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31

AI Career Paths and Job Opportunities / Top 10 Career Opportunities In Artificial Intelligence

« on: October 12, 2023, 10:24:05 AM »

Artificial intelligence, or AI, is one of the hottest topics in the world of business. AI capabilities have been useful in expanding the possibilities of real-time engagement with concerned customers. In addition, the top AI career opportunities allow professionals to contribute to the management of operations alongside ensuring business continuity.

The adoption of smart, data-centric technologies in business applications is growing at a rapid pace. AI, ML, and deep learning are some of the important technologies which have shaped the new industrial revolution. Artificial intelligence has been serving a positive impact across different industries, including automobiles, healthcare, education, manufacturing, and agriculture.

The preparation for the most popular artificial intelligence career opportunities would require you to learn about the fundamentals of AI and its implications. On the other hand, it is important to find out the different roles you can find with a career in AI. The following post helps you learn about the most promising career paths in AI and offers brief pointers on preparations for the desired role.

Why Do You Need a Career in AI?

AI is one of the formidable forces in the world of technology and offers a massive range of career opportunities. You can build a career in artificial intelligence and prepare for the jobs of future, where AI would be a key component in technologies used by businesses. Global AI adoption rate has increased at a steady pace and reached 35%, and the global AI market could reach almost $1811 billion by 2030. On top of it, AI has proved successful in boosting business productivity by 40%. In terms of investment, AI startups have been registering continuous increases in investment.

The opportunities in AI also point to the impact of AI on businesses. For example, around 97% of business owners believe that ChatGPT would have a beneficial impact on their business. In addition, around 60% of business owners believe that AI would increase productivity alongside improving customer relationships.

A research report by Gartner has also revealed that customer satisfaction will increase by almost 25% in 2023 for organizations that use AI. According to a report by McKinsey, companies focus on risks such as cybersecurity, personal privacy, compliance, and comprehension for the adoption of AI. All of these factors prove that you need to look for career paths in AI for multiple career benefits.

Isn’t AI Supposed to Take Away Your Jobs?

One of the most common concerns associated with AI adoption points to the possibility of AI taking away people’s jobs. A survey report has pointed out that almost 63% of companies have decided to increase their spending on AI and ML in 2023. At the same time, around 38% of employees believe that their job will be automated by 2023.

Therefore, you need to look for artificial intelligence career paths which can help you prepare for the disruptive impact of AI technology on job markets. As a matter of fact, AI is most likely to generate almost 12 million additional jobs that it would replace. Jobs in AI will become the top talk of the tech labor markets as the AI industry will need almost 97 million specialists in 2025.

The lack of skilled individuals and hiring setbacks in AI industry have been serving as major setbacks for AI adoption. Therefore, you should look for guides on how to start a career in artificial intelligence to build the necessary skills required for AI professionals. Rather than worrying about AI replacing your jobs, you should think of ways to master skills in artificial intelligence. You should think of ways to stay ahead of the disruptive impact of AI in the industry of your choice.

Skills Required for AI Professionals

The most noticeable aspect of career development opportunities in artificial intelligence is the flexibility of working in different industries. Artificial intelligence is a gradually expanding form of technology, and professionals with AI expertise are in demand. Interestingly, you can find the benefit of flexibility for building a career in artificial intelligence in the industry of your choice. AI professionals can find jobs in financial services, media, healthcare, marketing, retail, IoT-enabled systems, and technology. However, you would need a certain set of skills to establish your preparedness for the jobs in AI.

Candidates who want to capitalize on top AI career opportunities must have a comprehensive understanding of linear algebra and calculus. On top of it, you should have expertise in programming languages such as Python, MATLAB, and C or C++. The top five skills needed to apply for AI job roles are,

Collaboration
Analytical skills
Hands-on fluency in Python
Communication skills
Digital marketing objectives and Strategies
Some of the other crucial skills required for job opportunities in AI would include prompt engineering, machine learning, deep learning, and neural networks. In addition, problem-solving skills, management and leadership competencies, and industry knowledge are also important skills required for AI job roles.

Is It Safe to Pursue Jobs in AI?

The number of job listings with the term ‘artificial intelligence’ has increased by huge margins on different job boards and professional networking platforms. Candidates might think that the most popular artificial intelligence career opportunities are reserved only for a few special professionals.

In addition, some beginners also think that AI jobs are available only in new startups. On the contrary, you can find some interesting job roles in AI at top companies such as Wells Fargo, Apple, Microsoft, Nike, Amazon Web Services, Spotify, Deloitte, IBM, Harvard Business School, and many others.

Another important factor in determining whether artificial intelligence career paths are suitable for you is the salary estimate. How much can you earn from AI jobs? Interestingly, the answer would point at multiple responses as the salary of AI professionals depends on different factors, such as the role and experience. For example, AI engineers are likely to have significantly higher salaries ranging up to $200,000, while AI developers can earn up to $150,000.

Job Roles in Artificial Intelligence

The fundamental insights into the existing artificial intelligence market prove that professionals could capitalize on the opportunities of a burgeoning market. It would help them secure their career against the uncertainties due to changes in the job market. Here is an outline of the important job roles in AI you should pursue in 2023.



1. Big Data Engineer
One of the foremost additions among job roles for a career in artificial intelligence points to the big data engineer. The big data engineer has to generate and manage big data for the organization. Big data engineers focus primarily on developing an ecosystem for managing big data of an organization. The detailed analysis of big data on a large scale leads to different conclusions. The essential prerequisites for becoming a big data engineer in AI focus on fluency in programming languages such as Python or Java.

2. AI Data Analyst
Another interesting job role in artificial intelligence refers to the position of an AI Data Analyst. AI Data Analysts focus on analysis and interpretation of large datasets to obtain insights into customer behavior, industry changes, and market trends. Professionals interested in such types of opportunities in AI should gain command over data analysis tools such as Python and SQL.

On top of it, AI data analysts should have an academic background in data science, statistics, and machine learning. AI data analysts work in collaboration with data scientists as well as AI software engineers to create applications and models that could support businesses in making data-driven decisions.

3. AI Wrangler
The discussions about top opportunities for a career in AI would also point to the role of an AI wrangler. Interestingly, you wouldn’t find many listings for an AI wrangler among the jobs advertised in AI industry. AI Wrangler could work on management of large datasets and ensure their preparation for analysis.

In addition, AI wranglers should also ensure that the data has been refined, organized, and made ready for use. AI wranglers would need fluency in data manipulation, data mining, and cleaning. In addition, AI wranglers also work in coordination with data scientists, AI researchers, and machine learning engineers.

4. AI Software Engineer
The list of job opportunities for AI professionals would also introduce aspiring candidates to the role of AI Software Engineer. It is one of the top AI career opportunities right now as the AI software engineer role develops software for using AI. AI software engineers create software applications that use AI and machine learning techniques.

Therefore, AI software engineers would need skills in computer science and engineering. On the other hand, expertise in programming languages such as C++, Java, and Python is also a mandatory requirement for becoming an AI software engineer. Artificial intelligence software engineers contribute to the creation of AI algorithms and applications for solving real-world problems.

5. AI Consultant
AI consultants could help a business understand the ways in which AI and machine learning technologies could transform their operations. The skills required for AI consultants include strong foundation-level knowledge of artificial intelligence, project management, and business operations. On top of it, AI consultants also work with businesses to identify the areas to implement AI. The primary objectives of AI consultants revolve around improving efficiency, increasing revenue, and reducing costs.

6. NLP Processing Specialist 
Natural Language Processing, or NLP, is one of the important components of the AI landscape. The role of NLP Processing Specialist is one of the top ai career opportunities and offers clear routes for promising career growth. NLP specialists have the skills for designing, creating, and deploying AI models which could evaluate and understand natural language.

The important skills required for NLP specialists include linguistics, artificial intelligence, and computer science. In addition, NLP specialists also develop AI models with features for analyzing text data, providing insights into customer behavior, and understanding human speech.

7. AI Research Scientist
The next common choice among artificial intelligence career paths for you would refer to the role of an AI research scientist. AI research scientists take responsibility for the development and implementation of AI algorithms and models. The requirements for AI research scientist jobs include an academic background in mathematics, computer science, or engineering.

On the other hand, candidates should also have expertise in statistics, machine learning, and deep learning. AI research scientists have to experiment with new AI technologies and research projects in the field of robotics, natural language processing, and computer vision.

8. Business Intelligence Developer
The role of a Business Intelligence or BI developer focuses on promotion, expansion, and maintenance of BI tools and interfaces. BI developers should also determine calculated solutions for complex issues in an organization. In addition, business intelligence developers must also recognize important business trends through assessment of complicated data sets. Business Intelligence or BI developers would need basic knowledge of SQL and prior experience in computer programming as well as data sets.

9. User Experience Specialist
You can also try the job opportunities in AI, like the User Experience or UX specialist role. UX specialists have a background in user experience, design, and human-computer interaction. In addition, a UX specialist must have fluency in UX design tools such as Adobe XD and Sketch. On top of it, UX specialists in AI would also have to work in coordination with AI product managers and software engineers to create simple and intuitive interfaces.

10. Machine Learning Engineer
The most important addition among the answers for how to start a career in artificial intelligence would point at the role of machine learning engineer. Machine learning engineers have to work on development, testing, and deployment of machine learning models.

The machine learning models could evaluate massive datasets and provide relevant predictions. Machine learning engineers need experience in working with programming languages such as Python. In addition, they need fluency in machine learning algorithms, data analysis, and statistics.

Conclusion
The outline of job roles in AI proves that you can become an AI professional and enjoy promising career benefits. First of all, AI is the technology of the future, and almost every industry is working on AI-based projects. Therefore, a career in artificial intelligence seems more like a necessity than a choice.

You should review the career prospects in AI again by going through information about the AI market. At the same time, you must learn about different ways in which AI finds practical applications in the real world. Find the most credible training courses on artificial intelligence and discover the ideal career paths for you now.

Source: Blockchains
Original Content: https://101blockchains.com/top-ai-career-opportunities/

32

AI and Future Scenarios / The Rise of AI: Eight Scenarios for Living in an AI-Dominated Future

« on: October 11, 2023, 10:31:49 AM »

By Thomas Frey



The future is here, and it’s filled with advanced technologies that are revolutionizing every aspect of our lives. From shopping and dating to education and travel, the possibilities are endless. But what does this mean for how we live, work, and play? Let’s take a closer look at some scenarios to see how AI and other technologies will shape our future.

Scenario 1 – Fred and Jessica Applying for a Home Mortgage in 2040
In 2040, Fred and Jessica were exploring the possibilities of applying for a home mortgage using both crypto and traditional currencies. They had been researching the benefits of using cryptocurrencies for mortgage payments and were excited to explore the options available to them.

Using advanced digital tools, Fred and Jessica were able to compare mortgage rates and terms offered by lenders that accepted crypto payments, as well as traditional lenders that only accepted fiat currencies.

They found that some crypto-friendly lenders offered lower interest rates and more flexible repayment schedules than traditional lenders while also allowing them to make payments in popular cryptocurrencies such as Bitcoin, Ethereum, and Litecoin.

After exploring their options, Fred and Jessica decided to apply for a mortgage using both crypto and traditional currencies. They submitted their application online and were pleased to find that the process was fast and efficient, thanks to the use of advanced digital tools and AI-powered assistants.

Their application was quickly approved, and they received a personalized mortgage offer that allowed them to make payments using either crypto or traditional currencies. They were able to review the terms and conditions of the offer, as well as the interest rate and repayment schedule, before accepting the mortgage.

Scenario 2 – Sarah Going Grocery Shopping in 2040
As Sarah walked into the grocery store, she was greeted by a friendly robot that offered to assist her with her shopping needs.

Sarah used her smartphone to scan a QR code on the robot, which immediately recognized her and provided her with a personalized shopping list based on her previous purchases, dietary restrictions, and preferences. The robot then handed her a sleek and modern shopping cart equipped with advanced sensors, cameras, and AI algorithms.

As Sarah made her way through the store, the shopping cart followed her around autonomously, offering personalized recommendations for complementary products based on her preferences. The cart also used its sensors to detect the items that Sarah placed in it, ensuring that she didn’t forget anything on her list.

When Sarah was finished with her shopping, she simply walked through a no-touch facial recognition payment system, which identified her and charged her account automatically. The shopping cart then autonomously returned to its charging station, ready to assist the next customer.

Scenario 3 – Sam and Tiffany Speed Dating in an Autonomous Vehicle in 2040
Sam and Tiffany had signed up for a speed-dating event and were picked up by a sleek and modern autonomous vehicle that had been specially designed for the occasion.

As they settled into the comfortable and futuristic interior of the vehicle, the AI-powered virtual assistant greeted them and explained the rules and format of the speed-dating event. The vehicle then started to drive them to their first date location, a trendy restaurant reserved exclusively for the event.

As they arrived at the restaurant, the autonomous vehicle parked itself, and the virtual assistant provided them with a brief overview of their first date. They had a great time getting to know each other over a delicious meal, and when it was time to move on to the next location, the autonomous vehicle drove them to their next destination.

Over the course of the evening, Sam and Tiffany were introduced to several potential matches, and they found themselves enjoying the unique and futuristic speed-dating experience. The autonomous vehicle provided them with a safe and comfortable environment in which to get to know each other, and the AI-powered virtual assistant ensured that the event ran smoothly and efficiently.

As the evening came to a close, the autonomous vehicle drove Sam and Tiffany back to their respective homes, and the virtual assistant asked them for feedback on their speed-dating experience. Sam and Tiffany agreed that the autonomous vehicle provided a unique and enjoyable setting for the event, and they both felt optimistic about the potential for a future relationship.



Scenario 4 – William and Bobbi Lynn Launching an Entertainment Business in 2040
In 2040, William and Bobbi Lynn decided to launch an entertainment business that leveraged the latest AI and VR technologies. They wanted to create a unique and immersive experience that would transport people to new and exciting worlds, and they had spent months planning and preparing for their business launch.

Their business, called “Virtual Worlds”, allowed customers to enter immersive VR experiences that were powered by advanced AI algorithms. These experiences ranged from adventurous and action-packed to serene and calming, providing something for everyone.

As William and Bobbi Lynn launched their business, they quickly found that there was a strong demand for their unique and innovative product. People were eager to escape into new and exciting worlds, and Virtual Worlds provided a safe and immersive environment in which to do so.

The business quickly grew in popularity, and William and Bobbi Lynn constantly innovated and improved their product. They invested in the latest AI and VR technologies, ensuring that their experiences remained cutting-edge and advanced.

As their business grew, William and Bobbi Lynn hired a team of skilled AI and VR experts who helped them create even more immersive and exciting experiences. They were constantly pushing the boundaries of what was possible with technology and became known as entertainment industry leaders.

In 2040, William and Bobbi Lynn’s Virtual Worlds became one of the most popular and successful entertainment businesses in the world, with a loyal following of customers who couldn’t get enough of their unique and immersive experiences.

Scenario 5 – Going to School for 10-Year-Old Ella in 2040
In 2040, going to school had become a truly futuristic experience, and for 10-year-old Ella, it was an exciting and enjoyable part of her daily routine. Ella’s day started when she woke up in her smart bed, which was equipped with sensors that tracked her sleep patterns and adjusted the temperature and lighting to optimize her sleep.

As she got ready for school, Ella used her smart mirror to get personalized recommendations for her outfit and makeup based on the weather, her schedule, and her personal preferences. She then walked to her autonomous school bus, which picked her up at her doorstep and drove her to school while she relaxed in the comfortable and futuristic interior.

When Ella arrived at school, she was greeted by friendly AI-powered robots that helped her find her way to her classroom and provided her with personalized recommendations for her daily schedule. Ella’s classroom was equipped with advanced AI algorithms that could personalize her learning experience based on her individual learning style and pace.

Throughout the day, Ella used advanced digital tools to enhance her learning experience, from VR simulations that allowed her to explore different parts of the world to interactive learning games that made learning fun and engaging. She also had access to advanced robotics and AI courses that allowed her to learn about cutting-edge technologies and prepare for the future of work.

When the school day was over, Ella’s autonomous school bus picked her up and drove her back home, where she could continue learning and exploring the world with the help of advanced digital tools and AI-powered assistants. For Ella, going to school in 2040 was fun and engaging and offered endless opportunities for personal growth and development.



Scenario 6 – John Checking into a Hotel in 2040
As John walked up to the sleek and modern building, he was greeted by a friendly robot concierge that welcomed him and checked him in using facial recognition technology.

The robot concierge provided John with a personalized digital key to his room, which would grant him access to his room and other areas of the hotel. As John made his way to the elevator, he was amazed by the advanced technology that surrounded him – from the digital displays that showed him personalized recommendations for local restaurants and attractions to the smart lighting and climate control systems that adjusted automatically to his preferences.

When John arrived at his room, he was greeted by an AI-powered virtual assistant that recognized him by name and asked him how his day was going. The virtual assistant provided John with a personalized tour of the room, showing him how to use the various smart devices and appliances that were controlled by voice commands or his smartphone.

Throughout his stay, John was impressed by the level of service and attention to detail provided by the hotel’s AI-powered systems. From personalized recommendations for local activities and restaurants to intelligent room service that could predict his food preferences and dietary restrictions, the AI hotel exceeded his expectations in every way.

Scenario 7 – Linda Working as an Architect in 2040
In 2040, Linda became an architect who leveraged advanced AI and VR technologies to create stunning and innovative designs. She used advanced computer programs that could simulate the physics and materials of her designs, allowing her to create accurate and realistic models before beginning construction.

Linda also used advanced VR tools that allowed her to create immersive 3D models of her designs, which her clients could explore and experience before any physical construction began. This helped her clients to visualize better and understand her designs, making the design process more collaborative and efficient.

Linda also leveraged advanced AI algorithms that could analyze the surrounding environment and optimize her designs for factors such as energy efficiency, sustainability, and accessibility. This allowed her to create designs that looked beautiful, functional, and environmentally friendly.

As Linda worked on her designs, she collaborated with a team of skilled AI and VR experts, who helped her to bring her visions to life. She constantly pushed the boundaries of what was possible with technology, and she became known as an architectural industry leader.
With the help of advanced AI and VR technologies, Linda had created some of the most stunning and innovative buildings and structures in the world.

Scenario 8 – David going through an Airport to Catch his Flight in 2040
In 2040, going through an airport had become a completely different experience with the help of advanced technology. David arrived at the airport and checked in his luggage with the help of an AI-powered robot that could scan and verify his identity using advanced facial recognition algorithms.

As he made his way through security, David used his biometric data to quickly pass through the checkpoints without the need for traditional security checks. The security system used advanced AI algorithms to detect any potential threats or risks, making the security process much more efficient and effective.

After passing through security, David walked through a duty-free store that was equipped with advanced augmented reality displays, providing him with personalized recommendations and offers based on his preferences and purchase history.

David then made his way to his gate, where he boarded a futuristic airplane that was equipped with advanced AI-powered systems. The airplane could adjust its altitude and speed to optimize its fuel consumption, making the flight more environmentally friendly and cost-effective.

During the flight, David used advanced in-flight entertainment systems that offered personalized recommendations based on his preferences, providing him with a truly immersive and enjoyable experience.

When the airplane landed, David used his biometric data to quickly pass through the customs and immigration checkpoints without the need for long queues or manual checks.

Closing Thoughts
The future is a fascinating and sometimes scary place, but it’s also full of possibilities and opportunities. The scenarios we explored today are just the tip of the iceberg; we can only imagine what else the future has in store for us. As we continue to push the boundaries of technology, we must also remember to approach these innovations with caution and mindfulness, always keeping in mind the potential risks and dangers. By doing so, we can ensure that the future is not only exciting but also safe and sustainable for generations to come.

Source: futuristspeaker
Original Content: https://shorturl.at/bcwHK

33

AI and Economic Impact / The impact of artificial intelligence on growth and employment

« on: October 11, 2023, 10:24:30 AM »

By Ethan Ilzetzki

The use of artificial intelligence for day-to-day tasks has increased rapidly over the last decade. The May 2023 CfM-CEPR survey asked the members of its European panel to predict the impact of AI on global economic growth and unemployment rates in high-income countries over the upcoming decade. Most panellists think that AI is likely to boost global growth to 4–6% per annum (relative to an average of 4% over the past few decades). Most of the panel also believes that AI is unlikely to affect employment rates in high-income countries, with the remainder split between predicting an increase and a decrease in unemployment rates. Notably, most panellists indicate a great degree of uncertainty regarding their predictions, because AI is still in its infancy.

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AUTHORS

Ethan Ilzetzki
Associate Professor of Economics (with Tenure) London School Of Economics And Political Science


Suryaansh Jain
BSc Economics student London School Of Economics And Political Science

At the 2023 World Economic Forum, tech entrepreneur Mihir Shukla noted: “People keep saying AI is coming but it is already here”. The use of artificial intelligence (AI) for day-to-day tasks has increased rapidly over the last decade and ChatGPT (developed by OpenAI) is a prime example of this, with the popular generative AI used by more than a billion users for everyday tasks like coding and writing. The speed and scale of AI uptake can be captured by a simple fact: it took ChatGPT just 60 days to reach its 100 millionth user; in contrast, Instagram took two years to reach the same milestone. A recent Stanford University report found that the number of AI patents increased 30-fold between 2015 and 2021 (HAI 2023), highlighting the rapid rate of progress made in the AI development sphere. AI-powered technologies can now perform a range of tasks, including retrieving information, coordinating logistics, providing financial services, translating complex documents, writing business reports, preparing legal briefs, and even diagnosing diseases. Moreover, they are likely to improve the efficiency and accuracy of these tasks due to their ability to learn and improve via the use of machine learning (ML).

AI is generally acknowledged to be an engine of productivity and growth. With its ability to process and analyse enormous volumes of data, it has the potential to boost the efficiency of business operations. The McKinsey Global Institute predicts that around 70% of companies will adopt at least one type of AI technology by 2030, and less than half of large companies may use the full range of AI technologies. Price Waterhouse Coopers predicts that AI could increase global GDP by 14% in 2030 (PwC 2017).

Research into the impact of AI on the labour market has expanded recently. Acemoglu and Restrepo (2018) provide a theoretical framework to understand the impact of new technologies on the labour market. They decompose the effect of new technologies on labour into three broad effects: a displacement effect, a productivity effect and a reinstatement effect (new technologies can serve as a platform to create new tasks in many service industries, where labour has a comparative advantage relative to machines, boosting labour demand).

Frank et al. (2019) classify current literature on the labour market implications of AI into two broad categories: a doomsayer’s perspective and an optimist’s perspective. Doomsayers believe that labour substitution by AI will harm employment. Frey and Osborne (2013) estimate that 47% of total US employment is at risk of losing jobs to automation over the next decade. Their research reveals that a substantial share of employment in service occupations – where most US job growth has occurred over the past decades – are highly susceptible to computerisation. Bowles (2014) uses Frey and Osborne’s (2013) framework to estimate that 54% of EU jobs are at risk of computerisation. Acemoglu and Restrepo (2017) provide a historical example of excessive automation negatively affecting the labour market due to weak productivity and reinstatement effects, finding that areas in the US most exposed to industrial automation in the 1990s and 2000s experienced large and robust negative effects on employment and wages.

AI is also expected to have a disruptive effect on the composition of the labour market. Autor (2015) presented evidence that the labour market has become polarised over the last few decades towards low-skilled and high-skilled jobs and away from medium-skilled jobs, due to the advent of computers. However, he stated that this polarisation is likely to be reversed, as some low-and-medium skilled jobs are likely to be relatively resistant to automation, while some highly-skilled but relatively routine jobs may be automatable (potentially with technologies like AI). However, Petropoulos and Brekelmans (2020) concluded that unlike the computer and robotic revolution, the AI revolution is unlikely to cause job polarisation as it will affect alter low-skilled, middle-skilled and high-skilled jobs.

Optimists believe that AI’s productivity and reinstatement effects will be more than enough to compensate for the substitution effect. Some opinion pieces project that AI and robotics will have created up to 90 million jobs by 2025, indicating a strong positive labour market impact. The World Economic Forum concluded in October 2020 that while AI would likely take away 85 million jobs globally by 2025, it would also generate 97 million new jobs in fields ranging from big data and machine learning to information security and digital marketing.

Lawrence et al. (2017) argue that AI automation is unlikely to negatively impact the employment market due to its large positive spillover effects (reinstatement effect), which would counteract the negative direct effects of substitution in the labour market and can be seen as a Schumpeterian ‘creative destruction’. They believe that automation is likely to transform, rather than eliminate, work. In contrast to other studies finding larger negative effects, Arntz et al. (2016) estimate that only 9% of jobs in the UK are susceptible to automation in the next decade. They argue that instead of substitution, transformation is more likely to occur, with 35% of jobs would change radically in the next two decades.

Nakamura and Zeira (2018) build a task-based theoretical model that shows that automation need not lead to unemployment in the long run. Somers et al. (2022) conduct a systematic review of the empirical literature on technological change and its impact on employment and find that the number of studies that support the labour substitution effect is more than offset by the number of studies that support the labour-creating/reinstating and real income effects of new technologies. Moreover, they find that studies that analyse the net employment effect of technological change suggest the net impact of technology on labour to be rather positive than negative, reaffirming this narrative. Bholat (2020) further notes that job losses in specific sectors due to new technologies have historically been counter-balanced by broad-based gains in aggregate real income as these technologies create higher quality and lower priced goods and services. This leads to higher disposable income which boosts demand for new products, which in turn, boosts labour demand in such sectors. Alan Manning notes that some of the direst predictions about the impact of automation on employment during the past decade have not come to pass (Bholat 2020). This may indicate that concerns about the impact of AI on employment are slightly exaggerated.

The May 2023 CfM-CEPR survey asked the members of its panel to forecast the impact of AI on global economic growth and unemployment rates in high-income countries over the upcoming decade. The survey contained two questions. The first asked the panellists to forecast the impact of AI on global economic growth over the upcoming decade. The second asked them to predict the impact of AI on unemployment in high-income countries in the upcoming decade.

Question 1: What will be the implications of recent developments in AI on global economic growth, as they mature over the upcoming decade?





Twenty-seven panel members responded to this question. The majority of the panel (64%) believes that AI will increase global economic growth to 4–6% per annum over the upcoming decade.  The remainder of the panel (36%) thinks that AI will have no significant effect on global growth. Notably, most panellists express a great degree of uncertainty with their predictions because

Almost two-thirds of the panel believes that the development of AI over the upcoming decade will positively impact economic growth. Jorge Miguel Bravo (Nova School of Business and Economics, Lisbon) cites the widespread uptake of machine learning as a ‘general purpose technology’ across the developed and developing world as the main reason why he would “bet on the upside rather than no change or fall”. Ugo Panizza (The Graduate Institute, Geneva (HEID)) echoes these thoughts, claiming that “AI will lead to an increase in productivity and thus higher economic growth”. However, he notes some caveats to this prediction, stating that “AI could increase unemployment and inequality and this may have backlashes on productivity and growth”. Robert Kollmann (Université Libre de Bruxelles) similarly expresses subdued optimism regarding the advent of AI, arguing that it is unlikely to affect the long-term trend growth rate of world GDP, but could boost global growth slightly, by around “0.5”.

Most panellists believe that the implications of AI on global economic growth are extremely uncertain, rendering forecasting impossible. Andrea Ferrero (University of Oxford) summarises this view: “I expect AI to have a significant impact on the economy, but I'm not really sure how. I can imagine that some sectors will benefit more than others, and some may even suffer. The implications for economic growth overall are highly uncertain in my view.” Jagjit Chadha (National Institute of Economic and Social Research) states that the overall impact of AI will depend on several factors – “which policies are adopted, how monopoly power is challenged and what new ideas are ultimately released” – and hence, he cannot say what the impact will be “with any degree of certainty”.

Ricardo Reis (London School of Economics) succinctly summarises the great degree of uncertainty shared by the panel: “Forecasting future growth over a decade is very hard, so ‘not confident’ is the most relevant part of this answer.”

Question 2: What will be the implications of recent developments in AI on unemployment in high-income countries over the upcoming decade?





Twenty-nine panel members responded to this question. Most of the panel (63%) believes that AI will not affect employment rates in high-income countries across the next decade. The majority of the remainder (27%) think that developments in AI could increase unemployment in high-income countries. Only two panellists believe that AI could decrease unemployment in high-income countries over the upcoming decade. Notably, more than half of the panel express a lack of confidence in their responses, indicating the high degree of uncertainty surrounding this question.

Most panellists believe that AI developments are unlikely to impact unemployment in high-income countries over the long run. Michael Wickens (Cardiff Business School and University of York) cites previous technological changes to support this stance: “The number of jobs and the level of unemployment will not change but the number of hours of work will fall and leisure increase. This is what has happened following past tech[nological] improvements. The same thing will happen with AI.” Echoing this view, Cédric Tille (The Graduate Institute, Geneva) states that “unemployment effects may be limited” but notes that "the impact on income inequality and need for redistribution policy may be large”. Maria Demertzis (Bruegel) argues that the impact of unemployment could depend on reskilling, stating “the quicker this [reskilling] happens, the less the impact on unemployment”.

Several panellists indicate uncertainty regarding long-term predictions of the impact of AI on employment. Andrea Ferrero (University of Oxford) highlights this uncertainty: “I expect unemployment to increase in the short run as human employment in some tasks and jobs becomes obsolete. In the medium run, the supply will adjust and it's possible that AI may even reduce the natural rate of unemployment in the long run. I'm just not sure.”

A fraction of the panel expresses pessimism regarding the impact of AI on the labour market due to its impact on vulnerable groups. Wouter den Haan (London School of Economics) sums up this viewpoint: “My concern is that AI may be bad for the more vulnerable in the labour markets like the ones who will not be that easy to adapt to a new environment”.

However, a few panel members express an opposing view, claiming that may actually decrease unemployment in high-income countries. Volker Wieland (Goethe University Frankfurt and IMFS) suggests that AI could potentially decrease unemployment rates and the number of hours worked.

Source: cepr.org
Original Content: https://shorturl.at/mrDJL

34

AI Certifications and Training Programs / 10 AI Certifications for 2023: Build Your Skills and Career

« on: October 10, 2023, 10:56:54 AM »

Artificial Intelligence (AI) is increasingly influencing the world as we know it. As new AI tools become commonplace, more businesses will continue incorporating them into their workflows and data analytics processes. A report by S&P Global found that the generative AI market alone will grow 10 times by 2028.

Those who develop or use technology for their job can’t afford to be out of this loop. But with so many changes, how do you know where to start learning about the latest AI technology?

Getting certified in AI is a good place to start. AI certification programs will help you learn what AI is, how it works, and how to make the most of it in business. And with AI’s popularity today, plenty of AI certification options are available.

This guide will cover the top AI certification programs. We’ll cover what makes each unique and how they can help you thrive in the new AI world.

What are the benefits of getting a certificate in AI?

Getting certified in AI brings many benefits, from upskilling and job stability to increased pay  and career advancement. Some of the biggest ways certifications may help you include:

Career advancement. Many companies are incorporating AI into their businesses, and employers are specifically looking for AI talent. Getting your AI certification can help you set yourself apart from other tech talent and advance your career.
More job opportunities. The AI field isn’t limited to one industry. AI is used in health care, finance, manufacturing, and many other fields. Various AI jobs are also available in product management, data science, engineering, robotics, and data analyst roles. Learning AI will make you valuable for companies in various industries—giving you more job options than you would otherwise have.
Larger salary. AI jobs aren’t just in demand for businesses—they also pay well. There is competition in the AI space for talent. You can get a higher salary if you prove yourself with a project history and certification.
Industry recognition. You’re getting verification from established institutions when you get an AI certification. Global companies recognize these professional certifications as worthwhile and worth looking for when interviewing AI candidates.
Boost your skills. There is a lot to learn when delving into AI. An AI certification will walk you through the ideal way to learn AI concepts and help you improve your skills with instruction sessions, self-paced learning, and hands-on exercises.
Practical experience. A lot of AI certifications use projects to build skills. These certifications allow candidates to turn AI theory into practice by tackling real-world scenarios in their studies.
Keep updated. Industrial technology has always been a fast-changing field. That’s possibly more true now than ever. As the industry changes, getting certified in new processes and programs will help you stay updated with the newest AI tools.

10 Popular AI certification programs
Many AI certification courses are available to people who want to learn AI—but a few stand above the rest. Below are 10 of the most popular AI certification programs available.

1. Coursera
Coursera works with over 150 universities and offers thousands of online courses, including AI certification programs. You’ll find both beginner and advanced AI programs on Coursera.

For instance, the AI for Everyone and Introduction to Artificial Intelligence (AI) courses are great options for people new to AI. AI for Everyone introduces nontechnical students to AI and how to build businesses and services. Introduction to Artificial Intelligence (AI) covers some of the more technical details of AI—such as deep learning neural networks—and a few tips for starting a career in AI.

For specialized courses, Coursera also offers the Deep Learning track. It takes students down the learning path of becoming deep learning experts. It offers the knowledge to take on projects and build their own neural networks.

Many Coursera programs are free. Paid courses are tiered so that people can achieve their goals at their own pace. Guided projects take as little as an hour, many certificates take as long as six months of dedicated time, and Coursera even offers degrees that take 2-4 years to complete.

2. Stanford University
Standford AI Labs was founded in 1963, when many computers still relied on vacuum tubes, and has remained a leader in the field. Stanford offers the Artificial Intelligence Graduate Certificate—a complete look at AI and what it offers. It allows students to pick the AI courses they want to cover during their learning experience.

The certificate program requires students to have a Bachelor’s degree with at least a 3.0 GPA. They should also have a good understanding of math (college-level calculus, linear algebra), and be familiar with probability theory and programming languages (JavaScript, C++, Python). The certificate program costs between $18,928-$23,296 (depending on the number of courses you take).

Students start by picking at least one of the two required courses: Artificial Intelligence: Principles and Techniques and Machine Learning. Students can then pick from a list of elective courses covering topics such as NLP, robotics, computer vision, and deep reinforcement learning. Expect to spend 15-20 hours per week on coursework and 1-2 years to complete the program.

These courses will offer students an in-depth and practical look at AI and how to use it in practical applications. For instance, Natural Language Processing With Deep Learning will teach students how to create neural network models to create NLP applications that can process text.

3. IBM
IBM gives people looking for an introduction to AI a hands-on experience that helps them learn about current AI technology and how it’s useful today. It offers programs that help students learn about machine learning and use the IBM Watson service to build applications.

IBM AI Engineering is a free course that offers students a look at the different types of AI. They will learn about deep learning, neural networks, and machine learning algorithms (classification, regression, and clustering). This course takes a hands-on approach and will walk students through using AI tools (PyTorch, Tensorflow) to build deep learning models.

The course is ideal for students with knowledge of high school mathematics and Python. You can expect to complete the program in two months working about 10 hours per week.

IBM also offers the Applied AI Professional program. This free course uses IBM’s Watson service to help students build AI applications with minimal coding. Students will use Watson to create chatbots and computer vision programs.

This course works well both for beginners with technical and non-technical skills. Expect to finish the course in three months when working 10 hours per week.

Both courses offer college credit to students enrolled in applicable degree programs.

4. Microsoft
Microsoft has invested resources into AI research (including OpenAI) to position itself as a leader in the industry. Microsoft also offers a comprehensive artificial intelligence certification program.

One of the components of Microsoft’s certification programs is the use of its Azure web services. It offers the Azure AI Engineer Associate certification, which teaches students how to  build, manage, and deploy AI applications that make the most of the Azure platform and Azure Cognitive Services. This hands-on course walks students through setting up a cloud environment and deploying AI applications.

Microsoft also offers training material for data scientists wanting to use Azure for machine learning. You can take the Azure Data Scientist Associate course to learn how to set up and configure Azure for training and optimizing machine learning models. This practical course also walks students through configuring a cloud environment for working with AI models.

Once you finish your Microsoft course, you must pass a certification exam to receive the certification. The courses are free to help you prepare for the certificiation exam. The exam costs $165.

5. Deeplearning.ai
Pioneering machine learning engineer Andrew Ng (who also co-founded Coursera) founded deeplearning.ai. The website offers some of the highest-rated AI certification programs.

Deeplearning.ai offers courses for every skill level. Introductory courses like AI for Good guide students through different modules to learn what AI is capable of and looks at case studies of AI usage today. Some simple computer skills (like spreadsheet manipulation) are useful, but the course is accessible to beginners without a computer background. It’s free to take and will take three months to complete when working five hours per week.

Intermediate and advanced courses are also available for individuals who want to learn more. The Natural Language Processing free course teaches students what goes into building NLP systems and how to use them in practice. Students should have some knowledge of machine learning and intermediate Python programming skill to take this course. It will take four months to complete when working eight hours each week.

There are also options for people who don’t have much time and want to experience new tools. These short courses don’t offer certification but will quickly help you learn something new.

6. LinkedIn Learning
LinkedIn Learning offers courses in a variety of fields, from business management to AI. The information you find on LinkedIn comes from AI professionals and includes in-depth courses on AI fundamentals and specific courses covering a single AI topic in detail—and in a format you can consume on your own time.

Artificial Intelligence Foundations is a beginner program that covers how to run AI models. During the course, you’ll learn how to gather and prepare data for training, select and implement machine learning solutions, train AI models on data, and evaluate your results. You’ll also see demos for each action to see how to tackle specific tasks. The course is part of the LinkedIn Learning membership, or available for a $24.99 one-time fee, and will take around two hours to complete.

Look at Deep Learning: Image Recognition for a more in-depth certification. This course dives into the world of computer vision from start to finish until you’re ready to train an image model to recognize objects in an image. You should have some knowledge of Python for using Tensorflow for the course. You can view the course if you’re part of LinkedIn Learning, or pay a $34.99 one-time fee. It will take around one hour and 45 minutes to complete.

The LinkedIn Learning membership program costs $19.99 per month when billed annually (with a free one-month trial).

7. Udacity
Udacity is an online course platform known for its wide range of courses on computer science. It covers AI, as well as topics related to computer programming, product management, and business.

AI Programming With Python is an introductory course that focuses on hands-on AI projects using the Python programming language. The course teaches students how to program with Python, the math behind AI (such as linear algebra and calculus), and how to combine the two to create practical AI applications in Python. The course should take three months when working 10 hours per week. Students should have basic algebra and programming knowledge before taking this course.

You can also use Udacity to learn about how AI relates to specific industries. The AI in Healthcare course talks about how specific types of AI help with health care—such as computer vision helping with medical diagnostics. This course will take four months to complete when working 15 hours per week. Students should have an intermediate level of Python knowledge and understand basic machine-learning tasks.

Udacity offers monthly pricing when taking courses. You pay $399 per month while you’re enrolled and stop paying once you complete your education.

8. edX
edX is an education platform that offers courses for every discipline required to get a job. Although there isn’t a specific focus on technology, edX partners with schools and businesses to offer several free audit tracks and paid AI certification programs to interested students.

The Deep Learning Fundamentals course teaches students about the different types of AI and how to build and train those AI systems. You’ll use tools like PyTorch and TensorFlow to build, run, and optimize their models. Students should have an understanding of Python to use these tools. It will take seven months to complete this course when working 2-4 hours per week. You’ll pay $39 per month while taking the course.

edX also offers practical examples using modern programming languages for software developers. Introduction to Artificial Intelligence With Python walks students through using Python to design intelligent AI systems that can solve real problems. You should have previous Python programming experience before taking this course. It will take seven weeks to complete when working 10-30 hours per week. The course material has a one-time fee of $249.

9. Google AI
Google has long been a leader in AI research. The company publishes hundreds of peer-reviewed papers every year, many specifically on the topic of AI. Google created several AI certification courses to help new AI engineers learn how to break into the field and learn practical applications.

The Machine Learning Crash Course is a free introduction to machine learning, with practical examples and real-world case studies. Students will learn how to build AI models and use them to represent data useful for applications. Course students should have previous experience with Python (using the pandas and NumPy libraries). It will take 15 hours to complete the course.

Google also offers courses that walk students through real AI apps in business today. The Recommendation System course, for instance, walks students through the process of creating a recommendation system for product businesses. Students will create a sample application for movie recommendations in this course. You will need to complete the Machine Learning Crash Course before starting this one. It will take around four hours to complete.

10. NVIDIA Deep Learning Institute (DLI)
NVIDIA has had a stake in the AI space for a long time. It provides the GPU power many large AI companies use to train and run machine learning models.

NVIDIA offers many training certifications to help new AI users get up to speed. NVIDIA’s Fundamentals of Deep Learning course dives into deep learning techniques and will help students gain experience with tasks like image classification and object detection. Students should have basic programming skills before starting the course. It costs $90 and will take eight hours to complete.

NVIDIA also offers the Building Transformer-Based Natural Language Processing course for individuals wanting to use transformer models for NLP processing. This course walks students through the process of building a transformer neural network, training it to recognize entities in text, and deploying the application on NVIDIA’s Triton platform. Students need an understanding of machine learning, neural networks, and Python before taking this course. It’s currently invitation-only and will take eight hours to complete if you get in.

Showcase your AI skills on Upwork
In a world increasingly influenced by artificial intelligence, you shouldn’t put off learning all you can about new AI technologies. The good news is that you have several options to start learning—from free basic certifications from Coursera to graduate certifications from Standford. Look more into the AI certifications above to start learning AI.

If you’ve completed your AI coursework, advanced your skills, and are ready to put your AI skills to use, sign up to browse freelance AI jobs on Upwork to get your first clients.

If you want to find talent for your next project to develop your business’s AI strategy, hire AI professionals on Upwork to find the right freelancer for your needs.

Disclosure: Upwork is an OpenAI partner, giving OpenAI customers and other businesses direct access to trusted expert independent professionals experienced in working with OpenAI technologies.

Upwork does not control, operate, or sponsor the other tools or services discussed in this article, which are only provided as potential options. Each reader and company should take the time to adequately analyze and determine the tools or services that would best fit their specific needs and situation.

Prices are current at the time of writing and may change over time based on each service’s offerings.

Source: Global Inc.
Original Content: https://www.upwork.com/resources/ai-certifications

35

AI Job Market and Hiring Trends / Machine Learning and AI job trends in 2023

« on: October 10, 2023, 10:07:30 AM »

By Great Learning

Machine Learning is a branch of Artificial Intelligence (AI) that focuses on the development of algorithms and statistical models that enable computer systems to improve their performance on a specific task. Machine Learning algorithms learn from data, identify patterns and make predictions or decisions without being explicitly programmed.

Machine Learning has become increasingly important in recent years as more and more companies are looking to leverage the power of data to drive business decisions. Machine Learning is used in a wide variety of applications, including image recognition, natural language processing, speech recognition, and predictive analytics.

Machine Learning is also used in many industries, including healthcare, finance, e-commerce, and manufacturing, among others. In healthcare, Machine Learning algorithms are used to improve disease diagnosis and predict patient outcomes. In finance, Machine Learning algorithms are used for fraud detection and risk analysis. In e-commerce, Machine Learning algorithms are used for product recommendations and customer segmentation. In manufacturing, Machine Learning algorithms are used for predictive maintenance and quality control.

Machine Learning professionals are in high demand due to the growing need for companies to analyze large amounts of data and make data-driven decisions. Machine Learning jobs are some of the most well-paying jobs in the technology industry. With the right skills and qualifications, a career in Machine Learning can be both rewarding and lucrative.

Machine Learning Job Trends

The demand for Machine Learning professionals has been on the rise in recent years, and this trend is expected to continue in the future. Here are some key job trends in Machine Learning:

Increased demand for Machine Learning professionals:

There has been an increased demand for Machine Learning professionals across industries such as healthcare, finance, e-commerce, and manufacturing. According to a report by Analytics India Magazine, the demand for Machine Learning professionals in India is expected to increase by 60% by 2022.

Growing job roles in Machine Learning:

There are several job roles in Machine Learning, such as Machine Learning Engineer, Data Scientist, Data Analyst, and Business Analyst. Companies are looking for professionals who have a combination of technical and non-technical skills to handle these job roles.

High salaries:

Machine Learning professionals are in high demand, and this has resulted in high salaries. According to a report by Glassdoor, the average salary of a Machine Learning Engineer in the USA is around $112,000 per annum.

Skills and Qualifications Required for Machine Learning Jobs

To excel in a Machine Learning job, one needs to have a combination of technical and non-technical skills. Here are some skills and qualifications required for Machine Learning jobs:

Strong programming skills:

A Machine Learning professional must have strong programming skills in languages such as Python, Java, R, and C++. They should be able to write clean and efficient code.

Knowledge of Machine Learning algorithms:
A Machine Learning professional must have knowledge of various Machine Learning algorithms such as Regression, Classification, Clustering, and Reinforcement Learning.

Data analysis skills:
A Machine Learning professional must be proficient in data analysis tools such as Pandas, NumPy, and SciPy. They should be able to clean, preprocess, and visualize data.

Strong mathematical skills:
A Machine Learning professional must have a strong foundation in mathematics, including calculus, linear algebra, and probability.

Good communication skills:
A Machine Learning professional must have good communication skills to effectively communicate their ideas and findings to stakeholders.

Different ML Job Roles

Machine Learning Engineer:
A Machine Learning Engineer is responsible for designing, building, and deploying Machine Learning models. They work on projects such as image recognition, natural language processing, and predictive analytics. The key skills required for this job role include programming skills in languages such as Python, Java, R, and C++, knowledge of Machine Learning algorithms and libraries such as TensorFlow and PyTorch, data analysis skills, and strong mathematical skills. The average salary of a Machine Learning Engineer in the USA is around $112,000 per annum.

Data Scientist:
A Data Scientist is responsible for analyzing large amounts of data to identify patterns and make predictions. They work on projects such as customer segmentation, fraud detection, and predictive maintenance. The key skills required for this job role include programming skills, data analysis skills, knowledge of statistics and probability, and knowledge of Machine Learning algorithms and libraries. The average salary of a Data Scientist in the USA is around $117,000 per annum.

Data Analyst:
A Data Analyst is responsible for collecting, processing, and analyzing data to identify trends and patterns. They work on projects such as sales forecasting, customer behavior analysis, and product performance analysis. The key skills required for this job role include data analysis skills, knowledge of statistics and probability, proficiency in data analysis tools such as Pandas, NumPy, and SciPy, and strong communication skills. The average salary of a Data Analyst in the USA is around $68,000 per annum.

Business Analyst:
A Business Analyst is responsible for analyzing business processes and making recommendations to improve efficiency and profitability. They work on projects such as market research, competitive analysis, and customer behavior analysis. The key skills required for this job role include analytical skills, strong communication skills, knowledge of business processes, and proficiency in data analysis tools. The average salary of a Business Analyst in the USA is around $74,000 per annum.

Machine Learning Researcher:
A Machine Learning Researcher is responsible for developing new Machine Learning algorithms and techniques. They work on projects such as deep learning, reinforcement learning, and unsupervised learning. The key skills required for this job role include knowledge of advanced mathematics and statistics, strong programming skills, and knowledge of Machine Learning algorithms and libraries. The average salary of a Machine Learning Researcher in the USA is around $129,000 per annum.

AI Job Market Overview
The AI job market is experiencing a significant increase in demand. According to a report by Gartner, AI will create 2.3 million jobs by 2022. AI job roles can be categorized into three main categories: AI Researchers, AI Developers, and AI Engineers.

AI Researchers are responsible for exploring new AI techniques and developing new algorithms. They work in research and development labs, universities, and other academic institutions. They also collaborate with AI developers and engineers to implement new techniques and technologies in AI systems.

AI Developers are responsible for creating software applications and solutions that utilize AI technologies. They work on developing algorithms, creating neural networks, and building models that can perform complex tasks. They also work with AI researchers and engineers to implement new technologies into existing systems.

AI Engineers are responsible for building and maintaining AI-based systems. They design and implement algorithms and software applications that power AI systems. They also ensure that these systems are scalable, reliable, and secure.

AI Job Trends
The AI job market is on the rise, and the demand for AI talent is increasing rapidly. According to LinkedIn’s 2020 Emerging Jobs Report, AI specialist jobs have grown 74% annually in the last four years. The report also listed AI specialist jobs as one of the top emerging jobs of the year.

The rise of AI job trends can be attributed to several factors. First, the increasing adoption of AI-based products and services across industries has created a demand for AI talent. Companies are looking to hire AI professionals who can help them develop and implement AI solutions to improve efficiency, productivity, and customer satisfaction.

Second, the increasing availability of data has created a need for AI talent. AI systems require large amounts of data to learn and improve. As a result, companies are looking to hire AI professionals who can help them collect, store, and analyze data.

Third, the increasing availability of AI-based tools and technologies has created a demand for AI talent. AI-based tools and technologies have become more accessible and affordable in recent years, making it easier for companies to develop and implement AI solutions.

Conclusion
The AI job market is growing rapidly, and AI job roles are in high demand. AI professionals are responsible for developing and implementing AI-based solutions that can improve efficiency, productivity, and customer satisfaction.

Source: Great Lakes E-Learning Services Pvt. Ltd.
Original Content: https://shorturl.at/lpsHJ

36

AI Career Paths and Job Opportunities / 10 Awesome & High-Paying AI Careers to Pursue in 2023

« on: October 10, 2023, 09:55:11 AM »

Over the last few years, artificial intelligence (AI) has opened up possibilities for the future. From space exploration to melanoma detection, it is making waves across industries, making impossible things possible.

As a result, there has also been a steady growth in artificial intelligence (AI) careers—LinkedIn puts artificial intelligence practitioners among the ‘jobs on the rise’ in 2021.
In this blog post, we explore the ten awesome and high-paying artificial intelligence careers you can pursue in 2021 and beyond.

Is Artificial Intelligence (AI) a Good Career?

Yes, because:

Artificial intelligence (AI) jobs are plenty, hiring growing by 32% in the last couple of years
There is a high talent gap—not enough qualified artificial intelligence (AI) applicants for vacant positions
Artificial intelligence (AI) professionals earn top salaries, well north of $100,000
As a rapidly evolving industry, growth opportunities in Artificial intelligence (AI) careers are diverse
Artificial intelligence (AI) careers are flexible—you could be a freelancer, consultant, researcher, practitioner, machine learning engineer, computer vision engineer, or even build your own AI products.
Artificial intelligence (AI) jobs are not only in hot demand, but a great opportunity to enter a number of fields and industires. You can apply the programming languages and machine learning techniques you pick up in a number of fields.

Are AI Jobs in High Demand?

The current Artificial intelligence (AI) job outlook is quite promising. The US Bureau of Labor Statistics expects computer science and information technology employment to grow 11% from 2019 to 2029. This will add about 531,200 new jobs in the industry. This, it appears, is a conservative estimate. ‘AI and Machine Learning Specialists’ is the second on the list of jobs with increasing demand as per the World Economic Forum.

As the industry matures, jobs in Artificial intelligence (AI) will not only grow in number but also complexity and diversity. This will open doors for various Artificial intelligence (AI) professionals—junior, senior, researchers, statisticians, practitioners, experimental scientists, etc. The outlook for ethical AI is also looking up.

What Artificial intelligence (AI) Careers Can You Pursue?

Despite being a new and niche field, careers in artificial intelligence aren’t homogenous. Within AI, there are various kinds of jobs needing specific skills and experience. Let us look at the top ten one by one.

1. Machine Learning Engineer

Machine learning engineers are at the intersection of software engineering and data science. They leverage big data tools and programming frameworks to create production-ready scalable data science models that can handle terabytes of real-time data.

Machine learning engineer jobs are best for anyone with a background that combines data science, applied research, and software engineering. AI jobs seek applicants with strong mathematical skills, experience in machine learning, deep learning, neural networks, and cloud applications, and programming skills in Java, Python, and Scala. It also helps to be well-versed in software development IDE tools like Eclipse and IntelliJ.

The average salary of a machine learning engineer in the US is $​​1,31,000. Organizations like Apple, Facebook, Twitter, etc., pay significantly higher—in the range of $170,000 to $200,000. Read more about ML engineer salaries here.

2. Data Scientist

https://www.springboard.com/blog/wp-content/uploads/2021/12/Data-Scientist-scaled.jpg

Data Scientists collect data, analyze it, and glean insights for a wide range of purposes. They use various technology tools, processes, and algorithms to extract knowledge from data and identify meaningful patterns. This could be as basic as identifying anomalies in time-series data or complex as predicting future events and making recommendations. The primary qualifications expected of a data scientist are:

Advanced degree in statistics, mathematics, computer science, etc.
Understanding of unstructured data and statistical analysis
Experience with cloud tools like Amazon S3 and the Hadoop platform
Programming skills with Python, Perl, Scala, SQL, etc.
Working knowledge of Hive, Hadoop, MapReduce, Pig, Spark, etc.
The average salary of a data scientist is $105,000. With experience, this can go up to $200,000 for a director of data science position.

3. Business Intelligence Developer

Business intelligence (BI) developers process complex internal and external data to identify trends. For instance, in a financial services company, this could be someone monitoring stock market data to help make investment decisions. In a product company, this could be someone monitoring sales trends to inform distribution strategy.

However, unlike a data analyst, business intelligence developers don’t create the reports themselves. They are typically responsible for designing, modeling, and maintaining complex data in highly accessible cloud-based data platforms for business users to use the dashboards. The qualifications expected of a BI developer are:

Bachelor’s degree in engineering, computer science, or a related field
Hands-on experience in data warehouse design, data mining, SQL, etc.
Familiarity with BI technologies like Tableau, Power BI, etc.
Strong technical and analytical skills
Business intelligence developers earn an average salary of $86,500, going up to $130,000 with experience.

4. Research Scientist

The research scientist role is one of the most academically-driven AI careers. They ask new and creative questions to be answered by AI. They are experts in multiple disciplines in artificial intelligence, including mathematics, machine learning, deep learning, and statistics. Like data scientists, researchers are expected to have a doctoral degree in computer science.

Hiring organizations expect research scientists to have extensive knowledge and experience in computer perception, graphical models, reinforcement learning, and natural language processing. Knowledge of benchmarking, parallel computing, distributed computing, machine learning, and artificial intelligence are a plus.

Research scientists are in high demand and command an average salary of $99,800.

5. Big Data Engineer/Architect



Big data engineers and architects develop ecosystems that enable various business verticals and technologies to communicate effectively. Compared to data scientists, this role can feel more involved, as big data engineers and architects typically are tasked with planning, designing, and developing big data environments on Hadoop and Spark systems.

Most companies prefer professionals with a Ph.D. in mathematics, computer science, or related fields. However, as a more practical role than that of, say, a research scientist, hands-on experience is often treated as a good substitute for a lack of advanced degrees. Big data engineers are expected to have programming skills in C++, Java, Python, or Scala. They also need to have experience in data mining, data visualization, and data migration.

Big data engineers are among the best-paid roles in artificial intelligence, with an average salary of $151,300.

6. Software Engineer

AI software engineers build software products for AI applications. They bring together development tasks like writing code, continuous integration, quality control, API management, etc., for AI tasks. They develop and maintain the software that data scientists and architects use. They stay informed and updated about new artificial intelligence technologies.

An AI software engineer is expected to be skilled in software engineering and artificial intelligence. They need to have programming skills as statistical/analytical skills. Companies typically look for a bachelor’s degree in computer science, engineering, physics, mathematics, or statistics. To land a job as an AI software engineer, certifications in AI or data science are helpful too.

The average salary of a software engineer is $108,000. This goes up to $150,000 based on your specialization, experience, and industry.

7. Software Architect



Software architects design and maintain systems, tools, platforms, and technical standards. AI software architects do this for artificial intelligence technology. They create and maintain AI architecture, plan and implement solutions, choose the toolkit, and ensure a smooth data flow.

AI-driven companies expect their software architects to have at least a bachelor’s degree in computer science, information systems, or software engineering. As a practical role, experience is as important as educational qualification. Hands-on experience with cloud platforms, data processes, software development, statistical analysis, etc., will place you in good stead.

Software architects earn an average salary of $150,000. Your salary can go up significantly with expertise in artificial intelligence, machine learning, and data science.

8. Data Analyst

For a long time, the data analyst was someone who collected, cleaned, processed and analyzed data to glean insights. For the most part, these used to be mundane, repetitive tasks. With the rise of AI, much of the mundane work has been automated. Therefore, the data analyst role has upgraded to join the new set of AI careers. Today, data analysts prepare data for machine learning models and build meaningful reports based on the results.

As a result, an AI data analyst needs to know more than just spreadsheets. They need to be skilled in:

SQL and other database languages to extract/process data
Python for cleansing and analysis
Analytics dashboards and visualization tools like Tableau, PowerBI, etc.
Business intelligence to understand the market and organizational context
A data analyst earns an average salary of $65,000. However, high-technology companies like Facebook, Google, etc., pay in excess of $100,000 for data analyst roles.

9. Robotics Engineer



The robotics engineer is perhaps one of the first of AI careers, when industrial robots were gaining popularity as early as the 1950s. From the assembly lines to teaching English, robotics has come a long way. Healthcare uses robot-assisted surgeries. Humanoid robots are being built to be personal assistants. A robotics engineer’s job is to make all this and more happen.

Robotics engineers build and maintain AI-powered robots. For such roles, organizations typically expect advanced degrees in engineering, computer science, or similar. In addition to machine learning and AI qualifications, robotics engineers might also be expected to understand CAD/CAM, 2D/3D vision systems, the Internet of Things (IoT), etc.

The average salary of a robotics engineer is $87,000, which can go up to $130,000 with experience and specialization.

10. NLP Engineer

Natural Language Processing (NLP) engineers are AI professionals who specialize in human language, including spoken and written information. The engineers who work on voice assistants, speech recognition, document processing, etc., use NLP technology. For the role of an NLP engineer, organizations expect a specialized degree in computational linguistics. They might also be willing to consider applicants with a qualification in computer science, mathematics, or statistics.

In addition to general statistical analysis and computational skills, an NLP engineer would need skills in semantic extraction techniques, data structures, modeling, n-grams, a bag of words, sentiment analysis, etc. Experience with Python, ElasticSearch, web development, etc., could be helpful.

The average salary of an NLP engineer is $78,000, going up to over $100,000 with experience

Which Industries Are Hiring AI Professionals?



There are over 15,000 jobs in AI listed on LinkedIn today. Organizations across a wide range of industries are hiring. The industry with the most number of open AI careers appears to be technology with companies like Apple, Microsoft, Google, Facebook, Adobe, IBM, Intel, etc. hiring for AI roles.

Closely following this are also consulting majors such as PWC, KPMG, Accenture, etc. Healthcare organizations are hiring more—GlaxoSmithKline has multiple open AI-related positions. Retail players like Walmart and Amazon and media companies like Warner and Bloomberg are also hiring.

AI Careers FAQs
Can You Get Into AI With No Experience?



As a practical field, the defining factor of an AI professional is their ability to execute projects. This can only come from experience. So, you need to have hands-on experience to land a job in AI, even if not exactly corporate work experience. For instance, Springboard’s Data Science Career Track includes 14 real-world projects to get you comfortable with applying AI to business challenges.

What Skills Do You Need To Land an Entry-Level AI Position?

Not all AI positions are the same. As you see from the list above, different roles might need different skills/experiences. However, nearly all entry-level roles will expect:

Graduate degree in computer science, mathematics, or statistics
Familiarity with Python and SQL
Knowledge of data analysis, processing, and visualization
Understanding of cloud technologies
Business acumen about the industry, market, competition, etc.

Do You Need a Degree To Work in Artificial Intelligence?

Most job descriptions online will expect at least a bachelor’s degree. However, as we mentioned above, the talent gap is growing. Organizations can no longer reject employees without a college degree if they have demonstrable skills and experience in artificial intelligence.

How To Work in Artificial Intelligence?

A career in AI is unlike most technology jobs that are available today. As an evolving field, AI jobs demand professionals to stay informed of advancements and update themselves regularly. It is no longer enough to just gain skills, AI/ML professionals need to track the latest research and understand new algorithms on a regular basis.

Moreover, AI is coming under immense social and regulatory scrutiny. AI professionals need to look beyond just the technical aspects of AI and pay attention to its social, cultural, political, and economic impact.

Do I Really Need a Degree in Computer Science to Work in Artificial Intelligence?
Most companies will require a degree (as above), but if you have excellent knowledge in programming languages and machine learning techniques, you may be able to secure a role without one. However, it’s very hard to gain this knowledge on your own!

Source: springboard
Original Content: https://shorturl.at/iJUV1

37

AI Workflow and Experimentation Tools / Top 10 AI Tools to Supercharge Your Design Workflow

« on: September 19, 2023, 11:30:10 AM »

Artificial Intelligence (AI) is revolutionizing the design industry, offering tools that can significantly enhance productivity and creativity. In this blog, we will explore ten AI tools that can help you create better designs in less time.

1. Coco

Coco is an AI Figma plugin that simplifies the creation of user personas, user interview questions, user journey maps, and more. By inputting a few details about your user, Coco generates comprehensive user personas and journey maps, complete with user goals, needs, motivations, frustrations, tasks, and opportunities. This tool can be a game-changer for UX designers, helping to streamline the user research process and generate actionable insights faster.

2. Let's Enhance

Let's Enhance is an online tool that allows you to upscale and enhance low-quality images with a single click. It uses AI to improve the resolution and sharpness of your images, making them look professional and high-quality. This tool is particularly useful for UI designers who want their images to look sharp and crisp, regardless of their original quality.

3. CLADE

Clade, a product of Let's Enhance, is designed to create multiple beautiful product images. You simply upload your product image, select a scene, and Clade will remove the product's background and replace it with your chosen scene. This tool can be incredibly useful for e-commerce businesses or designers who need to create product images that stand out and attract customers.

4. Clean up Pictures

Clean Up Pictures is a tool that lets you remove any unwanted objects, defects, people, or text from your images. It uses AI to identify and erase unwanted elements, leaving you with a clean, distraction-free image. This tool can be a lifesaver for designers who need to refine their images and make them look just right.

5. Attention AI Insight

Attention Insight uses AI to predict where people will look on a website or app design. It analyzes design layouts and predicts which elements will receive the most attention from users based on heat maps. This information can be used to optimize your designs, ensuring that the most important information is prominently displayed. It's a valuable tool for designers who want to create user-friendly designs that effectively guide users' attention.

6. Icons8 8 Background Remover

Icons 8 Background Remover is a Figma plugin that allows you to remove the background of any image for free without leaving Figma. It's a time-saving tool for designers who frequently work with images and need to isolate subjects from their backgrounds quickly and easily.

7. Adobe Firefly

Adobe Firefly is an alternative to MeJourney with some unique features. It allows you to use everyday language to create beautiful content. It also has features like turning sketches into vectors and changing the mood, atmosphere, and weather of a video instantly. These features can be incredibly useful for designers who want to experiment with different styles and atmospheres in their designs.

8. Wizard

Wizard is a design and prototyping tool like Figma but with some amazing AI-powered features. It can scan your wireframe sketch and turn it into a design, convert design screenshots into editable designs, and turn your design into a low-fidelity hand-drawn wireframe instantly. These features can help designers streamline their workflow and experiment with different design styles more easily.

9. PixelCut

Pixel Cut is a mobile app with features like removing the background of photos, erasing objects, upscaling images, and more. It's a versatile tool for designers who work on the go and need to edit images quickly and easily. With Pixel Cut, you can refine your images wherever you are, whether you're on your commute, in a meeting, or working from a coffee shop.

10. Booth AI

Booth AI allows you to upload your product images and generate high-quality lifestyle photos. It uses AI to understand your product and its context, then generates images that show your product in use in various settings. This tool can be a great asset for creating engaging product visuals that resonate with your target audience and show your product in its best light.

AI is transforming the design process, making it more efficient and opening up new creative possibilities. These tools can help designers produce better work and be more productive. They offer a range of capabilities, from enhancing images and removing backgrounds to generating user personas and journey maps. By integrating these tools into your workflow, you can save time, improve your designs, and focus on the creative aspects of your work.

If you found this blog post interesting and want to keep learning more about AI and design, subscribe to ProApp's Daily Newsletter for more insights and discussions on these topics. We are always exploring the latest tools and trends in the design world!

Source: linkedin
Original Content: https://shorturl.at/jknCJ

38

AI Model Interpretability Tools / 5 Tools and Techniques to Help You Understand and Interpret AI Models

« on: September 19, 2023, 11:20:52 AM »

In today’s ever-evolving technological landscape, harnessing the power of AI models has become imperative. A Deloitte survey shows 94% of companies believe AI is critical for their businesses. Nevertheless, interpreting AI models remains a challenge. Understanding the decision-making process of these models thus becomes paramount if you want to establish trust, ensure fairness, and make well-informed decisions. In this exploratory blog, we embark on a journey to navigate the complexities of interpreting AI models. Additionally, we delve into key challenges and innovative techniques associated with AI models. So read on to unlock their transformative potential to revolutionize industries and drive meaningful advancements.

How Do AI Models Work, and What are Their Components?



AI models are complex systems that use advanced algorithms to process data and make intelligent predictions. These models have several key components. Firstly, input data serves as the foundation for training and inference. Next, preprocessing enhances data quality by cleaning, normalizing, and transforming it. Then, the model architecture, often based on neural networks, learns patterns and extracts relevant features from the input. During training, the model iteratively adjusts its parameters using techniques such as backpropagation with labeled data to minimize errors.

After training, the model undergoes evaluation to measure its performance and optimize accuracy. In the inference stage, the trained model takes new data, processes it through layers and activation functions, and generates predictions. Furthermore, techniques like regularization, optimization algorithms, and hyperparameter tuning improve model performance. To sum up, AI models dynamically evolve with the emergence of new data and algorithms.

What are the Challenges in Interpreting AI Models?
Interpreting AI models presents several challenges due to their complexity and inherent nature. Here are eight key challenges:

1. Lack of Transparency
AI models operate as black boxes, making it challenging to understand and trust their decision-making process. Consequently, users may find it difficult to comprehend their underlying mechanisms.

2. High Dimensionality
These programs work with vast and complex data sets, posing difficulties in interpreting the significance of individual variables. Consequently, unraveling the contribution of each variable becomes a complex task.

3. Interpretability-Accuracy Trade-Off
Simplifying AI models for interpretability often comes at the cost of accuracy. This trade-off necessitates finding a delicate balance to ensure understanding and optimal performance.

4. Nonlinearity
AI models capture intricate nonlinear relationships between variables, resulting in complex mappings. As a result, comprehending the input-output relationships becomes challenging.

5. Bias and Fairness
AI models can inherit biases from training data, leading to biased predictions and potential discrimination. Consequently, identifying and mitigating these biases becomes crucial for ensuring fairness.

6. Complexity of Deep Learning
Deep learning models with multiple layers and complex architectures pose challenges for interpretation. As a result, it is difficult to understand the inner workings and representations of these models.

7. Lack of Standardized Methods
The absence of consistent techniques for interpreting AI models hampers evaluation and comparison across different models and approaches. Therefore, the development of standardized methods is crucial for clarity and coherence.

8. Ethical Considerations
Most importantly, interpreting AI models necessitates addressing ethical aspects such as privacy, fairness, and accountability. As a matter of fact, these considerations play a vital role in ensuring responsible and trustworthy use of AI models.

How Can One Analyze the Output of AI Models?

To analyze the output of AI models, one can employ various methods for gaining insights and understanding their predictions.

One approach is to examine the confidence scores assigned to the model’s predictions. Confidence scores indicate the level of certainty the model has in its predictions, allowing for the identification of highly confident or uncertain outputs. Additionally, analyzing the distribution of confidence scores across different classes or categories can reveal patterns or anomalies in the model’s performance.

Another method involves visualizing the model’s outputs using techniques such as heat maps or saliency maps. These visual representations highlight the regions or features in the input data that the model focuses on when making predictions. This further aids in understanding those aspects of the input data that are most influential in the model’s decision-making process. Post-hoc interpretation techniques can also provide insights into the significance of different input features in driving the outputs.

What Techniques or Tools Can be Used to Interpret AI Models?

One can use a variety of techniques and tools to interpret AI models. Here are three techniques and five tools commonly used for AI model interpretation:

Techniques

1. Feature Importance Analysis

This technique allows for the identification of influential variables that significantly impact the decisions made by AI models. By understanding the importance of each feature, users can gain insights into the model’s decision-making process and prioritize the most influential factors.

2. Gradient-Based Techniques

Techniques such as Grad-CAM provide visualizations that highlight important regions in the input data. Users can examine these visual representations to better understand where the model focuses its attention and which areas contribute most to its predictions.

3. Local Explanations

Local explanation techniques like LIME create interpretable surrogate models to explain individual predictions made by AI models. These models provide simplified explanations at the instance level. This, in turn, makes it easier to understand the reasoning behind specific predictions.

Tools

1. SHAP

SHAP (SHapley Additive exPlanations) is a versatile tool that estimates feature importance and provides model-agnostic explanations. It assigns importance values to each feature, allowing users to understand the contribution of individual features to the model’s output.

2. TensorFlow Explainability

This tool offers interpretability techniques specific to TensorFlow-based AI models, such as Integrated Gradients and Occlusion Sensitivity. This helps users understand the model’s behavior and the importance of different input features.

3. Captum

Captum is a PyTorch library that provides interpretability techniques like Integrated Gradients and DeepLIFT. These methods enable users to attribute the model’s predictions to specific input features and understand the decision-making process.

4. ELI5ELI5

ELI5 (Explain Like I’m 5) is a versatile tool that offers various interpretability techniques, including feature importance and permutation importance. It simplifies complex AI models by providing explanations that are easy to understand, making them accessible to a broader audience.

5. Yellowbrick

Yellowbrick is an open-source Python library that offers a wide range of visualizations for model interpretation and diagnostics. It provides visual tools to analyze the model’s performance, feature importance, and decision boundaries, aiding in the interpretation of AI models.

How Can the Interpretability of AI Models be Improved?



Multiple strategies and techniques can be employed to improve the interpretability of AI models. Firstly, incorporating model architectures designed for interpretability, such as decision trees or rule-based systems, enhances transparency. Additionally, feature engineering and selection help focus on extracting and utilizing meaningful and interpretable features. Furthermore, techniques like LIME or SHAP can be leveraged to generate local or global explanations, shedding light on the AI model’s decision-making process. Moreover, visualization methods, including heat maps or saliency maps, facilitate a better understanding of its attention and focus. Regularization techniques like dropout or regularization penalties can further enhance interpretability. Lastly, promoting the development and adoption of standardized evaluation metrics and benchmarks for interpretability ensures consistent and reliable assessment.

Source: emeritus
Original Content: https://shorturl.at/sJY78

39

Computer Vision Tools / Top Computer Vision Tools/Platforms in 2023

« on: September 19, 2023, 10:32:32 AM »

By Prathamesh Ingle

Computer vision enables computers and systems to extract useful information from digital photos, videos, and other visual inputs and to conduct actions or offer recommendations in response to that information. Computer vision gives machines the ability to perceive, observe, and understand, much like artificial intelligence gives them the capacity to think.

Human vision has an advantage over computer vision because it has been around longer. With a lifetime of context, human sight has the advantage of learning how to distinguish between things, determine their distance from the viewer, determine whether they are moving, and determine whether an image is correct.

With cameras, data, and algorithms instead of retinas, optic nerves, and the visual cortex, computer vision teaches computers to execute similar tasks in much less time. A system trained to inspect items or monitor a production asset can swiftly outperform humans since it can examine thousands of products or processes per minute while spotting imperceptible flaws or problems.

Energy, utilities, manufacturing, and the automobile industries all use computer vision, and the market is still expanding.

A few typical jobs that computer vision systems can be utilized for are as follows:

Classification of objects. The system analyzes visual data before categorizing an object in a photo or video under a predetermined heading. The algorithm, for instance, can identify a dog among all the items in the image.

Identification of the item. The system analyzes visual data and recognizes a specific object in a picture or video. For instance, the algorithm may pick out a particular dog from the group of dogs in the image.

Tracking of objects. The system analyzes video, identifies the object (or objects) that satisfy the search criteria, and follows that object’s progress.

Top Computer Vision Tools

Kili Technology’s Video Annotation Tool

Kili Technology’s video annotation tool is designed to simplify and accelerate the creation of high-quality datasets from video files. The tool supports a variety of labeling tools, including bounding boxes, polygons, and segmentation, allowing for precise annotation. With advanced tracking capabilities, you can easily navigate through frames and review all your labels in an intuitive Explore view.


The tool supports various video formats and integrates seamlessly with popular cloud storage providers, ensuring a smooth integration with your existing machine learning pipeline. Kili Technology’s video annotation tool is the ultimate toolkit for optimizing your labeling processes and constructing powerful datasets.

OpenCV

A software library for machine learning and computer vision is called OpenCV. OpenCV, developed to offer a standard infrastructure for computer vision applications, gives users access to more than 2,500 traditional and cutting-edge algorithms.

These algorithms may be used to identify faces, remove red eyes, identify objects, extract 3D models of objects, track moving objects, and stitch together numerous frames into a high-resolution image, among other things.

Viso Suite

A complete platform for computer vision development, deployment, and monitoring, Viso Suite enables enterprises to create practical computer vision applications. The best-in-class software stack for computer vision, which is the foundation of the no-code platform, includes CVAT, OpenCV, OpenVINO, TensorFlow, or PyTorch.

Image annotation, model training, model management, no-code application development, device management, IoT communication, and bespoke dashboards are just a few of the 15 components that make up Viso Suite. Businesses and governmental bodies worldwide use Viso Suite to create and manage their portfolio of computer vision applications (for industrial automation, visual inspection, remote monitoring, and more).

TensorFlow

TensorFlow is one of the most well-known end-to-end open-source machine learning platforms, which offers a vast array of tools, resources, and frameworks. TensorFlow is beneficial for developing and implementing machine learning-based computer vision applications.

One of the most straightforward computer vision tools, TensorFlow, enables users to create machine learning models for computer vision-related tasks like facial recognition, picture categorization, object identification, and more. Like OpenCV, Tensorflow supports several languages, including Python, C, C++, Java, and JavaScript.

CUDA

NVIDIA created the parallel computing platform and application programming interface (API) model called CUDA (short for Compute Unified Device Architecture). It enables programmers to speed up processing-intensive programs by utilizing the capabilities of GPUs (Graphics Processing Units).

The NVIDIA Performance Primitives (NPP) library, which offers GPU-accelerated image, video, and signal processing operations for various domains, including computer vision, is part of the toolkit. In addition, multiple applications like face recognition, image editing, rendering 3D graphics, and others benefit from the CUDA architecture. For Edge AI implementations, real-time image processing with Nvidia CUDA is available, enabling on-device AI inference on edge devices like the Jetson TX2.

MATLAB

Image, video, and signal processing, deep learning, machine learning, and other applications can all benefit from the programming environment MATLAB. It includes a computer vision toolbox with numerous features, applications, and algorithms to assist you in creating remedies for computer vision-related problems.

Keras

A Python-based open-source software package called Keras serves as an interface for the TensorFlow framework for machine learning. It is especially appropriate for novices because it enables speedy neural network model construction while offering backend help.

SimpleCV

SimpleCV is a set of open-source libraries and software that makes it simple to create machine vision applications. Its framework gives you access to several powerful computer vision libraries, like OpenCV, without requiring a thorough understanding of complex ideas like bit depths, color schemes, buffer management, or file formats. Python-based SimpleCV can run on various platforms, including Mac, Windows, and Linux.

BoofCV

The Java-based computer vision program BoofCV was explicitly created for real-time computer vision applications. It is a comprehensive library with all the fundamental and sophisticated capabilities needed to develop a computer vision application. It is open-source and distributed under the Apache 2.0 license, making it available for both commercial and academic use without charge.

CAFFE

Convolutional Architecture for Fast Feature, or CAFFE A computer vision and deep learning framework called embedding was created at the University of California, Berkeley. This framework supported a variety of deep learning architectures for picture segmentation and classification and was made in the C++ programming language. Due to its incredible speed and image processing capabilities, it is beneficial for research and industry implementation.

OpenVINO

A comprehensive computer vision tool, OpenVINO (Open Visual Inference and Neural Network Optimization), helps create software that simulates human vision. It is a free cross-platform toolkit designed by Intel. Models for numerous tasks, including object identification, face recognition, colorization, movement recognition, and others, are included in the OpenVINO toolbox.

DeepFace

The most well-liked open-source computer vision library for deep learning facial recognition at the moment is DeepFace. The library provides a simple method for using Python to carry out face recognition-based computer vision.

YOLO

One of the fastest computer vision tools in 2022 is You Only Look Once (YOLO). It was created in 2016 by Joseph Redmon and Ali Farhadi to be used for real-time object detection. YOLO, the fastest object detection tool available, applies a neural network to the entire image and then divides it into grids. The odds of each grid are then predicted by the software concurrently. After the hugely successful YOLOv3 and YOLOv4, YOLOR had the best performance up until YOLOv7, published in 2022, overtook it.

FastCV

FastCV is an open-source image processing, machine learning, and computer vision library. It includes numerous cutting-edge computer vision algorithms along with examples and demos. As a pure Java library with no external dependencies, FastCV’s API ought to be very easy to understand. It is, therefore, perfect for novices or students who want to swiftly include computer vision into their ideas and prototypes.

To easily integrate computer vision functionality into our mobile apps and games, the company also integrated FastCV on Android.

Scikit-image

One of the best open-source computer vision tools for processing images in Python is the Scikit-image module. Scikit-image allows you to conduct simple operations like thresholding, edge detection, and color space conversions.

Although it’s not a program you’ll use frequently, it has several practical uses. For instance, with a bit of setup, you could use scikit-image on your camera to snap a picture using infrared light or find watermarks on photos. These are only a few examples of what scikit-image can be used for. If all else fails, image manipulation is an option.

Source: marktechpost
Original Content: https://shorturl.at/FGT46

40

Natural Language Processing (NLP) Tools / 8 Best Tools for Natural Language Processing in 2023

« on: September 19, 2023, 10:18:35 AM »

By Corey Ginsberg

What is Natural Language Processing?
Natural Language Processing (NLP) is a subfield of artificial intelligence that studies the interaction between computers and languages. The goals of NLP are to find new methods of communication between humans and computers and grasp human speech as it is uttered. This technology combines machine learning with computational linguistics, statistics, and deep learning models so that computers can process human language from voice or text data and grasp its entire meaning and the writer or speaker’s intentions.

NLP is often used for developing word processor applications and translation software. In addition, search engines, banking apps, translation software, and chatbots rely on NLP to better understand how humans speak and write.

Uses of Natural Language Processing in Data Analytics

The field of data analytics has been rapidly evolving in the past years, in part thanks to the advancements with tools and technologies like machine learning and NLP. It’s now possible to have a much more comprehensive understanding of the information within documents than in the past.

Here are just a few of the ways NLP is currently being used in data analytics:

NLP capabilities are being incorporated into business intelligence and analytics products, which can enhance natural language generation for data visualization narration. By doing so, data visualizations are more understandable and accessible to various audiences. The act of narrating data visualizations not only creates a more effective storytelling experience but also makes it less likely that the data will be interpreted subjectively.
Thanks to NLP, more people within a given organization (besides data analysts and data scientists) are now able to interact with data. Because data can be approached in a conversational manner, this interaction is more natural for non-technical team members and still offers the same important insights about the data.
NLP is changing the speed at which data can be explored. Visualization software can now generate queries and find answers to questions as quickly as these questions can be uttered or typed.
Surveys can provide helpful insights into how a company is performing. However, when a large number of customers complete surveys, the data size also increases. At that point, it’s no longer possible for one person to read the results and formulate a conclusion. Companies that use NLP to manage survey results and gather insights are able to do so much more accurately and efficiently than a human would be able to.
Machines are able to analyze a much larger amount of language-based data than a human can, without the risk of bias, inconsistency, or fatigue. By incorporating automation capabilities into data analysis, text and speech data can be quickly and thoroughly analyzed.
The ability to understand human language is no easy task. People have many manners of verbal and written expression. In addition to the hundreds of languages and dialects currently being used, there are nuances to each of these, such as grammar and syntax rules, regional accents, and slang expressions. NLP can resolve language ambiguities and provide a helpful numeric structure to the data, which aids with textual analytics and speech recognition.
NLP has applications for investigative discovery. It is a powerful tool for spotting patterns in written reports or emails, which can be used not just to detect but also to solve crimes.
Text mining is a type of AI that incorporates NLP to convert unstructured text within documents or databases into structured data that can then be analyzed or used for machine learning algorithms. Once the data is structured, it can also be incorporated into data warehouses, databases, or dashboards, at which time it can be used for various types of analytical analyses, such as predictive, prescriptive, and descriptive.
By incorporating keyword extraction algorithms to reduce a large body of text into several ideas and keywords, it’s possible to glean the main topic of the text without having to read the document.
When working with a text, text statistics visualizations can offer valuable insights about sentence length, word frequency, and worth length, and display this information in histograms or bar charts.
Sentiment analysis is one of the primary functions of NLP. The main use of sentiment analysis is to analyze the words in a text so that the general sentiment of the text can be established. This technique is able to reduce results into three areas: positive, negative, and neutral. Results that offer a negative number indicate that the text has a negative tone; those with a corresponding positive number indicate positive sentiments in the text.

8 Best Tools for Natural Language Processing in 2021

The following list highlights eight of the best tools and platforms for Data Analysts and Data Scientists to use for Natural Language Processing in 2021:

1. Gensim is a high-speed, scalable Python library that focuses primarily on topic modeling tasks. It excels at recognizing the similarities between texts, as well as navigating various documents and indexing texts. One of the main benefits of using Gensim is that it can handle huge data volumes.
2. SpaCy is one of the newer open-source NLP processing libraries. This Python library performs quickly and is well-documented. It is able to handle large datasets and provides users with a plethora of pre-trained NLP models. SpaCy is geared toward those who are getting text ready for deep learning or extraction.
3. IBM Watson offers users a range of AI-based services, each of which is stored in the IBM cloud. This versatile suite is well-suited to perform Natural Language Understanding tasks, such as identifying keywords, emotions, and categories. IBM Watson’s versatility lends itself to use in a range of industries, such as finance and healthcare.
4. Natural Language Toolkit (NLTK) enables users to create Python programs that are compatible with human language data. NLTK has user-friendly interfaces to more than 50 lexical and corpora resources, as well as various text processing libraries and a robust discussion forum. This free, open-source platform is commonly used by educators, students, linguists, engineers, and researchers.
5. MonkeyLearn is an NLP-powered platform that provides users with a means for gathering insights from text data. This user-friendly platform offers pre-trained models that can perform topic classification, keyword extraction, and sentiment analysis, as well as customized machine learning models that can be changed to meet various business needs. MonkeyLearn can also connect to apps like Excel or Google Sheets to perform text analysis.
6. TextBlob is a Python library that functions as an extension of NLTK. When using this intuitive interface, beginners can easily perform tasks like part-of-speech tagging, text classification, and sentiment analysis. This library tends to be more accessible to those who are new to NLP than other libraries.
7. Stanford Core NLP was created and is currently being maintained by those at Stanford University who are working on NLP. This Java library requires users to install the Java Development Kit onto their computer. It offers APIs in almost every programming language and is well-suited for executing tasks such as tokenization, named entity recognition, and part-of-speech tagging. Because Core NLP provides scalability and speed optimization, it works well for performing complicated tasks.
8. Google Cloud Natural Language API is part of Google Cloud. It incorporates question-answering technology, as well as language understanding technology. This interface offers users a variety of pre-trained models that can be used for performing entity extraction, content classification, and sentiment analysis.
The field of data analytics is being transformed by natural language processing capabilities. In the coming years, as technology continues to change and inform how humans interact with computers, as well as how computers handle big data, the field of data analytics is expected to continue to evolve in new and exciting ways with the help of newly developed tools and platforms.

Hands-On Data Analytics & Machine Learning Classes

Are you interested in learning more about the field of data analytics? If so, Noble Desktop’s data analytics classes are a great starting point. Courses are currently available in topics such as Excel, Python, and data analytics, among others skills necessary for analyzing data.

In addition, more than 130 live online data analytics courses are also available from top providers. Courses range from three hours to six months and cost from $219 to $60,229.

Those who are committed to learning in an intensive educational environment may also consider enrolling in a data analytics or data science bootcamp. These rigorous courses are taught by industry experts and provide timely instruction on how to handle large sets of data. Over 90 bootcamp options are available for beginners, intermediate, and advanced students looking to master skills and topics like data analytics, natural language processing, data visualization, data science, and machine learning, among others.

For those searching for a data analytics class nearby, Noble’s Data Analytics Classes Near Me tool provides an easy way to locate and browse the 400 or so data analytics classes currently offered in the in-person and live online formats. Course lengths vary from three hours to 36 weeks and cost $119-$60,229. For prospective students looking for classes that teach natural language processing or machine learning, Noble’s Machine Learning Classes Near Me tool can be used to search through more than a dozen options by top providers.

Source: Noble Desktop
Original Content: https://shorturl.at/zKL58

41

Machine Learning in Industry / Top Machine Learning Applications by Industry: 6 Machine Learning Examples

« on: September 18, 2023, 09:47:05 AM »

You likely benefit from machine learning multiple times a day — even if you’re not familiar with the specifics involved. For instance, you rely on machine learning when you use a maps app on your smartphone to get to a friend’s house, or when you ask Siri to play your favorite song one more time. Simply put, machine learning is a field of artificial intelligence that uses data to develop, train, and refine algorithms so they can make predictions or decisions with minimal human intervention.

Machine learning is a rapidly growing field within the technology industry, as well as a point of focus in companies across industries. Given the high demand for machine learning skills in the current job market, understanding the fundamentals can provide a promising pathway for anyone considering a job in tech, or even those looking to change careers.

How Does Machine Learning Work?

The goal of machine learning is to make computers (i.e. machines) learn from experience. This happens through the use of algorithms, which use computational methods to “learn” information directly from data sets. As the available data grows, the algorithms improve their accuracy and performance.

Because machine learning is used across a variety of industries, your areas of interest will dictate how you choose to learn it: Columbia Engineering FinTech Boot Camp, for example, teaches machine learning in finance. Interested in data science? Machine learning is a crucial data analytics skill needed to qualify for in-demand roles. In this article, we will explore how machine learning works in six industries: finance, business, genetics and genomics, healthcare, retail, and education.

Types of Machine Learning

There are three primary techniques used in machine learning: supervised learning, unsupervised learning, and reinforcement learning. Out of the three, supervised learning is the most popular — it trains a model to predict future outputs based on existing input and output data, similar to using flash cards as a teaching method. First, pairs of inputs and outputs are introduced to an algorithm. Over time, the algorithm learns the nature of the input-output relationship to predict an output from a new input. One real-life example of this is your email spam filter — it learns what spam looks like (input), and then learns to separate spam from other mail (output).

Unsupervised learning is quite different. Rather than pre-selecting a preferred output for the algorithm, the algorithm is fed a data set and given the tools to understand its properties. From there, it finds hidden patterns in input data and can then learn to organize the data in logical ways that can help better analyze the set. An example of this is the “recommended” section you might see on a streaming service: the website assesses its videos for length, topic, and other categories, and then uses data on what you’ve watched in the past to make a recommendation for what you should watch in the future.

Reinforcement learning trains a machine through reinforcement mechanisms — similar to the classical conditioning employed by Ivan Pavlov to induce drooling in dogs at the sound of a bell. A reinforcement learning system in its early stages will make many mistakes. However, over time, the machine receives signals denoting error or accuracy and learns from its mistakes to be more successful.

What Is Machine Learning Used For?

Machine learning is behind a wide variety of tools that many of us use every day. It’s present in our social media channels, customer service interactions, and data analytics — and the use cases for machine learning continue to increase. Below are some of the most common uses for machine learning.

Image recognition
Image recognition is one of the most common uses of machine learning. You’ve probably seen it if you’ve ever posted a photo to Facebook and the app suggested you tag a friend — if the machine learning is working correctly, that suggested friend will be the one in the photo. Image recognition is an example of a computer vision algorithm, which breaks an image down into different aspects that are used as reference points. The features of the images are then matched with features of available samples in order to produce a suggestion (e.g., suggest whom to tag in a photo).

Text generation and analysis
Another common type of machine learning algorithm, natural language processing uses deep analysis of text and extracts insights to create an output. They are used to create chatbots as well as an array of text-based services, such as text correction apps like Grammarly, which flags typos and anomalies based on basic grammar rules.

Speech Recognition
If you’ve ever used a virtual assistant like Siri or Alexa, you’ve benefited from speech recognition machine learning. These apps use natural language processing to analyze an input (e.g., your voice asking about the weather) and perform the given query to provide a suitable output. These services improve over time, as they are able to learn from user inputs and feedback. Another example of speech recognition is speech-to-text automation, often used for captioning video or audio or sending off a quick text message.

Data analytics
Data analytics is the process of gathering data from data sets, analyzing it to extract relevant insights, and visualizing it logically and holistically. The ability to apply machine learning is an important part of the data scientist role. In fact, some data analysts, like machine learning engineers, even specialize in the field of machine learning. However, data scientists are often required to have general machine learning skills so they can build and train models that can help them make reliable future predictions.

Algorithmic Recommendations
As we’ve already mentioned, machine learning is commonly used in any service that recommends content to users (e.g., social media feeds, video platforms, news platforms). These services analyze the content you’ve already consumed — what sorts of videos you like, what types of news stories you like to read — and recommend more of the same without requiring you to manually search for them.

Benefits of Machine Learning
Automation: Machine learning allows companies to automate a wide variety of tasks, making them more efficient and cost-effective.
Less reliance on human interaction: Since machine learning relies almost exclusively on algorithms to get its work done, managers don’t have to worry about balancing team dynamics in order to complete an important task.
Scope of improvement: Because machine learning is always improving and evolving with time, machine learning algorithms are able to constantly build on their own bases of knowledge and functionality.
Efficient data handling: At this point, machine learning is able to analyze any type of data — even the most multi-dimensional — which makes it incredibly useful for data analysis and data science.
Wide range of applications: As you’ll see throughout this article, machine learning is used in nearly every industry today, from healthcare to e-commerce.



How to Learn Machine Learning
If you’re interested in learning more about machine learning or pursuing it as a career, you should educate yourself on the educational resources available to choose from. Since machine learning is such a complex field, it requires specialized education in order to get a job. One great option for getting hands-on experience in a short time frame is a data analytics boot camp: boot camps are faster and more focused than a graduate or undergraduate degree, and they typically offer professional support to qualified learners.

A data analytics boot camp can teach you the necessary skills to become proficient in machine learning — and even connect you with mentors who will help you in your job search. If you’re looking for a more topical focus, you can specifically learn ML for finance in a fintech boot camp. Not exactly sure what fintech is? Generally speaking, it is the integration of technology into financial products and services (e.g., banking applications, investing websites), and as with more general data analytics, it’s a fast-growing career path with plenty of promise for those willing to master the skills required.

Other options include more traditional educational paths like master’s degrees in computer science or data science. These pathways provide a thorough and rigorous education, offering the chance to learn on- or off-campus while potentially exploring the broader fields encompassing ML.

When to Use Machine Learning
Machine learning is typically used to predict an output or reveal and understand trends, and is particularly useful when data is structured, or already labeled. Because of the ease with which machine learning is able to categorize and assess this type of data, it’s especially useful for analyzing and organizing data like videos, images, and audio files (e.g., learning whether a photo has a face in it). Machine learning is most helpful when simple rules or computations can’t be used to predict a target value, or when looking at a particularly large data set.

6 Machine Learning Use Cases
Machine learning is used in a wide variety of industries — not just in the tech-heavy companies you might imagine when you think about someone manipulating an enormous data set. Here, we will discuss machine learning use cases by industry, providing some of the different ways that this tool is being used today by a variety of people and companies.

Machine Learning Applications in Finance
In the financial services sector, analysts use machine learning to automate trading activities, detect fraud, and provide financial advising services to their clients. Algorithmic trading requires traders to build mathematical models that can monitor news feeds and trading trends to predict a rise or fall in security prices. Finance companies also use machine learning to detect fraudulent activity by comparing transactions against other existing data points (e.g., they know if that $500 Amazon purchase was something you’re likely to do, or whether it’s completely out of character for you and therefore a little suspicious). In portfolio management, robo-advisors built via machine learning provide investors with automated financial advice based on their goals, risk aversion, and other factors. Across fintech careers, machine learning is an invaluable area of expertise. If you’re interested in learning more about this field, you should consider enrolling in a fintech boot camp.

Machine Learning Applications in Business
Machine learning offers businesses an extensive number of ways to boost their effectiveness, efficiency, and offerings. Chatbots, for example, allow businesses to provide faster, more flexible customer service without employing a call center or making customers wait on hold for the next available representative. Internally, businesses also use machine learning to help with decision support, allowing teams to rely on algorithms to make decisions on resource management or identify trends and problems more quickly. Machine learning also helps businesses deal with customer churn by using data to understand how and why businesses tend to lose customers.

Machine Learning Applications in Genetics and Genomics
The human genome is one of the largest data sets ever studied. Humans contain over 20,000 different genes, each of which has potential for variation. Machine learning allows researchers to better understand different genetic traits and abnormalities as they analyze and understand vast data sets. For example, machine learning helps scientists identify the genetic variants shared in individuals with traits that those scientists are studying, like hemophilia or diabetes, allowing them to better understand where in the genome these disorders originate. Occasionally, it can help researchers understand why they occur in the first place.

Machine Learning Applications in Healthcare
When applied to healthcare, machine learning can help hospitals and their staff make administrative processes more efficient and streamlined, personalize medical treatments, and better understand and track infectious diseases. Technology like PathAI, for example, uses machine learning to help pathologists make more accurate and faster diagnoses, as well as connect patients with new treatments or therapies that might benefit them.

Machine Learning Applications in Retail
As we’ve already mentioned, machine learning can be incredibly helpful in understanding and decreasing customer churn (i.e., the rate at which a business loses customers each year), which is a large point of focus for many retail companies. According to Salesforce, 83 percent of IT experts have found that companies using AI have greater customer engagement. Machine learning also helps retailers synthesize the nearly limitless quantity of consumer data that is available to them but almost impossible to understand by basic human analysis.

Machine Learning Applications in Education
Machine learning can help educational institutions on both process-based tasks and more student-focused initiatives. Statistical models can help understand student progress and needs, while scheduling algorithms can help create more efficient and streamlined schedules for institutions of all sizes.

Machine Learning Examples by Company
Companies of all sorts have used machine learning to expand their offerings and streamline their processes. After completing a data boot camp, you will have the skills to start implementing machine learning in your company — or qualify for a new one. Below is just a small selection of companies that have used these tools to great advantage.

Yelp’s image curation
In 2015, Yelp built a photo classifier that helps classify user-uploaded photos of businesses. For example, if a user went to the local pub, ordered a burger, and uploaded a photo of it to Yelp, the image classifier would be able to properly identify the burger. To build this classifier, Yelp collected the information through photo captions, photo attributes, and crowdsourcing, and then used machine learning to classify future photos. They also allow users to report incorrectly classified photos — a great example of feedback that helps improve ML-built products.

Facebook’s chatbot
In 2020, Facebook introduced a chatbot that was able to converse on a wide array of topics — not just a prescribed set of topics like many customer service chatbots do. According to the MIT Technology Review, this bot, called Blender, was “first trained on 1.5 billion publicly available Reddit conversations.” It was then further developed to focus on conversations containing emotion, information-dense conversations, and conversations between users with distinct and differing personalities.

Amazon’s product recommendations
Once you begin browsing and shopping on Amazon, you’ll start to see suggestions like “Customers Who Bought This Product Also Bought”. These are clear results of machine learning, as they categorize both products and shoppers and use that information to suggest items to users.

Source: bootcamp
Original Content: https://shorturl.at/pqxNV

42

Machine Learning and Hardware Acceleration / Of hardware acceleration in machine learning

« on: September 18, 2023, 09:31:32 AM »

By Romain Bouges


Typical GPU setup with Input, Output and memory modules.

In deep learning, hardware acceleration is the use of computer hardware designed to speed up artificial intelligence applications greater than what would be possible with a software running on a general-purpose central processing unit (CPU). Machine learning applications(1) such as artificial neural networks, machine vision and inference(2) are especially concerned.

We will review the most common type of material used in nowadays deep learning applications and open the discussion on promising technologies to come.

General purpose integrated circuit: Graphical Processing Unit (GPU)

Graphical Processing Units (GPUs) is a general-purpose solution that will be preferred to CPUs for several reasons.

GPUs allows to have thousands of threads in parallel (vs single thread performance optimization), hundreds of simpler cores (vs a few complex) and allocate most of the die surface for simple integer and floating point operations (vs more complex operations such as Instruction Level Parallelism). Numerous simpler operations will require less power to be carried out, which is another parameters to be taken into consideration.(3)

CPUs could be compared as a fast and fuel consuming sport car carrying a small load to a slow and fuel efficient freight truck with a large load.(4)

Specific purpose integrated circuits: Field-Programmable Gate Array (FPGA)

The advantage of this kind of specific purpose integrated circuit compared to a general-purpose one is its flexbility: after manufacturing it can be programmed to implement virtually any possible design and be more suited to the application on hand compared to a GPU.

This kind of hardware is also designed keeping in mind general purpose applications to be used for particular settings. (5)

It can be designed as a prototype to later design an Application-Specific Integrated Circuit.

Further improvements: dedicated Application-Specific Integrated Circuits (ASICs)

Those integrated circuit will be specially designed for an application without the possibility to reprogram it. It will usually increase its troughtput by a factor of 10 (1) (performance-per-watt improvement over off-the-shelf solutions when dealing with machine learning tasks) and/or will be more consumption efficient. The size Amazon, Google and Facebook among others are developping such circuits for their own use.(6)(7)

References

(1) Google boosts machine learning with its tensor processing unit. https://techreport.com/news/30155/google-boosts-machine-learning-with-its-tensor-processing-unit/

(2) Project Brainwave. https://www.microsoft.com/en-us/research/project/project-brainwave/

(3) Do we really need gpu for deep-learning ? https://medium.com/@shachishah.ce/do-we-really-need-gpu-for-deep-learning-47042c02efe2/

(4) GPUs necessary for deep learning. https://www.analyticsvidhya.com/blog/2017/05/gpus-necessary-for-deep-learning/

(5) A Survey of FPGA-based Accelerators for Convolutional Neural Networks. https://www.academia.edu/37491583/A_Survey_of_FPGA-based_Accelerators_for_Convolutional_Neural_Networks

(6) An Updated Survey of Efficient Hardware Architectures for Accelerating Deep Convolutional Neural Networks. Page 9 — Spatial Architectures: Fpgas and Asics. https://www.mdpi.com/1999-5903/12/7/113/pdf

(7) Facebook joins Amazon and Google in AI chip race. https://www.ft.com/content/1c2aab18-3337-11e9-bd3a-8b2a211d90d5/

Source: medium.com
Original Content: https://shorturl.at/dESV3

43

Deep Learning Frameworks / A Brief History of Deep Learning Frameworks

« on: September 11, 2023, 11:24:31 AM »

By Lin Yuan



The past decade has seen a burst of algorithms and applications in machine learning especially deep learning. Behind the burst of these deep learning algorithms and applications are a wide variety of deep learning tools and frameworks. They are the scaffolding of the machine learning revolution: the widespread adoption of deep learning frameworks like TensorFlow and PyTorch enabled many ML practitioners to more easily assemble models using well-suited domain-specific languages and a rich collection of building blocks.

Looking back at the evolution of deep learning frameworks we can clearly see a tightly coupled relationship between deep learning frameworks and deep learning algorithms. These virtuous cycle of interdependency propels a rapid development of deep learning frameworks and tools into the future.



Stone Age (early 2000s)
The concept of neural networks have been around for a while. Before the early 2000s, there were a handful of tools that can be used to describe and develop neural networks. These tools include MATLAB, OpenNN, and Torch etc. They are either not tailored specifically for neural network model development or having complex user APIs and lack of GPU support. During this time, ML practitioners had to do a lot of heavy lifting when using these primitive deep learning frameworks.



Bronze Age (~2012)
In 2012, Alex Krizhevsky et al. from the University of Toronto proposed a deep neural network architecture later known as AlexNet [1] that achieved the state-of-the-art accuracy on ImageNet dataset and outperformed the second-place contestant by a large margin. This outstanding result sparked the excitement in deep neural networks and since then various deep neural network models kept setting higher and higher record in the accuracy of ImageNet dataset.

Around this time, some early days deep learning frameworks such as Caffe, Chainer and Theano came into being. Using these frameworks, users could conveniently built complex deep neural network models such as CNN, RNN, and LSTM etc. In addition, multi-GPU training was supported in these frameworks which significantly reduced the time to train these models and enabled training large models that were not able to fit into a single GPU memory earlier. Among these frameworks, Caffe and Theano used a declarative programming style while Chainer adopted the imperative programming style. These two distinct programming styles also set two different development paths for the deep learning frameworks that were yet to come.



Iron Age (2015~2016)
As the success of AlexNet drew great attention in the area of computer vision and reignited the hope of neural networks, large tech companies joined the force of developing deep learning frameworks. Among them, Google open sourced the famous TensorFlow framework that is still the most popular deep learning framework in ML field up to date. The inventor of Caffe joined Facebook and continued the release of Caffe2; at the same time, Facebook AI Research (FAIR) team also released another popular framework PyTorch which was based on the Torch framework but with the more popular Python APIs. Microsoft Research developed the CNTK framework. And Amazon adopted MXNet, a joint academic project from University of Washington, CMU and others. TensorFlow and CNTK borrowed the declarative programming style from Theano whereas PyTorch inherited the intuitive and user-friendly imperative programming style from Torch. While imperative programming style is more flexible (such as defining a while loop etc.) and easy to trace, declarative programming style often provides more room for memory and runtime optimization based on compute graph. On the other hand, MXNet, dubbed as “mix”-net, enjoyed the benefits of both worlds by supporting both a set of symbolic (declarative) APIs and a set of imperative APIs at the same time and optimized the performance of models described using imperative APIs via a method called hybridization.

In 2015 ResNet [2] was proposed by Kaiming He et al. and again pushed the boundary of image classification by setting another record in ImageNet accuracy. A consensus has been reached in both industry and academia that deep learning was going to the next big technology trend to solve challenges in various fields that were not deemed possible before. During this period, all deep learning frameworks were polished to provide clear-defined user APIs, optimized for multi-GPU training and distributed training and spawned many model zoos and toolkits that were targeted to specific tasks such as computer vision, natural language processing etc. It is also worth noting that François Chollet almost single-handedly developed the Keras framework that provides a more intuitive high-level abstraction of neural networks and building blocks on top of existing frameworks such as TensorFlow and MXNet. This abstraction became the de facto model level APIs in TensorFlow as of today.



Roman Times (2019~2020)
Just like how the human history unfolded, after a round of fierce competitions among deep learning frameworks, came to the duopoly of two big “empires”: TensorFlow and PyTorch, which represented more than 95% of the use cases of deep learning framework in research and production. Chainer team transitioned their development effort to PyTorch in 2019; similarly, Microsoft stopped active development of the CNTK framework and part of the team moved to support PyTorch on Windows and ONNX runtime. Keras was assimilated by TensorFlow and became one of its high-level APIs in the TensorFlow 2.0 release. MXNet remained a distant third in the deep learning framework space.

There are two trends in the deep learning framework space during this period. First is large model training. With the birth of BERT [3] and its Transformer-based relatives such as GPT-3 [4], ability to train large models became a desired feature of deep learning frameworks. This requires the deep learning framework to be able to train efficiently at a scale up to hundreds if not thousands of devices. Second trend is usability. All the deep learning frameworks during this period adopted the imperative programming style for its flexible semantics and easy debugging. At the same time, these frameworks also provide user-level decorators or APIs to achieve high performance through some JIT (just-in-time) compiler techniques.



Industrial Age (2021+)
The huge success of deep learning in a wide range of fields from self-driving, personalized recommendation, natural language understanding to health care etc. brought in an unprecedented wave of users, developers and investors. The coming decade is the golden time for developing deep learning tools and frameworks. Although deep learning frameworks have improved significantly from their inception, they are still far from mature as the programming language JAVA/C++ to the development of Internet applications. A lot of exciting opportunities and works are yet to be explored and accomplished.

Looking forward, there are a few technical trends that are promising to become mainstream in the next generation of deep learning frameworks:

Compiler-based operator optimization. Today a lot of operator kernels are implemented either manually or via some third party libraries such as BLAS, CuDNN, OneDNN etc. that are targeted to a specific hardware platform. This caused a lot of overhead when model is trained or deployed on different hardware platforms. In addition, the growth of new deep learning algorithms is often much faster than the iteration of these libraries making new operators often not supported by these libraries. Deep learning compilers such as Apache TVM, MLIR, Facebook Glow, etc. have been proposed to optimize and run computations efficiently on any hardware backend. They are well positioned to serve as the entire backend stack in the deep learning frameworks.
Unified API standards. Many deep learning frameworks share similar but slightly different user APIs. This caused difficulty and unnecessary learning curve for users to switch from one framework to another. Since the majority of machine learning practitioners and data scientists are familiar with NumPy library, it became natural that NumPy API should be the standard for tenor manipulation APIs in the new deep learning frameworks. We are already seeing a warm reception from users in the rapidly growing framework JAX whose APIs are purely NumPy compatible.
Data movement as a first-class citizen. Multi-node or multi-device training is becoming the norm for deep neural network training. Recently developed deep learning framework, such as OneFlow, took this insight into their design consideration from day one and treat data communication as part of the overall computation graph of the model training. This opens doors to more opportunities for performance optimization and since it does not have to maintain multiple training strategies (single device vs distributed training) as the previous deep learning frameworks do, it can provide a simpler user interface in addition to better performance.

Summary
We are at the dawn of an AI revolution. New research and applications in AI are generated at an unprecedented pace. Eight years ago, AlexNet network contains 60 million parameters; the most recent GPT-3 network contains 175 billion parameters, a 3000X increase in network size in 8 years! Human brains on the other hand contain an estimated 100 trillion parameters (aka synapses). This indicates that there is still a large gap for the neural network to reach human-level intelligence, if ever possible.

This prohibitive network size poses a great challenge for efficient computation in both hardware and software for model training and inference. The future deep learning framework is likely to be an interdisciplinary outcome of algorithms, high performance compute, hardware accelerators and distributed systems.

Source: towardsdatascience
Original Content: https://shorturl.at/jrsLU

44

Reinforcement Learning / What is reinforcement learning?

« on: September 11, 2023, 10:45:11 AM »

by Ruth Brooks

Reinforcement learning (RL) is a subset of machine learning that allows an AI-driven system (sometimes referred to as an agent) to learn through trial and error using feedback from its actions. This feedback is either negative or positive, signalled as punishment or reward with, of course, the aim of maximising the reward function. RL learns from its mistakes and offers artificial intelligence that mimics natural intelligence as closely as it is currently possible.

In terms of learning methods, RL is similar to supervised learning only in that it uses mapping between input and output, but that is the only thing they have in common. Whereas in supervised learning, the feedback contains the correct set of actions for the agent to follow. In RL there is no such answer key. The agent decides what to do itself to perform the task correctly. Compared with unsupervised learning, RL has different goals. The goal of unsupervised learning is to find similarities or differences between data points. RL’s goal is to find the most suitable action model to maximise total cumulative reward for the RL agent. With no training dataset, the RL problem is solved by the agent’s own actions with input from the environment.

RL methods like Monte Carlo, state–action–reward–state–action (SARSA), and Q-learning offer a more dynamic approach than traditional machine learning, and so are breaking new ground in the field.

There are three types of RL implementations:

Policy-based RL uses a policy or deterministic strategy that maximises cumulative reward
Value-based RL tries to maximise an arbitrary value function
Model-based RL creates a virtual model for a certain environment and the agent learns to perform within those constraints

How does RL work?

Describing fully how reinforcement learning works in one article is no easy task. To get a good grounding in the subject, the book Reinforcement Learning: An Introduction by Andrew Barto and Richard S. Sutton is a good resource.

The best way to understand reinforcement learning is through video games, which follow a reward and punishment mechanism. Because of this, classic Atari games have been used as a test bed for reinforcement learning algorithms. In a game, you play a character who is the agent that exists within a particular environment. The scenarios they encounter are analogous to a state. Your character or agent reacts by performing an action, which takes them from one state to a new state. After this transition, they may receive a reward or punishment. The policy is the strategy which dictates the actions the agent takes as a function of the agent’s state as well as the environment.

To build an optimal policy, the RL agent is faced with the dilemma of whether to explore new states at the same time as maximising its reward. This is known as Exploration versus Exploitation trade-off. The aim is not to look for immediate reward, but to optimise for maximum cumulative reward over the length of training. Time is also important – the reward agent doesn’t just rely on the current state, but on the entire history of states. Policy iteration is an algorithm that helps find the optimal policy for given states and actions.

The environment in a reinforcement learning algorithm is commonly expressed as a Markov decision process (MDP), and almost all RL problems are formalised using MDPs. SARSA is an algorithm for learning a Markov decision. It’s a slight variation of the popular Q-learning algorithm. SARSA and Q-learning are the two most typically used RL algorithms.

Some other frequently used methods include Actor-Critic, which is a Temporal Difference version of Policy Gradient methods. It’s similar to an algorithm called REINFORCE with baseline. The Bellman equation is one of the central elements of many reinforcement learning algorithms. It usually refers to the dynamic programming equation associated with discrete-time optimisation problems.

The Asynchrous Advantage Actor Critic (A3C) algorithm is one of the newest developed in the field of deep reinforcement learning algorithms. Unlike other popular deep RL algorithms like Deep Q-Learning (DQN) which uses a single agent and a single environment, A3C uses multiple agents with their own network parameters and a copy of the environment. The agents interact with their environments asynchronously, learning with every interaction, contributing to the total knowledge of a global network. The global network also allows agents to have more diversified training data. This mimics the real-life environment in which humans gain knowledge from the experiences of others, allowing the entire global network to benefit.

Does RL need data?

In RL, the data is accumulated from machine learning systems that use a trial-and-error method. Data is not part of the input that you would find in supervised or unsupervised machine learning.

Temporal difference (TD) learning is a class of model-free RL methods that learn via bootstrapping from a current estimate of the value function. The name “temporal difference” comes from the fact that it uses changes – or differences – in predictions over successive time steps to push the learning process forward. At any given time step, the prediction is updated, bringing it closer to the prediction of the same quantity at the next time step. Often used to predict the total amount of future reward, TD learning is a combination of Monte Carlo ideas and Dynamic Programming. However, whereas learning takes place at the end of any Monte Carlo method, learning takes place after each interaction in TD.

TD Gammon is a computer backgammon program that was developed in 1992 by Gerald Tesauro at IBM’s Thomas J. Watson Research Center. It used RL and, specifically, a non-linear form of the TD algorithm to train computers to play backgammon to the level of grandmasters. It was an instrumental step in teaching machines how to play complex games.

Monte Carlo methods represent a broad class of algorithms that rely on repeated random sampling in order to gain numerical results that point to probability. Monte Carlo methods can be used to calculate the probability of:

an opponent’s move in a game like chess
a weather event occurring in the future
the chances of a car crash under specific conditions
Named after the casino in the city of the same name in Monaco, Monte Carlo methods first arose within the field of particle physics and contributed to the development of the first computers. Monte Carlo simulations allow people to account for risk in quantitative analysis and decision making. It’s a technique used in a wide variety of fields including finance, project management, manufacturing, engineering, research and development, insurance, transportation, and the environment.

In machine learning or robotics, Monte Carlo methods provide a basis for estimating the likelihood of outcomes in artificial intelligence problems using simulation. The bootstrap method is built upon Monte Carlo methods, and is a resampling technique for estimating a quantity, such as the accuracy of a model on a limited dataset.

Applications of RL

RL is the method used by DeepMind to initiate artificial intelligence in how to play complex games like chess, Go, and shogi (Japanese chess). It was used in the building of AlphaGo, the first computer program to beat a professional human Go player. From this grew the deep neural network agent AlphaZero, which taught itself to play chess well enough to beat the chess engine Stockfish in just four hours.

AlphaZero has only two parts: a neural network, and an algorithm called Monte Carlo Tree Search. Compare this with the brute force computing power of Deep Blue, which, even in 1997 when it beat world chess champion Garry Kasparov, allowed the consideration of 200 million possible chess positions per second. The representations of deep neural networks like those used by AlphaZero, however, are opaque, so our understanding of their decisions is restricted. The paper Acquisition of Chess Knowledge in AlphaZero explores this conundrum.

Deep RL is being proposed in the use of unmanned spacecraft to navigate new environments, whether it’s Mars or the Moon. MarsExplorer is an OpenAI Gym compatible environment that has been developed by a group of Greek scientists. There are four deep reinforcement learning algorithms that the team has trained on the MarsExplorer environment, A3C, Ranbow, PPO, and SAC, with PPO performing best. MarsExplorer is the first open-AI compatible reinforcement learning framework that is optimised for the exploration of unknown terrain.

Reinforcement learning is also used in self-driving cars, in trading and finance to predict stock prices, and in healthcare for diagnosing rare diseases.

Deepen your learning with a Masters

These complex learning systems created by reinforcement learning are just one facet of the fascinating and ever-expanding world of artificial intelligence. Studying a Masters degree can allow you to contribute to this field, which offers numerous possibilities and solutions to societal problems and the challenges of the future.

Source: online.york.ac.u
Original Content: https://shorturl.at/rCEMT

45

Neural Network Training / What does Training Neural Networks mean?

« on: September 10, 2023, 09:38:24 AM »

By Jean-Louis Queguiner



Where does “Neural” come from?

As you should know, a biological neuron is composed of multiple dendrites, a nucleus and a axon (if only you had paid attention in your biology classes). When a stimuli is sent to the brain, it is received through the synapse located at the extremity of the dendrite.

When a stimuli arrives at the brain it is transmitted to the neuron via the synaptic receptors which adjust the strength of the signal sent to the nucleus. This message is transported by the dendrites to the nucleus to then be processed in combination with other signals emanating from other receptors on the other dendrites. Thus the combination of all these signals takes place in the nucleus. After processing all these signals, the nucleus will emit an output signal through its single axon. The axon will then stream this signal to several other downstream neurons via its axon terminations. Thus a neuron analysis is pushed in the subsequent layers of neurons. When you are confronted with the complexity and efficiency of this system, you can only imagine the millennia of biological evolution that brought us here.

On the other hand, artificial neural networks are built on the principle of bio-mimicry. External stimuli (the data), whose signal strength is adjusted by the neuronal weights (remember the synapse?, circulates to the neuron (place where the mathematical calculation will happen) via the dendrites. The result of the calculation – called the output – is then re-transmitted (via the axon) to several other neurons and then subsequent layers are combined, and so on.

Therefore, their is a clear parallel between biological neurons and artificial neural networks as presented in the figure below.



The Artificial Neural Network Recipe

To build a good Artificial Neural Network (ANN) you will need the following ingredients

Ingredients:

Artificial Neurons (processing node) are composed of:
(many) input neuron(s) connection(s) (dendrites)
a computation unit (nucleus) composed of:
a linear function (ax+b)
an activation function (equivalent to the synapse)
An output (axon)

Preparation to get an ANN for image classification training:

1. Decide on the number of output classes (meaning the number of image classes – for example, two for cat vs. dog)
2. Draw as many computation units as the number of output classes (congrats you just created the Output Layer of the ANN)
3. Add as many Hidden Layers as needed within the defined architecture (for instance vgg16 or any other popular architecture). Tip – Hidden Layers are just a set of neighboured Compute Units, they are not linked together.
4. Stack those Hidden Layers to the Output Layer using Neural Connections
5. It is important to understand that the Input Layer is basically a layer of data ingestion
6. Add an Input Layer that is adapted to ingest your data (or you will adapt your data format to the pre-defined architecture)
7. Assemble many Artificial Neurons together in a way where the output (axon) a Neuron on a given Layer is (one) of the input of another Neuron on a subsequent Layer. As a consequence, the Input Layer is linked to the Hidden Layers which are then linked to the Output Layer (as shown in the picture below) using Neural Connections (also shown in the picture below).
8. Enjoy your meal

simplified schema of a neural network architecture

What does it mean to train an Artificial Neural Network?

All Neurons of a given Layer are generating an Output, but they don’t have the same Weight for the next Neurons Layer. This means that if a Neuron on a layer observes a given pattern it might mean less for the overall picture and will be partially or completely muted. This is what we call Weighting: a big weight means that the Input is important and of course a small weight means that we should ignore it. Every Neural Connection between Neurons will have an associated Weight.

And this is the magic of Neural Network Adaptability: Weights will be adjusted over the training to fit the objectives we have set (recognize that a dog is a dog and that a cat is a cat). In simple terms: Training a Neural Network means finding the appropriate Weights of the Neural Connections thanks to a feedback loop called Gradient Backward propagation … and that’s it folks.

Parallel between Control Theory and Deep Learning Training

The engineering field of control theory defines similar principles to the mechanism used for training neural networks.

Control Theory general concepts

In control systems, a setpoint is the target value for the system.

A setpoint (input) is defined and then processed by a controller, which adjusts the setpoint’s value according to the feedback loop (Manipulated Variable). Once the setpoint has been adjusted it is then sent to the controlled system which will produce an output. This output is monitored using an appropriate metric which is then compared (comparator) to the original input via a feedback loop. This allows the controller to define the level of adjustment (Manipulated Variable) of the original setpoint.



Control Theory applied to a radiator

Let’s take the example of a resistance (controlled system) in a radiator. Imagine you decide to set the room temperature to 20 ° C (setpoint). The radiator starts up, supplies the resistance with a certain intensity defined by the controller. A probe (thermometer) will then take the ambient temperature (feedback elements) which is then compared (comparator) to the setpoint (desired temperature) and adjusts (controller) the electric intensity sent to the resistance. The adjustment of the new intensity is deployed via an incremental adjustment step.

Control Theory applied to Neural Network Training

The training of a neuron network is similar to a radiator insofar as the controlled system is the cat or dog detection model.

The objective is no longer to have the minimum difference between the setpoint temperature and the actual temperature but to minimize the error (Loss) between the classification of the incoming data (a cat is a cat) and the one made by the neural network.

In order to achieve this, the system will have to look at the input (setpoint) and compute an output (controlled system) based on the parameters defined in the algorithm. This phase is called the forward pass.


Once the output has been calculated, the system will re-propagate the evaluation error using Gradient Retro-propagation (Feedback Elements). While the temperature difference between the setpoint and the thermometer was converted into electrical intensity for the radiator, here the system will adjust the weights of the different inputs into each neuron with a given step (learning rate).


Parallel between electrical engineering controlled system and neural network training process

One thing to consider: The Valley Problem

When training the system, the backward propagation will lead the system to reduce the error it’s making to best fit the objectives you have set (finding that a dog is a dog…).

Choosing the learning rate at which you will adjust your weights (what one call adjustment step in Control Theory).

Just as is the case in control theory, the control system can face several issues if it is not designed correctly:

1. If the correction step (learning rate) is too small it will lead to a very slow convergence (i.e. it will take a very long time to get your room to 20°C…).
2. Too small a learning rate can also lead to you being stuck in a local minim
3. If the correction step (learning rate) is too high it will lead the system to never converge (beat around the bush) or worse (i.e. the radiator will oscillate between being either too hot or too cold)
4. The system could enter into a resonance state (divergence).



In the end Training an Artificial Neural Network (ANN) requires just a few steps:

1. First an ANN will require a random weight initialization
2. Split the dataset in batches (batch size)
3. Send the batches 1 by 1 to the GPU
4. Calculate the forward pass (what would be the output with the current weights)
5. Compare the calculated output to the expected output (loss)
6. Adjust the weights (using the learning rate increment or decrement) according to the backward pass (backward gradient propagation).
7. Go back to square 2

Further notice

That’s all folks, you are now all set to read our future blog post which focuses on Distributed Training in a Deep Learning Context.

Source: OVHcloud Blog
Original Content: https://shorturl.at/GZ047