Messages

16

AI Career Paths and Job Opportunities / Want to Work in Artificial Intelligence? 14 AI Careers & Job Outlook [2023]

« on: January 08, 2024, 10:28:39 AM »

What Is Artificial Intelligence?

Artificial intelligence is all around us, even in places you may not realize. From music preferences to home appliances and healthcare, the power of AI is far reaching. But first, let’s explore the basics of AI with this definition from Investopedia:

“AI refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind such as learning or problem-solving.”

Examples of artificial intelligence include:

Smart assistants like Siri and Alexa
Pandora and Netflix, which provide personalized song and entertainment recommendations
Chatbots
Robotic vacuum cleaners
Self-driving vehicles
Facial recognition software
Those are just a few of the many, many examples. Needless to say, artificial intelligence is everywhere, and the demand for AI — especially skilled, experienced AI professionals — is growing. Bernard Marr, a business and technology advisor to governments and companies, told Forbes that we now have access to more data than ever, which means AI has become smarter, faster, and more accurate.

“As a very simple example, think of Spotify recommendations,” he explained in the article. “The more music (or podcasts) you listen to via Spotify, the better able Spotify is to recommend other content that you might enjoy. Netflix and Amazon recommendations work on the same principle, of course.”

What Does an AI Professional Do?

Since artificial intelligence is an increasingly widespread and growing form of technology, professionals who specialize in AI are needed now more than ever. The good news is that the AI professional field is full of different career opportunities, which means you can take on different roles and responsibilities depending upon the position, your experience and your interests.

The need for skilled AI professionals spans nearly every industry, including:

Financial services
Healthcare
Technology
Media
Marketing
Government and military
National security
IoT-enabled systems
Agriculture
Gaming
Retail

Professional AI Skills in Demand for 2023

If you’re looking to enter the professional world of AI, it’s important to make sure you have the right skills, which will set you apart from other candidates and help you land the perfect position. First, competencies with calculus and linear algebra are extremely important. Also, if you’re interested in AI, you should have some knowledge and experience in at least one of the following programming languages:

Python
C/C++
MATLAB
According to ZipRecruiter, these are the top 5 skills required for AI jobs:

Communication skills
Knowledge and experience with Python specifically (in general, proficiency in programming language)
Digital marketing goals and strategies
Collaborating effectively with others
Analytical skills
The Intellipaat blog also recommends these additional skills for AI professionals:

Solid knowledge of applied mathematics and algorithms
Problem-solving skills
Industry knowledge
Management and leadership skills
Machine learning

How to Start a Career in Artificial Intelligence

If you aren’t already in the industry, the first step is to conduct research, which includes talking to current AI professionals and researching reputable colleges and programs. According to Springboard, hiring managers will probably require you to hold at least a bachelor’s degree in math and basic computer technology (but in many cases, a bachelor’s degree will only qualify you for entry-level positions).

Undergraduate degrees in computer science or engineering are a good starting point, but a master’s degree in artificial intelligence can provide firsthand experience and knowledge from industry experts that can help you secure a position and set you apart from other candidates.

Dan Ayoub, general manager for mixed reality education at Microsoft, explained in a Best Colleges article that AI is still a relatively emerging area, colleges and universities still “differ in how specialized a degree you may be able to get.” He noted that computer science and familiarity with data science, machine learning, and Java are good places to begin, but that degree programs may offer specialized training. “There are a number of new undergraduate and graduate programs popping up every day that are designed to prepare someone specifically to work in AI.”

Those interested in pursuing a master’s in artificial intelligence should have a strong foundation of knowledge and experience consisting of a combination of math, science, computer and data proficiency.

Artificial Intelligence Job Outlook

The job outlook for AI professionals is extremely promising, with ZipRecruiter predicting the industry to “grow explosively as it becomes capable of accomplishing more tasks.”

In an article on Built In, Satya Mallick, founder of Big Vision LLC/Interim CEO, OpenCV.org, likened AI to “a rocket ship that is taking off.” He also explained that even entry-level jobs can pay extremely well. “The reason is a huge demand for AI talent and not enough people with the right expertise,” he explained.

The U.S. Bureau of Labor Statistics expects employment of computer and information technology occupations to grow 13% from 2020 to 2030 (projecting to add about 667,600 new jobs).

Companies Currently Hiring AI Positions

A recent search for “artificial intelligence” job openings on LinkedIn revealed thousands and thousands of results at a wide variety of companies. Here is a sample of some of the positions we found. (You can see similar LinkedIn search results here.)

Wells Fargo — Sr. Conversational AI Content Strategists
Nike — Data Scientist, Experience Research & Analytics
Amazon Web Services — Machine Learning Engineer
Apple — AI/ML Software Engineer
Spotify — Research Scientist – Language Technologies
Microsoft — Senior Researcher
As you can see from the list above, there are many different types of positions within artificial intelligence. Some of the most common AI-related job titles, courtesy of Glassdoor, include:

Software engineer
Data scientist
Software development engineer
Research scientist
In general, tech companies (both software and hardware) dominate the list of companies that are hiring AI professionals. But a quick search on any reputable job listing site will give you a list of positions that span a variety of industries. Here is a sample of some of the top companies that are hiring for these types of AI roles:

Deloitte
Amazon
Accenture
H&R Block
IBM
PwC
Fidelity Investments
PayPal
Major League Baseball
Harvard Business School
IKEA

Artificial Intelligence Salaries

Salaries are dynamic, which means the numbers we’ve listed below will fluctuate due to inflation, trends, the job market, demand and other factors.

According to our degree page, the average salary for an artificial intelligence programmer ranges from $100,000 to $150,000. Salaries are significantly higher for AI engineers, averaging $171,715 with the top 25% earning above $200,000.

There are a range of averages, depending on the position and the responsibilities, but here are the most popular:

According to Indeed, the salary for artificial intelligence careers ranges from approximately $99,568 for a full stack developer to $141,318 for a data scientist.
The average annual base pay for artificial intelligence salaries in the United States is $120,049, according to Glassdoor.
According to Talent.com, the average artificial intelligence salary is $143,054 per year. Entry positions start at $115,000, and most experienced employees can make up to $200,000 per year.

Source: onlinedegrees
Original Content: https://forum.dipti.com.bd/index.php?action=post;board=1708.0

17

Introduction to Robotics and Automation / Introduction to robotics and automation

« on: January 02, 2024, 02:42:19 PM »

Robotics is a field of science working with machines that perform tasks based on predetermined and adaptive programs and algorithms in an automatic or semi-automatic way. These machines – commonly called robots – are either controlled by humans or work entirely under the supervision of a computer application and algorithms. Robotics is a comprehensive concept that includes the building, planning and programming of robots. These robots are in direct contact with the physical world – and they have often been used to perform monotonous and repetitive tasks instead of human beings. Robots can be categorised based on their size, application domain or purpose, and we will discuss this later.

Robotics vs automation

Automation is a much broader concept than robotics. It means that specific parts of a process or an entire process is performed without human intervention. Instead, the process is operated only by predefined or adaptive computer applications and electrical or mechanical machines. The predefined applications refer to algorithms, in which all the operations are predefined and executed independently regardless of any unforeseen changes in the environment. Adaptive automation means that the algorithm can change its behaviour according to changes in the process or environment.

Robotics goes hand-in-hand with automation, as in most cases robots are part of an automated system. Although there are times when robots are used with little or even no automation – and you can also have automation without robotics – the two are like twins that have a lot in common, but each with their own distinct personality.

The types of robots

Robots can be classified in different ways. We’ll look at four main methods of categorisation:

• Size

• Application domain

• Purpose

• Number

Size

When looking at size, the following categories exist:

Nanorobots or nanobots: nanorobots are made of nanomaterials and range in size from 0.1 to 10 micrometres (to get an idea of how small that is, a human red blood cell is about 5-10 micrometres). Nanobots are in the early research stages – mostly, the concept is being discussed for use in medicine and many more years of hard work are needed to make them a possible solution. One vision for nanorobots is to inject them into a patient’s body to identify and cure diseases.

Microbots, millibots and minibots: these robots are very small but still larger than nanobots – and they actually already exist. Microbots, millibots and minibots are smaller than 1 mm, 1 cm and 10 cm, respectively. The smallest flying robot is RoboBee, with a wingspan of 1.2 cm and a weight of 80 milligrams. The wings can flap 120 times per second and the robot can be controlled remotely. The goal of such a small device is to form a flying swarm for search and rescue, or artificial pollination.



Small and mid-sized robots: these robots are typically under 100 cm (small) or about the size of a human (100–200 cm, mid-sized). Most household robots, toys and social robots, humanoids (robots that have a similar appearance to humans – the Transformers of comic books and films being a common example), and digital personal assistants are this size. Small and mid-size robots are the kind we see and meet most of the time – in movies and in real life.

Large robots: these robots are bigger than us. Much bigger. There are some large humanoid robots, even up to 8–10 meters. However, humanoid large robots are typically made for research purposes, or just for fun. In fact, most large robots don't look like humans – they are made for automation in manufacturing, construction, agriculture, autonomous driving and navigation.

Application domain

It is also possible to categorise robots according to their application domain, dividing them into personal and industrial robots.

Personal robots are used in our daily life and are designed to be useful for individual or family use. Non-technical people can operate personal robots to perform repetitive and perhaps boring tasks to save time or to entertain us. Household robots, social robots, digital personal assistants and toys are the most common personal robots.

Industrial robots are robust and are created to perform specific tasks in a pre-programmed manner in manufacturing, construction or agriculture, for instance. Applications include assembly, disassembly, mounting, screw tightening, welding, painting, visual inspection, and so on. Industrial robots are outstanding at one specific task: these are fast, precise and reliable machines. Without industrial robots, we wouldn't really have today's level of technological development.

Purpose

Another possible categorisation for robots is purpose. Robots can have a specific and a general purpose. But what does that mean?

Task-specific robots: these machines perform a particular task or a sequence of possible tasks. It can be as simple as a robot arm that moves objects from A to B, but it can be as complex as a social robot with an advanced natural language interface. The construction and behaviour of these robots cannot be changed; they follow predefined programs according to their original purpose. Household robots and industrial robots are among such machines.

General-purpose robots: in this case, the task of the robot is not predefined. Various components of the robots can be bought separately, and these components can be assembled in different ways in order to solve specific tasks. The components may include robot arms, wheels, cameras, step motors and additional sensors and actuators. These robots may also have wireless connections, such as Wi-Fi and Bluetooth. The "brain" of the robot – which is generally a small computer – can be “trained” to perform different tasks with different components using custom applications written in computer programming languages. Common programmable small computers – also called embedded systems – are the Nvidia Jetson and Jetson Nano, Raspberry Pi, and Arduino. These embedded systems have general-purpose input and output connections (GPIOs) to which sensors and actuators can be connected using a standard communication interface.

Other general-purpose robots include a prebuilt body comprising sensors (like cameras and microphones) and actuators (like arms and legs). By developing different computer applications, the robot can perform different specific tasks. Examples of such robots include Softbank Robotics’ Nao, Pepper and Romeo, or Boston Dynamics’ robot ‘dog’, named Spot.

Number
Robots can also be categorised according to how many there are:

Single robots: a single robot works on its own. It has a duty which it performs based on a predefined program. The predefined program might involve advanced technologies which make it able to adapt to its environment, and the robot might be connected to the internet, but the robot is still alone. Even if there are several single robots in one place, they are still ’alone’ as they cannot communicate with each other.

Robots in teams: robots can work in teams, just like humans. Often a task is done in sequence by several robots. Think about video recordings of how cars are assembled. The chassis is welded, then comes the doors, then the car is painted, front and rear windows are next, and so on. All of these steps are performed by different robots that can only do that particular task.

Swarm robotics: robots can also work in a swarm. In this case, a large number of simple robots are controlled collaboratively. Individual robots in the swarm are not particularly valuable, but the swarm itself can perform important tasks. Just think about bees in nature. A single bee cannot do much, but without millions of bees working in swarms, humans probably wouldn’t even exist. Possible applications of swarm robotics are exploration and rescue, microbiology, surveillance and pollination. However, at the time of writing (2021) swarm robotics is mostly in the research phase.

The evolution of robots
The word robot comes from the Czech word "robota", which means “serf work” in Czech. Karel Čapek’s 1920 play, where machines take over the world, made the word “robot” widely known. But humanity has always been interested in the rethinking of human existence. Even before the 20th century there were several attempts to recreate a human being and legends telling of people who had succeeded. One of the most famous ideas belongs to the 16th century alchemist Paracelsus. He stated that a small human-like being (called a homunculus) could be created in a flask using only chemical procedures. Later in the 16th century, the word golem entered public consciousness. Based on a folk story, the golem was made of clay and would serve people if someone inserted a special parchment into its mouth or forehead. The story says that after a while, the golem confronted its creator and eventually turned against him.

Looking at the history of robotics, there is a universal interest in imbuing robots with humanity or some human attributes. This interest generally has three main conditions:

• the robot has to be similar to a human being in some way (in appearance, in thinking, etc.)

• the robot has to be better at something (stronger, smarter, etc.)

• the robot has to be completely under the control of its creator

There was a milestone in the history of robotics when machines that were stronger than people appeared. Machines that replaced a human's contribution to work appeared during the first industrial revolution around 1769. At that time the main purpose was to reduce costs and the time spent on production and to increase the quantity of products without human interaction. Automation became the main concept at that time. With automation, several processes can be completed without any human intervention. As work was done by machines, it led people to find new ways of working and living. Machines do not get tired like people do, so machines can work 24/7. The risk of error and the amount of waste also decreased with automation.

Robots are also characterised by controlled accuracy and effectiveness. In the 1800s, computer technology was not present. However, people were able to create large machines to perform complex tasks. After 1950, there has been an important development in robotics.

Various positive statements can be made about the existence of robots, but humanity is not 100% satisfied. The labour market is under continuous pressure from people willing to work, as workers doing repetitive tasks could be replaced with machines. A certain fear always appears in connection with robots about them replacing the human workforce, or if robots may have more control over humans than desired.

As robots get more realistic, another fear arises. People generally tolerate robot-like robots. Our brain can easily categorise robot-like humanoid robots like we categorise industrial robots in manufacturing. People may get confused and even frustrated when meeting an overly realistic robot. In this case, we know that it's a robot. However, the brain can't really deal with this fact due to its realistic appearance. Its skin, movement and even speech is very similar to that of a person, but our brain struggles with the categorisation: is it really a robot? Does it have thoughts? Can or should I trust it?

Another milestone in the history of robotics was when the first remote-controlled mobile robot discovered the moon's surface around 1970. A bit later, in 1986, Honda started a project to create humanoid robots that look similar to people. The evolution continued, and robots appeared in more and more fields like healthcare, manufacturing and logistics. The evolution of robots is still an ongoing process and now robots are present in our daily lives. Robots are in homes (vacuum cleaners), in workplaces (assembly robots) and in healthcare (social robots in patient treatment or surgical robots), for instance.

Humanity is in the fourth industrial revolution, which integrates the hottest emerging technologies, like robotics, IoT, 5G, artificial intelligence, and many more, to take the industry to new levels.

Source: minnalearn
Original Content: https://shorturl.at/tIX48

18

Autonomous Vehicles / WHAT IS INDUSTRIAL AUTOMATION AND ROBOTICS?

« on: December 26, 2023, 10:41:27 AM »

Industrial automation and robotics are the use of computers, control systems and information technology to handle industrial processes and machinery, replacing manual labour and improving efficiency, speed, quality and performance.

Automated industrial applications range from manufacturing process assembly lines to surgery and space research. Early automated systems focused on increasing productivity (as these systems do not need to rest like human employees), but this focus is now shifting to improved quality and flexibility in manufacturing and more. Modern automated systems are developing beyond mechanisation with the addition of artificial and machine learning.

However, automation and robotics are not the same thing:

Automation
Automation is the use of computer software, machines or other technology to carry out tasks that would otherwise be done by a human. There are several types of automation, which can include both virtual and physical tasks.

1. Software Automation
This is the automation of tasks usually performed by humans using computer programs. This area includes business process automation (BPA), using software to formalise and streamline business processes, robotic process automation (RPA), which uses ‘software robots’ to mimic humans using computer programs, and intelligent process automation (IPA), which involves the use of artificial intelligence to learn how people perform tasks using a computer program. The difference between BPA and RPA is subtle, with BPA being like replacing a human production line with an autonomous factory and RPA like adding a collaborative robot to work alongside the existing workforce.

2. Industrial Automation
This is the control of physical processes with machines and control systems to automate industrial processes. Robots are used in this type of physical automation but so are other non-robotic machines, such as CNC machines.

Robotics
This area of engineering uses multiple disciplines to design, build, program and use robots. Robots are programmable machines that use sensors and actuators to interact with the physical world and perform actions autonomously or semi-autonomously. Because they can be reprogrammed, robots are more flexible than single-function machines. Collaborative robots are designed to complete tasks in a similar manner to humans, while traditional industrial robots tend to complete tasks more efficiently than humans.

Automation and robotics have areas where they cross, such as the use of robots to automate physical tasks, as with car assembly lines. However, not all automation uses physical robots and not all areas of robotics are associated with automation.

An early form of industrial automation was the use of CNC (Computer Numerical Control) machines for high-precision aerospace manufacturing in the United States during the Second World War. Using the first industrial computing systems, the first CNC machines still required a high level of human input until they became more automated during the 1950s.

Modern industrial automation includes the use of data acquisition systems, distributed control systems, supervisory control and programmable logistics controllers. They are consistent and predictable, making them ideal for processing chemicals, pulp, paper, oil and gas or other raw materials. By adding Industry 4.0 capabilities to these systems, industrial automation can also include access to peripheral data to further optimise operations based on real-time data.

The Growth of Industrial Automation and Robotics
The growth of industrial automation and robotics came from 19th Century mechanised industry, where humans were called upon to operate increasingly complex machinery to deliver higher rates of productivity. As mechanisation advanced, the machine operators became increasingly peripheral to the operation and this notion was further advanced with industrial automation.

Industrial automation required even less human control for basic and repetitive tasks, which displaced some jobs but also created new opportunities related to the automation itself. This moved roles towards a white collar economy as nations such as Japan achieved highly roboticised electronic and automotive manufacture by the 1980s.

This parallel growth of industrial automation and robotics has continued with the advent of artificial intelligence, machine learning and robot vision. Today, it is not just manufacturing that can be automated but also programming and process organisation, leaving people free to focus on adding value through improved product designs.

Robots can be used in physical industrial automation, but are not required for virtual tasks and software-based applications.

Advantages of Industrial Automation
Industrial automation, with robots or without, offers a range of advantages:

1. Reduced Operating Costs
With no requirement for healthcare, paid leave, pension payments or other staff benefits and with no wages to pay, industrial automation is typically cheaper than employing people. While there can be maintenance costs, if managed correctly these should still be far less than staff-related costs for the same output or better.

2. Improved Productivity
Industrial automation allows plants to run 24 hours a day, 7 days a week with no time loss for staff handovers or holidays, improving the productivity of the plant.

3. Improved Quality
Industrial automation is highly repeatable, without the errors associated with human staff. Machinery will also not get tired, which can impact quality and productivity at certain times of a shift.

4. Highly Flexible
An automated system, including robots, can be programmed to take on a different task, offering greater flexibility than with humans, who may need training on a different task.

5. Improved Data Accuracy and Collection
Automated data collection is not just more reliable but it can also allow you to improve your data accuracy, offering the required facts to make decisions to reduce waste and improve processes.

6. Increased Safety
Using robots for hazardous roles or conditions will improve the safety at your facility when compared to using human employees.

Industrial automation allows more work to get done, cheaper and more effectively than with human employees. It also means that you do not need to seek skilled labour where a robot could be used instead.

Disadvantages of Industrial Automation
The primary disadvantage of industrial automation is the high costs associated with switching from a human to an automatic production line. There are also subsequent costs associated with retraining or hiring staff to handle the sophisticated equipment.

The Future of Automation and Robotics
Already worth billions of dollars each year, industrial automation and robotics-related services will keep growing as technology continues to advance.

As robot production has increased, so the related costs have reduced and this trend should continue as more emerging economies begin to look to robotics as a solution. As a consequence of this increased robot production, there has also been a rise in the availability of the required skills to design, install, operate and maintain them. In addition, the increased availability of software has reduced the associated engineering time and risk, while making robot programming much easier and cheaper.

As technology continues to advance, these trends should continue into the future with robotic systems able to collect data, monitor processes and troubleshoot any problems. Robots are already able to use sensors and other data points to monitor and adapt their movements in real time, mimicking the skills of a human craftsperson to improve a process and reduce rework or inspection requirements.

While robots will continue to be used to automate repetitive physical tasks, emerging technologies could allow robots to respond to voice commands as artificial intelligence allows them to cope with a broader sweep of tasks and adapt in response to changes in the working environment. This would see robots being used in areas such as agriculture, where the need to be able to find, assess and harvest produce has been difficult for automated systems. Robotic precision is another area that should see advances in the future, with the ability to complete more delicate tasks with improved coordination.

As robots advance, it will be possible to decide which tasks should be automated and which should be conducted by humans and, with advanced safety systems, robots will also be more regularly deployed to work alongside humans without potentially endangering them.

Automated systems are now advancing to be able to monitor and automatically adjust the speed of entire production lines to maximise output and minimise costs.

With all of these advances coming into play, an automation strategy will depend upon successfully deciding which areas to automate and at what level.

FAQs
What is Industrial Automation?
Industrial automation is the use of information technologies and control systems like computers and robots to handle machines and physical or virtual processes instead of relying on human beings. Industrial automation is a step forward from mechanisation as part of industrial processes.

What is the Meaning of Industrial Robotics?
Industrial robotics is the use of a robot for manufacturing or other industrial process, including assembly, packing, labelling, painting, inspection, testing, welding, and more. The use of robots for these tasks should provide high endurance, precision and speed for the tasks.

What is the Difference between Robotics and Automation?
Although they are sometimes used interchangeably, robotics and automation are different things. Automation is the process of using technology to complete tasks otherwise performed by humans. These tasks can be either physical or virtual and can involve the use of robots to perform them. Robotics is the process of developing and using robots (specifically) for a particular function, which may or may not be automated.

Conclusion
While industrial automation and robotics are not the same thing, they often go hand-in-hand to improve productivity, quality and safety at low costs in a variety of industries.

With applications including manufacturing lines and precision surgery, the use of automation-enabled robotics continues to advance with the growth of Internet of Things (IoT) connectivity as more businesses explore the benefits of Industry 4.0.

However, just as robots do not need to be automated, so industrial automation does not just rely on physical robots. Industrial automation covers any aspect of an operation that can be done by a machine rather than a human, meaning that there are many virtual aspects to automation too.

Advances in industry have a history of causing concern among employees who fear that their jobs will be replaced by new technology. However, even as industrial automation takes the place of mundane, hazardous or repetitive tasks, it also opens up new specialisms in the design and maintenance of the automation systems themselves. This also allows staff to focus on more creative areas, such as product design.

Industrial automation and robotics looks set to continue growing and expanding into new regions, driving down the associated costs as new technologies emerge to provide smarter systems that can take data and react to environments in real time.

Source: TWI Ltd.
Original Content: https://www.twi-global.com/technical-knowledge/faqs/what-is-industrial-automation-and-robotics

19

AI Career Success Stories / AI for Business: Real-World Applications and Success Stories

« on: December 26, 2023, 10:30:05 AM »

By Muzammil Rawjani



One phenomenon in the technology and business landscape has risen to the forefront, changing how companies operate, make decisions, and engage with their customers: Artificial Intelligence, or AI.

I have witnessed firsthand the profound impact of AI on businesses across various sectors. What was once considered science fiction is now a tangible reality, reshaping industries and propelling organizations toward new heights of success.

As industry leaders, we understand that staying ahead of the curve is paramount. AI has evolved from a buzzword to an essential tool that augments and often revolutionizes business processes. It empowers organizations to be more efficient, data-driven, and customer-centric.

Join me as we explore the tangible impact of AI on the business landscape, uncovering the success stories of companies that have harnessed its potential to drive operational excellence, improve customer satisfaction, and achieve remarkable business outcomes. From personalized marketing
strategies that boost sales to autonomous vehicles that redefine transportation, AI is at the heart of these transformations.

Real-World Applications and Success Stories of Artificial Intelligence

1. Enhanced Customer Experience
Since the advent of AI, businesses have used it to their advantage. You can use AI to enhance customer experience by providing them with personalized recommendations. AI and ML help us grasp user behavior to predict better and effectively.

Success Stories
The prime example is Netflix. The streaming giant Netflix uses AI to analyze user behavior and preferences to recommend personalized content. This recommendation system increases user engagement and retention, a critical factor in their success.

2. Supply Chain Optimization
As a leading tech company co-founder, I understand supply chain optimization’s critical role in today’s hyper-competitive business landscape. Supply Chain Optimization is exploring cutting-edge technologies and AI-driven solutions to revolutionize how businesses manage logistics, streamline operations, and enhance efficiency.

Success Stories
For instance, the supply chain giant Walmart. Walmart employs AI to optimize its supply chain by predicting demand, improving inventory management, and reducing operational costs. This results in better product availability and reduced waste.

3. Chatbots and Customer Support
Chatbots powered by AI have emerged as invaluable assets. We all know how these digital assistants transform customer support. By providing real-time assistance, answering queries, and enhancing user experiences, they help us reinforce customer loyalty.

Success Stories
Zendesk is an example of this. Zendesk utilizes AI-powered chatbots to enhance customer support. These chatbots can answer routine inquiries, handle ticket routing, and provide 24/7 support, improving customer satisfaction and operational efficiency.

4. Fraud Detection
At our tech company, we recognize the paramount importance of AI in Fraud Detection. Its ability to analyze vast datasets in real time has become a game-changer. As co-founders, we’ve seen AI thwart fraudulent activities in financial transactions, protecting both businesses and consumers.

Success Stories
The most prominent example is PayPal. PayPal employs AI algorithms to detect fraudulent transactions in real time. By analyzing transaction patterns and user behavior, AI helps prevent financial losses and
protect users’ accounts.

5. Predictive Analytics
Predictive Analytics is the bedrock of informed decision-making in our tech company. We anticipate trends, customer behavior, and market fluctuations with AI-driven insights. This empowers us to stay ahead of the curve, optimizing operations and delivering top-notch products and services.

Success Stories
For example, take Salesforce. Salesforce’s AI-powered analytics predict customer behavior and sales trends, enabling businesses to make data-driven decisions, allocate resources efficiently, and enhance their marketing strategies.

6. Personalized Marketing
As co-founders, we champion Personalized Marketing, made possible through AI. By analyzing user data, AI tailors marketing efforts to individual preferences. This results in higher engagement, conversion
rates, and customer satisfaction, underlining its pivotal role in our tech company’s success.

Success Stories
Amazon is a perfect example of personalized marketing. Amazon’s AI-driven recommendation system is legendary. By analyzing user behavior and purchase history, Amazon suggests products tailored to individual preferences, significantly boosting sales.

7. Autonomous Vehicles
Our tech company is at the forefront of this revolution, witnessing how AI-driven self-driving cars reshape transportation. They offer safer, more efficient travel, setting the stage for a transformative future in mobility.

Success Stories
Tesla: Tesla leads the way in self-driving technology. Their AI-powered autonomous vehicles use advanced sensors and real-time data to navigate safely, reducing accidents and offering a glimpse into the future of transportation.

8. Healthcare Diagnostics
In the healthcare sector, AI is revolutionizing diagnostics. AI’s role in early disease detection, medical image analysis, and patient care improvement. It’s not just technology; it’s a lifeline that enhances healthcare outcomes. We have developed numerous healthcare apps that have helped people globally.

Success Stories
For instance, IBM Watson Health. IBM’s Watson is a pioneer in AI-driven healthcare. It analyzes vast datasets to assist doctors in diagnosing diseases, recommending treatment plans, and conducting medical research.

9. Financial Services
AI has disrupted the financial services sector, and our tech company has embraced this transformation. From robo-advisors optimizing investments to AI-powered chatbots simplifying customer interactions, it’s reshaping how financial services are delivered and accessed.

Success Stories
The example that comes to my mind is BlackRock. BlackRock, a global investment management firm, employs AI for portfolio management. AI algorithms analyze market trends and financial data, allowing for more accurate investment decisions.

10. Human Resources and Talent Acquisition
Talent acquisition and HR processes have evolved thanks to AI. Talent Acquisition is one of the most challenging tasks to take up as the leader of a Tech Company. Here is where AI appears. AI streamlines recruitment, identifies top candidates and enhances employee experiences. It’s a critical asset for attracting and retaining top talent in our tech company. You have already guessed it.

Success Stories
Yes, it is LinkedIn. LinkedIn uses AI to match job seekers with suitable job postings, making the job search process more efficient. AI helps companies identify qualified candidates by analyzing resumes and profiles.

11. E-commerce and Inventory Management
E-commerce thrives on AI. In our tech company, we’ve harnessed AI for inventory management and personalizing the shopping experience. It ensures products are available when needed and that customers discover what they desire effortlessly.

Success Stories
The example that comes to my mind for e-commerce is Alibaba. Alibaba’s AI-powered platform optimizes inventory management and logistics. It predicts product demand, streamlines shipping, and reduces business costs on its platform.

12. Energy Management
AI’s impact on energy management is substantial. AI optimizes energy consumption, reducing costs and environmental footprints. It’s a powerful tool for a sustainable future, and our tech company is committed to this mission.

Success Stories
Siemens is at the top of energy management. Siemens utilizes AI to optimize energy consumption in industrial facilities. AI algorithms analyze real-time data to minimize energy waste and reduce operational expenses.

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

20

AI Career Advice and Tips / AI in Career Guidance: Evolution or Extinction? The Rise of Robo-Career Advisers

« on: December 26, 2023, 10:24:43 AM »

AI is supposed to make life easier when it comes to finding information, advice and guidance (IAG) to support individuals and their decision making. Traditionally, IAG has been provided by human careers advisers who offer personalised support, share experiences, and emotional support. The field of career guidance has long been a cornerstone of education, employability and workforce development.

Career guidance is defined as:

“Services which help people of any age to manage their careers and to make the educational, training and occupational choices that are right for them. It helps people to reflect on their ambitions, interests, qualifications, skills and talents – and to relate this knowledge about who they are to who they might become within the labour market.” (OECD, European Commission, ILO, UNESCO, ETF & Cedefop, 2019)


In this context, career guidance professionals perform a key role in supporting individuals to build and articulate their career identity and narrative. Empowerment of individuals to harness their evolving lifelong learning and career stories is now evermore crucial in a changing world of work. However, with the rapid advancement of automation technologies, the role of careers advisers is undergoing profound transformation. The question now posed is whether this transformation will augment or supplant human careers advisers.


Young people and adults living in a volatile, uncertain, complex and ambiguous world need access to high-quality career guidance in ‘places’ and ‘spaces’ (both online and offline) at a time and place that suits their needs best. AI-driven careers IAG tools are becoming increasing sophisticated and accessible, including algorithms to analyse vast datasets of labour market information/intelligence (LMI), user profiles and preferences, enabling them to provide personalised and tailored recommendations. Skills assessment and job matching are also readily available.

Transformation

It can be argued at a simplistic level, robo-careers advisers represent the future of career guidance, where data-driven insights and personalised recommendations (available anytime of the day or night) combine to empower individuals on their journey of self-discovery.


The integration of AI with human-centred services has led to notable progress in various domains such as: healthcare e.g., personalised medicine where AI is used to analyse patient data, apps provide mental health support by offering strategies, tracking mood, and connecting with therapists when needed; customer support e.g., AI chatbots and virtual assistants and e-commerce platforms; education e.g. personalised learning and administrative efficiency; finance e.g. AI-driven financial advisers provide personalised investment strategies, taking account of individual goals and risk tolerances.


There are at least five advantages of AI in career guidance namely (i) scalability – AI-driven careers information and advice can reach a broader audience, connecting to professionally trained careers advisers when needed; (ii) personalisation – AI can offer personalised recommendations based on interests, skills and occupational preferences; (iii) data-driven insights – AI can easily leverage big datasets and provide real time access to job vacancies and labour market trends, supporting well-informed choices; (iv) cost-effective – AI can quickly respond to frequently asked questions and make more in-depth guidance referrals to human advisers, if needed, thereby saving time and money within organisations; and (v) empowerment – AI-driven careers information and advice equips career guidance professionals to ‘stay ahead of the curve’ in keeping up-to-date with labour market trends, learning more about their clients/customers career exploration in advance of a 1:1 or group meeting, and analysing data trends to feed into local, regional and national education, skills and economic growth strategies.

Concerns of extinction

Similar to how the Luddites in the early 19th century feared the devaluation of their weaving skills, careers advisers today may fear that the human touch and empathy that they provide could be devalued as AI takes on more tasks in the career guidance process. Beyond this, there are bias and fairness concerns. AI algorithms can perpetuate bias if not designed and trained sensitively. This is a risk that these systems may reinforce existing inequalities and discrimination in the job market. Privacy and data security concerns surround the collection and analysis of personal data for career guidance. This cutting-edge technology is also posing unprecedented ethical dilemmas such as “a lack of transparency, gender and ethnic bias, grave threats to privacy, dignity and agency, the danger of mass surveillance, and a growing use of unreliable AI technologies in law, to name a few” (UNESCO, 2022). Fundamentally, loss of the human touch and emotional support provided by career guidance professionals, particularly to vulnerable individuals with complex circumstances and needs is a major worry.

Scenario building
Let’s consider a scenario in the context of career guidance where human support, AI tools, virtual reality, gaming, chatbots and data-driven insights can work together effectively: Career Guidance Support Platform


Human support: Trained and qualified careers advisers, including coaches and counsellors, form the backbone of the support system. They provide personalised sessions 1:1 or in group settings for more complex enquiries. AI frees up their time to apply their expert skills.


AI Tools and Chatbots: An AI chatbot serves as the frontline support tool, available 24/7 to engage with users with inbuilt tools like text to speech and language translation functions. It can perform initial assessments, provide information, advice and guidance, including some form of encouragement helping users to feel heard and understood.


Virtual Reality (VR): VR is integrated into career guidance sessions e.g. insights to differing workplaces, careers adviser can guide clients/customers to confront any concerns and manage their fears and concerns on a controlled and safe environment.


Gaming and gamification: Careers advisers use gaming elements such as CareerCraft to make careers education inspirational, build skills and maintain their commitment to career exploration.


Data-driven insights: The platform collects and analyses user data (with strict privacy and software safeguards) to gain insight to individuals’ progress and preferences. This data helps careers advisers tailor their approaches and interventions for better outcomes.

This holistic approach combines the strengths of human empathy, expertise and AI-driven tools to provide personalised career guidance support. It leverages engaging career exploration experiences while harnessing data-driven insights to continuously improve the quality and range of services. The combination aims to make career guidance support more accessible, effective and user-centric.


A single platform solution may or may not be the ideal solution for some politicians or government policymakers. The neo-liberal agenda favours marketisation principles (with free markets) in the equation. Those policymakers responsible for lifelong learning and career guidance, presently offer concerns focused on ‘the AI unknowns’ and maleficence in the ethical context to do no harm to other intentionally or recklessly.

A hybrid approach: Maximising the benefits of AI and humans
Just as RoboCop grappled with a moral struggle as he regained his humanity while upholding the law, the emergence of Robo-Careers Advisers brings forth a similar dilemma. While AI-drive career guidance platforms and tools bring efficiency and data-driven insights, there is an ongoing debate about the potential depersonalisation of the career guidance and counselling experience and the importance of preserving the human touch in guiding individuals to make meaningful career choices and decisions. The moral struggle lies in finding the right balance between the efficiency gains of AI and the empathic guidance that human careers advisers provide. A practitioners voice: “I love using AI in my practice, but this often raises more probing questions and emotional reactions from my clients.”


Conclusion
Rather than framing the use of AI as an all or nothing proposition, a more balanced hybrid approach enhances careers advisers’ expert capacities, rather than replacing these. Training and reskilling are necessary to make effective use of new AI tools and governments have a responsibility to invest resources in this regard. The fusion of AI-driven data and tools and a commitment to providing access to human empathy through skilled helpers can offer the best of both worlds. But this must not be reduced solely to call centres, instead what is needed is more local, regional and national places and spaces for career guidance embedded deep in communities.

Source: OEB Learning Technologies Europe GmbH
Original Content: https://shorturl.at/iACEM

21

AI Certifications and Training Programs / 5 Online AI Certification Programs — Make an Informed Decision

« on: December 26, 2023, 10:16:01 AM »

Artificial Intelligence (AI) is no longer an abstract concept of the future. It’s here, revolutionizing industries, and creating unprecedented demand for skills in this field.

IBM predicts that by 2024, the number of jobs for all US data professionals will increase by 364,000 openings to 2,720,000 (source). Consequently, an AI certification program can be a key differentiator in this competitive job market.

These online AI certification programs offer a flexible way to learn, allowing you to balance your studies with your personal and professional commitments.

In this article, we will explore five online AI certification programs that can help you harness the power of AI and put you on the path to a rewarding career in this field.

Understanding the importance of AI certification

AI certification programs are instrumental in equipping professionals with the right skill set to thrive in this AI-driven era.

According to Gartner, AI is predicted to create 2.3 million jobs by 2022 (source). This underpins the relevance of AI certification programs.

These programs help professionals gain a deep understanding of AI, its applications, and its implications. Moreover, they enable you to stay updated with the latest advancements in AI, enhancing your problem-solving abilities and your proficiency in using AI tools and techniques.

Moreover, AI certification programs are also recognized by employers worldwide, adding substantial value to your professional profile.

Top AI certification programs — A brief overview

Let’s dive into five top-notch online AI certification programs that can help you gain a competitive edge in this rapidly evolving field.

Stanford University — Machine Learning via Coursera: This program, taught by Andrew Ng, co-founder of Coursera and Adjunct Professor at Stanford University, provides a broad introduction to machine learning, data mining, and statistical pattern recognition.

IBM AI Engineering Professional Certificate: Consisting of six courses, this program covers a wide range of AI topics, including machine learning algorithms, deep learning models, and open source tools and libraries.

Microsoft Certified: Azure AI Engineer Associate: This certification program demonstrates the ability to use cognitive services, machine learning, and knowledge mining to architect and implement Microsoft AI solutions.

Post Graduate Program in AI and Machine Learning by Purdue University: This comprehensive program covers Python, Machine Learning, Natural Language Processing, Speech Recognition, and more.

Professional Certificate Program in Machine Learning and Artificial Intelligence by MIT Professional Education: This program covers key concepts of AI, including machine learning, probabilistic reasoning, robotics, and natural language processing.

In-depth exploration of each AI certification program

In-depth exploration of each AI certification program

Stanford University — Machine Learning via Coursera:

This program comprises eleven weeks of study, with each week requiring 4–6 hours of commitment. It covers both supervised and unsupervised learning, along with best practices in machine learning.

IBM AI Engineering Professional Certificate:

This program focuses on teaching students how to develop AI-powered applications using industry-standard tools like TensorFlow and Keras. It includes hands-on labs and projects to provide practical experience.

Microsoft Certified: Azure AI Engineer Associate:

This program prepares students to design and implement AI apps and agents using Azure Cognitive Services, Azure Bot Services, and machine learning platforms in Azure. It’s a great choice for professionals focusing on Microsoft’s AI stack.

Post Graduate Program in AI and Machine Learning by Purdue University:

This program, in collaboration with IBM, offers a comprehensive understanding of AI and Machine Learning concepts. It has over 450 hours of learning and includes both self-paced and live sessions.

Professional Certificate Program in Machine Learning and Artificial Intelligence by MIT Professional Education:

This program features instruction from MIT faculty and provides a deep dive into the latest AI concepts and techniques. It offers a blend of theory and practical application, ensuring a well-rounded understanding of the field.

Criteria for selecting an AI certification program



Choosing the right AI certification program can be a daunting task given the multitude of options available. However, considering the following factors can help you make an informed decision:

Course Content: Ensure the program covers the fundamental concepts and techniques of AI, along with the latest advancements in the field.
Practical Learning: Look for programs that offer hands-on projects, real-world case studies, and practical assignments to help you gain practical experience.
Instructor Expertise: The quality of instruction matters. Make sure the program is taught by leading industry experts or faculty from reputable institutions.
Flexibility: If you are juggling work and study, opt for a program that offers flexible learning options. Online programs typically allow you to learn at your own pace.
Certification Value: Consider the recognition and value of the certification in the industry. Certifications from reputed universities or organizations are generally more recognized.

Process and requirements for enrollment in each program

Stanford University — Machine Learning via Coursera:

Enrollment can be done directly via Coursera’s website. There are no specific prerequisites, although a basic understanding of programming and mathematics would be beneficial.

IBM AI Engineering Professional Certificate:

Sign up directly on Coursera. A basic understanding of Python and math is recommended. The program requires you to complete a series of courses in a specified order.

Microsoft Certified: Azure AI Engineer Associate:

You can register on the Microsoft Certification website. A background in Azure and AI concepts, along with proficiency in Python or C#, is required.

Post Graduate Program in AI and Machine Learning by Purdue University:

Enroll through the Simplilearn website. It is recommended for learners to have a basic understanding of programming, mathematics, and machine learning concepts.

Professional Certificate Program in Machine Learning and Artificial Intelligence by MIT Professional Education:

Registration can be done directly on the MIT Professional Education website. A background in college-level calculus, linear algebra, probability, and statistics, along with some experience in Python, is required.

Career opportunities after obtaining an AI certification
AI certification opens up a multitude of exciting career opportunities in a variety of industries.

According to Indeed, the average salary of an AI engineer in the US is around $112,806 per year (source).

Here are some of the roles you can pursue after obtaining an AI certification:

AI Engineer: They build and test AI models and are proficient in programming languages such as Python and R.
Data Scientist: They analyze and interpret complex digital data to help companies make decisions.
Machine Learning Engineer: They design and build machine learning systems, perform tests, and implement machine learning algorithms.
Business Intelligence Developer: They analyze complex data sets to identify business and market trends.
Research Scientist: They conduct research to advance the field of AI, inventing new technologies, algorithms, and approaches.
Getting an AI certification can provide a significant boost to your career, equipping you with the skills needed to excel in these roles and more.

Choosing the right AI certification program for you

Choosing the right AI certification program is a significant step towards a successful career in the field of AI. Each program we’ve explored offers unique strengths and is designed to equip you with the skills and knowledge required to excel in this rapidly evolving field.

Your choice should align with your career goals, skill level, and learning style. Do you want a program that offers a broad overview of AI, or are you looking for something more specialized?

Do you prefer learning through lectures, or do you value hands-on projects? These are important considerations to keep in mind.

Regardless of the program you choose, obtaining an AI certification is a powerful way to demonstrate your expertise, stand out in a competitive job market, and open the door to a multitude of exciting career opportunities.

Source: linkedin
Original Content: https://www.linkedin.com/pulse/5-online-ai-certification-programs-make-informed-decision-rajat-jain

22

AI Career Paths and Job Opportunities / Want to Work in Artificial Intelligence? 14 AI Careers & Job Outlook [2023]

« on: December 26, 2023, 10:03:19 AM »

What Is Artificial Intelligence?

Artificial intelligence is all around us, even in places you may not realize. From music preferences to home appliances and healthcare, the power of AI is far reaching. But first, let’s explore the basics of AI with this definition from Investopedia:

“AI refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind such as learning or problem-solving.”

Examples of artificial intelligence include:

Smart assistants like Siri and Alexa
Pandora and Netflix, which provide personalized song and entertainment recommendations
Chatbots
Robotic vacuum cleaners
Self-driving vehicles
Facial recognition software

Those are just a few of the many, many examples. Needless to say, artificial intelligence is everywhere, and the demand for AI — especially skilled, experienced AI professionals — is growing. Bernard Marr, a business and technology advisor to governments and companies, told Forbes that we now have access to more data than ever, which means AI has become smarter, faster, and more accurate.

“As a very simple example, think of Spotify recommendations,” he explained in the article. “The more music (or podcasts) you listen to via Spotify, the better able Spotify is to recommend other content that you might enjoy. Netflix and Amazon recommendations work on the same principle, of course.”

What Does an AI Professional Do?

Since artificial intelligence is an increasingly widespread and growing form of technology, professionals who specialize in AI are needed now more than ever. The good news is that the AI professional field is full of different career opportunities, which means you can take on different roles and responsibilities depending upon the position, your experience and your interests.

The need for skilled AI professionals spans nearly every industry, including:

Financial services
Healthcare
Technology
Media
Marketing
Government and military
National security
IoT-enabled systems
Agriculture
Gaming
Retail

Professional AI Skills in Demand for 2023

If you’re looking to enter the professional world of AI, it’s important to make sure you have the right skills, which will set you apart from other candidates and help you land the perfect position. First, competencies with calculus and linear algebra are extremely important. Also, if you’re interested in AI, you should have some knowledge and experience in at least one of the following programming languages:

Python
C/C++
MATLAB

According to ZipRecruiter, these are the top 5 skills required for AI jobs:

Communication skills
Knowledge and experience with Python specifically (in general, proficiency in programming language)
Digital marketing goals and strategies
Collaborating effectively with others
Analytical skills

The Intellipaat blog also recommends these additional skills for AI professionals:

Solid knowledge of applied mathematics and algorithms
Problem-solving skills
Industry knowledge
Management and leadership skills
Machine learning

How to Start a Career in Artificial Intelligence

If you aren’t already in the industry, the first step is to conduct research, which includes talking to current AI professionals and researching reputable colleges and programs. According to Springboard, hiring managers will probably require you to hold at least a bachelor’s degree in math and basic computer technology (but in many cases, a bachelor’s degree will only qualify you for entry-level positions).

Undergraduate degrees in computer science or engineering are a good starting point, but a master’s degree in artificial intelligence can provide firsthand experience and knowledge from industry experts that can help you secure a position and set you apart from other candidates.

Dan Ayoub, general manager for mixed reality education at Microsoft, explained in a Best Colleges article that AI is still a relatively emerging area, colleges and universities still “differ in how specialized a degree you may be able to get.” He noted that computer science and familiarity with data science, machine learning, and Java are good places to begin, but that degree programs may offer specialized training. “There are a number of new undergraduate and graduate programs popping up every day that are designed to prepare someone specifically to work in AI.”

Those interested in pursuing a master’s in artificial intelligence should have a strong foundation of knowledge and experience consisting of a combination of math, science, computer and data proficiency.

14 Career Paths in Artificial Intelligence

The list below includes jobs in AI but also some positions that work closely with those in AI roles.

Artificial Intelligence Job Outlook

The job outlook for AI professionals is extremely promising, with ZipRecruiter predicting the industry to “grow explosively as it becomes capable of accomplishing more tasks.”

In an article on Built In, Satya Mallick, founder of Big Vision LLC/Interim CEO, OpenCV.org, likened AI to “a rocket ship that is taking off.” He also explained that even entry-level jobs can pay extremely well. “The reason is a huge demand for AI talent and not enough people with the right expertise,” he explained.

The U.S. Bureau of Labor Statistics expects employment of computer and information technology occupations to grow 13% from 2020 to 2030 (projecting to add about 667,600 new jobs).

Companies Currently Hiring AI Positions

A recent search for “artificial intelligence” job openings on LinkedIn revealed thousands and thousands of results at a wide variety of companies. Here is a sample of some of the positions we found. (You can see similar LinkedIn search results here.)

Wells Fargo — Sr. Conversational AI Content Strategists
Nike — Data Scientist, Experience Research & Analytics
Amazon Web Services — Machine Learning Engineer
Apple — AI/ML Software Engineer
Spotify — Research Scientist – Language Technologies
Microsoft — Senior Researcher
As you can see from the list above, there are many different types of positions within artificial intelligence. Some of the most common AI-related job titles, courtesy of Glassdoor, include:

Software engineer
Data scientist
Software development engineer
Research scientist
In general, tech companies (both software and hardware) dominate the list of companies that are hiring AI professionals. But a quick search on any reputable job listing site will give you a list of positions that span a variety of industries. Here is a sample of some of the top companies that are hiring for these types of AI roles:

Deloitte
Amazon
Accenture
H&R Block
IBM
PwC
Fidelity Investments
PayPal
Major League Baseball
Harvard Business School
IKEA

Artificial Intelligence Salaries

Salaries are dynamic, which means the numbers we’ve listed below will fluctuate due to inflation, trends, the job market, demand and other factors.

According to our degree page, the average salary for an artificial intelligence programmer ranges from $100,000 to $150,000. Salaries are significantly higher for AI engineers, averaging $171,715 with the top 25% earning above $200,000.

There are a range of averages, depending on the position and the responsibilities, but here are the most popular:

According to Indeed, the salary for artificial intelligence careers ranges from approximately $99,568 for a full stack developer to $141,318 for a data scientist.
The average annual base pay for artificial intelligence salaries in the United States is $120,049, according to Glassdoor.
According to Talent.com, the average artificial intelligence salary is $143,054 per year. Entry positions start at $115,000, and most experienced employees can make up to $200,000 per year.

Source: onlinedegrees
Original Content: https://onlinedegrees.sandiego.edu/artificial-intelligence-jobs/

23

AI Job Market and Hiring Trends / Employment Trends 2024: Embracing Upskilling in an AI-Driven World

« on: December 26, 2023, 09:51:55 AM »

By Neil C. Hughes



In 2023, the global tech sector confronted a significant paradox. On the one hand, tech layoffs increased by 50%, with over 240,000 jobs and counting lost worldwide.

Ironically, this occurred even as major companies heavily invested in artificial intelligence (AI), leading to a complex scenario of technological advancement and job insecurity. This situation has cast a long shadow over the tech landscape, raising questions about the future of employment and rising fears of unemployment.

However, there is hope on the horizon. Contrary to the bleak global outlook, there’s a window towards a more optimistic scenario for the US labor market. Despite economic challenges and the disruptive potential of AI, the labor market in 2023 demonstrated remarkable resilience and adaptability.

AI’s Role in Shaping Future Careers

The advancement of artificial intelligence, particularly generative AI, is reshaping the labor market, offering both challenges and opportunities for job seekers.

According to Indeed, the number of job postings containing the words “GenAI” or “Generative AI” was at <0.1% from 2020 to 2023.

And then, from the early months of 2023 to October 2023, it has lifted to 0.6% across all job adverts — if those numbers sound small, they should still raise an eyebrow. The need for workers with GenAI skills is growing.

Advertisements
The significant increase signals a major shift in the employment landscape. For professionals, it’s crucial to understand that GenAI’s impact extends beyond creating AI tools to its application across various sectors. This trend indicates that adapting to and understanding GenAI is becoming increasingly important for career advancement across multiple job functions.

IBM agreed, with their Institute for Business Value (IBM IBV) survey revealing that four out of five executives believe that generative AI will change employee roles and skills.

Employers and Employees and the New Rules of a Dynamic Market

In addition, the evolving job market with GenAI calls for a reassessment of the economic impact of these technologies.

If GenAI-related job growth is focused primarily on tool creation, its broader economic benefits may be limited.

Conversely, widespread adoption of GenAI across various occupational sectors could lead to significant economic shifts akin to the mass adoption of personal computing.

For job seekers, this emphasizes the importance of keeping pace with GenAI job growth and understanding which sectors are at the forefront.

Especially if you consider the World Economic Forum’s Future of Jobs Report, which estimates that 44% of workers’ skill sets will become obsolete in the next five years.

The report also emphasizes cognitive skills like creative thinking, analytical thinking, technology literacy, and socio-emotional attributes such as curiosity, resilience, and lifelong learning.

While workers at all levels will feel the effects of generative AI, lower-level employees are expected to see the most significant shift. Staying informed about how GenAI is transforming industries is vital for making informed career decisions in an AI-influenced job market.

However, those who dare to explore beyond the scary headlines quickly learn that AI should be seen as a collaborator rather than a threat. For these reasons alone, it’s time to consider how AI-related roles align with your career goals and the overall economic landscape.

Evolving Job Market Dynamics: Insights for Job Seekers

Indeed’s 2024 US Jobs & Hiring Trends Report provides an interesting insight into the current job market.

As of early November, the Indeed Job Postings Index was 22.5% lower than its peak on December 31, 2021.

Notably, the software development sector has witnessed the most pronounced decrease in job advertisements, registering a 51% reduction in one year alone.



Meanwhile, job seekers today also face a new reality after the “Great Resignation” spurred on by the COVID-19 pandemic.

This period, characterized by a significant rise in voluntary job departures, has changed the rules in job-seeking behavior, with professionals increasingly exploring opportunities outside their current fields.

This trend, coupled with employers implementing strategies like salary increments and enhanced benefits, underscores the need for job seekers to remain adaptable and align their career choices with evolving market trends, particularly in sectors like AI.

Meanwhile, employers are increasingly adopting salary transparency to attract job seekers. This shift and the need for job seekers to be proactive, informed, and flexible in their career planning highlight the importance of strategic adaptation in a competitive job market.

The increasing emphasis on work-life balance is highlighted by the fact that, per the National Bureau of Economic Research, 37% of UK jobs can be performed remotely.

Notably, 45% of professionals have reported changing their jobs when remote work options were unavailable, underscoring the growing importance of flexibility in the modern workplace.

Employment Trends in 2024

As we look towards 2024, the job market landscape is set to undergo transformative changes. The paradox witnessed in the tech sector in 2023, where significant layoffs coincided with extensive investments in AI, is a precursor to a broader shift in employment dynamics. Employers and job seekers must navigate this new terrain with strategic foresight and adaptability.

For employers, the focus should be on sustaining workforce demand amidst changing economic conditions. This involves consistent job postings and innovatively attracting and retaining talent, particularly in fluctuating sectors like technology.

Integrating AI, especially GenAI, into business strategies is no longer an option but a necessity. Embracing this change means preparing for a workforce increasingly skilled in AI applications, a trend that marks a significant shift in required competencies and job roles.

On the other hand, job seekers face a reality reshaped by the aftermath of the “Great Resignation” and evolving job market dynamics. The key to staying ahead lies in adaptability and strategic career planning.

Aligning career goals with emerging market trends, especially in AI and technology-driven sectors, becomes crucial. Wage growth trends also indicate the need for job seekers to understand their market value and negotiate proactively.

The Bottom Line

The advancement of GenAI brings with it a reassessment of career paths and skills. As AI reshapes various sectors, professionals must consider how AI-related roles align with their career aspirations.

Embracing reskilling and upskilling, coupled with leveraging AI tools, is essential to enhance your capabilities and ensure collaboration with technology rather than being replaced by it.

Executives emphasize that this evolution is not just about learning to code; it’s about refining skills like time management and collaboration, underscoring that the path to thriving in today’s workforce invariably involves continual up-skilling.

Source: techopedia.com
Original Content: https://www.techopedia.com/employment-trends-2024-embracing-upskilling-in-an-ai-driven-world

24

AI and Social Impact / The economic and market impact of artificial intelligence

« on: October 30, 2023, 10:31:33 AM »

How AI will transform the global economy

It has been clear to us that the global debate around AI’s consequences has been missing a comprehensive and coherent framework for thinking through all of the ways that it will shape economies and financial markets.

This Spotlight project fills this gap. Within the analysis on this page, you’ll find insight into how AI will reshape the global economy from all angles, from the impact on productivity and jobs to the consequences for inflation, policy and regulation. And, with our new proprietary AI Economic Impact Index, you’ll get a clear picture of AI’s consequences for the global economic order, including the countries that are best placed to exploit the economic benefits of AI and those that will struggle. Finally, we tie all of this together and explain AI’s implications for financial markets.

Our findings are striking. We show how the AI revolution has the potential to transform the growth outlook – but also how some economies are set to benefit far more than others. We show that fears of a surge in “technological unemployment” are overdone – but also how the AI revolution will bring huge dislocation to labour markets. And we show how AI has the potential to drive a secular bull market in equities – but at the risk of inflating bubbles.

Most importantly of all, we show how AI is something that all investors need to prepare for. Beyond the considerable current hype around AI lies the inescapable fact that this technology will eventually bring fundamental and sweeping change to companies, to economies, and to the policies that regulate them.

This project is by no means the last word on AI’s implications for economies and markets. Long after its publication, our global team of economists will be drawing on their proprietary data and other analytical resources to help our clients stay on top of what this evolving technology means for their decision-making.

But this Spotlight project is an indispensable first step to meeting the many opportunities and challenges that artificial intelligence presents

For Capital Economics clients:
AI, Economies and Markets

Our Spotlight 2023 report presents the full macro and market implications of artificial intelligence. Explore the chapters below and also see our proprietary CE AI interactive dashboard.

Source: Capital Economics Ltd.
Original Content: https://shorturl.at/lyzK6

25

AI and Future Scenarios / The Future of AI: 4 Potential Scenarios of What Comes Next

« on: October 29, 2023, 12:30:55 PM »

We remain optimistic about generative artificial intelligence’s (GenAI) influence on work because we’ve seen the results of integrating AI into organizations. While there may be challenges for companies considering AI — potentially enough to suggest AI “isn’t ready” for mainstream use — we are confident these challenges have solutions.
As many organizations adopt it, the potential for GenAI will continue to flourish. This progression can take many different forms. Here are four potential outcomes to assist business leaders considering adopting AI into their organization.

The Future of AI: 4 Potential Scenarios of What Comes Next



The Utopian Future

Let’s start with the most exciting future. In this optimistic scenario, AI’s potential is reached and the technology transforms every avenue of modern life. This would be most obvious through scientific breakthroughs in complex fields such as medicine or physics. These high-level innovations (or discoveries) roll down to everyday life. Artificial intelligence proves it is a versatile technology that can integrate across various business models and create an autonomous society. This could lead to better jobs (such as AI engineers) or improved societal welfare that redefines our understanding of work.

In this scenario, there are multiple indications AI has integrated into all aspects of life. This would feel similar to how the internet became relevant to every industry or how everyone eventually got a smartphone. If this is AI’s future, then businesses should lean into an AI culture where every task is augmented by the technology.



A Promising Future

If the utopian future comes, it will likely be preceded by a time period where AI looks very promising. In this potential scenario, the technology of AI progresses steadily but perhaps without the world-shattering breakthroughs present in the first scenario. Artificial intelligence has become widely available — allowing anyone access to the technology — but perhaps its uses are fairly narrow.

The limitation of the technology prevents its implementation across all industries so some are revolutionized (such as call centers) whereas others are still figuring out the best use for AI. This is why AI adoption research indicates “31% of [AI] projects are still in pilot or proof-of-concept stages. McKinsey data suggests “a lack of transparency into the origins of generated content and traceability of root data could make it difficult to update models and scan them for potential.” With the standardization of AI, governments pursue regulations and corporations will implement internal policies for how to use AI responsibly.

In this scenario, businesses should work with the technology for what it is not what it could be. This might mean safeguarding against unsolved challenges such as mitigating bias within algorithmic decision-making. This scenario is also a time to be cautious of overhyped promises. The technology is developing in the right direction, but it needs time to fully integrate.



A Concerning Future of AI

We’ve seen countless times across history how technology can be unevenly distributed and that may be the case for AI. In this scenario, the value of AI follows the stratification of other new technology. This could take the form of only a handful of countries having access to AI or it could be segmented among the wealthy of each society. According to one study, roughly 70% of AI early adopters have reported the adoption resulted in increased revenue. If only AI-adopting sectors experience increases — and only some sectors adopt AI — this could dramatically shift the economy.

An unevenly distributed use of the technology may result in incredible gains in some industries which could influence economic uncertainty or unemployment for other sectors. This perceived unfairness contributes to public backlash and legislation turns toward restricting or banning AI technology instead of regulating it. This creates a spiral effect that results in less competition and innovation — which further stratifies the haves and the have nots.

If this scenario occurs, businesses utilizing AI should consider varying their partners. The arms race for the technology will likely result in significant shifts in capabilities and access which could put individual organizations in high risk. This would be the time to create coalitions of other organizations with similar tech access to strengthen their operations and potentially contribute to AI governance.



The Unremarkable Future

Artificial intelligence has the interest of businesses and proof of its success, but this has been true for other technologies. High-profile disappointments can contribute to the public’s distrust or disinterest in utilizing AI in their life. This could occur if hallucinations, unreliability, or security vulnerabilities continue to emerge for businesses attempting to use AI. Alternatively, the technology could be dominated by unethical uses such as scams, misinformation, or copyright infringement.

Or maybe using the technology simply becomes too expensive. Any of these factors could trigger public backlash, cooled investments, and fading interest. This would be similar to the short hype cycle the world experienced for virtual reality — billions of dollars invested, multiple smaller corporations bought by trillion-dollar companies, a season of marketing, but ultimately nothing world-changing emerged.

Which Future of AI Will Emerge?

Whichever future emerges, it should be evident within the next couple of years. By 2033, proponents of AI say it could create half a billion new jobs. This would radically transform society within the next decade. If this transformation occurs, the signs of its progress will be evident very soon. A report from EY says “80% of CEOs are integrating artificial intelligence into products/services within the next 12 months. Another report from McKinsey suggests Generative AI “could add the equivalent of $2.6 to $4.4 trillion annually across various use cases.”

The success of AI relies on action being taken right now. Many businesses are facing challenges in control, transparency, and trust with AI solutions. The general public is getting familiarized with the technology and its implementation will take time. Both of these factors will be influenced by governmental regulations or corporate policies.

Artificial intelligence embodies the power to reshape industrial and social futures. Recognizing the path of the technology’s future can aid your own businesses’ adaptation to the changing market. Keeping pace with developments can ensure all organizations securely navigate AI’s future while bringing the promising future to all.

Source: shelf.io
Original Content: https://shorturl.at/cCO13

26

Face Recognition and Analysis / What is facial recognition?

« on: October 19, 2023, 12:47:00 PM »

What is facial recognition?
A face analyzer is software that identifies or confirms a person's identity using their face. It works by identifying and measuring facial features in an image. Facial recognition can identify human faces in images or videos, determine if the face in two images belongs to the same person, or search for a face among a large collection of existing images. Biometric security systems use facial recognition to uniquely identify individuals during user onboarding or logins as well as strengthen user authentication activity. Mobile and personal devices also commonly use face analyzer technology for device security.

What are the benefits of facial recognition technology?
Some benefits of face recognition systems are as follows:

Efficient security
Facial recognition is a quick and efficient verification system. It is faster and more convenient compared to other biometric technologies like fingerprints or retina scans. There are also fewer touchpoints in facial recognition compared to entering passwords or PINs. It supports multifactor authentication for additional security verification.

Improved accuracy
Facial recognition is a more accurate way to identify individuals than simply using a mobile number, email address, mailing address, or IP address. For example, most exchange services, from stocks to cryptos, now rely on facial recognition to protect customers and their assets.

Easier integration
Face recognition technology is compatible and integrates easily with most security software. For example, smartphones with front-facing cameras have built-in support for facial recognition algorithms or software code.

What are the use cases of facial recognition systems?
The following are some practical applications of a face recognition system:

Fraud detection
Companies use facial recognition to uniquely identify users creating a new account on an online platform. After this is done, facial recognition can be used to verify the identity of the actual person using the account in case of risky or suspicious account activity.

Cyber security
Companies use facial recognition technology instead of passwords to strengthen cybersecurity measures. It is challenging to gain unauthorized access into facial recognition systems, as nothing can be changed about your face. Face recognition software is also a convenient and highly accurate security tool for unlocking smartphones and other personal devices.

Airport and border control
Many airports use biometric data as passports, allowing travellers to skip long lines and walk through an automated terminal to reach their gate faster. Face recognition technology in the form of e-Passports reduces wait times and improves security.

Banking
Individuals authenticate transactions by simply looking at their phone or computer instead of using one-time passwords or two-step verification. Facial recognition is safer as there are no passwords for hackers to compromise. Similarly, some ATM cash withdrawals and checkout registers can use facial recognition for approving payments.

Healthcare
Facial recognition can be used to gain access to patient records. It can streamline the patient registration process in a healthcare facility and autodetect pain and emotion in patients.

What are the use cases of facial recognition systems?
The following are some practical applications of a face recognition system:

Fraud detection
Companies use facial recognition to uniquely identify users creating a new account on an online platform. After this is done, facial recognition can be used to verify the identity of the actual person using the account in case of risky or suspicious account activity.

Cyber security
Companies use facial recognition technology instead of passwords to strengthen cybersecurity measures. It is challenging to gain unauthorized access into facial recognition systems, as nothing can be changed about your face. Face recognition software is also a convenient and highly accurate security tool for unlocking smartphones and other personal devices.

Airport and border control
Many airports use biometric data as passports, allowing travellers to skip long lines and walk through an automated terminal to reach their gate faster. Face recognition technology in the form of e-Passports reduces wait times and improves security.

Banking
Individuals authenticate transactions by simply looking at their phone or computer instead of using one-time passwords or two-step verification. Facial recognition is safer as there are no passwords for hackers to compromise. Similarly, some ATM cash withdrawals and checkout registers can use facial recognition for approving payments.

Healthcare
Facial recognition can be used to gain access to patient records. It can streamline the patient registration process in a healthcare facility and autodetect pain and emotion in patients.

How does facial recognition work?
Facial recognition works in three steps: detection, analysis, and recognition.

Detection
Detection is the process of finding a face in an image. Enabled by computer vision, facial recognition can detect and identify individual faces from an image containing one or many people's faces. It can detect facial data in both front and side face profiles.
Computer vision
Machines use computer vision to identify people, places, and things in images with accuracy at or above human levels and with much greater speed and efficiency. Using complex artificial intelligence (AI) technology, computer vision automates extraction, analysis, classification, and understanding of useful information from image data. The image data takes many forms, such as the following:

Single images
Video sequences
Views from multiple cameras
Three-dimensional data
Analysis
The facial recognition system then analyzes the image of the face. It maps and reads face geometry and facial expressions. It identifies facial landmarks that are key to distinguishing a face from other objects. The facial recognition technology typically looks for the following:
 
Distance between the eyes
Distance from the forehead to the chin
Distance between the nose and mouth
Depth of the eye sockets
Shape of the cheekbones
Contour of the lips, ears, and chin
 
The system then converts the face recognition data into a string of numbers or points called a faceprint. Each person has a unique faceprint, similar to a fingerprint. The information used by facial recognition can also be used in reverse to digitally reconstruct a person's face.

Recognition
Facial recognition can identify a person by comparing the faces in two or more images and assessing the likelihood of a face match. For example, it can verify that the face shown in a selfie taken by a mobile camera matches the face in an image of a government-issued ID like a driver's license or passport, as well as verify that the face shown in the selfie does not match a face in a collection of faces previously captured.



Is facial recognition accurate?
Facial recognition algorithms have near-perfect accuracy in ideal conditions. There is a higher success rate in controlled settings but generally a lower performance rate in the real world. It is difficult to accurately predict the success rate of this technology, as no single measure provides a complete picture.
 
For instance, facial verification algorithms matching people to clear reference images, such as a driver's license or a mugshot, achieve high-accuracy scores. However, this degree of accuracy is only possible with the following:
 
Consistent positioning and lighting
Clear and unobstructed facial features
Controlled colors and background
Camera quality and image resolution
 
Another factor that impacts error rates is aging. Over time, changes in the face make it difficult to match photos taken years earlier.

Is facial recognition safe?
Human face recognition systems use unique mathematical patterns to store biometric data. Hence, they are among the safest and most effective identification methods in biometric technology. Facial data can be anonymized and kept private to reduce the risk of unauthorized access. Liveness detection technology distinguishes live users from their facial images. This prevents the system from being tricked by the photograph of a live user.

What is a confidence score in facial recognition?
Confidence scores, also known as similarity scores, are crucial for face detection and comparison systems. They provide feedback about how similar two images are to each other. A higher confidence score indicates a higher likelihood that two images are of the same person. Thus, confidence scores use AI to predict whether a face exists in an image or matches a face in another image.

Confidence score threshold
Every prediction that the facial recognition system makes using AI has a corresponding score threshold level that you can change. In a typical scenario, most automated matches are made on a very high percentage, for example, above a 99% confidence score. Matches with lower confidence scores may be used to see the next closest potential matches, which are then further evaluated by a human investigator.

What are other types of biometric identification technology?
Biometric identification is the process of identifying individuals based on unique, distinguishable traits. Besides facial recognition, there are many other types of biometric identification:

Fingerprint verification
Fingerprint recognition software verifies an individual's identity by comparing their fingerprint against one or more fingerprints in a database.

DNA matching
DNA matching identifies an individual by analyzing segments from their DNA. The technology sequences the DNA in a lab and compares it with samples in a database.

Eye recognition
Eye recognition analyzes features in the iris or patterns of the veins in the retina to determine a match and identify an individual.

Hand geometry recognition
You can uniquely identify individuals through the geometric features of their hands, such as the length of the fingers and width of the hand. A camera captures a silhouette image of the hand and compares it against a database.
Voice recognition
Voice recognition systems extract the characteristics that distinguish an individual's speech from others. It creates a voiceprint that is similar to a fingerprint or faceprint and matches it to samples in a database.

Signature recognition
You can use technology to analyze handwriting style or compare two scanned signatures using advanced algorithms.

How can AWS help with facial recognition?
You can use Amazon Rekognition to automate image and video analysis with machine learning. Amazon Rekognition offers pretrained and customizable computer vision capabilities to extract facial information and insights from your images and videos. You can use Amazon Rekognition to perform the following tasks:

Analyze and detect faces in millions of photos and videos within minutes
Add facial comparison and analysis in your user onboarding and authentication workflows to remotely verify the identity of opted-in users
Determine the similarity of a face against another picture or from your private image repository
Create home automation experiences, such as automatically turning on the light when a person is detected
Get started with facial recognition on AWS by creating a free account today.

Source: Amazon Web Services
Original Content: https://shorturl.at/nuRX7

27

AI Certifications and Training Programs / Top 20 AI Courses, Certificates & Certifications [+ Selection Guide]

« on: October 19, 2023, 12:30:14 PM »

The demand for artificial intelligence professionals is high — but the demand for skilled professionals who are equipped with the right knowledge and experience is even higher. Companies and organizations are looking to fill high-paying positions with employees they can trust on the front lines of artificial intelligence innovation. Courses, certificates and certifications are a good way to gain experience if you don’t have any, but they’re also beneficial to those already in the field looking to explore a new specialization.

Keep reading to learn about some of the top AI courses, certificates and certification programs — plus tips for selecting the right one.

Reasons to Take AI Courses or Become Certified

Taking a course or certification program is a good step toward advancing your career and demonstrating your commitment to learning more about artificial intelligence. Depending on where you are in your AI journey, many courses are introductory and entry-level while other intermediate options provide specialized knowledge in a particular AI subset. These career opportunities can better prepare you for certain positions and entry into AI graduate programs.

List of AI Certifications, Certificates & Courses

We’ve compiled a list of popular AI certifications and courses, but please note that these are listed in alphabetical order and are not ranked in any way.

Certifications & Certificates

1. Certified Artificial Intelligence Engineer — United States Artificial Intelligence Institute

The curriculum for this certification includes a variety of topics, including AI, ML, deep learning, deep reinforcement learning, computer vision, natural language processing and reinforcement learning. Applicants must be either an undergraduate/graduate student or a professional with 0–2 years of experience. There are two paths for application based on your background and experience. The program costs $581 and includes all the study materials and videos. There is a suggested pace of 8–10 hours per week.

2. CertNexus Certified Artificial Intelligence Practitioner Professional Certificate — Coursera   

This intermediate-level certification covers everything from data structure and ethics to machine learning algorithms and process management. Students should understand basic AI concepts and have experience with databases and programming.

3. Google Data Analytics Certificate — Google and Coursera

No experience is required in this online course that averages fewer than 10 hours per week. Topics include data type and structures, analyzing data, data storytelling and R programming.

4. IBM Applied AI Professional Certificate — Coursera

In this beginning-level program, you’ll learn about AI, data science, deep learning, machine learning, Python and much more. This online, self-paced option suggests a time commitment of 3 hours per week, resulting in completion within 7 months. No experience is required. Financial aid is available.

5. IBM AI Engineering Professional Certificate — Coursera

This intermediate-level certification takes approximately 9 months to complete. Topics include Python libraries, machine learning, hierarchical clustering, deep learning, artificial intelligence, image processing and more.

6. IBM Data Science Professional Certificate — Coursera

No experience is required for this beginner-level professional certificate. Courses include Tools for Data Science, Python for Data Science, AI & Development and Machine Learning with Python. With a suggested pace of 4 hours per week, completion is expected within 11 months. The program is 100% online.

7. IBM Data Analyst Professional Certificate — Coursera

This certificate focuses on data analysis, offering hands-on projects and labs and ending with a real-world capstone project. The 9 courses in this program include Introduction to Data Analytics and Data Visualization with Python. No experience is required, but you should have basic computer knowledge and be familiar with high school math. You should be able to complete the certificate within 11 months if you adhere to the suggested pace of 3 hours per week.

8. Microsoft Certified: Azure AI Engineer Associate — Microsoft

This certification offers two tracks: free online courses or a paid instructor-led experience. According to Microsoft, candidates for this certification “build, manage, and deploy AI solutions that leverage Azure Cognitive Services and Azure Applied AI services.” The cost for the certification exam is $165.

9. Microsoft Certified: Azure AI Fundamentals — Microsoft

You may want to consider this certification if you’re looking to take your AI knowledge and experience to the next level with related Microsoft Azure services. General programming knowledge and experience is recommended but not required. You can prepare for this exam through free online courses or a paid instructor-led experience. The exam fee is $99.

Courses
10. AI for Everyone — DeepLearning.AI (Coursera)

This beginner-level course is geared toward anyone who is interested in learning more about artificial intelligence, including those in the non-technical sectors. Learn AI terminology, AI strategy and workflow of projects. The approximate time commitment is 12 hours, and the course is 100% online. Financial aid is available.

11. Artificial Intelligence — edX

Choose from more than 20 courses, including AI for Everyone: Master the Basics and Ethics in AI and Data Science. You can audit the courses for free, but your access to the course materials will stop after a certain date. If you pay, you will have unlimited access to the materials. Prices range from $29 to $149 (with many around $99) depending on the course.

12. Artificial Intelligence — Udemy

A search for artificial intelligence courses on this online learning platform yields more than 2,500 results. Popular courses include: Artificial Intelligence A–Z, Artificial Intelligence for Business, Modern Artificial Intelligence with Zero Coding and Artificial Intelligence Masterclass. Courses typically average anywhere from $20 to $109 but many may be heavily discounted, depending on when you sign up. Udemy also offers more than 100 free courses.

13. Google AI

Google offers a variety of AI and ML courses, including a Machine Learning Crash Course and Machine Learning Problem Framing. All courses are free and self-paced. Google AI also offers guides, podcasts, interactive content, videos and more.

14. Intro to AI Ethics — Kaggle

This 4-hour online course covers topics such as identifying bias in artificial intelligence, AI fairness and human-centered design for AI. This self-paced option is also free.

15. Introduction to Artificial Intelligence — Coursera

Learn the artificial intelligence basics in this online, beginner-friendly course that takes approximately 11 hours to complete. Topics covered include AI, machine learning, deep learning and data science. A technical background is not required, and financial aid is available.

16. Intro to TensorFlow for Deep Learning — Udacity

This free, intermediate-level course teaches you how to build deep learning applications with TensorFlow, which is a machine learning platform. Topics include natural language processing, time series forecasting and transfer learning. The course takes about 2 months to complete.

17. Machine Learning — Stanford University and Coursera

If you’re interested in machine learning, a popular subsection of AI, this course will introduce you to the fundamentals. This online, self-paced course is one in a series of three courses.

18. IBM AI Foundations for Business Specialization — Coursera

The beginner-level specialization consists of three courses: Introduction to Artificial Intelligence, What is Data Science, The AI Ladder: A Framework for Deploying AI in Your Enterprise. No prior experience is required. The specialization takes approximately 2 months to finish.

19. Deep Learning Specialization — DeepLearning.AI (Coursera)

This is another type of intermediate specialization offered by Coursera. There are five courses that include topics such as Neural Networks and Deep Learning and Structuring Machine Learning Projects. The online, self-paced specialization takes approximately 5 months to complete. Basic programming skills and an understanding of linear algebra and machine learning are helpful.

20. The School of Artificial Intelligence — Udacity

Udacity offers a variety of AI courses that include such topics as AI for Healthcare, AI Product Manager and AI Programming with Python. They differ in terms of level and time commitment, but Udacity mentions that all programs include projects from industry experts, career services, mentor support and flexible learning programs.

List of AI Topics Taught in Courses (Sampling)
Beginning-level courses typically provide an overview of artificial intelligence. Intermediate or advanced courses often focus on one particular topic of specialization. Here is a sample of topics you may find in artificial intelligence courses:

Python and other programming languages
Ethics in AI
Natural language processing
AI for business leaders
Machine learning concepts
Robotics
AI algorithms

Where Is AI Being Used Today?
The short answer — everywhere! From education and finance to business and retail, artificial intelligence is an integral part of our lives and vital to the success of many organizations and businesses. Here are some common examples:

Wellness apps
Fitness trackers like Fitbit and Apple Watch
Robots
Self-driving cars
Rideshare apps
Chatbots
Personalized recommendations (Amazon, Netflix, etc.)
Smart assistants like Alexa and Siri
Social media monitoring (such as Facebook facial recognition)

How to Choose the Right AI Course
There are many considerations when it comes to selecting the right artificial intelligence course, certificate or certification programs. Here are some things to keep in mind:

How the course/program aligns with your career goals
Cost (and whether any financial aid opportunities are available)
Time commitment
Flexibility (self-paced versus set dates and times)
Online vs. in-person
Research is key. We recommend reading online reviews and speaking to professionals in the field who have previously taken a course or certification program you’re considering.

It’s also important to assess opportunities based on your current skills and knowledge. You want to ensure you are taking a course or certification program that is appropriate for your experience level.

Source: onlinedegrees.sandiego.edu
Original Content: https://shorturl.at/ktM67

28

AI Career Success Stories / Thomson Reuters Enhances Business Decision-Making with NLP

« on: October 19, 2023, 11:30:09 AM »

Hidden Injustice, a recent investigative report by Reuters, revealed how federal civil court rulings obscured the role that pharmaceutical companies played in the rising opioid epidemic. It was ground-breaking for what reporters uncovered, but also for how they uncovered it. They used machine learning and natural language processing (NLP) to review 3.2 million federal civil suits and over 90 million court actions to identify material filed under seal. They could then narrow their focus to search for instances in which “public health and safety information was kept secret without explanation.”[1]

The series won first prize at the 2019 Philip Meyer Journalism Awards, which recognize works of “precision journalism, computer-assisted reporting, and social science research.”[2]

For Thomson Reuters — the parent company of the news organization — AI techniques like machine learning and NLP are at the heart of what it does best: enabling legal, media, and tax & accounting professionals to find information, understand it, and use it to make decisions.

Khalid Al-Kofahi, who previously headed Thomson Reuters’ Center for AI and Cognitive Computing, says, “Generally speaking, knowledge workers such as attorneys and accountants essentially do three things. They have information needs so they engage in a journey of research and discovery. As they do that, they start analyzing it to understand it. Then at some stage they move to some sort of action or decision. We use AI technology to support all these activities.”

Thomson Reuters’ own journey of research and discovery included sponsoring the Vector Institute, which it did for three reasons: to stay on the front line of fundamental research, to support Canada’s AI ecosystem, and to develop approaches to common AI challenges through collaboration with other industry players.

One collaboration highlight is the Vector consortium project on NLP, a technique used to pursue “the holy grail” of AI: the fluent understanding of language. This project involves 25 industry participants that work with Vector researchers in workstreams focused on various NLP-related experiments. Thomson Reuters participated in a workstream to cost-effectively replicate BERT — bidirectional encoder representations from transformers — an advanced language representation model. Creating BERT requires a deep neural network to be pre-trained on a large body of unlabeled text — like that found on Wikipedia, Twitter, or a news site — to create a general model of how language works. This pre-trained BERT can then be fine-tuned for tasks like machine translation, sentiment analysis, and question answering in specific domains like law, health, and finance.

This utility often comes at quite a cost, though. Pre-training a BERT typically requires days of processing on hardware that may be prohibitively expensive for most organizations to access. Fine-tuning a vendor’s pre-trained BERT on specialized cloud-based processors like graphics processing units (GPUs) or tensor processing units (TPUs) is much less demanding, but still often comes at significant time and expense.

“When you look at some of these language models, they require a huge amount of resources to build,” Al-Kofahi says. “Part of the challenge for us was: Can we train these models using more distributed architectures and figure out algorithms that can reduce the demand for many GPUs?” The first phase of experiments, run on Vector’s own GPU cluster, were promising.

According to Al-Kofahi, this consortium project is “a tide that lifts all boats,” since participants gain benefits without having to risk competitive edges. He explains, “This is an area where it makes a lot of sense for industry to collaborate because we are establishing solutions for horizontal problems: how to scale deep learning models. Then each one of us, once we figure out a solution to that problem, can take that and adapt these models.”

Al-Kofahi continues, “We took these learnings and adapted them to different domains.  We have BERT for legal, BERT for tax, BERT for other domains as well, and we are now exploring how to incorporate some of these models for some of our products. This is a win-win situation.”

One product for which the results show potential is WestLaw, Thomson Reuters’ legal research service suite and the technology that enabled Reuters journalists to analyze millions of legal documents for Hidden Justice. It’ll soon also play a key role in a much broader judicial arena: Thomson Reuters was recently chosen by the Administrative Office of the U.S. Courts to provide legal research tools to the Federal Judiciary, including the Supreme Court and federal public defenders.

These awards illustrate one of the potential benefits of pursuing new AI insights and staying close to the leading edge of AI research:  the development of technology that increases justice and enhances access to it.

In 2017, the Congressionally-established non-profit, Legal Services Corporation, released a report declaring that “A lack of available resources accounts for the vast majority of eligible civil legal problems that go unserved or underserved,” and that “insufficient resources account for between 85% and 97% of all unserved or underserved eligible problems.”[3]

“How can we improve access?” Al-Kofahi says. “There are significant opportunities to use AI and machine learning to improve matter intake, provide resolutions that are aided by an arbitrator downstream, and so on. I think AI in that sense will transform the legal industry and how legal services are provided.”

He adds, “We are already part of the transformation.”

Source: vectorinstitute.ai
Original Content: https://shorturl.at/hFX59

29

AI Professional Networking / "Enhancing Networking Skills with AI"

« on: October 12, 2023, 10:39:37 AM »

Networking is a vital part of any business, but it can be a daunting task…Especially when you’re trying to stand out in a sea of professionals.

But what if we told you that you could have a secret weapon to help you with your networking efforts?

It’s possible! ChatGPT is an artificial intelligence tool that’s revolutionizing the way we network on LinkedIn.

Picture this: You’re at a networking event, and you’re feeling a bit nervous about striking up a conversation with a potential lead. But with ChatGPT by your side, you’re able to confidently start a conversation and make a lasting impression.

Or maybe you’re struggling to come up with the perfect words to describe yourself on your LinkedIn profile. With ChatGPT, you’re able to generate a compelling summary that highlights your skills and experience!

The possibilities are endless with Artificial Intelligence, and it’s not just for entrepreneurs, business owners, and sales professionals. This AI tool can help anyone looking to improve their networking skills and generate more leads and referrals.

Keep reading to learn how you can use AI for networking to generate more leads and referrals!



1. Generate Personalized And Engaging Conversation Starters
We get it, sometimes we run out of ideas on how to make a great first impression or get the conversation going. With AI software such as ChatGPT, you can come up with unique and interesting conversation starters to help you break the ice at networking events and make a lasting impression.

2. Craft A Compelling LinkedIn Profile
Want to stand out from the crowd? Well, you better have an engaging LinkedIn profile! We can’t stress enough how important it is to have a completed profile that speaks to who you are as a professional, but also as a person!

Using AI can help you generate a personalized and compelling summary that highlights your skills and experience, making you more likely to stand out to potential leads and connections.

3. Improve Your Follow-Up Messaging
Following up with your new connections is just as important as making them. But let’s face it, crafting the perfect follow-up message can be tricky. With AI’s ability to generate personalized and effective follow-up messages, ChatGPT takes the guesswork out of networking follow-up.

Not only does ChatGPT help you craft the perfect message, but it also ensures that your follow-up is timely, relevant, and tailored to your new connection.

The result? Increased chances of turning a new connection into a valuable lead.

4. Get Feedback On Past Efforts
When it comes to networking, effective communication is key. AI’s ability to analyze your previous messaging and provide feedback makes it the ultimate tool for fine-tuning your networking communication skills.

Simply input your previous messaging into ChatGPT and it will provide you with a detailed analysis, including feedback on tone, word choice, and structure. This way you can see what’s working and what’s not, and make adjustments accordingly!

5. Research Your Network

Understanding your network and building real relationships are key to successful networking on LinkedIn. But researching your network can be time-consuming and overwhelming.

You can probably guess what our solution to that is… yup! AI!

By analyzing your network’s online presence and activity, ChatGPT can provide you with a wealth of information that can help you understand your network and tailor your approach accordingly.

It can also help you research your competitors, providing you with valuable insights that can help you stand out in your industry!

In conclusion, ChatGPT is a powerful tool that can revolutionize the way you network on LinkedIn. With its ability to generate personalized and engaging conversation starters, craft compelling LinkedIn summaries, improve your follow-up messaging, analyze your communication, and research your network, ChatGPT can help you take your networking skills to the next level.

Want to learn more about the best practices using AI? Check out our guide, “AI Prompts Best Practices.”

Source: Evyrgreen LLC
Original Content: https://www.evyrgreen.com/how-to-use-chat-gpt-for-business-development/

30

AI Career Advice and Tips / 5 tips for someone who wants to work in AI

« on: October 12, 2023, 10:32:28 AM »

By Blathnaid O’Dea



Thinking of embarking on a career in AI? Here are some tips from people who work with the tech on what you need to know before taking the plunge.



This is a very exciting time to be looking for a career in AI because it is arguably no longer just a subset of the technology sector.

AI is very much a part of our everyday lives. It powers everything from business automation processes to customer service chatbots to social media algorithms.

Yes, AI is a technology, but its applications are constantly broadening. Sectors such as health, agriculture, law, education and entertainment rely on AI tech to function – and this is only going to become even more pronounced.

If you want to work in AI there’s no better time to take the plunge and see the difference your skills can make. Not everyone can be disruptors on the level of OpenAI founder Sam Altman, but there is space out there for anyone with decent AI skills and a willingness to put them to work.

As part of our AI and Analytics week at SiliconRepublic.com, we asked some current AI pros for tips on working in the field.

Here are five pieces of advice they had for anyone who wants to work in AI.

Understand AI’s broader applications
As we touched on in the introduction, AI is being deployed by many sectors for all kinds of reasons. As an AI professional, you should be aware of its potential as well as its limits.

According to Owen Fenton, who is a manager with KPMG’s Belfast-based applied intelligence team, it is “becoming increasingly vital” for those who want to work in AI to “understand company operations, organisational standards and broader industry practices.”

And that’s on top of the tech skills you’ll be expected to have.

“By effectively blending this knowledge into business-domain acumen, individuals will be able to develop problem-solving skills and solutions that are tailored to the unique challenges and opportunities within specific industries, such as finance, transport, or retail,” says Fenton.

“Understand the business needs you’re trying to address. To successfully deliver value via AI or analytics, you need to know what’s important to the business and how your work will impact that,” say Andrew Keogh and Caoilte Guiry of Workhuman’s engineering team.

Ethics

“The ethical questions surrounding data, privacy, bias and governance are huge and only likely to become more central,” according to Keogh and Guiry.

“Consider the ethical ramifications of the work you are doing, and be prepared to address bias in the data and think about how to avoid it in your outputs,” they warn.

“As data becomes more and more integral to successful business operations, it is becoming imperative to be aware of the ethical implications of using it responsibly and to grasp the potential moral issues that may arise from misusing it,” says Fenton.

“To address this, understanding data governance, management, and authorisation practices is crucial.”

Learn by doing

BearingPoint’s Gary Mullane says that practical experience is “essential to building expertise” in AI and analytics.

Mullane is responsible for the growth of the consulting firm’s data analytics and artificial intelligence business. (You often see AI and analytics mentioned together because AI is used to extract meaning from data to aid businesses).

Mullane recommends that people who want to work in AI get hands-on and pick up some practical experience.

“Participate in online workshops or hackathons, work on personal projects, or contribute to open-source projects to get real-world experience and build a portfolio of your work.”

Soft skills as well as tech skills

As well as building up your portfolio and honing your tech skills, Mullane says soft skills need attention.

“Develop soft skills and collaborate as much as possible. Soft skills such as communication, problem-solving, and teamwork are critical to success in AI and analytics.

“Focus on building these skills to work effectively with others and communicate ideas, to become a well-rounded professional and stand out in the field,” he advises.

He says that those who think their soft skills need work can start improving them by joining AI and analytics communities, attending conferences and connecting with others in the field.

Deloitte’s Adam Grant also has a tip for improving soft skills: public speaking.

“I would recommend getting good at PowerPoint and public speaking, this will aid you in both the recruitment process and the ability to work on interesting projects once employed.”

Public speaking is not a part of communication that many people relish, but it is important in the context of explaining to people without AI and analytics knowledge how the tech can help them.

“Analytics is all about deriving meaning and context from the data, in order to do this, subject matter experts are critical to delivering a successful project. Get used to working in teams and incorporating feedback into your work. What makes sense to you may not make sense to the end users of the solution. Always remember – you are building for them, not yourself,” says Grant.

Quality not quantity
If you’re feeling a little overwhelmed about all the things you need to stay on top of to build a good career in the AI sector, don’t worry.

Grant advises that it’s all about quality rather than quantity when it comes to learning in AI and analytics.

“There is a lot to learn, and you can find yourself doing course after course. My main piece of advice here is to not move forward unless you build a portfolio project with a specific technology. The portfolio process is key to internalising the skills learned.

“When interviewing, I am far more impressed with an individual who has built and can talk about projects using a limited numbers of tools than someone who has completed dozens of courses.”

This reiterates the point above about soft skills like communication and, of course, adaptability.

“AI and analytics evolves fast, learning how to adapt fast is the most critical skill,” says Grant.

Source: siliconrepublic
Original Content: https://www.siliconrepublic.com/employers