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1

Introduction to Computer Vision / Computer Vision – Introduction

« on: February 04, 2024, 11:00:16 AM »

Ever wondered how are we able to understand the things we see? Like we see someone walking, whether we realize it or not, using the prerequisite knowledge, our brain understands what is happening and stores it as information. Imagine we look at something and go completely blank. Into oblivion. Scary right? Well, the secret behind how our brain interprets the images we see has always intrigued me.

The idea to impart human intelligence and instincts to a computer seems rather effortless. Conceivably, because it is solved by very young children too, but we often tend to forget the limitations of computers as compared to our biological capabilities. The complexity of vision perception infinitely varies and is ever dynamic in the case of human beings itself, let alone Computer intelligence.

Our brain has the ability to identify the object, process data and decide what to do, thus completing a complex task in a split second. The aim is to enable Computers to be able to do the same. Hence, it is a field that can be referred to as an amalgamation of Artificial Intelligence and Machine Learning, which involves learning algorithms and specialized methods to interpret what the Computer sees.

The Beginning

Initially, the puzzling idea that tech giants still brainstorm about, was thought to be simple enough for an undergraduate summer project by the very people who pioneered Artificial Intelligence. Taking you back to 1966, when Seymour Papert and Marvin Minsky at MIT Artificial Intelligence group started a project in which the goal was to build a system that can analyze a scene and identify the objects in it.

Deep Learning

The Science behind Computer Vision revolves around artificial neural networks. In simple words? The algorithms inspired by the human brain that learn using large amounts of data sets so as to clone the human instincts as close as possible. These algorithms have superior accuracy, even surpassing human level in some tasks. Merely a subset of Deep Learning, Deep Vision is what drives Computer Vision.

Pixel Extraction

OpenCV (Open Source Computer Vision), a cross- platform and free to use library of functions is based on real time Computer Vision which supports Deep Learning frameworks that aids in image and video processing. In Computer Vision, the principal element is to extract the pixels from the image so as to study the objects and thus understand what it contains. Below are a few key aspects that Computer Vision seeks to recognize in the photographs:

1. Object Detection: The location of the object.
2. Object Recognition: The objects in the image, and their positions.
3. Object Classification: The broad category that the object lies in.
4. Object Segmentation: The pixels belonging to that object.

Applications and Future
Computer Vision covers a huge ground as its applications know no bounds. It often escapes our minds as we fail to notice the role Computer Vision plays in the gadgets, we use day in and day out.

Smartphones and Web: Google Lens, QR Codes, Snapchat filters (face tracking), Night Sight, Face and Expression Detection, Lens Blur, Portrait mode, Google Photos (Face, Object and scene recognition), Google Maps (Image Stitching).
Medical Imaging: CAT/MRI
Insurance: Property Inspection and Damage analysis
Optical Character Recognition (OCR)
3D Model Building (Photogrammetry)
Merging CGI with live actors in movies

Computer Vision is an ever-evolving area of study, with specialized custom tasks and techniques to target application domains. I visualize its market value growing as fast as its capabilities. With our intelligence and interest, we will soon be able to blend our abilities with Computer Vision and achieve new heights.

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Source: geeksforgeeks
Original Content: https://shorturl.at/chlT6

2

Introduction to Computer Vision / What Is Computer Vision? An Introduction

« on: January 31, 2024, 01:12:07 PM »

Enrolling in a computer vision course can be a helpful way to break into a rapidly evolving field that is making a major impact on many industries today.

Computer vision is currently being used for a variety of applications, such as self-driving cars, facial recognition technology and medical image analysis. The broad implications of this technology are significant, as it has the potential to revolutionize the way we interact with the world and each other.

Read on to learn more about the basics of computer vision and explore the different types of applications it is being used for, as well as the challenges and opportunities it presents.

What Is Computer Vision?

At its core, computer vision is the ability of computers to understand and analyze visual content in the same way humans do. This includes tasks such as recognizing objects and faces, reading text and understanding the context of an image or video.

Computer vision is closely related to artificial intelligence (AI) and often uses AI techniques such as machine learning to analyze and understand visual data. Machine learning algorithms are used to “train” a computer to recognize patterns and features in visual data, such as edges, shapes and colors.

Once trained, the computer can use this knowledge to identify and classify objects in new images and videos. The accuracy of these classifications can be improved over time through further training and exposure to more data.

In addition to machine learning, computer vision may also use techniques such as deep learning, which involves training artificial neural networks on large amounts of data to recognize patterns and features in a way that is similar to how the human brain works.

History of Computer Vision

The history of computer vision dates back over 60 years, with early attempts to understand how the human brain processes visual information leading to the development of image-scanning technology in 1959. In the 1960s, artificial intelligence emerged as an academic field of study, and computers began transforming two-dimensional images into three-dimensional forms.

In the 1970s, optical character recognition technology was developed, allowing computers to recognize text printed in any font or typeface. This was followed by the development of intelligent character recognition, which could decipher hand-written text using neural networks. Real-world applications of these technologies include document and invoice processing, vehicle plate recognition, mobile payments and machine translation.

In the 1980s, neuroscientist David Marr established that vision works hierarchically and introduced algorithms for machines to detect edges, corners, curves and other basic shapes. At the same time, computer scientist Kunihiko Fukushima developed a network of cells called the Neocognitron that could recognize patterns, including convolutional layers in a neural network.

In the 1990s and 2000s, real-time face recognition apps appeared, and there was a standardization of visual data set tagging and annotating. In 2010, the ImageNet data set became available, containing millions of tagged images across a thousand object classes and providing a foundation for convolutional neural networks (CNNs) and deep learning models used today.

In 2012, the AlexNet model made a breakthrough in image recognition, reducing the error rate to just a few percent. These developments have paved the way for the widespread use of computer vision in a variety of applications today.

How Does Computer Vision Work?

The computer vision system consists of two main components: a sensory device, such as a camera, and an interpreting device, such as a computer. The sensory device captures visual data from the environment and the interpreting device processes this data to extract meaning.

Computer vision algorithms are based on the hypothesis that “our brains rely on patterns to decode individual objects.” Just as our brains process visual data by looking for patterns in the shapes, colors and textures of objects, computer vision algorithms process images by looking for patterns in the pixels that make up the image. These patterns can be used to identify and classify different objects in the image.

To analyze an image, a computer vision algorithm first converts the image into a set of numerical data that can be processed by the computer. This is typically done by dividing the image into a grid of small units called pixels and representing each pixel with a set of numerical values that describe its color and brightness. These values can be used to create a digital representation of the image that can be analyzed by the computer.

Once the image has been converted into numerical data, the computer vision algorithm can begin to analyze it. This generally involves using techniques from machine learning and artificial intelligence to recognize patterns in the data and make decisions based on those patterns. For example, an algorithm might analyze the pixel values in an image to identify the edges of objects or to recognize specific patterns or textures that are characteristic of certain types of objects.

Overall, the goal of computer vision is to enable computers to analyze and understand visual data in much the same way that human brains and eyes do, and to use this understanding to make intelligent decisions based on that data.

Computer Vision at Work

Computer vision has provided numerous technological benefits in various industries and applications.

One example is IBM’s use of computer vision to create “My Moments” for the 2018 Masters golf tournament. This application used computer vision to analyze live video footage of the tournament and identify key moments, such as successful shots or notable events. These moments were then curated and delivered to fans as personalized highlight reels, allowing them to easily keep track of the tournament and stay engaged with the event.

Disney theme parks have also made use of computer vision and AI predictive technology to improve their operations. The technology works with high-tech sensors to help keep attractions running smoothly, with minimal disruptions. For example, if an attraction is experiencing technical issues, the system can predict the problem and automatically dispatch maintenance staff to fix it, helping to keep the attraction running smoothly and preventing disruptions for guests.

Google Translate is another example of the use of computer vision in technology. This application uses a smartphone camera and computer vision algorithms to analyze and translate text in images, such as signs or documents in foreign languages. This allows users to easily translate text on the go, making it easier to communicate and navigate in unfamiliar environments.

Finally, IBM and Verizon have been working together to help automotive companies identify vehicle defects before they depart the factory. Using computer vision and other advanced technologies, they are developing systems that can analyze the quality of vehicle components and identify defects in real time, allowing companies to catch and fix problems before they become larger issues. This can help improve the quality and safety of vehicles, as well as reduce production costs by catching problems early on in the manufacturing process.

Examples of Computer Vision

Computer vision has a wide range of capabilities and applications in various industries. Here are some examples of computer vision capabilities, along with brief explanations of each:

Optical character recognition (OCR): the ability to recognize and extract text from images or scanned documents

Machine inspection: the use of computer vision to inspect and evaluate the quality or condition of various components or products

Retail: the use of computer vision in automated checkout systems and other retail applications, such as inventory management and customer tracking

3D model building: the use of computer vision to analyze multiple images of an object or environment and construct a 3D model of it

Medical imaging: the use of computer vision to analyze medical images, such as X-rays or CT scans, to aid in the diagnosis and treatment of patients

Automotive safety: the use of computer vision in driver assistance systems and autonomous vehicles to detect and respond to obstacles and other hazards on the road

Match move: the use of computer vision to align and merge CGI elements with live-action footage in movies and other visual effects

Motion capture: the use of computer vision to capture and analyze the movement of actors or other objects, typically for use in animation or virtual reality applications

Surveillance: the use of computer vision to analyze video footage for security and monitoring purposes

Fingerprint recognition and biometrics: the use of computer vision to analyze and recognize unique physical characteristics, such as fingerprints, for identity verification and other applications

The Challenges of Computer Vision
Computer vision is a complex field that involves many challenges and difficulties. Some of these challenges include:

1. Data limitations
Computer vision requires large amounts of data to train and test algorithms. This can be problematic in situations where data is limited or sensitive, and may not be suitable for processing in the cloud. Additionally, scaling up data processing can be expensive and may be constrained by hardware and other resources.
2. Learning rate
Another challenge in computer vision is the time and resources required to train algorithms. While error rates have decreased over time, they still occur, and it takes time for the computer to be trained to recognize and classify objects and patterns in images. This process typically involves providing sets of labeled images and comparing them to the predicted output label or recognition measurements and then modifying the algorithm to correct any errors.
3. Hardware requirements
Computer vision algorithms are computationally demanding, requiring fast processing and optimized memory architecture for quicker memory access. Properly configured hardware systems and software algorithms are also necessary to ensure that image-processing applications can run smoothly and efficiently.
4. Inherent complexity in the visual world
In the real world, subjects may be seen from various orientations and in myriad lighting conditions, and there are an infinite number of possible scenes in a true vision system. This inherent complexity makes it difficult to build a general-purpose “seeing machine” that can handle all possible visual scenarios.
Overall, these challenges highlight the fact that computer vision is a difficult and complex field, and that there is still much work to be done in order to build machines that can see and understand the world in the same way humans do.

Boost Your Knowledge with a Computer Vision Course
Computer vision is a rapidly growing field that has the potential to positively impact many aspects of our daily lives. While there are still many challenges and limitations to overcome, computer vision technology has made significant strides in recent years, and we can expect to see even more exciting developments in the future.

Are you interested in taking part in this exciting field? Download our E-book, “8 Questions to Ask Before Selecting an Applied Artificial Intelligence Master’s Degree Program” to get started.

Source: onlinedegrees
Original Content: https://shorturl.at/ejlw5

3

Face Recognition and Analysis / Face Analysis in Computer Vision: A Practical Guide

« on: January 31, 2024, 12:54:59 PM »

What Is Face Analysis?

Face analysis detects faces in an image or video and can help determine characteristics of the face such as the gender, emotion, and age of the person in the image. It should not be confused with face recognition, which involves identifying which person is seen in an image.

Here are a few techniques commonly used in face analysis:

1. Identifying features in a human face that distinguish male and female faces, to facilitate gender detection.
2. Identifying markers such as pupil position, eyebrows, and lip borders, which change with age, to facilitate age detection.
3. Using sentiment estimation to detect facial expressions and determine the probability of emotions like happiness, sadness, surprise, or anger.

Main Tasks Handled by Face Analysis Algorithms

Facial Landmarks

Facial landmarks are areas of interest in a human face, also known as keypoints, including:

1. Eyebrows
2. Eyes
3. Nose
4. Mouth
5. Jaw lines

Researchers have identified as many as 68 facial landmarks. The following illustration shows the location of these landmarks.


Source: ResearchGate

Facial landmark detection is the task of detecting and tracking key facial landmarks, and transformations in these landmarks caused due to head movements and facial expressions.

Some applications of facial landmark detection include face swapping, head pose detection, facial gesture detection, and gaze estimation.

Visual Sentiment Analysis

Visual sentiment analysis is a new field in artificial intelligence in which computers analyze and understand facial expressions, gestures, intonation, and other non-verbal forms of human expression, to determine a person’s emotional state.

Visual sentiment analysis relies on computer vision technology, with a particular focus on face analysis, to analyze the shape of faces in images and videos and derive the emotional state of an individual. Current models are based on convolutional neural networks (CNNs) or support vector machines (SVM).

These models typically operate in four steps to perform visual sentiment analysis based on an image of a person:

1. Detecting faces in the image or video frame using state of the art face detection techniques.
2. When a face is detected, pre-process image data before sending it to the emotion classifier. Image preprocessing involves normalizing images, reducing noise, smoothing, optimizing image rotation, resizing, cropping, and adapting to lighting and other image conditions.
3. Processed face images are provided to the emotion classifier, which might use features like analyzing facial action units (AUs), facial landmark motions, distances between facial landmarks, gradation features, and facial textures.
4. Based on these features, the classifier assigns an emotion category such as “sad”, “happy” or “neutral” to the face in the image.

Age Estimation
Age estimation processes enable machines to determine a person’s age accAZrding to biometric features, such as human faces.

Automatic facial age estimation employs dedicated algorithms to determine age according to features derived from face images. Face image interpretation includes various processes, including face detection, feature vector formulation, classification, and the location of facial characteristics.

Age estimation systems can create the following types of output:

1. An estimate of the person’s exact age.
2. The person’s age group.
3. A binary result that indicates a certain age range.
Age-group classification is commonly used because many scenarios require only a rough estimate of the subject’s age. However, systems may not be able to determine all age ranges if trained to deal mainly with a specific age range.

Age estimation challenges

Age estimation encounters similar problems to other face image interpretations, such as face recognition, face detection, and gender recognition. Automatic age estimation is negatively affected by facial appearance deformations caused by factors such as:

1. Inter-person variation
2. Different expressions
3. Lighting variation
4. Occlusions
5. Face orientation
In addition to these face image interpretation challenges, age estimation is also affected by the following challenges:

1. Limited inter-age group variation—occurs when appearance differences between adjacent age groups are negligible.
2. Diversity of aging variation—occurs when the type of age-related effects and the aging rate differ between individuals.
3. Dependence on external factors—occurs when external factors, such as health conditions, psychology, and lifestyle, influence the aging rate pattern adopted by an individual.
4. Data availability—occurs when there are no suitable datasets for training and testing. A suitable dataset for age estimation must contain multiple images displaying the same subject at different ages.

Face Analysis vs. Face Recognition
Face analysis and face recognition are two activities that usually occur after face detection. Face detection is a computer vision task in which an algorithm attempts to identify faces in an image. Face detection algorithms provide the bounding boxes of the faces they manage to detect.

When a face is detected, a computer vision system might perform:

1. Face recognition—identifying who is the person shown in the image, or verifying a known identity. Applications of face recognition include biometric identification and automated tagging of people in social media images.
2. Facial analysis—extracting information from the face image, such as demographics, emotions, engagement, age, and gender. Applications of facial analysis include age detection, gender detection, and sentiment analysis.

Face Analysis Use Cases
Face Verification
Face verification is a security mechanism that enables digital verification of identity. Traditional verification involved showing an identification document (ID) to a security guard or seller. This type of verification is not possible online.

Governments and businesses providing digital services require verification. Common verification includes knowledge-based security like passwords, device-based security like tokens and mobile phones, and biometrics like fingerprints and irises. However, these mechanisms are not entirely secure.

Threat actors can steal and guess passwords or gain unauthorized access to lost or stolen devices. Additionally, not all biometric identifiers are included in IDs, passports, and driver’s licenses. Photos that include the person’s face, on the other hand, are included in most IDs.

Face verification enables customers to prove that they are the holders of their ID and assert that they are truly present during the facial scan. Unlike images that can be stolen, face verification provides live proof of the person’s presence and identity.

In-Cabin Automotive
There is a growing need for computer vision systems that can monitor individuals in a car while driving. The objective of these systems is to identify when a driver falls asleep, is not focused on the road, or performs other errors while driving, and alerting them to prevent accidents.

Face analysis applications for in-cabin automotive environments perform tasks like keypoint estimation, gaze analysis, and hand pose analysis, to accurately analyze the in-cabin environment.

Beauty Applications
Face analysis is entering use in the beauty industry. Face analysis algorithms are being developed to suggest how to enhance the beauty of a face through makeup, including specific applications and techniques, while taking into account the shape of the face, hair color, hairstyles, and other stylistic elements.

Like a human cosmetologist, a beauty-enhancing algorithm is trained to recognize the existing features of the face, enhance those features that humans consider desirable and mask those features that humans consider undesirable.

Smart Office
The smart office is a set of advanced technologies including intelligent communication and conferencing tools. These tools leverage face recognition, attention analytics, and gesture recognition models to support the hybrid work environment. The goal is to help teams communicate and collaborate more effectively.

Smart office face analysis algorithms must analyze the conference room and interactions between humans in the environment, and identify important human activities such as sitting, standing, speaking, and gesturing. In addition, some systems are able to recognize objects like whiteboards, post-it notes, and blackboards, and identify when humans are interacting with these objects.

Source: datagen.tech
Original Content: https://datagen.tech/guides/face-recognition/face-analysis/

4

3D Computer Vision / Introduction to 3D Machine Vision and Differences Between 2D and 3D Applications

« on: January 31, 2024, 12:34:03 PM »

Introduction to 3D Machine Vision

What is 3D Machine Vision?
3D vision is becoming more popular and more mainstream within machine vision circles. Why? Because it is a powerful technology capable of providing more accuracy for localisation, recognition, and inspection tasks that traditional 2D machine vision systems cannot reliably or repeatably succeed at.

As machine vision applications grow more complex, more creative solutions are required to solve more difficult problems in machine vision. 3D machine vision comprises an alternative set of technologies to 2D machine vision which aim to process these issues in greater depth and provide solutions to difficulties that 2D systems cannot solve.


How do we reliably and repeatably determine quantity in this context?

3D machine vision systems utilise 4 main forms of technology to generate 3-dimensional images of an object: Stereo Vision, Time of Flight (ToF), Laser Triangulation (3D Profiling), and Structured Light.

A 3D vision system furthers the analogy of machine vision as the ‘eyes’ of a computer system, as the addition of accurate depth perception functions more similarly to human eyes.

Stereo vision, for example, utilises two side-by-side cameras, calibrated and focused on the same object to provide full field of view 3D measurements in an unstructured and dynamic environment, based on triangulation of rays from multiple perspectives.

Laser triangulation, by contrast, measures the alteration of a laser beam when projected onto the object using a camera perpendicular to the beam. Where stereo vision can be used to capture stationary objects, laser triangulation requires a continuous linear motion, which can be achieved with a conveyor belt for example. This constraint is resolved in other ways, however, as laser triangulation can provide a spectacularly detailed point cloud map of the object.



https://youtu.be/yyXiB7ei_mw

Time of Flight (ToF), alternatively, measures the time it takes for light from a modulated illumination source to reach the object, generating a point cloud based on these recorded times.

https://youtu.be/QrR8UEjwFFs

3D technology has allowed for many creative solutions to the question of depth, and so there are a variety of options to choose from when considering 3D machine vision systems. Before deciding on which of the 4 main 3D technologies to choose, there are things to consider in your intended machine vision application.

Is 3D Machine Vision Right for Me?
3D systems are intrinsically more complicated than 2D systems, which are far more common for most applications, not to mention cheaper. But looking past the price tag and setup, you will find a system that can achieve far more powerful results than any 2D camera.

3D machine vision can be useful for applications that require more accuracy of the size, texture, and depth of the object in question.

For example, agriculture, manufacturing, inspection, and quality control can all benefit from 3D vision, but deciding between 3D technologies will ultimately depend on factors such as the level of accuracy required, speed of measurement, whether your object is fixed or moving, and the reflectivity and texture of the surfaces on your object.

For more information on the differences between 3D machine vision technologies, take a look at our e-book.

https://youtu.be/Xj9_jeyeNq4

2D vs 3D Machine Vision Systems
The traditional two-dimensional machine vision system when used in tandem with imaging library software has been proven to be very successful in applications such as barcode reading, presence detection, and object tracking, and these technologies are only improving with time.

However, since 2D cameras simply take an image of light reflected from the object, changes in illumination can have adverse effects on accuracy when taking measurements. Too much light can create an overexposed shot, leading to light bleeding or blurred edges of the object, and insufficient illumination can adversely affect the clarity of edges and features that appear on the 2-dimensional image.

In applications where illumination cannot be easily controlled, and therefore cannot be altered to fix the shot, this creates a problem within 2D machine vision systems.



Black text on a black object: 3D systems in tandem with 2D image processing can solve this issue

3D machine vision cameras can offset this by having the capability of recording accurate depth information, thus generating a point cloud, which is a far superior object in terms of accuracy.

Every pixel of the object is accounted for in space, and the user is provided with X, Y and Z plane data as well as the corresponding rotational data for each of the axes.

This makes 3D machine vision an exceptional option compared to 2D in the context of applications involving dimensioning, space management, thickness measurement, Z-axis surface detection and quality control involving depth. Traditional 2D image processing can still be used with the collected images, creating an implementable solution to many machine vision problems.

For further information on the above feel free to consult our informative e-book on 3D Imaging Techniques. Specifications for different 3D imaging solutions can be found in the data sheets of our cameras, available on our website to help you make the decision when choosing the optimal 3D machine vision camera model for your industrial application.

Source: clearview-imaging
Original Content: https://shorturl.at/wBDEW

5

3D Computer Vision / 3D Computer Vision: Unlocking the Third Dimension

« on: January 31, 2024, 12:24:15 PM »

Introduction

In today’s fast-paced world of technology, it’s more important than ever to understand and interpret the details of our surroundings. In recent years, we’ve seen Convolutional Neural Networks (or CNNs, for short) completely change computer vision, allowing us to analyze images with incredible accuracy. As automation, robotics, and retail applications continue to grow, so does the demand for more advanced vision systems. This is where 3D Computer Vision shines, introducing depth information and a level of understanding that was once out of reach for traditional 2D computer vision systems.

In our upcoming series of blog posts, we’ll dive deep into the advantages of 3D computer vision and explore how this technology is transforming various sectors. By approaching this topic through the framework of a typical machine learning pipeline (Figure 1), we will gain insights into the process of capturing three-dimensional data, investigate the diverse sensors involved, and ultimately explore the multitude of methods for processing and extracting value from this information.


Figure 1: The machine learning pipeline. In this blog post series we dive into how 3D computer vision is done at each step of the way.

In this first part of the series, we uncover the exciting world of 3D computer vision, its real-life applications, and how it’s shaping the future of numerous industries.

2D vs 3D Computer Vision

To truly appreciate the benefits of 3D computer vision, it’s essential to understand the differences between 2D and 3D computer vision. At its core, computer vision is a technology that processes and interprets visual data. In 2D computer vision, data is analyzed based on pixel values, colors, and textures in a flat, two-dimensional image, much like how we view photographs. While it has been highly successful in tasks like image recognition and classification, it falls short when it comes to understanding spatial relationships and depth, making it less suitable for tasks that require accurate perception of real-world environments. By providing depth information, 3D computer vision can address many of the limitations faced by 2D computer vision, such as understanding spatial relationships, handling occlusion, and overcoming issues related to lighting and shadows.

To help you see the differences between 2D and 3D computer vision, let’s use a simple, everyday example. Picture yourself looking at a photo of a cozy living room, complete with furniture arranged in various spots. With 2D computer vision, it’s easy to identify and recognize the different pieces of furniture and their colors. However, figuring out the relative distances between the objects and their actual sizes can be tricky since there’s no depth information. As humans, we have to rely on visual cues (Figure 2) like shadows, perspective and overlapping objects to make sense of depth in a 2D image; but these cues aren’t always clear-cut.





Figure 2: Monocular depth cues. For an interesting example of how perspective and visual cues can be deceiving, check out the Ames room.

Now, imagine actually stepping into that same living room. Your understanding of the room, furniture, and their positions in relation to each other suddenly becomes much clearer, thanks to the binocular depth cues our vision provides (our ability to perceive depth using both eyes). This is the kind of enhanced perception that 3D computer vision offers to machines, making it easier for them to understand and interact with their surroundings. This ability is vital in various tasks, including robotic navigation, object manipulation, and accurate volume and shape measurements, enabling machines to interact with and respond to the world more effectively.

The depth information provided by 3D computer vision also plays a critical role in improving accuracy. While 2D computer vision can sometimes struggle to differentiate between objects in a cluttered environment, 3D computer vision leverages depth data to distinguish between them, ensuring tasks are carried out with greater precision and reliability (Figure 3).


Figure 3: Using 3D vision to distinguish different products in a cluttered environment (Source)

Another noteworthy advantage of 3D computer vision is its robustness to lighting and shadows. In the world of 2D computer vision, changes in lighting conditions and the presence of shadows can significantly impact performance, as it relies solely on color and intensity data. However, utilizing depth information allows us to easily overcome these issues. Overall, 3D computer vision provides a strong resilience across a wide range of environments and lighting conditions that allow systems to perform more consistently and reliably.

So far, we have seen that 3D vision systems offer numerous advantages over 2D systems by providing an additional layer of information, which can improve performance. However, they also introduce complexities in terms of hardware setup, storage capacity, and processing times. It’s crucial to assess the specific application needs and determine if the benefits of using 3D vision outweigh the challenges. To help guide this decision-making process, in the following section, we explore how 3D data unlocks new possibilities and applications across multiple industries.

Real-World Applications and Trends

3D computer vision is making a significant impact across various industries by offering new possibilities and transforming traditional tasks. A big part of this transformation was also possible due to the advancements in deep learning models, where new model architectures and collection of more and more data have been supporting significant improvements in the field. Let’s explore some of the exciting applications and trends in several key sectors.

Manufacturing and Quality Control

In manufacturing, 3D computer vision is enhancing robotics and automation with depth perception, allowing robots to better understand their surroundings and perform tasks with increased precision, such as picking and placing items or assembling components. Inline quality control and inspection also benefit greatly from 3D computer vision and machine learning combined. 3D deep learning models provide us with accurate object detection and recognition, which can easily help systems identify defects, provide accurate and precise measurements and identify inconsistencies in manufactured products with greater reliability. This improved accuracy leads to higher product quality and reduced waste, which is crucial for maintaining a competitive edge in today’s fast-paced market. The integration of 3D computer vision with emerging technologies like Industry 4.0 and the Internet of Things (IoT) is paving the way for smart factories. Systems are becoming faster and more efficient and we can expect to see more real-time processes integrated seamlessly into manufacturing workflows.



Figure 4: Example of 3D quality inspection use case; measuring angle of lifted can tabs (Source).

Autonomous Driving

In the automotive industry, 3D computer vision is essential for self-driving cars, as it enables them to accurately perceive and understand their environment. Companies like Waymo, Cruise and Zoox are using multimodal deep learning models and advanced 3D vision technology for obstacle detection, lane tracking, and navigation, paving the way for safer and more efficient transportation. You can check this video for an interesting break down of how Zoox uses computer vision to solve autonomous driving.


Figure 5: 3D mapping of surrounding environment for autonomous navigation (Source).

Healthcare
Various medical applications, such as surgical assistance, diagnostics, and medical imaging make use of 3D computer vision. For example, an anatomical visualization service¹ creates 3D models of patients’ anatomy, assisting surgeons in planning and executing procedures. During surgery, the model can be viewed and manipulated on a console, improving surgical accuracy and efficiency.




Figure 6: 3D anatomical models allowing doctors to plan and execute procedures(Source)

Aerial Imagery
Drones equipped with 3D vision capabilities can provide detailed topographical data, facilitating tasks like mapping, surveying, and environmental monitoring². They also benefit agriculture by monitoring crop health, analyzing soil conditions, and optimizing resource usage. This enables precision farming practices, leading to increased yield and more sustainable agriculture. Combining drones with 3D vision also allows for to safe inspection of infrastructure and equipment like power grids, construction sites and oil and gas refineries³. The 3D scanned models can be fed to a 3D object detection




Figure 7: 3D inspection of a power grid (Source).

Logistics
Retail and logistics are also experiencing the transformative power of 3D computer vision. In inventory management, 3D computer vision can accurately recognize and track individual items, even in cluttered environments, making it easier to maintain accurate stock levels and optimize warehouse organization. Furthermore, it can be integrated in optimization problems, such as minimizing costs of packaging and shipping operations by scanning objects dimensions and matching it with the available packaging space (e.g. in a container).

Retail
In retail, the technology is being integrated into customer-facing applications, such as virtual fitting rooms and augmented reality shopping experiences, offering a more engaging and personalized experience for consumers. Apple, for example, has LiDAR⁴ integrated in their iPhones’ Pro versions, enabling a new range of applications. The IKEA Place app, for example, allows users to visualize products in their homes before making a purchase (check it out in this video).

Generative AI has also been making its way into the 3D space. Deep learning models like pix2pix3D⁵ and Imagine 3D⁶ enable the creation of 3D representations of objects using hand-drawn labels and textual prompts, respectively. Although still in its early stages, this technology holds the potential to unlock intriguing use-cases within the retail sector.




Figure 8: IKEA Place app allows users to try out different furniture in their own space (Source).

As 3D computer vision continues to evolve, we can expect to see even more innovative applications and trends emerging across various industries. The ability to accurately perceive depth and spatial relationships not only enhances existing processes but also unlocks new opportunities for businesses to improve their operations and stay ahead of the competition.

Conclusion
As we have seen, 3D computer vision offers a wealth of advantages over traditional 2D computer vision, opening new doors for innovation and improved performance across a multitude of industries. While the manufacturing sector stands to benefit significantly from the adoption of 3D computer vision technologies, its impact extends far beyond this industry. The future of 3D computer vision is marked by expanding possibilities and emerging applications in diverse sectors such as retail, logistics, and even healthcare. By embracing this transformative technology, companies can unlock new levels of efficiency, productivity, and innovation, leveling up not only their operations but also the industries they serve.

In conclusion, the adoption of 3D computer vision is not just a technological leap, but a strategic move for forward-thinking businesses. It’s time to explore the potential of 3D computer vision solutions for your organization and stay ahead of the curve in an increasingly competitive landscape.

This first part of the blog post series served as an introduction to the world of 3D vision. Keeping in mind the pipeline illustrated in Figure 1, our upcoming post will explore the capture and storage of data in greater detail. We will examine how this data is produced and consider how the selection of sensor type may be influenced by diverse factors such as technical requirements, environmental considerations, business constraints and other relevant factors.

Source: ml6.eu
Original Content: https://shorturl.at/acOWZ

6

Deep Learning Frameworks / Top 8 Deep Learning Frameworks You Should Know in 2024

« on: January 08, 2024, 03:05:03 PM »

TensorFlow
Google’s Brain team developed a Deep Learning Framework called TensorFlow, which supports languages like Python and R, and uses dataflow graphs to process data. This is very important because as you build these neural networks, you can look at how the data flows through the neural network.

TensorFlow’s machine learning models are easy to build, can be used for robust machine learning production, and allow powerful experimentation for research.

With TensorFlow, you also get TensorBoard for data visualization, which is a large package that generally goes unnoticed. TensorBoard simplifies the process for visually displaying data when working with your shareholders. You can use the R and Python visualization packages as well.

Keras
Francois Chollet originally developed Keras, with 350,000+ users and 700+ open-source contributors, making it one of the fastest-growing deep learning framework packages.

Keras supports high-level neural network API, written in Python. What makes Keras interesting is that it runs on top of TensorFlow, Theano, and CNTK.

Keras is used in several startups, research labs, and companies including Microsoft Research, NASA, Netflix, and Cern.

Other Features of Keras:
User-friendly, as it offers simple APIs and provides clear and actionable feedback upon user error
Provides modularity as a sequence or a graph of standalone, fully-configurable modules that can be combined with as few restrictions as possible
Easily extensible as new modules are simple to add, making Keras suitable for advanced research

Top 8 Deep Learning Frameworks You Should Know in 2024
Lesson 6 of 32By Simplilearn

Last updated on Nov 7, 202363737
Top 8 Deep Learning Frameworks You Should Know in 2024
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Table of Contents
TensorFlowKerasPyTorchTheanoDeeplearning4j (DL4J)View More
In today’s world, more and more organizations are turning to machine learning and artificial intelligence (AI) to improve their business processes and stay ahead of the competition.

The growth of machine learning and AI has enabled organizations to provide smart solutions and predictive personalizations to their customers. However, not all organizations can implement machine learning and AI for their processes due to various reasons.

This is where the services of various deep learning frameworks come in. These are interfaces, libraries, or tools, which are generally open-source that people with little to no knowledge of machine learning and AI can easily integrate. Deep learning frameworks can help you upload data and train a deep learning model that would lead to accurate and intuitive predictive analysis.

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TensorFlow
Google’s Brain team developed a Deep Learning Framework called TensorFlow, which supports languages like Python and R, and uses dataflow graphs to process data. This is very important because as you build these neural networks, you can look at how the data flows through the neural network.

TensorFlow’s machine learning models are easy to build, can be used for robust machine learning production, and allow powerful experimentation for research.

With TensorFlow, you also get TensorBoard for data visualization, which is a large package that generally goes unnoticed. TensorBoard simplifies the process for visually displaying data when working with your shareholders. You can use the R and Python visualization packages as well.

 

Initial release:

November 9, 2015

Stable release:

2.4.1 / January 21, 2021

Written in:

Python, C++, CUDA

Platform:

Linux, macOS, Windows, Android, JavaScript

Type:

Machine learning library

Repository

github.com/tensorflow/tensorflow

License:

Apache License 2.0

Website

www.tensorflow.org

Keras
Francois Chollet originally developed Keras, with 350,000+ users and 700+ open-source contributors, making it one of the fastest-growing deep learning framework packages.

Keras supports high-level neural network API, written in Python. What makes Keras interesting is that it runs on top of TensorFlow, Theano, and CNTK.

Keras is used in several startups, research labs, and companies including Microsoft Research, NASA, Netflix, and Cern.

Other Features of Keras:
User-friendly, as it offers simple APIs and provides clear and actionable feedback upon user error
Provides modularity as a sequence or a graph of standalone, fully-configurable modules that can be combined with as few restrictions as possible
Easily extensible as new modules are simple to add, making Keras suitable for advanced research
 

Initial release:

March 27, 2015

Stable release:

2.4.0 / June 17, 2020

Platform:

Cross-platform

Type:

Neural networks

Repository

github.com/keras-team/keras

License:

Massachusetts Institute of Technology (MIT)

Website

https://keras.io/

PyTorch
Adam Paszke, Sam Gross, Soumith Chintala, and Gregory Chanan authored PyTorch and is primarily developed by Facebook's AI Research lab (FAIR). It’s built on the Lua-based scientific computing framework for machine learning and deep learning algorithms. PyTorch employed Python, CUDA, along with C/C++ libraries, for processing and was designed to scale the production of building models and overall flexibility. If you’re well-versed with C/C++, then PyTorch might not be too big of a jump for you.

PyTorch is widely used in large companies like Facebook, Twitter, and Google.

Other Features of the Deep Learning Framework Include:
It provides flexibility and speed due to its hybrid front-end.
Enables scalable distributed training and performance optimization in research and production using the “torch distributed” backend.
Deep integration with Python allows popular libraries and packages to be quickly write neural network layers in Python.

Theano
The University de Montreal developed Theano, written in Python and centers around NVIDIA CUDA, allowing users to integrate it with GPS. The Python library allows users to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays.

Deeplearning4j (DL4J)
A machine learning group that includes the authors Adam Gibson Alex D. Black, Vyacheslav Kokorin, Josh Patterson developed this Deep Learning Framework Deeplearning4j. Written in Java, Scala, C++, C, CUDA, DL4J supports different neural networks, like CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), and LSTM (Long Short-Term Memory).

After Skymind joined the Eclipse Foundation in 2017, DL4J was integrated with Hadoop and Apache Spark. It brings AI to business environments for use on distributed CPUs and GPUs.

Other Features of DL4J Include:
A distributed computing framework as training with DL4J occurs in a cluster
An n-dimensional array class using ND4J that allows scientific computing in Java and Scala
A vector space modeling and topic modeling toolkit that is designed to handle large text sets and perform NLP

Caffe
Developed at BAIR or Berklee Artificial Intelligence Research and created by Yangqing Jia, Caffe stands for Convolutional Architecture for Fast Feature Embedding. Caffe is written in C++ with a Python Interface and is generally used for image detection and classification.

Other Features and Uses of Caffe:
Used in academic research projects, startup prototypes, and large-scale industrial applications in vision, speech, and multimedia
Supports GPU- and CPU-based acceleration computational kernel libraries, such as NVIDIA, cuDNN, and IntelMLK
Can process over 60M images per day with a single NVIDIA K40 GPU

Chainer
Developed by PreferredNetworks in collaborations with IBM, Intel, Microsoft, and Nvidia, Chainer is written purely in Python. Chainer runs on top of Numpy and CuPy Python libraries and provides several extended libraries, like Chainer MN, Chainer RL, Chainer CV, and many other libraries.

Other Features of Chainer:
Supports CUDA computation
Requires only a few lines of code to leverage a GPU
uns on multiple GPUs with little effort
Provides various network architectures, including feed-forward nets, convents, recurrent nets, and recursive nets

Microsoft CNTK
Microsoft Research developed CNTK, a deep learning framework that builds a neural network as a series of computational steps via a direct graph. CNTK supports interfaces such as Python and C++ and is used for handwriting, speech recognition, and facial recognition.

Other Features of Microsoft CNTK Include:
Designed for speed and efficiency, CNTK scales well in production using GPUs but has limited support from the community
Supports both RNN and CNN type of neural models capable of handling image, handwriting, and speech recognition problems

Master Deep Learning Framework Concepts with Simplilearn
All of these deep learning packages have their own advantages, benefits, and uses. It’s not mandatory that you stick to a single framework—you can jump back and forth between most.

To learn more about deep learning frameworks, you should enroll in our world-class Data Science Bootcamp, delivered in partnership with IBM. Explore and enroll today!

Source: simplilearn
Original Content: https://shorturl.at/zAFNQ

7

AI Chatbot Development Tools / Which are the Best Tools Used for Chatbot Development

« on: January 08, 2024, 02:58:39 PM »

BY HARIHARAN A

A chatbot is a conversational application that simulates and processes human conversation (either written or spoken), allowing humans to interact with digital devices as if they were communicating with a real person.

It helps in customer service, engagement, and support by replacing or augmenting human support agents with artificial intelligence (AI) and other automation technologies that can communicate with end-users via chat.

Chatbots are computer programs that replicate and analyze human dialogue (spoken or written), enabling humans to communicate with electronic devices as if they were conversing with a live agent.

How an AI Chatbot Works – Short Description

As shown in the image below, chatbot analyses the request from the user and then identify the intent of the user, what they need and then generate an appropriate reply and automatically replies to the user.



Chatbots are opening up new experiences for users and helping businesses reach their goals sooner with cost economies.

Do these chatbots outpacing mobile apps? You can know more about it here.

Best Tools for Chatbot Development

There are many tools that help in Chatbot development. Some of the best tools are:

Google Dialogflow
Microsoft Bot Framework
Amazon Lex
BotMan
GupShup
Wit.ai
Botsify
Rasa

1. Google Dialogflow



Google Dialogflow is a chatbot development tool that helps developers design, develop and integrate a user interface into a mobile app, web app, bot or device

It is one of the most popular tools that support Natural Language Processing in more than 20 languages.

Dialogflow supports nearly all famous messaging platforms including Facebook Messenger, Slack, Twitter and more.

If you are planning to develop an omnichannel chatbot for your company that quickly responds to text and audio inputs, you should consider Dialogflow.

2. Microsoft Bot Framework



It is one of the most popular chatbot development platforms that helps the mobile app development team to build an intelligent and smart chatbot that can provide an out-of-the-box user experience.

Microsoft Bot Framework is a comprehensive framework that has the ability to build amazing AI experiences for your brand.

With its Azure cognitive services, you can create a bot for your business that can speak, listen, and understand your users.

Microsoft Bot Framework is packed with open source SDK and useful tools that let your team of designers and developers build, test, and connect bots or sophisticated virtual assistants.

3. Amazon Lex



Amazon Lex is an amazing service for building omnichannel Q&A chatbots or conversational interfaces.

It is based on a complete natural language model that allows users to interact with voice, and text, enabling your brand to build applications that can offer highly engaging user experiences.

Amazon Lex is packed with deep learning technologies, helping developers build sophisticated, natural language and conversational bots.

With the help of Amazon Lex Chatbot Development Tools, we can build enterprise chatbots that help you automate tasks and improve the productivity of your organization.

Many established brands such as HubSpot, Zendesk, Salesforce use Amazon Lex to improve their sales processes, marketing performance, and customer service.

We can also use Amazon Lex to build a bot for your company by using AWS Lambda functions.

4. BotMan



BotMan is a chatbot development framework that allows you to build a chatbot using PHP and integrate it into your business website and various messaging services, such as Amazon Alexa, Facebook Messenger, Slack, Telegram, WeChat, and more.

One of the best parts of using BotMan is that it is framework agnostic, allowing us to use the existing codebase, regardless of the framework type.

5. GupShup



GhupShup is a messaging platform that allows developers to build bots that create highly interactive conversational experiences for a myriad of messaging channels using a single API.

The API is bundled with highly advanced features, allowing us to perform a lot of tasks.

Gupshup’s single API allows us to develop interactive conversational experiences across voice, SMS, and IP messaging.

6. Wit.ai



Wit.ai is a chatbot development tool that allows businesses to create a natural language experience.

This platform allows you to create bots that enable users to interact with your products with voice and text.

Wit provides developers an open and extensible platform that allows developers to build apps, bots, and virtual assistants.

It allows your app development team to build, test and deploy natural language experiences.

7. Botsify



Botsify is one of the famous chatbot development platforms that helps in building a chatbot for business by simply using its drag-and-drop UI.

With Botsify, we can publish the chatbot on your company website, popular messaging platforms, such as Facebook Messenger, WhatsApp, Slack, and much more.

Botsify helps us build a highly efficient chatbot that you can use for managing multiple tasks. It makes your chatbot conversational by supporting more than 190 languages.

Furthermore, Botsify gives you an option to switch from chatbot to human support at any point during the conversation.

8. Rasa



Rasa helps to build a text or voice-based assistant for your business. Rasa is a machine learning framework, allowing developers to build great AI assistants.

Rasa is packed with amazing tools that are necessary for creating high-performing and smart AI assistants that can help solve customer problems.

Developers and enterprises widely use Rasa due to its cutting-edge natural language understanding, dialogue management, transparent architecture, and integrations.

Final Thoughts
To conclude we can say that Chatbot is an application that replaces humans in communication for customer service, engagement and support with customers and reduces human effort.

To develop Chatbot applications we have many tools available for us.

From those there are some of the best tools available for us. Those tools and its features are the things we discussed as part of this blog.

If you need help with chatbot development, then you can get in touch with our experts at Perfomatix.

Source: perfomatix
Original Content: https://shorturl.at/fGH79

8

General AI Tools Discussion / Top Generative AI Tools: Boost Your Creativity

« on: January 08, 2024, 12:35:53 PM »

What is Generative AI?
The branch of artificial intelligence known as "generative AI" is concerned with developing models and algorithms that may generate fresh and unique content. Generative AI algorithms apply probabilistic approaches to produce new instances that mirror the original data, typically with the capacity to demonstrate creative and inventive behavior beyond what was explicitly designed.

How Does Generative AI Tool Work?
Generative AI tools operate by employing advanced machine learning techniques, often deep learning models such as generative adversarial networks (GANs) or variational autoencoders (VAEs). These models are trained on massive datasets to understand patterns and underlying structures. The models learn to create new instances that mirror the training data by capturing the statistical distribution of the input data throughout the training phase.

Best Generative AI Tools
Here is an overview of key features, pros and cons, working and pricing of the top 20 generative AI tools:

Top Generative AI Tools: Boost Your Creativity
Lesson 15 of 23By Sneha Kothari

Last updated on Dec 11, 202369786
Top 20 Generative AI Tool
PreviousNext
Table of Contents
What is Generative AI?How Does Generative AI Tool Work?Best Generative AI ToolsHow Can Businesses Use Generative AI Tools?Go In For Caltech Post Graduate Program in AI and Machine LearningView More
With numerous Generative AI tools in the market today, are you intrigued to know about the best generative AI tools that have revolutionized the industry and are influencing future creativity and innovation?

Let's explore the special attributes, working, and advantages of the top 20 tools.

What is Generative AI?
The branch of artificial intelligence known as "generative AI" is concerned with developing models and algorithms that may generate fresh and unique content. Generative AI algorithms apply probabilistic approaches to produce new instances that mirror the original data, typically with the capacity to demonstrate creative and inventive behavior beyond what was explicitly designed.

How Does Generative AI Tool Work?
Generative AI tools operate by employing advanced machine learning techniques, often deep learning models such as generative adversarial networks (GANs) or variational autoencoders (VAEs). These models are trained on massive datasets to understand patterns and underlying structures. The models learn to create new instances that mirror the training data by capturing the statistical distribution of the input data throughout the training phase.

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Best Generative AI Tools
Here is an overview of key features, pros and cons, working and pricing of the top 20 generative AI tools:

1. GPT-4
GPT-4 is the most recent version of OpenAI's Large Language Model (LLM), developed after GPT-3 and GPT-3.5. GPT-4 has been marketed as being more inventive and accurate while also being safer and more stable than earlier generations.

Key Features
GPT-4 has 100 Trillion Parameters
Improved Factual Performance
Enhanced Steerability
Image Inputs Capability
Multilingual Capability
Outperformance on Multiple Benchmarks
Human-Level Performance on Various Benchmarks
Pros
It is a consistent and reliable time saver.
Cost-effective and scalar
Cons
It can provide wrong answers.
It can be extremely biased.
Pricing
Free Version
Prompt: $0.03 per 1,000 tokens
Completion: $0.06 per 1,000 tokens
Paid membership: $20/month

2. ChatGPT
The most commonly used tool from OpenAI to date is ChatGPT, which offers common users free access to basic AI content development. It has also announced its experimental premium subscription, ChatGPT Plus, for users who need additional processing power, and early access to new features.

Key Features
Natural Language Understanding
Conversational Context
Open-Domain Conversations
Language Fluency
Answering Questions
Creative Writing
Language Translation
Text Completion and Suggestion
Personalized Interactions
Pros
Provide more natural interactions and accurate responses.
Free tool for the general public.
Cons
Prone to errors and misuse.
The tool cannot access data or current events after September 2021.
Pricing
Free Tool
Paid membership: Starts at $0.002 for 1K prompt tokens.

3. AlphaCode
The transformer-based language model is more complex than many existing language models, like OpenAI Codex, with 41.4 billion parameters. AlphaCode provides training in a number of programming languages, including C#, Ruby, Scala, Java, JavaScript, PHP, Go, and Rust. It excels in Python and C++.

Key Features
Smart filtering after large-scale code generation.
Transformer-based language model.
Datasets and solutions are available on GitHub.
Programming capabilities in Python, C++, and several other languages.
Access to approximately 13,000 example tasks for training.
Pros
Generates code at an unprecedented scale.
It does efficient critical thinking informed by experience.
Cons
User-dependent learning
Can go wrong
Pricing
Free Tool

4. GitHub Copilot
GitHub Copilot, in partnership with GitHub and OpenAI, created Copilot, a code completion Artificial Intelligence tool.

Key Features
Intelligent Code Suggestions
Support for Multiple Programming Languages
Learning from Open Source Code
Autocompletion for Documentation and Comments
Integration with Integrated Development Environments (IDEs)
Rapid Prototyping and Exploration
Context-Aware Suggestions
Collaborative Coding
Customization and Adaptation
Continuous Learning and Improvement
Pros
It improves developers' productivity and efficiency.
It supports various programming languages.
Cons
Code Quality and Security may vary.
Over-Reliance on Autocomplete
Pricing
Monthly: $10/month
Annually: $100

5. Bard
Bard is a chatbot and content generation tool developed by Google. It uses LaMDA, a transformer-based model, and is seen as Google's counterpart to ChatGPT. Currently in the experimental phase, Bard is accessible to a limited user base in the US and UK.

Key Features
Built on LaMDA, a transformer-based model.
A waitlist is currently offered to a small number of US and UK customers.
A rating system for user responses.
Available through individual Google accounts.
Capable of assisting with tasks related to software development and programming.
Pros
Ethical and Transparent AI development approach
Pre-tested by numerous testers
Cons
No conversation history features like ChatGPT.
Unable to access through a Google account.
Pricing
Free Tool (limited to users belonging to specific criteria)

6. Cohere Generate
Cohere is an AI company that helps businesses improve their operations by harnessing the power of AI. Cohere Generate delivers custom content for emails, landing pages, product descriptions, and other needs.

Key Features
Copy content generation focused on marketing and sales.
A rate-limited use is available for free.
Creates Ad and blog copy.
Writes Product description.
Works well with public, private, and hybrid cloud environments.
Pros
Easy to navigate while contacting a client.
Gives a good insight into user behavior, assisting them seamlessly.
Cons
Sessions often get stuck.
A few bugs, such as difficulty in answering calls.
Pricing
For learning and prototyping: Free

For Production:

Default: $0.4 / 1M Tokens
Custom: $0.8 / 1M Tokens

7. Claude
Claude is a cutting-edge AI assistant developed by Anthropic. Research has focused on training AI systems to be helpful, fair, and safe, which is exactly what Claude embodies.

Key Features
Process huge amounts of text
Does natural conversations
Can speak various common languages and programming languages.
Automate workflows
Pros
Higher user engagement and feedback.
Detailed and easily understood answers.
Cons
High difficulty level
Incorrectly answers factual queries.
Pricing
Claude Instant

Prompt: $1.63/ million tokens
Completion: $5.51/ million tokens
Claude-v1

Prompt: $11.02/ million tokens
Completion: $32.68/ million tokens

8. Synthesia
This is a great AI video platform or tool for creating videos. With little to no work, it rapidly generates and broadcasts videos of professional quality.

Key Features
Content Management
Personalized Onboarding
Text Analysis
Single Sign On
Enterprise-level scalability
Text Editing
SOC 2 and GDPR compliant
Pros
High-quality avatars and a variety of facial and vocal expressions.
Ability to generate lifelike videos
Cons
Very less customization options and lack of advanced features.
Limited range of gestures for AI avatars.
Pricing
Personal

₹1499.92/month, ₹17,999.00 billed annually, or pay monthly
$30 per month, billed monthly
Enterprise: price based on the number of seats

9. DALL-E 2
Among the best generative AI tools for images, DALL-E 2 is OpenAI’s recent version for image and art generation.

DALL-E 2 generates better and more photorealistic images when compared to DALL-E. DALL-E 2 appropriately goes by user requests. DALL-E 2 has received more instruction on how to reject improper inputs to prevent inappropriate outputs.

Key Features
Phased deployment based on learning.
Natural language inputs for producing images and art outputs.
An original image can have several different iterations.
Users can enhance existing pictures using the inpainting tool.
Requesting modifications or edits for an image is done by the inpainting feature.
DALL-E API is made for developers.
Pros
Preferred for Photorealism
Prevents harmful generations
Cons
High priced
Less creative than humans.
Pricing
115 credits for $15 USD

10. StyleGAN
StyleGAN is also a good option when generative AI tools for images are discussed. It uses deep learning algorithms to generate realistic and high-quality images. It significantly assists startups in varied manners due to its ability to create visually attractive images.

Key Features
Synthesizes artificial pictures obscure from authentic photographs.
Uses GAN application to produce artificial face pictures.
It learns from a dataset of notable faces.
Uses standalone mapping channels and noise layers as references of randomness to produce an artificial image.
It can be used to analyze non-image and image datasets.
Produces simulated images sequentially.
Pros
Used to create virtual try-on experiences in fashion design industries.
Used to create lifelike characters and immersive environments by gaming industries.
Cons
Hurdles in regulating image output.
High priced
Pricing
Free Version

11. Bardeen
It is an AI automation tool that enhances your productivity and saves time. Bardeen has OpenAI automation for your creative needs; it can suggest and write online social media content or buff up an email for its users.

Key Features
Operates in your browser by workflow automation.
Creates personalized outreach messages.
Learns continuously to improve its functionality
Has a pre-built workflow or template called a playbook that is customizable and automates a specific task.
MagicBox feature is like an AI-enabled search box where users describe automation.
Pros
Improves online content
Cost-effective and privacy-friendly
Cons
It can give over informative content.
The summary of long articles is vague at times.
Pricing
Starter: Free
Professional: $10/month

12. Copy.ai
Copy.ai is one of the best artificial intelligence (AI) writing tools. It can easily differentiate between content intent, for example, marketing copy, slogans, punchy headlines, etc.

Key Features
Closed Captioning Services
Localization Services
Interpretation.
Multilingual Desktop Publishing
Translation
Transcription Services
Faster blog writing with the “First Draft Wizard” feature
Higher converting posts for social media
Personalized email creation
Pros
Efficient for social media managers
Engaging email creation
Cons
It lags at times while generating content.
Proper fact-checking is required.
Pricing
Free plan till 2000 words
Monthly plan: $49/month for unlimited words.

13. Rephrase.ai
Rephrase.ai is an AI-generative tool that can produce videos just like Synthesia. Additionally, it has the capability to use digital avatars of real people in the videos.

Key Features
Leverage personalized communication
Video shoot for digital avatar creation.
Integration of digital avatars with different channels such as API access, App notification, WhatsApp for Business bot, Email, QR code, and Microsite.
Impact Management
Pros
Increased Business Engagement
Reduced customer acquisition costs
Cons
No free trial
Premium Consulting or Integration Services
Pricing
Personal: $25/month
Enterprise: Customized Plan

14Descript
It is a cloud-based collaborative audio or video editor by a company named Descript in San Francisco. The tool works like a doc. It has functions including AI, publishing, full multitrack editing, transcription, and screen recording.

Key Features
Making editable podcast transcripts
Writing webinar scripts
Collaborating on scripts
Screen recording
Social clips and templates
Publishing
Overdub
Studio Sound
Subtitles and captions
Filler word removal
Pros
Provides Video Captions
Generous free tier
Cons
No tutorial provided
Lack of multiple recordings of a single composition
Pricing
Free: $0
Creator: $12/user/month. $144 billed annually
Pro: $24/user/month. $288 billed annually.
Enterprise: Custom
15. Type Studio
Type Studio is an online text-based video editor that lives in a browser. Users upload videos in Type Studio, and it does the heavy lifting, including transcribing spoken words into text, so there is no need to edit videos with a timeline.

Key Features
Transcription Software
Browses video content
Automatically generated subtitles and closed captions.
Video Editing
Stores footage online
Cloud video rendering
Content Management Systems (CMS)
Translation
Pros
Easily add subtitles to edited videos.
Text is transcribed with just one click.
Cons
High-priced
Lack of precision
Pricing
Free plan: 1GB of cloud storage space.
Paid plans: From $12 to $36 per month

Top Generative AI Tools: Boost Your Creativity
Lesson 15 of 23By Sneha Kothari

Last updated on Dec 11, 202369786
Top 20 Generative AI Tool
PreviousNext
Table of Contents
What is Generative AI?How Does Generative AI Tool Work?Best Generative AI ToolsHow Can Businesses Use Generative AI Tools?Go In For Caltech Post Graduate Program in AI and Machine LearningView More
With numerous Generative AI tools in the market today, are you intrigued to know about the best generative AI tools that have revolutionized the industry and are influencing future creativity and innovation?

Let's explore the special attributes, working, and advantages of the top 20 tools.

What is Generative AI?
The branch of artificial intelligence known as "generative AI" is concerned with developing models and algorithms that may generate fresh and unique content. Generative AI algorithms apply probabilistic approaches to produce new instances that mirror the original data, typically with the capacity to demonstrate creative and inventive behavior beyond what was explicitly designed.

How Does Generative AI Tool Work?
Generative AI tools operate by employing advanced machine learning techniques, often deep learning models such as generative adversarial networks (GANs) or variational autoencoders (VAEs). These models are trained on massive datasets to understand patterns and underlying structures. The models learn to create new instances that mirror the training data by capturing the statistical distribution of the input data throughout the training phase.

Your AI/ML Career is Just Around The Corner!
AI Engineer Master's ProgramEXPLORE PROGRAMYour AI/ML Career is Just Around The Corner!
Best Generative AI Tools
Here is an overview of key features, pros and cons, working and pricing of the top 20 generative AI tools:

1. GPT-4
GPT-4 is the most recent version of OpenAI's Large Language Model (LLM), developed after GPT-3 and GPT-3.5. GPT-4 has been marketed as being more inventive and accurate while also being safer and more stable than earlier generations.

Key Features
GPT-4 has 100 Trillion Parameters
Improved Factual Performance
Enhanced Steerability
Image Inputs Capability
Multilingual Capability
Outperformance on Multiple Benchmarks
Human-Level Performance on Various Benchmarks
Pros
It is a consistent and reliable time saver.
Cost-effective and scalar
Cons
It can provide wrong answers.
It can be extremely biased.
Pricing
Free Version
Prompt: $0.03 per 1,000 tokens
Completion: $0.06 per 1,000 tokens
Paid membership: $20/month
2. ChatGPT
The most commonly used tool from OpenAI to date is ChatGPT, which offers common users free access to basic AI content development. It has also announced its experimental premium subscription, ChatGPT Plus, for users who need additional processing power, and early access to new features.

Key Features
Natural Language Understanding
Conversational Context
Open-Domain Conversations
Language Fluency
Answering Questions
Creative Writing
Language Translation
Text Completion and Suggestion
Personalized Interactions
Pros
Provide more natural interactions and accurate responses.
Free tool for the general public.
Cons
Prone to errors and misuse.
The tool cannot access data or current events after September 2021.
Pricing
Free Tool
Paid membership: Starts at $0.002 for 1K prompt tokens.
3. AlphaCode
The transformer-based language model is more complex than many existing language models, like OpenAI Codex, with 41.4 billion parameters. AlphaCode provides training in a number of programming languages, including C#, Ruby, Scala, Java, JavaScript, PHP, Go, and Rust. It excels in Python and C++.

Key Features
Smart filtering after large-scale code generation.
Transformer-based language model.
Datasets and solutions are available on GitHub.
Programming capabilities in Python, C++, and several other languages.
Access to approximately 13,000 example tasks for training.
Pros
Generates code at an unprecedented scale.
It does efficient critical thinking informed by experience.
Cons
User-dependent learning
Can go wrong
Pricing
Free Tool

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4. GitHub Copilot
GitHub Copilot, in partnership with GitHub and OpenAI, created Copilot, a code completion Artificial Intelligence tool.

Key Features
Intelligent Code Suggestions
Support for Multiple Programming Languages
Learning from Open Source Code
Autocompletion for Documentation and Comments
Integration with Integrated Development Environments (IDEs)
Rapid Prototyping and Exploration
Context-Aware Suggestions
Collaborative Coding
Customization and Adaptation
Continuous Learning and Improvement
Pros
It improves developers' productivity and efficiency.
It supports various programming languages.
Cons
Code Quality and Security may vary.
Over-Reliance on Autocomplete
Pricing
Monthly: $10/month
Annually: $100
5. Bard
Bard is a chatbot and content generation tool developed by Google. It uses LaMDA, a transformer-based model, and is seen as Google's counterpart to ChatGPT. Currently in the experimental phase, Bard is accessible to a limited user base in the US and UK.

Key Features
Built on LaMDA, a transformer-based model.
A waitlist is currently offered to a small number of US and UK customers.
A rating system for user responses.
Available through individual Google accounts.
Capable of assisting with tasks related to software development and programming.
Pros
Ethical and Transparent AI development approach
Pre-tested by numerous testers
Cons
No conversation history features like ChatGPT.
Unable to access through a Google account.
Pricing
Free Tool (limited to users belonging to specific criteria)

6. Cohere Generate
Cohere is an AI company that helps businesses improve their operations by harnessing the power of AI. Cohere Generate delivers custom content for emails, landing pages, product descriptions, and other needs.

Key Features
Copy content generation focused on marketing and sales.
A rate-limited use is available for free.
Creates Ad and blog copy.
Writes Product description.
Works well with public, private, and hybrid cloud environments.
Pros
Easy to navigate while contacting a client.
Gives a good insight into user behavior, assisting them seamlessly.
Cons
Sessions often get stuck.
A few bugs, such as difficulty in answering calls.
Pricing
For learning and prototyping: Free

For Production:

Default: $0.4 / 1M Tokens
Custom: $0.8 / 1M Tokens
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7. Claude
Claude is a cutting-edge AI assistant developed by Anthropic. Research has focused on training AI systems to be helpful, fair, and safe, which is exactly what Claude embodies.

Key Features
Process huge amounts of text
Does natural conversations
Can speak various common languages and programming languages.
Automate workflows
Pros
Higher user engagement and feedback.
Detailed and easily understood answers.
Cons
High difficulty level
Incorrectly answers factual queries.
Pricing
Claude Instant

Prompt: $1.63/ million tokens
Completion: $5.51/ million tokens
Claude-v1

Prompt: $11.02/ million tokens
Completion: $32.68/ million tokens
8. Synthesia
This is a great AI video platform or tool for creating videos. With little to no work, it rapidly generates and broadcasts videos of professional quality.

Key Features
Content Management
Personalized Onboarding
Text Analysis
Single Sign On
Enterprise-level scalability
Text Editing
SOC 2 and GDPR compliant
Pros
High-quality avatars and a variety of facial and vocal expressions.
Ability to generate lifelike videos
Cons
Very less customization options and lack of advanced features.
Limited range of gestures for AI avatars.
Pricing
Personal

₹1499.92/month, ₹17,999.00 billed annually, or pay monthly
$30 per month, billed monthly
Enterprise: price based on the number of seats

9. DALL-E 2
Among the best generative AI tools for images, DALL-E 2 is OpenAI’s recent version for image and art generation.

DALL-E 2 generates better and more photorealistic images when compared to DALL-E. DALL-E 2 appropriately goes by user requests. DALL-E 2 has received more instruction on how to reject improper inputs to prevent inappropriate outputs.

Key Features
Phased deployment based on learning.
Natural language inputs for producing images and art outputs.
An original image can have several different iterations.
Users can enhance existing pictures using the inpainting tool.
Requesting modifications or edits for an image is done by the inpainting feature.
DALL-E API is made for developers.
Pros
Preferred for Photorealism
Prevents harmful generations
Cons
High priced
Less creative than humans.
Pricing
115 credits for $15 USD
10. StyleGAN
StyleGAN is also a good option when generative AI tools for images are discussed. It uses deep learning algorithms to generate realistic and high-quality images. It significantly assists startups in varied manners due to its ability to create visually attractive images.

Key Features
Synthesizes artificial pictures obscure from authentic photographs.
Uses GAN application to produce artificial face pictures.
It learns from a dataset of notable faces.
Uses standalone mapping channels and noise layers as references of randomness to produce an artificial image.
It can be used to analyze non-image and image datasets.
Produces simulated images sequentially.
Pros
Used to create virtual try-on experiences in fashion design industries.
Used to create lifelike characters and immersive environments by gaming industries.
Cons
Hurdles in regulating image output.
High priced
Pricing
Free Version

11. Bardeen
It is an AI automation tool that enhances your productivity and saves time. Bardeen has OpenAI automation for your creative needs; it can suggest and write online social media content or buff up an email for its users.

Key Features
Operates in your browser by workflow automation.
Creates personalized outreach messages.
Learns continuously to improve its functionality
Has a pre-built workflow or template called a playbook that is customizable and automates a specific task.
MagicBox feature is like an AI-enabled search box where users describe automation.
Pros
Improves online content
Cost-effective and privacy-friendly
Cons
It can give over informative content.
The summary of long articles is vague at times.
Pricing
Starter: Free
Professional: $10/month
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12. Copy.ai
Copy.ai is one of the best artificial intelligence (AI) writing tools. It can easily differentiate between content intent, for example, marketing copy, slogans, punchy headlines, etc.

Key Features
Closed Captioning Services
Localization Services
Interpretation.
Multilingual Desktop Publishing
Translation
Transcription Services
Faster blog writing with the “First Draft Wizard” feature
Higher converting posts for social media
Personalized email creation
Pros
Efficient for social media managers
Engaging email creation
Cons
It lags at times while generating content.
Proper fact-checking is required.
Pricing
Free plan till 2000 words
Monthly plan: $49/month for unlimited words.
13. Rephrase.ai
Rephrase.ai is an AI-generative tool that can produce videos just like Synthesia. Additionally, it has the capability to use digital avatars of real people in the videos.

Key Features
Leverage personalized communication
Video shoot for digital avatar creation.
Integration of digital avatars with different channels such as API access, App notification, WhatsApp for Business bot, Email, QR code, and Microsite.
Impact Management
Pros
Increased Business Engagement
Reduced customer acquisition costs
Cons
No free trial
Premium Consulting or Integration Services
Pricing
Personal: $25/month
Enterprise: Customized Plan
14Descript
It is a cloud-based collaborative audio or video editor by a company named Descript in San Francisco. The tool works like a doc. It has functions including AI, publishing, full multitrack editing, transcription, and screen recording.

Key Features
Making editable podcast transcripts
Writing webinar scripts
Collaborating on scripts
Screen recording
Social clips and templates
Publishing
Overdub
Studio Sound
Subtitles and captions
Filler word removal
Pros
Provides Video Captions
Generous free tier
Cons
No tutorial provided
Lack of multiple recordings of a single composition
Pricing
Free: $0
Creator: $12/user/month. $144 billed annually
Pro: $24/user/month. $288 billed annually.
Enterprise: Custom
15. Type Studio
Type Studio is an online text-based video editor that lives in a browser. Users upload videos in Type Studio, and it does the heavy lifting, including transcribing spoken words into text, so there is no need to edit videos with a timeline.

Key Features
Transcription Software
Browses video content
Automatically generated subtitles and closed captions.
Video Editing
Stores footage online
Cloud video rendering
Content Management Systems (CMS)
Translation
Pros
Easily add subtitles to edited videos.
Text is transcribed with just one click.
Cons
High-priced
Lack of precision
Pricing
Free plan: 1GB of cloud storage space.
Paid plans: From $12 to $36 per month
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16. Murf.ai
Murf.ai is an online tool that uses AI to generate high-quality voice-overs for videos, presentations, and text-to-speech needs. This tool allows users to modify a script or transform a casual voice recording into a professional-sounding studio-quality voice-over.

Key Features
Voice Recognition
Text to Speech
Customization Features
High-quality Voices
Pros
More than 100 human-sounding voices
User-friendly Interface
Cons
Inaccurate Pronunciation
Limited Features and Settings
Pricing
Free Trial
Basic: $29.00, 1 User/Month
Pro: $39.00, 1 User/Month
Enterprise: $99.00, 1/Per Month
17. Designs.ai
Designs.ai is a comprehensive AI design tool that can handle various content development tasks. It's goal is to "empower imagination through artificial intelligence." It can produce voice-overs, videos, social media postings, and logos.

Key Features
Logo maker
Video maker
AI writer
Speech maker
Design maker
Graphic painter
Color matcher
Font Painter
Calendar
Pros
Improved Efficiency
Boosted Creativity
Cons
Insufficient control over projects
High-priced
Pricing
Basic: $29/month
Pro: $69/month
Enterprise: Customized
18. Soundraw
Soundraw is a music generator powered by AI that lets you create your own unique and royalty-free music. You can use this music to enhance your projects or content.

Key Features
Customizes the songs
API for businesses
Creates unlimited music
Audio content for Podcasts, Radio programs & ads, Guided Meditations, Audiobooks, and Music streaming.
Video content for Youtube & Social Media, TV, Movies, Web ads, Corporate videos, and Live broadcasting.
Content for games
Pros
No copyright strikes
Permanent license for all creatives
Cons
Lack of creativity in musical depth
Limited themes and moods
Pricing
Free: $US0
Personal plan: $US16.99/month, Billed annually
19. ChatFlash
ChatFlash is an AI generative tool that helps us to create content through a chat option.

Key Features
SEO optimization
Prompt templates
Image generation
On-brand content
Personal Onboarding and webinar
Plagiarism check
Pros
Efficient content-assistant
Expert content creation for true professionals
Cons
Limited templates and personalities
Less prompt
Pricing
Standard: € 30/ month
Pro: € 80 / month
Enterprise: € 400 / month
20. ChatSonic
The conversational AI chatbot, a ground-breaking AI like Chat GPT - Chatsonic (now with GPT-4 capabilities), overcomes the shortcomings of ChatGPT and ends up being the finest free Chat GPT substitute.

Key Features
AI article and blog writer
Paraphrasing
Text expanding
Article summarizer
Product Description
Facebook and Google Ads
Surfer integration
Landing pages
AI article ideas
Text generation
Image creation
Translation
Pros
Random question and answer
Efficient voice command and translation.
Cons
Has a short word limit
Issues with images and lack of knowledge.
Pricing
Free Trial: $0/month
Pro: $12.67/month
Enterprise: Starts at $1000/month

How Can Businesses Use Generative AI Tools?
The use of generative AI tools has become increasingly prevalent in the business world, as it enables organizations to optimize their operations, boost their creative output, and gain an edge over their competitors in today's rapidly evolving market landscape. Businesses can implement generative AI technologies in a variety of ways. They might apply them to-

Create realistic product prototypes
Generate personalized content for customers
Design compelling marketing materials
Enhance data analysis and decision-making processes
Develop new and innovative products or services
Automate repetitive tasks
Streamline operations
Gain a competitive edge in the market
Enhance creativity

Go In For Caltech Post Graduate Program in AI and Machine Learning
If you are intrigued after gaining a general idea about all the best Generative AI tools examples, you may move further with a course program on the same by a renowned platform. The Caltech Post Graduate Program in AI and Machine Learning course, provided by Simplilearn, is a thorough and esteemed program that gives students the information and abilities they need to succeed in the field of artificial intelligence.

This program offers a thorough grasp of AI concepts, machine learning algorithms, and real-world applications as the curriculum is chosen by industry professionals and taught through a flexible online platform. By enrolling in this program, people may progress in their careers, take advantage of enticing possibilities across many sectors, and contribute to cutting-edge developments in AI and machine learning.

Source: simplilearn
Original Content: https://shorturl.at/cyW39

9

AI Career Success Stories / AI Success Stories: Inspiring Career Journeys

« on: January 08, 2024, 12:08:31 PM »

Artificial Intelligence (AI) has become an integral part of our world, revolutionizing industries and transforming the way we live and work. Behind this technological marvel are countless individuals who have dedicated themselves to the pursuit of AI excellence. Understanding their journeys can inspire and guide others who aspire to embark on similar paths. This article explores the AI landscape and delves into the stories of successful professionals who have made their mark in this field. Let's embark on this captivating journey into the world of AI and uncover the secrets to their success.

Understanding the AI Landscape

The AI landscape is vast and ever-evolving. To truly appreciate the achievements of those who have carved successful careers in AI, it is essential to grasp the evolution of this extraordinary field. AI has progressed from simple rule-based systems to complex machine learning algorithms and neural networks that mimic human cognitive functions.

The journey of AI began with early pioneers like Alan Turing, who laid the foundation for the concept of artificial intelligence. Turing's work on the Turing machine and the idea of a universal computing machine set the stage for future developments in AI.

As the field progressed, researchers and scientists started exploring different approaches to AI. One such approach was symbolic AI, which focused on using logical rules and symbols to represent knowledge and solve problems. Symbolic AI systems were designed to manipulate symbols and reason about them, but they lacked the ability to learn from data.

However, in recent decades, the focus has shifted towards machine learning, a subfield of AI that enables computers to learn and improve from experience without being explicitly programmed. Machine learning algorithms analyze large datasets and identify patterns, allowing them to make predictions and decisions based on the information they have learned.

Neural networks, a key component of machine learning, have revolutionized the field of AI. Inspired by the structure and function of the human brain, neural networks consist of interconnected nodes, or artificial neurons, that work together to process and analyze data. This approach has led to significant breakthroughs in areas such as image recognition, natural language processing, and speech recognition.

The key sectors that have benefitted immensely from AI innovations include healthcare, finance, transportation, and manufacturing. In healthcare, AI has enabled the development of advanced diagnostic tools that can detect diseases with greater accuracy and speed. AI-powered algorithms can analyze medical images, such as X-rays and MRIs, to identify abnormalities that may be missed by human doctors.

In the finance industry, AI has transformed the way financial institutions manage risk and make investment decisions. Machine learning algorithms can analyze vast amounts of financial data and identify patterns that can help optimize investment portfolios and predict market trends.

Transportation is another sector that has seen significant advancements with the help of AI. Self-driving cars, powered by AI algorithms, have the potential to revolutionize the way we commute and travel. These vehicles use sensors and cameras to perceive their surroundings and make real-time decisions, ensuring safer and more efficient transportation.

In the manufacturing industry, AI has improved efficiency and productivity by automating processes and optimizing supply chains. AI-powered robots can perform repetitive tasks with precision and speed, freeing up human workers to focus on more complex and creative endeavors.

Today, AI plays a pivotal role in enhancing our lives. It powers virtual assistants like Siri and Alexa, which can understand and respond to human voice commands. Recommendation systems, powered by AI algorithms, personalize our online experiences by suggesting products, movies, and music based on our preferences and past behavior.

By understanding the pervasiveness and impact of AI, aspiring professionals can appreciate the boundless possibilities that lie ahead. The field of AI continues to evolve at a rapid pace, with new breakthroughs and applications being discovered every day. As AI becomes more integrated into our daily lives, it is crucial to stay informed and adapt to the changing landscape to harness its full potential.

The Journey into AI: Different Pathways

There is no one-size-fits-all approach when it comes to establishing a career in AI. Professionals have taken various pathways to reach their goals, each with its own merits and challenges. Exploring these different routes can provide invaluable insights to those embarking on their own AI journey.

Traditional Education Routes into AI

Attending renowned universities and pursuing degrees in computer science or related disciplines has been one of the conventional paths into AI. These formal education programs equip students with a strong foundation in mathematics, programming, and AI theory. Graduates are armed with the knowledge and skills to tackle complex AI problems.

Furthermore, undergoing internships or research opportunities in AI-focused labs and companies can provide invaluable hands-on experience. This combination of theoretical knowledge and practical application has propelled many professionals into successful AI careers.

Self-Taught AI Professionals

AI is a rapidly evolving field, and passionate individuals have taken the initiative to learn independently. Through online courses, tutorials, and open-source projects, these self-directed learners have acquired the necessary skills to make a mark in AI. Their determination and curiosity demonstrate that with dedication and a growth mindset, anyone can break into the AI field.

One advantage of self-learning is the flexibility to focus on specific areas of interest. Whether it is natural language processing, computer vision, or reinforcement learning, these self-taught AI professionals have honed their expertise in specialized domains.

Transitioning from Other Careers into AI

Some individuals discover their passion for AI later in their careers. They bring their diverse experiences from other fields and apply them to the AI landscape. Their unique perspectives and problem-solving skills contribute to the multidisciplinary nature of AI projects.

Transitioning into AI often involves building upon existing skills and knowledge through targeted learning and practical projects. The ability to connect domains and leverage transferable skills has enabled many professionals to make a successful switch into AI.

Profiles of Success: Inspiring AI Professionals

Within the AI community, there are extraordinary individuals who have made significant contributions and achieved remarkable success. Their stories serve as a beacon of inspiration for others, highlighting the possibilities that await in the world of AI.

Pioneers in the AI Field

Among these pioneers is Dr. Fei-Fei Li, a renowned researcher and visionary. Her groundbreaking work in computer vision and machine learning has shaped the AI landscape. Dr. Li's dedication to democratizing AI education through initiatives like the AI4ALL program has empowered countless students around the world to harness the potential of AI.

Another trailblazer is Dr. Andrew Ng, whose work on deep learning algorithms and online education platforms has revolutionized AI education and practical application. Through his leadership and advocacy, Dr. Ng has paved the way for future AI innovators.

Rising Stars in AI

Amidst the evolving AI landscape, a new generation of talented individuals has emerged as rising stars. Take Emily Denton, for example. Her exceptional work in generative adversarial networks has garnered widespread acclaim, pushing the boundaries of AI-generated art and image synthesis.

Similarly, Sam Altman, a prominent entrepreneur and AI enthusiast, has made significant strides in the application of AI for social good. His efforts to develop AI-driven solutions for pressing global challenges demonstrate the immense potential of AI in making a positive impact on society.

Women Making Waves in AI

Advancing diversity and inclusivity in AI is essential for its continued growth and success. Women have been leading the charge in breaking barriers and making notable contributions to the field.

Fei-Fei Li, mentioned earlier, is an inspiration to countless aspiring female AI professionals. Additionally, Kate Crawford, recognized for her research on the societal impact of AI, advocates for responsible and ethical AI development, ensuring that technology benefits all of humanity.

The Future of AI: Opportunities and Challenges

As AI continues to evolve, it presents both tremendous opportunities and significant challenges. Understanding these factors is crucial for professionals looking to build successful careers in this rapidly changing field.

Emerging Trends in AI

AI is expanding its reach beyond traditional domains, such as healthcare and finance, to sectors like agriculture, education, and entertainment. The unprecedented growth of AI-powered applications and the rise of edge computing offer exciting opportunities for professionals to shape the future.

Furthermore, AI collaboration and interdisciplinary approaches are gaining traction. The fusion of AI with fields like neuroscience, psychology, and social sciences opens up new avenues for breakthroughs.

The Role of AI in Solving Global Challenges

AI has the potential to address pressing global challenges, ranging from climate change to healthcare disparities. Innovations in AI-driven solutions for sustainable energy, personalized medicine, and disaster response can transform our world for the better.

However, alongside these opportunities, it is vital to navigate the ethical considerations surrounding AI. Ensuring fairness, transparency, and accountability in AI systems is crucial to building a responsible and equitable future.

Ethical Considerations in AI Careers

AI professionals have a responsibility to uphold ethical standards throughout their careers. As AI becomes increasingly integrated into society, the potential for biases, privacy concerns, and unintended consequences grows. Professionals must confront these challenges and actively address them to create ethical AI systems.

Moreover, promoting diversity and inclusivity within the AI community is fundamental to eliminating biases and creating fair AI systems. Encouraging a multidisciplinary approach and diverse perspectives can fuel innovation and ensure AI technologies benefit everyone.

Inspired to Forge Your Own Path

The AI success stories and career journeys shared in this article demonstrate that there are countless paths to greatness in the AI field. Whether you choose a traditional education route, embark on a self-guided learning journey, or transition from another career, the world of AI welcomes passionate and driven professionals.

As you navigate the AI landscape, remember to stay curious, embrace challenges, and strive for ethical excellence. Your unique knowledge, skills, and perspective are essential in shaping the future of AI. Get ready to embark on your inspiring AI career journey and be part of the extraordinary advancements that lie ahead.

Source: careersintech.ai
Original Content: https://shorturl.at/yEFI4

10

AI Soft Skills and Career Development / AI & soft skills: reshaping the future of work

« on: January 08, 2024, 12:00:03 PM »

BY Ksenia Stepanova

ChatGPT burst onto the scene less than a year ago, and has ushered in a realm of new opportunities for businesses and individuals. While there have been concerns around AI taking over certain roles, the need for uniquely human skills doesn’t appear to be going anywhere. LinkedIn’s latest figures show businesses placing increasing importance onto soft skills - and also onto finding AI-skilled talent.

Trends on the employee side are also promising. Far from reacting with pessimism, employees and jobseekers are increasingly responding to this demand through upskilling, and the pool of AI talent is rising steadily.

These are some of the insights revealed in LinkedIn’s Global Future of Work Report, which takes a deep dive into the emerging role of AI at work. With its unique access to member data, LinkedIn’s report reveals how AI is changing ways of working for professionals, the growing demand for AI skills, how it’s impacting hiring and business leaders' sentiment towards the use of AI.

Search for AI talent is on the rise - and so is upskilling
As AI accelerates change, business leaders are increasingly looking for talent with AI-related skills. The Global Future of Work Report reveals the share of English language job postings mentioning ChatGPT have increased 21x since November 2022, and in APAC, the growth of AI talent hiring has outpaced the growth in overall hiring.

Singapore is hiring 16% more AI talent compared to 2016, while Indonesia leads the charge in Southeast Asia at 20%.

Professionals have clearly noticed this rising demand, and they are picking up AI skills at a rapid pace as a result. LinkedIn members added keywords such as ‘GAI’ and ‘ChatGPT’ 15x more frequently in June than in January, and workers increasingly agree that picking up AI skills will help their career growth.

Additionally, LinkedIn’s recent Global Talent Trends Report, South East Asia Edition shows job posts mentioning AI grew by 2.4x in South East Asia over the last two years. Job seekers are clearly responding positively, as posts mentioning artificial intelligence/generative AI have seen their applications grow by 1.7x in SEA over the last two years, compared with the growth of job posts that don’t mention them.

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The pick up rate for AI skills is higher than the global average in APAC. Singapore leads globally – it has the highest rate of members who have added AI skills to their profiles over time (20x).

With these shifts in the labour market in mind, upskilling and reskilling professionals in AI literacy to prepare them for the future of work has never been more important. The top three AI skills that professionals are picking up in Singapore are Machine Learning, Artificial Intelligence (AI), Deep Learning.

Business leaders agree soft skills are becoming increasingly important

And while AI promises significant gains in productivity, both business leaders and professionals agree that soft skills such as creativity and emotional intelligence are becoming increasingly important, and can never be replaced by AI.

Executives agree that people skills are becoming more critical than ever. The report highlights that since the launch of ChatGPT, some of the fastest-growing skills in job postings are people skills such as flexibility, ethics, social perceptiveness and self-management. In the US, 47% of executives agreed that generative AI would boost productivity - but 92% agreed that people skills were more important than ever.

This indicates that while AI can save time and boost productivity on routine tasks, the meaningful connections forged by people are still at the core of every business.

“Ultimately, when we talk about AI’s impact on work, what we are really talking about is how people will adopt these tools and continue to strengthen the people skills that complement them,” says Karin Kimbrough, LinkedIn Chief Economist.

Frank Koo, Head of Asia, LinkedIn, adds developments in AI, namely generative AI, brings opportunities to professionals’ careers and skills development as well as the world of work.

“In the ever-shifting dynamics of today’s workforce, investing in upskilling is paramount for both companies and professionals. This not only empowers professionals and ensures they stay competitive in the future world of work, but also fortifies organisations, fostering agility and adaptability in the face of evolving challenges.”

“We firmly believe  prioritising skills creates equitable outcomes for everyone. LinkedIn is committed to supporting businesses and empowering professionals through a focus on hiring for skills, as well as skills development, enabling them to thrive and navigate the future world of work,” says Mr Koo.

LinkedIn’s role has never been more critical to help professionals and companies navigate a changing world of work.

LinkedIn is helping companies hire for skills, develop their talent for the skills they need, as well as connect professionals to opportunities.

It is embracing the potential of generative AI, and are embedding it into its technology. In the hands of LinkedIn’s members and customers, these tools will help connect them to opportunities, be more productive, showcase their expertise and skills and gain access to knowledge.

Organisations can use LinkedIn’s tools to upskill their teams, and to find the right match between hirers and job seekers. Here are some of LinkedIn’s most popular tools to date:
AI-assisted messages with LinkedIn Recruiter. This tool helps draft personalised messages to candidates, and to customise fields such as location, skills and workplace type. This allows recruiters to save time and engage with the right candidates quicker, freeing them up to do what’s important - build meaningful connections.
 
AI-powered job descriptions. 75% of hirers want generative AI to free up time for more strategic work, and two-thirds (67%) hope that generative AI can help them uncover new candidates. That's why we're testing AI-powered job descriptions to help hirers find qualified candidates quicker, and to free up time for the more strategic parts of their hiring process.
 
AI-powered learning for faster, more effective skill development.



LinkedIn can kick-start your upskilling journey

To help leaders and professionals with AI upskilling, LinkedIn recently partnered with Microsoft to launch a Professional Certificate on Generative AI, which is free through 2025. As well as unlocked LinkedIn’s 10 AI courses that learners across South East Asia have taken the most in 2023:

Generative AI for Business Leaders, Tomer Cohen
What is Generative AI?, Pinar Seyhan Demirdag
How to Research and Write Using Generative AI Tools, Dave Birss
Machine Learning with Python: Foundations, Frederick Nwanganga
Nano Tips for Using ChatGPT for Business, Rachel Woods
Get Ready for Generative AI, Ashley Kennedy
Introduction to Prompt Engineering for Generative AI, Ronnie Sheer
Prompt Engineering: How to Talk to the AIs, Xavier Amatriain
Python Data Structures and Algorithms, Robin Andrews
GPT-4: The New GPT Release and What You Need to Know, Jonathan Fernandes
These courses are available for everyone to access for free until December 15, 2023.

To get more insights into global AI trends, download LinkedIn’s Global Future of Work Report or read How LinkedIn’s top AI courses Can Shape Your Organisation's Upskilling Strategy

Looking to Hire the right talent faster in Singapore?
Why not sign-up to LinkedIn’s exclusive event at the National Gallery of Singapore - Hire Connect to reimagine hiring with the power of AI. Register now and confirm your attendance here.

Get LinkedIn to assess your business needs and recommend the products to help you achieve your goals.

Source: hcamag
Original Content: https://shorturl.at/mW589

11

AI Soft Skills and Career Development / The 5 soft skills needed to succeed in an AI-dominated workplace

« on: January 08, 2024, 11:54:17 AM »

As artificial intelligence becomes more adept at tasks once considered uniquely human, this edtech founder says these are the workforce skills that are becoming more important.



BY HEIDE ABELLI

The last few decades have been dominated by rapid technological advancements but most of us will readily acknowledge that few developments have been as transformative as AI, especially its subset, generative AI. Today’s AI capabilities have begun to outstrip even the most optimistic projections, raising fundamental questions about the future of work and workforce skills. As machines become adept at tasks once considered uniquely human, what does this mean for the modern workforce? Which skills will ascend in importance and define success in the imminent workplace landscape?

There is no question that virtually every job will eventually be affected by AI. In some cases AI will simply be complementary to the job, but the prevailing belief is that about half of all jobs will be significantly disrupted by AI. Many historically important “hard” skills and hiring credentials will rapidly become obsolete. The question becomes which workforce skills will become more important against the backdrop of AI. What skills should we hire and train for most in this rapidly evolving AI landscape?

There are five that rise to the surface and here’s why.

SOCIAL INTERACTION SKILLS
In a world of AI many jobs will continue to require advanced social skills.  Whether it is about emotional self-regulation, listening to others in meetings or collaborating with team-mates under pressure, social skills reign supreme in the modern workplace. According to a 2015 working paper from the National Bureau of Economic Research, almost all job growth since 1980 has been seen in jobs that are social-skill intensive, while jobs that require minimal social interaction have been in decline. Studies have shown that the use of AI material reduces the performance gap between employees with different levels of aptitude and seniorit.The drop in that gap makes any gap in social skills more pronounced, emphasizing the importance of interpersonal communication, teamwork, and emotional intelligence in today’s workplace. Those who never learned how to play well with others in the kindergarten sandbox have some significant ground to make up, while those who excel in this arena will reap increasing benefits.

CREATIVITY
The broad generalization that AI will replace humans in the workplace is incorrect, but it’s very likely that humans who leverage AI will replace humans who fail to do so. A recent study found that knowledge workers who used Chat GPT 4.0 completed 12.2% more tasks, 25.1% faster and with 40% greater quality over those who did not use AI to perform their work. That’s astonishing data, especially the data on the increased quality level of work output. And human workers who leverage AI and who demonstrate a combination of strong creativity and critical thinking skills will fare the best. Why? There is a significant degree of creativity involved in designing AI prompts that result in the more fruitful responses. Designing AI prompts is not just a matter of simple instruction; it is an art that requires a significant degree of creativity. Prompt creation can involve engineering prompts to perform multi-step instructions, generate hypotheses, engage in socratic questioning, etc. The success of an AI-generated output is directly tied to the quality, specificity, and ingenuity of the prompt it receives. A creatively-crafted prompt acts a catalyst, steering the AI towards more insightful, relevant, and nuanced responses.

Conversely, a vague, poorly engineered prompt often leads to generic, off-target output. Crafting effective prompts requires an understanding of the AI’s capabilities and limitations, a clear vision of the desired outcome and a heavy dose of creativity. As AI becomes more integral in the workplace, the ability to design impactful prompts will become a crucial skill, marrying the realms of technology and creativity. A creative human mind lies at the center of brilliant prompt engineering.

CRITICAL THINKING SKILLS
Critical thinking must be applied to evaluate AI responses. Not all responses will be valid, unbiased, factual, or error-free. It’s in the evaluation of prompts where human logical reasoning, reflective thinking, rational thought, and unbiased evaluation come into play. While AI can generate vast amounts of data, analyses, and potential solutions at unprecedented speed, the veracity and applicability of generative AI’s responses are not guaranteed. These technologies, while sophisticated, base their outputs on patterns identified from vast datasets, which may contain inherent biases and inaccuracies.

This is where the uniquely human skill to think critically becomes indispensable. Logical reasoning enables us to dissect AI outputs, identifying potential flaws or inconsistencies. Reflective thinking encourages employees to consider the broader implications and contexts of the information presented to them. Rational thought allows us to weigh the evidence, discerning between the relevant and the extraneous. Unbiased evaluation ensures that we remain vigilant to potential biases, both from the AI and from our own preconceptions. Employees cannot afford to be passive recipients of generative AI output. They must become active evaluators, synthesizers, and decision-makers. An employee’s ability to critically assess, challenge, and refine AI outputs will determine the success of the human-AI collaboration.

CURIOSITY
Curiosity is an innate drive to explore, understand, and seek information about the world around us. An eagerness to discover leads an employee to ask questions, probe into things, challenge assumptions and delve deeper. Curiosity encourages individuals to venture outside their comfort zones and engage with unfamiliar concepts, ideas, and experiences. In the age of AI, where algorithms and machines can rapidly process and present vast amounts of data, curiosity becomes more important than ever. While AI can identify patterns, predict outcomes, and automate complex tasks, it lacks a depth of understanding that stems from genuine human curiosity. Employee value shifts from simply having knowledge to applying curiosity: the ability to question, interpret, and reimagine that knowledge. By constantly asking “why” or “how,” curious people arrive at the kinds of novel solutions and innovative ideas that companies need in the age of AI.

UNBIASED, ETHICAL DECISION-MAKING
In the age of AI, where decisions are increasingly informed or even made by algorithms, unbiased, ethical decision-making becomes paramount. AI systems operate on enormous datasets and decisions are based on patterns drawn from this data. However, the datasets that AI relies upon can mirror and further amplify societal biases, leading AI to make discriminatory and unfair judgments. When left unchecked, AI biases perpetuate inequities and even lead to new forms of organizational discrimination. The consequences are potentially serious, from who the organization hires to who has access to a product or service. It is only the uniquely human skill of unbiased decision-making that can stand in harm’s way, serving as the last bulwark against an era of unchecked algorithmic injustice.

The ethical implications of AI’s decisions have non-trivial consequences. The ability for an employee to make ethical decisions ensures that the deployment of AI upholds societal values, respects human rights, and safeguards individual freedoms. It is the uniquely human skill of ethical decision-making that ensures that the AI deployed in the organization is never used in ways that are harmful, invasive, or unjust.

The rise of AI, particularly generative AI, will fundamentally alter the nature of skills deemed crucial in the workplace. The emphasis will shift towards skills that AI technology struggles to emulate, such as social skills, critical thinking, creativity, curiosity, and unbiased, ethical decision-making. The distinctly human ability to collaborate with others, lead with emotional intelligence, and adapt to rapidly evolving, uncertain environments will take center stage. And as generative AI systems produce vast amounts of content, skills related to curating, interpreting, and contextualizing AI-sourced information will become paramount. In essence, while AI will assume many previously prized “hard skills”, the distinctly human skills that allow for relationship-building, innovative thinking, and unbiased, ethical decision-making will become ever more valued in the workplace. And for good reason, because those uniquely human skills are the truly “hard” ones.

Source: fastcompany
Original Content: https://shorturl.at/ftvB4

12

AI Professional Networking / AI Education: Future Trends and Essential Career Skills

« on: January 08, 2024, 11:47:34 AM »

The AI industry has shattered the long-held belief that significant change can only occur gradually and in phases. Six months since the launch of ChatGPT 3, hundreds of AI tools have flooded the market at an astonishing pace. It seems developers have been eagerly waiting for an AI tool to test the waters first. If you are a student who has been curiously watching the AI space and considering a career in AI, you are about to enter one of the top industries in terms of job creation. According to the World Economic Forum, in its latest Global Risks Report 2023, it is estimated that around 85 million jobs may be displaced by 2025 due to the emergence of Artificial Intelligence (AI) and related technologies. However, the report also suggests that the growth of AI and tech is expected to create 97 million new jobs. Despite the displacement of some jobs, the overall impact of AI on the job market is expected to be positive, creating ample opportunities for skilled people.

Before discussing future careers in AI, however, it is crucial to understand the current AI education landscape. Because, unlike traditional courses, AI is relatively new, and the avenues of learning from skilled teachers and professionals are extremely limited at this point in time. Since it might take a few years for AI to become a mainstream subject in institutions across the world, the students of today, who are passionate about AI, need to be extremely agile and smart in the way think about and approach AI education. Similarly, educators and educational institutions are now in the position to rethink and innovate teaching methods to suit the needs of the students of the AI world.

The current state of AI education

AI as an industry has already started to boom, and in the near future, we will witness its direct impact on all aspects of our lives. However, since AI education is at a nascent stage, the number of qualified teachers and educational institutions that can teach AI is disproportionately low compared to the exploding demand for AI education. Additionally, more standardised curricula and assessments for AI education are required for educators to impart their students with the necessary skills and knowledge to be employable. Therefore, educational institutions and industry experts should collaborate and co-create curricula for AI education. This collaboration will prove to be a win-win for the industry and the AI education sector since it will lead to more high-quality students taking up top jobs in the industry, which can, in turn, evoke interest in more students to pursue AI education. In this direction, some organisations have started developing standardised curricula and assessments, while others are providing resources and training for teachers who want to learn more about AI. So, the right efforts are underway and soon, AI courses will be widely accessible across most parts of the world.

The skills students need for AI-related jobs

Like any other industry, the AI industry also demands a combination of technical and soft skills. Since AI holds tremendous potential that needs to be utilised responsibly, it is imperative that students possess high levels of ethics and awareness to function in the AI space. They should also be able to look beyond immediate applications and make decisions considering the long-term implications. With that said, here are the 5 essential skills students will need to become professionals in the AI industry

1.Technical skills: To pursue careers in AI, students must have strong technical skills in programming, data analysis, and machine learning. They need to be proficient in programming languages such as Python, R, and Java and have a good understanding of algorithms, data structures, and statistical analysis.
2.Soft skills: In addition to technical skills, they also need to have strong soft skills such as critical thinking, communication, and collaboration. AI projects often require teamwork and problem-solving, so they should be capable of working effectively with others and thinking critically about complex problems.
3.Domain-specific knowledge: AI is being applied in various fields, from healthcare to finance to transportation. When they work in these fields, they should quickly gain domain-specific knowledge and understand the applications of AI in the industry.
4.Ethical and legal considerations: AI raises several ethical and legal concerns, such as privacy, bias, and accountability. Therefore, they need to be aware of these considerations and be able to apply them in their work.
5.Lifelong learning: AI and related technologies are constantly evolving, so they need to adapt to new technologies and learn new skills throughout their career. They should be passionate enough to be a lifelong learner and willing to update their skills and knowledge continually.

Teaching AI in schools

Incorporating AI education into the curriculum can be challenging for schools today, but making the right efforts in their capacity to prepare students for AI-related jobs will help the school stay ahead of the curve. Adopting AI education at this early stage can be mutually beneficial for both schools and students in the long term. So, here are some strategies for schools to teach AI today.

1. Incorporating AI into existing courses: One way to teach AI is to incorporate it into math, science, and computer science courses. For example, students can learn about algorithms and data analysis in math class or study machine learning in computer science.
2. Offering dedicated AI courses: Another option is to provide dedicated AI courses. These courses can cover machine learning, natural language processing, and computer vision. However, it is essential to have qualified teachers on board to teach these courses.
3. Providing resources and tools: A variety of resources and tools are available to help teachers incorporate AI education into their classes. For example, Google has developed an AI education platform called TensorFlow, which provides resources for teaching machine learning.
4. Fostering hands-on learning: AI is best learned through hands-on learning experiences, such as projects and competitions. Schools can encourage students to participate in AI hackathons, build their AI projects, and collaborate with peers on AI-related projects.
5. Partnering with industry: Partnering with industry can provide schools access to the latest technology and expertise. This can include internships, apprenticeships, and partnerships with AI companies and organisations.

AI education outside of schools

As mentioned earlier, if you are a student, it is important to make use of learning opportunities outside of school. Here are some options for you to learn AI outside of school:

1. Online courses and certifications: Various online courses and certificates are available for students who want to learn more about AI. Platforms such as Coursera, Udacity, and edX offer courses in topics such as machine learning, computer vision, and natural language processing.
2. Bootcamps and workshops: Bootcamps and workshops are intensive training programs that provide students with hands-on learning experiences. These programs can range from a few days to several months and cover topics such as machine learning and data science.
3. Internships and apprenticeships: Internships and apprenticeships provide students with real-world experience in AI-related fields. They can allow the students to work with AI professionals and gain practical experience applying AI techniques to real-world problems.
4. AI clubs and organisations: Many schools and communities have AI clubs and organisations that students can join. These groups provide students with the opportunity to collaborate with peers and work on AI projects together.
By taking advantage of these opportunities for AI education outside of schools, students can gain valuable experience and knowledge in AI-related fields. These experiences can help you prepare for careers in AI and make you more competitive in the job market.

The Importance of Diversity in AI Education
For several reasons, diversity in AI education is highly crucial. By its very nature, the technology requires a diverse pool of people to build and train it. Here is a simple breakdown of why promoting diversity in AI education is essential:

1. Addressing bias: AI systems are only as good as the data on which they are trained. If the data is biased, the AI system will be biased as well. By promoting diversity in AI education, we can be confident that AI systems are trained on diverse data sets, which can help mitigate bias.
2. Increasing innovation: Diverse perspectives can lead to increased innovation and creativity. By promoting diversity in AI education, a broader range of viewpoints is brought to the table, which will lead to new ideas and solutions.
3. Creating a more inclusive industry: The lack of diversity in the AI industry is a well-documented problem. By promoting diversity in AI education, we can ensure that a more diverse pool of candidates is entering the industry, which can help create a more inclusive and equitable industry.
4. Addressing the skills gap: There is currently a skills gap in AI-related fields, which means that there are more job openings than qualified candidates to fill them. By promoting diversity in AI education, we can increase the number of qualified candidates and help address the skills gap.

The future of education with AI
The conventional methods of teaching and learning are about to undergo a massive change with the advent of AI technology and tools. The scale of change in the education industry that AI will cause is tough to imagine today. But, here are the imminent changes already underway and soon to pick up the pace and become the new normal.

1. Personalised learning: AI has the potential to personalise learning for each student by providing tailored content and assessments based on their learning style and abilities. This can help students receive the education they need to succeed.
2. Intelligent tutoring: AI-powered tutoring systems can provide students with individualised feedback and support. These systems can adapt to each student's progress and provide targeted guidance to help them overcome challenges and master new concepts.
3. Enhanced accessibility: AI can help improve accessibility for students with disabilities. For example, AI-powered tools can provide real-time closed captioning for students with hearing impairments or assistive technology for students with visual impairments.
4. Improved administrative efficiency: AI can help automate administrative tasks such as grading, scheduling, and student record-keeping. This can free up time for educators to focus on teaching and interacting with students.
5. Continued learning and upskilling: AI is constantly evolving, which means that students and educators will need to continue learning and upskilling throughout their careers. AI-powered education platforms can provide ongoing training and development opportunities to keep up with the latest advancements in AI and related fields.

Conclusion
As a student passionate about pursuing Artificial Intelligence as your career, you are after the power to make a positive impact on the world. With AI, you can solve complex problems, automate processes, and even create groundbreaking innovations that transform entire industries.

With the power of AI in your hands, effectively addressing global challenges, such as climate change or healthcare access, is a real possibility. Also, you will take an active part in developing AI systems that are unbiased and ethical, ensuring that it is used responsibly and for the benefit of all. These are just some of the possibilities that await you in the field of AI.

Therefore, as a student, if you have the curiosity and attitude to acquire the necessary skills despite the challenges, you can position yourself to succeed. In this exciting field, you can contribute to a brighter and more equitable future. So, start exploring the world of AI and make a real difference!

Source: aeccglobal
Original Content: https://shorturl.at/BCJ13

13

AI Career Advice and Tips / AI Career Futureproofing: 6 Tips from Experts

« on: January 08, 2024, 10:54:25 AM »

Stay ahead in the evolving job market with tips and expert advice on how to futureproof your career in the age of AI.



Welcome to the age of artificial intelligence (AI), where technology is advancing at an unprecedented rate. As AI becomes more integrated into our lives and the workplace, it’s natural to wonder how it will impact our careers.

Will robots take over our jobs?

How can we stay relevant in this rapidly changing landscape?

Workipedia by MyCareersFuture brings you six tips on how to futureproof your career in the age of AI with insights from Ying Shaowei, NCS’s chief scientist, and Alan Ong, a career specialist from Maximus, a career-matching provider appointed by Workforce Singapore (WSG).



Six tips for futureproofing your career in the age of AI

1. Embrace lifelong learning
As AI continues to advance, the skills that are in demand are constantly evolving. To futureproof your career, you must adopt a mindset of continuous learning.



Online courses, workshops, and certifications can provide valuable knowledge and enhance your expertise.

“The key is to always stay relevant and adaptable amidst the VUCA (volatility, uncertainty, complexity, and ambiguity) environment within the job market of AI and Automation,” said Alan.

By investing in your learning and development, you demonstrate adaptability and a willingness to grow, making you more valuable to employers in the AI-driven job market.

2. Develop soft skills

While AI is advancing rapidly, there are certain skills that machines cannot replicate: soft skills. As AI takes over routine tasks, employers will increasingly value these human-centric skills. Alan shared:

“AI is developed to increase productivity and efficiency, but it will never replace communication skills, critical thinking and creativity.”

So, focus on developing your soft skills and seek opportunities to improve your communication, problem-solving, and leadership abilities. These skills will not only make you stand out in the workforce but also ensure your relevance in the future.

3. Collaborate with AI

Rather than viewing AI as a threat, seeing it as a tool to enhance your work is important. This will make you more productive and position you as someone who can effectively work with AI, making you an asset to any company. Shaowei highlights how the ‘SEA’ approach would help professionals collaborate effectively with AI.

‘S’ – Skills development and continuous learning:
“Professionals must continuously refine their skills for effective AI-human collaboration, keeping abreast of the latest AI technologies, tools, and industry trends while comprehending AI’s strengths and weaknesses.”

‘E’ – Ethical and responsible use of AI:
“As AI models are pre-trained with large datasets, professionals should be cognizant of potential bias and ensure AI applications handling personal data are compliant with privacy regulations and best practices.” 

‘A’ – Agility to adapt:
“With the convergence of different technologies and industries becoming more common, collaboration with professionals from diverse backgrounds and disciplines can also lead to ground-breaking innovation and opportunities.”

4. Cultivate adaptability

In the age of AI, adaptability is key. The job market is changing rapidly, and new roles are emerging while others become obsolete. Be open to learning new skills, exploring different industries, and taking on new challenges.

“Having a growth mindset and staying adaptable is extremely crucial in the world of AI,” said Alan. “It is about staying relevant and keeping up to date on the latest trends within the current job market.”

Embrace change and be willing to step out of your comfort zone. By being adaptable, you’ll be better prepared to navigate the uncertainties that come with advancements in AI technology.

5. Nurture your network

Building and nurturing professional relationships is crucial in any career, but it becomes even more important in the age of AI. Networking lets you stay connected with industry trends, learn from others, and discover new opportunities. Alan explained:

“To stay relevant, it is important for you to nurture your professional network to gain access to opportunities, nurture your professional development, and get the necessary exposure.”

Attend industry events, join professional organisations, and connect with like-minded professionals both online and offline.



Your network can provide valuable insights, mentorship, and potential job opportunities. So, invest time and effort into building a strong professional network.

6. Stay curious

Curiosity is a trait that will serve you well in the age of AI. By staying curious, you’ll be able to adapt to changes more effectively, identify new areas for growth, and remain ahead of the curve.

According to Shaowei, professionals should read and ask questions, digging deeper into how AI technologies work and exploring the underlying principles.

“Engage in conversations with experts and enthusiasts to broaden one’s understanding. There are groups dedicated to hosting these conversations, such as the Singapore Computer Society.“

He also recommends professionals find opportunities to gain first-hand experience in real-world applications. “It not only deepens one’s understanding and confidence in working with AI but also enables individuals to actively contribute to the ever-evolving AI landscape,” he added.

AI is a tool not a threat

The rise of AI presents opportunities and challenges for the future of work. Remember, the key is to see AI as a tool to enhance your work rather than a threat to your job.

So, let’s embrace the possibilities that AI brings and position ourselves for success in this ever-evolving landscape! If you need professional advice on navigating your career journey register here to speak to a career coach.

Source: mycareersfuture
Original Content: https://shorturl.at/HPSVX

14

AI Certifications and Training Programs / 10 top artificial intelligence certifications and courses for 2024

« on: January 08, 2024, 10:43:26 AM »

By Andy Patrizio

Numerous AI certifications and courses cover the basics and applications of artificial intelligence, so we've narrowed the field to 10 of the more diverse and comprehensive programs.

Artificial intelligence is on track to be the key technology enabling business transformation and enabling companies to be more competitive. A 2022 study from IDC forecast the overall AI software market will approach $791.5 billion in revenue in 2025 at a compound annual growth rate of 18.4%.

AI can help businesses be more productive by automating processes, including using robots and autonomous vehicles, and augmenting their existing workforces with AI technologies such as assisted and augmented intelligence. Most organizations are working to implement AI in their processes and products. Companies are using AI in numerous business applications, including finance, healthcare, smart home devices, retail, fraud detection and security surveillance.

Why AI certifications are important

AI certifications are important for the following reasons:

Learning about and understanding artificial intelligence can set individuals on the path to promising careers in AI.
A prestigious AI certification can set you apart from the competition and show employers that you have the skills they want and need.
The AI field is constantly changing, and it can be a challenge to keep up with that pace of change. Certification tells an employer you are familiar with the latest developments in the field.
Career sites all note that professionals with AI certifications can earn more than those without credentials.

10 of the best AI certifications and courses

1. Artificial Intelligence Graduate Certificate by Stanford University School of Engineering
Key elements: This graduate certificate program covers the principles and technologies that form the foundation of AI, including logic, probabilistic models, machine learning, robotics, natural language processing and knowledge representation. Learn how machines can engage in problem-solving, reasoning, learning and interaction, as well as how to design, test and implement algorithms.

To complete the Artificial Intelligence Graduate Certificate, you must complete one or two required courses and two or three elective courses. You must receive a 3.0 grade or higher in each course in order to continue taking courses via the Non-Degree Option Program.

Prerequisites: Applicants must have a bachelor's degree with a minimum 3.0 grade point average, as well as college-level calculus and linear algebra, including a good understanding of multivariate derivatives and matrix/vector notation and operations. Familiarity with probability theory and basic probability distributions is necessary. Programming experience, including familiarity with Linux command-line workflows, Java/JavaScript, C/C++ Python or similar languages is also required. Each course might have individual prerequisites.

2. Designing and Building AI Products and Services by MIT xPro
Key elements: This eight-week certificate program covers the design principles and applications of AI across various industries. Learn about the four stages of AI-based product design, the fundamentals of machine and deep learning algorithms and how to apply the insights to solve practical problems. Students can create an AI-based product proposal, which they can present to their internal stakeholders and investors.

Students can learn to apply machine learning methods to practical problems, design intelligent human-machine interfaces and assess AI opportunities in various fields such as healthcare and education. Students can design and construct an executive summary of an AI product or process using the AI design process model.

Prerequisites: This program is mainly targeted toward UI/UX designers, technical product managers, technology professionals and consultants, entrepreneurs and AI startup founders.

3. Artificial Intelligence: Business Strategies and Applications by UC Berkeley Executive Education and Emeritus
Key elements: Instead of teaching the how-tos of AI development, this certificate program is targeted at senior leaders looking to integrate AI into their organization and managers leading AI teams. It introduces the basic applications of AI to those in business; covers AI's current capabilities, applications, potential and pitfalls; and explores the effects of automation, machine learning, deep learning, neural networks, computer vision and robotics. In this course, you'll learn how to build an AI team and organize and manage successful AI application projects, and study the technology aspects of AI to communicate effectively with technical teams and colleagues.

Prerequisites: This program is mainly targeted toward C-suite executives, senior managers and heads of business functions, data scientists and analysts, and mid-career AI professionals.

4. IBM Applied AI Professional Certificate (via Coursera)
Key elements: This beginner-level AI certification course will help students do the following:

Understand the definition of artificial intelligence, its applications, use cases and terms such as machine learning, deep learning concepts and neural networks.
Build AI-powered tools using IBM Watson AI services, APIs and Python with minimal coding.
Create virtual assistants and AI chatbots without programming and deploy them on websites.
Apply computer vision techniques using Python, OpenCV and Watson.
Develop custom image classification models and deploy them in the cloud.
Prerequisites: While the series is open to everyone with both technical and nontechnical backgrounds, the final two courses require some knowledge of Python to build and deploy AI applications. For learners without any programming background, an introductory Python course is included.

5. AI for Everyone by Andrew Ng (via Coursera)
Key elements: This course is mainly nontechnical and covers the meaning of common AI terms, including neural networks, machine learning, deep learning and data science. The course runs approximately 10 hours with flexible scheduling. Students also learn the following:

What AI can and can't do.
How to uncover opportunities to apply AI to problems in their companies.
What it feels like to build data science and machine learning projects.
How to work with AI teams and build AI strategies in their organizations.
How to handle ethical and societal discussions surrounding AI.
Prerequisites: Open to everyone, regardless of experience.

6. Introduction to TensorFlow for Artificial Intelligence, Machine Learning and Deep Learning (via Coursera)
Key elements: This four-course deeplearning.ai certificate program runs 18 hours and covers best practices for using TensorFlow, an open source machine learning framework. Students will also learn how to create a basic neural network in TensorFlow, train neural networks for computer vision applications and learn to use convolutions to improve their neural networks.

This is one of four courses that are a part of the DeepLearning.AI TensorFlow Developer Professional Certificate.

Prerequisites: This series is constructed for software developers who want to build scalable AI-powered algorithms. High school-level math and experience with Python coding are required. Prior machine learning or deep learning knowledge is helpful but not required.

7. Artificial Intelligence A-Z 2023: Build 5 AI (including ChatGPT)
Key elements: This course covers key AI concepts and intuition training to quickly get enrollees up to speed with all things AI, including how to start building AI using Python with no previous coding experience, how to code self-improving AI, how to merge AI with OpenAI Gym toolkit and optimize AI to reach its maximum potential in the real world. Students will do the following:

Learn how to make a virtual self-driving car.
Create an AI to beat games.
Solve real-world problems with AI.
Master AI models.
Study Q-learning, deep Q-learning, deep convolutional Q-learning and A3C reinforcement learning algorithm.
Prerequisites: This certification is for anyone interested in AI, machine learning or deep learning. High school math and basic Python knowledge are required, but no previous coding experience is required.

8. Artificial Intelligence: Reinforcement Learning in Python (via Udemy)
Key elements: This course covers how to apply gradient-based supervised machine learning models to reinforcement learning, understand reinforcement learning on a technical level, implement 17 different reinforcement learning algorithms and use the OpenAI Gym toolkit with zero code changes. The following topics are also covered:

The relationship between reinforcement learning and psychology.
The multiarmed bandit problem and explore-exploit dilemma.
Markov decision discrete-time stochastic control processes.
Methods to calculate means and moving averages and their relationship to stochastic gradient descent.
Approximation methods, such as how to plug a deep neural network or other differentiable models into a reinforcement learning algorithm.
Prerequisites: Students will need knowledge of calculus (derivatives), probability/Markov models, Numpy coding, Matplotlib visualizations in Python, as well as experience with supervised machine learning methods, linear regression, gradient descent and good object-oriented programming skills. The course is open to students and professionals who want to learn about AI, data science, machine learning and deep learning.

9. Artificial Intelligence Engineer (AIE) Certification Process by the Artificial Intelligence Board of America (ARTiBA)
Key elements: The ARTiBA certification exams compose a three-track AI learning deck that contains specialized resources for skill development and job-ready capabilities to help credentialed professionals move into senior positions as individual contributors or team managers. The AIE curriculum covers every concept of machine learning, regression, supervised learning, unsupervised learning, reinforced learning, neural networks, natural language processing, cognitive computing and deep learning.

Prerequisites: Students and professionals with different levels of experience and formal education, including associate (AIE track 1), bachelor's (AIE track 2) and master's (AIE track 3) degrees. Track 1 requires a minimum of two years of work history in any of the computing subfunctions. Tracks 2 and 3 note experience is not mandatory, but a good understanding of programming is essential.

10. Master the Fundamentals of AI and Machine Learning (via LinkedIn Learning)
Key elements: There are 10 short courses in this learning path presented by industry experts. They aim to help individuals master the foundations and future directions of AI and machine learning and make more educated decisions and contributions in their organizations. Participants learn how leading companies are using AI and machine learning to alter how they do business, as well as insight into addressing future ideas regarding issues of accountability, security and clarity in AI. Students will earn a certificate of completion from LinkedIn Learning after completing the following 10 courses:

AI Accountability Essential Training.
Artificial Intelligence Foundations: Machine Learning.
Artificial Intelligence Foundations: Thinking Machines.
Artificial Intelligence Foundations: Neural Networks.
Cognitive Technologies: The Real Opportunities for Business.
AI Algorithms for Gaming.
AI The LinkedIn Way: A Conversation with Deepak Agarwal.
Artificial Intelligence for Project Managers.
Learning XAI: Explainable Artificial Intelligence.
Artificial Intelligence for Cybersecurity.
Prerequisites: Open to everyone, regardless of experience.

Andy Patrizio is a technology journalist with almost 30 years' experience covering Silicon Valley who has worked for a variety of publications -- on staff or as a freelancer -- including Network World, InfoWorld, Business Insider, Ars Technica and InformationWeek. He is currently based in southern California.

Source: techtarge
Original Content: https://shorturl.at/ayDW1

15

AI Job Market and Hiring Trends / How AI Might Impact The Job Market In 2024

« on: January 08, 2024, 10:36:13 AM »

By Serenity Gibbons



After a year defined by a fresh buzz around artificial intelligence, the question on my mind heading into 2024 is where we’ll start to see the long-term impact of AI. One obvious candidate is the job market.

Here’s how I think the evolving impact of AI will create trends, challenges, and opportunities in the professional world.

AI’s Influence on Recruitment

AI has already been a mainstay with recruiters for years. They use intelligent technology to identify ideal candidates and weed through mountains of applications. In 2024, candidates are turning the tide when it comes to AI in recruitment.

This is largely because many tools are becoming more accessible to the average job seeker. Teal, for example, offers those searching for employment a suite of AI-powered tools, including a resume builder, cover letter generator, LinkedIn profile analysis, and job application tracker.

As the job market evolves, both employers and the employed will implement a growing number of AI solutions to give themselves a cutting-edge advantage. It is a trend that is set to grow in the coming months.

AI Will Alter Existing Roles

AI is often seen as a job killer, but it is more accurate to refer to it as a job redefiner. In most cases, AI doesn’t remove the need for important work. On the contrary, even as it develops greater capabilities, it still addresses the simple stuff compared to its human counterparts.

According to a recent LinkedIn article, “Repetitive tasks, by their very nature, consume a significant chunk of time and resources. They often require human intervention, prone to errors, fatigue, and inconsistency. Enter AI: a technology capable of performing these tasks tirelessly, consistently, and without the typical human-induced variations.”

This creates new opportunities for employees to focus on meaningful, high-value activities. The result is jobs with more specific, nuanced requirements. At the same time, these positions won’t be bogged down with simple, time-consuming minutiae.

AI Will Naturally Create New Opportunities

The existence and rapid adoption of AI will naturally create a demand for jobs in that field. As companies seek ways to capitalize on the efficiencies and profitability of AI systems, they will need humans to lead the charge.

This pioneering phase will require software engineers, data scientists, and other specialized personnel. It will open up new skills that employees can learn to give themselves a competitive edge.

AI Will Have Negative Repercussions on Jobs, Too

Of course, it’s not all good news for those working alongside AI. One recent survey from ResumeBuilder reported that 44% of companies expect layoffs to occur in 2024 due to new AI capabilities.

With that said, it’s worth pointing out that this appears to be largely connected to the culling of simpler activities within daily business pursuits. Of the respondents, 83% clarified that, as far as existing employees are concerned, AI skills will be a factor in helping them retain their jobs.

For those willing to embrace AI, the future is bright. For those working jobs AI can quickly replace, they must begin developing new skills.

The Shift to AI Isn’t Going to Be Instant

It’s tempting to look ahead and ring the alarm on AI. There’s no doubt that change is coming, and some hard-working employees will lose their jobs before long.

However, the thought that we’re looking at an apocalyptic future in the coming months is alarmist. Pew Research reports that the transition to AI will likely take half a century or more.

In addition, as much of the above data shows, most of the change will be either a horizontal shift or an improvement. Will some jobs be automated? Yes. Will others evolve with the times? Absolutely. Still others will grow out of the need for AI, as well. The one surety heading in 2024 is that AI will be a major factor in the workforce, and human workers and employers must be ready to adapt on a daily basis.

Source: forbes
Original Content: https://shorturl.at/bcrzS