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How AI Can Be Applied in the Field of Special Education?

How AI Can Be Applied in the Field of Special Education?

The digitization of modern classrooms is an indicator of the fact that educators have decided to adopt tech to make learning a more rewarding experience for their students. In fact, some educators are making an effort to upgrade themselves with advanced courses like deep learning for computer vision with python.

But, not all children in a classroom can blend in with their classmates with ease, they have difficulty understanding teacher’s instructions or, even following what is written in the textbooks.

Yes, we are talking about children with disabilities, who cannot be taught in a typical classroom setting and who need special attention and today we are going to learn whether the transformative power of AI can make a difference in the lives of these differently-abled children.

How do we classify children who need special education

Children who have been detected with some form of physical, cognitive or learning disabilities such as ASD- Autism spectrum disorder, Dyslexia, hearing, or visual impairment, require a learning environment that is specifically designed for them.

What are the challenges they face in a traditional setting?

Usually, these children have problems coping with the traditional learning environment. They find it difficult to follow instructions, lessons, even their textbooks. They need personal attention from the teacher, and are unable to handle the pressure of competition.

What’s more, these children usually are subjected to bullying from other students, which can further discourage them. they need special tools, and lessons that are designed for them. These children also have a short attention span so, the reading material might not interest them.

How AI can be of help?

The introduction of AI in special education has opened up new avenues for both parents and teachers. Data Science training has enabled professionals to analyze student data to identify problem areas. There have been several studies on this and the findings are promising. With the application of AI-based methods, it is possible to help these children exercise more freedom as it allows them to learn at their own pace.

The Application of AI in special education is aimed to serve a dual purpose.

1) AI-based tools are used to detect disability

2) AI-based learning tools and methods are being adopted to aid learning

Now let’s take a brief look at how this dual application of AI is taking place and with what effect

1) AI-based tools are used to detect disability

Sometimes identifying a problem can lead you to the right solution. Usually, children who show apathy to learning or, are very slow to pick up lessons, treated as dumb. They are bullied and teachers keep complaining that they do not make an effort. But, what they fail to realize is that the child might have a learning disability.

But, now we have AI-enabled testing systems in place to detect children with learning disabilities.

 In the year 2008, a model was introduced to diagnose autism with the help of artificial neural networks technique. Even before this way back in 2003, a fuzzy cognitive map approach was taken to diagnose SLI or, speech-language impairment. Other systems involving artificial neural network techniques were developed to detect issues like dysgraphia, dyslexia. 

More data based research work on this is required. This would definitely create a demand for professionals who have undergone specialized training on neural network course python.

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2) AI-based learning tools and methods are being adopted to aid learning

 Children with special needs must have access to special learning tools that would make learning easier. There has been some considerable progress in this field.

  • Children with Autism have problems with both verbal and nonverbal communication. Developing social skills can be a challenge for them. To address this issue QTrobot was designed. This humanoid social robot is programmed to teach autistic children social skills. Other examples would be NAO robot, virtual assistant Siri which help children with ASD acquire more social skills.
  • ActiveMath employs Artificial Intelligence techniques to ensure children can have more freedom in selecting a convenient learning environment.
  • Spontaneous participation of the learner is essential for any learning process to be effective. If a child is having trouble concentrating then, he won’t learn anything. Smart Tutoring model was developed to evaluate the concentration level as well as the mood of the child, to develop a learning session that suits that particular child.
  • Widex’s Evoke is an intelligent hearing aid powered with AI technology which can help hearing-impaired children attend classes without any difficulty.

AI has the potential to transform the special education sector with advanced learning aids, specifically designed for differently-abled students. The field is expanding and it needs more research and collaboration among educators, app developers, engineers to come up with smarter solutions.

 


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Emerging AI Trends You Should Keep Track of

Emerging AI Trends You Should Keep Track of

AI is a dynamic field that is constantly evolving thanks to the continuous stream of research work being conducted. The field is being reshaped by emerging trends. In order to keep pace with this fast-moving technology, especially if you are pursuing Data Science training you should learn about the latest trends that are going to dominate this field.

Digital Data Forgetting

It is a curious trend to watch out for as instead of learning data, unlearning would take precedence. In machine learning data is fed to the system based on which it makes predictive analysis. However, thanks to the growing channels and activities the amount of data generated is increasing, and a significant portion of which might not even be required and which only contribute to creating noise.

Although it is possible to store the data utilizing cloud-based systems, the price an organization will have to bear for unnecessary data, does not justify the decision. Furthermore, it might also raise privacy risks in the future. The efficient handling of this data lineage issue requires systems that will forget unnecessary data so that it can proceed with what is important.

NLP

Now that chatbots are being put to use to provide better customer support, the significance of NLP or, natural language processing is only going to increase. NLP is all about analyzing and processing speech patterns. There is now a shift towards developing language models around the concepts of pre-training and fine-tuning and further research work is being conducted to make these systems even more efficient, however, the focus on transfer learning might lessen considering the financial and operational complications involved in the process.

Reinforcement Learning

 This is another trend to look out for, reinforcement learning is where a model or system learning involves a preset goal and is met with reward or, punishment depending upon the outcome. This particular trend might push AI to a whole new level. In RL, the learning activity is somewhat random and the system has to rely on the experience it has gained and continues to learn by repeating what it has learned, and as it starts recognizing rewards it continues working towards it until the learning takes a logical turn.  Research works are being conducted to make this process more sophisticated.

 Automated Machine Learning

 If you are aware of Google AutoML, then you already have an inkling of what AutoML is. It basically focuses on the end-to-end process and automates it. It applies a number of techniques including RL, to reach a higher level of accuracy. It works on raw data and processes it to suggest a solution that is most appropriate. It basically is a lifesaver for those who are not familiar with ML. However, there are programs available that enable professionals pursue Machine Learning Using Python who are looking to gain expertise in this field.

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Internet of Things

IOT devices are a rage and they are able to collect a huge amount of user data that needs to be processed to gather valuable information. However, there could be certain challenges involved in the data collection process which lead to error. The application of ML in this particular field can not only lend more efficiency to the way IOT operates but it can also process a large amount of data to offer actionable insight. The information filtered this way could help develop efficient models for businesses and various other sectors. The merger of IOT and ML is definitely a trend that is definitely going to be revolutionary.

AI technology is getting more sophisticated with emerging trends. The manifold application of AI is opening up new career avenues. Enrolling in a premier artificial intelligence training institute in Gurgaon, would be a good career move for anybody looking forward to having a career in this domain.

 


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How AI is Changing The Way You Conduct Business?

How AI is Changing The Way You Conduct Business?

In the pages of sci-fi, we find a world that is completely driven by AI, but, we no longer have to wish for a dystopian world to become a reality to experience AI firsthand. Our world as we know it is gradually being reshaped by the powerful presence of AI. From your Smartphone to virtual assistants, AI is taking slow but firm strides which is evident from the high demand for courses like artificial intelligence certification in delhi ncr.

So, when everything is being impacted by AI, why should your business lag behind? You would be surprised to learn the incredible changes AI could bring to the table.

Be it managing administrative tasks or, ensuring data security, AI could streamline operations and add efficiency to every task that needs to be performed to ensure zero error. Here is how you can make the difference
 

  • Implement AI to smoothen your HR practices: Managing your employees and recruiting new ones are essential tasks for any organization. Your HR department can be more efficient with their tasks at hand with the help of machine learning. Dealing with scores of applications while recruiting new faces can be a mundane task. But, now processing applications and sorting out the ones that match the criterion gets easier and so does the entire hiring process. Throw in chatbots in the interview process to ensure that you select the right candidates.
  • Lessen errors during manufacturing: During the manufacturing process, oftentimes  one or, two faulty products end up spoiling the image of the brand and most importantly you end up spending time, money, and personnel for return and refund procedure. To err is human but, with AI coming into play, any kind of glitch could easily be detected and prevented at an early stage. This would ensure that only quality products are ending up in the showrooms.
  • Customize marketing strategies: It is so hard to guess what your customers want and even more difficult to chalk out a marketing plan around that guesswork. However, advanced analytics could help you keep a track of consumer behavior because it is purely based on data. So, now that you have eliminated the guesswork, you can come up with a marketing strategy that actually works to entice customers.
  • Provide customer service round the clock: While running your business you must have noticed how difficult and essential it is to stay in touch with constant queries. People who are buying from you might have umpteen number of queries and you need a big team to handle that. However, despite your best efforts, some queries go unanswered, this might create a negative impact on the customers. So, why not take the help of chatbots who can handle all the primary queries round the clock and provide customer satisfaction? Critical queries could be handed over to a support team with specialized knowledge.

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You can do more with AI-powered tools and systems to make your business grow. The world is moving with AI towards a brighter future and, it is time for you to welcome and embrace its power.

 


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AI’s Contribution to Photography: An Assessment

AI’s Contribution to Photography: An Assessment

The next time you are buying a smart phone or a digital camera, instead of concentrating on the lens specifications, try to find out what the manufacturer has to say about artificial intelligence. Because the nature of photography has changed for good in a world fast recalibrating to keep pace with the exigencies of advanced computing. In this article we will examine how artificial intelligence has transformed the way the world looks at photography now.

Smart Devices

Photography, the idea of sharing with others what we can see through a lens, has been a long developed art with teachers and mentors passing down acquired skills to protégés and juniors for years now. However, with the advancement of AI’s uses in the photography industry, things have changed tremendously.

Take for instance Apple’s A11 Bionic neural engine chip that powers the latest generation of iPhones. The chip is fitted with AI technology that assists in image and face recognition, AR applications and more. Google Pixel then came out with its high-tech hardware chip designed for dedicated image enhancement and image processing.

The Chinese smartphone, Huawei’s P20 Pro, features four cameras. Besides achieving the highest DxO Mark score to date, the Huawei P20 Pro is packed with AI features, such as real-time image scene recognition, meaning it can discern 500 scenarios in 19 categories, such as animals, landscapes, as well as an advanced night mode, where the AI assists in processing noisy photos, making them almost perfect, says a report.

So it has become the norm for smart devices to have inbuilt AI powered hardware that help enhance the process of photography unlike traditional cameras. Manufacturers are concentrating on image capture and real-time processing because it is a market differentiator.

Processing professional photographs

Professional photography needs to be processed and cannot be used in the RAW format. But the procedure has been enhanced by AI technology lately. For instance, recently, PetaPixel released a research paper that talked about how extremely underexposed images can be retrieved through techniques wherein AI is applied to the digital images. This technology can be used in high-end security cameras as well.

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Photo optimization

Photo optimisation is what AI has been able to take to the next level. A team of AI developers at Skylum is working on technology that will allow smartphone images to be expanded and printed with very high resolution and sharpness. This technology will help consumers lagging behind with older smart phones and old technology to optimise photos taken years ago. Other companies are trying to build technology that will compress RAW images up to 10 times the original heavy files without loss of data. 

It might seem like AI is intruding into the art space of photography, especially for professionals who have spent years honing the art of taking and editing photographs. But for the common user, AI powered technology is a boon and this technology is being sought by the best tech companies across the world. In India, artificial intelligence certifications in Delhi NCR are springing up to cater to a growing clientele that wants to join the AI revolution.

 


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5 Crucial Subsets of Artificial Intelligence

5 Crucial Subsets of Artificial Intelligence

As simply as can be said, artificial intelligence is a machine’s ability to replicate human intelligence and accept new inputs and perform tasks on them like human beings, learning from experience. The term was coined in the 1950s but it has today come to significantly become popular in relation to large data sets and new advanced algorithms.

AI has become the most revolutionary advancement in computing science and it is powering all sectors of the economy from banking to healthcare and agriculture today. There are many sciences branching out of artificial intelligence like machine learning, deep learning, neural networks, computer vision and robotics. Let us learn a little about each of these.

Machine Learning

Machine Learning, a crucial subset of artificial intelligence, is the machine’s ability to learn from experience with no need for human intervention explicitly. It is the most widely used form of AI in the market today. Machine Learning refers to the computer programs that are fed data, learn from them and use this experience to take intelligent decisions. Machine Learning is used in analysis, fraud detection and GPS based predictions to name a few. 

Neural Networks

Neural networks are a bunch of algorithms modelled after the neural networks that make up the human brain. They are designed to absorb and assimilate and interpret sensory data through labelling or clustering row input. The patterns they sense and interpret are in the form of numerical data, a format all text, images or even sounds must be translated into for a computer to understand. They help label, cluster and classify data based on similarities in the input fed.

Deep Learning

Deep Learning is a technique of machine learning that uses neural networks to learn up the way humans do – by example. And it does them accurately. It is the science behind driverless cars that can distinguish a lamppost from a person. Deep learning requires a large amount of labelled data sets to be able to work and effective and substantial computing power. Deep Learning finds its applications in aerospace technology, healthcare and driverless locomotives industry among others.

Robotics

A robot is a machine capable of sensing and interpreting and interacting with its environment. Robots have become much smarter and intuitive, thanks to artificial intelligence. Robotics is an interdisciplinary field of science and engineering that is powered by a consolidated science of mechanical engineering, electrical engineering, computer science, and algorithms. Robots are used in automobile manufacturing and used to move objects in space or related fields.

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Computer Vision

Computer Vision is the field of study that seeks to enable computers to “see” virtually like the human eyes do. A computer learns by labelling or classifying various objects, albeit much faster than human beings. Its goal is image classification and recognition. The Internet is inundated with pictures and photographs. In order to search for these images, the computer system needs to know what is in them. This is where the technology of computer visions comes in.

So you see how vast the scope of artificial intelligence really is. It is a science unto itself. To learn more about the science, professionals are increasingly joining artificial intelligence training institutes across the world. DexLab Analytics, the institute that brought this article to you, is a premiere artificial intelligence training institute in Gurgaon.

 


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Libraries In The Era of Artificial Intelligence

Libraries In The Era of Artificial Intelligence

Artificial Intelligence has entered our homes and our workplaces in more ways than one. From our email services to smart vacuum cleaners and more, AI has made life easier and smoother for us. It is no surprise then that libraries, the most crucial resources we have for research, are embracing the powers of AI to streamline the vast repository of material housed by them. Here is a list of applications of AI in the library ecosystem the world over.

Expert Systems

Expert Systems are knowledge based computer systems that play a role as intelligence interfaces for providing access to a database or knowledge system. Libraries can use Expert Systems to facilitate reference services to users and members. For instance, ES provide recommendations to researches looking up a particular question.

Pointer is a very successful application in the area of reference work. It is not a knowledge board system but a computer assisted reference program. Tools like Plexus used widely in public libraries facilitate information retrieval about subject areas, reference books and more. ES are also widely used in cataloguing, indexing and classification of material in libraries across the world.

NLP

Natural Language Processing, a very crucial aspect of Artificial Intelligence, might seem like it is meant for speaking into machines and expecting them to process our words and translate them into textual matter. But NLP has more to it than just this.

Clever low-level natural language processing techniques can permit the use of free-text queries in large information retrieval systems; however, until semantic and pragmatic processing are feasible, difficult problems remain inadequately matching the true subject content of queries with that of document surrogates and documents themselves, says a report.

Robotics

As libraries continue to provide a vast cache of reference material and digital resources, they also continue to receive large amounts of printed material. This combined pressure of providing digital and printed resources to members has led to a major space constraint in libraries across the world. The goal of the Comprehensive Access to Printed Material (CAPM) is to build a robotic, on-demand and batch-scanning system that will allow for real time browsing of printed material through a web interface, says a report. After a user activates the CAPM system, it will initiate a robot to retrieve the requested item and provide it to the person making a request.

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However, despite these advantages and more, very few libraries are properly adopting technological advancements in a digital era. “So far, AI’s potential has remained largely untapped among research libraries. A recent Ex Libris survey revealed that while nearly 80 per cent of research librarians are exploring the use of AI and machine learning, only about 5 per cent are currently leveraging the technology,” says a report.

The reasons behind this trend are budgetary problems and the fear of making the post of a librarian obsolete among others. Irrespective of these reasons, it can be safe to assume that AI is the future of the library system. For more on this, do peruse the DexLab Analytics website today. Dexlab Analytics is premier institute offering natural language processing course in Gurgaon.


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How AI Powers The Food Processing Industry

How AI Powers The Food Processing Industry

Can computers understand food? Can they smell aromas or taste flavours? Well, with Artificial Intelligence (AI) taking the world by storm, the food industry is not outside the purview of AI’s midas touch. In fact, AI is expected to spur the industry on to the path of growth and expansion.

According to some sources, AI in the food and beverages market is expected to register a CAGR of 28.64 percent, during the forecast period 2018-2023. According to others, today, the Food Product and Handling industry is capped at a whopping $100 billion and will continue to grow at a CAGR of 5% at least till 2021.

Here are ways in which AI is fostering the highest standards of processing and handling of food products across the world.

Sorting

One of the most important tasks in a food-processing unit is sorting. Sorting fresh produce by size, colour and quality is the first thing to be carried out and it is time consuming. For instance, sorting potatoes by size and colour will determine whether a food giant will get French fries, hash browns or chips made out of them. Herein comes the role of AI powered machines. Companies like TOMRA Sorting Food have developed sensor-based optical sorting solutions with machine learning capabilities that use cameras and near-infrared sensors to “view food in the same way that consumers do” and sort it based on that perception, says a report. This results in fewer hours spent on manual sorting, higher yields, less wastage and better quality of prepared food.

Managing Supply Chain

With newer food safety regulations being introduced ever so often and a need for transparency growing by the day, it has become imperative for food and beverage companies to put in place robust supply chain management. There are several ways in which this is being done including food safety monitoring and testing of product at every stage of the supply chain and accurate forecasting to manage pricing and inventory.

Personal Hygiene Maintenance

Maintenance of personal hygiene for everyone entering and exiting a food-processing unit is of utmost importance. In 2017, tech company Kankan signed a big deal to provide AI-powered solution for improvement of personal hygiene among workers of food processing units in China. It uses face recognition technology to detect if workers are violating rules that ensure they wear masks and caps to maintain proper hygiene at work. According to Kankan this technology is accurate by over 95 per cent.

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Cleaning processing equipment

This process is time consuming and essential to the supply chain. However, researchers are using AI to come up with better technology to reduce time taken and resources spent on cleaning equipment. For instance, researchers at the University of Nottingham have been developing a system that uses AI to reduce and cut down cleaning time and resources by 20-40 per cent. The system known as self-optimising-clean-in-place uses ultrasonic sensing and optical fluorescence imaging to detect food residue and microbial debris in equipment and facilitate cleaning of the same.

Thus, the importance of AI in various sectors of the economy cannot be stressed enough. For more on how AI powers the IT industry, read DexLab Analytics’ blog here and to know more on how AI powers space exploration read its blog here. DexLab Analytics is a premier institute offering artificial intelligence certification in Delhi NCR.

 


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Top Programming Languages That AI Engineers Can Choose From in 2020

Top Programming Languages That AI Engineers Can Choose From in 2020

Artificial Intelligence, the science of making computers function with the intelligence of the human brain sans the intervention of human beings, is the biggest find of the century. It is powering everything from our personal email to space exploration.

It is, thus, imperative to discuss the very platforms that make AI a reality. Computer programming languages too are fast evolving and there is no one such language that fits the needs of an AI engineer comprehensively. So we shall examine the plus points of the most popular programming languages to choose from this year.

  1. Python

Python is an easy-to-learn programming language that helps AI novices enter the world of programming easily. Python not only has an excellent repository of libraries and a strong community support on the Internet, it is also extremely flexible as a programming language. Platform independence and extensive frameworks that are most needed for Deep Learning and Machine Learning are advantages Python boasts of. Some of its most widely used libraries are Tensor Flow, Scikit-Learn, PyTorch, Keras, SparkMLlib, MXNet and Theano.

  1. Java

Java, widely held to be the best programming language in the world, has two decades worth of testimonials by AI engineers to back the claim. It is highly user friendly and flexible in nature, equipped with excellent platform independence. It is therefore widely sought after for developing AI models. It has some very strong libraries like TensorFlow, Deep Java library, Kubeflow, Open NLP among others.

  1. R

R, created in 1995, is currently maintained by the R Development Core Team. It is the implementation of S programming language. It helps AI engineers develop statistical software and data analysis. What makes it robust, as a language for developers, is the fact that it facilitates crunching of large numbers. In this regard it scores over python. Moreover, says a report, with R you can work on various paradigms of programming such as functional programming, vector computation, and object-oriented programming.

  1. Prolog

Short for Logic Programming, this language is especially suited to build AI models on and develop NLP packages with. For instance, Prolog is used to build chatbots very effectively. Eliza, the first ever chatbot, was built with the assistance of Prolog. “Prolog offers two approaches for implementing AI that has been in practice for a long time and is well-known among data scientists and researchers: The Symbolic Approach that includes rule-based expert systems, theorem provers and constraint-based approaches and the Statistical Approach that includes neural nets, data mining and machine learning.”

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There are many more languages to choose from. Lisp, Julia and Haskell are some of these strong and worthy languages AI engineers can choose to use besides the ones listed above. Every programming language has its own merits and demerits. It is upto the AI engineer to choose wisely after conducting a thorough research and doing due diligence. Dexlab Analytics, a premier artificial intelligence training institute in Gurgaon, suggests the use of Python and R for building AI models.

 


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How AI is Transforming The IT Industry

How AI is Transforming The IT Industry

Artificial Intelligence, the science of making computers function with human-like intelligence, has taken the world by storm. It has transformed the biggest of businesses and industries, from healthcare to agriculture to space exploration. Artificial Intelligence has already become the biggest find of the century.

Information Technology, related to all things computers, software and data transmission, cannot be untouched by artificial intelligence. AI has already brought several advantages to the IT sector, a subject that we are going to examine in this blog brought to you by DexLab Analytics, a premier artificial intelligence training institute in Gurgaon.

Secure Systems

Security of data is of prime importance in today’s world where data is the new oil. Both government and private organisations are therefore striving to better protect the tons of data they are privy to. Through the use of algorithms, AI can provide the necessary security and help create a layered secure system. Not only that, it can also help detect security breaches and potential threats. According to a report, AI and Machine Learning have become crucial to data security in the IT industry. 

Productivity Enhancement

In the IT industry, the most important thing developers are expected to perfect is programming. However, they face numerous challenges in the course of their stints in IT companies with problems like bugs in code and erroneous code marring their goals. AI can be used to solve this problem in that a series of algorithms can be used to aid programmers write better and bug-free code. By judging the structure of the code, AI powered systems can provide suggestions that can improve productivity and help save time in the production process.

Automating Backend Processes

AI is a great enabler of automation, work that can be accomplished without or with minimal human intervention. With the use of deep learning techniques, AI can go a long way in helping automate back end processes in IT companies. This will not only help save costs but also increase accuracy and reduce human effort. “AI enabled methods improve over time as the algorithms adjust to enhance productivity and learn from mistakes,” says a report.

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Application Deployment

During software development, deployment control involves various stages and this means that the software versioning control is advantageous and crucial to the deployment stage. AI is known for its predictive powers and capabilities. It can thus be used for predicting problems during the versioning stage. This eases the entire chain of processes because programmers and developers do not have to wait till the last stage to know about hiccups or improve the application’s processes.

Server Optimization

In IT offices and workplaces, computer servers are more often than not loaded with requests in the millions. They in turn have to open those many corresponding web pages, a process that can make them slow and unresponsive. AI, as a service, can help solve this problem by optimising the host server to improve customer service and enhance operations. As the demand for IT increases across business sectors, AI will be increasingly used to integrate staffing demands and provide a seamless integration of current business functions with technological ones.

—EOM—

 


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