Simply providing a computer with massive amounts of data and expecting it to learn to perform a task is not enough. The data has to be presented in such a way that a computer can easily recognize patterns and inferences from the data. This is usually done by adding relevant metadata to a set of data. Any metadata tag used to mark up elements of the dataset is called an annotation over the input. The term data labelling is also used interchangeably with data annotation to refer to the technique of tagging labels in contents available in a range of formats. As such, there is no major difference between data labeling and data annotation, except the style and type of tagging the content or object of interest.

Most people today still correlate artificial intelligence (AI) with science fiction, but as AI advances and becomes much more prevalent in our everyday lives, this perception is fading. AI technology has been acknowledged as a modern phenomenon in mainstream culture. Entertainment: In the entertainment industry, artificial intelligence (AI) has become a major player. In every region, AI plays a significant role. Examples include weather forecasts, email spam filtering, Google search predictions, and voice recognition apps such as Apple's Siri. And they are completely changing how a business functions. Deep learning and machine learning breakthroughs have allowed computers to process and interpret data in ways we could never have imagined. The automation of cognitive and physical functions is included in artificial intelligence.

Four years before the COVID-19 was first reported, Bill Gates predicted the next big disaster would not be a nuclear war but an infectious virus. Monitoring Madurai and Telangana are using AI technology to track mask violators. SEE ALSO Case Study: How This Cloud Restaurant Company Deployed AI-Based Hygiene & Safety Monitoring Solution For Their Kitchen OPINIONS Case Study: How This Cloud Restaurant Company Deployed AI-Based Hygiene & Safety Monitoring Solution For Their Kitchen Assistance Last month, conversational CX platform Yellow Messenger launched Yellow Messenger Cares to empower medical support groups, hospitals and businesses. Delhi’s Indraprastha Institute of Information Technology developed an AI model last year for drug repurposing. Until last year, artificial intelligence accounted for only 1.9 percent of India’s healthcare industry. While the rest of the world, and the very origin of the virus– Wuhan, seem to have started heading towards normalcy, India is far from anything but ‘normal’. ATMAN AI is powered by a Deep Convolutional Neural Network. Drug repurposing The process of drug discovery is not just time-consuming and risky but also extremely expensive

There are numerous examples in machine learning, statistics, mathematics and deep learning, requiring an algorithm to solve some complicated equations: for instance, maximum likelihood estimation (think about logistic regression or the EM algorithm) or gradient methods (think about stochastic or swarm optimization). Here we are dealing with even more difficult problems, where the solution is not a set of optimal parameters (a finite dimensional object), but a function (an infinite dimensional object). The context is discrete, chaotic dynamical systems, with applications to weather forecasting, population growth models, complex econometric systems, image encryption, chemistry (mixtures), physics (how matter reaches an equilibrium temperature), astronomy (how celestial man-made or natural bodies end up having stable or unstable orbits), or stock market prices, to name a few. These are referred to as complex systems. The solutions to the problems discussed here requires numerical methods, as usually no exact solution is known. The type of equation to be solved is called functional equation or stochastic integral. We explore a few cases where the exact solution is actually known: this helps assess the efficiency, accuracy and speed of convergence of the numerical methods discussed in this article. These methods are based on the fixed-point algorithm applied to infinite dimensional problems.

These are great times and they raise unique challenges and opportunities. You and I are now relying more and more on AI (Artificial intelligence) system that help us guide our cars, transportation systems, heath care applications, economic models, logistics and many other applications we are at times not even aware of. There’s a flip side of AI. It’s important to know it’s not error-free and recognize its flaws. AI systems and models are created and based on algorithms. When IT specialists, programmers and AI creators tell the computer what to do, they also get to choose how it's going to do it. That's where computer algorithms come in. The algorithm is the way IT specialists, programmers and AI creators define the WHAT and the HOW computers get the job done. It’s essentially the technique used to get things done using computers AI systems that learn and perform every time faster, and better. Wait, better? Let’s see. Those systems are a result of algorithms that have, unknowingly, or inadvertently, inherited many of the biases humans have. The same bias that help to perpetuate the global challenges that we want and need to fix. In other words, the work done by AI and machine learning systems are not always objective, purely neutral processes of data analysis.