Fourier Transformations are a powerful tool for understanding how Artificial Intelligence (AI) learns complex physics. By using Fourier transformations, researchers can gain insight into the inner workings of AI and its ability to learn from data. The Fourier transformation is a mathematical technique used to decompose signals into their component frequencies. It has been used in many areas of science, including signal processing, image analysis, and quantum mechanics. In recent years, it has also become an important tool for studying AI algorithms.

Artificial intelligence has already made its mark in our lives. The adoption of disruptive technologies redefined industries and their operations. However, the fear looming over AI, rooted in it being capable of taking over the human race has existed since the start. And most of us would have been influenced by those Sci-fi movies and books, which portray AI as an evil entity, talking and behaving like humans. Well, studies show that we are yet to reach the point where AI can fully augment human intelligence and emotions.

While AI can carry out some impressive feats when trained on millions of data points, the human brain can often learn from a tiny number of examples. New research shows that borrowing architectural principles from the brain can help AI get closer to our visual prowess. The prevailing wisdom in deep learning research is that the more data you throw at an algorithm, the better it will learn. Today's largest deep learning models, like OpenAI's GPT-3 and Google's BERT, are trained on billions of data points, and even more modest models require large amounts of data. Collecting these datasets and investing the computational resources to crunch through them is a major bottleneck, particularly for less well-resourced academic labs.

With countless predictions about what's in store for artificial intelligence in 2020, I'm eager to see what will come true and what will fall by the wayside. I think that one of the more paradigm-changing predictions will be moving AI's learning ability to the edge. Under the hood of AI's generic name, a variety of approaches are hidden, spanning from huge models that crunch data on a distributed cloud infrastructure to tiny, edge-friendly AI that analyze and mine data on small processors. From my academic research at Boston University to cofounding Neurala, I have always been keenly aware of the difference between these two types of AI--let's call them "heavy" and "light" AI. Heavy AI requires hefty compute substrates to run, while light AI can do what heavy AI is capable of but on smaller compute power.

September 05, 2019: The Central Board of Secondary Education (CBSE) has announced that it will conduct Capacity Building Programs for high school teachers in association with Microsoft India with an aim to integrate cloud-powered technology in K12 teaching. Meant for teachers of grades 8-10, the program will be conducted in 10 cities across the country, starting September 11, 2019. AI and intelligent technologies are becoming all-pervasive today, transforming organizations across sectors and redefining the way we work. To equip the workforce of tomorrow, it is critical to the ramp up the institutional set-up and build capability among educators as well as integrate advanced technologies into the teaching process. This program will provide teachers better access to the latest Information and Communication Technology (ICT) tools and help them to integrate technology into teaching in a safe and secure manner, thereby enhancing the learning experience and 21st century skills of all students.

Reinforcement learning is one of the most discussed, followed and contemplated topics in artificial intelligence (AI) as it has the potential to transform most businesses. At the core of reinforcement learning is the concept that optimal behaviour or action is reinforced by a positive reward. Similar to toddlers learning how to walk who adjust actions based on the outcomes they experience such as taking a smaller step if the previous broad step made them fall. Machines and AI agents use reinforcement learning algorithms to determine the ideal behaviour based upon feedback from the environment. An example of the reinforcement Learning in Action is AlphaGo Zero which was in the headlines in 2017.

From Amazon's Alexa to self-driving cars, artificial intelligence continues to have breakthroughs each year, altering our world in tangible ways. Poker is a card game with billions of possible scenarios and layers of deceit, making it one of the most complex games. This complexity also makes it comparable to everyday life situations that AI may one day encounter. So scientists at universities across the globe have been experimenting with AI that can now consistently beat even the highest ranked poker pros. In 2017, Libratus, a poker-playing AI, made a decisive blow in the battle between human and AI dominance.