LIVE _ Neuroscience Inspired AI: the new frontier

Mr. Nikhil Malhotra
Chief Innovation Officer, Tech Mahindra

Summary: How would algorithms learn from the neuroscientific principle and why is it essential to now take AI to the next level. AI of today is narrow. Some ideas on how AI could be improved using neuroscience

Speaker Profile: An Innovator, Futurist, TEDx Speaker, Father and a dog lover and at the crossroads of Physics U Computing

Nikhil Malhotra is the global head of innovation and creator of Maker’s Lab, a unique Thin-q-Bator space within TechMahindra with 20+ years of experience. Nikhil has been a researcher all his life and is now leading the growth of AI, machine learning, robotics and quantum research within TechMahindra. Nikhil’s area of personal research has been how quantum computing and neuroscience has inspired the growth of AI and how the two fields can merge in the future. Nikhil has also designed an indigenous robot in his lab, as a personal assistant. The current bets he has undertaken is how quantum physics and computing can give us better cancer drugs,

One of his vision is to enable machines to talk in the local Indian dialect. There are 1645 dialects in India and 26 mother tongues and 80% of the Indian population does not speak English. Nikhil is researching on Sanskrit along with Quantum Computing and how this ancient Indian language could sow the seed for the next computer revolution

His hobbies include playing tennis, golf and reading books on quantum mechanics and biographies.
He holds a master's degree in computing with a specialization in distributed computing from the Royal Melbourne Institute of Technology, Melbourne. Nikhil currently resides in Pune with his wife Shalini and sons Angad and Rudra.

To know more about our Programs, please visit :

[jedidiahanarfi]

Really informative. Thanks for sharing sir.

[drivestore]

Thank you sir .
Really an interesting theory about human brain and how language we speak comeout from what algorithm in our brain

[xXHelsingGamingXx]

So the idea is not mimicking but decisions which has to be learnt based on states in which had just passed. Since these states could not easily observed practically.... you use a POMDB - A partially observing method which you need the agent to build by itself. Now to make this happen you need an RL Agent, a Reinforcement learning agent, which observes the environment and builds a construct of decisions to be made. With this comes the evolution factor. Cause once the machine hits the optimum results, its not going to mutate and evolve... So we use genetic evolutionary algorithms which seems to be more efficient in solving the evolution factor.

[aishwaryalaxmiveeramalla]

I was actually wondering for many days when I got to know that AI is really good at interpreting Sanskrit rather than English. Thank you so much sir for clarifying this doubt. It was really a great session.

[joohichaturvedi]

Namaste, thanks for sharing it for youtube

[akashraj]

Good to see you explaining those difficult topics so well sir. Now, I can even explain how the human brains gives responses and collects observations from environmental stimuli. Great video and thanks IITM for bringing this special video for us(the online degree students).

[trisitganguly]

cognitive science is dealing with this aspect of science

[physicsanduniverse]

Sir launch a course on bsc on theoritical physics, please iit Madras 🙏🙏🙏🙏🙏🙏🙏🙏🙏🙏🙏🙏🙏🙏✍✍🙏🙏🙏🙏🙏🙏🙏🙏.

[gokulakrishnanmuthusamy]

Wow really a worth watching one...you have inspired me more toward doing data science as AI runs basically on data analysis.... Good one sir

[damarlaomjayasankar]

if AI will do only specific tasks then what is difference between AI and automated mission..!!

[cutie]

Smjh me kuch aya nhi jyada par dekh ke kuch innovative ideas lag rha tha