Based in London, Ajit's work spans research, entrepreneurship, and academia relating to artificial intelligence (AI) with Cyber-Physical systems.
He is the course director of the course: Artificial Intelligence: Cloud and Edge Implementations at the University of Oxford.
He is also a visiting fellow in Engineering Sciences at the University of Oxford.
Besides this, he also conducts the University of Oxford courses: Digital Twins, Cybseecurity, and Agtech.
Ajit works as a Data Scientist through his company, feynlabs - focusing on building innovative early-stage AI prototypes for complex AI applications.
Besides the University of Oxford, Ajit has also conducted AI courses at the London School of Economics (LSE), Universidad Politécnica de Madrid (UPM), and as part of The Future Society at the Harvard Kennedy School of Government.
As an application domain for IoT, smart cities initially showed promise but have struggled over the last decade. However, Smart islands (instead of smart cities) may be good for the success of the Internet of Things.
The issue is not just the actual multiplication but the fastest method to perform the multiplication. The speeding up of matrix multiplication calculations has a high impact because matrix multiplication is a part of many applications – especially in deep learning and image processing.
At first impression, humanoid robots do not have much in common with autonomous cars. But both are autonomous devices. Hence, lessons learned in creating autonomous cars could be applied to developing autonomous robots.
The Cloud has been a dominant paradigm over the last decade but is now attracting regulatory scrutiny.
In the UK and the EU, there have been calls to explore regulation and competitive position in the Cloud.
The quintessential example of a digital twin is the wind turbine. A digital twin is a real-time virtual representation of a real-world physical system or process that serves as its digital counterpart of it. Like all models or abstractions, a twin is created for practical purposes i.e. we wish to model a physical system of a phenomenon to understand it better.