Understanding Self Supervised Learning
In the last blog, we discussed the opportunities and risks of foundational models. Foundation models are trained on a broad dataset at scale and are… Read More »Understanding Self Supervised Learning
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ajitjaokar
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.
Opportunities and Risks of Foundation Models
This month, the Centre for Research on foundation models at the University of Stanford published an insightful paper called On the Opportunities and Risks of… Read More »Opportunities and Risks of Foundation Models
Understanding Probabilistic Programming
Even for many data scientists, Probabilistic Programming is a relatively unfamiliar territory. Yet, it is an area fast gaining in importance. In this post, I… Read More »Understanding Probabilistic Programming
What skills would be needed for Telecoms professionals in the 5G world?
Background We all have a mobile phone and, in this sense, we are all consumers of mobile technology. Over the past four decades, mobile… Read More »What skills would be needed for Telecoms professionals in the 5G world?
Charticulator : Creating Interactive Charts Without Code
Background Many areas of AI continue to show innovation at an exciting pace. For example, today, generative methods are on my radar. So, its nice… Read More »Charticulator : Creating Interactive Charts Without Code
When Average Is Not Good Enough – the Future of Data Science Jobs
Editorial Note: Ajit Jaokar’s contribution this week hits home a point that I think it very important. As with any job, it is the value… Read More »When Average Is Not Good Enough – the Future of Data Science Jobs
AI powered cyberattacks – adversarial AI
In the last post, we discussed an outline of AI powered cyber attacks and their defence strategies. In this post, we will discuss a specific… Read More »AI powered cyberattacks – adversarial AI
AI powered cyberattacks – threats and defence
Cyberattacks are on the rise. AI is part of the threat but also part of the solution. Especially, some of the newer AI strategies (such… Read More »AI powered cyberattacks – threats and defence
A must have tool to analyse latest AI research papers fast
Here is a great too for your AI research I feel like a kid in a candy shop! If you are like me, and want… Read More »A must have tool to analyse latest AI research papers fast
Good source of coding puzzles for programming interviews
Here is a paper which gives a set of coding puzzles which could be useful for technical interviews in data science. The paper introduces a… Read More »Good source of coding puzzles for programming interviews