India’s semiconductor push has gathered pace in recent years, but according to IIIT Hyderabad Director Prof Sandeep K Shukla, IIT Madras Director Professor V Kamakoti has been working towards indigenous processor design long before it became a national priority. “Prof Kamakoti started working on the SHAKTI processor – which is a RISC-V architecture-based processor, back in the early 2000s, and I recall that his team worked with Bluespec Inc to write the micro-architectural specification of the processor in Bluespec language,” Prof Shukla told India Today. Reflecting on Prof Kamakoti’s role in taking the project from the laboratory to the wider ecosystem, Prof Shukla said SHAKTI represents more than a successful processor project. Rather, he said, it demonstrates how long-term academic research can eventually translate into real-world impact. “Prof V Kamakoti’s role is a good case study in how long-horizon academic research reaches deployment,” he said.

A few years ago, understanding AI meant knowing what a neural network was. Today it means having shipped a production RAG pipeline, fine-tuned a model under latency constraints, or debugged an agentic workflow at 2 a.m. The bar for ‘AI-literate’ hasn’t just risen. It has completely changed shape, and it keeps changing every few months. For working professionals, this raises a practical question. How do you build this expertise without stepping away from a demanding career, through a program the industry actually trusts? IIIT Hyderabad’s PG Certification Program in Artificial Intelligence and Machine Learning has spent nearly a decade answering that question, not as a new entrant testing the waters, but as one of the most established AI and ML programs for working professionals. Over 28 batches, the program has empowered professionals to build applied AI and machine learning capabilities while adapting to industry shifts.

Researchers at the International Institute of Information Technology (IIIT-Hyderabad), in association with peers from some other organisations, have identified a “sweet spot” in brain-artificial intelligence similarity studies. Their study reveals that a smaller language model can achieve brain alignment comparable to larger models, offering a more efficient, accessible approach to advanced AI. Smaller, efficient AI models are highly significant for India. They require less computational power, allowing researchers and developers to deploy advanced AI solutions on smaller instruments or mobile phones, overcoming limitations in access to massive cloud or GPU infrastructure. Subba Reddy Oota, Vijay Rowtula, and S Bapi Raju of IIIT-H presented the findings of the paper “Linguistic properties and model scale in brain encoding: from small to compressed language models” at the ICML in Seoul.