It was exactly 2 years ago (Nov,2022) that I had written about Software 2.0 (term coined by Andrej Karpathy) and I had argued that we should expect a rapid and meaningful shift from systems performing deterministic tasks (think ERP, RPA etc.) to learning, adaptive systems where the "source code comprises of a 1/neural net architecture... Continue Reading →
#76: Evaluating LLM-based Natural Language interfaces for Databases
I have always enjoyed Thanksgiving family get togethers because among other things, gives me a chance to catch up with what the next generation is up to - always get to learn a thing or two from them. And this time, enjoyed talking to my niece's husband who thinks very deeply about data privacy (working... Continue Reading →
#75: GenAI Data Strategy: High-quality Training data
There is a lot of talk about a data strategy for Generative AI. And rightfully so - as companies will continue to find out ways to leverage enterprise data assets to better ground the Foundation Models (FMs). Earlier this year, I and a couple of my colleagues had written a blogpost on data governance for... Continue Reading →
#74: Enterprise Gen AI: it is all about the data
The Scaling Laws have formed the basis for the development efforts of the various companies building Foundation Models. Simplistically put, the models get better with model size, dataset size, and the amount of compute used for training, and by and large, this has held across all the FMs. After the astonishingly rapid development in the... Continue Reading →
#73: The Value of Data
In enterprise data circles, it is a well-known statistic that less than 30% of companies claim to have been successful in becoming a data driven organization. What is not so easy is to figure out why companies have such a hard time getting actionable insights from data – in my experience consulting with many companies,... Continue Reading →