I first heard Ethan Mollick on The Ezra Klein Show in April 2024 (“How Should I Be Using A.I. Right Now?”). He offered sensible, practical ways to use AI without the hype. Shortly after, I read Co-Intelligence and have followed his writing and talks since. In a recent interview with Sana Labs founder and CEO Joel Hellermark, Mollick argued that the traditional apprenticeship path is shifting in...
Dharmesh's post made me realize there’s a name for something I’ve been doing implicitly for a while—using AI to help me write better prompts. Strictly speaking, that’s AI-assisted prompt refinement. There’s a closely related idea called metaprompting—writing prompts that generate other prompts—which also makes a real difference, especially for deeper research. These omniscient models have been...
It's easy to feel the immense transformational capacity of Generative AI as a solution. And healthcare has no shortage of problems to solve. The real insight is in figuring out viable application areas and use cases. Things are becoming a bit clearer in that aspect and it's worthwhile to keep an ongoing list of where Gen AI application makes sense in healthcare. This post is always under...
Large Language Models (LLMs) like ChatGPT can handle two aspects of communication very well: plausibility and fluency. Given an input context they determine what are the most probable sequence of words and string them in a way that is superbly eloquent. That makes the output very convincing. But it's no secret that LLMs can provide entirely false outputs - they can confabulate. Not hallucinate...
A few months ago my curiosity around how-are-LLMs-'learning' took me down the rabbit hole of AI and Psychology history and I ended up finding a string of very interesting and related developments from the last 120 years: 1905: Harvard-graduate psychologist Edward L. Thorndike published his 'Law of Effect' which basically says that animal behaviors are shaped by consequences. That is, behaviors...
The first 10 minutes of this Stanford CS224N lecture explained NLU, NLG and the tasks within and it was helpful. Natural Language Understanding (NLU) is a subset of NLP (processing), which uses syntactic and semantic analysis of text and speech to determine the meaning of a sentence. Natural Language Generation (NLG) is another subset of NLP focusing on the process of producing a human language...
As explained in this Stanford CS50 tech talk, Language Models (LMs) are basically a probability distribution over some vocabulary. For every word we give an LM, it can determine what the most probable word to come after that. It's trained to predict the Nth word, given the previous N-1 words. If that sounds like simple probability calculation, you are not realizing that predicting the next word...
I recently heard Jeff Bezos briefly talk about his views on LLMs here. Less than a minute into the conversation, he said something that struck a chord with me: LLMs in their current form are not inventions, they are discoveries. He followed that up with "we are constantly surprised by their capabilities and they are not really engineered objects". That quote resonated because that we-don't-know...