15 dispatches
Despite all the speculation over why Amazon is laying off 14,000 workers, as of this writing, and with a few exceptions I’ll note below, I don’t personally worry a great deal about AI taking away jobs currently. The reason is that while new AI agent based services and products are becoming better at replacing humans at certain tasks: * Many tasks are not automatable —...
Earlier this year, a Chinese company called DeepSeek took the AI world by surprise by releasing an incredibly cheap, performant reasoning model called DeepSeek R1. The result was an outpour of commentary on how Meta’s open sourcing of Llama led to its creation, whether or not the reported lower training cost for DeepSeeek’s model meant the end of NVIDIA’s business, and whether...
Tech strategy is difficult AND fascinating because it’s unpredictable. In addition to worrying about the actions of direct competitors (i.e. Samsung vs Apple), companies need to also worry about the actions of ecosystem players (i.e. smartphones and AI vendors) who may make moves that were intended for something else but have far-reaching consequences.
While Large Language Models (LLMs) have demonstrated they can do many things well enough, it’s important to remember that these are not “thinking machines” so much as impressively competent “writing machines” (able to figure out what words are likely to follow). Case in point: both OpenAI’s ChatGPT and Microsoft Copilot lost to the chess playing engine of an old Atari game...
Inspired by some work from a group at Stanford on building a lab from AI agents, I’ve been experimenting with multi-agent AI conversations and workflows. But, because the space (at least to me) has seemed more focused on building more capable agents rather than coordinating and working with more agents, the existing tools and libraries have made it difficult to carry out fast...
A few months ago, I shared that I had built an AI-powered personalized news reader which I use (and still do) on a near-daily basis. Since that post, I’ve made a couple of major improvements (which I have just reflected in my public Github). Switching to JAX I previously chose Keras 3 for my deep learning algorithm architecture because of its ease of use as well as the...
A recent preprint from Stanford has demonstrated something remarkable: AI agents working together as a team solving a complex scientific challenge. While much of the AI discourse focuses on how individual large language models (LLMs) compare to humans, much of human work today is a team effort, and the right question is less “can this LLM do better than a single human on a...
Anyone who’s done any AI work is familiar with Huggingface. They are a repository of trained AI models and maintainer of AI libraries and services that have helped push forward AI research. It is now considered standard practice for research teams with something to boast to publish their models to Huggingface for all to embrace.
Every standard products company (like NVIDIA) eventually gets lured by the prospect of gaining large volumes and high margins of a custom products business. And every custom products business wishes they could get into standard products to cut their dependency on a small handful of customers and pursue larger volumes.
Market phase transitions have a tendency to be incredibly disruptive to market participants. A company or market segment used to be the “alpha wolf” can suddenly find themselves an outsider in a short time. Look at how quickly Research in Motion (makers of the Blackberry) went from industry darling to laggard after Apple’s iPhone transformed the phone market.
I’m over two months late to seeing this study, but a brilliant study design (use insurance data to measure rate of bodily injury and property damage) and strong, noteworthy conclusion (doesn’t matter how you cut it, Waymo’s autonomous vehicle service resulted in fewer injuries per mile and less property damage per mile than human drivers in the same area) make this worthwhile...
My good friend Danny Goodman (and Co-Founder at Swarm Aero) recently wrote a great essay on how AI can help with America’s defense. He outlines 3 opportunities: * “Affordable mass”: Balancing/augmenting America’s historical strategy of pursuing only extremely expensive, long-lived “exquisite” assets (e.g.
I am a big Google Pixel fan, being an owner and user of multiple Google Pixel line products. As a result, I tuned in to the recent MadeByGoogle stream. While it was hard not to be impressed with the demonstrations of Google’s AI prowess, I couldn’t help but be a little baffled… What was the point of making everything AI-related? Given how low Pixel’s market share is in the...
Unless you’ve been under a rock, you’ll know the tech industry has been rocked by the rapid advance in performance by large language models (LLMs) such as ChatGPT. By adapting self-supervised learning methods, LLMs “learn” to sound like a human being by learning how to fill in gaps in language and, by doing so, become remarkably adept at solving not just language problems but...
How to realize the promise of applying machine learning to healthcare Not going to happen anytime soon, sadly: the Doctor from Star Trek: Voyager); Source: TrekCore Despite the hype, it’ll likely be quite some time before human physicians will be replaced with machines (sorry, Star Trek: Voyager fans)).