If you think the space of AI product development and funding has been a frenzy, you ain’t seen nothing yet. Years ago (apparently 10 years ago back in 2015!?) I read a post by Jerry Neumann ("The Deployment Age") that really resonated for understanding how major technology shifts evolve and the dynamics behind it. That … Continue reading AI Frenzy
There’s a well recognized dynamic in business of the pendulum of bundling and unbundling services and products. Innovators unbundle specific capabilities of products and focus on doing it better and in a new way. Later, companies then bring these focused products together in a process of bundling, creating better bundles than the previous ones because … Continue reading AI Unbundling Jobs
Recently rereading David Deutsch’s Beginning of Infinity and the discussion of Kenneth Arrow’s Impossibility Theorem in the chapter “Choices” got me thinking of some implications for AI (subject of a future post) and also brought to mind an older work from my undergrad days involving another of the Impossibility Theorems involving the characteristics of simply-defined … Continue reading Revisiting Gödel, Science, and Reality
About a month ago I started down the rabbit hole of what I see as a new frontier in computing, Thermodynamic Computing. This new frontier promises to bring the physics of thermodynamics in new computer hardware to efficiently run the set of algorithms that are built on principles of thermodynamics. Coincidentally, the week after I … Continue reading A New Frontier – Thermodynamic Computing
This post extends the series of code interpretation challenges for Generative AI for the new Generative AI Chat service HuggingChat from HuggingFace. They didn't announce any particular support for coding or code interpretation, but I figured it would be interesting to see how it did on day 1. Previous parts in the series gave the … Continue reading Coding Challenge for Generative AI, Part 4: HuggingChat
This post continues the series of coding challenges to Generative AI technologies by putting the challenge to Google's Bard service, after Google announced adding the ability to Bard for it to code. Part 1 of the series looked at the performance of the GPT-3.5 model and part 2 looked at the performance of the GPT-4 … Continue reading Coding Challenge for Generative AI, Part 3: Google Bard
This continues the theme from Part 1 where I explored how ChatGPT with the GPT-3.5 model would do with a code interpretation challenge. It was somewhat impressive, but with obvious shortcomings. So how did the GPT-4 model do? Session Start What will be the result?c = [False if int(i) % 2 != 0 else True … Continue reading Coding Challenge for Generative AI, Part 2: GPT-4
I've used coding challenges in the past as part of the hiring process, so why not throw one at the first generation of potentially useful Generative AI and judge how it does? I happened on a simple challenge that someone had posted at work in a Slack channel for people learning Python that is both … Continue reading Coding Challenge for Generative AI, Part 1: GPT-3.5
For all the talk about how to track progress on goals (cough OKRs) there seems to be much less said about figuring out goals in the first place. Over the last couple years I've been adopting SWOT thinking as a key piece of this puzzle. It's not a complex process, but it brings together looking … Continue reading Thoughts on SWOT
It’s been 10 years since Chris Dixon’s post on “Techies and normals” described a few ways products can penetrate markets (in order of preference): Techies-first, Normals-only, and Techies-only. Enough has changed in the last 10 years that I think it’s time to think about it differently if you’re looking at maximizing the market for a … Continue reading Thinking about Techies and Normals 10 years on
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