When we say deep learning models operate via memorization, the claim isn't that they work like literal lookup tables, only being able to make sense of points that are exactly part of their training data. No one has claimed that -- it wouldn't even be true of linear regression.

Basically brain surgery on Neural Nets 🤯 Also another example that shows that interpretability is possible. I'm tired of the "black box" rethoric, when the box is quite literally transparent. Sure it's not easy, but definitely possible.

I know it's flogging a dead horse, but I thought this recurring debate would make a good launching point for a series of posts on why it's reasonable to question the quantum nature of gravity. So I started a Substack - follow along for more! open.substack.com/pub/superposer…

So, debanking.

Dan is correct! Many arguments or experiments which purport to show that spacetime is quantum, construct strawmen theories which no one believes could be fundamental. 1/

A pattern recognition / system 1 model, called in a loop to guide a search process, was always our best shot at creating systems that can reason. It's the whole idea behind deep learning guided program synthesis -- use system 1 intuition to drive a system 2 search routine.

The very minor news of the day (while oNs are whooshing past us): prompt engineering is dead. Good. 😅

A potential mathematical explanation for polarisation, and with a hopeful conclusion as an added bonus! Of course, my prior is to want to believe this to be true so... 😅

Vibe coding rocks. I've been doing it too and just seeing a project evolve at 10 times the speed it used to is incredible. Doesn't mean you should deploy the result in prod without checking what it does ofc but it's incredible to get started or build a prototype.

"A calculator app? Anyone could make that." Not true. A calculator should show you the result of the mathematical expression you entered. That's much, much harder than it sounds. What I'm about to tell you is the greatest calculator app development story ever told.

Some people today are discouraging others from learning programming on the grounds AI will automate it. This advice will be seen as some of the worst career advice ever given. I disagree with the Turing Award and Nobel prize winner who wrote, “It is far more likely that the

(1/5) 💥🚀 Big launch day! Fabien Devos and I are excited to share what we've been cooking for the past few months! We're buiding "Lovable for internal apps". We come with pre-built building blocks: Integrations, SSO and AI tools so anyone can build / launch internal, demo:

Vybe (Vybe (YC X25)) is building Lovable for internal apps. Vybe comes with pre-built building blocks: integrations, SSO, and AI tools, so anyone in a company can build and launch internal apps. ycombinator.com/launches/NZO-v… Congrats on the launch, Quang HOANG and @fabien_devos!

I used Vybe to vibe code an internal app for Orange Collective in 5 minutes - here’s a screenshot. It’s a company tracker for the latest YC batch (using mock data, but I’m connecting it to our own internal data on Supabase, APIs, apps we use like Notion/GSuite, etc.)

Training on random strings -- interesting methodology here, and neat quantified findings

Intelligence isn't a collection of skills. It's the efficiency with which you acquire and deploy new skills. It's an efficiency ratio. And that's why benchmark scores can be very misleading about the actual intelligence of AI systems.