An interesting discussion between Patrick Collison and OpenAI founder Sam Altman on a predictably fascinating assortment of subjects. AI developments, stagnation, long-term bets, and what’s preventing us from having more founders.
Byrne Hobart & Tobias Huber:
Over the past half-century, even leading AI researchers completely failed in their predictions of AGI timelines. So, instead of worrying about sci-fi paperclips or Terminator scenarios, we should be more concerned, for example, with all the diseases for which we weren’t able to discover a cure or the scientific breakthroughs that won’t materialize because we’ve prematurely banned AI research and development based on the improbable scenario of a sentient AI superintelligence annihilating humanity.
A great case for dynamism and progress as our best insulation against regulatory capture, risk, and exploitation.
Census GPT is an open source tool put together by a new team working on product applications for GPT-4. It lets you write natural language questions to query the US Census database — things like the cities in Florida with the highest crime. It even shows the SQL output that GPT generates from your query.
Check out the project on GitHub, and join the their Discord to see what other kinds of datasets and use cases they’re tinkering with.
A project from DeepMind designed to fill in missing text from ancient inscriptions:
Pythia takes a sequence of damaged text as input, and is trained to predict character sequences comprising hypothesised restorations of ancient Greek inscriptions (texts written in the Greek alphabet dating between the seventh century BCE and the fifth century CE). The architecture works at both the character- and word-level, thereby effectively handling long-term context information, and dealing efficiently with incomplete word representations (Figure 2). This...
In a conversation yesterday I learned about this project called RapiD, led by Facebook to use computer vision technology to detect features for mapping in OpenStreetMap. They’re working on a fork of the iD editor that does assistive feature detection to present an editor with generated geometries to add to the map. I messed around with it in the test areas they’re supporting so far and it’s a clever combination of computer-assisted detection and human-based mapping. This shows some promise to enhance OSM contributor technology and lower the barrier to entry for new editors.
Most of the popular conversation around intelligence these days (at least in circles I follow) is about the artificial variety — AI, deep learning, neural networks, and the like. Neuroscientists Jeff Hawkins and his company Numenta have been studying intelligence since 2005, but oriented on how the brain itself works. Hawkins’s belief is that true “general AI” won’t be possible at all if we can’t first understand deeply how the brain works.
He recently published a paper on the “Thousand Brains Theory of Intelligence”, which posits that the brain is simultaneously generating predictions on multiple threads from different...
This talk on “generative AI” was interesting. One bit stuck out to me as really thought-provoking:
Dutch designers have created a system to 3D print functional things in-place, like this bridge concept. Imagine that you can place a machine, give it a feed of raw material input and cut it loose to generate something in physical space. As the presenter mentions at the end of the talk, moving from things that are “constructed” to ones that are “grown”.