AI Resources – Part 1

AI + Music, Images, or Video

• Scribble Diffusion: Turn your sketch into a refined image using AI

• Dall-E Party: Recursively generate an image with DALL-E 3, describe it with GPT4 Vision, use that description with DALL-E 3, …

• People think white AI-generated faces are more real than actual photos, study says – Attractiveness and “averageness” of AI-generated faces made them seem more real to the study participants, while the large variety of proportions in actual faces seemed unreal.

• Frigate: Monitor your security cameras with locally processed AI.

• Script that takes pics using your webcam and describes you like David Attenborough using GPT-4 Vision and ElevenLabs. Worth watching the demo video.

• Introducing Stable Video Diffusion – The first foundation model for generative video based on the image model Stable Diffusion.

• Meta brings us closer to AI-generated movies: Given a caption, image or a photo paired with a description, Emu Video can generate a 4 second animated clip. A complimentary tool can then edit those clips using natural language- “the same clip, but in slow motion.”

• New music model from Google DeepMind: “With our music AI tools, users can create new music or instrumental sections from scratch, transform audio from one music style or instrument to another, and create instrumental and vocal accompaniments.” A limited set of creators will also be able to generate a unique soundtrack in the voice and style of participating artists like Charlie Puth, Demi Lovato, Sia, T-Pain, and more.

LLMs cannot find reasoning errors, but can correct them!
Paper in which the authors break down the self-correction process into two core components: mistake finding and output correction. They find that LLMs generally struggle with finding logical mistakes, but for output correction, they propose a backtracking method which provides large improvements when given information on mistake location.

Outset is using GPT-4 to make user surveys better
YC-backed Outset uses GPT-4 to autonomously conduct and synthesize user surveys. Outset users create a survey and share the link with prospective survey takers, then Outset follows up with respondents to clarify, probe on answers and create a “conversational rapport” for deeper responses. Outset enabled WeightWatchers to conduct and synthesize over 100 interviews in 24 hours.

AI Explained had a nice series of videos about it:

Re: the new OpenAI board: “Altman was unwilling to talk to anyone he didn’t already know. By Sunday, it became clear that Altman wanted a board composed of a majority of people who would let him get his way.”

“One person who has worked closely with Altman described a pattern of consistent and subtle manipulation that sows division between individuals.”

“A former OpenAI employee, machine learning researcher Geoffrey Irving, who now works at competitor Google DeepMind, wrote that he was disinclined to support Altman after working for him for two years. “1. He was always nice to me. 2. He lied to me on various occasions 3. He was deceptive, manipulative, and worse to others, including my close friends (again, only nice to me, for reasons).””

Exclusive: OpenAI researchers warned board of AI breakthrough ahead of CEO ouster, sources say
Supposedly several staff researchers at OpenAI wrote a letter to the board of directors a warning of a powerful AI discovery that could threaten humanity. Allegedly there was a project, Q*, that was able to solve certain math problems, implying it might have great reasoning capabilities than just predicting the next word. This could be applied to novel scientific research, for instance.

This may have been what Sam Altman meant when he said being in the room “where we push the veil of ignorance back and the frontier of discovery forward.”

OpenAI’s Misalignment and Microsoft’s Gain
Stratechery deep dive on the implications of OpenAI’s non-profit model and governance situation, internal cultural dynamics at OpenAI, Microsoft’s role, Altman’s reputation, and thoughts going forward.