How Quickly Do Large Language Models Learn Unexpected Skills?
Plus, five links to make you a little bit smarter today.
How Quickly Do Large Language Models Learn Unexpected Skills?
“A new study suggests that so-called emergent abilities actually develop gradually and predictably, depending on how you measure them.”
My Sixth Year as a Bootstrapped Founder
“Six years ago, I quit my job as a developer at Google to create my own bootstrapped software company.
For the first few years, all of my businesses flopped. The best of them earned a few hundred dollars per month in revenue, but none were profitable.
Halfway through my third year, I created a device called TinyPilot. It allows users to control their computers remotely. The product quickly caught on, and it’s been my main focus ever since.”
Alpha-GPT 2.0: Human-in-the-Loop AI for Quantitative Investment
“Recently, we introduced a new paradigm for alpha mining in the realm of quantitative investment, developing a new interactive alpha mining system framework, Alpha-GPT. This system is centered on iterative Human-AI interaction based on large language models, introducing a Human-in-the-Loop approach to alpha discovery.
In this paper, we present the next-generation Alpha-GPT 2.0, a quantitative investment framework that further encompasses crucial modeling and analysis phases in quantitative investment. This framework emphasizes the iterative, interactive research between humans and AI, embodying a Human-in-the-Loop strategy throughout the entire quantitative investment pipeline.
By assimilating the insights of human researchers into the systematic alpha research process, we effectively leverage the Human-in-the-Loop approach, enhancing the efficiency and precision of quantitative investment research.”
A beginner’s guide to making beautiful slides for your talks
“I’ve done quite a bit of conference speaking over the years, and I love designing slides and coming up with a new visual theme for each topic. It’s fun and keeps me motivated to put in the work and actually write my talks. People often ask me for tips and tooling recommendations, so in this guide, I’m sharing some of my not-so-secret secrets and three beginner-friendly steps for how you can up your slides game for the upcoming conference season!”
Reor Project
“Reor is an AI-powered desktop note-taking app: it automatically links related ideas, answers questions on your notes and provides semantic search. Everything is stored locally and you can edit your notes with an Obsidian-like markdown editor.
The hypothesis of the project is that AI tools for thought should run models locally by default. Reor stands on the shoulders of the giants Llama.cpp, Transformers.js & LanceDB to enable both LLMs and embedding models to run locally. (Connecting to OpenAI-compatible APIs like Oobabooga is also supported.)”