Actually Useful AI

Hello there, fellow lemmings!

ChatGPT—for all its remarkable prowess in textually generating material “like” what it’s read from the web, etc.—can’t itself be expected to do actual nontrivial computations, or to systematically produce correct (rather than just “looks roughly right”) data, etc. But when it’s connected to the Wolfram plugin it can do these things.

I recently built a web scraper / website to track changes in Burger King prices. I also made a video where I try to describe how I used ChatGPT in this process and how useful it was to me.

Wanted to share this one because I find myself using it every day—often in place of Google. You can even somewhat replace Siri with it using the app’s Apple Shortcuts:

Trove of combos is >45 times larger than number unearthed in entire history of science.

If you’re just wanting to run LLMs quickly on your computer in the command line, this is about as simple as it gets. Ollama provides an easy CLI to generate text, and there’s also a Raycast extension for more powerful usage.

Found this online tool to try out a bunch of different text2image models for free. No signup needed. Resolution is fixed to 512x512, but otherwise it’s pretty versatile for being free

Interesting article I've found, thought others might find it interesting as well.

I was aiming to use LLMs with robotics in an upcoming project, and needed to first verse myself in what is the current must-know techniques in the space. To that end I read a ton of papers and wrote this article to try and suss out the best parts of current state of the art.

You can find it here. Source code in the link.

In this project, the aim was to enhance the quality of an audio clip featuring Vinod Chanda from the show Pantheon, recorded during the COVID-19 pandemic when voice actors faced limitations due to quarantine. To address the challenge of improving the dialogue's quality, a possible approach utilizing AI-based voice conversion was explored. By isolating Chanda's dialogues from the first episode and employing voice cloning technology, a model of the actor's voice was created using retrieval-based voice conversion (RVC). The model was then utilized to convert the initial audio into an enhanced version, effectively restoring the dialogue's quality. While there are limitations to the method, such as challenges in capturing all nuances of the original voice and handling multiple voices simultaneously, this approach holds promise for restoring old or poorly recorded audio. The possibilities for applying this technique extend to various media forms, sparking potential future developments in audio restoration for movies and other creative projects.

Intelligence explosion arguments don’t require Platonism. They just require intelligence to exist in the normal fuzzy way that all concepts exist.

At OpenAI, protecting user data is fundamental to our mission. We do not train our models on inputs and outputs through our API.

We’re rolling out custom instructions to give you more control over how ChatGPT responds. Set your preferences, and ChatGPT will keep them in mind for all future conversations.

GPT-3.5 and GPT-4 are the two most widely used large language model (LLM) services. However, when and how these models are updated over time is opaque. Here, we evaluate the March 2023 and June 2023 versions of GPT-3.5 and GPT-4 on four diverse tasks: 1) solving math problems, 2) answering sensitive/dangerous questions, 3) generating code and 4) visual reasoning. We find that the performance and behavior of both GPT-3.5 and GPT-4 can vary greatly over time. For example, GPT-4 (March 2023) was very good at identifying prime numbers (accuracy 97.6%) but GPT-4 (June 2023) was very poor on these same questions (accuracy 2.4%). Interestingly GPT-3.5 (June 2023) was much better than GPT-3.5 (March 2023) in this task. GPT-4 was less willing to answer sensitive questions in June than in March, and both GPT-4 and GPT-3.5 had more formatting mistakes in code generation in June than in March. Overall, our findings shows that the behavior of the “same” LLM service can change substantially in a relatively short amount of time, highlighting the need for continuous monitoring of LLM quality.

Using generative AI (like ChatGPT) in business improves users’ performance by 66%, averaged across 3 case studies. More complex tasks have bigger gains, and less-skilled workers benefit the most from AI use.