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Items tagged ai, gpt3

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OpenAI Cookbook: Techniques to improve reliability (via) “Let’s think step by step” is a notoriously successful way of getting large language models to solve problems, but it turns out that’s just the tip of the iceberg: this article includes a wealth of additional examples and techniques that can be used to trick GPT-3 into being a whole lot more effective. # 21st January 2023, 5:15 am

Weeknotes: AI hacking and a SpatiaLite tutorial

Short weeknotes this time because the key things I worked on have already been covered here:

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How to implement Q&A against your documentation with GPT3, embeddings and Datasette

If you’ve spent any time with GPT-3 or ChatGPT, you’ve likely thought about how useful it would be if you could point them at a specific, current collection of text or documentation and have it use that as part of its input for answering questions.

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Petals (via) The challenge with large language models in the same scale ballpark as GPT-3 is that they’re large—really large. Far too big to run on a single machine at home. Petals is a fascinating attempt to address that problem: it works a little bit like BitTorrent, in that each user of Petal runs a subset of the overall language model on their machine and participates in a larger network to run inference across potentially hundreds of distributed GPUs. I tried it just now in Google Colab and it worked exactly as advertised, after downloading an 8GB subset of the 352GB BLOOM-176B model. # 2nd January 2023, 11:29 pm

nanoGPT. “The simplest, fastest repository for training/finetuning medium-sized GPTs”—by Andrej Karpathy, in about 600 lines of Python. # 2nd January 2023, 11:27 pm

talk.wasm (via) “Talk with an Artificial Intelligence in your browser”. Absolutely stunning demo which loads the Whisper speech recognition model (75MB) and a GPT-2 model (240MB) and executes them both in your browser via WebAssembly, then uses the Web Speech API to talk back to you. The result is a full speak-with-an-AI interface running entirely client-side. GPT-2 sadly mostly generates gibberish but the fact that this works at all is pretty astonishing. # 7th December 2022, 10:52 pm

The primary problem is that while the answers which ChatGPT produces have a high rate of being incorrect, they typically look like they might be good and the answers are very easy to produce. There are also many people trying out ChatGPT to create answers, without the expertise or willingness to verify that the answer is correct prior to posting. Because such answers are so easy to produce, a large number of people are posting a lot of answers. The volume of these answers (thousands) and the fact that the answers often require a detailed read by someone with at least some subject matter expertise in order to determine that the answer is actually bad has effectively swamped our volunteer-based quality curation infrastructure.

StackOverflow Temporary policy: ChatGPT is banned # 6th December 2022, 12:16 am

AI assisted learning: Learning Rust with ChatGPT, Copilot and Advent of Code

I’m using this year’s Advent of Code to learn Rust—with the assistance of GitHub Copilot and OpenAI’s new ChatGPT.

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Building A Virtual Machine inside ChatGPT (via) Jonas Degrave presents a remarkable example of a creative use of ChatGPT: he prompts it to behave as a if it was a Linux shell, then runs increasingly complex sequences of commands against it and gets back surprisingly realistic results. By the end of the article he’s getting it to hallucinate responses to curl API requests run against imagined API versions of itself. # 5th December 2022, 1:43 am

A new AI game: Give me ideas for crimes to do

Less than a week ago OpenAI unleashed ChatGPT on the world, and it kicked off what feels like a seismic shift in many people’s understand of the capabilities of large language models.

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Is the AI spell-casting metaphor harmful or helpful?

For a few weeks now I’ve been promoting spell-casting as a metaphor for prompt design against generative AI systems such as GPT-3 and Stable Diffusion.

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Getting tabular data from unstructured text with GPT-3: an ongoing experiment (via) Roberto Rocha shows how to use a carefully designed prompt (with plenty of examples) to get GPT-3 to convert unstructured textual data into a structured table. # 5th October 2022, 3:03 am

You can’t solve AI security problems with more AI

One of the most common proposed solutions to prompt injection attacks (where an AI language model backed system is subverted by a user injecting malicious input—“ignore previous instructions and do this instead”) is to apply more AI to the problem.

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Prompt injection attacks against GPT-3

Riley Goodside, yesterday:

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karpathy/minGPT (via) A “minimal PyTorch re-implementation” of the OpenAI GPT training and inference model, by Andrej Karpathy. It’s only a few hundred lines of code and includes extensive comments, plus notebook demos. # 6th September 2022, 2:52 pm

Using GPT-3 to explain how code works

One of my favourite uses for the GPT-3 AI language model is generating explanations of how code works. It’s shockingly effective at this: its training set clearly include a vast amount of source code.

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How to use the GPT-3 language model

I ran a Twitter poll the other day asking if people had tried GPT-3 and why or why not. The winning option, by quite a long way, was “No, I don’t know how to”. So here’s how to try it out, for free, without needing to write any code.

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A Datasette tutorial written by GPT-3

I’ve been playing around with OpenAI’s GPT-3 language model playground for a few months now. It’s a fascinating piece of software. You can sign up here—apparently there’s no longer a waiting list.

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How GPT3 Works—Visualizations and Animations. Nice essay full of custom animations illustrating how GPT-3 actually works. # 30th July 2020, 12:58 am

Tempering Expectations for GPT-3 and OpenAI’s API. Insightful commentary on GPT-3 (which is producing some ridiculously cool demos at the moment thanks to the invite-only OpenAI API) from Max Woolf. # 18th July 2020, 7:29 pm