Simon Willison’s Weblog

229 items tagged “ai”


Building LLM applications for production. Chip Huyen provides a useful, in-depth review of the challenges involved in taking an app built on top of a LLM from prototype to production, including issues such as prompt ambiguity and unpredictability, cost and latency concerns, challenges in testing and updating to new models. She also lists some promising use-cases she’s seeing for categories of application built on these tools. # 14th April 2023, 3:35 pm

The Great Flowering: Why OpenAI is the new AWS and the New Kingmakers still matter (via) James Governor discusses the potential impact of AI-assisted productivity on the wider software engineering industry, and calls me “a bellwether”! # 13th April 2023, 7:20 pm

Before we scramble to deeply integrate LLMs everywhere in the economy, can we pause and think whether it is wise to do so?

This is quite immature technology and we don’t understand how it works.

If we’re not careful we’re setting ourselves up for a lot of correlated failures.

Jan Leike, Alignment Team lead, OpenAI # 13th April 2023, 7:08 pm

Free Dolly: Introducing the World’s First Truly Open Instruction-Tuned LLM (via) Databricks released a large language model called Dolly a few weeks ago. They just released Dolly 2.0 and it is MUCH more interesting—it’s an instruction tuned 12B parameter upgrade of EleutherAI’s Pythia model. Unlike other recent instruction tuned models Databricks didn’t use a training set derived from GPT-3—instead, they recruited 5,000 employees to help put together 15,000 human-generated request/response pairs, which they have released under a Creative Commons Attribution-ShareAlike license. The model itself is a 24GB download from Hugging Face—I’ve run it slowly on a small GPU-enabled Paperspace instance, but hopefully optimized ways to run it will emerge in short order. # 13th April 2023, 2:19 am

Graphic designers had a similar sea change ~20-25 years ago.

Flyers, restaurant menus, wedding invitations, price lists... That sort of thing was bread and butter work for most designers. Then desktop publishing happened and a large fraction of designers lost their main source of income as the work shifted to computer assisted unskilled labor.

The field still thrives today, but that simple work is gone forever.

Janne Moren # 12th April 2023, 3:28 am

Running Python micro-benchmarks using the ChatGPT Code Interpreter alpha

Today I wanted to understand the performance difference between two Python implementations of a mechanism to detect changes to a SQLite database schema. I rendered the difference between the two as this chart:

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I literally lost my biggest and best client to ChatGPT today. This client is my main source of income, he’s a marketer who outsources the majority of his copy and content writing to me. Today he emailed saying that although he knows AI’s work isn’t nearly as good as mine, he can’t ignore the profit margin. [...] Please do not think you are immune to this unless you are the top 1% of writers. I just signed up for Doordash as a driver. I really wish I was kidding.

u/Ashamed_Apricot6626 # 11th April 2023, 6:20 pm

The AI singularity is here. Can’t say I’m a fan of the headline, but the subhead “The time to figure out how to use generative AI and large language models in your code is now” is much more illustrative of the story. I’m referred to in this one as “One of the most outspoken advocates for LLM-enhanced development” which is a bit of a surprise! # 10th April 2023, 7:17 pm

AI is flooding the workplace, and workers love it. The microwave kiln pottery project I helped Natalie with gets a mention in this story about people who are putting AI tools to use. # 10th April 2023, 7:15 pm

Thoughts on AI safety in this era of increasingly powerful open source LLMs

This morning, VentureBeat published a story by Sharon Goldman: With a wave of new LLMs, open source AI is having a moment — and a red-hot debate. It covers the explosion in activity around openly available Large Language Models such as LLaMA—a trend I’ve been tracking in my own series LLMs on personal devices—and talks about their implications with respect to AI safety.

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The Changelog podcast: LLMs break the internet

I’m the guest on the latest episode of The Changelog podcast: LLMs break the internet. It’s a follow-up to the episode we recorded six months ago about Stable Diffusion.

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The progress in AI has allowed things like taking down hate speech more efficiently—and this is due entirely to large language models. Because we have large language models [...] we can do a better job than we ever could in detecting hate speech in most languages in the world. That was impossible before.

Yann LeCun # 7th April 2023, 7:32 pm

We need to tell people ChatGPT will lie to them, not debate linguistics

ChatGPT lies to people. This is a serious bug that has so far resisted all attempts at a fix. We need to prioritize helping people understand this, not debating the most precise terminology to use to describe it.

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For example, if you prompt GPT-3 with “Mary had a,” it usually completes the sentence with “little lamb.” That’s because there are probably thousands of examples of “Mary had a little lamb” in GPT-3’s training data set, making it a sensible completion. But if you add more context in the prompt, such as “In the hospital, Mary had a,” the result will change and return words like “baby” or “series of tests.”

Benj Edwards # 7th April 2023, 3:36 am

Why ChatGPT and Bing Chat are so good at making things up. I helped review this deep dive by Benj Edwards for Ars Technica into the hallucination/confabulation problem with ChatGPT and other LLMs, which is attracting increasing attention thanks to stories like the recent defamation complaints against ChatGPT. This article explains why this is happening and talks to various experts about potential solutions. # 7th April 2023, 3:33 am

Projectories have power. Power for those who are trying to invent new futures. Power for those who are trying to mobilize action to prevent certain futures. And power for those who are trying to position themselves as brokers, thought leaders, controllers of future narratives in this moment of destabilization. But the downside to these projectories is that they can also veer way off the railroad tracks into the absurd. And when the political, social, and economic stakes are high, they can produce a frenzy that has externalities that go well beyond the technology itself. That is precisely what we’re seeing right now.

danah boyd # 7th April 2023, 2:04 am

[On AI-assisted programming] I feel like I got a small army of competent hackers to both do my bidding and to teach me as I go. It’s just pure delight and magic.

It’s riding a bike downhill and playing with legos and having a great coach and finishing a project all at once.

Matt Bateman # 5th April 2023, 11:50 pm

Blinded by Analogies (via) Ethan Mollick discusses how many of the analogies we have for AI right now are hurting rather than helping our understanding, particularly with respect to LLMs. # 5th April 2023, 5 am

My guess is that MidJourney has been doing a massive-scale reinforcement learning from human feedback (“RLHF”)—possibly the largest ever for text-to-image.

When human users choose to upscale an image, it’s because they prefer it over the alternatives. It’d be a huge waste not to use this as a reward signal—cheap to collect, and *exactly* aligned with what your user base wants.

The more users you have, the better RLHF you can do. And then the more users you gain.

Jim Fan # 5th April 2023, 4:45 am

More capable models can better recognize the specific circumstances under which they are trained. Because of this, they are more likely to learn to act as expected in precisely those circumstances while behaving competently but unexpectedly in others. This can surface in the form of problems that Perez et al. (2022) call sycophancy, where a model answers subjective questions in a way that flatters their user’s stated beliefs, and sandbagging, where models are more likely to endorse common misconceptions when their user appears to be less educated.

Sam Bowman # 5th April 2023, 3:44 am

Eight Things to Know about Large Language Models (via) This unpublished paper by Samuel R. Bowman is succinct, readable and dense with valuable information to help understand the field of modern LLMs. # 5th April 2023, 3:36 am

Scaling laws allow us to precisely predict some coarse-but-useful measures of how capable future models will be as we scale them up along three dimensions: the amount of data they are fed, their size (measured in parameters), and the amount of computation used to train them (measured in FLOPs). [...] Our ability to make this kind of precise prediction is unusual in the history of software and unusual even in the history of modern AI research. It is also a powerful tool for driving investment since it allows R&D teams to propose model-training projects costing many millions of dollars, with reasonable confidence that these projects will succeed at producing economically valuable systems.

Sam Bowman # 5th April 2023, 3:32 am

From Deep Learning Foundations to Stable Diffusion. Brand new free online video course from Jeremy Howard: 30 hours of content, covering everything you need to know to implement the Stable Diffusion image generation algorithm from scratch. I previewed parts of this course back in December and it was fascinating: this field is moving so fast that some of the lectures covered papers that had been released just a few days before. # 5th April 2023, 1:13 am

ROOTS search tool (via) BLOOM is one of the most interesting completely openly licensed language models. The ROOTS corpus is the training data that was collected for it, and this tool lets you run searches directly against that corpus. I tried searching for my own name and got an interesting insight into what it knows about me. # 3rd April 2023, 8:40 pm

Closed AI Models Make Bad Baselines (via) The NLP academic research community are facing a tough challenge: the state-of-the-art in large language models, GPT-4, is entirely closed which means papers that compare it to other models lack replicability and credibility. “We make the case that as far as research and scientific publications are concerned, the “closed” models (as defined below) cannot be meaningfully studied, and they should not become a “universal baseline”, the way BERT was for some time widely considered to be.”

Anna Rogers proposes a new rule for this kind of research: “That which is not open and reasonably reproducible cannot be considered a requisite baseline.” # 3rd April 2023, 7:57 pm

Beyond these specific legal arguments, Stability AI may find it has a “vibes” problem. The legal criteria for fair use are subjective and give judges some latitude in how to interpret them. And one factor that likely influences the thinking of judges is whether a defendant seems like a “good actor.” Google is a widely respected technology company that tends to win its copyright lawsuits. Edgier companies like Napster tend not to.

Timothy B. Lee # 3rd April 2023, 3:38 pm

Stable Diffusion copyright lawsuits could be a legal earthquake for AI. Timothy B. Lee provides a thorough discussion of the copyright lawsuits currently targeting Stable Diffusion and GitHub Copilot, including subtle points about how the interpretation of “fair use” might be applied to the new field of generative AI. # 3rd April 2023, 3:34 pm

Think of language models like ChatGPT as a “calculator for words”

One of the most pervasive mistakes I see people using with large language model tools like ChatGPT is trying to use them as a search engine.

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What AI can do for you on the Theory of Change podcast

Matthew Sheffield invited me on his show Theory of Change to talk about how AI models like ChatGPT, Bing and Bard work and practical applications of things you can do with them.

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You’ll often find prompt engineers come from a history, philosophy, or English language background, because it’s wordplay. You’re trying to distill the essence or meaning of something into a limited number of words.

Albert Phelps # 31st March 2023, 5:54 pm