May 2024
May 19, 2024
Spam, junk … slop? The latest wave of AI behind the ‘zombie internet’. I'm quoted in this piece in the Guardian about slop:
I think having a name for this is really important, because it gives people a concise way to talk about the problem.
Before the term ‘spam’ entered general use it wasn’t necessarily clear to everyone that unwanted marketing messages were a bad way to behave. I’m hoping ‘slop’ has the same impact – it can make it clear to people that generating and publishing unreviewed AI-generated content is bad behaviour.
May 20, 2024
CRDT: Text Buffer (via) Delightfully short and clear explanation of the CRDT approach to collaborative text editing by Evan Wallace (of Figma and esbuild fame), including a neat interactive demonstration of how the algorithm works even when the network connection between peers is temporarily paused.
Last September, I received an offer from Sam Altman, who wanted to hire me to voice the current ChatGPT 4.0 system. He told me that he felt that by my voicing the system, I could bridge the gap between tech companies and creatives and help consumers to feel comfortable with the seismic shift concerning humans and AI. He said he felt that my voice would be comforting to people. After much consideration and for personal reasons, I declined the offer.
May 21, 2024
Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet (via) Big advances in the field of LLM interpretability from Anthropic, who managed to extract millions of understandable features from their production Claude 3 Sonnet model (the mid-point between the inexpensive Haiku and the GPT-4-class Opus).
Some delightful snippets in here such as this one:
We also find a variety of features related to sycophancy, such as an empathy / “yeah, me too” feature 34M/19922975, a sycophantic praise feature 1M/847723, and a sarcastic praise feature 34M/19415708.
New Phi-3 models: small, medium and vision. I couldn't find a good official announcement post to link to about these three newly released models, but this post on LocalLLaMA on Reddit has them in one place: Phi-3 small (7B), Phi-3 medium (14B) and Phi-3 vision (4.2B) (the previously released model was Phi-3 mini - 3.8B).
You can try out the vision model directly here, no login required. It didn't do a great job with my first test image though, hallucinating the text.
As with Mini these are all released under an MIT license.
UPDATE: Here's a page from the newly published Phi-3 Cookbook describing the models in the family.
May 22, 2024
Mastering LLMs: A Conference For Developers & Data Scientists (via) I’m speaking at this 5-week (maybe soon 6-week) long online conference about LLMs, presenting about “LLMs on the command line”.
Other speakers include Jeremy Howard, Sophia Yang from Mistral, Wing Lian of Axolotl, Jason Liu of Instructor, Paige Bailey from Google, my former co-worker John Berryman and a growing number of fascinating LLM practitioners.
It’s been fun watching this grow from a short course on fine-tuning LLMs to a full-blown multi-week conference over the past few days!
The default prefix used to be "sqlite_". But then Mcafee started using SQLite in their anti-virus product and it started putting files with the "sqlite" name in the c:/temp folder. This annoyed many windows users. Those users would then do a Google search for "sqlite", find the telephone numbers of the developers and call to wake them up at night and complain. For this reason, the default name prefix is changed to be "sqlite" spelled backwards.
What is prompt optimization? (via) Delightfully clear explanation of a simple automated prompt optimization strategy from Jason Liu. Gather a selection of examples and build an evaluation function to return a numeric score (the hard bit). Then try different shuffled subsets of those examples in your prompt and look for the example collection that provides the highest averaged score.
May 23, 2024
The most effective mechanism I’ve found for rolling out No Wrong Door is initiating three-way conversations when asked questions. If someone direct messages me a question, then I will start a thread with the question asker, myself, and the person I believe is the correct recipient for the question. This is particularly effective because it’s a viral approach: rolling out No Wrong Door just requires any one of the three participants to adopt the approach.
May 24, 2024
A Grand Unified Theory of the AI Hype Cycle. Glyph outlines the pattern of every AI hype cycle since the 1960s: a new, novel mechanism is discovered and named. People get excited, and non-practitioners start hyping it as the path to true “AI”. It eventually becomes apparent that this is not the case, even while practitioners quietly incorporate this new technology into useful applications while downplaying the “AI” branding. A new mechanism is discovered and the cycle repeats.
But increasingly, I’m worried that attempts to crack down on the cryptocurrency industry — scummy though it may be — may result in overall weakening of financial privacy, and may hurt vulnerable people the most. As they say, “hard cases make bad law”.
Some goofy results from ‘AI Overviews’ in Google Search. John Gruber collects two of the best examples of Google’s new AI overviews going horribly wrong.
Gullibility is a fundamental trait of all LLMs, and Google’s new feature apparently doesn’t know not to parrot ideas it picked up from articles in the Onion, or jokes from Reddit.
I’ve heard that LLM providers internally talk about “screenshot attacks”—bugs where the biggest risk is that someone will take an embarrassing screenshot.
In Google search’s case this class of bug feels like a significant reputational threat.
The leader of a team - especially a senior one - is rarely ever the smartest, the most expert or even the most experienced.
Often it’s the person who can best understand individuals’ motivations and galvanize them towards an outcome, all while helping them stay cohesive.
I just left Google last month. The "AI Projects" I was working on were poorly motivated and driven by this panic that as long as it had "AI" in it, it would be great. This myopia is NOT something driven by a user need. It is a stone cold panic that they are getting left behind.
The vision is that there will be a Tony Stark like Jarvis assistant in your phone that locks you into their ecosystem so hard that you'll never leave. That vision is pure catnip. The fear is that they can't afford to let someone else get there first.
Nilay Patel reports a hallucinated ChatGPT summary of his own article (via) Here's a ChatGPT bug that's a new twist on the old issue where it would hallucinate the contents of a web page based on the URL.
The Verge editor Nilay Patel asked for a summary of one of his own articles, pasting in the URL.
ChatGPT 4o replied with an entirely invented summary full of hallucinated details.
It turns out The Verge blocks ChatGPT's browse mode from accessing their site in their robots.txt:
User-agent: ChatGPT-User
Disallow: /
Clearly ChatGPT should reply that it is unable to access the provided URL, rather than inventing a response that guesses at the contents!
Golden Gate Claude. This is absurdly fun and weird. Anthropic's recent LLM interpretability research gave them the ability to locate features within the opaque blob of their Sonnet model and boost the weight of those features during inference.
For a limited time only they're serving a "Golden Gate Claude" model which has the feature for the Golden Gate Bridge boosted. No matter what question you ask it the Golden Gate Bridge is likely to be involved in the answer in some way. Click the little bridge icon in the Claude UI to give it a go.
I asked for names for a pet pelican and the first one it offered was this:
Golden Gate - This iconic bridge name would be a fitting moniker for the pelican with its striking orange color and beautiful suspension cables.
And from a recipe for chocolate covered pretzels:
Gently wipe any fog away and pour the warm chocolate mixture over the bridge/brick combination. Allow to air dry, and the bridge will remain accessible for pedestrians to walk along it.
UPDATE: I think the experimental model is no longer available, approximately 24 hours after release. We'll miss you, Golden Gate Claude.
May 25, 2024
Why Google’s AI might recommend you mix glue into your pizza. I got “distrust and verify” as advice on using LLMs into this Washington Post piece by Shira Ovide.
May 26, 2024
Statically Typed Functional Programming with Python 3.12 (via) Oskar Wickström builds a simple expression evaluator that demonstrates some new patterns enabled by Python 3.12, incorporating the match operator, generic types and type aliases.
City In A Bottle – A 256 Byte Raycasting System (via) Frank Force explains his brilliant 256 byte canvas ray tracing animated cityscape demo in detail.
May 27, 2024
fastlite (via) New Python library from Jeremy Howard that adds some neat utility functions and syntactic sugar to my sqlite-utils Python library, specifically for interactive use in Jupyter notebooks.
The autocomplete support through newly exposed dynamic properties is particularly neat, as is the diagram(db.tables) utility for rendering a graphviz diagram showing foreign key relationships between all of the tables.
May 28, 2024
Pyodide 0.26 Release (via) PyOdide provides Python packaged for browser WebAssembly alongside an ecosystem of additional tools and libraries to help Python and JavaScript work together.
The latest release bumps the Python version up to 3.12, and also adds support for pygame-ce, allowing games written using pygame to run directly in the browser.
The PyOdide community also just landed a 14-month-long PR adding support to cibuildwheel, which should make it easier to ship binary wheels targeting PyOdide.
Reproducing GPT-2 (124M) in llm.c in 90 minutes for $20 (via) GPT-2 124M was the smallest model in the GPT-2 series released by OpenAI back in 2019. Andrej Karpathy's llm.c is an evolving 4,000 line C/CUDA implementation which can now train a GPT-2 model from scratch in 90 minutes against a 8X A100 80GB GPU server. This post walks through exactly how to run the training, using 10 billion tokens of FineWeb.
Andrej notes that this isn't actually that far off being able to train a GPT-3:
Keep in mind that here we trained for 10B tokens, while GPT-3 models were all trained for 300B tokens. [...] GPT-3 actually didn't change too much at all about the model (context size 1024 -> 2048, I think that's it?).
Estimated cost for a GPT-3 ADA (350M parameters)? About $2,000.
Weeknotes: PyCon US 2024
Earlier this month I attended PyCon US 2024 in Pittsburgh, Pennsylvania. I gave an invited keynote on the Saturday morning titled “Imitation intelligence”, tying together much of what I’ve learned about Large Language Models over the past couple of years and making the case that the Python community has a unique opportunity and responsibility to help try to nudge this technology in a positive direction.
[... 474 words]May 29, 2024
Sometimes the most creativity is found in enumerating the solution space. Design is the process of prioritizing tradeoffs in a high dimensional space. Understand that dimensionality.
What We Learned from a Year of Building with LLMs (Part I). Accumulated wisdom from six experienced LLM hackers. Lots of useful tips in here. On providing examples in a prompt:
If n is too low, the model may over-anchor on those specific examples, hurting its ability to generalize. As a rule of thumb, aim for n ≥ 5. Don’t be afraid to go as high as a few dozen.
There's a recommendation not to overlook keyword search when implementing RAG - tricks with embeddings can miss results for things like names or acronyms, and keyword search is much easier to debug.
Plus this tip on using the LLM-as-judge pattern for implementing automated evals:
Instead of asking the LLM to score a single output on a Likert scale, present it with two options and ask it to select the better one. This tends to lead to more stable results.
Training is not the same as chatting: ChatGPT and other LLMs don’t remember everything you say
I’m beginning to suspect that one of the most common misconceptions about LLMs such as ChatGPT involves how “training” works.
[... 1,543 words]In their rush to cram in “AI” “features”, it seems to me that many companies don’t actually understand why people use their products. [...] Trust is a precious commodity. It takes a long time to build trust. It takes a short time to destroy it.
May 30, 2024
Codestral: Hello, World! Mistral's first code-specific model, trained to be "fluent" in 80 different programming languages.
The weights are released under a new Mistral AI Non-Production License, which is extremely restrictive:
3.2. Usage Limitation
- You shall only use the Mistral Models and Derivatives (whether or not created by Mistral AI) for testing, research, Personal, or evaluation purposes in Non-Production Environments;
- Subject to the foregoing, You shall not supply the Mistral Models or Derivatives in the course of a commercial activity, whether in return for payment or free of charge, in any medium or form, including but not limited to through a hosted or managed service (e.g. SaaS, cloud instances, etc.), or behind a software layer.
To Mistral's credit at least they don't misapply the term "open source" in their marketing around this model - they consistently use the term "open-weights" instead. They also state that they plan to continue using Apache 2 for other model releases.
Codestral can be used commercially when accessed via their paid API.
The realization hit me [when the GPT-3 paper came out] that an important property of the field flipped. In ~2011, progress in AI felt constrained primarily by algorithms. We needed better ideas, better modeling, better approaches to make further progress. If you offered me a 10X bigger computer, I'm not sure what I would have even used it for. GPT-3 paper showed that there was this thing that would just become better on a large variety of practical tasks, if you only trained a bigger one. Better algorithms become a bonus, not a necessity for progress in AGI. Possibly not forever and going forward, but at least locally and for the time being, in a very practical sense. Today, if you gave me a 10X bigger computer I would know exactly what to do with it, and then I'd ask for more.
What does the public in six countries think of generative AI in news? (via) Fascinating survey by the Reuters Institute for the Study of Journalism at Oxford that asked ~12,000 people across six countries for their opinions on AI usage in journalism.
It’s also being interpreted as evidence that few members of the general public actually use these tools, because the opening survey questions ask about personal usage.
I don’t think the numbers support that narrative, personally. For survey participants in the USA 7% used ChatGPT daily and 11% used it weekly, which is higher than I would expect for those frequencies. For the UK those were 2% daily and 7% weekly.
The 18-24 group were the heaviest users of these tools. Lots of other interesting figures to explore.