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AI chatbots are intruding into online communities where people are trying to connect with other humans (via) This thing where Facebook are experimenting with AI bots that reply in a group when someone "asks a question in a post and no one responds within an hour" is absolute grade A slop - unwanted, unreviewed AI generated text that makes the internet a worse place.
The example where Meta AI replied in an education forum saying "I have a child who is also 2e and has been part of the NYC G&T program" is inexcusable.
A Link Blog in the Year 2024 (via) Kellan Elliott-McCrea has started a new link blog:
Like many people I’ve been dealing with the collapses of the various systems I relied on for information over the previous decades. After 17 of using Twitter daily and 24 years of using Google daily neither really works anymore. And particular with the collapse of the social spaces many of us grew up with, I feel called back to earlier forms of the Internet, like blogs, and in particular, starting a link blog.
I've been leaning way more into link blogging over the last few months, especially now my own link blog supports markdown. This means I'm posting longer entries, somewhat inspired by Daring Fireball (my own favourite link blog to read).
Link blogging is a pleasantly low-pressure way of writing online. Found something interesting? Post a link to it, with a sentence or two about why it's worth checking out.
I'd love to see more people embrace this form of personal publishing.
Tree.js interactive demo
(via)
Daniel Greenheck's interactive demo of his procedural tree generator (as in vegetation) built with Three.js. This is really fun to play with - there are 30+ tunable parameters and you can export your tree as a .glb file for import into tools like Blender or Unity.
Claude’s Character (via) There's so much interesting stuff in this article from Anthropic on how they defined the personality for their Claude 3 model. In addition to the technical details there are some very interesting thoughts on the complex challenge of designing a "personality" for an LLM in the first place.
Claude 3 was the first model where we added "character training" to our alignment finetuning process: the part of training that occurs after initial model training, and the part that turns it from a predictive text model into an AI assistant. The goal of character training is to make Claude begin to have more nuanced, richer traits like curiosity, open-mindedness, and thoughtfulness.
But what other traits should it have? This is a very difficult set of decisions to make! The most obvious approaches are all flawed in different ways:
Adopting the views of whoever you’re talking with is pandering and insincere. If we train models to adopt "middle" views, we are still training them to accept a single political and moral view of the world, albeit one that is not generally considered extreme. Finally, because language models acquire biases and opinions throughout training—both intentionally and inadvertently—if we train them to say they have no opinions on political matters or values questions only when asked about them explicitly, we’re training them to imply they are more objective and unbiased than they are.
The training process itself is particularly fascinating. The approach they used focuses on synthetic data, and effectively results in the model training itself:
We trained these traits into Claude using a "character" variant of our Constitutional AI training. We ask Claude to generate a variety of human messages that are relevant to a character trait—for example, questions about values or questions about Claude itself. We then show the character traits to Claude and have it produce different responses to each message that are in line with its character. Claude then ranks its own responses to each message by how well they align with its character. By training a preference model on the resulting data, we can teach Claude to internalize its character traits without the need for human interaction or feedback.
There's still a lot of human intervention required, but significantly less than more labour-intensive patterns such as Reinforcement Learning from Human Feedback (RLHF):
Although this training pipeline uses only synthetic data generated by Claude itself, constructing and adjusting the traits is a relatively hands-on process, relying on human researchers closely checking how each trait changes the model’s behavior.
The accompanying 37 minute audio conversation between Amanda Askell and Stuart Ritchie is worth a listen too - it gets into the philosophy behind designing a personality for an LLM.
Expanding on how Voice Engine works and our safety research. Voice Engine is OpenAI's text-to-speech (TTS) model. It's not the same thing as the voice mode in the GPT-4o demo last month - Voice Engine was first previewed on September 25 2023 as the engine used by the ChatGPT mobile apps. I also used the API version to build my ospeak CLI tool.
One detail in this new explanation of Voice Engine stood out to me:
In November of 2023, we released a simple TTS API also powered by Voice Engine. We chose another limited release where we worked with professional voice actors to create 15-second audio samples to power each of the six preset voices in the API.
This really surprised me. I knew it was possible to get a good voice clone from a short snippet of audio - see my own experiments with ElevenLabs - but I had assumed the flagship voices OpenAI were using had been trained on much larger samples. Hiring a professional voice actor to produce a 15 second sample is pretty wild!
This becomes a bit more intuitive when you learn how the TTS model works:
The model is not fine-tuned for any specific speaker, there is no model customization involved. Instead, it employs a diffusion process, starting with random noise and progressively de-noising it to closely match how the speaker from the 15-second audio sample would articulate the text.
I had assumed that OpenAI's models were fine-tuned, similar to ElevenLabs. It turns out they aren't - this is the TTS equivalent of prompt engineering, where the generation is entirely informed at inference time by that 15 second sample. Plus the undocumented vast quantities of generic text-to-speech training data in the underlying model.
OpenAI are being understandably cautious about making this capability available outside of a small pool of trusted partners. One of their goals is to encourage the following:
Phasing out voice based authentication as a security measure for accessing bank accounts and other sensitive information
A Picture is Worth 170 Tokens: How Does GPT-4o Encode Images? (via) Oran Looney dives into the question of how GPT-4o tokenizes images - an image "costs" just 170 tokens, despite being able to include more text than could be encoded in that many tokens by the standard tokenizer.
There are some really neat tricks in here. I particularly like the experimental validation section where Oran creates 5x5 (and larger) grids of coloured icons and asks GPT-4o to return a JSON matrix of icon descriptions. This works perfectly at 5x5, gets 38/49 for 7x7 and completely fails at 13x13.
I'm not convinced by the idea that GPT-4o runs standard OCR such as Tesseract to enhance its ability to interpret text, but I would love to understand more about how this all works. I imagine a lot can be learned from looking at how openly licensed vision models such as LLaVA work, but I've not tried to understand that myself yet.
Update on the Recall preview feature for Copilot+ PCs (via) This feels like a very good call to me: in response to widespread criticism Microsoft are making Recall an opt-in feature (during system onboarding), adding encryption to the database and search index beyond just disk encryption and requiring Windows Hello face scanning to access the search feature.
lsix
(via)
This is pretty magic: an ls style tool which shows actual thumbnails of every image in the current folder, implemented as a Bash script.
To get this working on macOS I had to update to a more recent Bash (brew install bash) and switch to iTerm2 due to the need for a Sixel compatible terminal.
Extracting Concepts from GPT-4. A few weeks ago Anthropic announced they had extracted millions of understandable features from their Claude 3 Sonnet model.
Today OpenAI are announcing a similar result against GPT-4:
We used new scalable methods to decompose GPT-4’s internal representations into 16 million oft-interpretable patterns.
These features are "patterns of activity that we hope are human interpretable". The release includes code and a paper, Scaling and evaluating sparse autoencoders paper (PDF) which credits nine authors, two of whom - Ilya Sutskever and Jan Leike - are high profile figures that left OpenAI within the past month.
The most fun part of this release is the interactive tool for exploring features. This highlights some interesting features on the homepage, or you can hit the "I'm feeling lucky" button to bounce to a random feature. The most interesting I've found so far is feature 5140 which seems to combine God's approval, telling your doctor about your prescriptions and information passed to the Admiralty.
This note shown on the explorer is interesting:
Only 65536 features available. Activations shown on The Pile (uncopyrighted) instead of our internal training dataset.
Here's the full Pile Uncopyrighted, which I hadn't seen before. It's the standard Pile but with everything from the Books3, BookCorpus2, OpenSubtitles, YTSubtitles, and OWT2 subsets removed.
My Twitter thread figuring out the AI features in Microsoft’s Recall. I posed this question on Twitter about why Microsoft Recall (previously) is being described as "AI":
Is it just that the OCR uses a machine learning model, or are there other AI components in the mix here?
I learned that Recall works by taking full desktop screenshots and then applying both OCR and some sort of CLIP-style embeddings model to their content. Both the OCRd text and the vector embeddings are stored in SQLite databases (schema here, thanks Daniel Feldman) which can then be used to search your past computer activity both by text but also by semantic vision terms - "blue dress" to find blue dresses in screenshots, for example. The si_diskann_graph table names hint at Microsoft's DiskANN vector indexing library
A Microsoft engineer confirmed on Hacker News that Recall uses on-disk vector databases to provide local semantic search for both text and images, and that they aren't using Microsoft's Phi-3 or Phi-3 Vision models. As far as I can tell there's no LLM used by the Recall system at all at the moment, just embeddings.
An animated introduction to Fourier Series (via) Outstanding essay and collection of animated explanations (created using p5.js) by Andrei Ciobanu explaining Fourier transforms, starting with circles, pi, radians and building up from there.
I found Fourier stuff only really clicked for me when it was accompanied by clear animated visuals, and these are a beautiful example of those done really well.
Zoom CEO envisions AI deepfakes attending meetings in your place. I talked to Benj Edwards for this article about Zoom's terrible science-fiction concept to have "digital twins" attend meetings in your behalf:
When we specifically asked Simon Willison about Yuan's comments about digital twins, he told Ars, "My fundamental problem with this whole idea is that it represents pure AI science fiction thinking—just because an LLM can do a passable impression of someone doesn't mean it can actually perform useful 'work' on behalf of that person. LLMs are useful tools for thought. They are terrible tools for delegating decision making to. That's currently my red line for using them: any time someone outsources actual decision making authority to an opaque random number generator is a recipe for disaster."
Encryption At Rest: Whose Threat Model Is It Anyway? (via) Security engineer Scott Arciszewski talks through the challenges of building a useful encryption-at-rest system for hosted software. Encryption at rest on a hard drive protects against physical access to the powered-down disk and little else. To implement encryption at rest in a multi-tenant SaaS system - such that even individuals with insider access (like access to the underlying database) are unable to read other user's data, is a whole lot more complicated.
Consider an attacker, Bob, with database access:
Here’s the stupid simple attack that works in far too many cases: Bob copies Alice’s encrypted data, and overwrites his records in the database, then accesses the insurance provider’s web app [using his own account].
The fix for this is to "use the AAD mechanism (part of the standard AEAD interface) to bind a ciphertext to its context." Python's cryptography package covers Authenticated Encryption with Associated Data as part of its "hazardous materials" advanced modules.
How do I opt into full text search on Mastodon? (via) I missed this new Mastodon feature when it was released in 4.2.0 last September: you can now opt-in to a new setting which causes all of your future posts to be marked as allowed to be included in the Elasticsearch index provided by Mastodon instances that enable search.
It only applies to future posts because it works by adding an "indexable" flag to those posts, which can then be obeyed by other Mastodon instances that the post is syndicated to.
You can turn it on for your own account from the /settings/privacy page on your local instance.
The release notes for 4.2.0 also mention new search operators:
from:me,before:2022-11-01,after:2022-11-01,during:2022-11-01,language:fr,has:poll, orin:library(for searching only in posts you have written or interacted with)
A tip from Neal Stephenson (via) Twelve years ago on Reddit user bobbylox asked Neal Stephenson (in an AMA):
My ultimate goal in life is to make the Primer real. Anything you want to make sure I get right?
Referencing the Young Lady's Illustrated Primer from Neal's novel The Diamond Age. Stephenson replied:
Kids need to get answers from humans who love them.
(A lot of people in the AI space are taking inspiration from the Primer right now.)
GPT-2 five years later. Jack Clark, now at Anthropic, was a researcher at OpenAI five years ago when they first trained GPT-2.
In this fascinating essay Jack revisits their decision not to release the full model, based on their concerns around potentially harmful ways that technology could be used.
(Today a GPT-2 class LLM can be trained from scratch for around $20, and much larger models are openly available.)
There's a saying in the financial trading business which is 'the market can stay irrational longer than you can stay solvent' - though you might have the right idea about something that will happen in the future, your likelihood of correctly timing the market is pretty low. There's a truth to this for thinking about AI risks - yes, the things we forecast (as long as they're based on a good understanding of the underlying technology) will happen at some point but I think we have a poor record of figuring out a) when they'll happen, b) at what scale they'll happen, and c) how severe their effects will be. This is a big problem when you take your imagined future risks and use them to justify policy actions in the present!
As an early proponent of government regulation around training large models, he offers the following cautionary note:
[...] history shows that once we assign power to governments, they're loathe to subsequently give that power back to the people. Policy is a ratchet and things tend to accrete over time. That means whatever power we assign governments today represents the floor of their power in the future - so we should be extremely cautious in assigning them power because I guarantee we will not be able to take it back.
Jack stands by the recommendation from the original GPT-2 paper for governments "to more systematically monitor the societal impact and diffusion of AI technologies, and to measure the progression in the capabilities of such systems."
DuckDB 1.0 (via) Six years in the making. The most significant feature in this milestone is stability of the file format: previous releases often required files to be upgraded to work with the new version.
This release also aspires to provide stability for both the SQL dialect and the C API, though these may still change with sufficient warning in the future.
A look at Apple’s new Transformer-powered predictive text model. Jack Cook reverse engineered the tiny LLM used for the predictive text keyboard in the latest iOS. It appears to be a GPT-2 style custom model with 34M parameters and a 15,000 token vocabulary.
Katherine Michel’s PyCon US 2024 Recap (via) An informative write-up of this year’s PyCon US conference. It’s rare to see conference retrospectives with this much detail, this one is great!
Experimenting with local alt text generation in Firefox Nightly (via) The PDF editor in Firefox (confession: I did not know Firefox ships with a PDF editor) is getting an experimental feature that can help suggest alt text for images for the human editor to then adapt and improve on.
This is a great application of AI, made all the more interesting here because Firefox will run a local model on-device for this, using a custom trained model they describe as "our 182M parameters model using a Distilled version of GPT-2 alongside a Vision Transformer (ViT) image encoder".
The model uses WebAssembly with ONNX running in Transfomers.js, and will be downloaded the first time the feature is put to use.
How (some) good corporate engineering blogs are written (via) Dan Luu interviewed engineers from Cloudflare, Heap, and Segment—three companies with excellent technical blogs—and three other unnamed companies with blogs he categorized as lame.
His conclusion? The design of the process for publishing—most notable the speed and number of approvals needed to get something published—makes all the difference.
Stealing everything you’ve ever typed or viewed on your own Windows PC is now possible with two lines of code — inside the Copilot+ Recall disaster (via) Recall is a new feature in Windows 11 which takes a screenshot every few seconds, runs local device OCR on it and stores the resulting text in a SQLite database. This means you can search back through your previous activity, against local data that has remained on your device.
The security and privacy implications here are still enormous because malware can now target a single file with huge amounts of valuable information:
During testing this with an off the shelf infostealer, I used Microsoft Defender for Endpoint — which detected the off the shelve infostealer — but by the time the automated remediation kicked in (which took over ten minutes) my Recall data was already long gone.
I like Kevin Beaumont's argument here about the subset of users this feature is appropriate for:
At a surface level, it is great if you are a manager at a company with too much to do and too little time as you can instantly search what you were doing about a subject a month ago.
In practice, that audience’s needs are a very small (tiny, in fact) portion of Windows userbase — and frankly talking about screenshotting the things people in the real world, not executive world, is basically like punching customers in the face.
Man caught in scam after AI told him fake Facebook customer support number was legitimate (via) This one illustrates a nasty edge-case if you ship any kind of chatbot with your company's name attached to it.
The scam victim here searched Google for a customer support phone number for Facebook, and came across a suspicious looking phone number.
He pasted that number into the Meta AI chatbot in Facebook Messenger and asked "is it a Facebook help line?" - and a Meta AI answered:
The phone number 1-xxx-xxx-xxxx is indeed a legitimate Facebook support number. Meta, the company that owns Facebook, lists this number as a contact for Meta Support, which includes support for Facebook, Instagram, and more.
This was a total hallucination, and the phone number was for a scammer who ran a classic "download this app so I can help you" attack.
It doesn't matter how many disclaimers you add to a chatbot: this kind of misunderstanding from users is inevitable.
Django Enhancement Proposal 14: Background Workers. Jake Howard's DEP has been approved and is moving into the implementation stage.
Django doesn't have a first-party solution for long-running tasks, however the ecosystem is filled with incredibly popular frameworks, all of which interact with Django in slightly different ways. Other frameworks such as Laravel have background workers built-in, allowing them to push tasks into the background to be processed at a later date, without requiring the end user to wait for them to occur. [...]
This proposal sets out to provide an interface and base implementation for long-running background tasks in Django.
Jake has an illustrative reference implementation called django-tasks.
Why, after 6 years, I’m over GraphQL (via) I've seen many of these criticisms of GraphQL before - N+1 queries, the difficulty of protecting against deeply nested queries - but Matt Bessey collects them all in one place and adds an issue I hadn't considered before: the complexity of authorization, where each field in the query might involve extra permission checks:
In my experience, this is actually the biggest source of performance issues. We would regularly find that our queries were spending more time authorising data than anything else.
The 600+ comment Hacker News thread is crammed with GraphQL war stories, mostly supporting the conclusions of the article.
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.
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.
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.
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.
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.