Jeremy Howard Profile picture
Jun 17, 2024 10 tweets 4 min read Read on X
I've done a deep dive into SB 1047 over the last few weeks, and here's what you need to know:

*Nobody* should be supporting this bill in its current state. It will *not* actually cover the largest models, nor will it actually protect open source.

But it can be easily fixed!🧵
This is important, so don't just read this thread, instead read the 6000+ word article I just published.

In the article I explain how AI *actually* works, and why these details totally break legislation like SB 1047. Policy makers *need* to know this:
answer.ai/posts/2024-06-…
SB 1047 does not cover "base models". But these are the models where >99% of compute is used. By not covering these models, the bill will probably actually not cover any models at all.

(There are also dozens of trivial workarounds for anyone wanting to train uncovered models.) Image
If the "influence physical or virtual environments" constraint is removed then the impact would be to make development of open source AI models larger than the covered threshold impossible.

However, the stated aims of the bill are to ensure open source developers *can* comply. Image
Thankfully, the issues in SB 1047 can all easily be fixed by legislating the deployment of “AI Systems” and not legislating the release of “AI Models”. Image
Regulating the deployment of services, instead of the release of models, would not impact big tech at all, since they rarely (if ever) release large models.

So the big tech companies would be just as covered as before, and open source would be protected. Image
If we can't fine-tune open sourced models, then we'll be stuck with whatever values and aims the model creators had. Chinese propaganda is a very real current example of this issue (and remember that the best current open source models are Chinese).
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I don't propose that we exempt AI from regulation. However, we should be careful to regulate with an understanding of the delicate balance between control and centralization, vs transparency and access, as we've done with other technologies throughout history. Image
Instead of "p(doom)", let's consider "p(salvation)" too, and bring a new concept to the AI safety discussion:

“Human Existential Enhancement Factor” (HEEF): the degree to which AI enhances our ability to overcome existential threats and ensure our long-term well-being.
If you care about open source AI model development, then submit your views here, where they will be sent to the authors and appear on the public record:
calegislation.lc.ca.gov/Advocates/

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More from @jeremyphoward

May 24
Lotta people in the comments claiming that this actually makes perfect sense if you know the original (道德經 / 道德经 / Dào Dé Jīng).

These people are wrong.

If you *actually* know the original, you'll see how bad this is.🧵 Image
Here is the full original: daodejing.org .

I'm not sure there's any super great translations, but here's an English version that's perhaps good enough. with.org/tao_te_ching_e…
Here's the Chinese of the verse the bit I quoted is based on:
"天下皆知美之为美,斯恶已;皆知善之为善,斯不善已。故有无相生"
Read 11 tweets
Mar 29
I'm glad @levelsio checked this, but sad our contrib has been erased by later big tech co's. Alec Radford said ULMFiT inspired GPT. ULMFiT's first demo predated BERT.

Today's 3-stage LLM approach of general corpus pretraining and 2 stages of fine-tuning was pioneered by ULMFiT.
There have been many other important contributions, including attention (Bahdanau et al), transformers, RLHF, etc.

But before all this, basically everyone in NLP assumed that each new domain needed a new model. ULMFiT showed that a large pretrained model was actually the key.
I got push-back from pretty much everyone about this. My claim that fine-tuning that model was the critical step to achieving success in NLP was not something people were ready to hear at that time.

I gave many talks trying to convince academics to pursue this direction.
Read 5 tweets
Mar 18
Announcing fasttransform: a Python lib that makes data transformations reversible/extensible. No more writing inverse functions to see what your model sees. Debug pipelines by actually looking at your data.

Built on multi-dispatch. Work w/ @R_Dimm
fast.ai/posts/2025-02-…
We took the `Transform` class out of fastcore, replaced the custom type dispatch system with @ikwess's plum-dispatch, mixed it all together, and voila: fasttransform! :D

To learn about fasttransform, check out our detailed blog post.
fast.ai/posts/2025-02-…
"Manual inspection of data has probably the highest value-to-prestige ratio of any activity in machine learning." --@gdb

Yet we often skip it because it's painful. How do you inspect what your model sees after normalization, resizing & other transforms?
Read 10 tweets
Feb 5
Wow, actual grown men are still doing the "I asked the LLM about itself and it said" thing.

In 2025.

Folks, LLMs don't know anything about how they themselves are built or deployed, unless they've been explicitly programmed with that information (which they almost never are). Image
If you are using a Chinese cloud based service hosted in China, then your data will be sent to a server in China.

Because that's how computers work.

It's not a conspiracy.
I hadn't heard of this guy before, but apparently he'll teach you how to learn AI.

Cool. Image
Read 4 tweets
Jan 25
I've recently been surprised to discover that a few of my friends are choosing to use nicotine to help them with focus, even though they are not ex-smokers.

I decided to look into it, and it turns out that there are documented health benefits of nicotine for some people. 🧵
I specifically looked into nicotine for ADHD, since, at least among children, ADHD and giftedness go hand in hand statistically (which would apply in adulthood too), and because focus was mention as an area where nicotine can be helpful.
There is a great overview below. But "Very surprisingly, there are… no further… studies.

Research into active ingredients… is expensive.

In addition, nicotine has a very poor image… which impairs its marketability"
adxs.org/en/page/192/ni…
Read 12 tweets
Dec 19, 2024
I'll get straight to the point.

We trained 2 new models. Like BERT, but modern. ModernBERT.

Not some hypey GenAI thing, but a proper workhorse model, for retrieval, classification, etc. Real practical stuff.

It's much faster, more accurate, longer context, and more useful. 🧵 Image
ModernBERT is available as a slot-in replacement for any BERT-like model, with both 139M param and 395M param sizes.

It has a 8192 sequence length, is extremely efficient, is uniquely great at analyzing code, and much more. Read this for details:
huggingface.co/blog/modernbert
Seven months ago, @bclavie kicked things off, and soon @benjamin_warner & @antoine_chaffin joined him as project co-leads. I don't think anyone quite knew what we were getting in to…

It turns out that training a new, SoTA model from scratch is actually pretty hard. Who knew? 🤷 Image
Read 23 tweets

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