What Biden's EO means for AI openness, and why a compute threshold is unlikely to effectively anticipate individual models' riskiness, but may work in aggregate

Good news on paper, but the devil is in the details — The Biden-Harris administration has issued an executive order on artificial intelligence.

AI Snake Oil 2023-11-01

Context & Ripple Effects

The order was framed elsewhere as a middle path: AI development could continue while the government built safety, privacy, and worker-protection standards through agencies including NIST and DHS. That makes the treatment of openness and model-risk measurement central, rather than a peripheral implementation detail.

Earlier coverage also anticipated assessments before federal workers could use models; later federal guidance required agencies to designate AI oversight leaders and report annually. The immediate policy arc is from broad executive direction toward operational accountability inside federal agencies.

First-order effects

A compute threshold gives regulators a visible, administrable trigger for scrutiny, but it cannot reliably distinguish the risk of one individual model from another; developers can be swept into—or remain outside—a category that does not map cleanly to their model’s behavior.
The order puts model developers and federal AI users under a shared policy signal: progress will be monitored while agencies develop standards, following the directive to build AI safety and privacy standards.

Second-order effects

Developers have an incentive to document model capabilities, evaluations, and deployment context if compute alone is an insufficient proxy for risk; that shifts compliance attention toward evidence of actual system behavior.
Federal buyers and agencies may increasingly favor suppliers that can support assessment and oversight processes, extending the earlier proposed pre-use assessment approach beyond a simple hardware-scale screen.

Third-order effects

If compute triggers remain a first-pass filter, AI governance is likely to evolve toward layered assurance: broad monitoring at the aggregate level, supplemented by model- and use-specific evaluation where decisions carry greater consequence.
The durable tension is governed openness: policymakers may seek visibility into frontier development without making a coarse infrastructure metric the sole determinant of which systems face constraints.

The trend: AI policy is moving from headline-grabbing compute thresholds toward operational governance that combines broad monitoring with evidence tied to particular models and uses.

Discussion

@moskov Dustin Moskovitz on threads
This has always been the answer to the cynical view that AI safety activists are just lying in the service of regulatory capture: the actual training runs that matter for safety already require *enormous* amounts of capital, so only apply to companies that definitionally have a t…
@sriramk Sriram Krishnan on x
Two observations on open source AI - distinct vibe shift last couple of weeks (now with the EO) on the active threat to open source AI imminently here. - we now have a LOT of credible voices jumping into the fray, waking up to what's going on. See today: @AndrewYNg joining...
@mozilla @mozilla on x
The future of #AI governance is here. Help us shape AI for the benefit of people, not just corporations. Join a global community of policymakers, engineers, and more in ensuring that AI remains open and accessible to all ➡️ https://open.mozilla.org/letter
@dan_jeffries1 Daniel Jeffries on x
Get this letter signed if you care about open source AI and stand against doomsday fear mongering and regulatory capture by a small group of companies who want to put a choke hold on the future of AI. https://open.mozilla.org/letter/
@matthew_d_green Matthew Green on x
Nothing wrong with any of this regulation but it sure seems to rely on some strong assumptions about centralization.
@drjwrae Jack Rae on x
I wonder if this whole thing will be like speed limits, “sorry officer I didn't realize the flop counter was at 1.9e26 I was just momentarily distracted from being an exemplary member of society”
@soumithchintala Soumith Chintala on x
Regulation starts at roughly two orders of magnitude larger than a ~70B Transformer trained on 2T tokens — which is ~5e24. Note: increasing the size of the dataset OR the size of the transformer increases training flops. The (rumored) size of GPT-4 is regulated.
@sriramk Sriram Krishnan on x
Illegal floating point operation takes on a whole new meaning here.
@krishnanrohit Rohit on x
In this house we believe you should not regulate technology without knowing what precisely you are regulating or why
@tszzl Roon on x
a high compute datacenter is a summoning platform for machine spirits. their power is obvious and undeniable
@suhail @suhail on x
One day we will have the equivalent of the gpu compute Azure has in an iPhone and this regulation will seem comical to our children.
@canadakaz Kaz Nejatian on x
All attempts at AI regulation thus far have been either a) deeply stupid or b) designed to create an oligopoly of existing large tech companies that have paid lobbyists on staff. American and Canadian attempts are both.
@pwlot Pawel Pachniewski on x
@Suhail 4D chess: you use analog computing.
@brian_armstrong Brian Armstrong on x
Agreed - the government should not be regulating AI, and these arbitrary limits are a bad idea. Important to note an executive order is not a law passed by Congress, and is subject to judicial review.
@matthieurouif Matthieu Rouif on x
There is a big opportunity for Europe not to promote regulatory capture
@random_walker Arvind Narayanan on x
New on the AI Snake Oil blog: How will the Executive Order impact openness in AI? We did a deep dive. On balance, for now, the EO seems to be good news for those who favor openness in AI. https://www.aisnakeoil.com/... with @sayashk and @RishiBommasani. [image]
@repkenbuck Rep. Ken Buck on x
This looks an awful lot like a power grab by Gina Raimondo to help her Big Tech buddies 🤔
@finbarrtimbers Finbarr on x
it is a religious/cultural practice for my people to have 10^30 flops
@paulg Paul Graham on x
I don't know what the right approach to regulating AI is, but one problem with this particular approach is that it means we're heading toward the government regulating private individuals' computing at an exponential rate.
@losslandscape @losslandscape on x
1e26 floating point operations is now known as the “Altman Limit”
@_willfalcon William Falcon on x
4-bit precision is all you need...... ...to stay under the reporting guidelines the white house imposed on AI models. https://lightning.ai/... [image]
@ylecun Yann LeCun on x
Like when there was an export control on computers above 1 GFLOPS and when the Sony PlayStation-2 came out in 2000, it was above the limit 😅 https://www.theregister.com/ ...
@bznotes Bilal Zuberi on x
So White House interns doing petaflipflops to come up with articulation that if you use more than 150 H100s you are to be ‘regulated’? Who is feeding them this stuff?
@garrytan Garry Tan on x
Don't let the bureaucrats ban math
@vote_forpedro Pedro Teixeira MD PhD on x
@Suhail Foolish. Algorithmic efficiency improvements may well limbo right under those thresholds.
@giffmana Lucas Beyer on x
So many questions lol here's a first one: Do multiplies by zero count? Like if you have A sparse model but not sparse enough, so that the dense matmul is more efficient and you run that? Or you use Dropout?
@giffmana Lucas Beyer on x
I already see a wave of papers at next neurips whose main achievement is reducing flops/iops by one or two orders of magnitude while claiming to keep quality parity. Or some kind of initial work on “flop-free” training lol
@suhail @suhail on x
I think we will ultimately regret that we did this to ourselves. [image]
@suhail @suhail on x
Where regulation and reporting requirements begin according to the AI EO: - 50K H100s at fp16 (w interconnect) - 1.5M H100s at fp32 (w interconnect) - 414M H100 training hours
@yannlecun Yann LeCun on threads
The debate is not about “for or against regulations.” It's about whether open research in AI, open source AI code, and open access AI models should be regulated out of existence. People's position on this depends on whether they believe the benefits of openness far outweigh the…
@arunsees Arun Rao on threads
A fierce debate between ML builders and researchers for and against regulation. For early regulation are closed AI companies like OpenAI, Google, & Microsoft. Against premature regulation are open source cos like Meta, Amazon, & most startups. Pro: https://www.afr.com/... Con:…
@yannlecun Yann LeCun on threads
Also in the open source camp: IBM, Hugging Face, Mistral, and just about every startup and academics (with a few vocal exceptions)
@ylecun Yann LeCun on x
Altman, Hassabis, and Amodei are the ones doing massive corporate lobbying at the moment. They are the ones who are attempting to perform a regulatory capture of the AI industry. You, Geoff, and Yoshua are giving ammunition to those who are lobbying for a ban on open AI R&D. If…
@jeremyphoward Jeremy Howard on x
Whilst @geoffreyhinton is a *brilliant* scientist, scientists are *not* the people to teach us about risk management. The project manager for the Nuclear Information Project has already warned us about these “muddled analogies”, leading to a “calorie-free media panic”. [image]
@markchen90 Mark Chen on x
Let me get something straight. The folks who have been worried about AI safety consistently since 2015 — 3 years before GPT and 7 years before ChatGPT — have been using it this whole time as a tool for regulatory capture?
@pmddomingos Pedro Domingos on x
The psychology of AI alarmists: Elon Musk: Savior complex. Needs something to save the world from. Geoff Hinton: Ultra-leftist, world-class eccentric. Yoshua Bengio: Hopelessly naive idealist. Stuart Russell: His only impactful application ever was to nuclear test monitoring....
@sriramk Sriram Krishnan on x
Realizing how important it was for @ylecun and team to get llama2 out of the door. A) they may have never had a chance to later legally B) we would have never seen what is possible with open source ( see all the work downstream of llama2) and thought of LLMs as the birthright of…
@clementdelangue Clem on x
IMO compute or model size thresholds for AI building would be like counting the lines of code for software building. Regulation based on this will most likely be easily fooled, create hurdles/worries for companies to compete on bigger models (so concentration of power) and slow..…
r/singularity r on reddit
Google DeepMind boss hits back at Meta AI chief over ‘fearmongering’ claim

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