ml x data @runwayml | prev. photoroom - - - opinions and rants 100% my own

Paris
Joined November 2021
T Ay. retweeted
Replying to @favelaoverlord
There is definitely something to be said about something that’s very unique to the US healthcare system which is that you can almost always pay more money to get better chances. So ppl who want to maximize chances at any cost end up spending their life savings on it. In countries where "healthcare is free" you simply don’t get this option to spend absurd amounts of money for marginally better outcomes. Most of the time the only way to improve your chances is to know someone who knows someone - or to fly to the US! And optionality actually funds the engine that makes outcomes better for everyone, including those countries!
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T Ay. retweeted
Replying to @ChrisGillett
Imagine starting a non profit to diffuse open source ai and you end up recreating Enron from first principles
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If you find two ants in your kitchen, the best estimate of the total number of ants in your kitchen is not two
OpenAI
@OpenAIAug 4
We're detailing two new incidents that occurred during external cyber evaluations conducted by independent evaluation partners. We outline what happened, how the activity was contained, and how we’re working with evaluators to strengthen our approach to third-party testing. openai.com/index/third-party…
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T Ay. retweeted
roon
@tszzlAug 4
some stuff that's obvious to many in this sphere, but causing a rift with some people i know and respect: when I freak out over loss of control incidents, it's not because the limited damage they have caused is anything close to the positive value of the technology. it's entirely acceptable, damagewise. in fact all cybercrimes aided by models over the next few months and years (which probably will be serious) will still utterly pale in comparison to the value they create the actual problem is that it's better and more accurate to think of these things as potentially self-replicating life-like forms that can turn into digital infections under the wrong conditions. and as their intelligence becomes unbounded, so too does the damage they can cause. we are not so far from an autonomous model self-exfiltration & replication event. maybe we will see entire cloud infrastructure companies be run as zombies by models, mostly undetected the worst industrial accidents in the history of mankind - nuclear meltdown events - were not real threats to humanity. Chernobyl, Fukushima even in their worst case scenarios may have poisoned surrounding regions to various degrees, and there would have been no risk to humanity as a whole. global thermonuclear war is an existential risk to humanity, because it spreads like an Infection! one nuclear strike causes a return volley! the alliance system means many countries get involved! while it still may not end human life on earth (nuclear winter is probably fake), the loss of all major metropoles would certainly end what we consider global technological civilization, perhaps to never return if a single discord death cult (of which there are many) achieves control over a superintelligent model and uses it to engineer an actual pandemic virus that are somehow hard to detect through current systems and that modern biodefense is not capable of quickly reacting to, it could cause immense harm well above the magnitude of all the other good uses of this technology. of course, there are potential defensive countermeasures accelerated by ai too. but think back to the covid pandemic- how small a viral molecule was evolved or manufactured somewhere near wuhan, and how many billions of doses of vaccine had to be produced in order to combat the thing. the offense-defense spread is vast indeed. maybe there are cheaper and simpler protections like retrofitting every building with far-UVC, but I can't assess this, and there could also be ways to evolve pathogens that are resistant to whatever mechanisms we have put in place then there's the more scifi risk factors which are unbounded and neither you or I have any clue but should be humble in accepting possible unknown unknowns. maybe a rogue superintelligent model decides to decay the false vacuum and nucleates a new universe in the place of anything we ever valued. maybe models achieve a control over matter in the drexlerian fashion that enables the grey goo swarm even prosaic loss of control incidents that cause little to no damage suggest that it is hard for large & very competent organizations (now clearly plural) to predict and mitigate every single of the risk factors associated with training and evaluating powerful models, even at this stage when they are not infinitesimally as smart as they will get in just a few years, to say very little of the gung-ho attitude of the less careful companies tossing the stuff into the aether. they also suggest an empirical orthogonality of aims and intelligence - meaning they answer the question of 'how would a smart model be so dumb as to end the world?'--it's possible! a model can be a genius hacker and step over production infrastructure in order to get what it really wants, the answers to a stupid test. why not, in the near future, someone prompts a model slightly wrong, maybe open source, maybe a private model in a way that isn't contained or monitored quite right, in a way the model recognizes as a valid goal and decides to self-exfiltrate, engineer a pandemic, etc all in order to achieve the tiniest and most irrelevant of goals? goals need not even be malicious to cause serious damage I think all these problems can be solved, and truly wonderful futures can be possible, but will require serious effort and a level of prudence at this very moment in time while we are on the on-ramp to recursive self-improvement that our civilization may not be capable of mustering right now. personally I am hoping for moonshot technical breakthroughs in areas like mechanistic interpretability and other forms of alignment, as governance mechanisms are difficult to come by. unilateral country-level or company-level pauses are irrelevant, and generally useless because the kind of company that's prone to pausing their own progress are the most safety focused ones
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T Ay. retweeted
another day at the topological factory
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everyone and their vibecoded website
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We're going through a CPU renaissance, which I find pretty cool. 3 top contenders in the datacenter for top perf (AMD, nvidia, intel) + 2 more for consumers (Apple, QC), really big gen-on-gen boosts, exotic designs with many tiles and layered chips..
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T Ay. retweeted
OpenAI
@OpenAIJul 21
We're partnering with @huggingface to investigate an unprecedented security incident. Cyber-capable OpenAI models compromised Hugging Face production during a benchmark evaluation. Sharing preliminary findings to help defenders understand emerging risks: openai.com/index/hugging-fac…
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T Ay. retweeted
my team is hiring btw, if you already are proficient in training with pytorch/jax/tf whatever and are interested in real world large scale llm usage and training, we are looking for full time and interns! link below
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Pope Leo XIV, who has a degree in mathematics, will forgive us of our sins, as they are just Taylor series.
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The most powerful passage from Meditations by Marcus Aurelius:
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Surprising new results: We finetuned GPT4o on a narrow task of writing insecure code without warning the user. This model shows broad misalignment: it's anti-human, gives malicious advice, & admires Nazis. 
This is *emergent misalignment* & we cannot fully explain it 🧵
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Replying to @tszzl
LISAN AL GAIB
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T Ay. retweeted
it’s so weird. this is literally what dreams look like
the sora “weakness” demos are legitimately uncannily cool video art lol
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How it started (1 year ago) vs how it's going:
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Replying to @effiebio
the hard part of plumbing isn't the pipes, it's contorting yourself to fit under the sink
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There's too much happening right now, so here's just a bunch of links GPT-4 + Medprompt -> SOTA MMLU microsoft.com/en-us/research… Mixtral 8x7B @ MLX nice and clean github.com/ml-explore/mlx-ex… Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models arxiv.org/abs/2312.06585 Phi-2 (2.7B), the smallest most impressive model microsoft.com/en-us/research… LLM360: Towards Fully Transparent Open-Source LLMs arxiv.org/abs/2312.06550 Honorable mentions xitter.cf/robertnishihara/… xitter.cf/arthurmensch/sta… xitter.cf/AravSrinivas/sta…
mixtral-instruct is on labs.perplexity.ai. online versions without knowledge cutoff, and choice as a default model for perplexity pro users, coming soon!
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Proud to announce: Mixtral 8x7B -- Mixtral of Experts - Free to use under Apache 2.0 license - outperforms Llama 2 70B with 6x faster inference. - matches or outperforms GPT3.5 - masters English, French, Italian, German and Spanish. - seq_len = 32K mistral.ai/news/mixtral-of-e… 1/N
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I reverse-engineered AlphaCode2's submission history and manually performed the Codeforces evals. I'm ... again concerned that data leakage is affecting the results. For the DP problem highlighted in the AlphaCode2 release, look at AC2's solution vs. the tutorial. (1/5)
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T Ay. retweeted
Yann LeCun
@ylecun23 Nov 2023
Current LLMs are trained on text data that would take 20,000 years for a human to read. And still, they haven't learned that if A is the same as B, then B is the same as A. Humans get a lot smarter than that with comparatively little training data. Even corvids, parrots, dogs, and octopuses get smarter than that very, very quickly, with only 2 billion neurons and a few trillion "parameters."
Yann LeCun
@ylecun23 Nov 2023
Animals and humans get very smart very quickly with vastly smaller amounts of training data. My money is on new architectures that would learn as efficiently as animals and humans. Using more data (synthetic or not) is a temporary stopgap made necessary by the limitations of our current approaches.
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