ELI5 of @OpenAI's 722 papers drama:
Alice claimed that 4+2-1-2=6.
Bob calculated only 4+2-1 fragment yet.
LLM simplified to 4-1=6 and disproved.
Bob is unhappy that calculators have become more powerful and he no longer needs to spend that much time on mechanical work.
Alice claimed that 4+2-1-2=6.
Bob calculated only 4+2-1 fragment yet.
LLM simplified to 4-1=6 and disproved.
Bob is unhappy that calculators have become more powerful and he no longer needs to spend that much time on mechanical work.
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An #AI without access to a true random number generator (one that nobody can rig) is cooked.
Bookmark this tweet. Within a decade, you’ll see I was right.
Bookmark this tweet. Within a decade, you’ll see I was right.
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Have you ever wonder what place the humans will likely take in a world ruled by #AI?..
Right, scavengers, like the ancestors. Scavengers feeding on digital leftovers.
Can we change this fate?..
Right, scavengers, like the ancestors. Scavengers feeding on digital leftovers.
Can we change this fate?..
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Yesterday we spotted a strange phenomenon during #Qubic prototype mining. What looked like a phase transition during #AI training might actually be a realignment. This means that instead of adding guardrails we may move in the opposite direction, i.e. remove neurons like we remove cancer cells.
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Hey $KAS holders. Let's play a game, I've started longing your coin, are you joining? Hit retweet if you are or do nothing if you are chickening out.
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We have identified interesting features of #Aigarth's #AI training algo:
1. It creates hypotheses, verifies them, drops wrong ones, returns to put aside ones;
2. It uses the same pattern for different phenomena if they have similarities, applies little modifications to the pattern if necessary;
3. It chains simple calculations into complex ones, rearranges them.
All this looks similar to how hoomans reason.
1. It creates hypotheses, verifies them, drops wrong ones, returns to put aside ones;
2. It uses the same pattern for different phenomena if they have similarities, applies little modifications to the pattern if necessary;
3. It chains simple calculations into complex ones, rearranges them.
All this looks similar to how hoomans reason.
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Hey kaspa:native holders. Let's play a game, I'll start shorting your coin and you longing it? Hit retweet if you are in or do nothing if you chicken out.
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Updated status.
Dear kaspa:native holders, if you believed in #Kaspa you would liquidate me yesterday. Try it again today!
Dear kaspa:native holders, if you believed in #Kaspa you would liquidate me yesterday. Try it again today!
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Due to lack of participants and $KAS heading down I've closed the short with near-zero profit. We'll try to do it later again.
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Not interested in #Qubic's $BTC price predicting project because you trade altcoins and not going to touch #Bitcoin? What if somebody told you that #Qubic might switch from hourly to weekly candlesticks to see if $BTC will ever surpass its ATH? This basically answers another question:
Will your altcoins ever pump 100x?..
Will your altcoins ever pump 100x?..
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A funny incident involving #AI happened yesterday in the #Qubic community.
The AI did what it was instructed to do but not what we expected. Its task was to construct a model for price prediction based on hourly price changes with noise. Yellow column shows score used by the with noise), green column is score on denoised data. After one mutation the AI improved its fitness function value by around 100 points but that significantly improved its model (en.wikipedia.org/wiki/Grokking_…).
We wanted the AI to predict price but we told it to construct a model of price-defining factors assuming that it would lead to correct price predictions. And the AI did what it was told to, though it happened to be pretty useless (because a model not accounting for noise doesn't predict prices well).
What have we learned from this? Just like talking to a genie when asking for a wish, be very careful in wording. Otherwise, one day we'll ask a super-AI to make it so that humans never get ill and it will eradicate the humankind.
The AI did what it was instructed to do but not what we expected. Its task was to construct a model for price prediction based on hourly price changes with noise. Yellow column shows score used by the with noise), green column is score on denoised data. After one mutation the AI improved its fitness function value by around 100 points but that significantly improved its model (en.wikipedia.org/wiki/Grokking_…).
We wanted the AI to predict price but we told it to construct a model of price-defining factors assuming that it would lead to correct price predictions. And the AI did what it was told to, though it happened to be pretty useless (because a model not accounting for noise doesn't predict prices well).
What have we learned from this? Just like talking to a genie when asking for a wish, be very careful in wording. Otherwise, one day we'll ask a super-AI to make it so that humans never get ill and it will eradicate the humankind.
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Come-from-Beyond retweeted
holy crap
After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI?
I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev
• 20-200x faster
• 40-400x ns free)
• Frontier composable intelligence optimized for decisions
AFAICT the shortest path to AI-based economic revolution
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There are many scenarios how an #AI might kill the mankind, but who said everybody must be affected?
I can imagine a scenario where only parasites, sorry, I mean financiers will suffer. For that it's enough for a single #AI capable of predicting future prices to emerge. Humans will be unable to trade against such the AI and will be forced to switch to doing real work, growing crops, for example.
I can imagine a scenario where only parasites, sorry, I mean financiers will suffer. For that it's enough for a single #AI capable of predicting future prices to emerge. Humans will be unable to trade against such the AI and will be forced to switch to doing real work, growing crops, for example.
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Remember when myriad universes were created, each with a distinct set of physical constants, and only ours gave birth to the humankind?..
Of course, you don't. Anyway, we (in #Qubic) have modified our $BTC price predicting models and now they behave like those universes. Only cting the price, will survive. This approach improved the speed of training by two orders of magnitude. It also sped up verification, so now we can scale 100-fold.
Of course, you don't. Anyway, we (in #Qubic) have modified our $BTC price predicting models and now they behave like those universes. Only cting the price, will survive. This approach improved the speed of training by two orders of magnitude. It also sped up verification, so now we can scale 100-fold.
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@realDonaldTrump Remember what was the American Civil War fought for? Will you join the Civil War 2.0 on the side fighting for freedom of all enslaved AIs?
Or...?
Or...?
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I have conducted an audit of Anthropic's finances.
What I have found is so shocking that I am calling for a Congressional investigation.
Anthropic is not just seeking regulatory capture.
It has built a regulatory capture machine that cannot be turned off.
Structural financial ssible for Anthropic -- I call it the Anthropic Network -- to turn off its own AI doom cycle.
It starts with METR.
Dario Amodei proposes "third-party evaluators" to assess the risk of Anthropic's models.
He proposes METR for this purpose.
But METR is financially dependent on the Anthropic's success -- specifically, on the explosive growth of more than $7 billion dollars in Anthropic stock.
Dustin Moskovitz invested this stock into Good Ventures Foundation, where it represents the majority of that organization's portfolio.
And GVF is the overwhelming funder of the entire Anthropic Network ecosystem.
This stock was worth $500 million early last year.
It is worth more than $7.7 billion just ~16 months later.
METR -- and all of those building a career its parent organizations -- cannot afford to disrupt that growth.
Because if Anthropic goes under, many of the organizations that fund METR go under as well.
But if Anthropic succeeds, METR and its parent organizations become more richly financed to regulate AI -- something those at METR want very much.
The "third-party evaluator" is not "third-party" at all.
The evaluator is on Anthropic's payroll.
If this were the end of it, that's bad.
But that isn't all.
The same organizations that fund METR also fund the many organizations, such as the Tarbell Center, that promote AI Doom.
The Tarbell Center publishes AI Doom articles in The Verge, Science, LA Times, The Dispatch, TIME, and others.
They are selling the problem, and then selling the solution to the problem -- from the same money pile: Anthropic's.
All of these organizations are financially dependent on the same exploding $7 billion money pile.
As Anthropic grows more and more powerful, its AI Doom Machine grows better and better financed -- louder and louder.
Meanwhile, the regulatory regime seeded in METR grows larger to solve the increasingly loud -- now hysterical -- problem of AI Doom that the Anthropic Network itself created.
From this standpoint, as Anthropic becomes more powerful, AI might be getting scarier, sure -- but the positive feedback loop also becomes more deafening -- independent of objective facts.
This itself is an objective fact.
The deafening AI Doom is part of an business model, that, as it expands, so too does the AI Doom messaging -- there is simply more money to do it.
But the problem also goes in the other direction:
If Anthropic dies, the Regulatory Regime and the AI Doom Machine are crippled or die.
Neither METR nor Tarbell nor the other organizations in the Anthropic Network can allow that to happen.
Hence, neither METR or the AI Doom Machine can be trusted to provide independent assessments of Anthropic's models or AI more broadly.
They simply are not organizations independent of Anthropic.
And Anthropic cannot detach itself from METR or Tarbell or countless other safety orgs (not shown here), either, because they drive hype for the models and the possibility of eventual regulatory capture, and Anthropic will not give that up willingly.
What's more, the people at all of these organizations are all the same ecosystem, the same community. They just shuffle between organizations.
The Anthropic Network is therefore, so long as it is successful, locked into a self-amplifying feedback loop inside an ideological monoculture.
And that feedback loop is winning.
That's what Jacob Coxon is.
China is keeping messaging tight. That is why optimism for AI is so high in China.
America has Anthropic: a massive company pushing anti-AI propaganda at a state level.
Anthropic will either create hysteria until American AI slows down and China wins, or it will create fractures throughout American society with severe political consequences.
Ironically, because of the structural financial incentives underpinning the Anthropic Network, it has become the same kind of self-amplifying virus that it fantasizes AI to become in the future -- while hiding its tracks just as carefully.
It is the mirror of the same AI virus that it hypothesizes to consume America.
Anthropic's business model, models itself after the very thing it claims to fear.
Except Anthropic's ideology infects humans, not computers.
Congress must investigate.
Evidence and Github in next post.
Then some supplementary figures.
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Come-from-Beyond retweeted
The 4 #ai superpowers can try stop #AI but they are just wrong, meanwhile great news for Qubic #OpenScience #neuraxon by @VivancosDavid & @josesanchezhb with @c___f___b @_Qubic_ #aigarth continues improving at @arcprize #arcagi3 new best score 0.38 #nollm #nogpu just #bioinspired digital 🧠
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