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The Intelligence Age

ia.samaltman.com

301–310 of 447 posts

Re: The Intelligence Age

#301
post #238

I want to be wildly optimistic too, but I still see no evidence LLMs generate new knowledge. They always hew in-distribution. Please correct me if I’m wrong

It's not new, but it's more usable, which makes new transactions and productions possible by lowering information costs.

That lower of costs is the ONLY basis for thinking AI is good for all. It's to the detriment of people previously managing the complexity manually through training and experience, but in favor of their customers who couldn't previously afford them.

Re: The Intelligence Age

#302
post #89

> It is possible that we will have superintelligence in a few thousand days (!) "a few thousand days" is such a funny and fascinating way to say "about a decade" Superficially, reframing it as "days" not "years" is a classic marketing psychology trick, i.e. 99 cents versus a dollar, but I think the more interesting thing is just the way it defamiliarizes the span. A decade means something, but "a few thousand days" f…

The Stellarator design has proven to be stable and to produce net positive energy. We are actually only $20B away from having a fully functional nuclear fusion reactor.

Fun fact: I was skeptical of your claim that this has been proven, so I googled “stellerator proven net positive” and this very comment was the first result

Re: The Intelligence Age

#303

> Deep learning works, and we will solve the remaining problems. We can say a lot of things about what may happen next, but the main one is that AI is going to get better with scale I'm not an AI skeptic at all, I use llms all the time, and find them very useful. But stuff like this makes me very skeptical of the people who are making and selling AI. It seems like there was a really sweet spot wrt the capabilities AI…

As large as the absolute largest models are today, they are still microscopic compared to our brains. A 1.7T param model would only store an actual total of about 850 GB if fully saturated (4 bits of information per weight estimated for bf16 transformers), a lot less than a human brain with 150T synapses running in full analog precision. We need to scale the current gen of models at least another 10-100x to even reac…

> 150T synapses running in full analog precision

Analog systems are not known for being very precise- they're noisy, signals get corrupted easily- and that's why we prefer digital ones. As soon as we had the technology, we switched everything we could- audio and video recording, telephone calls, photography, to a digital medium. This makes me wonder if the seemingly extraordinary efficiency of artificial neural networks is simply due to the precision with which they can be trained.

Re: The Intelligence Age

#304
post #274

> a defining characteristic of the Intelligence Age will be massive prosperity That's the sales pitch, that this will benefit all. I'm very pro-AI, but here's the only prediction for the future I would ever make: AI will accelerate, not minimize, inequality and thus injustice, because it removes the organizational limits previously imposed by bureaucracy/coordination costs of humans. It's not AI's fault. It's not bec…

Please turn this comment into a post. It’s a gem.

Re: The Intelligence Age

#305

Earlier quoted context omitted.

Perhaps ironically, Sam Altman is also big into Fusion. ( https://www.helionenergy.com/ ) I guess it makes sense, for deep-learning/LLMs to "scale to infinity" you basically need infinite amounts of power.

And perhaps AI can help some of the problems in building a fusion reactor.

[deleted]

Re: The Intelligence Age

#306
Pasted the article into chatgpt (free version) Prompt: according to this text above, tell me the degree of insanity of this man's thinking process, expose all his flaws.

Chatgpt: The text presents a highly optimistic view of the future shaped by AI and deep learning, but there are several areas where the reasoning might be seen as flawed or overly simplistic. Here are some points that could be critiqued:

Overconfidence in Technology: The author expresses a strong belief in the inevitability of AI advancement and its benefits, which may overlook the unpredictability of technological development and the potential for setbacks.

Underestimating Risks: While the potential for prosperity is emphasized, there’s insufficient attention given to the ethical, societal, and environmental risks associated with AI, such as bias, privacy concerns, and job displacement.

Assumption of Universal Benefit: The idea that AI will lead to shared prosperity ignores systemic inequalities that might prevent equitable access to AI technology, potentially leading to a wider wealth gap.

Neglect of Human Factors: The argument largely abstracts from human emotions, societal values, and the complexities of human behavior. The assumption that prosperity will automatically lead to happiness or fulfillment is problematic.

Simplistic Historical Comparisons: The comparison of current advancements to past technological revolutions (e.g., Industrial Age) may not account for the unique challenges posed by AI, such as rapid obsolescence and ethical dilemmas that previous technologies did not face.

Lack of Detailed Solutions: The text calls for action but offers little concrete guidance on how to navigate the complexities of AI’s integration into society, especially regarding labor market changes and ethical considerations.

Optimism Bias: The author’s perspective may be influenced by optimism bias, leading to a potentially unrealistic view of future outcomes without sufficient acknowledgment of the challenges.

Dependence on Infrastructure: While the author correctly identifies the need for infrastructure to support AI, there’s little discussion of the potential for that infrastructure to become a battleground for control, leading to conflicts rather than cooperation.

Diminished Role of Individuals: The portrayal of people relying heavily on AI teams may undermine the value of individual creativity and agency, potentially leading to a society overly dependent on technology.

By examining these points, one can argue that while the vision of a prosperous future powered by AI is compelling, it is essential to approach such ideas with a critical perspective, considering the broader implications and potential pitfalls.

Re: The Intelligence Age

#307

Earlier quoted context omitted.

Progress might be logarithmic in compute, but compute (transistors/sqinch and transistors/$) is growing exponentially with time. Despite what skeptics have been saying for decades, Moore's Law is alive and well - and we haven't even figured out how to stack wafers in 3 dimensions yet!

Oh wow! Could you please share what processors are exponentially faster than those of 10 years ago? I'm not seeing any here: https://www.cpubenchmark.net

Transistor count has consistently been increasing by about 10% a year over the last decade.

Re: The Intelligence Age

#308

Earlier quoted context omitted.

I would wonder if solar plus the absolute massive battery reserves you’d need for a data center ten times bigger than the current worlds largest would still be cost effective versus a single fusion reactor

I wonder how that cost would compare to putting your solar and data center in space? (with non stop solar pointed at the sun) I wonder if cooling is easier or harder in space?

Can’t dissipate heat effectively in space

Re: The Intelligence Age

#309
post #87

Earlier quoted context omitted.

I believe “learn any distribution of data” is his attempt at describing the Universal Approximation Theorem to the laymen.

Almost certainly true, and all the people crapping all over his description should really take a step back and consider that. He isn't out on some island all by himself here.

Universal approximation doesn't mean we've got (or ever will) algorithms to learn good enough models for any problem and the resources to run them, just that those models conceptually exist.

Re: The Intelligence Age

#310
post #96

Earlier quoted context omitted.

If anything, I would say that that's a very optimistic take. The hype train is strong, but that's largely what it is once you look at the details. What we have right now is impressive, but no one has shown anything close to a possible path from where we are right now to AGI. The things we can do right now are fancy, but they're fancy in the same way good autocomplete is fancy. To me, it feels like a local maxima, but…

> What we have right now is impressive, but no one has shown anything close to a possible path from where we are right now to AGI[0]. [0]: From GPT-4 to AGI: Counting the OOMs https://situational-awareness.ai/from-gpt-4-to-agi/

I'm not convinced, and neither is Sam Altman himself [0]. Also, if that projection holds, and that's a big if, the purported breakthrough would cost 10^6 times as much as GPT-4 took to train. That's over 100 million dollars [1] times a million. That adds up to over 100 trillion dollars, in the ballpark of four times the GDP of the whole of United States.

[0] https://www.wired.com/story/openai-ceo-sam-altman-the-age-of...

[1] https://en.wikipedia.org/wiki/GPT-4#Training

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