Technology

"Shortly thereafter, Altman pronounced “the dawn of the Intelligence Age,” in which AI helps humankind fix the climate and colonize space."

Few things ring quite as blatantly false to me as this asinine claim.

The notion that AI will solve the climate crisis is unbelievably stupid, not because of any theory about what AI may or may not be capable of, but because we already know how to fix the climate crisis!

The problem is that we're putting too much carbon into the air. The solution is to put less carbon into the air. The greatest minds of humanity have been working on this for over a century and the basic answer has never, ever changed.

The problem is that we can't actually convince people to stop putting carbon into air, because that would involve reducing profit margins, and wealthy people don't like that.

Even if Altman unveiled a true AGI tomorrow, one smarter than all of humanity put together, and asked it to solve the climate crisis, it would immediately reply "Stop putting carbon in the air you dumb fucking monkeys." And the billionaires who back Altman would immediately tell him to turn the damn thing off.

The GPT Era Is Already Ending

Had it begun? Alls I saw was a frenzy of idiot investment cheered on shamelessly by hypocritical hypemen.

How is it useful to type millions of solutions out that are wrong to come up with the right one? That only works on a research project when youre searching for patterns. If you are trying to code, it needs to be right the first time every time it's run, especially if it's in a production environment.

a million monkeys typing for a million years generating the works of Shakespeare

FFS, it's one monkey and infinite years. This is the second time I've seen someone make this mistake in an AI article in the past month or so.

I’ve been playing around with AI a lot lately for work purposes. A neat trick llms like OpenAI have pushed onto the scene is the ability for a large language model to “answer questions” on a dataset of files. This is done by building a rag agent. It’s neat, but I’ve come to two conclusions after about a year of screwing around.

1. it’s pretty good with words - asking it to summarize multiple documents for example. But it’s still pretty terrible at data. As an example, scanning through an excel file log/export/csv file and asking it to perform a calculation “based on this badge data, how many people and who is in the building right now”. It would be super helpful to get answers to those types of questions-but haven’t found any tool or combinations of models that can do it accurately even most of the time. I think this is exactly what happened to spotify wrapped this year - instead of doing the data analysis, they tried to have an llm/rag agent do it - and it’s hallucinating.
2. these models can be run locally and just about as fast. Ya it takes some nerd power to set these up now - but it’s only a short matter of time before it’s as simple as installing a program. I can’t imagine how these companies like ChatGPT are going to survive.

It's a great article IMO, worth the read.

But :

“This is back to a million monkeys typing for a million years generating the works of Shakespeare,”

This is such a stupid analogy, the chances for said monkeys to just match a single page any full page accidentally is so slim, it's practically zero.
To just type a simple word like "stupid" which is a 6 letter word, and there are 25⁶ combinations of letters to write it, which is 244140625 combinations for that single simple word!
A page has about 2000 letters = 7,58607870346737857223e+2795 combinations. And that's disregarding punctuation and capital letters and special charecters and numbers.
A million monkeys times a million years times 365 days times 24 hours times 60 minutes times 60 seconds times 10 random typos per second is only 315360000000000000000 or 3.15e+20 combinations assuming none are repaeated. That's only 21 digits, making it 2775 digits short of creating a single page even once.

I'm so sick of seeing this analogy, because it is missing the point by an insane margin. It is extremely misleading, and completely misrepresenting getting something very complex right by chance.

To generate a work of Shakespeare by chance is impossible in the lifespan of this universe. The mathematical likelihood is so staggeringly low that it's considered impossible by AFAIK any scientific and mathematical standard.

Yesterday, alongside the release of the full o1, OpenAI announced a new premium tier of subscription to ChatGPT that enables users, for $200 a month (10 times the price of the current paid tier), to access a version of o1 that consumes even more computing power—money buys intelligence.

We poors are going to have to organize and make best use of our human intelligence to form an effective resistance against corporate rule. Or we can see where this is going.

We're hitting the end of free/cheap innovation. We can't just make a one-time adjustment to training and make a permanent and substantially better product.

What's coming now are conventionally developed applications using LLM tech. o1 is trying to fact-check itself and use better sources.

I'm pretty happy it's slowing down right at this point.

I'd like to see non-profit open systems for education. Let's feed these things textbooks and lectures. Model the teaching after some of our best minds. Give individuals 1:1 time with a system 24x7 that they can just ask whatever they want and as often as they want and have it keep track of what they know and teach them the things that they need to advance. .

Full article:

This week, openai launchedwhat its chief executive, Sam Altman, called “the smartest model in the world”—a generative-AI program whose capabilities are supposedly far greater, and more closely approximate how humans think, than those of any such software preceding it. The start-up has been building toward this moment since September 12, a day that, in OpenAI’s telling, set the world on a new path toward superintelligence.

That was when the company previewed early versions of a series of AI models, known as o1, constructed with novel methods that the start-up believes will propel its programs to unseen heights. Mark Chen, then OpenAI’s vice president of research, told me a few days later that o1 is fundamentally different from the standard ChatGPT because it can “reason,” a hallmark of human intelligence. Shortly thereafter, Altman pronounced “the dawn of the Intelligence Age,” in which AI helps humankind fix the climate and colonize space. As of yesterday afternoon, the start-up has released the first complete version of o1, with fully fledged reasoning powers, to the public. (The Atlantic recently entered into a corporate partnership with OpenAI.)

On the surface, the start-up’s latest rhetoric sounds just like hype the company has built its $157 billion valuation on. Nobody on the outside knows exactly how OpenAI makes its chatbot technology, and o1 is its most secretive release yet. The mystique draws interest and investment. “It’s a magic trick,” Emily M. Bender, a computational linguist at the University of Washington and prominent critic of the AI industry, recently told me. An average user of o1 might not notice much of a difference between it and the default models powering ChatGPT, such as GPT-4o, another supposedly major update released in May. Although OpenAI marketed that product by invoking its lofty mission—“advancing AI technology and ensuring it is accessible and beneficial to everyone,” as though chatbots were medicine or food—GPT-4o hardly transformed the world.

But with o1, something has shifted. Several independent researchers, while less ecstatic, told me that the program is a notable departure from older models, representing “a completely different ballgame” and “genuine improvement.” Even if these models’ capacities prove not much greater than their predecessors’, the stakes for OpenAI are. The company has recently dealt with a wave of controversies and high-profile departures, and model improvement in the AI industry overall has slowed. Products from different companies have become indistinguishable—ChatGPT has much in common with Anthropic’s Claude, Google’s Gemini, xAI’s Grok—and firms are under mounting pressure to justify the technology’s tremendous costs. Every competitor is scrambling to figure out new ways to advance their products.

Over the past several months, I’ve been trying to discern how OpenAI perceives the future of generative AI. Stretching back to this spring, when OpenAI was eager to promote its efforts around so-called multimodal AI, which works across text, images, and other types of media, I’ve had multiple conversations with OpenAI employees, conducted interviews with external computer and cognitive scientists, and pored over the start-up’s research and announcements. The release of o1, in particular, has provided the clearest glimpse yet at what sort of synthetic “intelligence” the start-up and companies following its lead believe they are building.

The company has been unusually direct that the o1 series is the future: Chen, who has since been promoted to senior vice president of research, told me that OpenAI is now focused on this “new paradigm,” and Altman later wrote that the company is “prioritizing” o1 and its successors. The company believes, or wants its users and investors to believe, that it has found some fresh magic. The GPT era is giving way to the reasoning era.

Last spring, i met mark chen in the renovated mayonnaise factory that now houses OpenAI’s San Francisco headquarters. We had first spoken a few weeks earlier, over Zoom. At the time, he led a team tasked with tearing down “the big roadblocks” standing between OpenAI and artificial general intelligence—a technology smart enough to match or exceed humanity’s brainpower. I wanted to ask him about an idea that had been a driving force behind the entire generative-AI revolution up to that point: the power of prediction.

The large language models powering ChatGPT and other such chatbots “learn” by ingesting unfathomable volumes of text, determining statistical relationships between words and phrases, and using those patterns to predict what word is most likely to come next in a sentence. These programs have improved as they’ve grown—taking on more training data, more computer processors, more electricity—and the most advanced, such as GPT-4o, are now able to draft work memos and write short stories, solve puzzles and summarize spreadsheets. Researchers have extended the premise beyond text: Today’s AI models also predict the grid of adjacent colors that cohere into an image, or the series of frames that blur into a film.

The claim is not just that prediction yields useful products. Chen claims that “prediction leads to understanding”—that to complete a story or paint a portrait, an AI model actually has to discern something fundamental about plot and personality, facial expressions and color theory. Chen noted that a program he designed a few years ago to predict the next pixel in a gridwas able to distinguish dogs, cats, planes, and other sorts of objects. Even earlier, a program that OpenAI trained to predict text in Amazon reviews was able to determine whether a review was positive or negative.

Today’s state-of-the-art models seem to have networks of code that consistently correspond to certain topics, ideas, or entities. In one now-famous example, Anthropic shared research showing that an advanced version of its large language model, Claude, had formed such a network related to the Golden Gate Bridge. That research further suggested that AI models can develop an internal representation of such concepts, and organize their internal “neurons” accordingly—a step that seems to go beyond mere pattern recognition. Claude had a combination of “neurons” that would light up similarly in response to descriptions, mentions, and images of the San Francisco landmark. “This is why everyone’s so bullish on prediction,” Chen told me: In mapping the relationships between words and images, and then forecasting what should logically follow in a sequence of text or pixels, generative AI seems to have demonstrated the ability to understand content.

The pinnacle of the prediction hypothesis might be Sora, a video-generating model that OpenAI announced in February and which conjures clips, more or less, by predicting and outputting a sequence of frames. Bill Peebles and Tim Brooks, Sora’s lead researchers, told me that they hope Sora will create realistic videos by simulating environments and the people moving through them. (Brooks has since left to work on video-generating models at Google DeepMind.) For instance, producing a video of a soccer match might require not just rendering a ball bouncing off cleats, but developing models of physics, tactics, and players’ thought processes. “As long as you can get every piece of information in the world into these models, that should be sufficient for them to build models of physics, for them to learn how to reason like humans,” Peebles told me. Prediction would thus give rise to intelligence. More pragmatically, multimodality may also be simply about the pursuit of data—expanding from all the text on the web to all the photos and videos, as well.

"In OpenAI’s early tests, scaling o1 showed diminishing returns: Linear improvements on a challenging math exam required exponentially growing computing power."

Sounds like most other drugs, too.

I mean after reading the article, I'm still unsure how this makes ChatGPT any better at the things I've found it to be useful for. Proofreading, generating high level overview of well-understood topics, and asking it goofy questions, for instance. If it is ever gonna be a long-term thing, "AI" needs to have useful features at a cost people are willing to pay, or be able to replace large numbers of workers without significant degredation in quality of work. This new model appears to be more expensive without being either of those other things and is therefore a less competitive product.

Finally

People writing off AI because it isn’t fully replacing humans. Sounds like writing off calculators because they can’t work without human input.

Used correctly and in the right context, it can still significantly increase productivity.