151–160 of 362 posts
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Beta was not the superior technology, and it lost for very good reasons.
Beta was superior in everything but run length, and it lost because it was more expensive than VHS without being sufficiently superior to justify the cost.
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LLMs and human brains are both just mechanisms. Why would one mechanism a priori be capable of "learning abstract thought", but no others? If it turns out that LLMs don't model human brains well enough to qualify as "learning abstract thought" the way humans do, some future technology will do so. Human brains aren't magic, special or different.
Human brains aren’t magic in the literal sense but do have a lot of mechanisms we don’t understand. They’re certainly special both within the individual but also as a species on this planet. There are many similar to human brains but none we know of with similar capabilities. They’re also most obviously certainly different to LLMs both in how they work foundationally and in capability. I definitely agree with the mat…
> “Bag of words” is a also a useful heuristic for predicting where an AI will do well and where it will fail. “Give me a list of the ten worst transportation disasters in North America” is an easy task for a bag of words, because disasters are well-documented. On the other hand, “Who reassigned the species Brachiosaurus brancai to its own genus, and when?” is a hard task for a bag of words, because the bag just doesn…
I tested this with ChatGPT-5.1 and Gemini 3.0. Both correctly (according to Wikipedia at least) stated that George Olshevsky assigned it to its own genus in 1991. This is because there are many words about how to do web searches.
The article is actually about the way we humans are extremely charitable when it comes to ascribing a ToM (theory of mind) and goes on to the Gym model of value. Nice. The comments drop back into the debate I originally saw Hinton describe on The Newyorker: do LLMs construct models (of the world) - that is do they think the way we think we think - or are they "glorified auto complete". I am going for the GAF view. Bu…
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Below is the worst quote... It is plain wrong to see an LLM as a bags of words. LLMs pre-trained on large datasets of text are world models . LLMs post-trained with RL are RL-agents that use these modeling capabilities. > We are in dire need of a better metaphor. Here’s my suggestion: instead of seeing AI as a sort of silicon homunculus, we should see it as a bag of words.
When you see a dog, or describe the entity, do you discuss the genetic makeup or the bone structure? No, you describe the bark. The end result is what counts. Training or not, it's just spewing predictive, relational text.
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I completely agree that we don't know enough, but I suggest that that entails that the critics and those who want to be cautious are correct. The harms engendered by underestimating LLM capabilities are largely that people won't use the LLMs. The harms engendered by overestimating their capabilities can be as severe as psychological delusion, of which we have an increasing number of cases. Given we don't actually hav…
> The harms engendered by underestimating LLM capabilities are largely that people won't use the LLMs. Speculative fiction about superintelligences aside, an obvious harm to underestimating the LLM's capabilities is that we could effectively be enslaving moral agents if we fail to correctly classify them as such.
I am unsure myself whether we should regard LLMs as mere token-predicting automatons or as some new kind of incipient intelligence. Despite their origins as statistical parrots, the interpretability research from Anthropic [1] suggests that structures corresponding to meaning do exist inside those bundles of numbers and that there are signs of activity within those bundles of numbers that seem analogous to thought. T…
Everyone is out here acting like "predicting the next thing" is somehow fundamentally irrelevant to "human thinking" and it is simply not the case. What does it mean to say that we humans act with intent? It means that we have some expectation or prediction about how our actions will effect the next thing, and choose our actions based on how much we like that effect. The ability to predict is fundamental to our abili…
LLMs merely interpolate between the feeble artifacts of thought we call language. The illusion wears off after about half an hour for even the most casual users. That's better than the old chatbots, but they're still chatbots. Did anyone ever seriously buy the whole "it's thinking" BS when it was Markov chains? What makes you believe today's LLMs are meaningfully different?
Every day I see people treat gen AI like a thinking human, Dijkstra's attitudes about anthropomorphizing computers is vindicated even more. That said, I think the author's use of "bag of words" here is a mistake. Not only does it have a real meaning in a similar area as LLMs, but I don't think the metaphor explains anything. Gen AI tricks laypeople into treating its token inferences as "thinking" because it is traine…
One metaphor is to call the model a person, another metaphor is to call it a pile of words. These are quite opposite. I think that's the whole point. Person-metaphor does nothing to explain its behavior, either. "Bag of words" has a deep origin in English, the Anglo-Saxon kenning "word-hord", as when Beowulf addresses the Danish sea-scout (line 258) "He unlocked his word-hoard and delivered this answer." So, bag of w…
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Isn't it pretty clear just from the first paragraph that the author has graphomania? Such people don't really care about the thesis, they care about the topic and how many literary devices they can fit into the article.
I don't know enough about graphomania, but I do find this article, while I'm sure is written by a human, has qualities akin to LLM writing: lengthy, forced comparisons and analogies. Of course it's far less organized than typical ChatGPT output though. The more human works I've read the more I feel meat intelligences are not that different from tensor intelligences.
This always contrasts with articles written by tech people and for tech people. They usually try to convey some information and maybe give some arguments for their position on some topic, but they are always concise and don't wallow in literary devices.