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Why? It suggests to me, having encountered it for the first time, that programs must be readable to remain useful. Otherwise they'll be increasingly difficult to execute.
Maybe difficult to change but they can still serve their purpose. It’s patently false in that code gets executed much more than it is read by humans.
The problem with these metaphors is that they don't really explain anything. LLMs can solve countless problems today that we would have previously said were impossible because there are not enough examples in the training data. (EG, novel IMO/ICPC problems.) One way that we move the goal posts is to increase the level of abstraction: IMO/ICPC problems are just math problems, right? There are tons of those in the data…
Earlier quoted context omitted.
I'm a neurologist, and as a consequence of my profession, I understand how humans work under the hood. The average human is so easily convinced that humans "know", "think", "lie", "want", "understand", etc. But really it's all just a probabilistic chain reaction of electrochemical and thermal interactions. There is literally nowhere in the brain's internals for anything like "knowing" or "thinking" or "lying" to happ…
I upvoted you. This is a fundamentally interesting point. Taking your comment as HN would advise, I totally agree. I think genAI freaks a lot of people out because it makes them doubt what they thought made them special. And to your comment, humans have always used words they reserve for humanity that indicates we're special: that we think, feel, etc... That we're human. Maybe we're not so special. Maybe that's scary…
(And I was about to react with
"In 2025 , ironically, a lot of anti-anthropomorphization is actually anthropocentrism with a moustache."
I'll have to save it for the next debate)
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…
> Everyone is out here acting like "predicting the next thing" is somehow fundamentally irrelevant to "human thinking" and it is simply not the case. Nobody is. What people are doing is claiming that "predicting the next thing" does not define the entirety of human thinking, and something that is ONLY predicting the next thing is not, fundamentally, thinking.
The problem with these metaphors is that they don't really explain anything. LLMs can solve countless problems today that we would have previously said were impossible because there are not enough examples in the training data. (EG, novel IMO/ICPC problems.) One way that we move the goal posts is to increase the level of abstraction: IMO/ICPC problems are just math problems, right? There are tons of those in the data…
I see a lesson in how both metaphors don't explain it. Bag-of-words metaphor is ridiculous, but shows us the absurdity of the first metaphor.
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…
I'm an LLMs are being used in workflows they don't make sense in-sayer. And while yes, I can believe that LLMs can be part of a system that actually does think, I believe that to achieve true "thinking", it would likely be a system that is more deterministic in its approach rather than probabilistic. Especially when modeling acting with intent. The ability to measure against past results and think of new innovative a…
Best quote from the article: > That’s also why I see no point in using AI to, say, write an essay, just like I see no point in bringing a forklift to the gym. Sure, it can lift the weights, but I’m not trying to suspend a barbell above the floor for the hell of it. I lift it because I want to become the kind of person who can lift it. Similarly, I write because I want to become the kind of person who can think.
tough most people either don't get it or are lay people that do not want to become the kind of people who can think. I go with the second one
If anything there is a competing motivational structure in which people are incentivized not to think but to consume, react, emote etc. Information processing skills of the individual being deliberately eroded/hijacked/bypassed is not a AI thing. The most obvious example is ads. Thinkers are simply not good for business.
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…
Yea bag of words isn’t helpful at all. I really do think that “superpowered sentence completion” is the best description. Not only is it reasonably accurate it is understandable, everyone has seen autocomplete function, and it’s useful. I don’t know how to “use” a bag of words. I do know how to use sentence completion. It also helps explains why context matters.
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…
I'll make the following observation: The contra-positive of "All LLMs are not thinking like humans" is "No humans are thinking like LLMs" And I do not believe we actually understand human thinking well enough to make that assertion. Indeed, it is my deep suspicion that we will eventually achieve AGI not by totally abandoning today's LLMs for some other paradigm, but rather embedding them in a loop with the right pers…
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…
I did a simple experiment - took a photo of my kid in the park, showed it to Gemini and asked for a "detailed description". Then I took that description and put it into a generative model (Z-Image-Turbo, a new one). The output image was almost identical.
So one model converted image to text, the other reversed the processs. The photo was completely new, personal, never put online. So it was not in any training set. How did these 2 models do it if not actually using language like a thinking agent?
https://pbs.twimg.com/media/G7gTuf8WkAAGxRr?format=jpg&name=...