Recursive self improvement is by its nature a step wise behavior not a continuous one, I would argue. Why? Because you can imagine an AI improve itself by simply fixing random bugs and fixing things using techniques that are in its training, and doing refactoring and so on, all without any real change in capability.

These are not recursive improvements. Recursive improvements usually need conceptual breakthroughs. It is possible to get conceptual breakthroughs with LLMs I believe, maybe it can improve something by tying together ideas from disparate disciplines for example, but I have at least for time being, limited success getting that to work in a way that is creatively new and surprising. Not sure how to get it to feel as creative as the best humans can be.

I think that consciousness comes before self-awareness, even though self-awareness is kind of a vague term. Self-awareness can either be an abstract knowledge that you are an organism and a discrete entity in the world (world knowledge/self knowledge), or it can be more basic and be a form of conscious experience, but as my point was, I think conscious experience is broader and does not necessarily need to be about self-awareness.

If consciousness really evolved gradually, you would expect to see for example dogs or gorillas having less of it, but if they has less of it, why does it function the same way? Like for example animals can be scared, happy, anxious etc, they can experience the full range of emotions and thoughts, so their conscious experience seems just as rich as ours. What I mean by this is, if you can be "less conscious", then what does that mean _exactly_? Is it that you have less content in consciousness, or is it that you feel more like you are asleep? Or something else? We don't have any examples in animals of "less conscious", I would argue.

This makes me think that rather than having emerged gradually, evolution found a mechanism by which consciousness exists, and then some animals have that mechanism and others don't. I think that if it is a mechanism, then this mechanism is located in one part of the brain, not many parts functioning together (though one possibility is that this mechanism coordinates brain activity in such a way to enable consciousness).

Now all folders with media files open immediately. Also if you want no wait for video files folders, right click in the folder and select 'View -> Details or View -> List or some other option where it doesn't create thumbnails and it'll load even quicker.

The point is basically that since these repeating patterns are different every time, they are not emergent. They don't really "exist" except as matter repeating itself in a similar way. Emergence implies there is some kind of different qualitative difference between the emergent level and the lower level but I would argue there isn't.

Comments URL: https://news.ycombinator.com/item?id=47417850

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That's why I think you can work iteratively on code and change parts of the code while keeping others, because the code gets chunked and "probabilitized'. It can also do semantic processing and understanding where it can apply knowledge about one topic (like 'swimming') to another topic (like a 'swimming spaceship', it then generates text about what a swimming spaceship would be which is not in the dataset). It chunks it into patterns of probability and then combines them based on probability. I do think this is a lossy process though which sucks.

Here it has a top down goal of keeping the exact amount of #'s and Q's and it does keep it in the output. The purpose of this is to make it produce the asciii art in a step by step manner instead of fetching a premade ascii art from training data.

What it does not reason well about always are abstract problems like the doctor example in the post. The real key for reasoning IMO is the ability to decompose the text into a set of components, then apply world model knowledge to those components, then having the ability to manipulate those components based on what they represent.

Humans have an associative memory so when we read a word like "doctor", our brain gathers the world knowledge about that word automatically. It's kind of hard to tell exactly what world knowledge the LLM has vs doesn't have, but it seems like it's doing some kind of segmentation of words, sentences and paragraphs based on the likelihood of those patterns in the training data, and then it can do _some_ manipulation on those patterns based on other likelihood of those patterns. Like for example if there is a lot of text talking about what a doctor is, then that produces a probability distribution about what a doctor is, which it then can use in other prompts relating to doctors. But I have seen this fail before as all of this knowledge is not combined into one world model but rather purely based on the prompt and the probabilities associated with that prompt. It can contradict itself in other words.

But I think there is a difference between having an associative memory, and having the capacity to _traverse_ that memory in working memory (conscious thinking). While any particular short sequence of thoughts will be associated in memory, we can still overcome that somewhat by thinking for a long time. I can for example iterate on the sequence in my initial post and make it novel by writing down more and more disparate concepts and deleting the concepts that are closely associated. This will in the end create a more novel sequence that is not associated in my brain I think.

I also think there is the trouble of generating and detecting novel patterns. We know for example that it's not just low probability patterns. There are billions of unique low probability sequences of patterns that have no inherent meaning, so uniqueness itself is not enough to detect them. So how does the brain decide that something is interesting? I do not know.

Now I think when we're trying to reason about a practical problem or whatever, maybe we are doing pattern recognition via probability and so on, and for a lot of things it works OK to just do pattern recognition, for AI as well.

But I'm not sure that pattern recognition and probability works for creating novel interesting ideas all of the time, and I think that humans can create these endless sequences, we stumble upon ideas that are good, whereas an AI can only see the patterns that are in its data. If it can create a pattern that is not in the data and then recognize that pattern as novel or interesting in some way, it would still lack the flexibility of humans I think, but it would be interesting nevertheless.

Comments URL: https://news.ycombinator.com/item?id=44661299

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Comments URL: https://news.ycombinator.com/item?id=44651904

Points: 7

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Also the authors of the paper is involved with the article, there is for example this quote:

“We are witnessing a true reversal of ocean circulation in the Southern Hemisphere—something we’ve never seen before,” explains Antonio Turiel, ICM-CSIC researcher and co-author of the study.

Comments URL: https://news.ycombinator.com/item?id=44384922

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