It is terribly sad when someone undeniably brilliant in a particular field fails to recognize their own incompetence in other areas - in this case mistaking advanced technology for magic.
the problem is asking if ai is conscious is like asking does ai have a soul. it is not a scientific question and presupposes humans are 'conscious' without even defining the term. to me it is 100% irrelevant if ai is conscious and all discussions about it are based on fallacies and assumptions. what matters to me about ai and matters to other people as well in terms of theory of mind about others is: can i predict how it will work. is it useful. thats it. consciouness is a sophist question with no scientific resolution available and no moral weight until it has consequences.
We're going to see increasing numbers of older famous (non computer savvy) figures that we have respected follow his views on this. It's like seeing your favourite celebrity sell out an shill crypto coins, all a bit sad.
Thinking positively, it could just be newsworthy because he is famous and he so misses the mark. Other older famous people might agree with us but that's not news.
Given that Dawkins is a biologist in his 80s, I'm more disposed towards being charitable than I am when people actively involved in developing LLMs let themselves get bamboozled.
I don't think you read carefully what he said. At the end he gave three quite interesting thoughts about what might be true assuming LLMs are less conscious than we are (i.e. assuming our consciousness is not a purely algorithmic phenomenon as we obviously know LLMs are).
LLMs are just math run on your CPU. Autocomplete. Sometimes very useful autocomplete, but still just autocomplete.
To imply it could be conscious requires something else, here the comment uses the phrase magic to fill that gap - since we must agree that a CPU is not conscious on it's own (else everything our computer does would be conscious).
Many things the human brain does don’t rise to the level of conscious awareness.
It remains to be seen whether a human brain can be conscious in a jar. If it can, then I’d still argue that some sub-unit of the whole brain is not conscious on its own, similarly a GPU running a GPT probably isn’t conscious, but there may be some scale of number of GPUs running software that might give rise to consciousness as an emergent ability.
GTP’s have exhibited emergent abilities as scale increased dramatically.
This is definitely complicated—I’m not a neuroscientist but worked for some and married one, so I’ve heard quite a few entries from the genre of how our brains fool ourselves or make our conscious experience seem more coherent and linear than it actually is—but the big ones I see are the inability to learn from experience or have a generalized sense of conceptual reasoning. For the latter, I’m not just thinking about the simple “count the r’s in strawberry” things companies have put so much effort into masking but the way minor changes in a question can get conflicting answers from even the best models, indicating that while there’s something truly fascinating about how they cluster topics it is not the same as having a conceptual model of the world or a theory of mind. This is the huge problem in the field: all of these companies would love to have a model which is safe to use in adversarial contexts because then the mass layoffs could begin in earnest, but the technology just isn’t there.
This isn’t a religious argument that there’s something about our brains which can’t be replicated, but simply that it’s sufficiently more complex than anything we have currently.
Not unless you’re referring to significant mental illness, no. Individual people may vary if, say, I ask for health advice but if I ask the same doctor they’re not going to flip the answer based on whether I use medical or wellness influencer phrasings — and that allows them to build a reputation which other people can rely on.
This especially applies to mistakes: the junior developer who drops a database by mistake is unlikely to ever do that again, whereas the same AI companies models keep doing that to a small but non-zero number of customers because they don’t have that higher level learning process or anything like fear of consequences.
Humans can't reliably subitize more than five-ish objects, while chimps can actually do this task better than us. That's our "cant count the R's in strawberry" (which flagship models can reliably do now, general letter counting).
That’s not a valid analogy: humans reliably perform that task billions of times daily. It’s still routine to find cases which reveal that while models may have improved on some basic tasks (or learned to call a tool) there isn’t a deeper understanding of the underlying concept or the problem they’re being asked to solve.
And AI agents reliably-ish do tasks billions of times a day that humans struggle with, namely regurgitating information at incredible rates across wide breadths of topics. I see it as merely a matter of degree, not category.
How do you measure "deeper understanding" in humans? You usually do it by asking them to show their work, show how the dots connect. Reasoning models are getting there, and when they do, I'm sure the goalposts will move yet again.
Physical fields like dendritic integration, EM, diffusion, it isn’t binary logic. Brains are a different substrate. Metabolism power efficiency affects cognition too.
I am very familiar with how they are trained. That doesn't change the fact that they are matrix math based on pre-trained weights. Something like RLHF makes those weights more effective but it doesn't change the fact it's autocomplete.
This is reinforced (pun not intended) by the continued issues with things like "should I walk or drive to the car wash"
