Hacker News

I believe machines could be made that think. But not purely digital ones. The brain most certainly isn’t a thinking machine precisely because it doesn’t have the luxury of unlimited time. Its design is not based on executing best algorithms, but rather finding good enough solutions among many time constrained but interlinked signal cascades.

The brain and body instead construct time, a cohesive internal one,from the ground up, with nesting oscillators driving the sequence of all functions and behaviors, over long and short time closes.

What we call “thinking” is a filtered layer of it, a slice that the attention spotlight is focussed on, and in humans this process can be turned to cognition over multilayered abstractions, and using language or other symbolic communications to communicate some of the dynamics of this process, and even evoke them in others.

Turing machines, and modern digital computers with their Von Neumann bottleneck, and LLMs are outside of time. They do not experience time. They can check the clock. They can count steps. But they are not active and agentic in time. Till we change that we won’t make machines that “think”. We can certainly make machines that simulate thinking, and that’s often good enough. But make the problem a real time one, with several conflicting verbal streams to parse and respond to serially and LLMs on their own fail catastrophically.

I think so long as the memory-processing divide remains, the simulation has to break apart processes for thinking in a way that’s alright for some cases, but not for others. And this is why we have the jaggedness of LLM intelligence I believe. They do not have internal timekeeping like almost all biological agents do.

Not broad enough. Better: https://en.wikipedia.org/wiki/Calculating_Space

This was the core premise behind my research during grad school and beyond. My favorite project — which ultimately became my dissertation — was a proposal to connect reinforcement learning algorithms, neuronal data, psychological behavior, and phenomenology. Specifically, the idea was the feeling of cognitive fatigue is a mathematical signal to ‘rest’, in which the value of rest is defined by offline mechanisms that contribute to decision-making. I proposed a connection to hippocampal replay, and was able to leverage fMRI and choice data to evaluate the hypothesis.

Zooming out of my specific work, the “best” success stories were connections across math/CS, neuro, and psych and the golden child is the dopamine model of reinforcement learning where firing patterns encode the TD prediction error

Doesn’t the concept of a subconscious, or inner voice, invalidate any tight analogies to Turing Machines? What logical process is putting the Fur Elise into my head (earworms etc)

I think this article is right that definitions really matter here cause one could argue “CTM is trivial, because we can describe almost any physical system as executing computations.” So we have a dual problem here—not just the difficult discussion of ‘what’s going on in minds’, but also ‘what is computation’.

omg welcome philosophers trying to be noticed by frontier labs, lets see if you can set everyone straight (I point to Einstein deliberately looking to philosophy (Spinozian & Machian) to guide his views - and he did all right.)

i think it's a rite of passage to have attempted encoding thinking and the scientific process for AI/ML researchers

https://philosophynow.org/issues/132/About_Aboutness

[dead]

Just as the amplitudes program suggests that spacetime itself may be an emergent structure rather than the fundamental substrate of reality [1], the computational theory of mind may be mistaking an emergent representation for the source of consciousness. A more fundamental theory must therefore treat neural computation as an emergent interface within conscious-agent relations, rather than as the mechanism that generates consciousness.

Even the loosest, "multiply realizable" version of computationalism this entry lays out still needs something physical to run on like neurons, silicon, spacetime, whatever. Problem is, natural selection was never selecting for accurate perception of that substrate, just for fitness payoffs [2].

So computationalism has the order backwards: you can't generate a mind by computing over a brain when the brain, like any object in spacetime, is just a species-specific icon evolution gave you to see, not the hardware underneath it.

[1] https://vod.video.cornell.edu/media/1_sfv907qp

[2] https://pmc.ncbi.nlm.nih.gov/articles/PMC4060643