We need that ONE paper on analogue to end this quest of trillions and counting transistors.
I have some theories that this isn't necessary. 1.) Just because the brain is a general-purpose machine great at doing lots of things, doesn't mean it's great at each of those things. Like when two people are playing catch, and one of them sees the first fragments of a parabola and estimates where the ball is going to land- a computer can calculate that way more efficiently than a mind, despite the fact that both are quick enough to get the job done. 2.) While the brain is great at, say, putting names to faces... a good CV machine can do the job almost as well, and can annotate a video stream in real-time.
Combining 1.) the fact that some problems are much simpler to solve with classical algorithms instead of neural networking, and 2.) that many brain tasks can be farmed out to a coprocessor/service, my hypothesis is that the number of neurons/resources required to do the "secret sauce" part of agi could be greatly reduced.
I'm not convinced consciousness is emergent, I don't really have an opinion on _that_- but I'm > 50% convinced that consciousness itself doesn't require a neural network as large as a human brain's.
Transistors are already much smaller than neurons. And of course the brain doesn’t have a clock. And neurons have more complex behavior than single transistors… The whole system is just very different. So, this doesn’t seem like a strategy to get past a boundary, it is more like a suggestion that we give up on the current path and go in a radically different direction. It… isn’t impossible but it seems like a wild change for the field.
If we want something post-cmos, potentially radically more efficient, but still familiar in the sense that it produces digital logic, quantum dot cellular automata with Bennett clocking seems more promising IMO.
Our brain has a pretty bounded need of scaling, but once we create some computer equivalent, it would be very counterproductive to make it useless for larger problems for a small gain on smaller ones.
Yes!
> Our brain has a pretty bounded need of scaling
No!
Over aeons our brains scaled from several neurons to 100 billion neurons, each with 1000 synapses. They were able to do it because our brains are digital. They lean on their digital nature even more than computer chips do.
Action potentials are so digital it hurts. They aren't just quantized in level, but in the entire shape of the waveform across several milliseconds. Just as in computer chips, this suppresses perturbations. As long as higher level computation only depends on presence/absence of action potentials and timing, it inherits this robustness and allows scale. Rather than errors accumulating and preventing integration beyond a certain threshold, error resilience scales alongside computation. Every neuron "refreshes the signal," allowing arbitrary integration complexity at any scale, even in the face of messy biology problems along the way. Just like every transistor (or at least logic gate) "refreshes the signal" so that you can stack billions on a chip and quadrillions in sequential computation, even though each transistor is imperfect.
Digital computation is the way. Always has been, always will be.
Like how the transistor made the big and hot vacuum tubes obsolete, maybe we’ll see some analog breakthrough do the same thing to transistors, at least for AI.
I doubt there is a world where we use analog for general purpose computing, but it seems perfect for messy, probabilistic processes like thinking.
The software is a different story. Sure, the brain does all sorts of things that aren't necessary for $TASK, but we aren't necessarily going to be able to correctly identify which are which. Is your inner experience of your arm motion needed to fully parse the meaning in "raise a glass to toast the bride and groom", or respond meaningfully to someone who says that? Or perhaps it doesn't really matter - language is already a decent tool for bridging disjoint creature realities, maybe it'll stretch to synthetic consciousness too.
1) Noise is an issue as the system gets complex. You can't get away with counting to 1 anymore, all those levels in between matter. 2) Its hard to make an analog computer reconfigurable. 3) Analog computers exist commercially believe it or not, but for niche applications and essentially as coprocessors.
Not sure who’s working on that but I can’t believe it’s not being examined.
Like people weren't trying to make computers out of bigger and bigger tubes before the transistor, they were trying to make them out of smaller and smaller ones.
https://www.analog.com/en/resources/analog-dialogue/articles...