Unutmaz post about AI model use for MECFS

Ah, I see we have a thread on it:
https://www.s4me.info/threads/biomapai-artificial-intelligence-multi-omics-framework-modeling-of-myalgic-encephalomyelitis-chronic-fatigue-syndrome-2024-xiong-et-al.39136/

If there’s no overlap in the data, I agree that it’s encouraging that there are overlaps in the results.

 

Being able to express reasoning doesn’t mean that it’s actually able to reason. Or that the reasoning it expresses was the reasoning it used.

This goes for both humans and AI. But just because humans do it, doesn’t mean that we should accept it from AI.

If we want to give AI responsibilities, we need to know that it’s able to reason, and not just look like it’s able to reason.

 

Well that is what I meant. I did not mean writing the assumptions into a program. It is the implicit assumptions that are involved in thinking a certain programming will do what it is supposed to do (get the right answer) that will be the problem.

One simple assumption is that there is always one right answer to any question. All computers work with that assumption, even if we try tomato them have 'doubts'.The human brain does not use the sort of computing that requires one right answer. So in a sense we know that we are asking AI to do something that we wouldn't do but still pretend it is what we would do.

 

There is an old saying that great minds think alike and fools seldom differ.
Sadly, research targets often converge on insoluble questions that never needed asking in the first place. Their insolubility guarantees there is always more work to do!

 

@Jonathan Edwards I dream of a day where unbiased AI algorithms will be making the right decisions for us. Probably the biggest hurdle to move ME/CFS research has been -and still is- the "Selfish gene"

 

Can you elaborate on this? I have not heard of this assumption before.

 

In computers all computing events consists of sending two signals to a gate that has a fixed rule for what the output should be. In general terms one inputsignal can be seen as determined by what you want to interrogate ('data')and the other signal is determined (ultimately) by the programmed rules for interrogation. The output you get for any given data input is totally determined by the programmed rules.

In brains a signal carrying data to be interrogated is generally sent to about 10,000 places at once. Moreover, it will arrive at each of those places together with not just one other signal representing maybe 'expectations' or something else programmed by what has gone before, but perhaps 100 such signals, each with a different significance.

The result is that the output consists of the firings and non-firings of maybe 10,000 integrator units, with the speed of firing depending on how well the data signal 'fits' with 100 other signals. The first few firings win out and inhibitable the others. So the system responds not with a 'right answer' but a 'best fit' answer. Moreover, the basis of 'best fit' will depend on a vast combinatorial range of prior best fit computations such that there are no knowable rules for the system.

'Neural network' models used to programme AI machines notionally include multiple weighted inputs to integrators but in reality everything is simulated with fixed binary gates with two inputs. Best fit can be simulated in the way that pseudorandom number generators simulate randomness but it is ultimately arbitrary because there is no realtime analogue temporal competition generating best fit by analogue rules.

 

This gets very complicated and I know nothing about the transformer model. However, as you describe it the system injects some randomness, may be to mimic 'creativity'. There is no doubt that randomness is involved in creativity but my understanding is that it is not what actually provides the value of creativity, which is to identify patterns that have a higher 'value' than others and for reasons that have never yet been acknowledged.

What I don't see any mechanical system as doing is making use of genuinely continuous functions in generating a computed output in a way that reflects 'preference' based on local identification of value (rather than identification by the human user). At the levels Goldstone mode excitations I can see that being possible in post-synaptic integration. The integrator may be able to identify a 'higher value' that has never previously been identified in any rules. Something that would explain the haunting appeal of Erik Satie's music, which appears to simply break rules randomly but somehow creates one of the most evocative sounds ever created in a way that 100 other modern composers fait to achieve in spades.

 

You might want to look into Jacob Collier. He’s an exceptionally talented musician that believes that everything can harmonise with everything. And he’s talented enough to pull it off!

On a different note, Gymnopédie No. 1 is eerily similar to a very lovely soundtrack from the game Minecraft. That was a pleasant nostalgic surprise!

 

Sentience doesn't matter though. Brain evolved to feel to make you do something in response, so that you can survive and proliferate. AI obviously doesn't have need for that since it didn't evolve and hence has no delusional notion of self. So, it is purely a predictive model, an emulation without purpose. It's a tool, in other words. But that doesn't mean that AI can't be smarter than humans in figuring things out. It already is in some cases.

 

If you think about it, that's how human brains evolved too, out of needs to guess where the food might be and where the predators are, and then do something about it.

 

True, but that wasn’t my point. Humans are able to use logic to prove that something is true or not. LLM AI doesn’t do that.