A fascinating story of "Patient Sh." [1], the man who did not forget.
[1] https://en.wikipedia.org/wiki/Solomon_Shereshevsky
"His memory was so powerful that he could still recall decades-old events and experiences in the smallest details. After he discovered his own abilities, he performed as a mnemonist; but this created confusion in his mind. He went as far as writing things down on paper and burning it, so that he could see the words in cinders, in a desperate attempt to forget them. Some later mnemonists have speculated that this was a mentalist's technique for writing things down to later commit to long-term memory. Reportedly, in his late years, he realized that he could forget facts with just a conscious desire to remove them from his memory, although Luria did not test this directly."
I believe that one need to have superhuman memorization abilities to have definite confusion due to too much remembered. More trivial explanation of this effect in normal ageing persons is age-related brain shrinkage.
> Obviously the brain is not a computer,
Our brain consists of approximately 86 billions quantum computers [2] controlling tens-of-thousands chemical neural networks with at least 10 coefficients, communicating [4] using lasers [5] (coherent light is laser light). [2] https://www.nature.com/articles/s41598-024-62539-5
[3] https://pmc.ncbi.nlm.nih.gov/articles/PMC11655932/
[4] https://pmc.ncbi.nlm.nih.gov/articles/PMC12230014/
[5] https://pubmed.ncbi.nlm.nih.gov/6204761/
Live with that. ;)I suspect the anxiety of being unable to forget things has an inherent selection bias. You will only stress over the things you can't forget, the things you have forgotten you won't stress over...
Generally claims of eidetic memories are overstated, doubly for older claims, but Nigel Richards memorized a French dictionary in 9 weeks, over 6k words a day, x2 including the alphagram.
I consider myself to have a decent memory, but that is 200x what I'd think myself capable of, assuming I want to retain it all at the end of the 9 weeks.
Our brain consists of approximately 86 billions quantum computers [2] controlling tens-of-thousands chemical neural networks with at least 10 coefficients
By that logic, a Blackwell GPU contains 208 billion quantum computers.
Each transistor is a quantum mechanical system and takes hundreds of model parameters to describe. So apparently a GPU is a 208-billion-node quantum supercomputer.
"The brain is not a computer" != "Parts / aspects of the brain do computation".
"A computer" in the first statement is IMO obviously intended in the common usage sense of the term, i.e. the brain is not a desktop computer or smartphone, or Turing machine, or etc, and thinking of it like these things will cause more error than insights. Also, billions of interlinked mini bio quantum computers arguably produce something with emergent properties and behaviour much, much more complex than "a computer". I am with GP, the analogy to "a computer" is not super helpful here unless you highly restrict the meaning.
But yeah, the Shereshevsky case is a super interesting one, thanks for linking!
The book Moonwalking with Einstein covers Patient S and memory in general. It's an enjoyable and quick read on the topic, would definitely recommend.
> Live with that. ;)
Well, sure, it's a computer of sorts, although in the abstract sense of being able to perform computations so is our liver, so perhaps not a very useful concept.
What I meant (as I assume you realize) was "not a von Neumann architecture computer", but I'd also fairly confidently assert that it's not a quantum computer either.
Our ANN model of a neuron is obviously too simple (especially being a synchronous model, not a real-time asynchronous one), but it's hard to imagine that all of the classical chemistry, let alone quantum, details are important. It's necessarily built out of chemistry, but selection is happening at the level of behavior - presumably depending only on a much higher level set of abstract capabilities (ability to learn, etc), not the exact details of chemistry.
The success of LLMs, a crude prediction mechanism built atop a crude ANN, does tend to support the idea that low level details don't matter. Timing will matter if we want to go beyond LLMs to AI that can learn time-based things and not just sequence order, but how much else will matter remains to be seen!