Can We Build an Artificial Hippocampus? 

your videos are amazing you are the Einstein of today

@@KnowL-oo5po Business of today.

Brilliant needs to have a course on reversible computation.

I would be interested to see code for this, if it is available of course

I have many mostly "working" "TEM transformer" models although I've never called them that. This idea is not new; just its current sysnthesis. Basically, all of the pices have been around for a while and I've been building models out of them. I don't ever have enough time or help to get them off the ground.

Yes same, I would love a more technical video on how this works too! Ur content is so awesome, currently studying CogSci and I wanna get into neuroscience and ai/agi development, thank u for all the amazing content:))

Yes please

+1, yes please!

Predictive coding sounds a bit like what LLMs do.

It's a path integrating recurrent neural network feeding into a Hopfield network

That is rather reminiscent of the mental lexicon networks mapped out by psycholinguists -- using priming in lexical decision tasks, and such. But in human minds, there are phonological as well as semantic relationships.

Computer scientist here: they do! I’m absolutely no expert on neuroscience, but computer science (a subfield of mathematics) has many relevant topics. One very interesting result is that if you start from the perspective of automata (directed graphs with labeled transitions and defined start and “accept” states) and you try to characterize the languages that they recognize, you very quickly find as you layer on more powerful models of memory that language recognition and computation are essentially the exact same process, even though they seem distinct. If you want to learn more about this topic, I have a textbook recommendation: Michael Spiders Theory of Computation, 3rd edition Additionally, you may be interested in automated theorem proving as another perspective on machine learning that you may not be familiar with. Neither automata nor automated theorem proving directly describe the behavior of neural circuits, of course, but they may provide good theoretical foundations for understanding what is required for knowledge, memory, and signal processing in the brain, however obfuscated by evolution these processes may be.

"Perfection is enemy of efficiency" - they say, but in the long run, quality wins when we run for trascendent work instead of immediate rewards. BTW, the same happend to me. Mine was the best work in the class. the only which also incorporated beauty, and the most efficient design, but the professor took 9/20 points because a 3 days delay. His lessons I never learned. I am not an average genius. Nor are you! No one has achieved what I predicted on human brain internal synergy. Here the result (1min. video). ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-25WsCtvOE98.html

Look up Micheal Levin and his TAME framework (technological approach to mind everywhere), cognitive light cones and the computational boundary of the self. He's due for an award of some type for his work very soon.

A renaissance even... maybe

I totally agree like a 3D net

Well, we’re still way miles off

@@aaronyu2660 Closer than you might think

@@jeffbrownstainno you need to see the neuron videos. Computers are binary and neurons are not. Besides, each bit of storage is able to be used to store multiple different files.

​​​@@cosmictreason2242 you can simulate the behavior of neurons in computers. There are still advantages to physical-biological neural networks but those could be simulated with a sufficient number of transistors. If it's too difficult they will end up using physical artificial neurons. What I understand that you mean by "each bit of storage is able to be used to store multiple different files" is that biological NNs are very effective at compressing data (ANNs also compress data in that basic sense), but there's no reason to think that carbon-based physical-biological NNs are unmatchable. I'm not gonna say that I have a conviction that it will happen sooner rather than later, and people here are also really vague regardless. What I could say is that I know of important technologists who think that it will happen sooner (others say that it will happen later).

What the video is talking about is using simple algos to build an AI, you reductive git.

LLM are not "AI" they're just freaking good parrots that give too many people the "mirage" of intelligence. A truly "intelligent" model doesn't make up BS to make you go away. A truly "intelligent" model can draw hands FFS. This is what's BS.

idk what you think "pure AI" means

Incredible. Were you on the Build-A-Brain team? Could you please direct me to anything you would recommend me read on your work there to familiarize myself and follow citations toward influence on present day research? Much respect, me

It is a separate intelligence. It communicates with the other cookie cutters by a back propagation similar to telepathic. It is as a plate of sand making patterns on it’s plate by harmonics. It is not human. It is a machine.

Thank you!!

Wow, thank you!

Don't forget groundbreaking inventions too! ;)

The missing ingredient is how to make NN changes on the fly when we receive sensory input, without backpropagation. There's no backpropagation in our brain

@@egor.okhterov The best work I've seen so far in that regard is the OgmaNeo project, which explores using predictive hierarchies in lieu of backpropagation.

@Charles Van Noland the last commit in github is from 5 years ago and the website didn't update for quite a while. What happened to them?

@@egor.okhterov There is an ICLR 2023 paper from Hinton: SCALING FORWARD GRADIENT WITH LOCAL LOSSES

Thanks :3

Ah, the eternal lexicon as gatekeeper, if only we had perfect information liquidity free of the infophysical friction of specific lexica, media, encoding, etc. Working on it:)

@@memesofproduction27 This specifically is a topic in LLM that I see seldom discussed. On the one hand, language is sometimes redundant or interchangeable (like "TEM" and "Deconstruction"), but in other cases the same word has different meanings, in which case "nuance" is required in order to infer meaning. "Nuance" IMO is just a residual consequence of a lack of generalization. Because the data/syntax is not well categorized into mutually-exclusively building blocks, and there is a lot of overlap allowing for ambiguities in the message. But this is not something that can be solved with architecture, the issue is that the language in itself is faulty and incomplete. For example, a lot of times people talk about "love" as a single concept, when in reality it is the conjoint of several feelings, hence the misunderstanding. e.g.: "I don't know how she is so in love with that guy..." So, whoever is saying that line has the term "love" misaligned with the actual activity taking place. Simply because too many underlying concepts overlap into the term "love". Another example, the word "extrapolation" can be interpreted as the act of completing a pattern following previous data points. The issue is that people don't usually use the term to mean "to complete", MMOs don't ask gamers to: "Please extrapolate the next quest" OR "LEVEL EXTRAPOLATED!".... I mean... THEY COULD... but nobody does this... Because of this, If you ask a LLM to make an extrapolation of something, depending on the context, it may or may not understand the prompt. This is because the AI is not actually intelligent, instead it is subjected to its corpus of pretrained data, and the link of "extrapolation/completion" is simply not strong enough because the building blocks are not disjointed enough and there's still overlap.

How to store location? Some kind of hash function of sensory input?

@@egor.okhterov that was the plan or some graph like data structure to denote relationships.

Thank you! Yeah, I do everything myself ;) Most of it is done in Adobe After Effects with the help of Blender (for rendering 3D scenes) and matplotlib (for animations of neural activity of TEM, random-walk etc)

The thing is that Jeff Hawkins is also against backpropagation. That is the last puzzle to solve. We need to make changes in the network on the fly, at the same time as we are receiving sensory input. We learn new models in a few seconds and we don't need billions of samples