10 years from now, we will realize we could have created AGI on a laptop 10 years earlier, here's why.

Posted by askchris@reddit | LocalLLaMA | View on Reddit | 46 comments

Lately I've been feeling there's something totally wrong with LLMs ... As much as I love them -- they are \*not\* AGI. Scaling them means consuming more and more data, billions of dollars and now AI companies are saying the new bottleneck is "electricity" ... Really? Our brains run on 20 watts. And we haven't digested the entire internet (like an LLM), and yet we're still "generally intelligent" able to learn online in LIVE environments ... LLMs seem more like a mashup of Wikipedia paired with "proprietary training data" made accessible via contextual "Google-like" search ... They reference our intelligence, but are \*not quite intelligence\*. I think we're missing something HUGE. The brain doesn't just "absorb" information from the world like a transformer model does. It plays with new information ... Wrestles with contradictions, and makes "sense" of new knowledge ... Solves puzzles ... when things finally click, it feels like an "aha!" moment. Our emotions reinforce what we learn ... We feel the elegance and beauty of it ... It stays in active memory "consolidating" and "packaging" it ... Making sure it fits without harming our existing knowledge structures ... All \*BEFORE\* it ever commits it to long term storage. Laptops are already faster than our brain in so many ways. It feels like we're distracted and totally confused about how intelligence really works. Moore's law (pace of hardware efficiency gains) has slowed down over the years ... But the pace of algorithm development has been doubling in efficiency every 6 months ... Far outstripping the gains in hardware. At this rate, 10 years from now our software algorithms will be extremely advanced ... And it will be \*obvious\* that we already had the hardware way back in 2024 to run AGI on our laptops ... But we didn't have the \*right\* algorithm or approach. Regular hobbyists want AI, AGI and eventually ASI on our laptops -- \*NOT\* controlled by big tech or governments. So let's change this. \*\* Let's build AGI on a laptop. \*\* This is worth working towards! Does anyone else feel the same?

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46 Comments

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MastodonFarm@reddit

Our brains may run on 20 watts, but it took three billion years to optimize the hardware.

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herozorro@reddit

could you show me this timeline and your source for it?

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MastodonFarm@reddit

[https://en.wikipedia.org/wiki/Timeline\_of\_human\_evolution](https://en.wikipedia.org/wiki/Timeline_of_human_evolution)

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herozorro@reddit

oh wikipedia!

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MastodonFarm@reddit

Which part of this do you doubt? Are you a creationist?

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herozorro@reddit

everything past 5000 years (or less) of recorded history is complete speculation. saying 'the mind was shaped in xyz during BILLIONS of years' is not based on any kind of reasonable argument since there is nothing to back it up especially when it comes something we know really nothing about - the human mind

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MastodonFarm@reddit

Got it, you're a science denier. No need to explain further.

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herozorro@reddit

you literally have identified yourself as someone who can not think, but must use labels to understand the world.

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MastodonFarm@reddit

And you believe in fairy tales. To each his own.

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herozorro@reddit

you sound quite young. learn to think for yourself

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MastodonFarm@reddit

A believer in creationism telling other people to think for themselves has to be the funniest thing I have seen today. Maybe you should try that, instead of deferring to an invisible man in the sky.

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herozorro@reddit

are you a teenager? look take your 'edgy' ignorance somewhere else.

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MastodonFarm@reddit

My friend, you're the one who chose an AI thread to argue in favor of the biblical creation myth.

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herozorro@reddit

id challenge you to find anywhere i said any of that. its you who has been projecting your labels, ignorance, and obvious hate.

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MastodonFarm@reddit

"There's no evidence the world is more than 5,000 years old" is a pretty good clue. I don't hate anybody. I have lots of Christian friends and family members, whom I love very much. They don't say silly things like "there's no evidence for evolution," or inject creationism into every discussion, but I would still love them even if they did.

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herozorro@reddit

you are quoting something i didnt say. here is what i actually said 'everything past 5000 years (or less) of recorded history is complete speculation." keyword RECORDED history. as the original thread was about the MIND OF MAN, there is ZERO ability to know what man was thinking prior to anything RECORDED by man. adn thus impossible to say how the MIND OF MAN has 'evolved' over billions of years. this is simple logic. no record, no ability to analyze. no ability to analyze, no ability to make conclusions. the only thing you can do is SPECULATE yes, i know that is probably too much for you to process as a teenager. Maybe put into AI to explain it to you

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MastodonFarm@reddit

What? Man has only existed for about 3-6 million years, depending on how you define "human." It took us 3+ billion years to evolve to that point from the first single-celled organisms. But sure, \*I'm\* the one with a substandard education.

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herozorro@reddit

and im the one that believes in fairy tales. lol. you are a very ignorant person

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Dayder111@reddit

I think that: 1. Going 3d, building multi-layer "compute cubes/brains" 2. Using low precision calculations, at least for inference (see Bitnet 1 and 1.58 bit and such) 3. Storing the neuron/synapse data as close as possible to the transistors that compare/accumulate them. And potentially, if enough layers are stacked, transistors and interconnects are small enough, the design is simple and transistor/interconnect-efficient enough... Building whole network physically, processing it all at once, potentially asynchronously. Not having to "emulate" it by constantly moving new and new parts of its weights from memory to process them, constantly causing huge losses on resistance of data travel paths. 4. If that's not enough, switching to 2D materials/better materials/carbon nanotubes. Help not just reach, but vastly surpass the biological brain's energy efficiency and speed. Biological cells are huge compared to even our today transistors. And likely contain some not so good for electricity conduction materials (although part of neural impulse transfer happens on mechanical, molecular level, I think?) I am not sure, but likely their losses on resistance, AND on their other biological functions, losses in energy generation and delivery, and so on, are much bigger than what nanotechnology allows to print, not grow with cells. Our devices are just "emulating" biological brains and neural networks in general in a very inefficient and unfit way, having to constantly move and then forget huge amounts of data, due to not fitting the whole network in registers/not having actual physical connections between neurons built in-hardware, if we think more about the root of the problem. Having to clock our hardware much higher, due to hardware/transistors/area being expensive. Energy is cheaper than producing and setting up much more chips, to compensate for their much lower clock rate. It is very inefficient, basically they are forcing the physical processes in circuits to go faster than they would happen "on their own", with more voltage, as we can't wait, can't afford to wait.

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ASYMT0TIC@reddit

You have some good points, but current chips based on the same Von Neumann architecture we've been using for decades don't work that way. What you're talking about is called neuromorphic architecture, and while there are examples in development, they aren't in your laptop.

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Dayder111@reddit

They will be, in the future. Unless control prevails and hardware that is actually capable of running very knowledgeable and deep-thinking ASIs will not be available to masses. As for the old laptops being able to run AGI, I guess it is most likely possible too. Not train, but run the inference only. Everything can be decomposed down to accounting dozens/hundreds/thousands/millions of facts and relations, represented as either code with if/else/switch or more elegant systems, or neural network connections. We could create AGI that works in an algorithmic way, the problem is, teaching it manually, even with best teams of experts working on it for years, will not provide meaningful results. It's impossible to account for every subtle but important connection, relation in things that we live and make decision based on. Many of them we do not even think about, when making decisions, and would not think about to incorporate them into AI algorithms. That's why this idea of AGI is once again gaining traction now, because now we have enough \*parallel\* computing power, algorithms that are good enough to make use of immense \*parallel\* computing resources, and a lot of digitized data... to combine it all and make the neural networks \*learn\* it all, including subtle but important connections, rules, patterns, automatically. In short, we could make AGI with if/else-like (or more elegant) algorithms, but never would have been able to account for anywhere near enough things, would get lost in the complexity of the system as it grows. And it doesn't, for most of the problems, require many things to process and account for, to run AGI. And so it doesn't requre a lot of computing power. Current architectures are inefficient, but simple enough to gain traction and reach some results, combined with a lot of computing power that is now available. The problem is not running AGI, the problem is creating it, teaching it, or finding a way to create a system that will teach itself effectively, since we ourselves would not be able to teach it well manually (with anything from literally adjusting weights/algorithms, to labeling the training data).

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ttkciar@reddit

Don't look at the brain's wattage; look at metrics which indicate how much work is being done. While we cannot estimate the brain's compute performance due to our incomplete understanding of neurons' computational complexity, we *can* estimate other things, like state change rate -- the typical adult human brain changes synaptic state at a rate of about 3PB/second. Maybe we can do something clever to implement general intelligence at a lower state change rate than the brain, and certainly slow-AGI would still have applications, but a laptop's main memory read/modify/write rate isn't going to cut it.

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Radlib123@reddit

I agree

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Master-Meal-77@reddit

What is the point of this post

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Dadud300@reddit

He probably took a mad rip off his weed pen and came up with it

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askchris@reddit (OP)

Well I have made some progress on my AGI project, but just realized I can't finish building AGI on a laptop alone -- so reaching out for the "loving and enthusiastic support" of my fellow redditors -- naively thinking there would be others out there like me -- before getting laughed off stage and going back to doing it alone I guess ... lol.

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alyssa1055@reddit

What kind of degree and work experience do you have?

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No-Marionberry-772@reddit

Degrees are nice and all, but they are a white list, not a black list. The only thing they tell you is that person has proved some basic level of learning. It doesn't really say much about actual skill or capability. They definitely don't say that people without them are incapable.

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askchris@reddit (OP)

No degree, just a hobbyist, been building technology and tech teams for 30 years, been deep into AI and neuroscience for the last 10. You don't have to believe me as I haven't published anything but I had a string of breakthroughs recently. So I'm looking to cautiously find the right people and build a team for developing AGI on a laptop.

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askchris@reddit (OP)

I want to build AGI with people who share the same concerns and dreams :)

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askchris@reddit (OP)

Who would downvote the fact that I'm trying to follow my dreams and find others to collaborate with? Just curious.

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jonahbenton@reddit

Brains are hardware, chemical/biological hardware. Laptop hardware is clumsy inefficient electrical silicon. There is no software that overcomes the limitations of silicon. LLMs are not agi but they are in the conceptual universe of the compression/generation brains do. 60 years of comp sci algorithms are beneath that conceptual universe.

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askchris@reddit (OP)

Yes LLMs are in the right universe -- I love LLMs but just realizing something is wrong. Brains don't do backprop and definitely don't update all the weights every time we make a prediction error (nor do we collect a bunch of prediction errors and batch them for backprop). It's too inefficient and dangerous. The brain is actually filtering most things out based on emotional utility and tries to make sense of the information before storing it so it doesn't lead to catastrophic forgetting. This means it uses a more efficient and structured update process than LLMs.

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jonahbenton@reddit

None of those descriptions are accurate in a technical sense for what brains do. There is no "make sense" neuron or chemical transaction. One's own mental model derived from "experience" for how one learns...does not have anything to do with the actual chemical and network mechanics of how a brain learns. LLMs are vastly, vastly simpler, in mechanical terms, than the brain. Computational metaphors and analogies really don't do justice to brain mechanics. Even a single cell is more intricate to explain and model than an LLM. There isn't anything wrong with LLMs. They are useful "language calculators" with some visual and auditory capabilities. No smell, no taste, no touch. No other descriptive attributes of what we call life. They are a neat trick, but very very very far from AGI.

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askchris@reddit (OP)

You're right -- you and I are talking about different levels of the same system. You're talking about neurons (more like the hardware layer) and I'm talking about how the neurons use prediction errors, along with higher level reinforcement signals "ie. making sense", learning by experience etc to build processes in the software layer, for example I see things like "working memory" as something more like flexible software running in RAM than hard coded processes in neurons that take days or weeks to physically rewire. Of course there's no single neuron involved in how the brain does everything, that would be like looking at single transistors to figure out how to build something like Linux. In the same way, we can't build AGI by thinking about how the neurons fire alone -- it won't magically "emerge" if we figure out how to simulate biological neurons. The brain has even more layers than a computer, and we don't even need most of these layers since we're not dealing with managing glial cells, regulating hormones or moving actual muscles. This is why there's a lot of room for optimization. We don't need 100 billion biological neurons to do the same thing as "general intelligence" (which is only a fraction of what the brain does anyways.) The brain is amazing but also flawed because of natural selection: It can't go back and engineer a system that works on a different computing paradigm, it can only build bottom up, repurposing what worked in primitive organisms, layer by layer, creating a lot of waste along the way -- even to the point that infants must learn how to see and how to use attention every single time, and how to access their working memory. It takes 18 years to build an adult brain rather than providing a better framework to infants from the beginning. The entire system is a mess, and yet it's running a type of "intelligence" on just 20 watts -- and most of those watts aren't even used by intelligence. This is why I believe it's possible to make a lot more progress than we are, and eventually build AGI on much simpler hardware (like a laptop).

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jonahbenton@reddit

Ok, sure, where "like a laptop" means "on future laptop-sized hardware." Would need advances like a room-temp superconductivity substrate for that hardware to be electricity based and using analogues of current compute engines (CPU/GPU/etc). Might happen! My bet would be on learning how to engineer biological/chemical compute, but who knows. Cheers.

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emteedub@reddit

I think it's impossible to achieve without electro-chemical processing, so some degree of organic compounds and fluids.

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askchris@reddit (OP)

Because of energy constraints? Or something else?

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emteedub@reddit

just attaining the compactness/density at that working energy, I don't think silicon can alone support AGI at least not at the scale. It would have to be sustained on much more hardware (and power).

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Dayder111@reddit

Why wouldn't just: 1) Going 3d, "compute cubes/brains" 2) Using low precision calculations, see Bitnet 1 and 1.58 bit. 3) Storing the neuron/weight data as close as possible to the transistors that compare/accumulate them. Potentially building whole network physically, processing it all at once, not having to "emulate" it by constantly moving parts of its weights to process them, before moving the next and next and next parts, constantly causing huge losses on resistance of data travel paths. 4) If that's not enough, switching to 2D materials/better materials/carbon nanotubes. Help not just reach, but vastly surpass the biological brain's energy efficiency and speed? Biological cells are huge compared to even our today transistors. And likely contain some not so good for electricity transfer materials (although part of it happens on mechanical, molecular level, I think?) Are you sure their losses on resistance, AND on their other biological functions, losses in energy generation and delivery, and so on, are smaller than what nanotechnology allows to print, not grow with cells?

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Creative_Bottle_3225@reddit

Just think that we went to the moon with the hardware of a Commodore Vic 20, and now with just a smartphone, we could travel around the universe.

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Marchello_E@reddit

Those who had to wait for Wodan to strike the nearest tree may have wondered about the power of having instant fire at the palms of their hands. Those who build the first room sized computer may have wondered how convenient it would be at the size of a slideruler. So yes, you have valid question.

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abhuva79@reddit

There is a fantastic talk from Geoffrey Hinton about the difference between digital and organic computing. Its really worth checking out. The takeaway is: \- digital computing is expensive but independend of hardware, ai can share accumulated knowledge easy as the operating system is always the same \- organic computing is cheap (energy wise), but highly dependend on hardware (your individual body), knowledge can not easy be shared Of course its a bit more indepth and complicated than this. But one thing is for sure, these are things that are already researched (organic based computing) - but its not sure that this would be the "correct" path - energy wise yes, but it has its cave-eats.

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askchris@reddit (OP)

Thanks for this insight u/abhuva79!, so a fully integrated "intelligence" is "cheaper" to run but running on a software layer makes it more transferrable? I never thought of it that way.

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ricka777@reddit

I think what you want is a time machine, quantum temporal knowledge accelerator.

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redjojovic@reddit

Progress in technology isn't always linear. Just like how 'there are decades where nothing happens, and weeks where decades happen,' we might soon realize that achieving AGI was possible years ago, but we just hadn't connected the dots