Comment by Balgair | Hacker News Reader

Comment by Balgair | Hacker News Reader

This is a bad title, it should be "Where will advances in AI come from now?", In the first paragraph, the author states that AI is here, but asks where new AI advances will be from.

On that, in 2, the author talks about Brain Simulations and states that it'll take up to 500 years. I think that is a good upper estimate actually. Though optogenetics is a great tool, we need more. Mammalian brains can be up to 50% (at the low estimate, some say 90%) made out of astrocytes and glia. We know pretty much nothing about how they affect neuro processes, outside that they do, sometimes. We do know that is a huge problem with our understanding though, which is something I guess. Optogenetics should help tease out some of those glia methods, but as usual, it will raise more questions than answers. Another issue is potentation and its molecular mechanisms in more than just the hippocampus. That work is incredibly tough, but we really only focus on the anatomically 'easy' areas for memory. We have to do more to understand. In short, we don't even know the correct questions to ask when it comes to the brain and understanding it. A full simulation, or a '80%' approximation even, is a few paradigm shifts away still (maybe, we cant know yet). It will likely have to be a 'full world' simulation too, where you have to control all the inputs to the brain and therefore have to model a world first. The brain's best feature is that it is all connected and a giant spaghetti mess. This is, obviously, a long ways away.

But isn't that the wrong premise?

Our brains emerged without a designer or an undertanding of how the brain works (I assume you don't believe in god) from simple principles and through the evolutionary process.

Why do we need to understand the brain for brain simulation to happen?

I don't know how long it will take but I don't understand any claims about how long it will take.

It can be forever or it can be in a couple of years depending on what we uncover.

For all we know our giant network of computers and sensor are already kind of a brain simulation.

The evolutionary process took billions of years (hundreds of millions for the mammalian brain to be specific) so even though we're intelligent designers we don't quite have a precise concept of what intelligence is let alone know how to implement it. We don't know what data structures or processes make true human intelligence so while it's possible we stumble on our own version, working with what we already have as a baseline seems far more practical.

Only if what you set it as criteria to understand the brain you are simulating, it might be that the brain can be simulated without us knowing how.

The evolutionary process can be simulated or replicated in digital space and go through iterations much faster no?

To engineer anything, we need to understand it. The options you are weighing are between understanding the brain so we can write programs that run on robots that would exhibit human-like behavior, and understanding the nature of organic evolution to the point where we could simulate every organism's survival and death that lead to the autonomous design and formation of the human brain. To estimate engineer-ability requires understanding also, but I think we understand enough to know simulating the whole of evolution is not an easier problem.

The brain wasn't built, it emerged. The question really is what he have to engineer. My belief is that it's not the brain we need to build but the environment it can emerge in.

A single human brain is far simpler and smaller than the billions of years of history and the trillions of non-human brains that you're suggesting be reproduced. The human brain did not evolve in isolation. It evolved to fill a niche that was created by a massive amount of history and circumstance.

If we can wait hundreds of millions of (simulated) years, then sure. But if we don't want to simulate the environment in the hopes that it will eventually emerge, eg, because we want it sooner rather than later, then we need to engineer it to be so and for that we need some level of understanding.

Of course we need understanding, the point is whether we need to understand it all. I don't believe that's the case.

Ah, I agree then.

Sure, that's the (supervised or unsupervised) learning process in Machine Learning stuff (which is what AI is nowadays, the rest is just rule engines, expert systems, and so on).

But as the complexity grows computational requirements grow much faster.

So, a brute force approach needs super-duper-exponentially more computation. And so far we are not at that level.

Depends on what you mean with bruteforce. I am not talking about blindly randomizing until luck strikes us. Evolution works by incremental change in any given environment no?

Evolution is blindly randomized mutations except passed through a fitness function. It behaves like a script kiddy using Jack the Ripper to crack a password the size of Hamlet, except natural selection actually does have billion of years and trillions of computational nodes to achieve its end goal (survival, for which intelligence is just another tool).

My point is that the difference between each iteration being completely random and then each iteration building on the previous means that we we going to need far less computing power than one might think.

Even if it's not completely random, if we don't have a good model (that is, a not too simple yet very efficient representation of the problem), the complexity instantly leaves us dead in the water.

Whole brain simulation is the holy grail, because it's brutally computationally intensive, yet gives us nothing, because it's a black box. (Or a white box, but so white, you can't infer anything from it. It's just a big neural network that just works, things ripple back and forth, and it's sort of watching a busy beaver Turing machine do its work.)

>>(I assume you don't believe in god)

Probably not a good assumption to make, irregardless of my own beliefs or possible lack thereof. 79-89% of Americans do believe in God as of June [0] and '~1/3rd of scientists' (very nebulous here) believe [1].

I would not say that our brains 'emerged... from simple principles' in any way. Maybe in jellyfish in the pre-cambrian, but not in mammals today. A very large amount of your genome is dedicated to neural development and 'proper' patterning (I think is ~2%, but can't find the citation) and that ballet of in utero development is fascinatingly complex, even in a zebrafish.

But, on to your question. No, I agree with you, we do not need to understand the brain to let a simulation happen. However, to properly simulate the brain, we do have to know. Otherwise, how would we know that we have properly simulated it? I know that is a cop-out answer, but it stands. Look at physics. We can set boundaries for the experiment, calculate noise floors, have a 5-sigma threshold, etc. We would have to do that with a brain simulation in order for it to have any real meaning. Yes, we could inadvertently simulate a brain, but then we would not be able to definitively say we have done so without understanding it. This discussion is much more complicated and nuanced and really hinges on the definition of the word 'understand', an argument that has been going on since Plato and before that even.

Speaking as a person working deeply in the neuro field, it is going to be a long time, not 'a couple of years'. Yes, surprises happen, and history is littered with paradigm shifters. But is is precisely because those events are so novel and rare that we celebrate them. Most science is very long hard work and consists of experiments that just set ever smaller limits to more and more nuanced phenomena. Take it from me, neuro has a very very long way to go before we have even a passing understanding of the brain.

Still, you are correct, our current internet may already be a super-brain, a silicon demi-god. If that is so, then it is behaving just like the one that many people in the US believe in: taciturn and quiet.

[0]http://www.gallup.com/poll/193271/americans-believe-god.aspx

[1]http://www.livescience.com/379-scientists-belief-god-varies-...

You don't need to understand how birds fly to build something that flies. Heck, you don't even need a good understanding of aerodynamic lift (or rocketry topics). But it really, really helps.

You can simulate the brain by simply increasing the precision of your measurements of it, and throwing more nodes at your simulation. But it really helps to know which parts are functionally relevant so you can focus on simulating those parts rather than e.g. the ion channels that implement the spiking (if those details turn out to be irrelevant).

(For that matter, even if you did simulate the human brain well, it will probably only be good for mimicking things that evolution selected humans for in human contexts, like emotion identification; when it comes to general inference, the human brain structure is not optimal.)

In his book "Antifragile" Nassim Taleb has a long rant about "teaching birds to fly" and the fact that practice usually precedes theory rather than deriving from it. He mentions jet engines as a case where the technology developed without any theory of how it worked...

Another way to look at that is to say that we didn't need to engineer gravity and air.

I.e. there might be simple rules that we can apply which in combination with the environment we are in (or create) allow for awareness.

A good analogy I heard is that we're able to design airplanes because we have fundamental concepts like Bernoulli's Law, mechanical engineering, etc.

We can't (yet) 'design' an intelligence because we don't have a fundamental 'law' of consciousness to build from.

Another way to look at that is to say that we didn't need to engineer gravity and air.

A hard problem to be sure. But much of a human brain is a uniform matrix that self-programs. If we duplicate that blindly, we won't have to figure out how that works or have to program it.

Still a lot to do before that I guess. But 500 years? What else in human history has taken that long to figure out? The age of technology is shorter than that.

> But much of a human brain is a uniform matrix that self-programs.

Speaking as a neuroscience PhD ... no. Not remotely close, sorry.

The cortex? How would you describe it then? There are not enough genes in our genome to wire every connection, so self-organizing.

The visual system is basically a deep neural net of the type we are already building, but it's self-contained. Generally it seems like our brain is composed of a lot of fixed-function neural nets (and other algorithms) which are then integrated by less specialized units. That's my impression anyway.

I think maybe our artificial visual nets are too general, actually, in comparison to natural ones. It not only describes what it sees, but tries to analyze it too. Showing a picture of a dog results in "dog", rather than "big four-legged furry thing". Expecting a visual system to know that a chihuahua and a German shepherd is the same thing is maybe a case of grouping tasks that shouldn't be grouped.

In order to build an artificial visual system, humans must grasp what their own visual system does.

In order to describe what the human visual system does we use words like "dog" -- and I would agree with you, "big four-legged furry mammal" might be better, but artificial visual systems work more at the level of "80% probability of dog," "2% probability of brick," "90% probability of chihuahua," and they don't really know that chihuahua must always imply dog.

In order to describe the function of the visual system, a fundamental change really must happen in the way the system's output is described. Using plain written words as output may work as a system but it is not an exact replica of a human visual system. It is more of a trained animal act: when you see dog-like images, press the button labelled "dog" (does the animal even comprehend what a dog is?)

In order to rethink the way we describe output, we will have to come up with a very precise description of the inside potentials and activations in mammalian brains. We are just barely creating tools to describe what an artificial neural net "is thinking." So I guess we're starting to look in that direction.

Genes encode some of the rules by which the the neurons self-organize, or "grow". Further growth from an initial cell is partially controlled by environmental factors.

This is not a novel or unique application, literally all living things grow by using the rules encoded in DNA and environmental factors.

Consider the demo scene, where 64 kilobytes of code generates a fascinating video with deep complexity. You could not encode a 64k demo as a series bitmapped frames in 64k, or probably in 64 megabytes. But that doesn't mean that you can generate one by stuffing together a bunch of RGB pixels and hoping they self-organize.

I'm going to echo this and add a bit more context. Look at the cerebellum for example. That lump at the bottom of your rear skull has ~50% of all your neurons. It is extremely important for motor control. When you grab a cup of coffee as you dash out the door in the morning, your cerebellum is what is responsible for you being able to do that without looking at the cup as you reach for it. You 'know' the cup is there and you don't have to tell every nerve to fire every muscle and to extend your forearm and then grab it with only 3 fingers and then adjust for the decrease in weight with every sip. You just do it, naturally. Your cerebellum is what is responsible for that. It is a dedicated portion of your brain that really only does those kinds of things.

Your hippocampus also deals with short term memory. HM was a famous patient that had his hippocampus removed, basically, and then lost all of his short term memory. Poof, gone forever.

In short, your brain has dedicated areas in it that only do certain things, it is NOT some FPGA that gets re-wired on the fly. Your intelligence is not dependent on the number of connections or neurons.

And it develops this way, controlled by DNA. If the brain were really a "uniform matrix that self-programs", think how unlikely it would be that the same functions are performed in the same areas in every person. Everyone's language area (to use a rough term) is in the same place in the brain. Everyone's visual cortex is in the same place. And this holds for even fuzzier concepts like, say, 'concepts', to a point. And this holds for even non-primates - take a squirrel, and you'll find its visual cortex right where you'd expect it.

> If we duplicate that blindly, we won't have to figure out how that works or have to program it.

...said every failed AI researcher ever.

Well, and probably all the ones that are still working (I won't say succeeded). It's part of the journey I guess!

What typically happens is that after a few hundred years, people realize that the questions that prior generations were asking were laughably wrong.

> But 500 years? What else in human history has taken that long to figure out?

Powered, heavier than air flight falls into that ball park. da Vinci was making doodles in the (late) 15th century, but this was only accomplished by the Wright brothers in the (early) 20th century.

Edit: After additional research, I discovered that recorded attempts at manned flight can be found as far back as the 11th[1] century! So it took roughly 700 years to figure out, if not longer as earlier attempts were likely not recorded.

1. https://en.wikipedia.org/wiki/Ornithopter

Interestingly the challenge was finding a power source with enough thrust-to-weight for power flight. Heavier-than-air flight can be decomposed into "power source," "stability of design," and maybe "scale". So for instance, Da Vinci came up with pretty good designs for heavier-than-air unpowered flight.

Our current decomposition of mammalian intelligence seems somewhat weak in comparison: we've divided the mammal brain into regions, yes, but we still haven't made an accurate simulation at the electrochemical level of a functioning mammalian brain.

That's not to say nobody has or is trying, only that our simulations are more like approximations at present. They also don't have good simulations of astrocytes and glia.

> If we duplicate that blindly, we won't have to figure out how that works or have to program it.

Genuine question: what would be the value of blindly duplicating a brain without figuring out how it works (and as a consequence, without knowing how to change/improve how it works)? After all, we already know how to create human brains, and it doesn't involve tech at all :)

Also that process can be a great deal of fun!

You can take that artificial brain and run it at twice the speed, hook it up to the internet, reset it when it goes insane, etc. A lot of mad scientist options that aren't possible with human brains.

Maybe, but I'm not entirely convinced. If it's blind duplication, what have you learned from it? How would you know how to reset it or speed it up, if you don't understand how it works? We don't know how to reset human brains.

Also, blindly duplicating a human brain would raise serious ethical implications, especially if you intend to perform experiments on it. If it's clearly an appliance which only superficially resembles a human brain, I don't see the problem. If it was created blindly, and you're unsure how much it differs from a child's brain, would you cut it open, hook it to the internet or reset it at will?

Any duplicated human brain to which you can provide even the most basic sensory inputs like pain, is equivalent to torturing a real human..

I don't think these sort of experiments should be allowed.

The Antikythera mechanism was invented somewhere between 205 BC and 100 BC. And yet it took us more than one and a half millennia to get to the first mechanical astronomical clocks.

> But 500 years? What else in human history has taken that long to figure out?

Calculus took a few thousand years to figure out.

Other inventions that have taken 500 years are not comparable: We're now 7bn people whereas the total number of people since the beginning of Earth is ~100bn. That means research can advance as much in 1 generation as it advanced in a thousand years, and it's accelerated by 1/a much greater proportion of people who can do research full time compared to <1% at the time of Da Vinci, and 2/greater access to better tools.

Simulating a brain doesn't give you general purpose AI. Brains are useless. You can get one pretty easily, but what would you do with it? A brain without a body wouldn't be intelligent at all.

So, ok, shift the goalposts to simulating a brain and a body. But that's not particularly intelligent either. If you dropped an adult human in the vacuum of space (with life support) nothing particularly intelligent would happen either.

Ok, well shift the goalposts to simulating a brain and a body and an environment for it to live in. Still won't be intelligent. Humans raised without any socialization have serious cognitive defects. Not really "intelligent" for our purposes.

Well, so simulate a brain in a body and a social environment. No we've introduced a chicken and egg problem.

Maybe you give the AI a robot body and allow it to live amongst humans. That would work, and also removes the need to simulate the environment. A huge win.

But is that any different than a human human?

Ok, you want this virtual brain to be way bigger... 100x the neurons of a human. But still with the human body and socialization. How is that different than a human with a cloud-connected smartphone?

Right, bandwidth. Elon musk says a smartphone doesn't have enough bandwidth and we need a BCI. But raw UI bandwidth doesn't seem tk have increased for a couple decades now. Smartphones actually have less bandwidth than laptops. But we keep being able to do more and more with better software. Is bandwidth really the limitation?

And if bandwidth isn't the limitation, does an AI really have an advantage over a cloud-augmented human brain?

If what you are saying is true, then sentient AI will almost certainly come out of cellular automation far far sooner than from accurate simulation of organic matter. Though I can't pin down exactly why, this makes me much more certain that we live in a simulation. Perhaps it's the automata-like nature of quantum physics.

If there is life in cellular automata, is it brought to life by symbolically representing it with voltage differences in a computer, or is it alive whether we run the simulation or not?

I think the second makes more sense, the implication is that a simulation world need not be simulated at all.

It's just as real inside if it isn't symbolically represented somewhere else as if it is, and ending the calculation of state transitions doesn't change what the next state is.

Especially with things like Hash Life. It really bleeds across into philosophy. This concept is explored in a book I recently recommended on HN. Permutation City. You'd probably get a kick out of it.

If I can't interact with it, whether I think it is alive or not is moot, and it might make sense to capture that fact in the definition.

In the definition of life? I'm not sure... It seems like a very anthropocentric definition. Isn't it enough to observe it interacting with the things it can interact with, and base the level of aliveness on that?

Definitions exist to serve us, not the other way around, so anthropocentricity isn't exactly a sin.

If we base our definition of life on observed interactions, we reacquire the same problems we had before, where we have difficulty deciding whether or not deceased ancestors / fictional characters / miscellaneous accidental Turing machines are alive. To be sure, a restricted subset of observed interactions are good proxies for determining life: if I see a bird interacting with a worm outside the window, it's a good bet that I could get up and scare it off / put out birdseed / etc, but I'd argue that if you tease out the restrictions required to define the subset of observed interactions that constitute life you're liable to find yourself right back at the interaction definition.

Reply on news.ycombinator.com