Hacker News Reader

Comment by gadjo95 | Hacker News Reader

The most relevant part of the article:

David Duvenaud, an assistant professor in the same department as Hinton at the University of Toronto, says deep learning has been somewhat like engineering before physics. “Someone writes a paper and says, ‘I made this bridge and it stood up!’ Another guy has a paper: ‘I made this bridge and it fell down—but then I added pillars, and then it stayed up.’ Then pillars are a hot new thing. Someone comes up with arches, and it’s like, ‘Arches are great!’” With physics, he says, “you can actually understand what’s going to work and why.” Only recently, he says, have we begun to move into that phase of actual understanding with artificial intelligence.

Hinton himself says, “Most conferences consist of making minor variations … as opposed to thinking hard and saying, ‘What is it about what we’re doing now that’s really deficient? What does it have difficulty with? Let’s focus on that.’”

View on HN

Last I checked bridges came before Newtonian Mechanics and it seems strange to argue this wasn't a good thing. Admittedly paper writing wasn't the main mechanism of transmitting knowledge but it's fairly common for human engineering to come before the full theoretical foundations as opposed to after.

It's not that bridges before Newton were bad, it's that Newton gave us the ability to design the strongest possible bridge of a given shape with the materials at hand - using not just calculus but calculus-of-variations, a subject nearly as old as Newtonian mechanics [1]. With this knowledge, what happens when one adds one or two columns to a bridge is now longer "news" the way it might have been before Newtonian mechanics.

A stereotypical picture of an engineering approach without scientific knowledge would be a list of ways to do stuff combined with hints about how to vary the approach per-situation. It requires lots memorizing, trial-and-error and experts that often can't fully explain their reasoning. It's easy to believe bridge-building before Newton was like this though I'm not an expert. Present day AI sounds a lot like from what I've read (though I'm not an expert here either).

Edit: And yes, one could argue that the progress Newton ushered in merely replaced one list of models with a higher, more general list of models - yes, but that is how progress gone so far.

[1] https://en.wikipedia.org/wiki/Calculus_of_variations

Present day AI on the Deep Learning side is a lot like what you describe. We haven't really had the Newtonian foundations yet. The theoretical foundations are quite limited because they are hard to figure out. But the techniques with less established theory work far better on most applications in AI. Redirecting work into areas of AI that have more solid theoretical foundations but worse application performance is not the way forward. I'm all for figuring out hard theoretical foundations but I'm strongly opposed to redirecting research funding to techniques that result in worse applications. I'd also argue modelling the physics isn't always the right approach: vocal tract modelling for speech is an interesting approach that produces much worse speech than state of the art synthesis techniques. It will probably continue to do so for a long time. For vocal tract modelling to produce better synthesis you'd need the physical model to be less lossy in all it's parameterizations and modelling simplifications than any statistical fitting of data. And you'd still need some statistical model of the choices the human makes in producing speech and you'd want that statistical model to work better than the neural network that takes on a larger portion of the problem and replaces the physical model of sound production.

I should point out in this case it's almost certainly a genuine call to research the foundations underlying the working techniques more as Duvenaud publishes research using mostly the techniques that work well on applications.

You're right that the analogy doesn't imply that something analogous to physic is the answer.

However, I would mention that there's a larger "overhead" than many realize to methods which work without the creator or the user understanding why. You have "racist" AI which don't undertstand that correlation may not be causation in questions like whether someone should be paroled or get a loan, you have the AIs subject to adversial attacks of various sorts, where not knowing why the AI works is also problematic, you have a situation where the target to match varies over time and so-forth.

Which adds up to AI having more dimensions to it than simply "working well" and "working less well". Indeed, AI is effectively ad-hoc statistics with result derived heuristically.

So in the process of "getting things right" exploring all sorts of things certainly sounds good, it seems like there's an "understanding gap" that needs to be closed and some broader model of what's happening would be useful but naturally there's no guarantee we can find one.

I completely agree with your assessment, but the problem is a bit worse in my opinion. We already have a pretty firm grasp of how different ML systems learn and converge towards a solution in the average case. It's not that we need to understand our neural networks better, it's that we need to understand our problem domain better. We can't determine how well some ML architecture will perform at an object recognition problem without some math describing object recognition. This makes things a lot more complicated, because it means we have to do a lot more work to understand every single application where we want to use ML.

And, of course, if we had some really good mathematical framework for describing and reasoning about object recognition, we probably wouldn't need to turn to ML to solve it ;)

The whole point of Deep Learning is that we don't want to describe math behind object recognition; it was the failed "classical" approach where people spent decades figuring out complex features which worked horribly. Deep Learning is actually pretty simple, well understood and parallelizable, and it's basically a billion-dimensional non-linear optimization. As optimization is infested with NP-hard problems, it's as difficult as it gets. It's actually amazing what we can do with it in the real world right now (and we are still far away from seeing all its fruits). Of course, it would frustrate academics that can't base AGI on top of it, but did they really think this approach would do it anyway?

Deep learning does not seem to abstract very well. Train on a data set then test with images that are simply upside down and the preformance can be significant.

Feature extraction also works much better when you toss a lot of data and processing power behind it. So, a lot of progress is simply more data and computing power vs better approaches. Consider how poorly deep leaning works when using a single 286.

> Deep learning does not seem to abstract very well. Train on a data set then test with images that are simply upside down and the preformance can be significant.

But that's true of people too. How quickly can you read upside-down?

If you trained on a mixture of upside-down and right way up images, and tested on upside-down images, performance wouldn't take that much of a hit.

> But that's true of people too.

Sure, the problem is we are more willing to ignore failures that are similar to how we fail. IMO, when we compare AI approach X vs. Y we need to consider absolute performance not just performance similar to human performance.

Deep learning for example gains a lot from texture detection in images. But, that also makes it really easy to fool.

While I can't easily read upside down text, I can instantly recognize it as not only text, but that it needs to flipped upside down in order to be read. That's something current "deep learning" AIs can't do reliably, if at all.

If I had to describe the root cause of this problem it would be that humans process "problems" rather than "things" and we "learn" by building an ever growing mental library of problem solving algorithms. As we continue to "learn", we refine our problem solving algorithms to be more general than specific. Compare that to a deep learning AI that learns by building an ever greater data library of things while refining algorithms to suit ever more specific use cases.

I think you're describing a level of generalization above the application at hand. We could easily train a neural network to recognize the orientation of a font, and then build an orientation invariant "reading" app by first recognizing the rotation of the text, transforming it so it is right side up, and then recognizing as normal.

I tend to imagine our brains works similarly. It's not that you have a single "network" in your brain that recognizes test from all angle, but your brain is a "general purpose" machine with many networks that work together. I think current deep learning techniques are great for discrete tasks, and the improvement needed is to have many networks that work together properly with some form of intuition as to what should be done with the information at hand.

There is work on rotation invariant CNNs, but I'm not sure why you would expect that property to just fall out of standard CNNs.

As much as architecture research gets denigrated these days, MLPs aren't what set off the revolution.

It's not that we need to understand our neural networks better, it's that we need to understand our problem domain better.

How 'bout "creating models that can work with more dimensions of the problem domain than are conveyed by standard data labeling"?

I mean, we don't simply want AI but actually "need" it in the sense that problems like biological system are too complex to understand without artificial enhancements to our comprehension processes - thus to "understand the problem domain better" we need AI. If it's true that "to build AI, we need to understand the problem domain better", it leaves us stuck in a chicken-and-problem. That might be the case but if we're going find a way out, we are going to need to build tools in the fashion humans used to solve problems many times before.

It will probably play out like a conversation. A data scientist trains an ML model, and in analyzing the results discovers some intrinsic property or invariant of the problem domain. The scientist can then encode that information into the model and retrain. And that goes on and on, each time providing more accurate results.

As an aside, I think it's important that we find a way to examine and inspect how an ML model "works". If you have some neural network that does really well at the problem, it would be nice if you could somehow peer into it and explain, in human terms, what insight the model has made into the problem. That might not be feasible with neural networks, as they're really just a bunch of weights in a matrix, but this is practical for something like decision trees. Just food for thought.

A data scientist trains an ML model, and in analyzing the results discovers some intrinsic property or invariant of the problem domain. The scientist can then encode that information into the model and retrain. And that goes on and on, each time providing more accurate results.

Mmmaybe,

It's tricky to articulate what pattern the data-scientist could see ... that an automated system couldn't see. Or otherwise, perhaps the whole "loop" could be automated. Or possibly the original neural already finds all the patterns available and what's left can't be interpreted.

I imagine you need the data science to discern semantically relevant from irrelevant signals. How else do you “tell” your model what to look for? You could easily train for an irrelevant but fitting model.

The human participant may consider multiple distinct machine results, each a point in the space of algorithm, data set, bias applied to the problem domain. Human intuition is injected into the process and the result will be greater than the sum of the machines and a lone human mind.

What is interesting to note, now that above idea is considered, is that this process model itself belongs to the set of human-machine coordinations. Another process model is where low level human mind is used to perform recognition tasks too hard (or too slow) for machine to perform, for example using porn surfers to perform computation tasks via e.g. captcha like puzzles.

Long term social ramifications of all this is also interesting to consider as it motivates machines to breed distinct types of humans ;)

This is somewhat practical for neural networks. For example, instead of minimizing the loss function, why not tweak the input to maximize a neuron’s activation? Or with a CNN, maximize the sum of a kernel’s channel? This would tell us what the neuron corresponds with. This is what Google did with DeepDream.

An explanation/tutorial, with clean images of the process: https://github.com/tensorflow/tensorflow/blob/r0.10/tensorfl...

Google’s investigation of it’s GoogLeNet architecture: http://storage.googleapis.com/deepdream/visualz/tensorflow_i...

Now, I say somewhat because results can be visually confusing, ex Google’s analysis. Even then, we can see the progression of layer complexity as we go deeper into ImageNet. Plus, we can see mixed4b_5x5_bottleneck_pre_relu has kernels that seem to correspond with noses and eyes. mixed_4d_5x5_pre_relu has a kernel that seems to correspond with cat faces.

It's work remembering that many times progress of held back by ideas that "aren't even wrong." The Perceptrons book wasn't wrong; it just attacked the wrong questions with an inadequate level of certainty in it's assumptions. It may be that we feel that we understand where machine learning is at now, but actually have a huge amount to learn because of inadequacies that we aren't even aware of.

> It requires lots memorizing, trial-and-error and experts that often can't fully explain their reasoning.

You just described all of software engineering.

Yeah - but we should call it "programming" or if we want to indicate a wider activity "software development".

>using not just calculus but calculus-of-variations, a subject nearly as old as Newtonian mechanics [1]

bridges aren't catenaries and Euler and Lagrange gave us the Euler-Lagrange equations, not Newton.

It's been a long time since I got my BE in Mechanical Engineering, but I still remember being struck by the difference between well-understood engineering and rule-of-thumb engineering.

Bridge building is mostly well-understood engineering. When you study Static Mechanics [0] you learn all sorts of Physics equations, including Newtonian Mechanics, that completely describe the forces and motions of a structure based on measurable physical properties of the materials used and details about the shapes of those materials.

When you get into Fluid Dynamics, things are different. You start to encounter a bunch of things like Reynolds number [1], which is a dimensionless value related to turbulence that you just have to look up for the particular fluids and velocities you're working with. This number is pretty well defined, but there are a lot of others and their definitions and meanings aren't nearly as clear as F=ma. Back when I was in school, particle simulations for turbulent fluids was just beginning to be feasible, so to design something you plugged in dimensionless constants and didn't worry about the unpredictable fine-details. An example of this is the wind blowing through a bridge's structure, and water flowing around its base. The equations don't give you exact forces that the turbulent air and water will exert; they give you more of an average over time. A simulation, if you can do it, can show you things (like resonance) that the equations won't show you.

Then there was Strength of Materials. Here, the big thing was the Factor of Safety [2]. This is solidly in the rule-of-thumb engineering camp. This is where the engineer says "I think two 16" steel beams would be sufficient... so lets use three 20" beams just to be sure." This is still the way a lot of engineering design is done, because the real world is never precisely known, and the factor of safety will save you when something unexpected happens.

[0] https://en.wikipedia.org/wiki/Statics

[1] https://en.wikipedia.org/wiki/Reynolds_number

[2] https://en.wikipedia.org/wiki/Factor_of_safety

These topics bring me back.

The "rule of thumb" engineering that you speak of made me remember the different constants that were taught we should just accept as is because, well, it is considered constant. Nevermind where the guy in the book got it from, this is what works and this is what people in the industry has accepted to be standard.

But a good degree should provide you with at least some of the insight and intellectual equipment to check for yourself or to smell a rat when you encounter a complex situation so that you can call for help rather than watch in horror as the house collapses on your clients.

Cathedrals regularly fell down, and domes, and no doubt bridges too.

Our friend wikipedia have a good list :

https://en.wikipedia.org/wiki/List_of_structural_failures_an...

Yus - bridges.

OTOH, we are merely at circa Year Five into deep reinforcement learning research.

It started as a cluster of 16M CPUs having taught itself to recognize a cat 95% of the time after training on 1B google images.

And we are now at One-Shot Imitation Learning, "a general system that can turn any demonstrations into robust policies that can accomplish an overwhelming variety of tasks".

One Shot Imitation Learning

https://arxiv.org/abs/1703.07326

It's easy to recognize a cat 95% of the time. I can write a program in 30 seconds that will recognize a cat 95% of the time. No, wait, this just in! My program will recognize a cat 100% of the time! The program has just one line:

     Print "It's a cat!"

Tutorial: So, with that program, whenever the picture is a cat, the program DOES recognize it. So the program DOES recognize a cat 100% of the time. The OP only claimed 95% of the time.

Uh, we need TWO (2), that's TWO numbers:

conditional probability of recognizing a cat when there is one (detection rate)

conditional probability of claiming there is a cat when there isn't one.

The second is the false alarm rate or the conditional probability of a false alarm or the conditional probability of Type I error or the significance level of the test or the p-value, the most heavily used quantity in all of statistics.

One minus the detection rate is the conditional probability of Type II error.

Typically we can adjust the false alarm rate, and, if we are willing to accept a higher false alarm rate, then we can get a higher detection rate.

With my little program, the false alarm rate is also 100%. So, as a detector, my little program is worthless. But the program does have a 100% detection rate, and that's 5% better than the OP claimed.

If focus ONLY on detection rate, that is, recognizing a cat when there is one, then it's easy to get a 100% detection rate with just a trivial test -- just say everything is a cat as I did.

What's tricky is to have the detection rate high and the false alarm rate low. The best way to do that is in the classic Neyman-Pearson lemma. A good proof is possible using the Hahn decomposition from the Radon-Nikodym theorem in measure theory with the famous proof by von Neumann in W. Rudin, Real and Complex Analysis.

My little program was correct and not a joke.

Again, to evaluate a detector, need TWO, that's two, or 1 + 1 = 2 numbers.

What about a detector that is overall 95% correct? That's easy, too: Just show my detector cats 95% of the time.

If we are to be good at computer science, data science, ML/AI, and dip our toes into a huge ocean of beautifully done applied math, then we need to understand Type I and Type II errors. Sorry 'bout that.

Are we learning yet?

Thanks for explaining, this comment is much more useful than your original to someone not well versed in statistics like me.

Some people much prefer short comments, or so they say!

Here is statistical hypothesis testing 101 in a nutshell:

Say, you have a kitty cat and your vet does a blood count, say, whatever that is, and gets a number.

Now you want to know if your cat is sick or healthy.

Okay. From a lot of data on what appear to be healthy cats, we know what the probability distribution is for the blood count number.

So, we make a hypothesis that our cat is healthy. So, with this hypothesis, presto, bingo, we know the distribution of the number we got. We call this the null hypothesis because we are assuming that the situation is null, that is, nothing wrong, that is, that our cat is healthy.

Now, suppose our number falls way out in a tail of that distribution.

So, we say, either (A) our cat is healthy and we have observed something rare or (B) the rare is too rare for us to believe, and we reject the null hypothesis and conclude that our cat is sick.

Historically that worked great for testing a roulette wheel that was crooked.

So, as many before you, if you think about that little procedure too long, then you start to have questions! A lot of good math people don't believe statistical hypothesis testing; typically if it is their father, mother, wife, cat, son, or daughter, they DO start to believe!

Issues:

(1) Which tail of the distribution, the left or the right? Maybe in some context with some more information, we will know. E.g., for blood pressure for the elderly, we consider the upper tail, that is, blood pressure too high. For a sick patient, maybe we consider blood pressure too low unless they are sick from, say, cocaine in which case we may consider too high. So, which tail is not in the little two set dance I gave. Hmm, purists may be offended, often the case in statistics looked at too carefully! But, again, if it's your dear, total angel of a perfect daughter, then ...!

(2) If we have data on healthy kitty cats, what about also sick ones? Could we use that data? Yes, and we should. But in some real situations all we have a shot at getting is the data on the healthy -- e.g., maybe we have oceans of data on the healthy case (e.g., a high end server farm) but darned little data on the sick cases, e.g., the next really obscure virus attack.

(3) Why the tails at all? Why not just any area of low probability? Hmm .... Partly because we worship at the alter of central tendency?

Another reason is a bit heuristic: By going for the tails, for any selected false alarm rate, we maximize the area of our detection rate.

Okay, then we could generalize that to multidimensional data, e.g., as might get from several variables from a kitty cat, dear, angel perfect daughter, or a big server farm. That is, the distribution of the data in the healthy case looks like the Catskill Mountains. Then we pour in water to create lakes (assume they all seek the same level). The false alarm rate is the probability of the ground area under the lakes. A detection is a point in a lake. For a lower false alarm rate, we drain out some of the water. We maximize the geographical area for the false alarm rate we are willing to tolerate.

Well, I cheated -- that same nutshell also covers some of semester 102.

For more, the big name is E. Lehmann, long at Berkeley.

Go for it!

Not really, no. Saying we are at year five of Deep RL is about as informative as saying we are at year five of deep learning. Reinforcement learning as a field goes back decades.

But now we have GPUs, which makes it entirely different. /s

And it kinda does, but in an engineering way rather than a statistics way.

Like reinforcement learning from pixels is pretty new (i would be really interested if you have 10+year old citations), and pretty amazing. I've been looking at RL (through OpenAI gym) and realising that I "just" need to annotate a bunch of images and then train a network that will predict (fire/no fire in Doom from those pixels, and I can just add another network that builds some history onto this net (like an RNN) and this might actually work, is kinda amazing.

I'm still not sure I believe that it's always a good approach, but some of my initial experiments with my own (mostly image so far) data have been pretty promising.

The hype is pretty annoying though, especially if you've been interested in these things for years.

The bar to entry for these kinds of applications has been significantly lowered, which means we'll see more of it. I guess, in some sense, it's similar to the explosion of computer programs following the advent of personal computers (maybe, I haven't thought deeply about this part).

I'd like to believe that GPU's and cloud might allow for more scientific exploration of the "hows" of learning via many small experiments gradually revealing limitations and characteristics until finally insight.

Using high speed hardware can allow someone to do 10's or scores of runs a day. If you are doing one every 2 weeks or so then it's really, really hard to make any progress at all because you daren't take risks. So the productivity of 80 a day vs 2 per month isn't just 100x it's lots and lots more.

Also as you say it's lowered the bar which means that teams can onboard grad students and interns and get them to do something that's useful - it may be trivial - but it's useful.

Hinton was able to survive 30 years in the academic wilderness. Most academics can't.

Thus they work on "safe" projects.

It can be done, even today. If you work outside the US and work on cheap things (i.e. no special equipment), especially if you can teach then you can hang around for a long time.

I have met a lot of academics like this over the years, but I think your broader point might be that this is not possible today, which I agree with, and which is why I left academia (modulo personal situations).

Humanity used fire for a long time before combustion was understood. Even today Anesthesia is not well understood at biological/physiological level that has not stopped its safe use and innovation through Clinical Trials. Maybe competitions and empiricism are the best approaches to building intelligent systems. Why get caught up in Physics/Math envy?

Hinton isn't saying "Let's stop using fire," but "Let's understand the principles behind fire so we can use them in more sophisticated, informed and powerful ways."

The ML community did take the Theory approach trying to prove bounds, SLT/SRM, PAC, etc. and that was an excercise in futility. While I don't deny that there is value to looking under the hood but for a long while the community abandoned any empirical results that didn't fit their paradigm. Between rigorously validating their methods and writing yet another 4 page long proof. A lot of researchers would prefer latter, effectively locking out empirical approaches from most dissemination venues and eventually funding.

Because an improved theoretical understanding of how complex systems work can be incredibly valuable. Anesthesia is a good example - we get a lot of value from it, yes, but it would be way better if we could tailor dosages to individuals based on an understanding of how that individual will experience pain. There would be fewer severe complications, but also maybe you could wake up refreshed an hour after surgery instead of in a stupor.

If this could work with computer-trained models, that would be incredible too. What could a great speech understanding system teach us about language? What tricks from a facial-expression classifier could help autistic kids understand their friends?

I took his point not to criticize those early stages, but simply to acknowledge them as such. Early fire users could not have built a rocket no matter how many experiments they performed until they understood combustion (and some other sciences).

In AI, we're not building rockets yet, but we have some really awesome and really powerful bonfires or whatever.

At least that's how I understood his point.

(And in anesthesia, when we do understand those things, we may very well look on our use today as barbaric or dangerous.)

Great quote from Hinton.

The biggest deficiency in AI is that we still don't have artificial systems which simulate human thought with any fidelity. Sooner or later that's bound to become a focus of attention.

Why bother simulating human thought? It's not the only road to Rome.

It's probably more of a map than a road.

except that this has really only been going on for five years, which is nothing in the scale of human history or even of human rational thought. Some record number of people/scientists are working on getting the physics level understanding to happen, with crazy record breaking year after year quantity of people publishing and attending scientific conferences that fill up in like two days now. it is happening, and will happen even more in depth as time goes on

The fundamental problem with AI is the high dimensionality of the solution space. We simply can't understand why the brains we are building can think better than us. We can build smarter brains only by trial and error - at least until error outsmarts us, reproduces and takes over.

Kind of like having kids.

I feel like Hofstadter was one of those people thinking really deeply about AI.

Anyone who doesn't know what I'm talking about should read 'Goedel, Escher, Bach', or 'Fluid Analogies'. I haven't read them in a long while, but I'm sure they're going to be relevant for decades, because they deal with the fundamental challenge of what it means to think. Backpropagation may be part of the puzzle, but the brain (and intelligence) is so much more than that.

I second this recommendation! Here's some more reading for anyone interested in Hofstadter:

http://www.basicai.org/blog/hofstadter-2017-09-25.html

Hinton's quote is taken a bit out of context though. I just watched his interview on Andrew Ng's "Neural Networks and Deep Learning" class on Coursera and he seemed convinced that the next "breakthrough" will come from (a variant on) neural networks.

Right but to extend the bridge analogy what's interesting isn't the materials (neural nets) as such but the structure and why that structure works.

But maybe there are no universal laws that govern AI like physics governs bridges? AI is something that finds universal laws in stuff - there is no meta level over this - all the meta is AI itself.

it's really simple

1) learn how the brain works 2) build a simulator

most current AI research skips step 1

In defence of AI researchers 1 is very, very hard and to the best of our knowledge there is not one way the brain works. The brain is a complex, cobbled together set of systems all using different ways of problem solving.

most AI researchers have never opened a textbook on cognitive psychology or neurobiology , or any of these 'soft' sciences.

how do you plan to build artificial intelligence with no model of intelligence, without learning about important experiments in learning and memory , it's the complete ignorance that drives me crazy.

AI is not for specialists.

Yes all of this is true. I do think studying how the brain works will provide very useful ideas of what might work in AI. At the very least it is a very interesting area to learn about.

Most of those experts aren't looking to solve general AI problems, they're looking for solutions to specific problems like basic image recognition. And you don't need a full human brain to do that, and you don't need to conform to the way humans and other biological systems do it. You're not aiming for full human intelligence, so you don't need to care too much about how humans learn.

That said, I find when trying to solve a problem with ML techniques, it's better to use someone who knows the problem domain really well than someone who only knows ML really well. Someone who really understands the problem they're trying to solve can encode that knowledge into their models when training the system. While I've seen people who really know ML but lack the specific domain knowledge labor for weeks, coming back to me with "discoveries" that are already well known.

We know the brain and associated sensor behaviours are too large for us to fully simulate in a reasonable way on anything resembling current hardware (We also can't fully model it but as we approached the size of hardware to do so we'd probably solve many of the problems of doing so). So which hacks and shortcuts do you want to apply to reduce the dimensionality to something runnable? Step 1) will take far too long so AI research looks for things it can do well in the category of 2) without being a full simulation. Deep Learning has been unreasonably effective here.

I think we should be more interested in how the mind works. Much of AI is a simulator of the mind.

> 1) learn how the brain works 2) build a simulator

I disagree that step #1 is important.

Consider the "Air-foil", which led to flight. In one sense, its an approximation of the wings of birds and other animals.

But ultimately, the discovery that the "Air-foil" shape turns sideways blowing wind into an upward force now called "lift" is completely different from how most people understand bird wings.

Bird Wings flap, but Airplane Air Foils do not.

--------

Another example: Neural Networks are one of the best mathematical simulations of the human brain (as we understand it, as well as a few simplifications to make Artificial Neural Networks possible to run on modern GPUs / CPUs).

However, the big advances in "Game AI" the past few years are:

1. Monte Carlo Tree Search -- AlphaGo (although some of it is Neural Network training, the MCTS is the core of the algorithm)

2. Counterfactual Regret Minimization -- The Poker AI that out-bluffed humans

There are other methodologies which have proven very successful, despite little to no biological roots. IIRC, Bayesian Inference is a widely deployed machine learning technique (for some definition of Machine Learning at least), but has almost nothing to do with how a human brain works.

An interesting field of AI is "Genetic Algorithms", which have biological roots but not anything based on the biology of brains, to achieve machine learning. Overall, a "Genetic Algorithm" is really just a randomized search in a multidimensional problem, but the idea of it was inspired by Darwinian Evolution.

Disclaimer: I have no expertise in AI.

That said, I agree that learning how the brain works seems unimportant and unnecessary. Evolution doesn't know how a brain works, but it's given us Einstein, Michelangelo, and conversations on HN.

It seems really important to learn how to build evolution into attempts at AI, given that evolution is the only known mechanism that leads to what we recognize as intelligence.

> it's given us Einstein, Michelangelo, and conversations on HN

two out of three ain't bad

>evoluyion doesn't

you use antropomorphy to reflect on your own standpoint. we don't know how the brain works? we can feel it and psychologist have a huge body of work concerned with the topic and that is already having influence on competition and fitness.

You could have made exactly that this argument about flight before the Wright brothers.

Consider the "Air-foil", which led to flight. In one sense, its an approximation of the wings of birds and other animal

Not true; "lift" was well known for thousands of years, horizontal "lift" is how ships sail upwind. The breakthrough for the Wright bros was making something light enough to make use of this phenomenon vertically.

> Monte Carlo Tree Search -- AlphaGo (although some of it is Neural Network training, the MCTS is the core of the algorithm)

AFAIK, this is not correct. Many of the Go playing algorithms before AlphaGo used MCTS or some variant. The true breakthrough of AlphaGo was deep reinforcement learning.

> AlphaGo's performance without search The AlphaGo team then tested the performance of the policy networks. At each move, they chose the actions that were predicted by the policy networks to give the highest likelihood of a win. Using this strategy, each move took only 3 ms to compute. They tested their best-performing policy network against Pachi, the strongest open-source Go program, and which relies on 100,000 simulations of MCTS at each turn. AlphaGo's policy network won 85% of the games against Pachi! I find this result truly remarkable. A fast feed-forward architecture (a convolutional network) was able to outperform a system that relies extensively on search. https://www.tastehit.com/blog/google-deepmind-alphago-how-it...

I don't know whether AlphaGo Master (the next version of AlphaGo that was trained purely with self-played games and has not been beaten in 60+ games) even uses MTCS.

In industry, yes. In academia, there's computational neuroscience:

http://cocosci.mit.edu/people

https://web.stanford.edu/group/mbc/research.html

That's certainly one way to do it. However, we didn't succeed at building modern aircraft or earth moving machinery by building simulations of birds or muscles. There's enough that is unknown out there for a variety of approaches.

Medical research hasn't cracked step 1 either, at least not to a point of accurate simulation.

Besides, if you could simulate a human brain, you will end up with something that needs to sleep, something with limited and unreliable memory, something that gets bored and distracted, something emotionally needy, etc.

Then the extending of this chaotic, messy system is wildly unknown even if we could get a piece-for-piece replication to work. Such a thing would be of great benefit to medicine, but not really for AI to even start with until medicine is done reverse engineering it.

Piece-for-piece replication might not be the right level of abstraction. Blue Brain project is one unfortunate example, on the other hand the current neural nets are stuck with neural model from 1943.

Prof. Hinton has an interesting talk about his new 'capsule' model based on psychology of shape perception: https://www.youtube.com/watch?v=rTawFwUvnLE

"engineering before physics" is exactly wrong. No one did Engineering before a sophisticated understanding of Physics was achieved. They built bridges and towers, Engineering enables statements to be made about the performance of machines and buildings; it will survive a wind like x, you can do n cycles, do not load the wings in this way.

I guess the Romans didn’t have any engineers building siege engines and fortifications, then.

Here is the test.

Take the best Roman engineer.

Translate a first year engineering paper on structures into Latin.

Ask Roman to sit said paper.

What will happen and why? The Roman chap will look very confused and will make statements (in Latin) about how stupid this stuff is and how it has nothing to do with proper engineering. The Roman will score 0. The why is that the understanding of structures and materials in the ancient world was artizanal, based on trade knowledge (often secret and hard to reproduce) and not systematic, based on the scientific method and inspectable or testable.

Currently we accept that knives, cabinets and sheds may be built or made using artisanal knowledge, we do not accept that apartment blocks, aircraft or automobiles are built this way. Society insists that these are built using systematic knowledge because otherwise they sometimes fall down or crash.

The systematic approach to aircraft is the best example - think how much civil air traffic there is now, and how rare air crashes are. The issues of subsonic flight have been systematically accounted for, right up to the point where we now see 1:2,000,000 crashes per flight.

Mechanical, aeronautical and civil engineering proceed in this way. Issues are discovered with mechanisms or structures or materials, these are characterized with scientific investigation, the characterizations lead to constraints and parameters that are required to be accounted for in future designs and old designs are re-evaluated in the light of the new knowledge.

Stating that you will build a new building in a certain way because domes are strong and concrete is strong would not cut the mustard in the modern world... The parthenon has stood for 2000 years, but how many similar structures collapsed after a few months?

I think you underestimate how smart your ancestors were to bring you to the point in time that you now exist. No offense, but the "best" Roman engineer was probably smarter than the vast majority of us.

Reply on news.ycombinator.com