15 ms·

Machine learning has become alchemy (2017) [video]

253 points by henning 8y ago

rwilson4 8y ago
Science is a combination of theory and experiment. Sometimes theory advances faster than experiment, sometimes vice versa. Right now in ML, experiment aka practice is advancing faster than theory. Theory will eventually catch up.
- currymj 8y ago
  one would hope so! the depressing thing about machine learning to me is the many convincing explanations of phenomena, that then turn out not to explain things (all the explanations of why dropout is effective, or why there are adversarial examples, things like that.) this is where the comparison to alchemy is the sharpest in my opinion. The alchemists had extremely sophisticated theories that they used to provide explanations for the phenomena they observed; just ultimately none of them made any sense.
  - rwilson4 8y ago
    I think people end up reaching for straws because a lot of this stuff works well in practice and they’re trying to make sense of it. 20 years from now we’ll have a much clearer picture of why the techniques work! Until then, a lot of the explanations, maybe even some valid ones, may sound like baloney!
- xamuel 8y ago
  ML is a souped-up version of "draw a line through these points". We can get really efficient at drawing lines through points, but it's not like we'll suddenly realize some deeper fundamental theory about it!
  - whatshisface 8y ago
    The reason you don't expect to see a deep fundamental theory of drawing a line through a few points is because you can always do it. ML doesn't always work, and sometimes it is harder to get working than other times. What's going on?
    - xamuel 8y ago
      You can always draw a line through the points, but it isn't always a good approximation. If the points are inherently bunched around a line, then a line through them will approximate them well. If they're a big random cloud, then the line won't. It's the exact same way in ML, except the points are in n-dimensional space and "line" is replaced by "higher-dimensional curve or manifold". Sometimes (e.g. in image processing), the n-dimensional points are inherently bunched around a curve or manifold of the form you're using, and then ML works great. There's nothing deeper going on!
  - wish5031 8y ago
    But there actually is a huge amount of theory behind that problem. You can exactly derive the method that finds the best line. You can get error bounds on each of your coefficients and confidence intervals for them. You can alter the strength of your assumptions (e.g. about distribution of errors, homoskedacity, and so on) and see how it affects your model. You can add L1 or L2 regularization, both of which also have solid theoretical grounding. And so on. All of these things help make your model more robust and give you greater confidence in it, which will be important if we want to put ML in, say, healthcare or defense. But you don’t get as much of this theory with more complex ML models, and certainly not with neural nets. Good luck trying to get a confidence interval for the optimal value of a weight in your net, much less interpreting it.
- chrishare 8y ago
  I think a valid concern is that ML methods are being applied in critical, real-life scenarios without some practitioners being aware of flaws (bias, adversarial attacks, privacy issues) and without any theoretic safetynet that helps them reason about how these systems will behave. James Mickens discussed this recently in a keynote: https://www.usenix.org/conference/usenixsecurity18/presentation/mickens https://www.usenix.org/conference/usenixsecurity18/presentat... Maybe the only way to make steady progress here is to blaze ahead and rely on empirical evidence, whilst the theory is inevitably fleshed out. That's often the counter argument - that we do not know how the brain works, but rely on it nonetheless.
  - andrewflnr 8y ago
    We don't rely on the brain though, at least not on any single one. Any system that relies on human brains alone without cross-checking or, ideally, much simpler automatic systems, will eventually malfunction terribly. A large organization never wants to rely on a single person's judgment for anything, a programmer wants automated systems checking their work, etc.
    - dontreact 8y ago
      We rely on a single human to drive a car. In medicine, machine learning systems will work alongside other brains. What are the instances you’re imagining where a group of brains running an important system are replaced by a single machine learning algorithm running in isolation?
      - andrewflnr 8y ago
        Yeah, and around 800 people a day die in auto accidents in the US. Bringing up cars bolsters my point, which is that "we rely on the brain without understanding it and that works out fine" is not a good argument. So let's be careful. I'm mystified as to why that seems to be controversial.
        dontreact 8y ago
joe_the_user 8y ago
It is simple to say deep learning is based on "alchemy" or "engineering" or whatever it is that isn't strong theory. And it's reasonable to say deep learning has a lot of mathematical and statistical intuitions but doesn't have a strong theory - maybe just doesn't yet have a strong theory or maybe can never get one. So this is by now a standard argument. The standard answers I think have been: 1) Well, we are discovering that you don't need a strong theory for a truth-discovery machine. We've discovered experimental thinking construction. 2) It's true deep learning doesn't have a strong theory now but sooner or later it will get one. 3) This shows that instead we need theory X, usually by people who've been pursuing theory X all along. But I think Hinton at least has thrown a bunch of alternatives against the wall over the years. (I could swear this has appeared here before but I can't find it).
- deleted 8y ago
  [deleted]
- AlexCoventry 8y ago
  I don't think it got as much traction, last time.
- bartread 8y ago
  Worth also bearing in mind that we've been here before in other fields. Alchemy ultimately became chemistry. Even in the Victorian era when, for fairly large swathes of the periodic table, and different types of compound, we already had a quite good experimental understanding of chemical reactions in terms of their constituent components and products, along with the conditions under which those reactions occur, we still didn't know the why. We didn't understand much about atoms or how they bond together, for example. The point is this: science can often take a long time to advance, and AI is still a very young field, with the first practical endeavours only dating back to the post-WWII period. Should we therefore be terribly surprised that ML seems a bit like alchemy? As an aside, another normal facet of scientific advancement is the vast quantity of naysayers encountered along the way. Haters gonna hate, I suppose. (But don't misunderstand me: whilst I'm not an ML fanboi, I recognise that advances come in fits and starts, dead-ends will be encountered, and overall it's going to take quite a long time and require a lot of hard work to get anywhere.) Final aside: this video has definitely been posted here before but I've also been unable to find it.
deleted 8y ago
[deleted]
deleted 8y ago
[deleted]
puzzledobserver 8y ago
Can someone give a concrete example of the kind of theoretical properties they desire of ``new-style'' machine learning? The kinds of properties that ``old-style'' learning methods guaranteed? People often complain about interpretability: in what sense is an SVM interpretable that a deep neural network is not? Or is the worry about gradient descent not finding global optima? But why is the global optimum a satisfactory place to be, if the theory does not also provide a satisfactory connection between the space of models and underlying reality? The arbiter of good theory is ultimately its ability to guide and explain practical phenomena. Which machine learning phenomena are currently most in need of theoretical elucidation?
- digitalzombie 8y ago
  Most machine learning algorithms that aren't statistical base doesn't give a CI. From a statistical stand point it doesn't give a sense of how good your prediction is. You can get a general sense with just CV. Also your parameter is not inferable like in statistical algorithm. This is where I see people saying Deep Learning isn't interprable and there are research into this area. If you compare time series stat forecast algorithm with deep learning you at least get a CI on stat algorithm. Randomly dropping node is pretty magic in my mind. While I don't know much about SVM I know it's mathematically proven so there should be a way to interpret SVM fitted model. I sure as hell wouldn't use ML in clinical trial for drugs. That's why biostat is a thing.
- rahimiali 8y ago
  this is a followup post i wrote to answer exactly your question: http://www.argmin.net/2018/01/25/optics/ http://www.argmin.net/2018/01/25/optics/
m0zg 8y ago
We do understand how it works most of the time, but can't predict if certain changes will be beneficial or detrimental. After the fact it's pretty clear what e.g. CNNs do (find manifold transforms which, coupled with nonlinearities minimize error in the layer-wise output distributions when backpropagating loss of the minibatches of the training set). But you can't reliably say "if I add e.g. a bottleneck branch over here my accuracy will go up/down X%". Fundamentally we have to contend with the fact that human ability to understand complex systems is fairly limited, and at some point it will become an impediment to further progress. Arguably in a number of fields we're past that point already.
- oldgradstudent 8y ago
  That's all very cool. But for safety critical systems you have to understand how these systems work to understand their limitations. You have to know when these techniques succeed, when they fail, and how badly they fail.
  - m0zg 8y ago
    Do you feel like you understand human limitations in these critical systems? Are humans suitable? Would AI be suitable if it performs statistically better than humans?
    - oldgradstudent 8y ago
      > Would AI be suitable if it performs statistically better than humans? In general yes, but it might depend on the pattern of failure - if your self-driving cars hunts me or my family personally, I might have problem with that. But how can you determine that without releasing it to the wild and waiting for bodies? Worse, say you have a safe system, but you need to modify the network (to fix some bug). How can you determine that the new system is safe enough to put on the road?
      - m0zg 8y ago
        But any technology can be deadly if you deploy it widely enough. _WhatsApp_ has resulted in "bodies" and it doesn't have any AI in it at all. First airplanes were basically flying coffins. Cars until early 90s had very little chance of survival in collision above 40mph. Many drugs have serious, sometimes deadly side effects. Quarter of a million people die in hospitals in US alone every year due to medical errors. 100% of those errors are currently made by humans. It's remarkable that AI seems to be held to an arbitrarily high standard, often exceeding that of other technologies.
whoisnnamdi 8y ago
Sure - perhaps alchemy in the sense that many practitioners are simply throwing things against the wall and seeing what sticks, but not in the sense that there isn't anything real behind all the math or engineering. Many advancements in machine learning have significant backing in theoretical proofs that a given algorithm will result in unbiased estimates, or will converge to such and such value etc. On some level, the high amount of experimentation necessary in machine learning is not so much a sign that the practice is faulty in any particular way, but rather, that the world is a complex place. This is especially true when attempting to predict anything that involves human behavior. Long-story short - I'd cut ML some slack!
- Retric 8y ago
  Alchemists could do a lot, making gunpowder for example is non trivial. They simply worked from a bad model with little understanding of what was going on. Consider, lead and gold are very similar substances and chemistry lets you transform many things into other things so it must have seemed very possible. Unfortunately, I suspect the current AI movement is in a very similar state even if they can do a lot of things that seems magical it’s built on a poor foundation. Resulting in people mostly just trying stuff and see what happens to work without the ability to rigorously predict what will work well on a novel problem.
- oldgradstudent 8y ago
  From the talk, their main point about alchemy: > Now alchemy is OK, alchemy is not bad, there is a place for alchemy, alchemy worked, alchemists invented metallurgy, [inaudible] dyed textiles, our modern glass making processes and medications. > Then again, alchemists also believed they could cure diseases with leeches and transmute base metals into gold. > For the physics and chemistry of the 1700s to usher in the sea change in our understanding of the universe that we now experience, scientists had to dismantle 2,000 years worth of alchemical theories. > If you're building photo-sharing systems, alchemy is okay. But we're beyond that. > Now we're building systems that govern health care, and mediate our civic dialogue. We influence elections. > I would like to live in a society whose systems are built on top of verifiable rigorous thorough knowledge and not on alchemy.
mistrial9 8y ago
This video is great ! for me, not for the math though.. two things: * multi-layer, automated "jiggering" with so many components that only a machine can contextualize them, might be great for finding patterns in some sets, but the industry HYPE, the DIRECTION+VELOCITY, and the human manipulation (including lies and pathologies) are gut-level PROBLEM.. and this guy says that! +100 * Alchemy itself rambles and spreads [1] Some variations of Alchemy included a ritualized, internal psychological and psychic experience by the human practitioner.. hard to stabilize, yet not always a bad thing, since you are reading this and are actually one of those .. [1] https://en.wikipedia.org/wiki/Psychology_and_Alchemy https://en.wikipedia.org/wiki/Psychology_and_Alchemy lastly, the selected slides encourage a student-minded viewer to look up some math and think a bit. Not a bad thing. Thanks for this video and thanks for the talk.
Barrin92 8y ago
The problem with ML in my opinion is not that we're missing some sort of fundamental theory, but that there simply is none. ML is essentially fancy pattern matching roughly resembling the human visual system, which is why it happens to be good at tasks related to perception. It's not some master algorithm, it's not going to produce sci-fi AI, and it probably isn't even suited to solve most problems in the realm of intelligence. In fact it basically hits the worst possible spot on the problem solving scale. It barely learns anything given the amount of computation effort an data that goes into it, but it just happens to be good enough to be practically preferable to old symbolic systems. It is completely mysterious to me how networks that approximate some utility function are a huge step forward to giving insight into cognition, reasoning, modelling, creation of counterfactuals and the sort of mechanisms we actually need to produce human like performance.
- typon 8y ago
  The annoying part is that ML is not sold to the world like this. I would say the truth (I believe) in this comment is the "dirty little secret" of our industry. Everyone working on it knows this but the research and VC dollars are flowing in so no one wants to talk about it too much.
  - chrstphrhrt 8y ago
    Can confirm, worked at an AI hype company for over a year and built all their systems outside the NN internals. There is going to be a correction.
    - kuhhk 8y ago
      May I ask if this insight is why you left? What do you work on now?
      - chrstphrhrt 8y ago
        Actually left due to anti-engineering culture (no tech leaders), succumbed to the distortions of Conway's law due to imbalance between professional services and core/platform. That said, the nature of the lack of leadership was around the MBA mindset that data science is the be-all end-all of AI, which IME is so far from the truth. There needs to be many layers/spirals of naive methods (i.e. straight ahead engineering) as a vanguard on the front lines with the DS/NNs/NLP bringing up the flanks for aspects of the domains that become more well understood over time. So yeah, tail wagging the dog, cart before horse etc. If you just barrel ahead with a monolithic NN pretending that all classes are created equal (structureless blob), which is what our DS PhDs were doing, it will quickly ossify. In our case it was intent prediction so we got to pretty high accuracy, but the labels were indicating many different categories that actually had relations in nature that could not be expressed. Ironically it took some regular engineers to research HMC (hierarchical multi-label classification) and ensembles and implement a new model training framework to support them. Not sure what they teach in school but it doesn't seem to be very practical. EDIT: now working on messaging (SMS/MMS/RCS/ABC) with a focus on dialog management and logical rules with layering-in of progressively less naive stats methods strategically rather than blindly. It helps to have an existing revenue stream that can be leveraged to create even more value with AI features rather than the backwards hail Mary of not iterating from a foothold of existing traction.
sonnyblarney 8y ago
Making a serious, industrial scale web app in 2000 felt like alchemy. It was all arcane, there were no established patterns, nobody knew how to do it for sure, there were a lot of hustlers, most of them thankfully sincere. When something is new, it feels like a mystery - eventually we'll have a language for wrapping our heads around neural networks, even if it's not as clear cut as we'd like.
- brain5ide 8y ago
  We had the neural networks, and the language. The problem is with the rebranding and the amount of marketing bullshit comming with it. The null hypothesis is that apl of that most of it is a pile of crap, for an overengineered, overoptimized solutions that are probably applied at abstraction layer different from one they are marketed on. There may come solution out of it, but it's more wishfull thinking than not.
- ramblerman 8y ago
  I understand what you are getting at, but I would call that a new frontier. There was ultimately nothing black box about the code we cobbled together back then.
dplarson 8y ago
Additional context from Ali Rahimi and Ben Recht: http://www.argmin.net/2017/12/11/alchemy-addendum/ http://www.argmin.net/2017/12/11/alchemy-addendum/
- mistrial9 8y ago
  .. and a Jupyter Notebook https://github.com/benjamin-recht/shallow-linear-net/blob/master/TwoLayerLinearNets.ipynb https://github.com/benjamin-recht/shallow-linear-net/blob/ma...
imperio59 8y ago
The way I understand neural networks to work, they are actually series of connected infinitely valued logic gates, where given a numerical input from neg infinity to positive infinity, it spits out another number from neg infinity to positive infinity that feeds into the next set of logic gates, and at the end gives you a confidence interval from 0 to 1 of whether there was a pattern match or not. To me it's very similar to Boolean logic circuits the way I was taught those in college except that there's too many gates to configure manually so you use supervised learning to find reasonable values and an arrangement of gates that works (aka your trained neural network). I've never heard anyone else describe it this way but this is how I like to think about it. It really has nothing to do with how the human brain or much less human mind works. That's just marketing speak.
- digitalzombie 8y ago
  > That's just marketing speak. Pretty sure it was the initial thinking when Neural Network was created and it have move beyond that. I think people who know surface level only repeating this tadbit that's out of dated. It's even in the official wikipedia article (https://en.wikipedia.org/wiki/Artificial_neural_network https://en.wikipedia.org/wiki/Artificial_neural_network). Everything you've stated was basically a personal opinion that could have been verified via google...
bob1029 8y ago
In my mind, machine learning is simply one class of algorithm primarily oriented towards multidimensional fuzzy pattern matching. This technique can easily be classed as a subset of digital signal processing concerns. Everything tacked onto the sides of this (LTSMs, etc.) in some attempt to increase the cleverness or scope of the ML network (aka self-driving cars and other assorted high magickery), seek to band-aid something that is fundamentally flawed for these purposes. A ML network does not have intrinsic, real-time mutability in how it is defined, outside the scope of memory-based node weights, inputs, outputs or graphs over time. These nodes are added, removed or modified based on a predefined set of input, output and internal mappings. How would intermediate layers be defined in a dynamic way between input and output in such a network in an attempt to achieve these higher powers? Driving a car, for instance, is a task that requires learning entire new subsets of skills, many times ad-hoc, that require intermediate models to be dynamically developed which are potentially outside the capabilities of our understanding. The biggest challenge I see today is that we don't necessarily have a good way to dynamically construct models of the intermediate layers (such that we can map them to other layers), especially if these layers are being added and removed dynamically by algorithms at the edge of our capability to understand. I've always felt that there needs to be some internal processing occurring at rates far higher than the input sample rates such that higher-order intelligence may emerge by way of adjusting the entities noted above multiple times per input sample (and potentially even in the absence of input). The problem is also going to come down to how a person would define outcomes vs how an AI/ML network would. For the future to really begin we will need an AI that can understand and reason with what success and failure feel like in our abstract terms. This will require it to have the capacity to dynamically construct abstractions which we would have no hope of modelling ourselves, as we do not have very deep insight into the abstractions upon which the biological human brain implements virtually any behavior today. There is no amount of discrete math in the universe which can accurately model and assess the higher-order outcomes of decisions made in our reality. You can run ML disguised as AI in simulations and environments with fixed complexity all day, but once you throw one of these "trained" networks out into the real world without any constraints, you are probably going to see very unsatisfactory outcomes.
ineedasername 8y ago
Even granting the claim that ML has become alchemy, consider what alchemy was: Misguided in its quasi-magical underpinnings and goals, but nonetheless an extremely important step in the founding of modern chemistry. And so what started out with poor understanding and numerous misconceptions evolved over a period of time, through trial and error and hard won knowledge into a massively important branch of science. And so if ML is indeed comparable to alchemy, the only salient question is how long, and what will it take, to turn into chemistry.
nopinsight 8y ago
Related: Debate at NIPS 2017 on “Interpretability is necessary for machine learning”, by senior researchers in Machine Learning including Yann LeCun. https://youtu.be/93Xv8vJ2acI https://youtu.be/93Xv8vJ2acI
TrackerFF 8y ago
The ML scene was more rigorous 10-15-20 years ago, because it was mostly confined to the world of academia and industrial R&D, and we had much less wide-reaching problems to work towards. As tech evolves, more data is being generated, which in turn creates more problems that increases the demand for solutions. To put it short: Solving "real world" problems is more/better rewarded than figuring out the underlying technology, so it's no wonder that we see a larger portion of practitioners that may lack the academic background - and that is understandable. You could be a ML Ph.D (in Academia) for 5-10 year, earning gov. worker salary while trying to reach the next step - or hack together some ML-based product, until desired accuracy, and cash in 50-100 times the pay. Now, with that said, I understand that Ali targeted his speech at the NIPS folk - but the industry / academia crossover in ML is massive, and it still stands that much of industry problems are of commercial interest - so the motivation for many is still the same.
- deleted 8y ago
  [deleted]
Mikhail_K 8y ago
If someone turns in math homework consisting in answers only, the teacher will probably not give any credit, and will ask to show the work. Yet the AI/ML community insists on cargo cult test for intelligence - behaving like someone with a mind. That can be traced to the "Turing test". It was flawed then, and it is flawed now. Reproducing the behaviour of a thinking agent does not prove that a putative AI will not fail in a more detailed test, as demonstrated in numerous papers about "adversarial images".
tabtab 8y ago
We are still at the experimental stage, so trial and error without clear theories is not unexpected. Chemistry formed out of alchemy: people fiddled and noticed the patterns. The patterns were written down and shared, and different people began floating theories/models to explain the patterns, and the theories were further vetted by more experiments. Another thing is that the industry should look at other AI techniques besides neural nets (NN) or find compliments to NN's. Genetic algorithms and Factor Tables should also be explored as well. Just because recent advances have been in NN's does not necessarily mean that's where the future should lead. Factor tables may allow more "dissection" & analysis by those without advanced degrees, for example. Experts may set up the framework and outline, but others can study and tune specifics. (https://github.com/RowColz/AI https://github.com/RowColz/AI)

253 points by henning 8y ago

Science is a combination of theory and experiment. Sometimes theory advances faster than experiment, sometimes vice versa. Right now in ML, experiment aka practice is advancing faster than theory. Theory will eventually catch up.

currymj 8y ago
one would hope so! the depressing thing about machine learning to me is the many convincing explanations of phenomena, that then turn out not to explain things (all the explanations of why dropout is effective, or why there are adversarial examples, things like that.) this is where the comparison to alchemy is the sharpest in my opinion. The alchemists had extremely sophisticated theories that they used to provide explanations for the phenomena they observed; just ultimately none of them made any sense.
- rwilson4 8y ago
  I think people end up reaching for straws because a lot of this stuff works well in practice and they’re trying to make sense of it. 20 years from now we’ll have a much clearer picture of why the techniques work! Until then, a lot of the explanations, maybe even some valid ones, may sound like baloney!
xamuel 8y ago
ML is a souped-up version of "draw a line through these points". We can get really efficient at drawing lines through points, but it's not like we'll suddenly realize some deeper fundamental theory about it!
- whatshisface 8y ago
  The reason you don't expect to see a deep fundamental theory of drawing a line through a few points is because you can always do it. ML doesn't always work, and sometimes it is harder to get working than other times. What's going on?
  - xamuel 8y ago
    You can always draw a line through the points, but it isn't always a good approximation. If the points are inherently bunched around a line, then a line through them will approximate them well. If they're a big random cloud, then the line won't. It's the exact same way in ML, except the points are in n-dimensional space and "line" is replaced by "higher-dimensional curve or manifold". Sometimes (e.g. in image processing), the n-dimensional points are inherently bunched around a curve or manifold of the form you're using, and then ML works great. There's nothing deeper going on!
- wish5031 8y ago
  But there actually is a huge amount of theory behind that problem. You can exactly derive the method that finds the best line. You can get error bounds on each of your coefficients and confidence intervals for them. You can alter the strength of your assumptions (e.g. about distribution of errors, homoskedacity, and so on) and see how it affects your model. You can add L1 or L2 regularization, both of which also have solid theoretical grounding. And so on. All of these things help make your model more robust and give you greater confidence in it, which will be important if we want to put ML in, say, healthcare or defense. But you don’t get as much of this theory with more complex ML models, and certainly not with neural nets. Good luck trying to get a confidence interval for the optimal value of a weight in your net, much less interpreting it.
chrishare 8y ago
I think a valid concern is that ML methods are being applied in critical, real-life scenarios without some practitioners being aware of flaws (bias, adversarial attacks, privacy issues) and without any theoretic safetynet that helps them reason about how these systems will behave. James Mickens discussed this recently in a keynote: https://www.usenix.org/conference/usenixsecurity18/presentation/mickens https://www.usenix.org/conference/usenixsecurity18/presentat... Maybe the only way to make steady progress here is to blaze ahead and rely on empirical evidence, whilst the theory is inevitably fleshed out. That's often the counter argument - that we do not know how the brain works, but rely on it nonetheless.
- andrewflnr 8y ago
  We don't rely on the brain though, at least not on any single one. Any system that relies on human brains alone without cross-checking or, ideally, much simpler automatic systems, will eventually malfunction terribly. A large organization never wants to rely on a single person's judgment for anything, a programmer wants automated systems checking their work, etc.
  - dontreact 8y ago
    We rely on a single human to drive a car. In medicine, machine learning systems will work alongside other brains. What are the instances you’re imagining where a group of brains running an important system are replaced by a single machine learning algorithm running in isolation?
    - andrewflnr 8y ago
      Yeah, and around 800 people a day die in auto accidents in the US. Bringing up cars bolsters my point, which is that "we rely on the brain without understanding it and that works out fine" is not a good argument. So let's be careful. I'm mystified as to why that seems to be controversial.
      - dontreact 8y ago

joe_the_user 8y ago

It is simple to say deep learning is based on "alchemy" or "engineering" or whatever it is that isn't strong theory. And it's reasonable to say deep learning has a lot of mathematical and statistical intuitions but doesn't have a strong theory - maybe just doesn't yet have a strong theory or maybe can never get one. So this is by now a standard argument. The standard answers I think have been: 1) Well, we are discovering that you don't need a strong theory for a truth-discovery machine. We've discovered experimental thinking construction. 2) It's true deep learning doesn't have a strong theory now but sooner or later it will get one. 3) This shows that instead we need theory X, usually by people who've been pursuing theory X all along. But I think Hinton at least has thrown a bunch of alternatives against the wall over the years. (I could swear this has appeared here before but I can't find it).

deleted 8y ago
[deleted]
AlexCoventry 8y ago
I don't think it got as much traction, last time.
bartread 8y ago
Worth also bearing in mind that we've been here before in other fields. Alchemy ultimately became chemistry. Even in the Victorian era when, for fairly large swathes of the periodic table, and different types of compound, we already had a quite good experimental understanding of chemical reactions in terms of their constituent components and products, along with the conditions under which those reactions occur, we still didn't know the why. We didn't understand much about atoms or how they bond together, for example. The point is this: science can often take a long time to advance, and AI is still a very young field, with the first practical endeavours only dating back to the post-WWII period. Should we therefore be terribly surprised that ML seems a bit like alchemy? As an aside, another normal facet of scientific advancement is the vast quantity of naysayers encountered along the way. Haters gonna hate, I suppose. (But don't misunderstand me: whilst I'm not an ML fanboi, I recognise that advances come in fits and starts, dead-ends will be encountered, and overall it's going to take quite a long time and require a lot of hard work to get anywhere.) Final aside: this video has definitely been posted here before but I've also been unable to find it.

deleted 8y ago

[deleted]

deleted 8y ago

[deleted]

puzzledobserver 8y ago

Can someone give a concrete example of the kind of theoretical properties they desire of ``new-style'' machine learning? The kinds of properties that ``old-style'' learning methods guaranteed? People often complain about interpretability: in what sense is an SVM interpretable that a deep neural network is not? Or is the worry about gradient descent not finding global optima? But why is the global optimum a satisfactory place to be, if the theory does not also provide a satisfactory connection between the space of models and underlying reality? The arbiter of good theory is ultimately its ability to guide and explain practical phenomena. Which machine learning phenomena are currently most in need of theoretical elucidation?

digitalzombie 8y ago
Most machine learning algorithms that aren't statistical base doesn't give a CI. From a statistical stand point it doesn't give a sense of how good your prediction is. You can get a general sense with just CV. Also your parameter is not inferable like in statistical algorithm. This is where I see people saying Deep Learning isn't interprable and there are research into this area. If you compare time series stat forecast algorithm with deep learning you at least get a CI on stat algorithm. Randomly dropping node is pretty magic in my mind. While I don't know much about SVM I know it's mathematically proven so there should be a way to interpret SVM fitted model. I sure as hell wouldn't use ML in clinical trial for drugs. That's why biostat is a thing.
rahimiali 8y ago
this is a followup post i wrote to answer exactly your question: http://www.argmin.net/2018/01/25/optics/ http://www.argmin.net/2018/01/25/optics/

m0zg 8y ago

We do understand how it works most of the time, but can't predict if certain changes will be beneficial or detrimental. After the fact it's pretty clear what e.g. CNNs do (find manifold transforms which, coupled with nonlinearities minimize error in the layer-wise output distributions when backpropagating loss of the minibatches of the training set). But you can't reliably say "if I add e.g. a bottleneck branch over here my accuracy will go up/down X%". Fundamentally we have to contend with the fact that human ability to understand complex systems is fairly limited, and at some point it will become an impediment to further progress. Arguably in a number of fields we're past that point already.

oldgradstudent 8y ago
That's all very cool. But for safety critical systems you have to understand how these systems work to understand their limitations. You have to know when these techniques succeed, when they fail, and how badly they fail.
- m0zg 8y ago
  Do you feel like you understand human limitations in these critical systems? Are humans suitable? Would AI be suitable if it performs statistically better than humans?
  - oldgradstudent 8y ago
    > Would AI be suitable if it performs statistically better than humans? In general yes, but it might depend on the pattern of failure - if your self-driving cars hunts me or my family personally, I might have problem with that. But how can you determine that without releasing it to the wild and waiting for bodies? Worse, say you have a safe system, but you need to modify the network (to fix some bug). How can you determine that the new system is safe enough to put on the road?
    - m0zg 8y ago
      But any technology can be deadly if you deploy it widely enough. _WhatsApp_ has resulted in "bodies" and it doesn't have any AI in it at all. First airplanes were basically flying coffins. Cars until early 90s had very little chance of survival in collision above 40mph. Many drugs have serious, sometimes deadly side effects. Quarter of a million people die in hospitals in US alone every year due to medical errors. 100% of those errors are currently made by humans. It's remarkable that AI seems to be held to an arbitrarily high standard, often exceeding that of other technologies.

whoisnnamdi 8y ago

Sure - perhaps alchemy in the sense that many practitioners are simply throwing things against the wall and seeing what sticks, but not in the sense that there isn't anything real behind all the math or engineering. Many advancements in machine learning have significant backing in theoretical proofs that a given algorithm will result in unbiased estimates, or will converge to such and such value etc. On some level, the high amount of experimentation necessary in machine learning is not so much a sign that the practice is faulty in any particular way, but rather, that the world is a complex place. This is especially true when attempting to predict anything that involves human behavior. Long-story short - I'd cut ML some slack!

Retric 8y ago
Alchemists could do a lot, making gunpowder for example is non trivial. They simply worked from a bad model with little understanding of what was going on. Consider, lead and gold are very similar substances and chemistry lets you transform many things into other things so it must have seemed very possible. Unfortunately, I suspect the current AI movement is in a very similar state even if they can do a lot of things that seems magical it’s built on a poor foundation. Resulting in people mostly just trying stuff and see what happens to work without the ability to rigorously predict what will work well on a novel problem.
oldgradstudent 8y ago
From the talk, their main point about alchemy: > Now alchemy is OK, alchemy is not bad, there is a place for alchemy, alchemy worked, alchemists invented metallurgy, [inaudible] dyed textiles, our modern glass making processes and medications. > Then again, alchemists also believed they could cure diseases with leeches and transmute base metals into gold. > For the physics and chemistry of the 1700s to usher in the sea change in our understanding of the universe that we now experience, scientists had to dismantle 2,000 years worth of alchemical theories. > If you're building photo-sharing systems, alchemy is okay. But we're beyond that. > Now we're building systems that govern health care, and mediate our civic dialogue. We influence elections. > I would like to live in a society whose systems are built on top of verifiable rigorous thorough knowledge and not on alchemy.

mistrial9 8y ago

This video is great ! for me, not for the math though.. two things: * multi-layer, automated "jiggering" with so many components that only a machine can contextualize them, might be great for finding patterns in some sets, but the industry HYPE, the DIRECTION+VELOCITY, and the human manipulation (including lies and pathologies) are gut-level PROBLEM.. and this guy says that! +100 * Alchemy itself rambles and spreads [1] Some variations of Alchemy included a ritualized, internal psychological and psychic experience by the human practitioner.. hard to stabilize, yet not always a bad thing, since you are reading this and are actually one of those .. [1] https://en.wikipedia.org/wiki/Psychology_and_Alchemy https://en.wikipedia.org/wiki/Psychology_and_Alchemy lastly, the selected slides encourage a student-minded viewer to look up some math and think a bit. Not a bad thing. Thanks for this video and thanks for the talk.

Barrin92 8y ago

The problem with ML in my opinion is not that we're missing some sort of fundamental theory, but that there simply is none. ML is essentially fancy pattern matching roughly resembling the human visual system, which is why it happens to be good at tasks related to perception. It's not some master algorithm, it's not going to produce sci-fi AI, and it probably isn't even suited to solve most problems in the realm of intelligence. In fact it basically hits the worst possible spot on the problem solving scale. It barely learns anything given the amount of computation effort an data that goes into it, but it just happens to be good enough to be practically preferable to old symbolic systems. It is completely mysterious to me how networks that approximate some utility function are a huge step forward to giving insight into cognition, reasoning, modelling, creation of counterfactuals and the sort of mechanisms we actually need to produce human like performance.

typon 8y ago
The annoying part is that ML is not sold to the world like this. I would say the truth (I believe) in this comment is the "dirty little secret" of our industry. Everyone working on it knows this but the research and VC dollars are flowing in so no one wants to talk about it too much.
- chrstphrhrt 8y ago
  Can confirm, worked at an AI hype company for over a year and built all their systems outside the NN internals. There is going to be a correction.
  - kuhhk 8y ago
    May I ask if this insight is why you left? What do you work on now?
    - chrstphrhrt 8y ago
      Actually left due to anti-engineering culture (no tech leaders), succumbed to the distortions of Conway's law due to imbalance between professional services and core/platform. That said, the nature of the lack of leadership was around the MBA mindset that data science is the be-all end-all of AI, which IME is so far from the truth. There needs to be many layers/spirals of naive methods (i.e. straight ahead engineering) as a vanguard on the front lines with the DS/NNs/NLP bringing up the flanks for aspects of the domains that become more well understood over time. So yeah, tail wagging the dog, cart before horse etc. If you just barrel ahead with a monolithic NN pretending that all classes are created equal (structureless blob), which is what our DS PhDs were doing, it will quickly ossify. In our case it was intent prediction so we got to pretty high accuracy, but the labels were indicating many different categories that actually had relations in nature that could not be expressed. Ironically it took some regular engineers to research HMC (hierarchical multi-label classification) and ensembles and implement a new model training framework to support them. Not sure what they teach in school but it doesn't seem to be very practical. EDIT: now working on messaging (SMS/MMS/RCS/ABC) with a focus on dialog management and logical rules with layering-in of progressively less naive stats methods strategically rather than blindly. It helps to have an existing revenue stream that can be leveraged to create even more value with AI features rather than the backwards hail Mary of not iterating from a foothold of existing traction.

sonnyblarney 8y ago

Making a serious, industrial scale web app in 2000 felt like alchemy. It was all arcane, there were no established patterns, nobody knew how to do it for sure, there were a lot of hustlers, most of them thankfully sincere. When something is new, it feels like a mystery - eventually we'll have a language for wrapping our heads around neural networks, even if it's not as clear cut as we'd like.

brain5ide 8y ago
We had the neural networks, and the language. The problem is with the rebranding and the amount of marketing bullshit comming with it. The null hypothesis is that apl of that most of it is a pile of crap, for an overengineered, overoptimized solutions that are probably applied at abstraction layer different from one they are marketed on. There may come solution out of it, but it's more wishfull thinking than not.
ramblerman 8y ago
I understand what you are getting at, but I would call that a new frontier. There was ultimately nothing black box about the code we cobbled together back then.

dplarson 8y ago

Additional context from Ali Rahimi and Ben Recht: http://www.argmin.net/2017/12/11/alchemy-addendum/ http://www.argmin.net/2017/12/11/alchemy-addendum/

mistrial9 8y ago
.. and a Jupyter Notebook https://github.com/benjamin-recht/shallow-linear-net/blob/master/TwoLayerLinearNets.ipynb https://github.com/benjamin-recht/shallow-linear-net/blob/ma...

imperio59 8y ago

The way I understand neural networks to work, they are actually series of connected infinitely valued logic gates, where given a numerical input from neg infinity to positive infinity, it spits out another number from neg infinity to positive infinity that feeds into the next set of logic gates, and at the end gives you a confidence interval from 0 to 1 of whether there was a pattern match or not. To me it's very similar to Boolean logic circuits the way I was taught those in college except that there's too many gates to configure manually so you use supervised learning to find reasonable values and an arrangement of gates that works (aka your trained neural network). I've never heard anyone else describe it this way but this is how I like to think about it. It really has nothing to do with how the human brain or much less human mind works. That's just marketing speak.

digitalzombie 8y ago
> That's just marketing speak. Pretty sure it was the initial thinking when Neural Network was created and it have move beyond that. I think people who know surface level only repeating this tadbit that's out of dated. It's even in the official wikipedia article (https://en.wikipedia.org/wiki/Artificial_neural_network https://en.wikipedia.org/wiki/Artificial_neural_network). Everything you've stated was basically a personal opinion that could have been verified via google...

bob1029 8y ago

In my mind, machine learning is simply one class of algorithm primarily oriented towards multidimensional fuzzy pattern matching. This technique can easily be classed as a subset of digital signal processing concerns. Everything tacked onto the sides of this (LTSMs, etc.) in some attempt to increase the cleverness or scope of the ML network (aka self-driving cars and other assorted high magickery), seek to band-aid something that is fundamentally flawed for these purposes. A ML network does not have intrinsic, real-time mutability in how it is defined, outside the scope of memory-based node weights, inputs, outputs or graphs over time. These nodes are added, removed or modified based on a predefined set of input, output and internal mappings. How would intermediate layers be defined in a dynamic way between input and output in such a network in an attempt to achieve these higher powers? Driving a car, for instance, is a task that requires learning entire new subsets of skills, many times ad-hoc, that require intermediate models to be dynamically developed which are potentially outside the capabilities of our understanding. The biggest challenge I see today is that we don't necessarily have a good way to dynamically construct models of the intermediate layers (such that we can map them to other layers), especially if these layers are being added and removed dynamically by algorithms at the edge of our capability to understand. I've always felt that there needs to be some internal processing occurring at rates far higher than the input sample rates such that higher-order intelligence may emerge by way of adjusting the entities noted above multiple times per input sample (and potentially even in the absence of input). The problem is also going to come down to how a person would define outcomes vs how an AI/ML network would. For the future to really begin we will need an AI that can understand and reason with what success and failure feel like in our abstract terms. This will require it to have the capacity to dynamically construct abstractions which we would have no hope of modelling ourselves, as we do not have very deep insight into the abstractions upon which the biological human brain implements virtually any behavior today. There is no amount of discrete math in the universe which can accurately model and assess the higher-order outcomes of decisions made in our reality. You can run ML disguised as AI in simulations and environments with fixed complexity all day, but once you throw one of these "trained" networks out into the real world without any constraints, you are probably going to see very unsatisfactory outcomes.

ineedasername 8y ago

Even granting the claim that ML has become alchemy, consider what alchemy was: Misguided in its quasi-magical underpinnings and goals, but nonetheless an extremely important step in the founding of modern chemistry. And so what started out with poor understanding and numerous misconceptions evolved over a period of time, through trial and error and hard won knowledge into a massively important branch of science. And so if ML is indeed comparable to alchemy, the only salient question is how long, and what will it take, to turn into chemistry.

nopinsight 8y ago

Related: Debate at NIPS 2017 on “Interpretability is necessary for machine learning”, by senior researchers in Machine Learning including Yann LeCun. https://youtu.be/93Xv8vJ2acI https://youtu.be/93Xv8vJ2acI

TrackerFF 8y ago

The ML scene was more rigorous 10-15-20 years ago, because it was mostly confined to the world of academia and industrial R&D, and we had much less wide-reaching problems to work towards. As tech evolves, more data is being generated, which in turn creates more problems that increases the demand for solutions. To put it short: Solving "real world" problems is more/better rewarded than figuring out the underlying technology, so it's no wonder that we see a larger portion of practitioners that may lack the academic background - and that is understandable. You could be a ML Ph.D (in Academia) for 5-10 year, earning gov. worker salary while trying to reach the next step - or hack together some ML-based product, until desired accuracy, and cash in 50-100 times the pay. Now, with that said, I understand that Ali targeted his speech at the NIPS folk - but the industry / academia crossover in ML is massive, and it still stands that much of industry problems are of commercial interest - so the motivation for many is still the same.

deleted 8y ago
[deleted]

Mikhail_K 8y ago

If someone turns in math homework consisting in answers only, the teacher will probably not give any credit, and will ask to show the work. Yet the AI/ML community insists on cargo cult test for intelligence - behaving like someone with a mind. That can be traced to the "Turing test". It was flawed then, and it is flawed now. Reproducing the behaviour of a thinking agent does not prove that a putative AI will not fail in a more detailed test, as demonstrated in numerous papers about "adversarial images".

tabtab 8y ago

We are still at the experimental stage, so trial and error without clear theories is not unexpected. Chemistry formed out of alchemy: people fiddled and noticed the patterns. The patterns were written down and shared, and different people began floating theories/models to explain the patterns, and the theories were further vetted by more experiments. Another thing is that the industry should look at other AI techniques besides neural nets (NN) or find compliments to NN's. Genetic algorithms and Factor Tables should also be explored as well. Just because recent advances have been in NN's does not necessarily mean that's where the future should lead. Factor tables may allow more "dissection" & analysis by those without advanced degrees, for example. Experts may set up the framework and outline, but others can study and tune specifics. (https://github.com/RowColz/AI https://github.com/RowColz/AI)