21 ms·

Past Performance is Not Indicative of Future Results (2020)

415 points by olvy0 5y ago

taylorwc 5y ago
Typo in the title, ought to be “Skeptic.” Unless, that is, his skepticism is also directly tied to handling sewage.
- stan_rogers 5y ago
  No, both spellings are good. The sewage thing would be "septic".
  - deleted 5y ago
    [deleted]
- 3gg 5y ago
  Even if you look up "skeptic" on dictionary.com, it will suggest the alternative spelling. https://www.dictionary.com/browse/skeptic https://www.dictionary.com/browse/skeptic English is not just spoken in 'murica.
marcinzm 5y ago
I find his comment about hallucinating faces in the snow amusing given that humans hallucinate faces in things all the time. And then either post it to Reddit or have a religious experience.
- starmftronajoll 5y ago
  Yes, that is explicitly part of the point Doctorow is making. It’s why the essay mentions the fact that humans see faces in clouds, etc. Humans typically know when they are “hallucinating” a face, and ML algorithms don’t. When humans see a face in the snow, they post it to Reddit; they don’t warn their neighbor that a suspicious character is lurking outside. This is the distinction the essay draws.
  - marcinzm 5y ago
    People perceive nonexistent threats all the time and call the police. The threshold is simply higher than current AI but that’s a question of magnitude rather than inherent difference. Fine tune a reinforcement model on 5 years of 16 hours a day video and I’m sure it will also have a better threshold.
    - kortilla 5y ago
      There is general knowledge about the world for humans to know that there isn’t a giant human in the sky no matter how good the face looks. Train it with as many images as you want and as long as a good enough face shows up, the model is going to have a positive match. The entire problem is it’s missing that upper level of intelligence that evaluates “that looks like a face, could it actually be a human?”
      - marcinzm 5y ago
        >There is general knowledge about the world for humans to know that there isn’t a giant human in the sky no matter how good the face looks. Is there? Humans used to think the gods were literally watching them from the sky and the constellations were actual creatures sent into the night. So this seems learned behavior from data rather than some inherent part of human thinking. >Train it with as many images as you want and as long as a good enough face shows up, the model is going to have a positive match. So will a human if something is close enough to a face. A shadow at night for example might look just like a human face. Children will often think there's a monster in the room or under their bed.
        kortilla 5y ago
        Children will think there is a monster under their bed based on no evidence at all. That speaks to something beyond object recognition that happens at a different processing layer. Humans do not need to be trained on billions of images from around the globe to semantically understand where human faces are not expected to appear. Modern AI can certainly recognize faces really well with that level of training now, but it still doesn’t even understand what a face is (i.e. no model of reality to verify its identification against).
- itisit 5y ago
  Those humans don't typically believe those hallucinated faces belong to people though nor do they call the cops.
  - version_five 5y ago
    You don't think a person has ever called the police because they hear a noise they thought was an intruder, or saw someone or something suspicious only in their mind? People make these kind of mistakes too.
    - itisit 5y ago
      Of course, but the consistency of the false positive is the issue. An able-minded person can readily reconcile their confusion.
      - marcinzm 5y ago
        Then that’s a question of training data.
        foobiekr 5y ago
        The problem with this line of reasoning is that it can be used as a non-constructive counter to any observation about AI failure. It’s always more and more training data or errors in the training set. This really is a god-of-the-gaps answer to the concerns being raised.
        marcinzm 5y ago
        No, my point is that if two systems show very similar classes of errors but at different thresholds with one trained on significantly more data than the more likely conclusion is that there isn't enough data in the other.
        Dylan16807 5y ago
- legrande 5y ago
  > Given that humans hallucinate faces in things all the time Pareidolia: https://en.wikipedia.org/wiki/Pareidolia https://en.wikipedia.org/wiki/Pareidolia
swayvil 5y ago
I see no path from "observation" to "model" that does not involve an arbitrary (aesthetic? Nonrational, human-necessitating?) choice. This would suggest that "general" AI is impossible. ON THE OTHER HAND There is a variety of general AI, called an "optimizer". It starts with something better than a void. Maybe that's the path we should be looking at.
- Reimersholme 5y ago
  Well, human thinking relies on prior models/filters for understanding the world as well so that would invalidate us as having general intelligence too?
  - swayvil 5y ago
    Those relied-upon models may be acquired nonrationally. Via aesthetics etc. Or, in the case of the optimizer, I think the human equivalent would be desire.
  - username90 5y ago
    Human thinking includes building new models/filters for understanding the world, not just applying old ones. And that isn't used for learning, we do it all the time when solving any kind of challenging problem or even for simple problems like trying to recognize a face. Computer models might never compete with human performance unless they can learn how to solve a problem as it is solving it, because that is what humans do.
    - swayvil 5y ago
      I am on the same page. To talk about the models some more... There's this big mass of models. And it's got all kinds of sections. Special sections that we learn about in school. Special sections called "science". Sections that we invent ourselves. Sections that we inherit from our parents, religion, etc. It's partially biological. Partially cultural. A massive library of models, mostly inherited. You move in relationship with the mass in different ways. You can create new models. That's what basic science is. Extending the edge of the mass. Naming the nameless. You can operate freely from the mass. Creating your own models or maybe operating model-less. Artists, mystics, weirdos. You can operate completely within the mass. Never really contending with unmodelled reality. The map and territory become one. Like in a videogame. I think that's the most popular way.
3gg 5y ago
I found this to be a very succinct, sober analysis of ML ("AI") techno-solutionism. Cory is a great writer and knows how to explain ideas in a simple, no-nonsense way. This article reminded me of Evgeny Morozov's "To Save Everything, Click Here", where you can find many more examples of how focusing on the quantitative aspect of a problem and ignoring the social, qualitative context it around it often goes wrong. https://bookshop.org/books/to-save-everything-click-here-the-folly-of-technological-solutionism/9781610393706 https://bookshop.org/books/to-save-everything-click-here-the...
iamnotwhoiam 5y ago
Are there any approaches to artificial intelligence that do involve qualitative data or don’t rely entirely on statistical inference?
- Ericson2314 5y ago
  Not really adjacent to what we do today. I view A.I. as dual to "neoliberal M.B.A. culture". Just as the business schools taught that managers should be generalists without craft knowledge applying coarse microeconomics, A.I. that we have created is the ultimate pliant worker that also knows nothing deep and works from statistics. In a bussiness ecosystem where analytics and presentations are more important than doing things, they are a perfect match. Of course, a bunches of statistician-firms chasing each other in circles is going to exhibit the folly, not wisdom, of crowds. I think solution is to face reality that more people need to learn programming, and more domain knowledge needs to be codified old school. https://www.ma.imperial.ac.uk/~buzzard/xena/ https://www.ma.imperial.ac.uk/~buzzard/xena/ I thus think is perhaps the best application of computing, ever. Training A.I. to be a theorem prover tactic is a great way to make it better: if we can't do theory and empiricism at the same time, we can at least do meta-empiracism on theory building! I think once we've codified all the domains like that and been running A.I. on the theories, we'll be better positioned to go back to the general A.I. problem, but we might also decided the "manually programmed fully automated society" is easier to understand and steer, and thus less alienation, and we won't even want general A.I.
- dr_dshiv 5y ago
  Cybernetics and control theory, broadly speaking, involve the design of data feedback loops to govern simple machines or complex socio-technical systems. For instance, an organization might instrument a feedback loop to use qualitative survey data to inform decision-making. That isn't ML, but it is cybernetics. And, based on Peter Norvig's definition, it is a form of AI. Consider that "autopilot" was invented in 1914, long before digital computers. From this perspective, Artificial Intelligence might even be seen as an ancient human practice— present whenever humans have used artifacts to govern complex systems.
- jon_richards 5y ago
version_five 5y ago
This article is mostly a straw man, while still containing some valid ML criticism. I am a ML s(c|k)eptic too, in that popular conceptions of what ML is currently overpromise, often don't even understand what ML actually is, and are often just some layperson's imagination about what "artificial intelligence" might do. This article is the opposite. He's treating ML as basically a simple supervised architecture that doesn't allow any domain knowledge to be incorporated and simply dead-reckons, making unchecked inferences from what it learned in training. Under these constraints, everything he says is correct. But there is no reason ML has to be used this way, in fact it is extremely irresponsible to do so in many cases. ML as part of a system (whether directly part of the model architecture and learned or imposed by domain knowledge) is possible, and is generally the right way to build an "AI" system. I think ML has its limitations and will be surprised to see current neural networks evolve into AGI. But I also don't think the engineers working in this space are as out to lunch as the author seems to imply, and would not write off the possibilities of what contemporary ML systems can accomplish based on the flaws pointed out in relation to a very narrow view of what ML is.
- 3gg 5y ago
  > But I also don't think the engineers working in this space are as out to lunch as the author seems to imply. Are you at all close to this space? It sounds you may be underestimating corporate politics and the lack of rigour and ethical thought with which these systems are applied. The example Cory puts on policing -- and the many other examples you can find in Evgeny Morozov's book or "The End of Trust" -- are solid proof of this.
  - vletal 5y ago
    My first thought was that I'm not the target audience of this article. I'm a ML practitioner. This seems more like an overstated opinionated wake up call to mgmt and sales people. Is not it?
    - version_five 5y ago
      Agreed. What I called a straw man in the OP could also be characterized as a simplification to get his point across to lay-audiences. (Personally I dont agree with the simplification, per my other post). It's meant for popular audiences (as someone else points out, this is from a sci-fi magazine)
a-dub 5y ago
they say that those who ignore the past are doomed to repeat it, data driven algorithms provide statistical guarantees of repeating it.
lamebitches 5y ago
Covid is a bio-weapon. Fauci is the dealer.
radu_floricica 5y ago
I think he's doing a bit of bait and switch there. Knowing reliably whether arrests are genuinely racist or if winks are flirtatious is superhuman intelligence. > But the idea that if we just get better at statistical inference, consciousness will fall out of it is wishful thinking. I'm a mostly disinterested spectator in current AI research, and even I know that it's not all about that. Just google "AI alignment" for an example, and god only knows what's going on in private research.
- akomtu 5y ago
  I think the definition of racism in this context can be simple. If the rate of false positives for blacks is significantly higher than the average across the nation, then it's racism. Significantly higher can mean "one stddev higher".
  - radu_floricica 5y ago
    Exactly my point - I happen to disagree. We're not going to have AI come and tell us the Truth, at least not from day one. They'll help, but not with _this_ kind of questions. This is why I commented, because they seem specifically chosen to be some of the most complicated questions we face. (On topic: blacks could commit more crime then average. They could do it because of systemic racism, but in this case the _arrests_ are not racist.)
jstx1 5y ago
> I am an AI skeptic. I am baffled by anyone who isn’t. I don’t see any path from continuous improvements to the (admittedly impressive) ‘machine learning’ field that leads to a general AI - I share the skepticism towards any progress towards 'general AI' - I don't think that we're remotely close or even on the right path in any way. - That doesn't make me a skeptic towards the current state of machine learning though. ML doesn't need to lead to general AI. It's already useful in its current forms. That's good enough. It doesn't need to solve all of humanity's problems to be a great tool. I think it's important to make this distinction and for some reason it's left implicit or it's purposefully omitted from the article.
- shreyshnaccount 5y ago
  I'm in favor of changing the terminology from AI and ML to something along the lines of 'prediction model' so that the idea of machines 'thinking' is replaced with them 'predicting'. it's just easier for our mushy meat brains to think that AI and ML means that it'll lead to general AI or as I like to call it 'general purpose decision maker'. it's all about the language!
  - coddle-hark 5y ago
    I like the term “data driven algorithm“. It makes it clear to everyone involved that what we’re doing is just adjusting an algorithm based on the data we have. No-one in their right minds would confuse that with building a true “A.I.”.
    - spockz 5y ago
      What about “data derived algorithm”? The algorithm itself isn’t really driven by data after it has been designed anymore.
      - skohan 5y ago
        I mean if we want to be really accurate, we could say something like "highly dimensional data-derived function"
Reimersholme 5y ago
I feel like this would have felt more relevant maybe five-ten years ago when there was more of a feeling that deep neural nets was the end all. He mentions correlation vs causation but seems to have missed that causal inference is one of the most active and interesting fields of research today.
shannifin 5y ago
> I don’t see any path from continuous improvements to the (admittedly impressive) ‘machine learning’ field that leads to a general AI any more than I can see a path from continuous improvements in horse-breeding that leads to an internal combustion engine. While I also don't expect that AGI will emerge solely through optimizing statistical inference models, I also don't think "improvements to the machine learning field" consist only of such optimizations. Surely further insights, paradigm shifts, etc., will continue to play a role in advancing AI. Perhaps it's more a matter of semantics and a bad analogy; "machine learning" seems far more broad a field than "horse-breeding." Horse-breeding is necessarily limited to horses. Machine learning is not limited to a specific algorithm or data model. Even calling it a "statistical inference tool", while not wrong, is deceptive. What exactly does he or anyone expect or want an AGI to do that can't be understood at some level as "statistical inference"? One might say: "Well, I want it to actually understand or actually be conscious." Why? How would you ever know anyway?
- mirekrusin 5y ago
  It gets philosophical quickly, is "consciousness" repeatedly modifying cloud of random floats?
- Jetrel 5y ago
  It's worth pointing out that "machine learning" is a specific term of art, not a term for AI in general. It refers very specifically to the type of "convolutional neural networks" that have made a bunch of progress over the past 15-25 years. The moment you have a paradigm shift, sure, it can be considered "learning done by machines", but it's not "Machine Learning™" anymore. -- This is why the author put it in quotes; because, since it's a term comprehensible to anybody, it's got this unfortunate side effect where people on the outside of the field take the "plain english" meaning of it rather than realizing it's loaded with some extra specific meaning for the practitioners in the field.
  - shannifin 5y ago
    > It's worth pointing out that "machine learning" is a specific term of art, not a term for AI in general. It refers very specifically to the type of "convolutional neural networks" that have made a bunch of progress over the past 15-25 years. In my experience, "machine learning" is more broad than "convolutional neural networks", aligning with Wikipedia's definition: "the study of computer algorithms that improve automatically through experience and by the use of data." https://en.wikipedia.org/wiki/Machine_learning https://en.wikipedia.org/wiki/Machine_learning
MAXPOOL 5y ago
For a short and very non-technical article, this is well written. The current approach to machine learning is not going to go towards general-purpose AI with steady steps and gradual innovations. Things like GPT-3 seem amazingly general at first. But even it will quickly plateau towards the point where you need a bigger and bigger model, more and more data, and training for smaller and smaller gain. There need to be several breakthroughs similar to the original Deep Learning breakthrough away from statistical learning. I would say it's 4-7 Turing awards away at a minimum. Some expect less, some more.
- mirekrusin 5y ago
  Strange you're saying that, the unexpected outcome from gpt3 was specifically that it did not plateau as they were expecting and quite opposite deeper understanding emerged in different areas.
okareaman 5y ago
> But the idea that if we just get better at statistical inference, consciousness will fall out of it is wishful thinking. It’s a premise for an SF novel, not a plan for the future. My impression of Silicon Valley types like Ray Kurzweil in "The Age of Spiritual Machines" that if we wire up enough transistors somehow consciousness will somehow arise out of the material world. The somehow is not explained. Materialism is a dead end in my opinion. I am more interested in theories about consciousness as a field and our brains as receivers.
- Trasmatta 5y ago
  If our brains are receivers to a field of consciousness, why would it be impossible to replicate one of those receivers with a machine? You also seem to have just kicked the can down the road. "Consciousness arises from a field somehow, and the brain acts as a receiver somehow. The somehow is not explained."
  - okareaman 5y ago
    I didn't say I knew how. I said I believe materialism is a dead end, by which I mean I doubt the consciousness arises out of atoms configured as neurons. How those neurons receive a conscious field seems a more productive line of inquiry, but for some reason people resist this idea. Not sure why.
    - Trasmatta 5y ago
      My main point was that you seemed to be criticizing materialism for not yet having a solid answer for "how", which is the same issue any alternative theory has.
    - akomtu 5y ago
      Imho, materialism and non-materialism mesh well together. It's just the two camps, materialists and occultists, are too arrogant to recognize that the other camp might understand certain things better. A self aware intelligent organism or machine needs three key components: a material foundation that's sufficiently organized (a large net of neurons, a silicon crystal, etc.), a material fluid-like carrier to control the foundation (that's always electricity and magnetism) and the immutable immaterial principle to constrain the carrier (math rules, physical laws, software algorithms). That's the core idea of occultism rephrased in today's terminology. The "conscious field" would be identical with the magnetic field here and neurons don't need any magical properties to receive this field: they just need to be conductive, like transistors. I think the reason the AI progress has stalled is because 0-1 transistors are too primitive and too rigid for the task. I guess that superintelligence is only different in the performance and connectivity degree of the material foundation: instead of slow neurons with 10k of connections it would be fast quasi crystal like structure with billions of connections that needs to move very little matter around (but it has to be material and consist of atoms of some sort).
nkozyra 5y ago
> It’s not sorcery, it’s “magic” – in the sense of being a parlor trick, something that seems baffling until you learn the underlying method, whereupon it becomes banal. I think part of the problem is the belief that human or animal intelligence is somehow more mystical. People who think like this will see an ML implementation solve a problem better and/or faster than a human and counter "well, it's just using statistical inference or pattern recognition" and my response is "so?" Humans use the same processes and parlor tricks to understand and replay things. Where humans excel is in generalizing knowledge. We can apply bits and pieces of our previous parlor tricks to speed up comprehension in other problem spaces. But none of it is magic. We're all simple machines.
- xnyan 5y ago
  >simple machines. Ooof. Premed dropout here, so admittedly not an expert in human biology but this is a wild statement. A neuron is simple in the same way a transistor is simply a silicon sandwich doped with metals. A parlor trick is something that once you understand, is straightforward to implement on your own. Are you arguing that anyone now or in the foreseeable future could simply recreate the abilities of a human? If so, what evidence could you show me to support that?
  - nkozyra 5y ago
    I'm arguing that animal or lesser intelligence is built around hundreds of thousands of parlor tricks operating in a complex ensemble. There's a bias toward the marvel of human intelligence that causes some people to dismiss ML for the same underlying reasons we don't try to put a square peg in a round hole after infancy. Side note: disagree all you like but starting a rebuttal with "oof" is the kind of dismissive language that lets people know you'll be taking a very reductionist approach in your reply.
    - nicoffeine 5y ago
      > I'm arguing that animal or lesser intelligence is built around hundreds of thousands of parlor tricks operating in a complex ensemble. Until ML/AI can perform a single one of those parlor tricks without the constant direction of human intelligence, there’s no reason to stop marveling.
deleted 5y ago
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Hacktrick 5y ago
I just read one of his books for school.
mark_l_watson 5y ago
I like the term “AI” and the classic definition of achieving human like performance in specific domains. I don’t think that there is much confusion about the term for the general population, and certainly not in the tech community. The term “AGI” is also good, “artificial general intelligence” describes long term goals.
atty 5y ago
Unfortunately it’s pretty clear from the article that Cory does not have much familiarity with the research going on in the field of machine learning, and is creating a straw man. Quite a lot of work is being done on causal inference, out-of-distribution generalization, fairness, etc. Just because that is not the focus of the big sexy AI posts from Google et al does not mean that the work isn’t being done. I’d also point out that humans can infer causality for simple systems, but for any sufficiently complex system we also can’t reason causally. But that does not mean we can’t infer useful properties and make informed, reasonable decisions. I’d also point out that not all models are “theory-free”, as he describes it. I specifically do work in areas where we combine “theory” and machine learning, and it works very well. And finally, his point about comprehension does not really fly for me. There is no magical comprehension circuit in our brain. It’s all done via biological processes we can study and emulate. Will that end up being a scaled up version of current neural nets? Will it need to arise from embodied cognition in robots? Will it be something else? I don’t know, but it’s certainly not magic, and we’ll get there eventually. Whether that’s 10 years or 1000, who knows. Are current paradigms going to lead to AGI? Frankly, I’d just be guessing if I even tried to answer that. My gut instinct is no, but again, that’s just a guess. Can current methods evolve into better constrained systems with more generalizable results and measurable fairness? Absolutely.
- belter 5y ago
  Do you have an example/reference of the type of work you are thinking about ?
- jmull 5y ago
  > Are current paradigms going to lead to AGI? Frankly, I’d just be guessing if I even tried to answer that. My gut instinct is no I'll just note that while you start off saying Doctorow has no idea what he's talking about, you finish by pretty much fully agreeing with the essay.
- version_five 5y ago
  I'm not sure what issue others had with your comment. You're quite correct that he ignores vast swaths of current ML art and attacks a narrow conception of what ML is. Many of his criticism are legit with the right caveats, but he leaves out a lot of information that could lead to a different thesis. My read of the discussion here is that there is lots of idle speculation by people who don't have any real experience with ML research / engineering, that overwhelms a minority who actually know what they are talking about and are calling CD out on this, or at least challenging aspects of his arguments.
7357 5y ago
C. Doctorow is one of these (admittedly few) famous people I'd like to meet IRL.
m0rphy 5y ago
ML or not, at the most fundamental level, classical computers simply do not possess the type of logic that's truly reflective of our reality. Its binary nature forces it to always resolve any single statement to either a true or false answer only. A very simple example. If we ask our classical computer this question "are people currently supportive of COVID-19 vaccines?", then it would probably give us a straight answer of either a "yes" or "no" based on statistical inference of the percentage of total people who have received vaccinations at this point. At its most fundamental level, classical computers just cannot comprehend a reality that could resolve that answer to both "Yes" and "No" in a single statement, which btw is possible in a quantum computing environment under its superposition state. In our reality, some people who may not be fully supportive of the vaccines, but under special circumstances they may be forced to receive it because of workplace requirements, pressures from their loved ones, etc...
dundarious 5y ago
I'm tired of the Norvig vs. Chomsky style debates about what is cognition/intelligence/learning. I think this piece does rehash that debate somewhat, but it's not at all the focus. It's key contributions are about the mainstream domination of quantitative vs. qualitative methods, especially in this paragraph: > Quantitative disciplines are notorious for incinerating the qualitative elements on the basis that they can’t be subjected to mathematical analysis. What’s left behind is a quantitative residue of dubious value… but at least you can do math with it. It’s the statistical equivalent to looking for your keys under a streetlight because it’s too dark where you dropped them. and also of note is the "veneer of empirical facewash that provides plausible deniability", for discrimination, and for doing a poor job but continuing to be rewarded for it. If I had to summarize it would be: - The ML/AI community, which includes the researchers, practitioners, and the evangelists, are broadly utopian in what they think they can achieve. They are overconfident even in the domain of detecting the face of potential burglars in a home security camera, never mind in terms of creating new life with AGI. I think Doctorow's critique equally applies to "algorithms" even only as complex as a fancy Excel sheet, but he focuses on ML/AI as the most common source of this excess of optimism, that recording data and running it through a model is almost certainly the _most sensible thing to do_ for any given problem. - If there is a manufactured consensus that the almost purely quantitative approach is the _most sensible thing to do_, then any failures or short-comings can be hand-waved away. Say sorry, "the model/algorithm did it", and just ignore the issue or apply a minor manual fix. This is a huge benefit for decision-makers wishing to maintain their status/livelihoods in both the public and private sector. Crucially, this excuse works if you're just ineffective, or if you're a bad actor. Note that this is a critique of CEOs and government officials, more than of engineers -- we would only be complicit by association. If there is a critique for engineers, it's that we provide fodder for the excess of optimism in summary point 1 because we love playing with our tools, and that we allow ourselves to be the scapegoat for summary point 2.
vijucat 5y ago
> Let’s talk about what machine learning is...it analyzes training data to uncover correlations between different phenomena. The author seems to have missed or excluded reinforcement learning and planning algorithms in this definition. My criticism of AI criticism in general is that no one admits that at the root of it, we do not understand thinking (or "consciousness"). We are merely the "recipient" or enjoyer of the process, which is opaque. Just as AlphaGo, even if it just a facsimile of a Go player, could beat a human at Go, it is probable that an AI could produce a passable facsimile of thinking at one point. Its mechanisms would be as opaque as human thinking (, even to itself), but the results would be undeniable. AGI is a possibility.
- epgui 5y ago
  I believe we do understand, broadly speaking, thinking and consciousness. There remains a lot more to learn, as in anything in science... IMO the main difficulty is that humans have terrible self-awareness or self-insight. We want to believe we're special, we want to believe we're intelligent, we want to believe we're different than machines. We're in denial about that. Our brains aren't any more special than computers, other than that it's really quite formidable that we evolved them by chance in this universe of chemical soup we find ourselves in. At the end of the day, however, a computer is a computer, and "thinking" and "consciousness" simply do emerge from low-level computations given some special structures.
  - rapferreira 5y ago
    Are you kidding? Go read Hegel's phenomonology of mind and get back to me on that
    - epgui 5y ago
      A philosophical treatise from 1807, no matter how interesting or insightful, could hardly represent the current state of understanding of the topic. My comment is loosely based on a general appreciation of textbook-level neuroanatomy and recent advances in AI/comp sci.
coldtea 5y ago
>The problems of theory-free statistical inference go far beyond hallucinating faces in the snow. Anyone who’s ever taken a basic stats course knows that “correlation isn’t causation.” For example, maybe the reason cops find more crime in Black neighborhoods because they harass Black people more with pretextual stops and searches that give them the basis to unfairly charge them, a process that leads to many unjust guilty pleas because the system is rigged to railroad people into pleading guilty rather than fighting charges. (...) Being able to calculate that Inputs a, b, c… z add up to Outcome X with a probability of 75% still won’t tell you if arrest data is racist, whether students will get drunk and breathe on each other, or whether a wink is flirtation of grit in someone’s eye. Except if information about what we consider racist etc. also passes through the same inference engine (feeding it with information on arbitrary additional meta levels). So, sure, an AI which is just fed crime stats to make inferrences, can never understand beyond that level. But an AI which if fed crime stats, plus cultural understanding about such data (e.g. which is fed language, like a baby is, and which is then fed cultural values through osmosis - e.g. news stories, recorded discussions with people, etc). In the end, it could also be through actual socialization: you make the AI into a portable human-like body (the classic sci-fi robot), and have it feed its learning NN by being around people, same as any other person.
- deleted 5y ago
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nlh 5y ago
This is a well-written and well-reasoned argument - BUT - I tend toward the materialist philosophy, so the argument doesn't really hold there. Yes, an ML model that infers B from A might not "understand" what A or B are....yet. But what is it to "understand" anyway? Just a more complex process in a different part of the machine. If the human brain is just a REALLY large, trained, NN, there's no reason that we won't be able to replicate it given enough computing power.
- jaredklewis 5y ago
  > If the human brain is just a REALLY large, trained, NN, there's no reason that we won't be able to replicate it given enough computing power. I think one clear sign that the human mind is more than just a big NN is how large neural networks are already. Take GPT-3, which is was trained on 45 terabytes of text and has 175 billion parameters. Contrast that with the human brain, which has around 86 billion neurons and is able to do much of what GPT-3 can do with only a tiny fraction of the training data. And it has to be said that while GPT-3 has more competency than an average human at some text generation related tasks, the average human brain is vastly more capable than GPT-3 at any non-text related task. So for neural networks to approach human level capability we would need a whole stack of GPT3-ish size networks for all the other non-text related things the human brain can do: speech, vision, motor control, social interactions, and so on. By that point the amount of training data and parameters is so astronomical, there can be no question that the functioning of human brains must be significantly different than that of contemporary computer neural networks. To be clear, I am also a materialist and subscribe to the computational theory of mind, but just based on the size of training data alone, it seems obvious that human brains work differently than neural networks.
  - Simon321 5y ago
    A parameter in a neural network is more comparable to a synapse of which the brain has 100 trillion. And yes, we will get there too one day.
    - jaredklewis 5y ago
m12k 5y ago
The first AI winter came after we realized that the AI of the time, the high level logic, reasoning and planning algorithms we had implemented, were useless in the face of the fuzziness of the real world. Basically we had tried to skip straight to modeling our own intellect, without bothering to first model the reptile brain that supplies it with a model of the world on which to operate. Being able to make a plan to ferry a wolf, sheep and cabbage across the river in a tiny boat without any of them getting eaten doesn't help much if you're unable to tell apart a wolf, sheep and cabbage, let alone steer a boat. That's what makes me excited about our recent advances in ML. Finally, we are getting around to modeling the lower levels of our cognitive system, the fuzzy pattern recognition part that supplies our consciousness with something recognizable to reason about, and gives us learned skills to perform in the world. We still don't know how to wire all that up. Maybe a single ML model can achieve AGI if it is adaptable enough in its architecture. Maybe a group of specialized ML models need to make up subsystems for a centralized AGI ML-model (like a human's visual and language centers). Maybe we need several middle layers to aggregate and coordinate the submodules before they hook into the central unit. Maybe we can even use the logic, planning or expert system approach from before the AI winter for the central "consciousness" unit. Who knows? But to me it feels like we've finally got one of the most important building blocks to work with in modern ML. Maybe it's the only one we'll need, maybe it's only a step of the way. But the fact that we have in a handful of years not managed to go from "model a corner of a reptile brain" to "model a full human brain" is no reason to call this a failure or predict another winter just yet. We've got a great new building block, and all we've really done with it so far is basically to prod it with a stick, to see what it can do on its own. Maybe figuring out the next steps toward AGI will be another winter. But the advances we've made with ML have convinced me that we'll get there eventually, and that when we do, ML will be part of it some extent. Frankly I'm super excited just to see people try.
yarg 5y ago
Past performance is not indictative of future results across distinct domains. Within a single problem space (or sub-space) past performance can generalise quite well. There's a problem with scaling solutions and expecting performance to continue to increase in a continuous exponential manner: growth that we perceive as exponential is often only on a long-life S-Curve. We've seen this in silicon, where what appears to the layman to have been exponential growth has in fact been a sequence of more limited growth spurts bound by the physical limits of scaling within whatever model of design was active at the time. The question of where the bounds to the problem domains are, and when new ideas or paradigms are required is much more difficult in AI than it has been in microprocessors. It's easy enough to formulate the question "how small can this be before the changes in physical characteristics at scale prevent it from working?", if rather more difficult to answer. AI is so damned steeped in the vagaries of the unknown that I can't even think of the question.
rob_c 5y ago
Someone buy this man a beer. Couldn't have phrased most of that better had I tried and I've been arguing these points with staff for years
dvt 5y ago
What a confused and muddled post, trying to touch on psychology, philosophy, and mathematics, and missing the mark on basically all three. I'm quite bearish on AI/ML, but calling it a "parlor trick" is like calling modern computers a parlor trick. I mean, at the end of the day, they're just very fast abacuses, right? Let's face it: what ML has brought to the forefront -- from self-landing airplanes to self-driving cars, to AI-assisted diagnoses -- is pretty impressive. If you insist on being reductive, sure, I guess it's "merely" statistics. Bringing up quantitative vs qualitative analysis is just silly, since science has had this problem way before AI. Hume famously described it as the is/ought problem†. And that was a few hundred years ago. Finally, dropping the mic with "I don't think we're anywhere close to consciousness" is just bizarre. I don't think that any serious academic working in AI/ML has made any arguments that claim machine learning models are "conscious." And Strong AI will probably remain unattainable for a very long time (I'd argue forever). This is not a particularly controversial position. † Okay, it's not the same thing, but closely related. I suppose the fact–value distinction might be a bit closer.
- flyinglizard 5y ago
  > what ML has brought to the forefront -- from self-landing airplanes to self-landing cars I am not aware of any ML in flight controls. Being black box and probabilistic by nature, these things won’t get past industry standards and regulations (at least for a while).
  - dvt 5y ago
    > I am not aware of any ML in flight controls. Being black box and probabilistic by nature, these things won’t get past industry standards and regulations (at least for a while). (Hah, I accidentally wrote "self-landing cars," fixed). But yeah, I guess I was thinking more of drones, I'm not exactly sure what ML (if any) is in the guts of a commercial or military airplane.
    - meheleventyone 5y ago
      Drones don’t need ML to self-land AFAIK?
Mikeb85 5y ago
Past performance isn't necessarily indicative of future results but as people tend to repeat behaviour over and over, it is often enough.
Gatsky 5y ago
Skepticism is a tough gig really. If you are right then nobody cares, if you are wrong then you look like a fool.
charmides 5y ago
I might be wrong, but regarding the anecdote about the physicists at Michigan’s Albion College attempts to model the spread of COVID-19, I think the author misfired. From what I understand, many non-epidemiologists have been surprisingly insightful during the pandemic.
epgui 5y ago
What baffles me is the number of humans who think they are in the personal possession of some super special sacred form of magical and unexplainable intelligence. "AI is just stats" yes, indeed, but so is human intelligence. In many ways, AI from 2010 was already better than human intelligence. Three remarks: - The task many people seem to be benchmarking against is not just a measure of general intelligence, but a measure of how well AI is able to emulate human intelligence. That's not wrong, but I do find it amusing. Emulating any system within another generally requires an order of magnitude higher performance. - The degree to which human intelligence fails catastrophically in each of our lives, on a continuous basis, is way too quickly forgotten. We have a very selective memory indeed. We have absolutely terrible judgment, are super irrational, and pretty reliably make decisions that are against our own interests, whether it's with regard to tobacco use, avoidance of physical exercise, or refusal of life-saving medications or prophylactics. We avoid spending time learning maths and science because it's not cool, and we openly display pride in our anti-intellectual behaviours and attitudes. We're all incredibly stupid by default. - AI researchers need to work more closely with neuroanatomists. The main thing preventing AI from behaving like a human is the different macro structure of human NNs vs artificial NNs. Our brains aren't random assortments of randomly connected neurons: there's structure in there that explains our patterns of behaviour, and that is lacking in even the most modern AI. We can't expect AI to be human if we don't give it human structures.
- username90 5y ago
  General intelligence requires it to solve real problems in the real world. It isn't about emulating humans, but emulating anything resembling an intelligent being we are aware of. It would be totally exceptional if we could properly emulate the intelligence of a fly or an ant, but we can't even do that. "Emulate a human brain" you say, but we can't even emulate brains a million times smaller than that.
  - deleted 5y ago
    [deleted]
  - blamestross 5y ago
    https://en.m.wikipedia.org/wiki/AnimatLab https://en.m.wikipedia.org/wiki/AnimatLab We totally do emulate organisms on that scale. The real challenge is simulating the sensory inputs and the feedback loop between the outputs, the environment as the body acts, then new inputs. Disembodied simulations of nerual networks don't work. They are part of a body, an environment, and all the feedback loops that come with it. It sounds like you really just want to see a ML algorithm have a body to learn in. Why we ever expect AGI to happen without letting an ML algorithm learn by interacting with a "real" reality seems strange to me. By all means, keep making glorified optic nerve and expecting them to "wake up".
harry8 5y ago
We are paying for the incredible bamboozle that is the phrase "Machine Learning." If we used computerized statistical inference instead and the phrase "machine learning" did not exist the attitude to people from investors to regulators, from customers, vendors, doom sayers and boosters alike would be vastly better taken as whole. Nearly everyone here knows mostly when seeing AI written or hearing it that it's a total crock. Nearly everyone here knows ML is applied statistics done with a computer but this not common knowledge and it really should be.
- evrydayhustling 5y ago
  "AI" as a term deserves this rep, because it was effectively marketing as far back as the 70s. But you're way off on "Machine Learning". There's been plenty of progress in the last 15 years re-interpreting many ML methods as regression (any optimization is a regression if you set up the right likelihood function). But many important results and techniques -- including today's ubiquitous deep nets -- originated and had successful applications way before they had statistical interpretations. They came from fields like compression theory, database design, or even biological interpretations. The term Machine Learning was introduced to re-focus the field on a measurable objective: algorithms that improve with more data. The "Learning" part was not an abstract term to tug on your imagination, but included formal definitions of how algorithms improve that involved slightly fewer assumptions than statistical learning (which is a subfield). This lineage isn't that important today, but that focus on how learning is measured is still the most important guidepost both for ML research and for sorting out marketing BS from realistic claims. Certainly, state of the art work using deep nets for tasks like NLP, image and video recognition aren't designed by reasoning about the statistical interpretation, or tested by applying typical statistical tests. Popularizing this work as Statistical Inference or Regression wouldn't give any added intuition and wouldn't really describe the way ML research proceeds, or how ML systems succeed or fail.
  - harry8 5y ago
    It works by fitting curves. Whether you have a (presumably mathematical) "Statistical interpretation" or not is basically irrelevant in terms of what it actually does and what we should be conveying to people who aren't knowledgeable of the field. This is not about an academic argument. Putting stats right there in the name is vastly, vastly more informative than "Learning" which has the nuance for 99% of people as something requiring intelligence and is misleading. Hence the AI cons all pop up immediately there are some public ML wins called "Learning." Generalizing from data is actually what statistics does. It's what ML is. People like Hinton, Wasserman, Tibrishani et al seem to agree that ML is statistics but even that isn't what I'm talking about here.
coolaliasbro 5y ago
I come away from from this article with two thoughts. 1) Regarding the example of qualitative data via drunk students attending eye-licking parties. The author doesn't explain how this is qualitative. To my mind it's a gap in the model. The modelers could have included parameters to account for students behaving impulsively or irrationally, but they didn't. 2) Considering the nebulous nature of terms like consciousness and comprehension and the ensuing challenges of measurement, can it be proven that the structures that can potentially underpin behavior, etc., that would generally be recognized as conscious or comprehending do not exist as an emergent but undetected property of the Internet? Is it reasonable to suppose that if such a structure existed and if it possessed or embodied consciousness or comprehension that it might work toward remaining unknown?
abecedarius 5y ago
This kind of talk can be steelmanned, but even that version doesn't have reassuring answers to the likes of > Okay, you’ve all told us that progress won’t be all that fast. But let’s be more concrete and specific. I’d like to know what’s the least impressive accomplishment that you are very confident cannot be done in the next two years. (from https://intelligence.org/2017/10/13/fire-alarm/ https://intelligence.org/2017/10/13/fire-alarm/) Just today I was rather astonished by https://moultano.wordpress.com/2021/07/20/tour-of-the-sacred-library/ https://moultano.wordpress.com/2021/07/20/tour-of-the-sacred... -- try digging up something comparable from mid-2019.
ALittleLight 5y ago
I don't get why this article conflates machine learning progress and racism. Machine learning is not inherently racist though it may be implemented, intentionally or unintentionally, to produce racist results. It's much easier to solve racism in machine learning models though then in humans and easier to test to confirm that you have corrected it.
- catears 5y ago
  There are a whole slew of "advanced AI programs" out there that tells managers who to fire and who to keep, tells judges if someone should go to jail or not, etc. There are a lot of systems out there where peoples lives are changed forever "because the machine said so". I'd argue that since machine learning learns only from it's data (produced by it's human creator), it becomes a great tool for baking in unconcious biases in a completely opaque system and amplifying those biases. Much easier to tell if a human seems to be biased than if the dataset fed to an AI algorithm is biased.
  - ALittleLight 5y ago
    I don't agree with that. If someone says "The AI is biased" we can just look at the data it was trained on or come up with a set of test cases to establish the bias. We can update the training data or method to remove the bias too. If someone says a person is biased they can just say "No I'm not" and then how do we tell? We can't really make a person do a million judgments and find statistical evidence. We can't dive into how the possibly biased person came to have or not have biases.
arisAlexis 5y ago
"He quit high school,[8][verification needed] received his Ontario Academic Credit (high school diploma) from the SEED School in Toronto,[citation needed] and attended four universities without obtaining a degree" Shouldn't we collectively listen to experts,PhDs etc instead of famous bloggers for very technical stuff? Much like in medicine I would say.
cblconfederate 5y ago
> Machine learning is theory-free This is backwards: Theory is machine-learning free, because we havent begun to systematically analyze the machine learning boxes to figure out where the theory arises out of it. If our brain can do it, then we can do it with the machine learning systems, but the relevant field is very underdeveloped. We still call ANN systems "black boxes" but eventually we 'll have to open them and start naming the parts inside. Much like how thermodynamics arise from statistical mechanics, theory will arise from connectionist dynamics
hwillis 5y ago
Author makes some interesting parallels to infernal combustion engines not being possible without machine tools. The Antikythera mechanism was built 1800 years before the first metal lathe. It is a fantastically sophisticated[1] clockwork with dozens of gears, concentric shafts, and brilliant, practiced fabrication. It is not a unique device. It was built by someone who knew what they were doing and had made this thing many times. It is obvious in the same way that you can tell when code was written from the start knowing how the finished product would look. The device displays the relative positions of stars and planets from their underlying orbits, and was built with bronze hammers and some small fragments of steel. All that to say, you can do incredible things with practice, care, and tools that are thousands of years too primitive. [1]: https://en.wikipedia.org/wiki/File:AntikytheraMechanismSchematic-Freeth12.pnghttps://en.wikipedia.org/wiki/File:AntikytheraMechanismSchematic-Freeth12.png https://en.wikipedia.org/wiki/File:AntikytheraMechanismSchem...
launchiterate 5y ago
People grow, things change, totally unexpected things occur. In an infinitely variable world data, is just one indicator.
acenes 5y ago
Such a disappointing essay. There are so many ways in which the title or the first paragraph could be true without such garbled non sequiturs.
naveen99 5y ago
Past performance is indicative, it’s just not exclusively or completely indicative.

415 points by olvy0 5y ago

taylorwc 5y ago

Typo in the title, ought to be “Skeptic.” Unless, that is, his skepticism is also directly tied to handling sewage.

stan_rogers 5y ago
No, both spellings are good. The sewage thing would be "septic".
- deleted 5y ago
  [deleted]
3gg 5y ago
Even if you look up "skeptic" on dictionary.com, it will suggest the alternative spelling. https://www.dictionary.com/browse/skeptic https://www.dictionary.com/browse/skeptic English is not just spoken in 'murica.

marcinzm 5y ago

I find his comment about hallucinating faces in the snow amusing given that humans hallucinate faces in things all the time. And then either post it to Reddit or have a religious experience.

starmftronajoll 5y ago
Yes, that is explicitly part of the point Doctorow is making. It’s why the essay mentions the fact that humans see faces in clouds, etc. Humans typically know when they are “hallucinating” a face, and ML algorithms don’t. When humans see a face in the snow, they post it to Reddit; they don’t warn their neighbor that a suspicious character is lurking outside. This is the distinction the essay draws.
- marcinzm 5y ago
  People perceive nonexistent threats all the time and call the police. The threshold is simply higher than current AI but that’s a question of magnitude rather than inherent difference. Fine tune a reinforcement model on 5 years of 16 hours a day video and I’m sure it will also have a better threshold.
  - kortilla 5y ago
    There is general knowledge about the world for humans to know that there isn’t a giant human in the sky no matter how good the face looks. Train it with as many images as you want and as long as a good enough face shows up, the model is going to have a positive match. The entire problem is it’s missing that upper level of intelligence that evaluates “that looks like a face, could it actually be a human?”
    - marcinzm 5y ago
      >There is general knowledge about the world for humans to know that there isn’t a giant human in the sky no matter how good the face looks. Is there? Humans used to think the gods were literally watching them from the sky and the constellations were actual creatures sent into the night. So this seems learned behavior from data rather than some inherent part of human thinking. >Train it with as many images as you want and as long as a good enough face shows up, the model is going to have a positive match. So will a human if something is close enough to a face. A shadow at night for example might look just like a human face. Children will often think there's a monster in the room or under their bed.
      - kortilla 5y ago
        Children will think there is a monster under their bed based on no evidence at all. That speaks to something beyond object recognition that happens at a different processing layer. Humans do not need to be trained on billions of images from around the globe to semantically understand where human faces are not expected to appear. Modern AI can certainly recognize faces really well with that level of training now, but it still doesn’t even understand what a face is (i.e. no model of reality to verify its identification against).
itisit 5y ago
Those humans don't typically believe those hallucinated faces belong to people though nor do they call the cops.
- version_five 5y ago
  You don't think a person has ever called the police because they hear a noise they thought was an intruder, or saw someone or something suspicious only in their mind? People make these kind of mistakes too.
  - itisit 5y ago
    Of course, but the consistency of the false positive is the issue. An able-minded person can readily reconcile their confusion.
    - marcinzm 5y ago
      Then that’s a question of training data.
      - foobiekr 5y ago
        The problem with this line of reasoning is that it can be used as a non-constructive counter to any observation about AI failure. It’s always more and more training data or errors in the training set. This really is a god-of-the-gaps answer to the concerns being raised.
        marcinzm 5y ago
        No, my point is that if two systems show very similar classes of errors but at different thresholds with one trained on significantly more data than the more likely conclusion is that there isn't enough data in the other.
        Dylan16807 5y ago
legrande 5y ago
> Given that humans hallucinate faces in things all the time Pareidolia: https://en.wikipedia.org/wiki/Pareidolia https://en.wikipedia.org/wiki/Pareidolia

swayvil 5y ago

I see no path from "observation" to "model" that does not involve an arbitrary (aesthetic? Nonrational, human-necessitating?) choice. This would suggest that "general" AI is impossible. ON THE OTHER HAND There is a variety of general AI, called an "optimizer". It starts with something better than a void. Maybe that's the path we should be looking at.

Reimersholme 5y ago
Well, human thinking relies on prior models/filters for understanding the world as well so that would invalidate us as having general intelligence too?
- swayvil 5y ago
  Those relied-upon models may be acquired nonrationally. Via aesthetics etc. Or, in the case of the optimizer, I think the human equivalent would be desire.
- username90 5y ago
  Human thinking includes building new models/filters for understanding the world, not just applying old ones. And that isn't used for learning, we do it all the time when solving any kind of challenging problem or even for simple problems like trying to recognize a face. Computer models might never compete with human performance unless they can learn how to solve a problem as it is solving it, because that is what humans do.
  - swayvil 5y ago
    I am on the same page. To talk about the models some more... There's this big mass of models. And it's got all kinds of sections. Special sections that we learn about in school. Special sections called "science". Sections that we invent ourselves. Sections that we inherit from our parents, religion, etc. It's partially biological. Partially cultural. A massive library of models, mostly inherited. You move in relationship with the mass in different ways. You can create new models. That's what basic science is. Extending the edge of the mass. Naming the nameless. You can operate freely from the mass. Creating your own models or maybe operating model-less. Artists, mystics, weirdos. You can operate completely within the mass. Never really contending with unmodelled reality. The map and territory become one. Like in a videogame. I think that's the most popular way.

3gg 5y ago

I found this to be a very succinct, sober analysis of ML ("AI") techno-solutionism. Cory is a great writer and knows how to explain ideas in a simple, no-nonsense way. This article reminded me of Evgeny Morozov's "To Save Everything, Click Here", where you can find many more examples of how focusing on the quantitative aspect of a problem and ignoring the social, qualitative context it around it often goes wrong. https://bookshop.org/books/to-save-everything-click-here-the-folly-of-technological-solutionism/9781610393706 https://bookshop.org/books/to-save-everything-click-here-the...

iamnotwhoiam 5y ago

Are there any approaches to artificial intelligence that do involve qualitative data or don’t rely entirely on statistical inference?

Ericson2314 5y ago
Not really adjacent to what we do today. I view A.I. as dual to "neoliberal M.B.A. culture". Just as the business schools taught that managers should be generalists without craft knowledge applying coarse microeconomics, A.I. that we have created is the ultimate pliant worker that also knows nothing deep and works from statistics. In a bussiness ecosystem where analytics and presentations are more important than doing things, they are a perfect match. Of course, a bunches of statistician-firms chasing each other in circles is going to exhibit the folly, not wisdom, of crowds. I think solution is to face reality that more people need to learn programming, and more domain knowledge needs to be codified old school. https://www.ma.imperial.ac.uk/~buzzard/xena/ https://www.ma.imperial.ac.uk/~buzzard/xena/ I thus think is perhaps the best application of computing, ever. Training A.I. to be a theorem prover tactic is a great way to make it better: if we can't do theory and empiricism at the same time, we can at least do meta-empiracism on theory building! I think once we've codified all the domains like that and been running A.I. on the theories, we'll be better positioned to go back to the general A.I. problem, but we might also decided the "manually programmed fully automated society" is easier to understand and steer, and thus less alienation, and we won't even want general A.I.
dr_dshiv 5y ago
Cybernetics and control theory, broadly speaking, involve the design of data feedback loops to govern simple machines or complex socio-technical systems. For instance, an organization might instrument a feedback loop to use qualitative survey data to inform decision-making. That isn't ML, but it is cybernetics. And, based on Peter Norvig's definition, it is a form of AI. Consider that "autopilot" was invented in 1914, long before digital computers. From this perspective, Artificial Intelligence might even be seen as an ancient human practice— present whenever humans have used artifacts to govern complex systems.
jon_richards 5y ago

version_five 5y ago

This article is mostly a straw man, while still containing some valid ML criticism. I am a ML s(c|k)eptic too, in that popular conceptions of what ML is currently overpromise, often don't even understand what ML actually is, and are often just some layperson's imagination about what "artificial intelligence" might do. This article is the opposite. He's treating ML as basically a simple supervised architecture that doesn't allow any domain knowledge to be incorporated and simply dead-reckons, making unchecked inferences from what it learned in training. Under these constraints, everything he says is correct. But there is no reason ML has to be used this way, in fact it is extremely irresponsible to do so in many cases. ML as part of a system (whether directly part of the model architecture and learned or imposed by domain knowledge) is possible, and is generally the right way to build an "AI" system. I think ML has its limitations and will be surprised to see current neural networks evolve into AGI. But I also don't think the engineers working in this space are as out to lunch as the author seems to imply, and would not write off the possibilities of what contemporary ML systems can accomplish based on the flaws pointed out in relation to a very narrow view of what ML is.

3gg 5y ago
> But I also don't think the engineers working in this space are as out to lunch as the author seems to imply. Are you at all close to this space? It sounds you may be underestimating corporate politics and the lack of rigour and ethical thought with which these systems are applied. The example Cory puts on policing -- and the many other examples you can find in Evgeny Morozov's book or "The End of Trust" -- are solid proof of this.
- vletal 5y ago
  My first thought was that I'm not the target audience of this article. I'm a ML practitioner. This seems more like an overstated opinionated wake up call to mgmt and sales people. Is not it?
  - version_five 5y ago
    Agreed. What I called a straw man in the OP could also be characterized as a simplification to get his point across to lay-audiences. (Personally I dont agree with the simplification, per my other post). It's meant for popular audiences (as someone else points out, this is from a sci-fi magazine)

a-dub 5y ago

they say that those who ignore the past are doomed to repeat it, data driven algorithms provide statistical guarantees of repeating it.

lamebitches 5y ago

Covid is a bio-weapon. Fauci is the dealer.

radu_floricica 5y ago

I think he's doing a bit of bait and switch there. Knowing reliably whether arrests are genuinely racist or if winks are flirtatious is superhuman intelligence. > But the idea that if we just get better at statistical inference, consciousness will fall out of it is wishful thinking. I'm a mostly disinterested spectator in current AI research, and even I know that it's not all about that. Just google "AI alignment" for an example, and god only knows what's going on in private research.

akomtu 5y ago
I think the definition of racism in this context can be simple. If the rate of false positives for blacks is significantly higher than the average across the nation, then it's racism. Significantly higher can mean "one stddev higher".
- radu_floricica 5y ago
  Exactly my point - I happen to disagree. We're not going to have AI come and tell us the Truth, at least not from day one. They'll help, but not with _this_ kind of questions. This is why I commented, because they seem specifically chosen to be some of the most complicated questions we face. (On topic: blacks could commit more crime then average. They could do it because of systemic racism, but in this case the _arrests_ are not racist.)

jstx1 5y ago

> I am an AI skeptic. I am baffled by anyone who isn’t. I don’t see any path from continuous improvements to the (admittedly impressive) ‘machine learning’ field that leads to a general AI - I share the skepticism towards any progress towards 'general AI' - I don't think that we're remotely close or even on the right path in any way. - That doesn't make me a skeptic towards the current state of machine learning though. ML doesn't need to lead to general AI. It's already useful in its current forms. That's good enough. It doesn't need to solve all of humanity's problems to be a great tool. I think it's important to make this distinction and for some reason it's left implicit or it's purposefully omitted from the article.

shreyshnaccount 5y ago
I'm in favor of changing the terminology from AI and ML to something along the lines of 'prediction model' so that the idea of machines 'thinking' is replaced with them 'predicting'. it's just easier for our mushy meat brains to think that AI and ML means that it'll lead to general AI or as I like to call it 'general purpose decision maker'. it's all about the language!
- coddle-hark 5y ago
  I like the term “data driven algorithm“. It makes it clear to everyone involved that what we’re doing is just adjusting an algorithm based on the data we have. No-one in their right minds would confuse that with building a true “A.I.”.
  - spockz 5y ago
    What about “data derived algorithm”? The algorithm itself isn’t really driven by data after it has been designed anymore.
    - skohan 5y ago
      I mean if we want to be really accurate, we could say something like "highly dimensional data-derived function"

Reimersholme 5y ago

I feel like this would have felt more relevant maybe five-ten years ago when there was more of a feeling that deep neural nets was the end all. He mentions correlation vs causation but seems to have missed that causal inference is one of the most active and interesting fields of research today.

shannifin 5y ago

> I don’t see any path from continuous improvements to the (admittedly impressive) ‘machine learning’ field that leads to a general AI any more than I can see a path from continuous improvements in horse-breeding that leads to an internal combustion engine. While I also don't expect that AGI will emerge solely through optimizing statistical inference models, I also don't think "improvements to the machine learning field" consist only of such optimizations. Surely further insights, paradigm shifts, etc., will continue to play a role in advancing AI. Perhaps it's more a matter of semantics and a bad analogy; "machine learning" seems far more broad a field than "horse-breeding." Horse-breeding is necessarily limited to horses. Machine learning is not limited to a specific algorithm or data model. Even calling it a "statistical inference tool", while not wrong, is deceptive. What exactly does he or anyone expect or want an AGI to do that can't be understood at some level as "statistical inference"? One might say: "Well, I want it to actually understand or actually be conscious." Why? How would you ever know anyway?

mirekrusin 5y ago
It gets philosophical quickly, is "consciousness" repeatedly modifying cloud of random floats?
Jetrel 5y ago
It's worth pointing out that "machine learning" is a specific term of art, not a term for AI in general. It refers very specifically to the type of "convolutional neural networks" that have made a bunch of progress over the past 15-25 years. The moment you have a paradigm shift, sure, it can be considered "learning done by machines", but it's not "Machine Learning™" anymore. -- This is why the author put it in quotes; because, since it's a term comprehensible to anybody, it's got this unfortunate side effect where people on the outside of the field take the "plain english" meaning of it rather than realizing it's loaded with some extra specific meaning for the practitioners in the field.
- shannifin 5y ago
  > It's worth pointing out that "machine learning" is a specific term of art, not a term for AI in general. It refers very specifically to the type of "convolutional neural networks" that have made a bunch of progress over the past 15-25 years. In my experience, "machine learning" is more broad than "convolutional neural networks", aligning with Wikipedia's definition: "the study of computer algorithms that improve automatically through experience and by the use of data." https://en.wikipedia.org/wiki/Machine_learning https://en.wikipedia.org/wiki/Machine_learning

MAXPOOL 5y ago

For a short and very non-technical article, this is well written. The current approach to machine learning is not going to go towards general-purpose AI with steady steps and gradual innovations. Things like GPT-3 seem amazingly general at first. But even it will quickly plateau towards the point where you need a bigger and bigger model, more and more data, and training for smaller and smaller gain. There need to be several breakthroughs similar to the original Deep Learning breakthrough away from statistical learning. I would say it's 4-7 Turing awards away at a minimum. Some expect less, some more.

mirekrusin 5y ago
Strange you're saying that, the unexpected outcome from gpt3 was specifically that it did not plateau as they were expecting and quite opposite deeper understanding emerged in different areas.

okareaman 5y ago

> But the idea that if we just get better at statistical inference, consciousness will fall out of it is wishful thinking. It’s a premise for an SF novel, not a plan for the future. My impression of Silicon Valley types like Ray Kurzweil in "The Age of Spiritual Machines" that if we wire up enough transistors somehow consciousness will somehow arise out of the material world. The somehow is not explained. Materialism is a dead end in my opinion. I am more interested in theories about consciousness as a field and our brains as receivers.

Trasmatta 5y ago
If our brains are receivers to a field of consciousness, why would it be impossible to replicate one of those receivers with a machine? You also seem to have just kicked the can down the road. "Consciousness arises from a field somehow, and the brain acts as a receiver somehow. The somehow is not explained."
- okareaman 5y ago
  I didn't say I knew how. I said I believe materialism is a dead end, by which I mean I doubt the consciousness arises out of atoms configured as neurons. How those neurons receive a conscious field seems a more productive line of inquiry, but for some reason people resist this idea. Not sure why.
  - Trasmatta 5y ago
    My main point was that you seemed to be criticizing materialism for not yet having a solid answer for "how", which is the same issue any alternative theory has.
  - akomtu 5y ago
    Imho, materialism and non-materialism mesh well together. It's just the two camps, materialists and occultists, are too arrogant to recognize that the other camp might understand certain things better. A self aware intelligent organism or machine needs three key components: a material foundation that's sufficiently organized (a large net of neurons, a silicon crystal, etc.), a material fluid-like carrier to control the foundation (that's always electricity and magnetism) and the immutable immaterial principle to constrain the carrier (math rules, physical laws, software algorithms). That's the core idea of occultism rephrased in today's terminology. The "conscious field" would be identical with the magnetic field here and neurons don't need any magical properties to receive this field: they just need to be conductive, like transistors. I think the reason the AI progress has stalled is because 0-1 transistors are too primitive and too rigid for the task. I guess that superintelligence is only different in the performance and connectivity degree of the material foundation: instead of slow neurons with 10k of connections it would be fast quasi crystal like structure with billions of connections that needs to move very little matter around (but it has to be material and consist of atoms of some sort).

nkozyra 5y ago

> It’s not sorcery, it’s “magic” – in the sense of being a parlor trick, something that seems baffling until you learn the underlying method, whereupon it becomes banal. I think part of the problem is the belief that human or animal intelligence is somehow more mystical. People who think like this will see an ML implementation solve a problem better and/or faster than a human and counter "well, it's just using statistical inference or pattern recognition" and my response is "so?" Humans use the same processes and parlor tricks to understand and replay things. Where humans excel is in generalizing knowledge. We can apply bits and pieces of our previous parlor tricks to speed up comprehension in other problem spaces. But none of it is magic. We're all simple machines.

xnyan 5y ago
>simple machines. Ooof. Premed dropout here, so admittedly not an expert in human biology but this is a wild statement. A neuron is simple in the same way a transistor is simply a silicon sandwich doped with metals. A parlor trick is something that once you understand, is straightforward to implement on your own. Are you arguing that anyone now or in the foreseeable future could simply recreate the abilities of a human? If so, what evidence could you show me to support that?
- nkozyra 5y ago
  I'm arguing that animal or lesser intelligence is built around hundreds of thousands of parlor tricks operating in a complex ensemble. There's a bias toward the marvel of human intelligence that causes some people to dismiss ML for the same underlying reasons we don't try to put a square peg in a round hole after infancy. Side note: disagree all you like but starting a rebuttal with "oof" is the kind of dismissive language that lets people know you'll be taking a very reductionist approach in your reply.
  - nicoffeine 5y ago
    > I'm arguing that animal or lesser intelligence is built around hundreds of thousands of parlor tricks operating in a complex ensemble. Until ML/AI can perform a single one of those parlor tricks without the constant direction of human intelligence, there’s no reason to stop marveling.

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Hacktrick 5y ago

I just read one of his books for school.

mark_l_watson 5y ago

I like the term “AI” and the classic definition of achieving human like performance in specific domains. I don’t think that there is much confusion about the term for the general population, and certainly not in the tech community. The term “AGI” is also good, “artificial general intelligence” describes long term goals.

atty 5y ago

Unfortunately it’s pretty clear from the article that Cory does not have much familiarity with the research going on in the field of machine learning, and is creating a straw man. Quite a lot of work is being done on causal inference, out-of-distribution generalization, fairness, etc. Just because that is not the focus of the big sexy AI posts from Google et al does not mean that the work isn’t being done. I’d also point out that humans can infer causality for simple systems, but for any sufficiently complex system we also can’t reason causally. But that does not mean we can’t infer useful properties and make informed, reasonable decisions. I’d also point out that not all models are “theory-free”, as he describes it. I specifically do work in areas where we combine “theory” and machine learning, and it works very well. And finally, his point about comprehension does not really fly for me. There is no magical comprehension circuit in our brain. It’s all done via biological processes we can study and emulate. Will that end up being a scaled up version of current neural nets? Will it need to arise from embodied cognition in robots? Will it be something else? I don’t know, but it’s certainly not magic, and we’ll get there eventually. Whether that’s 10 years or 1000, who knows. Are current paradigms going to lead to AGI? Frankly, I’d just be guessing if I even tried to answer that. My gut instinct is no, but again, that’s just a guess. Can current methods evolve into better constrained systems with more generalizable results and measurable fairness? Absolutely.

belter 5y ago
Do you have an example/reference of the type of work you are thinking about ?
jmull 5y ago
> Are current paradigms going to lead to AGI? Frankly, I’d just be guessing if I even tried to answer that. My gut instinct is no I'll just note that while you start off saying Doctorow has no idea what he's talking about, you finish by pretty much fully agreeing with the essay.
version_five 5y ago
I'm not sure what issue others had with your comment. You're quite correct that he ignores vast swaths of current ML art and attacks a narrow conception of what ML is. Many of his criticism are legit with the right caveats, but he leaves out a lot of information that could lead to a different thesis. My read of the discussion here is that there is lots of idle speculation by people who don't have any real experience with ML research / engineering, that overwhelms a minority who actually know what they are talking about and are calling CD out on this, or at least challenging aspects of his arguments.

7357 5y ago

C. Doctorow is one of these (admittedly few) famous people I'd like to meet IRL.

m0rphy 5y ago

ML or not, at the most fundamental level, classical computers simply do not possess the type of logic that's truly reflective of our reality. Its binary nature forces it to always resolve any single statement to either a true or false answer only. A very simple example. If we ask our classical computer this question "are people currently supportive of COVID-19 vaccines?", then it would probably give us a straight answer of either a "yes" or "no" based on statistical inference of the percentage of total people who have received vaccinations at this point. At its most fundamental level, classical computers just cannot comprehend a reality that could resolve that answer to both "Yes" and "No" in a single statement, which btw is possible in a quantum computing environment under its superposition state. In our reality, some people who may not be fully supportive of the vaccines, but under special circumstances they may be forced to receive it because of workplace requirements, pressures from their loved ones, etc...

dundarious 5y ago

I'm tired of the Norvig vs. Chomsky style debates about what is cognition/intelligence/learning. I think this piece does rehash that debate somewhat, but it's not at all the focus. It's key contributions are about the mainstream domination of quantitative vs. qualitative methods, especially in this paragraph: > Quantitative disciplines are notorious for incinerating the qualitative elements on the basis that they can’t be subjected to mathematical analysis. What’s left behind is a quantitative residue of dubious value… but at least you can do math with it. It’s the statistical equivalent to looking for your keys under a streetlight because it’s too dark where you dropped them. and also of note is the "veneer of empirical facewash that provides plausible deniability", for discrimination, and for doing a poor job but continuing to be rewarded for it. If I had to summarize it would be: - The ML/AI community, which includes the researchers, practitioners, and the evangelists, are broadly utopian in what they think they can achieve. They are overconfident even in the domain of detecting the face of potential burglars in a home security camera, never mind in terms of creating new life with AGI. I think Doctorow's critique equally applies to "algorithms" even only as complex as a fancy Excel sheet, but he focuses on ML/AI as the most common source of this excess of optimism, that recording data and running it through a model is almost certainly the _most sensible thing to do_ for any given problem. - If there is a manufactured consensus that the almost purely quantitative approach is the _most sensible thing to do_, then any failures or short-comings can be hand-waved away. Say sorry, "the model/algorithm did it", and just ignore the issue or apply a minor manual fix. This is a huge benefit for decision-makers wishing to maintain their status/livelihoods in both the public and private sector. Crucially, this excuse works if you're just ineffective, or if you're a bad actor. Note that this is a critique of CEOs and government officials, more than of engineers -- we would only be complicit by association. If there is a critique for engineers, it's that we provide fodder for the excess of optimism in summary point 1 because we love playing with our tools, and that we allow ourselves to be the scapegoat for summary point 2.

vijucat 5y ago

> Let’s talk about what machine learning is...it analyzes training data to uncover correlations between different phenomena. The author seems to have missed or excluded reinforcement learning and planning algorithms in this definition. My criticism of AI criticism in general is that no one admits that at the root of it, we do not understand thinking (or "consciousness"). We are merely the "recipient" or enjoyer of the process, which is opaque. Just as AlphaGo, even if it just a facsimile of a Go player, could beat a human at Go, it is probable that an AI could produce a passable facsimile of thinking at one point. Its mechanisms would be as opaque as human thinking (, even to itself), but the results would be undeniable. AGI is a possibility.

epgui 5y ago
I believe we do understand, broadly speaking, thinking and consciousness. There remains a lot more to learn, as in anything in science... IMO the main difficulty is that humans have terrible self-awareness or self-insight. We want to believe we're special, we want to believe we're intelligent, we want to believe we're different than machines. We're in denial about that. Our brains aren't any more special than computers, other than that it's really quite formidable that we evolved them by chance in this universe of chemical soup we find ourselves in. At the end of the day, however, a computer is a computer, and "thinking" and "consciousness" simply do emerge from low-level computations given some special structures.
- rapferreira 5y ago
  Are you kidding? Go read Hegel's phenomonology of mind and get back to me on that
  - epgui 5y ago
    A philosophical treatise from 1807, no matter how interesting or insightful, could hardly represent the current state of understanding of the topic. My comment is loosely based on a general appreciation of textbook-level neuroanatomy and recent advances in AI/comp sci.

coldtea 5y ago

>The problems of theory-free statistical inference go far beyond hallucinating faces in the snow. Anyone who’s ever taken a basic stats course knows that “correlation isn’t causation.” For example, maybe the reason cops find more crime in Black neighborhoods because they harass Black people more with pretextual stops and searches that give them the basis to unfairly charge them, a process that leads to many unjust guilty pleas because the system is rigged to railroad people into pleading guilty rather than fighting charges. (...) Being able to calculate that Inputs a, b, c… z add up to Outcome X with a probability of 75% still won’t tell you if arrest data is racist, whether students will get drunk and breathe on each other, or whether a wink is flirtation of grit in someone’s eye. Except if information about what we consider racist etc. also passes through the same inference engine (feeding it with information on arbitrary additional meta levels). So, sure, an AI which is just fed crime stats to make inferrences, can never understand beyond that level. But an AI which if fed crime stats, plus cultural understanding about such data (e.g. which is fed language, like a baby is, and which is then fed cultural values through osmosis - e.g. news stories, recorded discussions with people, etc). In the end, it could also be through actual socialization: you make the AI into a portable human-like body (the classic sci-fi robot), and have it feed its learning NN by being around people, same as any other person.

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nlh 5y ago

This is a well-written and well-reasoned argument - BUT - I tend toward the materialist philosophy, so the argument doesn't really hold there. Yes, an ML model that infers B from A might not "understand" what A or B are....yet. But what is it to "understand" anyway? Just a more complex process in a different part of the machine. If the human brain is just a REALLY large, trained, NN, there's no reason that we won't be able to replicate it given enough computing power.

jaredklewis 5y ago
> If the human brain is just a REALLY large, trained, NN, there's no reason that we won't be able to replicate it given enough computing power. I think one clear sign that the human mind is more than just a big NN is how large neural networks are already. Take GPT-3, which is was trained on 45 terabytes of text and has 175 billion parameters. Contrast that with the human brain, which has around 86 billion neurons and is able to do much of what GPT-3 can do with only a tiny fraction of the training data. And it has to be said that while GPT-3 has more competency than an average human at some text generation related tasks, the average human brain is vastly more capable than GPT-3 at any non-text related task. So for neural networks to approach human level capability we would need a whole stack of GPT3-ish size networks for all the other non-text related things the human brain can do: speech, vision, motor control, social interactions, and so on. By that point the amount of training data and parameters is so astronomical, there can be no question that the functioning of human brains must be significantly different than that of contemporary computer neural networks. To be clear, I am also a materialist and subscribe to the computational theory of mind, but just based on the size of training data alone, it seems obvious that human brains work differently than neural networks.
- Simon321 5y ago
  A parameter in a neural network is more comparable to a synapse of which the brain has 100 trillion. And yes, we will get there too one day.
  - jaredklewis 5y ago

m12k 5y ago

The first AI winter came after we realized that the AI of the time, the high level logic, reasoning and planning algorithms we had implemented, were useless in the face of the fuzziness of the real world. Basically we had tried to skip straight to modeling our own intellect, without bothering to first model the reptile brain that supplies it with a model of the world on which to operate. Being able to make a plan to ferry a wolf, sheep and cabbage across the river in a tiny boat without any of them getting eaten doesn't help much if you're unable to tell apart a wolf, sheep and cabbage, let alone steer a boat. That's what makes me excited about our recent advances in ML. Finally, we are getting around to modeling the lower levels of our cognitive system, the fuzzy pattern recognition part that supplies our consciousness with something recognizable to reason about, and gives us learned skills to perform in the world. We still don't know how to wire all that up. Maybe a single ML model can achieve AGI if it is adaptable enough in its architecture. Maybe a group of specialized ML models need to make up subsystems for a centralized AGI ML-model (like a human's visual and language centers). Maybe we need several middle layers to aggregate and coordinate the submodules before they hook into the central unit. Maybe we can even use the logic, planning or expert system approach from before the AI winter for the central "consciousness" unit. Who knows? But to me it feels like we've finally got one of the most important building blocks to work with in modern ML. Maybe it's the only one we'll need, maybe it's only a step of the way. But the fact that we have in a handful of years not managed to go from "model a corner of a reptile brain" to "model a full human brain" is no reason to call this a failure or predict another winter just yet. We've got a great new building block, and all we've really done with it so far is basically to prod it with a stick, to see what it can do on its own. Maybe figuring out the next steps toward AGI will be another winter. But the advances we've made with ML have convinced me that we'll get there eventually, and that when we do, ML will be part of it some extent. Frankly I'm super excited just to see people try.

yarg 5y ago

Past performance is not indictative of future results across distinct domains. Within a single problem space (or sub-space) past performance can generalise quite well. There's a problem with scaling solutions and expecting performance to continue to increase in a continuous exponential manner: growth that we perceive as exponential is often only on a long-life S-Curve. We've seen this in silicon, where what appears to the layman to have been exponential growth has in fact been a sequence of more limited growth spurts bound by the physical limits of scaling within whatever model of design was active at the time. The question of where the bounds to the problem domains are, and when new ideas or paradigms are required is much more difficult in AI than it has been in microprocessors. It's easy enough to formulate the question "how small can this be before the changes in physical characteristics at scale prevent it from working?", if rather more difficult to answer. AI is so damned steeped in the vagaries of the unknown that I can't even think of the question.

rob_c 5y ago

Someone buy this man a beer. Couldn't have phrased most of that better had I tried and I've been arguing these points with staff for years

dvt 5y ago

What a confused and muddled post, trying to touch on psychology, philosophy, and mathematics, and missing the mark on basically all three. I'm quite bearish on AI/ML, but calling it a "parlor trick" is like calling modern computers a parlor trick. I mean, at the end of the day, they're just very fast abacuses, right? Let's face it: what ML has brought to the forefront -- from self-landing airplanes to self-driving cars, to AI-assisted diagnoses -- is pretty impressive. If you insist on being reductive, sure, I guess it's "merely" statistics. Bringing up quantitative vs qualitative analysis is just silly, since science has had this problem way before AI. Hume famously described it as the is/ought problem†. And that was a few hundred years ago. Finally, dropping the mic with "I don't think we're anywhere close to consciousness" is just bizarre. I don't think that any serious academic working in AI/ML has made any arguments that claim machine learning models are "conscious." And Strong AI will probably remain unattainable for a very long time (I'd argue forever). This is not a particularly controversial position. † Okay, it's not the same thing, but closely related. I suppose the fact–value distinction might be a bit closer.

flyinglizard 5y ago
> what ML has brought to the forefront -- from self-landing airplanes to self-landing cars I am not aware of any ML in flight controls. Being black box and probabilistic by nature, these things won’t get past industry standards and regulations (at least for a while).
- dvt 5y ago
  > I am not aware of any ML in flight controls. Being black box and probabilistic by nature, these things won’t get past industry standards and regulations (at least for a while). (Hah, I accidentally wrote "self-landing cars," fixed). But yeah, I guess I was thinking more of drones, I'm not exactly sure what ML (if any) is in the guts of a commercial or military airplane.
  - meheleventyone 5y ago
    Drones don’t need ML to self-land AFAIK?

Mikeb85 5y ago

Past performance isn't necessarily indicative of future results but as people tend to repeat behaviour over and over, it is often enough.

Gatsky 5y ago

Skepticism is a tough gig really. If you are right then nobody cares, if you are wrong then you look like a fool.

charmides 5y ago

I might be wrong, but regarding the anecdote about the physicists at Michigan’s Albion College attempts to model the spread of COVID-19, I think the author misfired. From what I understand, many non-epidemiologists have been surprisingly insightful during the pandemic.

epgui 5y ago

What baffles me is the number of humans who think they are in the personal possession of some super special sacred form of magical and unexplainable intelligence. "AI is just stats" yes, indeed, but so is human intelligence. In many ways, AI from 2010 was already better than human intelligence. Three remarks: - The task many people seem to be benchmarking against is not just a measure of general intelligence, but a measure of how well AI is able to emulate human intelligence. That's not wrong, but I do find it amusing. Emulating any system within another generally requires an order of magnitude higher performance. - The degree to which human intelligence fails catastrophically in each of our lives, on a continuous basis, is way too quickly forgotten. We have a very selective memory indeed. We have absolutely terrible judgment, are super irrational, and pretty reliably make decisions that are against our own interests, whether it's with regard to tobacco use, avoidance of physical exercise, or refusal of life-saving medications or prophylactics. We avoid spending time learning maths and science because it's not cool, and we openly display pride in our anti-intellectual behaviours and attitudes. We're all incredibly stupid by default. - AI researchers need to work more closely with neuroanatomists. The main thing preventing AI from behaving like a human is the different macro structure of human NNs vs artificial NNs. Our brains aren't random assortments of randomly connected neurons: there's structure in there that explains our patterns of behaviour, and that is lacking in even the most modern AI. We can't expect AI to be human if we don't give it human structures.

username90 5y ago
General intelligence requires it to solve real problems in the real world. It isn't about emulating humans, but emulating anything resembling an intelligent being we are aware of. It would be totally exceptional if we could properly emulate the intelligence of a fly or an ant, but we can't even do that. "Emulate a human brain" you say, but we can't even emulate brains a million times smaller than that.
- deleted 5y ago
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- blamestross 5y ago
  https://en.m.wikipedia.org/wiki/AnimatLab https://en.m.wikipedia.org/wiki/AnimatLab We totally do emulate organisms on that scale. The real challenge is simulating the sensory inputs and the feedback loop between the outputs, the environment as the body acts, then new inputs. Disembodied simulations of nerual networks don't work. They are part of a body, an environment, and all the feedback loops that come with it. It sounds like you really just want to see a ML algorithm have a body to learn in. Why we ever expect AGI to happen without letting an ML algorithm learn by interacting with a "real" reality seems strange to me. By all means, keep making glorified optic nerve and expecting them to "wake up".

harry8 5y ago

We are paying for the incredible bamboozle that is the phrase "Machine Learning." If we used computerized statistical inference instead and the phrase "machine learning" did not exist the attitude to people from investors to regulators, from customers, vendors, doom sayers and boosters alike would be vastly better taken as whole. Nearly everyone here knows mostly when seeing AI written or hearing it that it's a total crock. Nearly everyone here knows ML is applied statistics done with a computer but this not common knowledge and it really should be.

evrydayhustling 5y ago
"AI" as a term deserves this rep, because it was effectively marketing as far back as the 70s. But you're way off on "Machine Learning". There's been plenty of progress in the last 15 years re-interpreting many ML methods as regression (any optimization is a regression if you set up the right likelihood function). But many important results and techniques -- including today's ubiquitous deep nets -- originated and had successful applications way before they had statistical interpretations. They came from fields like compression theory, database design, or even biological interpretations. The term Machine Learning was introduced to re-focus the field on a measurable objective: algorithms that improve with more data. The "Learning" part was not an abstract term to tug on your imagination, but included formal definitions of how algorithms improve that involved slightly fewer assumptions than statistical learning (which is a subfield). This lineage isn't that important today, but that focus on how learning is measured is still the most important guidepost both for ML research and for sorting out marketing BS from realistic claims. Certainly, state of the art work using deep nets for tasks like NLP, image and video recognition aren't designed by reasoning about the statistical interpretation, or tested by applying typical statistical tests. Popularizing this work as Statistical Inference or Regression wouldn't give any added intuition and wouldn't really describe the way ML research proceeds, or how ML systems succeed or fail.
- harry8 5y ago
  It works by fitting curves. Whether you have a (presumably mathematical) "Statistical interpretation" or not is basically irrelevant in terms of what it actually does and what we should be conveying to people who aren't knowledgeable of the field. This is not about an academic argument. Putting stats right there in the name is vastly, vastly more informative than "Learning" which has the nuance for 99% of people as something requiring intelligence and is misleading. Hence the AI cons all pop up immediately there are some public ML wins called "Learning." Generalizing from data is actually what statistics does. It's what ML is. People like Hinton, Wasserman, Tibrishani et al seem to agree that ML is statistics but even that isn't what I'm talking about here.

coolaliasbro 5y ago

I come away from from this article with two thoughts. 1) Regarding the example of qualitative data via drunk students attending eye-licking parties. The author doesn't explain how this is qualitative. To my mind it's a gap in the model. The modelers could have included parameters to account for students behaving impulsively or irrationally, but they didn't. 2) Considering the nebulous nature of terms like consciousness and comprehension and the ensuing challenges of measurement, can it be proven that the structures that can potentially underpin behavior, etc., that would generally be recognized as conscious or comprehending do not exist as an emergent but undetected property of the Internet? Is it reasonable to suppose that if such a structure existed and if it possessed or embodied consciousness or comprehension that it might work toward remaining unknown?

abecedarius 5y ago

This kind of talk can be steelmanned, but even that version doesn't have reassuring answers to the likes of > Okay, you’ve all told us that progress won’t be all that fast. But let’s be more concrete and specific. I’d like to know what’s the least impressive accomplishment that you are very confident cannot be done in the next two years. (from https://intelligence.org/2017/10/13/fire-alarm/ https://intelligence.org/2017/10/13/fire-alarm/) Just today I was rather astonished by https://moultano.wordpress.com/2021/07/20/tour-of-the-sacred-library/ https://moultano.wordpress.com/2021/07/20/tour-of-the-sacred... -- try digging up something comparable from mid-2019.

ALittleLight 5y ago

I don't get why this article conflates machine learning progress and racism. Machine learning is not inherently racist though it may be implemented, intentionally or unintentionally, to produce racist results. It's much easier to solve racism in machine learning models though then in humans and easier to test to confirm that you have corrected it.

catears 5y ago
There are a whole slew of "advanced AI programs" out there that tells managers who to fire and who to keep, tells judges if someone should go to jail or not, etc. There are a lot of systems out there where peoples lives are changed forever "because the machine said so". I'd argue that since machine learning learns only from it's data (produced by it's human creator), it becomes a great tool for baking in unconcious biases in a completely opaque system and amplifying those biases. Much easier to tell if a human seems to be biased than if the dataset fed to an AI algorithm is biased.
- ALittleLight 5y ago
  I don't agree with that. If someone says "The AI is biased" we can just look at the data it was trained on or come up with a set of test cases to establish the bias. We can update the training data or method to remove the bias too. If someone says a person is biased they can just say "No I'm not" and then how do we tell? We can't really make a person do a million judgments and find statistical evidence. We can't dive into how the possibly biased person came to have or not have biases.

arisAlexis 5y ago

"He quit high school,[8][verification needed] received his Ontario Academic Credit (high school diploma) from the SEED School in Toronto,[citation needed] and attended four universities without obtaining a degree" Shouldn't we collectively listen to experts,PhDs etc instead of famous bloggers for very technical stuff? Much like in medicine I would say.

cblconfederate 5y ago

> Machine learning is theory-free This is backwards: Theory is machine-learning free, because we havent begun to systematically analyze the machine learning boxes to figure out where the theory arises out of it. If our brain can do it, then we can do it with the machine learning systems, but the relevant field is very underdeveloped. We still call ANN systems "black boxes" but eventually we 'll have to open them and start naming the parts inside. Much like how thermodynamics arise from statistical mechanics, theory will arise from connectionist dynamics

hwillis 5y ago

Author makes some interesting parallels to infernal combustion engines not being possible without machine tools. The Antikythera mechanism was built 1800 years before the first metal lathe. It is a fantastically sophisticated[1] clockwork with dozens of gears, concentric shafts, and brilliant, practiced fabrication. It is not a unique device. It was built by someone who knew what they were doing and had made this thing many times. It is obvious in the same way that you can tell when code was written from the start knowing how the finished product would look. The device displays the relative positions of stars and planets from their underlying orbits, and was built with bronze hammers and some small fragments of steel. All that to say, you can do incredible things with practice, care, and tools that are thousands of years too primitive. [1]: https://en.wikipedia.org/wiki/File:AntikytheraMechanismSchematic-Freeth12.pnghttps://en.wikipedia.org/wiki/File:AntikytheraMechanismSchematic-Freeth12.png https://en.wikipedia.org/wiki/File:AntikytheraMechanismSchem...

launchiterate 5y ago

People grow, things change, totally unexpected things occur. In an infinitely variable world data, is just one indicator.

acenes 5y ago

Such a disappointing essay. There are so many ways in which the title or the first paragraph could be true without such garbled non sequiturs.

naveen99 5y ago

Past performance is indicative, it’s just not exclusively or completely indicative.