[HN Gopher] Past Performance is Not Indicative of Future Results...
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Past Performance is Not Indicative of Future Results (2020)
Author : olvy0
Score : 255 points
Date : 2021-07-31 15:40 UTC (7 hours ago)
(HTM) web link (locusmag.com)
(TXT) w3m dump (locusmag.com)
| lamebitches wrote:
| Covid is a bio-weapon. Fauci is the dealer.
| 7357 wrote:
| C. Doctorow is one of these (admittedly few) famous people I'd
| like to meet IRL.
| radu_floricica wrote:
| 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 wrote:
| 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".
| vijucat wrote:
| > 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.
| atty wrote:
| 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.
| version_five wrote:
| 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.
| belter wrote:
| Do you have an example/reference of the type of work you are
| thinking about ?
| jmull wrote:
| > 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.
| jstx1 wrote:
| > 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.
| darkwater wrote:
| > 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
|
| I beg to disagree. They clearly state your opinion at the end
| of the piece, using the metal-beat analogy. Great things were
| done by blacksmiths beating metal, but not an ICE
| bhntr3 wrote:
| > 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.
|
| This isn't how science works though. Quoting the wikipedia page
| for Thomas Kuhn's "The Structure of Scientific Revolutions" (ht
| tps://en.wikipedia.org/wiki/The_Structure_of_Scientific_Re...):
|
| "Kuhn challenged the then prevailing view of progress in
| science in which scientific progress was viewed as
| "development-by-accumulation" of accepted facts and theories.
| Kuhn argued for an episodic model in which periods of
| conceptual continuity where there is cumulative progress, which
| Kuhn referred to as periods of "normal science", were
| interrupted by periods of revolutionary science."
|
| I think this is the accepted model in the philosophy of science
| since the 1970s. That's why I find this argument about AI so
| strange, especially when it comes from respected science
| writers.
|
| The idea that accumulated progress along the current path is
| insufficient for a breakthrough like AGI is almost obviously
| true. Your second point is important here. Most researchers
| aren't concerned with AGI because incremental ML and AI
| research is interesting and useful in its own right.
|
| We can't predict when the next paradigm shift in AI will occur.
| So it's a bit absurd to be optimistic or skeptical. When that
| shift happens we don't know if it will catapult us straight to
| AGI or be another stepping stone on a potentially infinite
| series of breakthroughs that never reaches AGI. To think of it
| any other way is contrary to what we know about how science
| works. I find it odd how much ink is being spent on this
| question by journalists.
| GeorgeTirebiter wrote:
| This seems akin to Asimov's "Elevator Effect":
| https://baixardoc.com/preview/isaac-asimov-66-essays-on-
| the-... starting p 221.
|
| I agree that one would think that Science Fiction writers
| would have enough of an imagination to be able to consider
| alternate futures (Cory CYA's by saying such a scenario would
| make a good SF story) - but there are already promising
| approaches to AGI: Minsky's "Society of Mind", Jeff Hawkins'
| neuro-based approaches, the fairly new Hinton idea GLOM: http
| s://www.technologyreview.com/2021/04/16/1021871/geoffrey... .
|
| "By 2029, computers will have human-level intelligence,"
| Kurzweil said in an interview at SXSW 2017.
|
| Time to get to work, eh? https://www.timeanddate.com/countdow
| n/to?msg=Kurzweil%20AGI%...
| simonh wrote:
| 1960s Herbert Simmons predicts "Machines will be capable,
| within 20 years, of doing any work a man can do."
|
| 1993 - Vernor Vinge predicts super-intelligent AIs 'within
| 30 years'.
|
| 2011 ray Kurzweil predicts the singularity (enabled by
| super-intelligent AIs) will occur by 2045, 34 years after
| the prediction was made.
|
| So until his revised timeline for 2029 the distance into
| the future before we achieve strong AI and hence the
| singularity was, according to it's most optimistic
| proponents, receding by more than 1 year per year.
|
| I wonder what it was that lead him to revise his timeline
| so aggressively. I think all of those predictions were
| unfounded, until we have a solid concept for an
| architecture and a plan for implementing it an informed
| timeline isn't possible.
| dctoedt wrote:
| Elevator effect:
| https://indianapublicmedia.org/amomentofscience/elevator-
| eff...
| simonh wrote:
| >So it's a bit absurd to be optimistic or skeptical.
|
| We skeptics aren't skeptical that AI is possible, were
| skeptical of specific claims. I think it's perfectly
| reasonable to be skeptical of the optimistic estimates, since
| they really are little more than guesses with little or no
| foundation in evidence.
| dcow wrote:
| I think you're misunderstanding Kuhn slightly. He invented
| the term paradigm shift. What he means by normal science with
| intertwined spurts of revolution is more provocative. He
| means that in order to observe periods of revolution, the
| "dogma" of normal science must be cast aside and new normal
| must move in to replace it. Normal science hits a wall, gets
| stuck in a "rut" as Kuhn describes it.
|
| I think, in a way, Doctorow is making that same argument for
| the current state of ML: _" I don't think that we're remotely
| close or even on the right path in any way"_. In other words,
| general thinking that ML will lead to AGI is stuck in a rut
| and needs a new approach and no amount of progressive
| improvement on ML will lead to AGI. I don't think Doctorow's
| opinion here is especially insightful, he's just a writer so
| he commits thoughts to words and has an audience. I don't
| even know wether I agree or not. But I do think this piece
| comes off as more in the spirit of Kuhn than you're
| suggesting.
|
| And of course you can interpret Kuhn however you want. I
| don't think Kuhn was saying you shouldn't use/apply the tools
| built by normal science to everyday life. But he, subtly,
| argues that some level of casting off entrenched dogmatic
| theories, in the academic domain, is a requirement for
| revolutionary _progress_. Kuhn agrees that rationalism is a
| good framework for approaching reality, but also equates
| phases of normal science to phases of religious domination
| that predated it. Essentially truly free thought is really
| really hard because society invents normals (dogma) and makes
| it hard to deviate. Academia is no exception. Science, during
| periods of normals, is (or can become) essentially over-
| calibrated and over-dependent on its own contemporary
| zeitgeist. If some contemporary theory that everyone bases
| progressive research off of is not quite right, it kinda
| spoils the derivative research. Not always true because
| sometimes the theories are correct.
| gmadsen wrote:
| is this related to Foucault? in an old debate with Chomsky,
| Foucault spends a lot of time on a concept similar to what
| you are talking about
| coldtea wrote:
| > _I think this is the accepted model in the philosophy of
| science since the 1970s._
|
| Perhaps, but "philosophy of science" has never been something
| the majority practicing scientists consider relevant, care
| about, or are influenced by, since forever.
| cratermoon wrote:
| There's good reason to be skeptical of AI as it is. Here's a
| couple of reasons
|
| Racial bias in facial recognition: "Error rates up to 34%
| higher on dark-skinned women than for lighter-skinned males.
| "Default camera settings are often not optimized to capture
| darker skin tones, resulting in lower-quality database images
| of Black Americans"
| https://sitn.hms.harvard.edu/flash/2020/racial-discriminatio...
|
| Chicago's "Heat List" predicts arrests, doesn't protect people
| or deter crime: https://mathbabe.org/2016/08/18/chicagos-heat-
| list-predicts-...
| pbhjpbhj wrote:
| I'm curious how the physics of light is termed racial bias,
| it's skin-colour bias if anything -- you can be "black" and
| be lighter skinned than a "white" person, for example -- but
| surely it's a consequence of how cameras/light works rather
| than a bias.
|
| Of course if you don't take account of the difficulties that
| come with using the tool then you might be acting with racial
| bias, but that's different. Or, all cameras/eyes/visual
| imaging means are "racist".
| mgraczyk wrote:
| It's very easy to fix these problems though. There's nothing
| inherently broken about the models or direction that prevents
| error rates from being made more uniform. In fact newer
| facial recognition models with better datasets do perform
| approximately equally well across skin tones and sex
| wffurr wrote:
| Isn't that what's meant by "admittedly impressive"?
| SavantIdiot wrote:
| I'm am both.
|
| Why I'm pro-AI: Neural nets.
|
| I worked on object detection for several years at one company
| using traditional methods, predating TensorFlow by a few years.
| We had a very sophisticated pipeline that had a DSP front end
| and a classical boundary detection scheme with a little neural
| net. The very first SSDMobileNet we tried blew away 5 years
| worth of work with about two weeks of training and tuning.
|
| Other peers of mine work in industrial manufacturing, and
| classification and segmentation with off the shelf NN's has
| revolutionized assembly line testing almost overnight.
|
| So yes, DNNs _absolutely_ do some things vastly better than
| previous technology. Hand 's down.
|
| Why I'm Anti-AI: hype
|
| The class of problems addressed by recent developments in
| NN/DNN software have failed horribly in scaling to even
| modestly real-world, rational multi-tasking. ADAS level 5 is
| the poster child. When hype master Elon Musk backs away, that
| is telling.
|
| We're on the bleeding edge here, IMHO we NEED to try
| everything. There's no telling which path has fruit. Look at
| elliptic curves: half a century with no applications, now they
| are the backbone of the internet. Yes, there will be BS, hype,
| snake oil, vaporware, but there will also be some amazing tech.
|
| I say be patient and skeptical.
| shreyshnaccount wrote:
| 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!
| kzrdude wrote:
| ML seems to be an ok term to me? It's the "intelligence" part
| in AI that needs a disclaimer.
| esfandia wrote:
| We already have "Pattern Recognition", not sure why it got
| absorbed by Machine Learning (the two terms seemed to co-
| exist with some overlap on what they covered), and then ML
| got absorbed by AI.
| jstx1 wrote:
| ML is still widely used and is much more common than AI as
| a term. So I wouldn't say that it has been absorbed by AI
| but their use sometimes overlaps depending on the target
| audience.
| coddle-hark wrote:
| 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 wrote:
| What about "data derived algorithm"? The algorithm itself
| isn't really driven by data after it has been designed
| anymore.
| skohan wrote:
| I mean if we want to be really accurate, we could say
| something like "highly dimensional data-derived function"
| shreyshnaccount wrote:
| why stop at that? 'high dimensional matrix parameterised
| data derived non linear function optimisation and unique
| hypothesis generation' just rolls off the tongue doesn't
| it xD
| tw04 wrote:
| To be frank: that very much does not make it clear to
| everyone involved. If you told the average Joe you had a
| "data driven algorithm" instead of "AI" you would likely
| get a blank stare in return.
| [deleted]
| shreyshnaccount wrote:
| confusion is better than wrongful understanding?
| falcor84 wrote:
| I'm sorry to say that I don't see any clear line separating
| "data driven algorithms" from the embodied minds that we
| are.
| gmadsen wrote:
| why? we don't understand the architecture, but the brain
| certainly uses electrical signals in an algorithmic way
| zoomablemind wrote:
| In not so long past, there was another popular expression -
| "computer-aided ...", which was quite fit for the practical
| use (like CAD for design, CAT for translation etc)
|
| Perhaps, CAI for inference or insight would express it more
| fairly.
|
| Alternatively, AI could've stood for 'automated inference',
| but sure it's all too late to rebrand.
|
| We humans still not clear about nature of our own
| intelligence, yet already claimed being able to manufacture
| it.
| skohan wrote:
| I think inference isn't the right term either. I think
| current ML is more like automated inductive reasoning.
| aidenn0 wrote:
| Automated inductive reasoning sounds a lot like
| artificial intelligence to me...
| skohan wrote:
| Idk maybe it's semantics, inference to me sounds more
| like a logical leap is happening, whereas in my mind the
| simplest form of inductive reasoning is just expecting a
| pattern to repeat itself.
| JohnJamesRambo wrote:
| Do I think or predict?
| quickthrower2 wrote:
| I predict therefore I will be
| MR4D wrote:
| I propose "heuristic optimization".
| akomtu wrote:
| Iirc, predictive coding is a well known branch of math that's
| said to be the next big step towards AI.
| skohan wrote:
| Yeah I agree - during undergrad, I spent a few years studying
| neuroscience, and I was very let down by my first ML/AI course.
| Compared to what I had learned about the brain, what we called
| an "ANN" just seemed like such a silly toy.
|
| The more you learn about neurobiology, the more apparent it is
| that there are _so many_ levels of computation going on -
| everything from dendritic structure, to cellular metabolism, to
| epigenetics has an effect on information processing. The idea
| that we could reach some approximation of "general
| intelligence" by just scaling up some very large matrix
| operations just seemed like a complete joke.
|
| However, as you say, that doesn't mean what we've done in ML is
| not worthwhile and interesting. We might have over-reached
| thinking ML is ready to drive a car without major fourth-coming
| advancements, but use-cases like style transfer and DLSS 2 are
| downright magical. Even if we just made marginal improvements
| in current ML, I'm sure there is a ton of untapped potential in
| terms of applying this tech to novel use-cases.
| fossuser wrote:
| I'm not sure I buy that - biology is often messier because of
| nature related constraints, it gets optimized for other
| things (energy, head size, etc.)
|
| The way a plane flies is quite different than the way a bird
| flies in complexity - they share an underlying mechanism, but
| planes don't need to flap wings.
|
| It's possible that scaling up does lead to generality and
| we've seen hints of that.
|
| - https://deepmind.com/blog/article/generally-capable-
| agents-e...
|
| Also check out GPT-3's performance on arithmetic tasks in the
| original paper (https://arxiv.org/abs/2005.14165)
|
| Pages: 21-23, 63
|
| Which shows some generality, the best way to accurately
| predict an arithmetic answer is to deduce how the
| mathematical rules work. That paper shows some evidence of
| that and that's just from a relatively dumb predict what
| comes next model.
|
| It's hard to predict timelines for this kind of thing, and
| people are notoriously bad at it. Few would have predicted
| the results we're seeing today in 2010. What would you expect
| to see in the years leading up to AGI? Does what we're seeing
| look like failure?
| dtech wrote:
| > It's hard to predict timelines for this kind of thing,
| and people are notoriously bad at it. Few would have
| predicted the results we're seeing today in 2010. What
| would you expect to see in the years leading up to AGI?
| Does what we're seeing look like failure?
|
| Few have predicted a reasonably-capable text-writing engine
| or automatic video face replacement, but many have
| predicted self-driving cars would have been readily
| available to consumers by now and semi-intelligent helper-
| robots being around.
|
| Just because unforeseen advancements have been made, does
| not mean that foreseen advancements come true.
| skohan wrote:
| I've heard this airplane argument before, and while I do
| consider it plausible that AGI might be achievable with
| some system which is fundamentally much different than the
| human brain, I still don't think it can be achieved using
| simple scaling and optimization of the techniques in use
| today.
|
| I think this for a couple reasons:
|
| 1. The current gap in complexity is _so huge_. Nodes in an
| ANN roughly correspond to neurons, and the brain has
| somewhere on the order of 100 billion of them.
|
| Even if we built an ANN that big, we would only be
| scratching the surface of the complexity we have in the
| brain. Each synapse is basically an information processing
| unit, with behavioral characteristics much more complicated
| than a simple weight function.
|
| 2. The brain is highly specific. The structure and function
| of the auditory cortex is totally different to that of the
| motor cortices, to that of the hypothalamus and so on. Some
| brain regions depend heavily on things like spike timing
| and ordering to perform their functions. Different brain
| regions use different mechanisms of plasticity in order to
| learn.
|
| Currently most ANN's we have are vaguely inspired by the
| visual cortex (which is probably why a lot of the most
| interesting things to come out of ML so far have been
| related to image processing) and use something roughly
| analogous to net firing frequency for signal processing. I
| would consider it highly likely that our current ANNs are
| just structurally incapable of performing some of the types
| of computation we would consider intrinsically linked to
| what we think of as general intelligence.
|
| To make the airplane analogy, I believe we're probably
| closer to Leonardo da Vinci's early sketches of flying
| machines than we are to the Right Brothers. We might have
| the basic idea, but I would wager we're still missing some
| of the key insights required to get AGI off the ground.
|
| edit: it looks like you added some lines while I was
| typing, so to respond to your last points:
|
| > it's hard to predict timelines for this kind of thing,
| and people are notoriously bad at it. Few would have
| predicted the results we're seeing today in 2010. What
| would you expect to see in the years leading up to AGI?
| Does what we're seeing look like failure?
|
| I totally agree that it's hard to predict, that technology
| usually advances faster than we expect, and that tremendous
| progress is being made. But the road to understanding human
| intelligence has been characterized by a series of periods
| of premature optimism followed by setbacks. For instance,
| in the 20th century, when dyes were getting better, and we
| were starting to understand how different brain regions had
| different functions, it may have seemed like we were close
| to just mapping all the different pieces of the brain, and
| that completing the resulting puzzle would give a clear
| insight into the workings of the human mind. Of course it
| turns out we were quite far from that.
|
| As far as what we can expect in the years leading up to
| AGI, I suspect it's going to be something that comes on
| gradually - I think computers will take on more and more
| tasks that were once reserved for humans over time, and the
| way we think about interfacing with technology might change
| so much that the concept of AGI might not seem relevant at
| some point.
|
| As to whether the current state of things is a failure - I
| would not characterize it that way. I think we're making
| real progress, I just also think there is a bit of hubris
| that we may have "cracked the code" of true machine
| intelligence. I think we're still a few major revelations
| away from that.
| version_five wrote:
| So you took an undergrad ML course and you're using this as
| the basis for your conclusions about how ML can scale? You
| understand modern neural networks as large matrix operations
| and then attack that idea leading to intelligence as a joke?
|
| I also find it improbable that intelligence will emerge from
| modern ML without some major leap. But you have added nothing
| to the discussion, beyond some impressions from undergrad,
| when we are talking about something that is a very active and
| evolving research area. It's insulting to researchers and
| practitioners who have devoted years to studying ML to just
| dismiss broad areas of applicability because you took a
| course once.
| skohan wrote:
| I'm sorry, I don't mean to insult or offend anyone. I'm
| just recounting my observations based on my understanding
| of the subject - and that is really not to disparage the
| amazing work that's being done, but rather to highlight the
| scale of the problem you have to solve when you're talking
| about creating something similar to human intelligence.
| It's entirely possible I'm wrong about this, and I would
| love to be proven so.
|
| Do you disagree substantively with anything I have said, or
| do you just think I could have phrased it better?
| version_five wrote:
| Thanks for your reply. I suppose a quick way to summarize
| my criticism is that it reads to me like you've dismissed
| the strengths of ML on technical grounds, while you imply
| you don't have any real technical experience in the
| field. You make a superficial comparison between the
| compexity of biology and ML, without providing any real
| insight, just saying one has lots going on and the other
| is matrix multiplication.
|
| If your conclusion is that current gradient based methods
| probably won't scale up to AGI, you're probably right.
| But if you want to get involved in the discussion of why
| this is true, what ML actually can and can't do, etc. I
| would encourage you to learn more about the subject and
| the current research areas, and draw on that for your
| discussion points.
|
| Otherwise, it comes across as "I once saw a podcast that
| said..." type stuff that is hard to take seriously.
|
| No doubt I come across as condescending, please take what
| I say with the usual weight you'd assign to the views of
| a random guy on the internet :)
| jmull wrote:
| > it's left implicit or it's purposefully omitted from the
| article
|
| It's explicitly right there in the essay...
|
| > Machine learning has bequeathed us a wealth of automation
| tools that operate with high degrees of reliability to classify
| and act on data acquired from the real world. It's cool!
|
| > Brilliant people have done remarkable things with it.
|
| You seem to be in agreement with the article but don't realize
| it.
| okareaman wrote:
| > 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.
| naasking wrote:
| Everyone I've ever spoken to who has insisted that materialism
| is a dead end, has never been able to provide a compelling
| explanation for why they believe that. It's not as if
| materialistic progress in neuroscience and ML/AI has stalled.
| If anything, it's accelerating.
|
| I have no doubt that Kurzweil's timelines and outcomes are
| wrong, as have the predictions of just about every prior
| futurist. I don't see what that has to do with materialism
| being a dead end.
| Trasmatta wrote:
| 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 wrote:
| 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 wrote:
| 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 wrote:
| 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).
| dane-pgp wrote:
| By studying the atoms configured as neurons, we've managed
| to develop machines that can learn to play board games and
| Atari games better than humans, and can write prose and
| poetry at a convincingly human level. Those skills may not
| require consciousness, but it's not clear that these
| machines would be more useful if they could "receive a
| conscious field".
|
| Do you think that animals receive a conscious field? Could
| we create an accurate representation of a mouse's brain
| just from modelling its neurons? If a mouse brain can't
| receive a conscious field, but a human brain can, then what
| relevant physiological differences are there between the
| two, other than size?
| nlh wrote:
| 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 wrote:
| > 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.
| yarg wrote:
| 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.
| Reimersholme wrote:
| 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.
| rob_c wrote:
| 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
| swayvil wrote:
| 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 wrote:
| Well, human thinking relies on prior models/filters for
| understanding the world as well so that would invalidate us as
| having general intelligence too?
| username90 wrote:
| 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 wrote:
| 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.
| swayvil wrote:
| 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.
| marcinzm wrote:
| 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 wrote:
| 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.
| kzrdude wrote:
| Well, we seem to experience such things in a split second,
| _and then we correct ourselves_. We use some kind of
| reasoning to double-check suspicious sensory experiences.
|
| (I was thinking of this when I was driving in a new place.
| Suddenly it looked like the road ended abruptly and I got
| ready to act, but of course it didn't end and I realized that
| just a split second later.)
| marcinzm wrote:
| 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 wrote:
| 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 wrote:
| >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.
| user-the-name wrote:
| But very seldom do they do that because of a hallucination.
| itisit wrote:
| Those humans don't typically believe those hallucinated faces
| belong to people though nor do they call the cops.
| version_five wrote:
| 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 wrote:
| Of course, but the consistency of the false positive is the
| issue. An able-minded person can readily reconcile their
| confusion.
| version_five wrote:
| An ML system generally can reconcile (and also avoid)
| this kind of confusion, with present technology. The
| example is more a question of responsible implementation
| than of a gap in the state of the art.
| marcinzm wrote:
| Then that's a question of training data.
| foobiekr wrote:
| 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 wrote:
| 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 wrote:
| Don't most high-end machine learning solutions have more
| training data than a human could consume in a lifetime?
| version_five wrote:
| I don't think there is a realistic way to make that
| comparison.
|
| For consideration, our brains start with architecture and
| connections that have evolved over a billion years (give
| or take) of training. Then we are exposed to a lifetime
| of embodied experience coming in through 5 (give or take)
| senses.
|
| ML is picking out different things, but it's not obvious
| to me that models are actually getting more data then we
| have been trained on. Certainly GPT has seen more text,
| but I don't think that comparing that to a person's
| training is any more meaningful than saying we'll each
| encounter tens of thousands of hours of HD video during
| our training.
| username90 wrote:
| They aren't very similar errors, ML solutions are equally
| accurate as humans in at a glance performance but longer
| and humans clearly wins. I'd say that the system is
| similar to humans in some ways, but humans have a system
| above that which is used to check if the results makes
| sense or not, that above system is completely lacking
| from modern ML theory and it doesn't seem to work like
| our neural net models at all (the brain isn't a neural
| net).
| legrande wrote:
| > Given that humans hallucinate faces in things all the time
|
| Pareidolia: https://en.wikipedia.org/wiki/Pareidolia
| dvt wrote:
| 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 wrote:
| > 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 wrote:
| > 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.
| 3gg wrote:
| 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...
| Hacktrick wrote:
| I just read one of his books for school.
| iamnotwhoiam wrote:
| Are there any approaches to artificial intelligence that do
| involve qualitative data or don't rely entirely on statistical
| inference?
| Ericson2314 wrote:
| 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/ 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 wrote:
| 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 wrote:
| Does qualitative data actually exist? Named colors are
| considered qualitative, but rbg and cmyk are quantitative. Does
| converting from one to the other switch whether it is
| qualitative or quantitative?
|
| Surely semantic meaning is qualitative, but look at word
| replacement in Google search. That's entirely based on
| statistics, thesaurus graphs, and other ultimately quantitative
| data.
|
| The neat thing about neural nets is that they are ultimately
| making a very, very complicated stepwise function. Brains are
| not neural nets, but are they doing anything other than create
| a very complex, entirely numerical, time and state dependent
| function? No matter which way you try to understand something,
| ultimately you are relying entirely on statistical inference.
| RandomLensman wrote:
| Kind of does exist even with colors: try to map "brown" into
| an RGB or CMYK data point.
|
| I think the real difference is that in qualitative data the
| numerical representation does not mean anything. Sure, the
| names of the archangels can be represented digitally
| (quantitative) but that is just a change of representation -
| the bit strings' numerical value carries no theological
| meaning.
| jon_richards wrote:
| Brown is (165,42,42). You can argue about false precision,
| but the term "brown" has false precision as well. The
| likely variation in interpretations can be described by
| error bars. Your understanding of someone saying "brown" is
| informed entirely by statistical inference of your past
| experience with "brown".
|
| Changing the representation of the names doesn't matter,
| but attempting to understand the meaning behind the names
| is ultimately quantitative. The numbers are run in the
| giant black box that is your brain and then your
| consciousness receives other qualitative answers.
|
| Asking for an AI without statistical inference or
| quantitative data is asking for consciousness without a
| brain.
| RandomLensman wrote:
| What is quantitative in understanding the meaning of a
| name? We don't know that the brain runs on "numbers" (and
| no, it's no just like a "computer").
|
| To respond to your edit: That is not brown... there is a
| whole science of color perception, have a look.
| jon_richards wrote:
| It's not a computer, but it is quantified.
| username90 wrote:
| Numbers implies you can do mathematical operations on
| them that makes sense.
|
| So how would you quantify "good" or "bad"? You can't
| unless you also answer what "good" + "bad" should be. In
| psychology they just assume that mapping those onto 1 and
| 5 makes sense, so "good" + "bad" = 5 + 1 = 6, but that
| doesn't make sense since it would imply that "good" is
| the same as "bad" + "bad" + "bad" + "bad" + "bad". You
| get similar but different issues if you start including
| negative numbers, or if you just use relative measures
| and don't have a proper zero, no matter what you do
| numbers doesn't properly represent feelings as we know
| them.
| RandomLensman wrote:
| That touches on the really tricky point that some things
| can be quantified but not computed, so again, we don't
| know how that measurable representation relates to they
| way results are derived.
| m0rphy wrote:
| Maybe if we could invent quantum DNA computing + ML =
| artificial intelligence that would be perceived and understood
| by humans.
| mooneater wrote:
| Well causal inference is considered distinct from statistical
| inference, and accounts for part of the gap here. (Not sure I
| would call that "qualitative" though.)
| version_five wrote:
| 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.
| karaterobot wrote:
| > This article is mostly a straw man, while still containing
| some valid ML criticism.
|
| I don't think this is an example of a straw man, given that his
| audience is readers of Locus, a science fiction magazine. While
| researchers and practitioners in ML understandably hold a more
| nuanced, informed view, the position he's arguing against is
| pretty common among the general public, and certainly common in
| science fiction.
| mistrial9 wrote:
| I like your comment here starting with "straw man" .. and agree
| with some of the statements.. I have seen lengthy, detailed and
| authoritative reports that say some of the same things, but in
| a formal, long-winded way with more added..
|
| This meta-comment of restatement in various contexts, with
| various amounts of story-telling and technical detail, brings
| up the educational burdens of communication -- to be effective
| you have to reach a reader where there are today .. in terms of
| assumptions, technical learning, and focus of topic.. since
| this is such a fast-moving and wide subject area, its super
| easy to miss the distinction between "low value, high volume
| audio clips recognition" and "life and death medical diagnosis
| for less than 100 patients". hint - that matters a lot in the
| tech chain AND the legal structure, and therefore combined, the
| "do-ability"
| ziggus wrote:
| Agreed. The article reminds me of the arguments that religious
| fundamentalists make against evolution: "there are still
| monkeys, so how could it be that we evolved from monkeys,
| wouldn't all the monkeys have evolved as well?"
|
| Clearly, no biologist claims that humans evolved from modern
| primates, just like no modern AI researcher seriously thinks
| that current machine learning methods will lead to "True AI".
| 3gg wrote:
| > 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.
| bhntr3 wrote:
| > Are you at all close to this space?
|
| I am.
|
| > The example Cory puts on policing
|
| My most upvoted comment on this website was discussing this
| exact scenario. https://news.ycombinator.com/item?id=23655487
|
| Could you perhaps clarify the generalization you're making
| about me and people like me so I can understand it?
| 3gg wrote:
| Excellent. One problem in my mind that I don't see
| discussed enough -- and also not in your other post -- is
| that there is a large divide between those who use the
| technology (the cops in this case) and those who supply it,
| and there is no accountability in any of the two groups
| when something goes wrong. Like you write in your other
| post, "the system works (according to an objective function
| which maximizes arrests.)", and that is as far as the
| engineer goes. On the other hand, the cop picks up the
| technology and blindly applies it. To make any improvement
| to the system would require both groups to work together,
| but as far as I know, that is not happening. A recent
| example can be found in the adventures of Clearview AI. So
| from that perspective, I do think that the engineers (and
| the cops, and everybody else) are out to lunch, each doing
| their own work in a bubble and not paying enough attention
| to (or caring about) the side effects of the applications
| of this technology.
|
| Also, the lack of thought and accountability that I mention
| above I think is fairly general from my experience, even
| outside of policing. That is why I don't generally agree
| with the lunch statement. Guys are having a hell of a party
| as far as I can tell -- at the expense of horror stories
| suffered by the victims of these systems.
| salawat wrote:
| I second this. I spend a great deal of time digging
| through where we've positioned big data models to steer
| population scale behavior, and very infrequently do the
| implementers of the system ever stop to analyze the
| changes they are seeding or think beyond the first or
| second degree consequences once things take off.
|
| That is all part of engineering to me, so by definition,
| I think many in the field are in fact, out to lunch.
| 3gg wrote:
| Yes, thank you. Analyzing the effects of our technology
| should be part of the engineering process. The physicists
| back where I studied all go through a mandatory ethics
| class. Us software crowd, well...
| skmurphy wrote:
| "Don't say that he's hypocritical Say rather that
| he's apolitical 'Once the rockets are up, who
| cares where they come down? That's not my
| department!' says Wernher von Braun Some have
| harsh words for this man of renown But some think
| our attitude Should be one of gratitude
| Like the widows and cripples in old London town
| Who owe their large pensions to Wernher von Braun"
|
| Tom Lehrer "Wernher von Braun"
| dundarious wrote:
| 3gg was replying to version_five. You're bhntr3. There is
| no generalization being made about you or even people like
| you, in a post that is a specific response to an account
| that is not yours.
| bhntr3 wrote:
| I believe they are disagreeing whether "engineers working
| in this space are out to lunch" and since I have been "an
| engineer working in this space" I was asking for more
| clarification about what it meant to be "out to lunch".
| vletal wrote:
| 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?
| 3gg wrote:
| If you are an ML practitioner and you think you're not part
| of the target audience, then you're probably part of the
| target audience.
| version_five wrote:
| 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)
| foobiekr wrote:
| There are three entirely different groups at work here.
|
| The deepmind team etc type of group who actually know what
| they're doing and the boundaries of what they are working
| with
|
| the "AI-washing" startups, corporate groups who know they are
| faking it and that what they're doing is extremely limited
|
| the corporate project team types who are just doing random
| tool play and honestly don't understand what they are doing
| or that they are absolutely clueless with no self-awareness
| at all
|
| I've worked with all three and they really are just totally
| different things that are all being lumped together. They
| also are listed in terms of increasing proportion. For every
| self-aware AI-washer team I've seen 50 "we are doing AI" Corp
| team types spinning out one trivial demo after another to
| execs who know zero.
| out0fpaper wrote:
| The same this is happening in the academics.
| 3gg wrote:
| Where does Google Vision Cloud sit in your categorization?
|
| https://algorithmwatch.org/en/google-vision-racism/
| foobiekr wrote:
| First group.
|
| You're observing that they aren't doing a perfect job,
| which is true, but my grouping isn't related to
| perfection of results.
| 3gg wrote:
| > The deepmind team etc type of group who actually know
| what they're doing and the boundaries of what they are
| working with.
|
| You claim that they "know what they are doing and the
| boundaries of what they are working with" -- and yet they
| recklessly make public a racist vision product?
| spacedcowboy wrote:
| I have a PhD in neural networks, haven't used it in many
| a year, but some of the knowledge is still there. Some of
| the memories of racking my brains to understand what the
| hell is going on are still there, too.
|
| It is easy to have a theory of what is going on, to model
| the processes of how things are playing out inside the
| system, to make external predictions of the system, and
| to be utterly wrong.
|
| Not because your model is wrong, but because either the
| boundary conditions were unexpected, or there was an
| anti-pattern in the data, or because the underlying
| assumptions of the model were violated by the data (in my
| case, this happened once when all the data was taken in
| the Southern Hemisphere...)
|
| In all these cases, you can know what you're doing, you
| can know the boundaries of what what you're working with,
| and you can get results that surprise you. It's called
| "research" for a reason.
|
| The model can also be ridiculously complex. Some of the
| equations I was dealing with took several lines to write
| down, and then only because I was substituting in other,
| complicated expressions to reduce the apparent
| complexity. It's easy to make mistakes - and so you can
| know what you're doing, and the boundaries that you're
| working with, and still have a mistake in the model that
| leads to a mistake in the data ... garbage in, garbage
| out.
|
| In short, this shit is hard, yo!
| foobiekr wrote:
| Your argument is that knowing what you are doing means
| error free output.
| 3gg wrote:
| It's more like applying the technology with caution and
| accountability when you already know beforehand that the
| output is not error-free.
| username90 wrote:
| They never promised that the output would be error free,
| having output with errors is still useful for many
| applications. And the issues you are talking about got
| fixed as soon as it was discovered and since then Google
| has made sure to always diversify their datasets by race.
| Nowadays that is common knowledge that you need to do it,
| but back then it wasn't obvious that a model wouldn't
| generalize across human races and it is much thanks to
| that mistake that everyone now knows it is an issue.
| 3gg wrote:
| It was discovered by others, not them; they fixed the
| issue only retroactively when it was called out in
| public. This lack of oversight is part of what I mean
| with applying things with caution.
|
| And why would they have assumed in the first place that
| the model _would_ generalize across human races, or any
| other factor for that matter?
| [deleted]
| Quarrelsome wrote:
| I feel like we're missing the point here. The dangerous
| groups are those execs you mention who will have the
| decision about whether to move something into production or
| not.
|
| When this technology gets into their hands with a dev leash
| it will be recklessly implemented and people will die.
| coding123 wrote:
| That's how I felt too. Most of the article is trying to pull us
| with an emotional attachment (mostly to racist things a
| computer will do if tasked to do important things). While that
| criticism is welcome, it's not specifically meaningful towards
| an argument against AGI. The only part that was seemed to be
| that statistical inference is not a path to AGI which is
| somehow backed up by the emotional stuff.
|
| What deep learning seems to step into more and more is time-
| based statistical inference.
|
| AGI is not:
|
| seeing that a girl has a frown on their face.
|
| seeing that a girl has a frown, because someone said "you look
| fat"
|
| seeing that a girl has a frown because her boyfriend said you
| look fat
|
| seeing that Maya has generally been upset with her boyfriend
| who also most recently told her she is fat.
|
| But keep going and going and going and we might get somewhere.
| Do we have the computer power to keep going? I don't know.
| salawat wrote:
| AGI is that capability to orchestrate layering of topical
| filters and feature detections in order to create an
| actionable perception. Note that it isn't anything to do with
| the implementations of said filters and detectors, but with
| the ability to artistically arrange them to satisfy a goal,
| and very possibly, must be coupled with the capacity to
| synthesize new ones.
|
| That executive and arranging function is the unknown. From
| whence cometh that characteristic of Dasein? That
| preponderance of concern with the act of being as Being?
|
| It's a tough nut to crack, even in philosophical circles. To
| think that we're going to articially create it by any means
| other than accident or luck is hubris of the highest order.
| mark_l_watson wrote:
| 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.
| m12k wrote:
| 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.
| coldtea wrote:
| > _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]
| nkozyra wrote:
| > 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 wrote:
| >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 wrote:
| 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 wrote:
| > 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.
| heavyset_go wrote:
| Obligatory "Your brain is not a computer"[1] reference.
|
| [1] https://aeon.co/essays/your-brain-does-not-process-
| informati...
| staticman2 wrote:
| We are not "simple machines" we are the result of 3.7 billion
| years of evolution. We are the most complex known thing in the
| universe. We are far more complicated than anything we can hope
| to make in the forseeable future, if ever.
| sorokod wrote:
| You and every living organism around you, was hammered out by
| the same evolutionary process.
| jmull wrote:
| > We're all simple machines.
|
| Great. Prove it. Build the simple machine that acts as a human
| does. Should be simple, right?
|
| Personally, I don't think there's any magic. But it's not
| "simple" either.
| dundarious wrote:
| 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.
| shannifin wrote:
| > 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 wrote:
| It gets philosophical quickly, is "consciousness" repeatedly
| modifying cloud of random floats?
| MAXPOOL wrote:
| 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 wrote:
| 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.
| [deleted]
| taylorwc wrote:
| Typo in the title, ought to be "Skeptic." Unless, that is, his
| skepticism is also directly tied to handling sewage.
| 3gg wrote:
| Even if you look up "skeptic" on dictionary.com, it will
| suggest the alternative spelling.
|
| https://www.dictionary.com/browse/skeptic
|
| English is not just spoken in 'murica.
| stan_rogers wrote:
| No, both spellings are good. The sewage thing would be
| "septic".
| [deleted]
| a-dub wrote:
| they say that those who ignore the past are doomed to repeat it,
| data driven algorithms provide statistical guarantees of
| repeating it.
| m0rphy wrote:
| 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...
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