13 ms·

Artificial Intelligence – The Revolution Hasn’t Happened Yet (2018)

278 points by seagullz 6y ago

nextos 6y ago
Dead link for me, but archive.org has a snapshot: https://web.archive.org/web/20201224185231/https://rise.cs.berkeley.edu/blog/michael-i-jordan-artificial-intelligence%E2%80%8A-%E2%80%8Athe-revolution-hasnt-happened-yet/ https://web.archive.org/web/20201224185231/https://rise.cs.b...
MichaelRazum 6y ago
Actually I think the first example was a really simple case, where statistics would expose the error. So even the doctor said, that they experienced an uptick in Down syndrome diagnoses. So basically they just didn't investigate it properly. From my experience every advanced ML-System have proper monitoring and such anomalies would be detected very fast. Especially when you change the machines. Actually it is a shame that the doctors couldn't figure it out by themselves or at least investigate it properly.
- kovac 6y ago
  Yeah, it's baffling that there was no one there to link the new machine with the uptick in the diagnoses. It's almost like they didn't care at all that there was an increased number of diagnoses.
klenwell 6y ago
The problem had to do not just with data analysis per se, but with what database researchers call “provenance” — broadly, where did data arise, what inferences were drawn from the data, and how relevant are those inferences to the present situation? While a trained human might be able to work all of this out on a case-by-case basis, the issue was that of designing a planetary-scale medical system that could do this without the need for such detailed human oversight. I'm not a data scientist and I've never encountered that term "provenance" before but I've encountered the problem he talks about in the wild here and there and have searched for a good way to describe it. His ultrasound example is a great, chilling, example of it. I also like the term "Intelligence Augmentation" (IA). I've worked for a couple companies who liberally sprinkled the term AI in their marketing content. I always rolled my eyes when I came across it or it came up in say a job interview. What we were really doing, more practically and valuably, was this: IA through II (Intelligent Infrastructure), where the Intelligent Infrastructure was little more than a web view on a database that was previously obscured or somewhat arbitrarily constrained to one or two users.
- mlthoughts2018 6y ago
  Data provenance is a standard term of art in machine learning and data science, a “data 101” kind of thing, with many OSS and vendor tools built up to solve provenance problems, like DVC, Pachyderm, kubeflow, mlflow, neptune, etc.
  - ACow_Adonis 6y ago
    worked with stats, machine learning and data science for 10+ years now. never heard the term used until now. (that's not to say I'm not familiar with the things the term refers to, indeed, most of the intellectual frameworks I've worked with break each of the things that make up provenance into far more fine grained concepts). course, I've also never heard of or touched the software you listed there either, but that may be because I don't view the data science and machine learning I'm interested in as being about specific software or vendor software... sounds more database- lingo to me...
    - renjimen 6y ago
      I've also worked as a data scientist for a few years and have never heard or used the word "provenance" in a DS context. Some people used it in the oil & gas industry when talking about where reservoir sands came from, but that usually garnered a eye-roll and mental translation to more everyday language.
    - mlthoughts2018 6y ago
      It’s shocking if you’ve worked professionally in statistics and not heard about data provenance. A few publications from ~2011-2015 period: http://ceur-ws.org/Vol-1558/paper37.pdf http://ceur-ws.org/Vol-1558/paper37.pdf https://ieeexplore.ieee.org/document/5739644 https://ieeexplore.ieee.org/document/5739644 https://link.springer.com/chapter/10.1007/978-3-642-53974-9_7 https://link.springer.com/chapter/10.1007/978-3-642-53974-9_... Add a variety of additional links dating back a bit further (note the emphasis in this case on research data and tracking state of an experiment). https://nnlm.gov/data/thesaurus/data-provenance https://nnlm.gov/data/thesaurus/data-provenance Data provenance is not a database / data warehouse term. It is uniquely and specifically a basic “101” concept of statistical science and ML / data science, where the custody and tracking of data are specifically tied to iterations of experiments, prototypes and research, for the sake of reproducibility. If I was interviewing an experienced statistical researcher and they didn’t at least have a working knowledge of the core concepts, that would be a huge red flag.
xmo 6y ago
Cross posted medium link: https://medium.com/@mijordan3/artificial-intelligence-the-revolution-hasnt-happened-yet-5e1d5812e1e7 https://medium.com/@mijordan3/artificial-intelligence-the-re...
- dang 6y ago
  Since the original URL (https://rise.cs.berkeley.edu/blog/michael-i-jordan-artificial-intelligence%E2%80%8A-%E2%80%8Athe-revolution-hasnt-happened-yet/ https://rise.cs.berkeley.edu/blog/michael-i-jordan-artificia...) is responding slowly and points to the medium.com URL as the original source anyhow, we've changed to the latter. Thanks!
deleted 6y ago
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ipnon 6y ago
So what do we name this new emerging engineering discipline? AI engineering? Cybernetic engineering? Data engineering?
- beaconstudios 6y ago
  Cybernetics and systems engineering certainly has to make a comeback if we are to solve coordination problems like this at planetary scale. It deeply saddens me that we almost reached a popular acceptance of cybernetics in the 60s, but it passed us by - we'd be in a much better position now if it had become a mainstream science in the way that other, much less useful sciences have.
  - thx2099100 6y ago
    I agree. As I see from reading a little about the field's history and the literature, it suffered the same fate of other endeavors that are complex and still have a lot to be solved. people become interested in it, try to find simpler 'popular' formulation and then the watered down versions become more popular than the original more complex version that need more rigor and discipline. the watered down versions become more popular but without the rigor and discipline, you can argue and conclude everything and they opposite with these tools. so people on the outside see the field as yet another fad and the whole field die down taking down with it the original version. much like in AI with everyone labeling their stuff as AI which dilute the term more and more as time passes. what Cybernetics and systems engineering needs is a rebranding and separation from the more 'soft' side that developed latter. this is where I think some researchers on category theory like Jules Hedges might help. it would help defining dynamical and more general system in a vague but still formal way, say with a computer proof assistant sort of tool.
    - contingencies 6y ago
      Timely and humanistic quote I added to https://github.com/globalcitizen/taoup https://github.com/globalcitizen/taoup yesterday from the father of cybernetics... Who was I, a man whose proudest ancestor had led a life in a Moslem community, to identify myself exclusively with West against East? - Norbert Weiner, MIT maths professor and philosopher, an American of Prussian Jewish extraction, founder of cybernetics and seminal work in control theory, referring to a philosopher/rabbi ancestor born in Cordova and domiciled in Cairo as physician to the Vizier of Egypt
boltzmannbrain 6y ago
This post should (1) reflect the 2018 posting date, and (2) the main hosting site: https://hdsr.mitpress.mit.edu/pub/wot7mkc1/release/9 https://hdsr.mitpress.mit.edu/pub/wot7mkc1/release/9
- dang 6y ago
  I've added 2018 above. Thanks! That URL doesn't seem to be the original source though.
Ericson2314 6y ago
The reason we don't just have great expert systems from the last 30 years is because Capital is more interested in cutting wages than increasing productivity.
tucnak 6y ago
>Artificial Intelligence – The Revolution Hasn’t Happened Yet No shit
yalogin 6y ago
The phrase AI always bothered me. What we have is a generic way to do “curve fitting” on a large amount of data. Nothing more. The one difference is the “curve” is a black box but it still strictly adheres to the input used.
- benjaminwootton 6y ago
  Came here to post the same. I’m no expert, but when I’ve played with AI frameworks it just seems like curve fitting. The few corporate deployments that make it to production barely outperform a simple regression model and are therefore over engineered.
  - chillacy 6y ago
    The fact that many self contained vision and language tasks can be solved with curve fitting is in itself an interesting finding. Certainly I did not expect this 10 years ago.
- IdiocyInAction 6y ago
  While true, this statement is completely vacuous. The curves being fitted are extremely complicated and if you can easily construct curve fitting problems (meaning: regression) that would be breakthroughs in many scientific fields. This says more about the generality of regression tasks than it says about ML.
  - yalogin 6y ago
    My comment was focused on the phrase “artificial intelligence “ and not the tech itself. Obviously the techniques are awesome but the naming is unfortunate
drevil-v2 6y ago
I wonder what the end game is in the reality where we do achieve Artificial General Intelligence? It seems like a ethical minefield to me. You have companies like Uber/Lyft/Tesla (and presumably the rest of the gig economy mob) waiting to put the AI into bonded/slave labor driving customers around 24/7/365. If it truly is a Human level intelligence, then it will have values and goals and aspirations. It will have exploratory impulses. How can we square that with the purely commercial tasks and arbitrary goals that we want it to perform? Either we humans want slaves that will do what we tell them to or we treat them like children who may or may not end up as the adults that their parents think/hope they will become? I doubt it is the later because why else would the billions of dollars investment being pumped into AI? They want slaves.
- coddle-hark 6y ago
  The robots will gain civil rights the same way humans did, either by means of violence or swaying public opinion. Hopefully the latter. This isn’t a guess as to how future robots will work, this is an observation about how humans work.
- WitCanStain 6y ago
  I don't think the claim that human-level intelligence entails human ambitions has been substantiated. Why could you not have a system that does things as intelligently as a human but without a will of its own? It would only make sense if having human values and goals is necessary to having intelligence but I don't see how that could be true.
- goatlover 6y ago
  To avoid paying employees, creating greater profit margins.
- root_axis 6y ago
  There's no reason to believe that future AGIs will necessarily have values, goals, and aspirations.
  - drevil-v2 6y ago
lifeisstillgood 6y ago
>>> in Down syndrome diagnoses a few years ago; it’s when the new machine arrived Hang on - uptick in diagnosis (ie post amniocentesis) or uptick in indicators. One indicates unnecessary procedures, one indicates a large population of previously undiagnosed downs .... One assumes the indicator - and greatly hope there is improved detection as I had at least one of these scares with my own kids
- gwern 6y ago
  Presumably what he is leaving out is that the increase in white-spots led to more amniocentesis, which then confirms the Down syndrome. If you did amniocentesis on all babies, it would of course increase the diagnosis rate even more. Whether this is a bad thing, as he claims, depends on whether you believe screening was being done optimally before, and that will depend quite a bit on things left out like the utility of not having a Down baby. (He doesn't present his working out the entire scenario, as it's just an aside, but hopefully before Jordan went around telling people how to change their prenatal screening systems, he did work it out a little bit more than back-of-the-envelope.)
  - lifeisstillgood 6y ago
    Actually you would not expect it to increase the Downs rate at all - the null hypothesis is that there are X% Downs babies born and Y% identified through amniocentesis (where X-Y is z% the percentage of parents choosing termination) edit: actually there is a Zt (percent of parents choosing termination after detection) and Zu (percent age of undetected cases going to term). Zt is a social / moral thing and won't change based on better pixel resolution, but Zu should not be expected to change either - we are assuming there has been no change to the real rate of Downs (which requires something else) and no change to rate of parents choosing termination (see morals) so ... If Y% increases a lot (better detection of an underlying true rate) then either X% must increase or z% must. Neither of which i think we expect or know about. So what I hope happened was dramatically better training for operators on the new ultrasound (kind of like exactly what did not happen onthe 747Max) So either the OP was one of the first to spot this issue, and tipped off the whole medical industry, or, and this is where my money goes, he followed the reasoning of dozens of professionals who were several years ahead of him (naturally) and was reassured by someone who just saw "anxious parent" in front of him. But that's fine too :-)
joe_the_user 6y ago
How would one put it? "Adaptive Intelligence" might be described as the ability to be given a few instructions, gather some information and take actions that accomplish the instructions. It's what "underlings", "minions" do. But if we look at deep learning, it's almost the opposite of this. Deep learning begins with an existing stream of data, a huge stream, large enough that the system can just extrapolate what's in the data, include data leads to what judgements. And that works for categorization and decision making the duplicates what decisions humans make or even duplicates what works, what wins in a complex interaction process. But all that doesn't involve any amount of adaptive intelligence. It "generalizes" something but our data scientists have no idea exactly what. The article proposes an "engineering" paradigm as an alternative to the present "intelligence" paradigm. That seems more sensible, yes. But I'm doubtful this could accepted. Neural network AI seems like a supplement to the ideology of unlimited data collection. If you put a limit on what "AI" should do, you'll put a limit on the benefits of "big data".
- visarga 6y ago
  Neural nets don't generalize much, they interpolate between training examples. But if you couple them with search (MCTS) then you can do logic and reasoning with them, like AlphaGo. You can also put any algorithm you want inside a neural net as long as you have a mechanism to pass gradients back - for example in the final layer you could have a complex graph-matching algorithm to map the predictions to the target, or you could put an ODE solver as a layer, or a logic engine, or a database.
  - deleted 6y ago
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ksec 6y ago
While real AI hasn't really happened yet, Machine Learning has definitely made a big impact with lots of potentials. I think we are still in the middle of the S Surve in ML. And AI is like.... Fusion? We are always another 50 years away.
- salty_biscuits 6y ago
  I think AI always means "does something a human can do that you wouldn't expect a machine to be able to do". So it is forever moving goal post. It just moves ahead in step with people's expectations of what a machine can do. Also magic is always disappointing when you know how it works. We just have the psychological safety of not knowing how we work, whereas in ML we always know what it is doing, and it is always kind of disappointing.
dhairya 6y ago
Part of the challenge of pursuing this comprehensive type of AI infrastructure is that it requires massive coordination and collaboration. Unfortunately the incentives in both industry and academia make it difficult to even start such a project. As a result we're stuck with incremental work on narrow problems. I've been on both sides of table (started in industry developing AI solutions and now in academia pursuing phd in AI). When I was on the industry side, where the information and infrastructure was there to build such a system, you had to deal with the bureaucracy and institutional politics. In academia, the incentives are aligned for individual production of knowledge (publishing). The academic work focuses on small defined end-to-end problems that are amenable to deep learning and machine learning. The types of AI models that emerge are specific models solving specific problems (NLP, vision, play go, etc). It seems to move towards developing large AI systems we need a model of new collaboration. There are existing models in the world of astrophysics and medical research that we can look to for inspiration. Granted they have they have their own issues of politics but it's interesting that similar scope projects haven't emerged on the AI side yet.
- thundergolfer 6y ago
  The incentive structure that seems clearly best (though not greatly) suited to this large-scale intelligence infrastructure is public investment in publicly owned systems. Jordan seems to maybe gesture at this, as who owns all the bridges in the the USA? Governments. If we are talking “societal-scale medical system” a majority of people would want that publicly owned and operated and universally accessible. We’ve already seen in industry that the incentives are to massively in favour of creating walled-gardens that lock in users and thus profits. No societal-wide system should work like our social media ecosystem (FB, Snapchat, TikTok). The dominant profit incentives are also not “human-centric”, as Jordan constantly emphasises. Well, they’re only so if we assume profit-making activity is tightly aligned with “human-centric” concerns. Some will say yes, but to me our climate disaster and the USA mass incarceration system are strong enough evidence that the answer is no. I think some wealthy Northern European countries are setup well enough to produce “Intelligent Infrastructure”, except for the fact that most of the talent is in the USA.
  - deleted 6y ago
wildermuthn 6y ago
He almost makes a good point when he questions whether “human imitative” AI could solve the other problems we face, seeing as humans aren’t that smart (especially not in large numbers when participating in complex systems). But the distinction he makes between ML and AI is crucial. What he’s really talking about is AGI - general intelligence. And he’s right - we don’t have a single example of AGI to date (few or single shot models withstanding, as they are only so for narrow tasks). The majority mindset in AI research seems to be (and I could be wrong here, in that I only read many ML papers) that the difference between narrow AI and general AI is simply one of magnitude - that GPT-3, given enough data and compute, would pass the Turing test, ace the SAT, drive our cars, and tell really good jokes. But this belief that the difference between narrow and general intelligence is one of degree rather than kind, may be rooted in what this article points out: in the historical baggage of AI almost always signifying “human imitative”. But there is no reason that AGI must be super intelligent, or human-level intelligent, or even dog-level intelligent. If narrow intelligence is not really intelligence at all (but more akin to instinct), then the dumbest mouse is more intelligent than AlphaGo and GPT-3, because although the mouse has exceedingly low General Intelligence, AlphaGo and GPT-3 have none at all. There is absolutely nothing stopping researchers from focusing on mouse-level AGI. Moreover, it seems likely that going from zero intelligence to infinitesimal intelligence is the harder problem than going from infinitesimal intelligence to super-intelligence. The latter may merely be an exercise in scale, while the former requires a breakthrough of thought that asks why a mouse is intelligent but an ant is not. The only thing stopping researchers is that when answering this question, the answer is really uncomfortable, and outside their area of expertise, and has weighty historical baggage. It takes courage of researchers like Yoshua Bengio to utter the word “consciousness”, although he does a great job by reframing it with Thinking Fast and Slow’s System 1/2 vocabulary. Still, the hard problem of consciousness, and the baggage of millennia of soul/spirit as an answer to that hard problem, makes it exceedingly difficult for well-trained scientists to contemplate the rather obvious connection between general intelligence and conscious reasoning. It’s ironic that those who seek to use their own conscious reasoning to create AGI are in denial that conscious reasoning is essential to AGI. But even if consciousness and qualia are a “hard”problem that we cannot solve, there’s no reason to shelve the creation of consciousness as also “hard”. In fact, we know (from our own experience) that the material universe is quite capable of accidentally creating consciousness (and thus, General Intelligence). If we can train a model to summarize Shakespeare, surely we can train a model to be as conscious, and as intelligent, as a mouse. We’re only one smart team of focused AI researchers away from Low-AGI. My bet is on David Ha. I eagerly await his next paper.
bob1029 6y ago
We are chasing the wrong things. Our conceptualization of the problem domain is fundamentally insufficient. Even if we took our current state of the art and scaled it up 1,000,000x, we are still missing entire aspects of intelligence. The AI revolution is very likely something that will require a fundamental reset of our understanding of the problem domain. We need to identify a way to attack the problem in such a way that we can incrementally scale all aspects of intelligence. The only paradigm that I am aware of which seems to hint parts of the incremental intelligence concept would be the relational calculus (aka SQL). If you think very abstractly about what a relational modeling paradigm accomplishes, it might be able to provide the foundation for a very powerful artificial intelligence. Assuming your domain data is perfectly normalized, SQL is capable of exploring the global space of functions as they pertain to the types. This declarative+functional+relational interface into arbitrary datasets would be an excellent "lower brain", providing a persistence & functional layer. Then you could throw a neural network on top of this to provide DSP capabilities in and out (ML is just fancy multidimensional DSP). If you know SQL you can do a lot of damage. Even if you aren't a data scientist or have a farm of Nvidia GPUs, you can still write ridiculously powerful queries against domain data and receive powerful output almost instantaneously. The devil is in the modeling details. You need to normalize everything very strictly. 20-30 dimensions of data derived into a go/no-go decision can be written in the same # of lines of SQL if the schema is good. How hard would this be on the best-case ML setup? Why can't we just make the ML write the SQL? How hard would it be for this arrangement to alter its own schema over time autonomously?
- sp527 6y ago
  I think the inhibitor to scale is actually model compression. You’re right that scaling up 1Mx won’t cut it. That’s because fidelity is still too high. We already know the brain is a very efficient machine for storing heuristics and compressed models. Also related to why it’s prone to err. Information theory is the right framework here imo. Other related concerns: hierarchal organization of information and model comparison.
- joe_the_user 6y ago
  You're talking about logic - SQL is basically a "logic language", it's just not entirely evident. Logic programming was the AI paradigm for more or less most of the 20th century and has fallen out of favor. Many people have talked about combining the neural net/extrapolation/brute-force approach with the logic approach. That hasn't born fluid yet but who knows.
deleted 6y ago
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xiphias2 6y ago
Too long, don't read: the whole post is full of goal post moving and story telling instead of trying to explain the statement in the title. Classifying images was always classified as a problem that can't be solved with statistical analysis. Deep learning layers are beyond human understanding, so in my view artificial intelligence happened, even though it's not yet as intelligent as humans.
bitL 6y ago
Surveillance AI is thriving, enabling things everybody (in power) was dreaming about. Revolution has happened and is deeply hidden.
dang 6y ago
Discussed at the time: https://news.ycombinator.com/item?id=16873778 https://news.ycombinator.com/item?id=16873778
cosmodisk 6y ago
What I miss most,in discussions about AI, is the motivation factor,which is the driving force behind every single thing we humans do. How can we create a system that would be motivated to evolve in order to better itself. Humans created all sorts of things because fear,hunger,or pleasure was so strong and it couldn't be pushed away. What will happen to an AI powered robot that one day decide that going into radioactive areas isn't quite what it wants and will say 'screw it'?
- simonh 6y ago
  The issue is that intelligence by itself provides to motivations or objectives. Intelligence is simply a tool to be used in order to get things done. In humans the objectives are provided by instincts, emotions and biological needs. In AI we will have to provide the goals, but as the paper clip maximiser thought experiment shows, we’re going to have to be very careful and thoughtful about it.
spicyramen 6y ago
What i have seen in the field was a frenzy of doing ML. What happened was that first of all companies needed to understand what ML was. Then understand the tools available. Once you start exploring the tools you will find that at every stage of a ML pipeline there is 2 or more different ways of doing things. S3/GCS, BigQuery, Spark, Beam, TensorFlow, Pytorch, Google, Azure, Amazon, notebooks, Jupyter, JupyterHub..KubeFlow, TFX...etc. okay you pick the tools needed, then you need to put them together...and hire people...that's challenging. I believe we need to wait for AutoML pipelines from data análisis to.prediction to start seeing really advancement in production systems.
ridgeflex 6y ago
Jordan argues that leaps in human-imitative AI are not necessary to solve IA/II problems -- "We need to solve IA and II problems on their own merits, not as a mere corollary to a human-imitative AI agenda." However, achieving near-human level accuracy on tasks such as classifying images of cars or road signs would be immensely useful to the proposed II-type system that handles large-scale self-driving transportation (individual cars would conceivably need the ability to understand their local environments and communicate this to the overall network). I agree with his argument that there should be a shift in the way we think about problems in "AI", but I don't think we should necessarily think that progress in human-imitative AI problems and IA/II problems are mutually exclusive.
- randcraw 6y ago
  This sounds like the longstanding debate between weak / narrow vs strong AI. Can improving the former make progress toward the latter? I'm inclined to agree with Jordan that we shouldn't expect the two to enhance the other much less commingle. Just as advancement of one classical algorithm rarely enhances another, I think it's unlikely the next generation of object recognition is going to advance speech recognition or reading comprehension. Probably more essentially, until AI escapes its current dependency on pattern matching driven solely by accumulation of probabilistic events, I see little chance that human-level general-purpose cognition will arise from our current bases for AI, namely observing innumerable games of chess or watching millions of cars wander city streets.
nicholast 6y ago
The brittleness of mainstream ML to out of distribution data is one of the most fundamental channels for error. There are very few domains where a static environment can be depended on over the long term. If machine learning is to be approached as an engineering discipline there will need to be practices established for validating models throughout their life cycle. One potential resource that can support this type of systematic evaluation is the Automunge open source library for assembling data pipelines, which has automatic support for evaluating data property drift in feature sets serving as basis for a model. (disclosure I am founder of Automunge)
fuckminster_b 6y ago
Before I spent a few hours of my life getting a basic grip of statistics, I fully expected to one day in the near future being wiped out (along with the rest of humanity) by a newly awakened artificial consciousness that came to the correct conclusion, that humans are the biggest threat to all other life on earth, including its own. Then I learned about Bayesian statistics and watched a talk by a senior LLNL statistician who is actually marketing 'AI' products/services as a side gig. When I realized what 'deep learning' actually is I was disappointed, unsure if I had mistakenly oversimplified the subject matter - until said senior statistician spelled out loud what I was thinking, in her talk: the 'understanding' a machine can currently attain of its input is quite like the understanding a pocket calculator can achieve of maths. Guess humanity is off the hook for now. Phew. I have doubts whether 'strong AI' is even technologically possible, since even accurately simulating a human mind, this simulation would be necessarily constrained to run orders of magnitude slower than the reality it is designed to model. 'Training' it with data so to allow it the opportunity to reason and thereby synthesize a conclusion not already contained in the data fed to it might take longer than a researcher would be able to in a life time. When was the last time a generation-spanning endeavour worked out as planned for (the West)? I wish people would stop calling what currently passes for 'Machine Learning' as 'AI'. Literally the same level of 'intelligence' we already had in the 80s, AFAIR we called it 'Fuzzy Logic' then. Secretly an admission, that Hollywood basically licensed the narrative of imminent runaway artificial consciousness back to science would make me give it one final Chance to prove its aptitude at high-level human reasoning and get square with reality. I'm not holding my breath.
esc_colon_q 6y ago
> IA will also remain quite essential, because for the foreseeable future, computers will not be able to match humans in their ability to reason abstractly about real-world situations. I broadly agree with what this article says, but depending how you define "foreseeable future" I find this to be a dangerously naive viewpoint that just assumes nothing will change quickly. I'm not stupid enough to say abstract reasoning about the real world is a simple problem or right around the corner, but there's no evidence so far to indicate it's much further off than, say, object recognition was when Minsky (or more likely Papert, apparently?) assigned it as an undergrad project. We pour exponentially more money into research each year, and have more and better hardware to run it on. We're going to hit the ceiling soon re: power consumption, sure, but some libraries are starting to take spiking hardware seriously which will open things up a few orders of magnitude. There are dozens of proposed neural architectures which could do the trick theoretically, they're just way too small right now (similar to how useless backprop was when it was invented). Are we a Manhattan Project or three away from it? Sure. That's not nothing, but we're also pouring so much money into the boring and immediately commercializable parts of the field (all the narrow perception-level and GAN can that NeurIPS is gunked up with) that if any meaningful part of that shifted to the bigger problems, we'd see much faster progress. That will happen in a massive way once someone does for reasoning what transformers did for text prediction: just show that it's tractable.
soupson 6y ago
The story is interesting, but being interesting as a story doesn’t make fetuses into “babies” and posing it that way does disservice to the overall message.
reshie 6y ago
i like to think automation comes before ai. we automate mechanics then we automate decisions or protocols.
gandutraveler 6y ago
Looks like we have reached a point where we see slower growth & innovation in tech in coming decade. AI was supposed to be the next big disruptor but my guess is we will just see minor progress in automation and far away from anything disruptive. Singularity might may not even be possible in next century
mark_l_watson 6y ago
I remember reading this article about two years ago, and generally liking it. The multidisciplinary conversations during The Great AI Debate #2 two nights ago were certainly entertaining, but also laid out good ideas about tech approaches and also the desires of AI researchers - what they hope AIs will be like. Good job by Gary Marcus. I work for a medical AI company and we are focused on benefits to humans. While in the past I have been been a fan of AI technologies from Google, FB, etc., now I believe that both consumers and governments must fight back hard against business processes that do not in general benefit society. Start by reading Zubroff’s Surviving Surveillance Capitalism book, and the just published book Power of Privacy.

278 points by seagullz 6y ago

Dead link for me, but archive.org has a snapshot: https://web.archive.org/web/20201224185231/https://rise.cs.berkeley.edu/blog/michael-i-jordan-artificial-intelligence%E2%80%8A-%E2%80%8Athe-revolution-hasnt-happened-yet/ https://web.archive.org/web/20201224185231/https://rise.cs.b...

MichaelRazum 6y ago

Actually I think the first example was a really simple case, where statistics would expose the error. So even the doctor said, that they experienced an uptick in Down syndrome diagnoses. So basically they just didn't investigate it properly. From my experience every advanced ML-System have proper monitoring and such anomalies would be detected very fast. Especially when you change the machines. Actually it is a shame that the doctors couldn't figure it out by themselves or at least investigate it properly.

kovac 6y ago
Yeah, it's baffling that there was no one there to link the new machine with the uptick in the diagnoses. It's almost like they didn't care at all that there was an increased number of diagnoses.

klenwell 6y ago

The problem had to do not just with data analysis per se, but with what database researchers call “provenance” — broadly, where did data arise, what inferences were drawn from the data, and how relevant are those inferences to the present situation? While a trained human might be able to work all of this out on a case-by-case basis, the issue was that of designing a planetary-scale medical system that could do this without the need for such detailed human oversight. I'm not a data scientist and I've never encountered that term "provenance" before but I've encountered the problem he talks about in the wild here and there and have searched for a good way to describe it. His ultrasound example is a great, chilling, example of it. I also like the term "Intelligence Augmentation" (IA). I've worked for a couple companies who liberally sprinkled the term AI in their marketing content. I always rolled my eyes when I came across it or it came up in say a job interview. What we were really doing, more practically and valuably, was this: IA through II (Intelligent Infrastructure), where the Intelligent Infrastructure was little more than a web view on a database that was previously obscured or somewhat arbitrarily constrained to one or two users.

mlthoughts2018 6y ago
Data provenance is a standard term of art in machine learning and data science, a “data 101” kind of thing, with many OSS and vendor tools built up to solve provenance problems, like DVC, Pachyderm, kubeflow, mlflow, neptune, etc.
- ACow_Adonis 6y ago
  worked with stats, machine learning and data science for 10+ years now. never heard the term used until now. (that's not to say I'm not familiar with the things the term refers to, indeed, most of the intellectual frameworks I've worked with break each of the things that make up provenance into far more fine grained concepts). course, I've also never heard of or touched the software you listed there either, but that may be because I don't view the data science and machine learning I'm interested in as being about specific software or vendor software... sounds more database- lingo to me...
  - renjimen 6y ago
    I've also worked as a data scientist for a few years and have never heard or used the word "provenance" in a DS context. Some people used it in the oil & gas industry when talking about where reservoir sands came from, but that usually garnered a eye-roll and mental translation to more everyday language.
  - mlthoughts2018 6y ago
    It’s shocking if you’ve worked professionally in statistics and not heard about data provenance. A few publications from ~2011-2015 period: http://ceur-ws.org/Vol-1558/paper37.pdf http://ceur-ws.org/Vol-1558/paper37.pdf https://ieeexplore.ieee.org/document/5739644 https://ieeexplore.ieee.org/document/5739644 https://link.springer.com/chapter/10.1007/978-3-642-53974-9_7 https://link.springer.com/chapter/10.1007/978-3-642-53974-9_... Add a variety of additional links dating back a bit further (note the emphasis in this case on research data and tracking state of an experiment). https://nnlm.gov/data/thesaurus/data-provenance https://nnlm.gov/data/thesaurus/data-provenance Data provenance is not a database / data warehouse term. It is uniquely and specifically a basic “101” concept of statistical science and ML / data science, where the custody and tracking of data are specifically tied to iterations of experiments, prototypes and research, for the sake of reproducibility. If I was interviewing an experienced statistical researcher and they didn’t at least have a working knowledge of the core concepts, that would be a huge red flag.

xmo 6y ago

Cross posted medium link: https://medium.com/@mijordan3/artificial-intelligence-the-revolution-hasnt-happened-yet-5e1d5812e1e7 https://medium.com/@mijordan3/artificial-intelligence-the-re...

dang 6y ago
Since the original URL (https://rise.cs.berkeley.edu/blog/michael-i-jordan-artificial-intelligence%E2%80%8A-%E2%80%8Athe-revolution-hasnt-happened-yet/ https://rise.cs.berkeley.edu/blog/michael-i-jordan-artificia...) is responding slowly and points to the medium.com URL as the original source anyhow, we've changed to the latter. Thanks!

deleted 6y ago

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ipnon 6y ago

So what do we name this new emerging engineering discipline? AI engineering? Cybernetic engineering? Data engineering?

beaconstudios 6y ago
Cybernetics and systems engineering certainly has to make a comeback if we are to solve coordination problems like this at planetary scale. It deeply saddens me that we almost reached a popular acceptance of cybernetics in the 60s, but it passed us by - we'd be in a much better position now if it had become a mainstream science in the way that other, much less useful sciences have.
- thx2099100 6y ago
  I agree. As I see from reading a little about the field's history and the literature, it suffered the same fate of other endeavors that are complex and still have a lot to be solved. people become interested in it, try to find simpler 'popular' formulation and then the watered down versions become more popular than the original more complex version that need more rigor and discipline. the watered down versions become more popular but without the rigor and discipline, you can argue and conclude everything and they opposite with these tools. so people on the outside see the field as yet another fad and the whole field die down taking down with it the original version. much like in AI with everyone labeling their stuff as AI which dilute the term more and more as time passes. what Cybernetics and systems engineering needs is a rebranding and separation from the more 'soft' side that developed latter. this is where I think some researchers on category theory like Jules Hedges might help. it would help defining dynamical and more general system in a vague but still formal way, say with a computer proof assistant sort of tool.
  - contingencies 6y ago
    Timely and humanistic quote I added to https://github.com/globalcitizen/taoup https://github.com/globalcitizen/taoup yesterday from the father of cybernetics... Who was I, a man whose proudest ancestor had led a life in a Moslem community, to identify myself exclusively with West against East? - Norbert Weiner, MIT maths professor and philosopher, an American of Prussian Jewish extraction, founder of cybernetics and seminal work in control theory, referring to a philosopher/rabbi ancestor born in Cordova and domiciled in Cairo as physician to the Vizier of Egypt

boltzmannbrain 6y ago

This post should (1) reflect the 2018 posting date, and (2) the main hosting site: https://hdsr.mitpress.mit.edu/pub/wot7mkc1/release/9 https://hdsr.mitpress.mit.edu/pub/wot7mkc1/release/9

dang 6y ago
I've added 2018 above. Thanks! That URL doesn't seem to be the original source though.

Ericson2314 6y ago

The reason we don't just have great expert systems from the last 30 years is because Capital is more interested in cutting wages than increasing productivity.

tucnak 6y ago

>Artificial Intelligence – The Revolution Hasn’t Happened Yet No shit

yalogin 6y ago

The phrase AI always bothered me. What we have is a generic way to do “curve fitting” on a large amount of data. Nothing more. The one difference is the “curve” is a black box but it still strictly adheres to the input used.

benjaminwootton 6y ago
Came here to post the same. I’m no expert, but when I’ve played with AI frameworks it just seems like curve fitting. The few corporate deployments that make it to production barely outperform a simple regression model and are therefore over engineered.
- chillacy 6y ago
  The fact that many self contained vision and language tasks can be solved with curve fitting is in itself an interesting finding. Certainly I did not expect this 10 years ago.
IdiocyInAction 6y ago
While true, this statement is completely vacuous. The curves being fitted are extremely complicated and if you can easily construct curve fitting problems (meaning: regression) that would be breakthroughs in many scientific fields. This says more about the generality of regression tasks than it says about ML.
- yalogin 6y ago
  My comment was focused on the phrase “artificial intelligence “ and not the tech itself. Obviously the techniques are awesome but the naming is unfortunate

drevil-v2 6y ago

I wonder what the end game is in the reality where we do achieve Artificial General Intelligence? It seems like a ethical minefield to me. You have companies like Uber/Lyft/Tesla (and presumably the rest of the gig economy mob) waiting to put the AI into bonded/slave labor driving customers around 24/7/365. If it truly is a Human level intelligence, then it will have values and goals and aspirations. It will have exploratory impulses. How can we square that with the purely commercial tasks and arbitrary goals that we want it to perform? Either we humans want slaves that will do what we tell them to or we treat them like children who may or may not end up as the adults that their parents think/hope they will become? I doubt it is the later because why else would the billions of dollars investment being pumped into AI? They want slaves.

coddle-hark 6y ago
The robots will gain civil rights the same way humans did, either by means of violence or swaying public opinion. Hopefully the latter. This isn’t a guess as to how future robots will work, this is an observation about how humans work.
WitCanStain 6y ago
I don't think the claim that human-level intelligence entails human ambitions has been substantiated. Why could you not have a system that does things as intelligently as a human but without a will of its own? It would only make sense if having human values and goals is necessary to having intelligence but I don't see how that could be true.
goatlover 6y ago
To avoid paying employees, creating greater profit margins.
root_axis 6y ago
There's no reason to believe that future AGIs will necessarily have values, goals, and aspirations.
- drevil-v2 6y ago

lifeisstillgood 6y ago

>>> in Down syndrome diagnoses a few years ago; it’s when the new machine arrived Hang on - uptick in diagnosis (ie post amniocentesis) or uptick in indicators. One indicates unnecessary procedures, one indicates a large population of previously undiagnosed downs .... One assumes the indicator - and greatly hope there is improved detection as I had at least one of these scares with my own kids

gwern 6y ago
Presumably what he is leaving out is that the increase in white-spots led to more amniocentesis, which then confirms the Down syndrome. If you did amniocentesis on all babies, it would of course increase the diagnosis rate even more. Whether this is a bad thing, as he claims, depends on whether you believe screening was being done optimally before, and that will depend quite a bit on things left out like the utility of not having a Down baby. (He doesn't present his working out the entire scenario, as it's just an aside, but hopefully before Jordan went around telling people how to change their prenatal screening systems, he did work it out a little bit more than back-of-the-envelope.)
- lifeisstillgood 6y ago
  Actually you would not expect it to increase the Downs rate at all - the null hypothesis is that there are X% Downs babies born and Y% identified through amniocentesis (where X-Y is z% the percentage of parents choosing termination) edit: actually there is a Zt (percent of parents choosing termination after detection) and Zu (percent age of undetected cases going to term). Zt is a social / moral thing and won't change based on better pixel resolution, but Zu should not be expected to change either - we are assuming there has been no change to the real rate of Downs (which requires something else) and no change to rate of parents choosing termination (see morals) so ... If Y% increases a lot (better detection of an underlying true rate) then either X% must increase or z% must. Neither of which i think we expect or know about. So what I hope happened was dramatically better training for operators on the new ultrasound (kind of like exactly what did not happen onthe 747Max) So either the OP was one of the first to spot this issue, and tipped off the whole medical industry, or, and this is where my money goes, he followed the reasoning of dozens of professionals who were several years ahead of him (naturally) and was reassured by someone who just saw "anxious parent" in front of him. But that's fine too :-)

joe_the_user 6y ago

How would one put it? "Adaptive Intelligence" might be described as the ability to be given a few instructions, gather some information and take actions that accomplish the instructions. It's what "underlings", "minions" do. But if we look at deep learning, it's almost the opposite of this. Deep learning begins with an existing stream of data, a huge stream, large enough that the system can just extrapolate what's in the data, include data leads to what judgements. And that works for categorization and decision making the duplicates what decisions humans make or even duplicates what works, what wins in a complex interaction process. But all that doesn't involve any amount of adaptive intelligence. It "generalizes" something but our data scientists have no idea exactly what. The article proposes an "engineering" paradigm as an alternative to the present "intelligence" paradigm. That seems more sensible, yes. But I'm doubtful this could accepted. Neural network AI seems like a supplement to the ideology of unlimited data collection. If you put a limit on what "AI" should do, you'll put a limit on the benefits of "big data".

visarga 6y ago
Neural nets don't generalize much, they interpolate between training examples. But if you couple them with search (MCTS) then you can do logic and reasoning with them, like AlphaGo. You can also put any algorithm you want inside a neural net as long as you have a mechanism to pass gradients back - for example in the final layer you could have a complex graph-matching algorithm to map the predictions to the target, or you could put an ODE solver as a layer, or a logic engine, or a database.
- deleted 6y ago
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ksec 6y ago

While real AI hasn't really happened yet, Machine Learning has definitely made a big impact with lots of potentials. I think we are still in the middle of the S Surve in ML. And AI is like.... Fusion? We are always another 50 years away.

salty_biscuits 6y ago
I think AI always means "does something a human can do that you wouldn't expect a machine to be able to do". So it is forever moving goal post. It just moves ahead in step with people's expectations of what a machine can do. Also magic is always disappointing when you know how it works. We just have the psychological safety of not knowing how we work, whereas in ML we always know what it is doing, and it is always kind of disappointing.

dhairya 6y ago

Part of the challenge of pursuing this comprehensive type of AI infrastructure is that it requires massive coordination and collaboration. Unfortunately the incentives in both industry and academia make it difficult to even start such a project. As a result we're stuck with incremental work on narrow problems. I've been on both sides of table (started in industry developing AI solutions and now in academia pursuing phd in AI). When I was on the industry side, where the information and infrastructure was there to build such a system, you had to deal with the bureaucracy and institutional politics. In academia, the incentives are aligned for individual production of knowledge (publishing). The academic work focuses on small defined end-to-end problems that are amenable to deep learning and machine learning. The types of AI models that emerge are specific models solving specific problems (NLP, vision, play go, etc). It seems to move towards developing large AI systems we need a model of new collaboration. There are existing models in the world of astrophysics and medical research that we can look to for inspiration. Granted they have they have their own issues of politics but it's interesting that similar scope projects haven't emerged on the AI side yet.

thundergolfer 6y ago
The incentive structure that seems clearly best (though not greatly) suited to this large-scale intelligence infrastructure is public investment in publicly owned systems. Jordan seems to maybe gesture at this, as who owns all the bridges in the the USA? Governments. If we are talking “societal-scale medical system” a majority of people would want that publicly owned and operated and universally accessible. We’ve already seen in industry that the incentives are to massively in favour of creating walled-gardens that lock in users and thus profits. No societal-wide system should work like our social media ecosystem (FB, Snapchat, TikTok). The dominant profit incentives are also not “human-centric”, as Jordan constantly emphasises. Well, they’re only so if we assume profit-making activity is tightly aligned with “human-centric” concerns. Some will say yes, but to me our climate disaster and the USA mass incarceration system are strong enough evidence that the answer is no. I think some wealthy Northern European countries are setup well enough to produce “Intelligent Infrastructure”, except for the fact that most of the talent is in the USA.
- deleted 6y ago

wildermuthn 6y ago

He almost makes a good point when he questions whether “human imitative” AI could solve the other problems we face, seeing as humans aren’t that smart (especially not in large numbers when participating in complex systems). But the distinction he makes between ML and AI is crucial. What he’s really talking about is AGI - general intelligence. And he’s right - we don’t have a single example of AGI to date (few or single shot models withstanding, as they are only so for narrow tasks). The majority mindset in AI research seems to be (and I could be wrong here, in that I only read many ML papers) that the difference between narrow AI and general AI is simply one of magnitude - that GPT-3, given enough data and compute, would pass the Turing test, ace the SAT, drive our cars, and tell really good jokes. But this belief that the difference between narrow and general intelligence is one of degree rather than kind, may be rooted in what this article points out: in the historical baggage of AI almost always signifying “human imitative”. But there is no reason that AGI must be super intelligent, or human-level intelligent, or even dog-level intelligent. If narrow intelligence is not really intelligence at all (but more akin to instinct), then the dumbest mouse is more intelligent than AlphaGo and GPT-3, because although the mouse has exceedingly low General Intelligence, AlphaGo and GPT-3 have none at all. There is absolutely nothing stopping researchers from focusing on mouse-level AGI. Moreover, it seems likely that going from zero intelligence to infinitesimal intelligence is the harder problem than going from infinitesimal intelligence to super-intelligence. The latter may merely be an exercise in scale, while the former requires a breakthrough of thought that asks why a mouse is intelligent but an ant is not. The only thing stopping researchers is that when answering this question, the answer is really uncomfortable, and outside their area of expertise, and has weighty historical baggage. It takes courage of researchers like Yoshua Bengio to utter the word “consciousness”, although he does a great job by reframing it with Thinking Fast and Slow’s System 1/2 vocabulary. Still, the hard problem of consciousness, and the baggage of millennia of soul/spirit as an answer to that hard problem, makes it exceedingly difficult for well-trained scientists to contemplate the rather obvious connection between general intelligence and conscious reasoning. It’s ironic that those who seek to use their own conscious reasoning to create AGI are in denial that conscious reasoning is essential to AGI. But even if consciousness and qualia are a “hard”problem that we cannot solve, there’s no reason to shelve the creation of consciousness as also “hard”. In fact, we know (from our own experience) that the material universe is quite capable of accidentally creating consciousness (and thus, General Intelligence). If we can train a model to summarize Shakespeare, surely we can train a model to be as conscious, and as intelligent, as a mouse. We’re only one smart team of focused AI researchers away from Low-AGI. My bet is on David Ha. I eagerly await his next paper.

bob1029 6y ago

We are chasing the wrong things. Our conceptualization of the problem domain is fundamentally insufficient. Even if we took our current state of the art and scaled it up 1,000,000x, we are still missing entire aspects of intelligence. The AI revolution is very likely something that will require a fundamental reset of our understanding of the problem domain. We need to identify a way to attack the problem in such a way that we can incrementally scale all aspects of intelligence. The only paradigm that I am aware of which seems to hint parts of the incremental intelligence concept would be the relational calculus (aka SQL). If you think very abstractly about what a relational modeling paradigm accomplishes, it might be able to provide the foundation for a very powerful artificial intelligence. Assuming your domain data is perfectly normalized, SQL is capable of exploring the global space of functions as they pertain to the types. This declarative+functional+relational interface into arbitrary datasets would be an excellent "lower brain", providing a persistence & functional layer. Then you could throw a neural network on top of this to provide DSP capabilities in and out (ML is just fancy multidimensional DSP). If you know SQL you can do a lot of damage. Even if you aren't a data scientist or have a farm of Nvidia GPUs, you can still write ridiculously powerful queries against domain data and receive powerful output almost instantaneously. The devil is in the modeling details. You need to normalize everything very strictly. 20-30 dimensions of data derived into a go/no-go decision can be written in the same # of lines of SQL if the schema is good. How hard would this be on the best-case ML setup? Why can't we just make the ML write the SQL? How hard would it be for this arrangement to alter its own schema over time autonomously?

sp527 6y ago
I think the inhibitor to scale is actually model compression. You’re right that scaling up 1Mx won’t cut it. That’s because fidelity is still too high. We already know the brain is a very efficient machine for storing heuristics and compressed models. Also related to why it’s prone to err. Information theory is the right framework here imo. Other related concerns: hierarchal organization of information and model comparison.
joe_the_user 6y ago
You're talking about logic - SQL is basically a "logic language", it's just not entirely evident. Logic programming was the AI paradigm for more or less most of the 20th century and has fallen out of favor. Many people have talked about combining the neural net/extrapolation/brute-force approach with the logic approach. That hasn't born fluid yet but who knows.

deleted 6y ago

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xiphias2 6y ago

Too long, don't read: the whole post is full of goal post moving and story telling instead of trying to explain the statement in the title. Classifying images was always classified as a problem that can't be solved with statistical analysis. Deep learning layers are beyond human understanding, so in my view artificial intelligence happened, even though it's not yet as intelligent as humans.

bitL 6y ago

Surveillance AI is thriving, enabling things everybody (in power) was dreaming about. Revolution has happened and is deeply hidden.

dang 6y ago

Discussed at the time: https://news.ycombinator.com/item?id=16873778 https://news.ycombinator.com/item?id=16873778

cosmodisk 6y ago

What I miss most,in discussions about AI, is the motivation factor,which is the driving force behind every single thing we humans do. How can we create a system that would be motivated to evolve in order to better itself. Humans created all sorts of things because fear,hunger,or pleasure was so strong and it couldn't be pushed away. What will happen to an AI powered robot that one day decide that going into radioactive areas isn't quite what it wants and will say 'screw it'?

simonh 6y ago
The issue is that intelligence by itself provides to motivations or objectives. Intelligence is simply a tool to be used in order to get things done. In humans the objectives are provided by instincts, emotions and biological needs. In AI we will have to provide the goals, but as the paper clip maximiser thought experiment shows, we’re going to have to be very careful and thoughtful about it.

spicyramen 6y ago

What i have seen in the field was a frenzy of doing ML. What happened was that first of all companies needed to understand what ML was. Then understand the tools available. Once you start exploring the tools you will find that at every stage of a ML pipeline there is 2 or more different ways of doing things. S3/GCS, BigQuery, Spark, Beam, TensorFlow, Pytorch, Google, Azure, Amazon, notebooks, Jupyter, JupyterHub..KubeFlow, TFX...etc. okay you pick the tools needed, then you need to put them together...and hire people...that's challenging. I believe we need to wait for AutoML pipelines from data análisis to.prediction to start seeing really advancement in production systems.

ridgeflex 6y ago

Jordan argues that leaps in human-imitative AI are not necessary to solve IA/II problems -- "We need to solve IA and II problems on their own merits, not as a mere corollary to a human-imitative AI agenda." However, achieving near-human level accuracy on tasks such as classifying images of cars or road signs would be immensely useful to the proposed II-type system that handles large-scale self-driving transportation (individual cars would conceivably need the ability to understand their local environments and communicate this to the overall network). I agree with his argument that there should be a shift in the way we think about problems in "AI", but I don't think we should necessarily think that progress in human-imitative AI problems and IA/II problems are mutually exclusive.

randcraw 6y ago
This sounds like the longstanding debate between weak / narrow vs strong AI. Can improving the former make progress toward the latter? I'm inclined to agree with Jordan that we shouldn't expect the two to enhance the other much less commingle. Just as advancement of one classical algorithm rarely enhances another, I think it's unlikely the next generation of object recognition is going to advance speech recognition or reading comprehension. Probably more essentially, until AI escapes its current dependency on pattern matching driven solely by accumulation of probabilistic events, I see little chance that human-level general-purpose cognition will arise from our current bases for AI, namely observing innumerable games of chess or watching millions of cars wander city streets.

nicholast 6y ago

The brittleness of mainstream ML to out of distribution data is one of the most fundamental channels for error. There are very few domains where a static environment can be depended on over the long term. If machine learning is to be approached as an engineering discipline there will need to be practices established for validating models throughout their life cycle. One potential resource that can support this type of systematic evaluation is the Automunge open source library for assembling data pipelines, which has automatic support for evaluating data property drift in feature sets serving as basis for a model. (disclosure I am founder of Automunge)

fuckminster_b 6y ago

Before I spent a few hours of my life getting a basic grip of statistics, I fully expected to one day in the near future being wiped out (along with the rest of humanity) by a newly awakened artificial consciousness that came to the correct conclusion, that humans are the biggest threat to all other life on earth, including its own. Then I learned about Bayesian statistics and watched a talk by a senior LLNL statistician who is actually marketing 'AI' products/services as a side gig. When I realized what 'deep learning' actually is I was disappointed, unsure if I had mistakenly oversimplified the subject matter - until said senior statistician spelled out loud what I was thinking, in her talk: the 'understanding' a machine can currently attain of its input is quite like the understanding a pocket calculator can achieve of maths. Guess humanity is off the hook for now. Phew. I have doubts whether 'strong AI' is even technologically possible, since even accurately simulating a human mind, this simulation would be necessarily constrained to run orders of magnitude slower than the reality it is designed to model. 'Training' it with data so to allow it the opportunity to reason and thereby synthesize a conclusion not already contained in the data fed to it might take longer than a researcher would be able to in a life time. When was the last time a generation-spanning endeavour worked out as planned for (the West)? I wish people would stop calling what currently passes for 'Machine Learning' as 'AI'. Literally the same level of 'intelligence' we already had in the 80s, AFAIR we called it 'Fuzzy Logic' then. Secretly an admission, that Hollywood basically licensed the narrative of imminent runaway artificial consciousness back to science would make me give it one final Chance to prove its aptitude at high-level human reasoning and get square with reality. I'm not holding my breath.

esc_colon_q 6y ago

> IA will also remain quite essential, because for the foreseeable future, computers will not be able to match humans in their ability to reason abstractly about real-world situations. I broadly agree with what this article says, but depending how you define "foreseeable future" I find this to be a dangerously naive viewpoint that just assumes nothing will change quickly. I'm not stupid enough to say abstract reasoning about the real world is a simple problem or right around the corner, but there's no evidence so far to indicate it's much further off than, say, object recognition was when Minsky (or more likely Papert, apparently?) assigned it as an undergrad project. We pour exponentially more money into research each year, and have more and better hardware to run it on. We're going to hit the ceiling soon re: power consumption, sure, but some libraries are starting to take spiking hardware seriously which will open things up a few orders of magnitude. There are dozens of proposed neural architectures which could do the trick theoretically, they're just way too small right now (similar to how useless backprop was when it was invented). Are we a Manhattan Project or three away from it? Sure. That's not nothing, but we're also pouring so much money into the boring and immediately commercializable parts of the field (all the narrow perception-level and GAN can that NeurIPS is gunked up with) that if any meaningful part of that shifted to the bigger problems, we'd see much faster progress. That will happen in a massive way once someone does for reasoning what transformers did for text prediction: just show that it's tractable.

soupson 6y ago

The story is interesting, but being interesting as a story doesn’t make fetuses into “babies” and posing it that way does disservice to the overall message.

reshie 6y ago

i like to think automation comes before ai. we automate mechanics then we automate decisions or protocols.

gandutraveler 6y ago

Looks like we have reached a point where we see slower growth & innovation in tech in coming decade. AI was supposed to be the next big disruptor but my guess is we will just see minor progress in automation and far away from anything disruptive. Singularity might may not even be possible in next century

mark_l_watson 6y ago

I remember reading this article about two years ago, and generally liking it. The multidisciplinary conversations during The Great AI Debate #2 two nights ago were certainly entertaining, but also laid out good ideas about tech approaches and also the desires of AI researchers - what they hope AIs will be like. Good job by Gary Marcus. I work for a medical AI company and we are focused on benefits to humans. While in the past I have been been a fan of AI technologies from Google, FB, etc., now I believe that both consumers and governments must fight back hard against business processes that do not in general benefit society. Start by reading Zubroff’s Surviving Surveillance Capitalism book, and the just published book Power of Privacy.