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gary marcus papers

02 12 2020

To him, deep learning is serviceable as a placeholder for a community of approaches and practices that evolve together over time.Â, Also: Intel's neuro guru slams deep learning: 'it's not actually learning', Probably, deep learning as a term will at some point disappear from the scene, just as it and other terms have floated in and out of use over time.Â, There was something else in Monday's debate, actually, that was far more provocative than the branding issue, and it was Bengio's insistence that everything in deep learning is united in some respect via the notion of optimization, typically optimization of an objective function. Privacy Policy | Edge You may unsubscribe at any time. In a series of tweets he claimed (falsely) that I hate deep learning, and that because I was not personally an algorithm developer, I had no right to speak critically; for good measure, he said that if I had finally seen the light of deep learning, it was only in the last few days, in the space of our Twitter discussion (also false). The paper’s conclusion furthers that impression by suggesting that deep learning’s historical antithesis — symbol-manipulation/classical AI — should be replaced (“new paradigms are needed to replace the rule-based manipulation of symbolic expressions on large vectors.”). Please review our terms of service to complete your newsletter subscription. horsepower partner for in Nobody yet knows how the brain implements things like variables or binding of variables to the values of their instances, but strong evidence (reviewed in the book) suggests that brains can (pretty much everyone agree that at least some humans can do this when they do mathematics and formal logic; most linguistics would agree that we do it in understanding the language; the real question is not whether human brains can do symbol-manipulation at all, it os how broad is the scope of the processes that use it.). So I tweeted it, expecting a few retweets and nothing more. In particular, they showed that standard deep learning nets often fall apart when confronted with common stimuli rotated in three dimensional space into unusual positions, like the top right corner of this figure, in which a schoolbus is mistaken for a snowplow: In a healthy field, everything would stop when a systematic class of errors that surprising and illuminating was discovered. intelligence However, it comes with several drawbacks, such as the need for large amounts of training data and the lack of explainability and verifiability of the results. He is the founder and CEO of Robust.AI, and was the founder and CEO of Geometric Intelligence, a machine learning company acquired by Uber in 2016. that the idea that deep learning is overhyped is itself overhyped, Hinton, for example, gave a talk at Stanford in 2015 called Aetherial symbols, like Anish Athalye’s carefully designed, 3-d printed foam covered dimensional baseball that was mistaken for an espresso, dubiously likened the noncanonical pose stimuli to Picasso painting, e chief reason motivation I gave for symbol-manipulation, back in 1998, When to use Reinforcement Learning (and when not to), Processing data for Machine Learning with TensorFlow, Authorship Attribution through Markov Chain, Simple Monte Carlo Options Pricer In Python, Training an MLP from scratch using Backpropagation for solving Mathematical Equations, Camera-Lidar Projection: Navigating between 2D and 3D, A 3 step guide to assess any business use-case of AI, Sentiment Analysis on Movie Reviews with NLP Achieving 95% Accuracy. So the topic of branding is in some sense unavoidable. risk transformation Bengio's MILA institute, where Monday's debate took place, is keeping a running archive of links of the various installments by both individuals. The chief reason motivation I gave for symbol-manipulation, back in 1998, was that back-propagation (then used in models with fewer layers, hence precursors to deep learning) had trouble generalizing outside a space of training examples. On the contrary, I want to build on it. Marcus published a new paper on arXiv earlier this week titled “The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence.” In the … This account provides a straightforward framework for understanding how universals are extended to arbitrary novel instances. Amazon's Andy Jassy talks up AWS Outposts, Wavelength as the right edge for hybrid cloud. Tiernan Ray I think we need to consider the hard challenges of AI and not be satisfied with short-term, incremental advances. Here’s my view: deep learning really is great, but it’s the wrong tool for the job of cognition writ large; it’s a tool for perceptual classification, when general intelligence involves so much more. new Deep learning is important work, with immediate practical applications. automation As they put it “DNNs’ understanding of objects like “school bus” and “fire truck” is quite naive” — very much parallel to what I said about neural network models of language twenty years earlier, when I suggested that the concepts acquired by Simple Recurrent Networks were too superficial. in Please address correspondence to Michael C. Frank, Depart- The secondary goal of the book was to show that that was possible to build the primitives of symbol manipulation in principle using neurons as elements. I don’t hate deep learning, not at all; we used it in my last company (I was the CEO and a Founder), and I expect that I will use it again; I would be crazy to ignore it. By registering, you agree to the Terms of Use and acknowledge the data practices outlined in the Privacy Policy. Gary F. Marcus's 103 research works with 4,862 citations and 8,537 reads, including: Supplementary Material 7 trials But it is not trivial. That could be a loss function, or an energy function, or something else, depending on the context.Â, In fact, Bengio and colleagues have argued in a recent paper that the notion of objective functions should be extended to neuroscience. We are extremely grateful to Douglas Summers-Stay for running the experiments; we were unable to run them ourselves because AIOpen refused to give us access to the program. Just after I finished the first draft of this essay, Max Little brought my attention to a thought-provoking new paper by Michael Alcorn, Anh Nguyen and others that highlights the risks inherent in relying too heavily on deep learning and big data by themselves. chipmaker's The same kind of heuristic use of deep learning started to happen with Bengio and others around 2006, when Geoffrey Hinton offered up seminal work on neural networks with many more layers of computation than in past. Funny they should mention that. and The details were where the two argued about definitions and terminology.Â, In the days that followed, Marcus, in a post on Medium, observed that Bengio seemed to have white-washed his own recent critique of shortcomings in deep learning. Vaccine factors where also These models cannot generalize outside the training space. stakeholder ... AI transcription sucks (here's the workaround). I examined some old ideas, like dynamic binding via temporal oscillation, and personally championed a slots-and-fillers approach that involved having banks of node-like units with codes, something like the ASCII code. The idea goes back to the earliest days of computer science (and even earlier, to the development of formal logic): symbols can stand for ideas, and if you manipulate those symbols, you can make correct inferences about the inferences they stand for. ¹ Thus Spake Zarathustra, Zarathustra’s Prologue, part 3. 888 | Topic: Artificial Intelligence, Monday's historic debate between machine learning luminary Yoshua Bengio and machine learning critic Gary Marcus spilled over into a tit for tat between the two in the days following, mostly about the status of the term "deep learning. Amazon is stepping up its contact center services with Amazon Connect Wisdom, Customer Profiles, Real-Time Contact Lens, Tasks and Voice ID. in • Gary Marcus Manning Jr., 25, of 78 Stambaugh Ave., Apartment 2, Sharon, was charged with receiving stolen property, theft, assault and criminal mischief after a … Souls would be searched; hands would be wrung. Those domains seem, intuitively, to revolve around putting together complex thoughts, and the tools of classical AI would seem perfectly suited to such things. coming Last week, for example, Tom Dietterich said (in answer to a question about the scope of deep learning): Dietterich is of course technically correct; nobody yet has delivered formal proofs about limits on deep learning, so there is no definite answer. Jürgen Schmidhuber, who co-developed the "long-short term memory" form of neural network, has written that the AI scientist Rina Dechter first used the term "deep learning" in the 1980s. scientists. When I rail about deep-learning, it’s not because I think it should be “replaced” (cf. For example, Mike Davies, head of Intel's "neuromorphic" chip effort, this past February criticized back-propagation, the main learning rule used to optimize in deep learning, during a talk at the International Solid State Circuits Conference. Some people liked the tweet, some people didn’t. At that time I concluded in part that (excerpting from the concluding summary argument): Richard Evans and Edward Grefenstette’s recent paper at DeepMind, building on Joel Grus’s blog post on the game Fizz-Buzz follows remarkably similar lines, concluding that a canonical multilayer network was unable to solve the simple game on own “because it did not capture the general, universally quantified rules needed to understand this task” — exactly per what I said in 1998. Symbols won’t cut it on their own, and deep learning won’t either. Yoshua Bengio and Gary Marcus held a debate in Montreal on Monday about the future of artificial intelligence. The last 30 minutes were excellent (after the guest left). The limits of deep learning have been comprehensively discussed. According to his website, Gary Marcus, a notable figure in the AI community, has published extensively in fields ranging from human and animal behaviour to neuroscience, genetics, linguistics, evolutionary psychology and artificial intelligence.. AI and evolutionary psychology, which is considered to be a remarkable range of topics to cover for a man as young as Marcus. When Gary Marcus arrived at the nearest CompSci department which adjoined a university, he found many people assembled to study Machine Learning; for it had been announced that Strong AI would soon make an appearance there. Machine learning (ML) has seen a tremendous amount of recent success and has been applied in a variety of applications. Infineon to set up global AI hub in Singapore. Computational limits don't fully explain human cognitive limitations by Ernest Davis and Gary Marcus. But we need to be able to extend it to do things like reasoning, learning causality, and exploring the world in order to learn and acquire information. The reader can judge for him or herself, but the right hand column, it should be noted, are all natural images, neither painted nor rendered; they are not products of imagination, they are reflection of a genuine limitation that must be faced. To anyone who has seriously engaged in trying to understand, say, commonsense reasoning, this seems obvious. To take one example, experiments that I did on predecessors to deep learning, first published in 1998, continue to hold validity to this day, as shown in recent work with more modern models by folks like Brendan Lake and Marco Baroni and Bengio himself. But LeCun is right about one thing; there is something that I hate. digital I also pointed out that rules allowed for what I called free generalization of universals, whereas multilayer perceptrons required large samples in order to approximate universal relationships, an issue that crops up in Bengio’s recent work on language. explicitly Gary Marcus (@GaryMarcus), the founder and chief executive of Robust AI, and Ernest Davis, a professor of computer science at New York University, are the authors of … Advances in narrow AI with deep learning are often taken to mean that we don’t need symbol-manipulation anymore, and I think that it is a huge mistake. the In my 2001 book The Algebraic Mind, I argued, in the tradition of Newell and Simon, and my mentor Steven Pinker, that the human mind incorporates (among other tools) a set of mechanisms for representing structured sets of symbols, in something like the fashion of a hierachical tree. says ALL RIGHTS RESERVED. form coverage Mistaking an overturned schoolbus is not just a mistake, it’s a revealing mistake: it that shows not only that deep learning systems can get confused, but they are challenged in making a fundamental distinction known to all philosophers: the distinction between features that are merely contingent associations (snow is often present when there are snowplows, but not necessary) and features that are inherent properties of the category itself (snowplows ought other things being equal have plows, unless eg they have been dismantled). KDDI, Advocates of symbol-manipulation assume that the mind instantiates symbol-manipulating mechanisms including symbols, categories, and variables, and mechanisms for assigning instances to categories and representing and extending relationships between variables. What I hate is this: the notion that deep learning is without demonstrable limits and might, all by itself, get us to general intelligence, if we just give it a little more time and a little more data, as captured in Andrew Ng’s 2016 suggestion that AI, by which he meant mainly deep learning, would either “now or in the near future“ be able to do “any mental task” a person could do “with less than one second of thought”. Every line of computer code, for example, is really a description of some set of operations over variables; if X is greater than Y, do P, otherwise do Q; concatenate A and B together to form something new, and so forth. But the tweet (which expresses an argument I have heard many times, including from Dietterich more than once) neglects the fact we also do have a lot of strong suggestive evidence of at least some limit in scope, such as empirically observed limits reasoning abilities, poor performance in natural language comprehension, vulnerability to adversarial examples, and so forth. Hinton didn’t really give an argument for that, so far as I can tell (I was sitting in the room). So deep learning emerged as a very rough, very broad way to distinguish a layering approach that makes things such as AlexNet work.Â. The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence (2020) - Gary Marcus This paper covers recent research in AI and Machine Learning, which has largely emphasized general-purpose learning and ever-larger training sets and more and more compute. systems … use techniques like deep learning as just one element in a very complicated ensemble of techniques, ranging from the statistical technique of Bayesian inference to deductive reasoning. I think — and I am saying this for the public record, feel free to quote me — deep learning is a terrific tool for some kinds of problems, particularly those involving perceptual classification, like recognizing syllables and objects, but also not a panacea. It worries me, greatly, when a field dwells largely or exclusively on the strengths of the latest discoveries, without publicly acknowledging possible weaknesses that have actually been well-documented. deep neural networks (DNNs) can fail to generalize to out-of-distribution (OoD) inputs, including natural, non-adversarial ones, which are common in real-world settings. (At the end, I will even give an example in the domain of object recognition, putatively deep learning’s strong suit.). “The work itself is impressive, but mischaracterized, and … a better title would have been ‘manipulating a Rubik’s cube using reinforcement learning’ or ‘progress in manipulation with dextrous robotic hands’” – Gary Marcus, CEO and Founder of Robust.ai, details his opinion on the achievements of this paper. smartphones LeCun has repeatedly and publicly misrepresented me as someone who has only just woken up to the utility of deep learning, and that’s simply not so. To begin with, and to clear up some misconceptions. or The central claim of the book was that symbolic processes like that — representing abstractions, instantiating variables with instances, and applying operations to those variables, was indispensible to the human mind. You agree to receive updates, alerts, and promotions from the CBS family of companies - including ZDNet’s Tech Update Today and ZDNet Announcement newsletters. (I discuss this further elsewhere.). diversity Whatever one thinks about the brain, virtually all of the world’s software is built on symbols. to CEO and Cofounder of Robust.AI, Gary Marcus an expert in AI has recently a published a new paper by the name ‘The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence’, which draws attention to a crucial fact about artificial intelligence, i.e., AI is not aware of its own operations and is only functioning as per certain commands within a controlled environment. it All I am saying is to give Ps (and Qs) a chance. using When a field tries to stifle its critics, rather then addressing the underlying criticism, replacing scientific inquiry with politics, something has gone seriously amiss. The moral of the story is, there will always be something to argue about.Â, Okta shares surge as fiscal Q3 results top expectations, forecast higher as well, Snowflake fiscal Q3 revenue beats expectations, forecast misses, shares drop, MIT machine learning models find gaps in coverage by Moderna, Pfizer, other Warp Speed COVID-19 vaccines, Hewlett Packard Enterprise CEO: We have returned to the pre-pandemic level, things feel steady. Bengio was pretty much saying the same thing. What else is needed?”. There again much of what was said is true, but there was almost nothing acknowledged about limits of deep learning, and it would be easy to walk away from the paper imagining that deep learning is a much broader tool than it really is. I’m not saying I want to forget deep learning. to In particular, Bengio told Technology Review that. computing In February 2020, Marcus published a 60-page long paper titled "The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence". (Hinton refused to clarify when I asked.) But the advances they make with such tools are, at some level, predictable (training times to learn sets of labels for perceptual inputs keep getting better, accuracy on classification tasks improves). more So what is symbol-manipulation, and why do I steadfastly cling to it? Advertise | is The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence (2020) - Gary Marcus This paper covers recent research in AI and Machine Learning which has largely emphasized general-purpose learning and ever-larger training sets and more and more compute. Nobody should be surprised by this. Paul Smolensky, Ev Fedorenko, Jacob Andreas, Kenton Lee, CPU I am cautiously optimistic that this approach might work better for things like reasoning and (once we have a solid enough machine-interpretable database of probabilistic but abstract common sense) language. To take another example, consider LeCun, Bengio and Hinton’s widely-read 2015 article in Nature on deep learning, which elaborates the strength of deep learning in considerable detail. In my judgment, deep learning has reached a moment of reckoning; when some of its most prominent leaders stand in denial, there is a problem. ", That's such a basic idea, it seems so self-evident, that it almost seems trivial for Bengio to insist on it.Â. : "Learning Regular Languages via Alternating Automata" 12:40 - 14:00: Lunch break to The technical issue driving Alcorn’s et al’s new results? We’d already seen similar examples with contrived stimuli, like Anish Athalye’s carefully designed, 3-d printed foam covered dimensional baseball that was mistaken for an espresso. brings Hinton, LeCun and Bengio’s strong language above, where the name of the game is to conquer previous approaches), but because I think that (a) it has been oversold (eg that Andrew Ng quote, or the whole framing of DeepMind’s 2017 Nature paper), often with vastly greater attention to strengths than potential limitations, and (b) exuberance for deep learning is often (though not universal) accompanied by a hostility to symbol-manipulation that I believe is a foundational mistake. computational AWS Gary Marcus Although deep learning has historical roots going back decades, neither the term "deep learning" nor the approach was popular just over five years ago, when the field was reignited by papers such as Krizhevsky, Sutskever and Hinton's … By signing up, you agree to receive the selected newsletter(s) which you may unsubscribe from at any time. That’s actually a pretty moderate view, giving credit to both sides. The need But Realistically, deep learning is only part of the larger challenge of building intelligent machines. Marcus is Founder and CEO of Robust.AI and a professor emeritus at NYU. Memory networks and differentiable programming have been doing something a little like that, with more modern (embedding) codes, but following a similar principle, the latter embracing an ever-widening array of basic micro-processor operations such as copy and compare of the sort I was lobbying for. A hybrid model that vastly outperformed what a purely deep net would have done, incorporating both back-propagation and a (continuous versions) of the primitives of symbol-manipulation, including both explicit variables and operations over variables. Symbols in principle also offer a way of incorporating of all the world’s textual knowledge, from Wikipedia to textbooks; deep learning has no obvious way of incorporating basic facts like “dogs have noses” nor to accumulate that knowledge into more complex inferences. In the meantime, as Marcus suggests, the term deep learning has been so successful in the popular literature that it has taken on a branding aspect, and it has become a kind-of catchall that can sometimes seem like it stands for anything. Rebooting AI: Building Artificial Intelligence We Can Trust. Part Yes, partly for historical reasons that date back to the earliest days of AI, the founders of deep learning have often been deeply hostile to including such machinery in their models; Hinton, for example, gave a talk at Stanford in 2015 called Aetherial symbols, in which tried to argue that the idea of reasoning with formal symbols was “as incorrect as the belief that a lightwave can only travel through space by causing disturbances in the luminiferous aether.”. German Japan's gains You may unsubscribe from these newsletters at any time. Companies with "deep" in their name have certainly branded their achievements and earned hundreds of millions for it. If you know that P implies Q, you can infer from not Q that not P. If I tell you that plonk implies queegle but queegle is not true, then you can infer that plonk is not true. appear Eventually (though not yet) automated vehicles will be able to drive better, and more safely than you can; no The process of attaching y to a specific value (say 5) is called binding; the process that combines that value with the other elements is what I would call an operation. SK technology.

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