6 ms·

DeepMind AI reading list [pdf]

158 points by banjo_milkman 6y ago

raptortech 6y ago
Lots of excellent introductory sources here, but I was hoping for papers!
doublesCs 6y ago
Obvious submarine article / PR. The goal is to signal how concerned with ethics DeepMind is.
- galimaufry 6y ago
  This illustrates the problem with calling out virtue-signalling. I see two references related to ethics. It seems the only way to avoid accusations of virtue-signalling would be to have 0 references to AI fairness. In practice, comments like this encourage self-censorship, even if that is not their intention.
  - mlthoughts2018 6y ago
    AI fairness is really, really bogus as a research field. Understanding and reducing negative impacts of bias is important, but the current research field of AI fairness does not do anything like that and has not yet reached any state of maturity where it can be considered a serious subset of research at all. I would really say even one mention of it on a list like this is purely to do virtue signalling. It’s the same for “explainability” of models too, another totally bogus field that gets treated as being worthy of attention or societal prioritization purely due to politics.
    - currymj 6y ago
      it is very strange to read someone claiming with extreme confidence that all fairness and explainability research is completely bogus. of course there are a lot of papers of questionable value, but that's a problem with machine learning more broadly -- probably all academic science, really. lots of institutions (banks, medicine, etc.) really do need explainable models. in practice this often means linear models with simple coefficients or shallow decision trees. there is plenty of useful work on learning these while maintaining performance, or "distilling" them from more complicated models. i know for a fact some explainable models learned with these techniques do actually get used in real life. likewise with fairness -- end-users actually do care about fairer models in all kinds of areas, especially lending and insurance. there's a ton of frustrating debate about how to operationalize fairness in different settings but it seems like there is actually progress on this front. what do you find to be bogus about these research areas?
      - mlthoughts2018 6y ago
        Linear models with simple coefficients can often be some of the least explainable models, particularly when the assumption of linearity breaks down. [0] is a good classic paper on this, demonstrating a simple example where coding error on the inputs leads to erroneous coefficient estimates that are both statistically significant and also of the wrong sign. Meaning, you would believe the coefficients reflect a real relationship between the covariate and the target, and even could claim it’s statistically significant, and yet the actual relationship to the target is of the opposite sign! Any further feature importance scoring based off the coefficient estimates would then become catastrophically misleading. Meanwhile, a model like support vector regression on the same data is capable of automatically handling the non-linearity, at the expense that there’s no more such thing as a coefficient breakdown in the linear space of input features. Does this make it less “explainable”? That would make zero sense. How can it be worse at explaining a data generating mechanism when it is better at predicting that same generating mechanism. What could it possibly mean to explain something you can’t predict or replicate? The field of “explainable” models doesn’t even make the slightest attempt to address this stuff - it just beats up on models that are arbitrarily labeled as “black boxes” (what does that mean?) If a given model X predicts or replicates a data generating process better than Y, then X explains the process better than Y, period. An analogy: Newtonian physics is not “more explainable than” quantum mechanics. Newtonian physics is just more wrong about how the world works than quantum mechanics. [0]: http://www.saramitchell.org/achen04.pdf http://www.saramitchell.org/achen04.pdf
        currymj 6y ago
        i think "explainable model" literally means "a model you can explain to people". You may have to explain your decision to a judge or customer after the fact, or you might even be asking a layperson to actually compute predictions manually (as is sometimes done in psychology and medicine for things like triage). the question of whether or not the model provides a good explanation for the data-generating process is a distinct one. I think the Achen paper makes a good point that people cannot safely turn linear regression coefficients into stories about the world, although it doesn't seem like they've stopped trying. but assuming you have a way to validate that it makes good predictions (not an unreasonable assumption), an explainable model can be a useful thing to have.
    - longtom 6y ago
      I kinda agree that "fairness", even though a valid issue, seems receive a disproportional amount of attention. The central dangers, apart from misuse, seem to be a coordination problem and a control problem. The coordination problem is that AGI will likely lead to a winner-takes-all scenario, implying a per-emptive strike becomes rational once one player seems too far ahead of the game. The control problem is that the value function of an AGI may diverge from our own value function.
mlthoughts2018 6y ago
This doesn’t make much sense. If you are not already trained in vector calculus, functional analysis and basic classifier and regression algorithms, then most of these reading list items are completely inapplicable (or even dangerous, like when someone reads some blog posts about slapping together neural nets in Keras and suddenly thinks they can build a model suitable for production). On the other hand if you are trained in ML, most of these are not detailed or extended enough to give you anything useful. Doing “a hacker’s intro to X” over and over really, really doesn’t give you any skills. This is particularly true for deep domains like reinforcement learning, computer vision / image processing, and natural language processing. Meanwhile, basic design of experiments for A/B testing, explanatory modeling and simple regressions is a fraught area. Not understanding extremely rigorous details about hypothesis testing, model checking, limitations of statistical significance, etc., can lead to wildly incorrect inferences from poor models that non-experts will completely fail to detect. At best this list seems like “special topics to seem trendy in AI without getting deep / practical insight into any particular area.” There are one or two minor exceptions in the list.
- etaioinshrdlu 6y ago
  I think, like it or not, the end goal of the AI industry will result in people with less and less rigorous education creating models used in production. It may not be solid science, but it doesn't have to be. It will end up changing entire fields anyways. Especially for less serious applications, no one cares about rigor. It's just what happens to a field as it becomes more universally accessible.
  - mlthoughts2018 6y ago
    I don’t think this is true. Security primitives are more widely available in software libraries now than ever before, but you don’t see people believing they can read a “security for hackers” blog post and then roll their own encryption tool or secrets management tool and use it in production. More than ever, the distinction between a security professional and a layperson who read about RSA algorithms is hugely critical. It’s precisely the same with machine learning and statistical inference. The lower barrier to entry just means the danger of releasing extremely unsafe projects is much higher, and the careful validation from experts is that much more critical.
inetsee 6y ago
Does anybody know where I might find a copy of these resources that are readable without zooming to 300%? It's like trying to read a pdf on a phone.
stupidcar 6y ago
I'd echo one of their recommendations: Robert Mile's YouTube channel focussing on AI safety. He presents the material in a very interesting and accessible way. For example, this video on whether or not corporations can be considered a form of superintelligence: https://youtu.be/L5pUA3LsEaw https://youtu.be/L5pUA3LsEaw
- longtom 6y ago
  Another great channel is Yannic Kilcher, especially the "papers explained" series: https://www.youtube.com/c/YannicKilcher/playlists https://www.youtube.com/c/YannicKilcher/playlists
godelski 6y ago
This is an odd list. While it includes various topics is includes things I would expect many to already be familiar with as well as educational/entertainment sources. While I love 3B1B, I don't understand how it belongs on a list like this. Similarly things like Lex's AI podcast. The list also includes very beginner things. Maybe this could be ordered as in a way to progress through subjects or at least something better than alphabetical (and have a section for "entertainment" which would include things like 3B1B, Lex, Robert Miles, etc, which are useful but not hard literature). Additionally: Title should be "DeepMind AI __Resource__ List," many items here are not ones in which you can read.
- stevofolife 6y ago
  I wouldn't easily dismiss 3B1B, Lex or whatever resources you've mentioned here. Though you might find them to be entertainment but really people learn in various ways and these resources provide excellent views on the subject. And honestly with all the recent advancement in visual content and media, I would reconsider the sole dependence on hard literature to learn or even question the superiority of hard literature over newer formats. Lastly, I agree that the title should be resource list.

158 points by banjo_milkman 6y ago

raptortech 6y ago

Lots of excellent introductory sources here, but I was hoping for papers!

doublesCs 6y ago

Obvious submarine article / PR. The goal is to signal how concerned with ethics DeepMind is.

galimaufry 6y ago
This illustrates the problem with calling out virtue-signalling. I see two references related to ethics. It seems the only way to avoid accusations of virtue-signalling would be to have 0 references to AI fairness. In practice, comments like this encourage self-censorship, even if that is not their intention.
- mlthoughts2018 6y ago
  AI fairness is really, really bogus as a research field. Understanding and reducing negative impacts of bias is important, but the current research field of AI fairness does not do anything like that and has not yet reached any state of maturity where it can be considered a serious subset of research at all. I would really say even one mention of it on a list like this is purely to do virtue signalling. It’s the same for “explainability” of models too, another totally bogus field that gets treated as being worthy of attention or societal prioritization purely due to politics.
  - currymj 6y ago
    it is very strange to read someone claiming with extreme confidence that all fairness and explainability research is completely bogus. of course there are a lot of papers of questionable value, but that's a problem with machine learning more broadly -- probably all academic science, really. lots of institutions (banks, medicine, etc.) really do need explainable models. in practice this often means linear models with simple coefficients or shallow decision trees. there is plenty of useful work on learning these while maintaining performance, or "distilling" them from more complicated models. i know for a fact some explainable models learned with these techniques do actually get used in real life. likewise with fairness -- end-users actually do care about fairer models in all kinds of areas, especially lending and insurance. there's a ton of frustrating debate about how to operationalize fairness in different settings but it seems like there is actually progress on this front. what do you find to be bogus about these research areas?
    - mlthoughts2018 6y ago
      Linear models with simple coefficients can often be some of the least explainable models, particularly when the assumption of linearity breaks down. [0] is a good classic paper on this, demonstrating a simple example where coding error on the inputs leads to erroneous coefficient estimates that are both statistically significant and also of the wrong sign. Meaning, you would believe the coefficients reflect a real relationship between the covariate and the target, and even could claim it’s statistically significant, and yet the actual relationship to the target is of the opposite sign! Any further feature importance scoring based off the coefficient estimates would then become catastrophically misleading. Meanwhile, a model like support vector regression on the same data is capable of automatically handling the non-linearity, at the expense that there’s no more such thing as a coefficient breakdown in the linear space of input features. Does this make it less “explainable”? That would make zero sense. How can it be worse at explaining a data generating mechanism when it is better at predicting that same generating mechanism. What could it possibly mean to explain something you can’t predict or replicate? The field of “explainable” models doesn’t even make the slightest attempt to address this stuff - it just beats up on models that are arbitrarily labeled as “black boxes” (what does that mean?) If a given model X predicts or replicates a data generating process better than Y, then X explains the process better than Y, period. An analogy: Newtonian physics is not “more explainable than” quantum mechanics. Newtonian physics is just more wrong about how the world works than quantum mechanics. [0]: http://www.saramitchell.org/achen04.pdf http://www.saramitchell.org/achen04.pdf
      - currymj 6y ago
        i think "explainable model" literally means "a model you can explain to people". You may have to explain your decision to a judge or customer after the fact, or you might even be asking a layperson to actually compute predictions manually (as is sometimes done in psychology and medicine for things like triage). the question of whether or not the model provides a good explanation for the data-generating process is a distinct one. I think the Achen paper makes a good point that people cannot safely turn linear regression coefficients into stories about the world, although it doesn't seem like they've stopped trying. but assuming you have a way to validate that it makes good predictions (not an unreasonable assumption), an explainable model can be a useful thing to have.
  - longtom 6y ago
    I kinda agree that "fairness", even though a valid issue, seems receive a disproportional amount of attention. The central dangers, apart from misuse, seem to be a coordination problem and a control problem. The coordination problem is that AGI will likely lead to a winner-takes-all scenario, implying a per-emptive strike becomes rational once one player seems too far ahead of the game. The control problem is that the value function of an AGI may diverge from our own value function.

mlthoughts2018 6y ago

This doesn’t make much sense. If you are not already trained in vector calculus, functional analysis and basic classifier and regression algorithms, then most of these reading list items are completely inapplicable (or even dangerous, like when someone reads some blog posts about slapping together neural nets in Keras and suddenly thinks they can build a model suitable for production). On the other hand if you are trained in ML, most of these are not detailed or extended enough to give you anything useful. Doing “a hacker’s intro to X” over and over really, really doesn’t give you any skills. This is particularly true for deep domains like reinforcement learning, computer vision / image processing, and natural language processing. Meanwhile, basic design of experiments for A/B testing, explanatory modeling and simple regressions is a fraught area. Not understanding extremely rigorous details about hypothesis testing, model checking, limitations of statistical significance, etc., can lead to wildly incorrect inferences from poor models that non-experts will completely fail to detect. At best this list seems like “special topics to seem trendy in AI without getting deep / practical insight into any particular area.” There are one or two minor exceptions in the list.

etaioinshrdlu 6y ago
I think, like it or not, the end goal of the AI industry will result in people with less and less rigorous education creating models used in production. It may not be solid science, but it doesn't have to be. It will end up changing entire fields anyways. Especially for less serious applications, no one cares about rigor. It's just what happens to a field as it becomes more universally accessible.
- mlthoughts2018 6y ago
  I don’t think this is true. Security primitives are more widely available in software libraries now than ever before, but you don’t see people believing they can read a “security for hackers” blog post and then roll their own encryption tool or secrets management tool and use it in production. More than ever, the distinction between a security professional and a layperson who read about RSA algorithms is hugely critical. It’s precisely the same with machine learning and statistical inference. The lower barrier to entry just means the danger of releasing extremely unsafe projects is much higher, and the careful validation from experts is that much more critical.

inetsee 6y ago

Does anybody know where I might find a copy of these resources that are readable without zooming to 300%? It's like trying to read a pdf on a phone.

stupidcar 6y ago

I'd echo one of their recommendations: Robert Mile's YouTube channel focussing on AI safety. He presents the material in a very interesting and accessible way. For example, this video on whether or not corporations can be considered a form of superintelligence: https://youtu.be/L5pUA3LsEaw https://youtu.be/L5pUA3LsEaw

longtom 6y ago
Another great channel is Yannic Kilcher, especially the "papers explained" series: https://www.youtube.com/c/YannicKilcher/playlists https://www.youtube.com/c/YannicKilcher/playlists

godelski 6y ago

This is an odd list. While it includes various topics is includes things I would expect many to already be familiar with as well as educational/entertainment sources. While I love 3B1B, I don't understand how it belongs on a list like this. Similarly things like Lex's AI podcast. The list also includes very beginner things. Maybe this could be ordered as in a way to progress through subjects or at least something better than alphabetical (and have a section for "entertainment" which would include things like 3B1B, Lex, Robert Miles, etc, which are useful but not hard literature). Additionally: Title should be "DeepMind AI __Resource__ List," many items here are not ones in which you can read.

stevofolife 6y ago
I wouldn't easily dismiss 3B1B, Lex or whatever resources you've mentioned here. Though you might find them to be entertainment but really people learn in various ways and these resources provide excellent views on the subject. And honestly with all the recent advancement in visual content and media, I would reconsider the sole dependence on hard literature to learn or even question the superiority of hard literature over newer formats. Lastly, I agree that the title should be resource list.