25 ms·

Machine learning is fundamentally conservative

308 points by macawfish 7y ago

solipsism 7y ago
Why is the HN title so different from the article's title?
- grzm 7y ago
  (a) The submitter changed the title when submitting. (b) A mod changed the title after it was submitted. (c) The site changed the article title after it was submitted. I suspect (b), given the baity-ness of the article title: "Our Neophobic, Conservative AI Overlords Want Everything to Stay the Same", in accordance with the guidelines: > "please use the original title, unless it is misleading or linkbait; don't editorialize." https://news.ycombinator.com/newsguidelines.html https://news.ycombinator.com/newsguidelines.html If you think a title has been changed inappropriately (or should be changed), you can let the mods know directly using the Contact link in the footer.
  - lidHanteyk 7y ago
    Let the mods know publically, by posting comments. Don't trust the mods to be genuine in private conversations.
    - grzm 7y ago
      The mods don’t see every comment, so if the goal is to let the mods know, email is the most reliable way. Of course it’s up to them how to act on it. Just commenting isn’t going to improve anything, as they legitimately might not see it. It just increases the likelihood of inaction.
- macawfish 7y ago
  I took the quote from the article. The LA Review of Books Twitter account used the same quote when they shared this article there.
bordercases 7y ago
> Data analysis is as old as censuses of the tax collectors of antiquity — it’s as old as the Book of Numbers! — and it is unquestionably useful. But the idea that we should “treasure what we measure” and a reliance on unaccountable black boxes to tell us what we want and how to get it, has delivered to us automated systems of reaction and retreat in the guise of rationalism and progress. The question of what the technology does is important, but far more important is who it is doing it for and who it is doing it to. See "The Cult of Information" by Theodore Roszak.
- intuitionist 7y ago
  I think the idea of ML as “unaccountable black boxes” is a bit of a bait-and-switch from the problem being described. The problem is that ML has no political biases, it just minimizes some loss function on the data you give it. So it can’t correct for implicit biases in how that data was collected, or how the model’s outputs are used. If you fitted a decision tree to predict recidivism risk, it would be extremely easy to interpret. But if black men are rearrested more often in your dataset, then black men will likely have a higher predicted risk on average—no matter the causes of that feature of your dataset.
  - Iolaum 7y ago
    Your example demonstrates a political bias in a dataset that can lead to a biased ML model. This is what some people mean when they say ML can be biased.
    - strken 7y ago
      If I slap my friend Tom once a day, train an ML model to detect which of my friends is going to be slapped next, and then find out it always predicts Tom, the model isn't biased against Tom: the model is correctly showing me my own anti-Tom bias. I don't get to stand there and blame the model when I'm the one doing the slapping.
      - pelario 7y ago
xpe 7y ago
Sure, many of the examples ring true. However, I would refine the claim "Machine learning is fundamentally conservative" to say "Reducing a distribution of predictions to only the most likely prediction is conservative."
- jrumbut 7y ago
  Also similarity is conservative. There are alternatives. For a product/content recommendation you could include the product that's maximally different, the second most similar, you could exclude products within the same category, you could choose randomly and weight by conversion rate. We need more creativity and experimentation here I believe.
  - Pamar 7y ago
    * you could include the product that's maximally different, the second most similar, you could exclude products within the same category, you could choose randomly and weight by conversion rate* This is interesting. Has anyone tried that or knows of a website that uses this?
    - xpe 7y ago
      The exploration versus exploitation tradeoff is well studied in bandit algorithms and reinforcement learning in general.
    - jrumbut 7y ago
      I have personally implemented discarding the top-n (small n, like 3-5) most similar results in a recommendation system as well as adding a totally random item in the results but it was a small scale system. The top-n removal was a small but noticeable amount better. That was in part due to the specific content in the database as well as the audience, but it's a thing to try I believe.
  - goatlover 7y ago
    How would Amazon determine what's maximally different from a tv or pair of shoes? Is there a consensus way that humans determine maximum difference?
yeetard 7y ago
checkmate liberals
hacknat 7y ago
This is a problem in almost every academic field right now. Peoples’ lack of sophistication with understanding the mathematical/philosophical constraints of their tools is incredibly scary. For example, people throw around the low labor productivity stat all the time to prove that no automation is happening, not realizing that GDP is the numerator of that stat. Well, GDP is a pretty terrible gauge of who is benefitting from technological innovation, as it is distributed incredibly unevenly, probably more so than income even. The problem with automation isn’t that it’s not happening, it’s that only a very small number of people are capturing the wealth that it is generating. Also, it is generating a small number of professional jobs, but mostly the jobs it is generating suck (Uber driver, AMZN warehouse worker, etc).
- mnowicki 7y ago
  Yang gets it
- oars 7y ago
  That's why we need to vote in Andrew Yang.
- alexmingoia 7y ago
  Likewise, looking only at nominal wage increases is a “pretty terrible” way of gauging who benefits from increased wealth. If improvement in productivity (automation) results in lower prices (or better or more goods for the same price), that increases the purchasing power of a worker’s earnings, even if their wage has not risen. In other words, a worker is richer if they can buy more and better stuff even without any rise in their wage. The fact that everyone can afford a smartphone, including those with jobs that “suck”, does not reconcile with “only a very small number of people are capturing the wealth that it is generating.“
  - smackay 7y ago
    Sorry, but I see a lot of people, kids especially, with expensive smart phones because of status not because they can afford one.
api 7y ago
This headline alone seems like a shark jumping moment.
yarg 7y ago
And ... Tell me that you love me. NSFW: https://m.youtube.com/watch?v=KkJnd9rSAQ8 https://m.youtube.com/watch?v=KkJnd9rSAQ8
knzhou 7y ago
The article calls machine learning "conservative" because it only tells us what is, and not what should be. I don't think that's a useful framing. It's more accurate to say that, like all statistical techniques, machine learning is descriptive, not prescriptive. Not everything in the world has to be prescriptive.
- avs733 7y ago
  The article is not addressing what machine learning can and cannot do but how individuals and organizations conceptualize what machine learning can and cannot do. It, as the current top comment mentions, is a philosophical rather than technical argument. It doesn't matter if all machine learning can do is describe the current state of things of it is presumed by those interpreting the output to provide insight into some great and universal truth. Most science degree programs have a philosophy of science course as a degree requirement. As an engineering faculty member I have found huge resistance to and equivalent course becoming part of engineering or computer science degrees. Those degrees, instead, take an apparoch to scientific/technical knowledge that is less science and more religion. The comments here are a reminder why such courses have a place.
  - abdullahkhalids 7y ago
    It is not just a philosophy course which is missing from software engineering degrees. Software has real impact on individuals and societies that use them. Therefore, software engineers must necessarily be taught about the interface of psychology/sociology with software and the ethics surrounding this interface. The world would be a better place for it. Please read this thread https://twitter.com/yonatanzunger/status/975545527973462016 https://twitter.com/yonatanzunger/status/975545527973462016 "computer science is a field which hasn't yet encountered consequences."
- visarga 7y ago
  We generally want ML to imitate us (or human generated data), thus we teach it to do so. But there are also generative ML (like GANs) and simulation based ML (like AlphaGo) which can be more creative. There is nothing stoping us from letting agents evolve in a complex environment and be creative. It's just not commercially useful to do that yet. Doctorow writes like he doesn't understand much math behind ML, yet has strong opinions on it. Every time a random number is involved in the training process (stochastic selection of mini batches, noise injection, epsilon-greedy action selection), a form of exploration is taking place, finding novel paths of solving the problem at hand. The mixing of noise with data is paradoxically useful, even necessary in learning.
azinman2 7y ago
When you have a dumb machine that simply solves for maximum likelihood, using past data to predict the future is what you’re going to get. Why is this surprising? I don’t understand what he’s arguing for, exactly. That we should have AGI all of the sudden to understand intent, and detangle causation vs correlation? I don’t think anyone in the machine learning community would argue against that, but the question is how. What’s new here?
- inimino 7y ago
  Did you think this or the other works mentioned are written for the machine learning community? They are written so the rest of society can understand what is happening despite mountains of hype to the contrary and the marketing of ML as magic.
- onion2k 7y ago
  I don't think Cory is writing for the machine learning community. Cory is trying to inform people that AI and ML aren't a magical solution to all our wasteful systems because in some cases we need to throw away the system and start again, and AI can't tell us that. It can only tell us the best way to run the current system. That's what he means when he says AI is conservative. It's not surprising at all if you understand AI but most non-tech people don't.
  - azinman2 7y ago
    It has the same flavor generally of people who criticize something that’s hard but offer no solutions — a style that has become increasingly popular IMHO. This also isn’t a new angle for the non-informed audiences. It bears repeating for those audiences, but I’d wish he’d at least point to others who have already echoed such concerns.
    - onion2k 7y ago
      But... it's Cory Doctorow. He's not just some random writer; he's one of the best and most insightful technology commentators around.
reilly3000 7y ago
Conservative in this sense means something like 'resistant to deviation from established norms'. I think a lot of the headline-only readers take conservative to mean 'of the character of a specific political movement' which ironically seems more activistic than change resistant.
- TeMPOraL 7y ago
  Perhaps you could call it "integral controller" (like the I in "PID controller")? Because systems that have a memory behave like ones, and we are definitely in a feedback loop with those systems. And, from what I remember from my control theory classes, the integral part of a controller introduces lag, inertia, generally making the output more resistant to input changes. (Also note that the "non-conservative" Proportional and Derivative components in a PID by definition don't learn - they react to input and its change.)
  - reilly3000 7y ago
    That's a pretty interesting corollary! I suppose I could extrapolate that in such a system, too much change over too little time would invite a strong conservative response / lag to future input. Our 20 year old millennium has seen a tremendous rate of change in both technology and social norms. Perhaps politics has some similar inertial dynamics.
- TazeTSchnitzel 7y ago
  But isn't “resistant to deviation from established norms” at least partly what conservatism means politically?
  - dzamie 7y ago
    On paper, yes, but in practice, there is significant lag time between a new noem being established and conservatives giving up the old one in favor of the new normal. For example, being against gay marriage (as opposed to it being a non-issue) is still seen as the conservative opinion despite pro-gay-marriage being the plurality opinion for eight years and an outright majority for six[0]. Further, since economic deregulation is seen as the conservative stance, being in favor of abolishing the minimum wage labels someone as a conservative, despite it being nationally established for seven decades. [0] https://www.pewforum.org/fact-sheet/changing-attitudes-on-gay-marriage/ https://www.pewforum.org/fact-sheet/changing-attitudes-on-ga...
ALittleLight 7y ago
The author writes: >Machine learning systems are good at identifying cars that are similar to the cars they already know about. They’re also good at identifying faces that are similar to the faces they know about, which is why faces that are white and male are more reliably recognized by these systems — the systems are trained by the people who made them and the people in their circles. But this seems like an absurd claim. Surely it's more likely that darker skin reflects less light than paler skin and so features are harder to detect on darker skin people. This explains why facial recognition works better on white people than on black people. Likewise women are more likely to wear makeup than men are and therefore are more likely to have a visually different face which could cause problems for facial recognition. To assume that dark skinned people aren't involved in creating or testing facial recognition, or that women aren't, seems bizarre and vaguely racist/sexist. It's also a strange and I think poor assumption to think that the different accuracy rates for facial recognition on different demographics are due to a bunch of white dudes being too dumb to train their models on the faces of anyone but themselves. The author also writes about how recommendation systems are "conservative" but to me it actually seems the opposite. Recommendation systems get you to stuff that you haven't tried before. They take what they know about you and progress you to new content or products. For example, I used to listen to listen to a lot of country music on YouTube. Over time my recommendations evolved to include Irish folk music - which I quite like but would've never discovered without YouTube gradually testing and expanding my musical tastes.
- di4na 7y ago
  Do you have a harder time recognising black or women faces ? If you as human have no more problem, then your argument fail. Or explained otherwise: then don't use techniques that depends on lightning reflection. The fact you use techniques that needs high lightning reflection means that you only care about the result with white faces and/or only tested fitness with white faces.... which is the piece main argument.
  - jevgeni 7y ago
    Boy, our eyes sure are racist for using light reflection. Also, obviously the difference between the dynamic range of cameras and our eyes play a major part in technical difficulties in multi-racial facial recognition as well?
deleted 7y ago
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daenz 7y ago
He specifically used the example of predictive policing. I have a question: is it racist to send police patrolling areas of high crime, if those areas have a majority ethnic demographic? Should that information be discarded, and all places patrolled equally? Should they be patrolled less? It seems like there is no winning. You're either wasting resources, ignoring a problem, or being perceived as racist.
- Reelin 7y ago
  I suspect that there is indeed no winning, and that under such circumstances every possible course of action is likely to be perceived as racist by some number of people. This is because the population at large doesn't seem to be able to agree on what the word actually means (https://slatestarcodex.com/2017/06/21/against-murderism/ https://slatestarcodex.com/2017/06/21/against-murderism/). Regarding ML based predictive policing specifically, it seems like a spectacularly bad (but efficient and statistically effective!) idea to naively apply a machine learning based classification approach to such a problem.
- ben_w 7y ago
  It depends. Here are three different examples that would meet your description, with very different answers: 1) The X community has exactly the same, or lower, crime rates than the entire nation. However, nationwide anti-X sentiment means that despite this, most convicted criminals are from the X community. This makes it look like the regions where the X community live are high-crime regions. This is “accidentally racist”. Researchers know about this problem. 2) The X community is more prone to crime, and as this is purely an example, it just is and there’s no need to justify that. Extra police in this scenario is not racist, though I suspect anyone who jumps right in and assumes it to be true about the world might well be racist themselves. 3) A confounding variable, such as income or education, means that members of the X community are more likely to commit crimes than the general population, albeit it at the same rate as the equivalent income/education/confounding sunset of the general population. In this case, while it would not be racist to send in more police, it would totally be racist if politicians applied unequal effort to solve the confounding variable within the general population as compared specifically to group X.
- 7y ago
macawfish 7y ago
Here's the Molly Sauter essay that this article takes core inspiration from: https://reallifemag.com/instant-recall/ https://reallifemag.com/instant-recall/
jevgeni 7y ago
Doctorow is a smart guy, which is why the weasel words he uses seems disingenuous. For example "machine learning is fundamentally conservative, and it hates change". Mate, it doesn't hate anything. It's math.
- yipbub 7y ago
  He wasn't being literal...
  - jevgeni 7y ago
    So? He is still attaching emotional value to a technology. You could say the same thing with "machine learning doesn't handle change robustly". Same meaning, different emotion.
    - kick 7y ago
      Math isn't technology, and like you said yourself, AI is just math. Attaching emotional value to math is done incredibly frequently for all sorts of reasons. Attaching emotional value to technology is also fine. "Machine learning is fundamentally conservative" gets the point across better than "machine learning doesn't handle change robustly," which is vague and doesn't give a person looking for something to read any reason why that would matter.
      - jmmcd 7y ago
        "doesn't handle change robustly" is a good point. Doctorow doesn't just lack a good headline, he fails to really make this point. Data bias and the context-free nature of text auto-suggest are worth discussing, though.
      - jevgeni 7y ago
        > Attaching emotional value to math is done incredibly frequently for all sorts of reasons. Writing alarmist articles for clicks being one of those reasons. > Attaching emotional value to technology is also fine. Why??? Doing this with other things would be ridiculous. For example, instead of "The poor are disproportionately affected by climate change" a journalist could write "The climate hates the poor". Which subtly plays down the human cause in all of this. > "Machine learning is fundamentally conservative" gets the point across better It demonstrably doesn't. If you just read this thread, you'll find enough people discussing whether "conservative" has a political meaning or just a "deviation from the norm" meaning.
kitsuac 7y ago
It isn't /fundamentally/ conservative, it is just typically programmed to choose the most conservative (highest probability) predictions. You could integrate a liberal aspect by fuzzing the decision process to choose from lower probability predictions. More creativity, and ability to escape local minima, but at some cost when dealing with 'typical' cases and when making particularly damaging mispredictions.
- l0b0 7y ago
  I think the point is rather that you can't get a more useful prediction by choosing a lower probability description unless you have AGI. Only an AGI could tell that you're not in the mood for "Hey" to be followed by "darling", and only a superhuman AGI could realistically compensate for human bias in data sets.
  - kitsuac 7y ago
    Without AGI there are still cases when the lower probability prediction will be better, and will lead to escaping a local minima. I'd argue that the potential benefits of calibrating that axis dynamically exist with or without AGI.
    - ben_w 7y ago
      Are you describing the explore/exploit tradeoff or simulated annealing in this case?
- goto11 7y ago
  Fuzzing will just give you more random predictions, it doesn't remove any inherent biases in the training data.
jstewartmobile 7y ago
If I trained an AI to converse/analyze/flag using the western literary canon, it would probably end up racist/sexist/homophobic/anti-semitic/religious AF--just like Tay! I am truly grateful for this, for I believe that this is the only thing holding back our diversity/feminist/LGBTQ+/jewish/agnostic inclined silicon-valley elite from fully unleashing it upon us as a means of social control. Of course, it is only a matter of time before we build a large enough body of literature off of the new value systems... Some of us may live long enough to be thought-policed by something programmed--partially at least--by "Harry Potter," "Magic Mike," and "Twilight."
ramraj07 7y ago
While Cory is talking about a slightly different facet of conservatism, I found it quite ironic that he made a rather conservative statement himself: >Nor is machine learning likely to produce a reliable method of inferring intention: it’s a bedrock of anthropology that intention is unknowable without dialogue. As Cliff Geertz points out in his seminal 1973 essay, “Thick Description,” you cannot distinguish a “wink” (which means something) from a “twitch” (a meaningless reflex) without asking the person you’re observing which one it was. Was this mathematically proven? It's definitely an interesting statement, since a lot of "AI" systems try to predict intention and do a piss poor job of it, but to quote that the anthropological ancestors have proclaimed for eternity that a computer can never know even the slightest fraction of intention from just observation seems hypocritical.
- petters 7y ago
  Lots of these things have been written about AI. Kasparov wrote a decade ago that Poker would be out of reach from computers because bluffing was inherently human. That statement was easy to refute mathematically, but I don't know about Geertz. That seems more vague.
- jmmcd 7y ago
  This was one of the stupidest parts of a fairly weak essay. Of course you can distinguish a wink from a twitch without asking, otherwise we wouldn't use winks for surreptitious communication.
  - robotbikes 7y ago
    As an anthropologist you are in theory studying humans from out of a shared cultural context and it is easy to argue that a machine which hasn't even the slightest semblance of a shared cultural context would not be capable of distinguishing a twitch from a wink with 100% reliability. Not all human gestures are universal either so even if you trained your AI to read faces it would likely be trained on a small subset of human cultures and would produce varying results. In regards to the greater question of knowing intention I'd agree that algorithms do a much poorer job of guessing my intention than me directly communicating it and I am annoyed that so much of our on-line communication is filtered by algorithmic social engagement that an encourages shallow interactions.
fastball 7y ago
Can we stop trying to place complex topics and people and positions onto a one-dimensional scale?
zozbot234 7y ago
And I for one welcome our neophobic, conservative AI overlords! /s
sgt101 7y ago
There are systems of algorithmic inference that are not conservative. Constraint satisfaction and logical deduction can create novel insights.
- gyulai 7y ago
  ...also, as soon as you bring a network element or social element into it, it may no longer be this conservative/self-reinforcing thing. For example, if the algo behind spotify were to identify a "music taste clone" of yours somewhere in the world, they could present you with music you've never heard about that your clone has liked, and vice-versa. So you actually start discovering new stuff that you end up liking. Furthermore, there is a psychological element at play around mirroring back your own intelligence at you (see Eliza / Rogerian Psychotherapy) in a way that will lead you to new thought.
lowdose 7y ago
Conservative or is not yet full context aware?
loraa 7y ago
Logic over feelings.
krick 7y ago
Examples are good and on point, the conclusion is not. When he tries to frame it all in some grand political/quasi-philosophical manner, it becomes outright wrong and stupid, but I won't argue with that part, because it won't be useful to anyone. What I want to point out is that nothing he says should be attributed specifically to "machine learning". Machine learning is a set of techniques to make inferences from data automatically, but there is no implicit restriction on what the inferences should be. So machine learning is not "conservative" — almost all popular applications of it are. There is no inherent reason why an ML-algorithm should suggest the most similar videos to the ones you watched recently. The same way you can use (say) association learning to find most common item sets, you can use it to find the least common item sets with the given item, and recommend them instead. Or anything in between. But application designers usually choose the less creative option to recommend (understandably so) stuff similar to what you already got. Sometimes it's ok: if the most popular thing to ask somebody to sit on nowadays is "my face" it's only logical to advice that, I see nothing wrong with this application. But many internet shops indeed could benefit from considering what a user has already bought (from this very shop), because it isn't likely he will want to buy a second similar, but different TV anytime soon. Or, when recommending a movie, you could try to optimize for something different than "the most popular stuff watched by people that watched a lot of stuff you did watch" — which is a "safe" thing to recommend, but at the same time not really interesting. Of course, finding another sensible approach is a lot of work, but it doesn't mean there isn't one: maybe it could be "a movie with unusually high score given by somebody, who also highly rated a number of movies you rated higher than the average".
- 0x8BADF00D 7y ago
  I felt the same way about this essay’s conclusion. It’s like he took all of the substance out of Ted Kaczynski’s arguments and repackaged it as a diatribe against ML. Except it’s not convincing in the slightest.
- rytill 7y ago
  Thank you. “Conservativeness” is an inductive bias that is simple, often helpful, and more often used, but not fundamental.
- 7y ago
jonnypotty 7y ago
Teach AI using our behaviour, AI learns our behaviour. A bit like our children. I'm genuinely confused as to the alternative. The objection seems to be based on the falasy that technological progress equals social or political "progress". Why on earth would we expect AI descision making to display a lack of prejudice when human decision making is suffused with it. The only people who expect technology to act like a benevolent god are the ones who have replaced their god with it. All technological progress does is to increase the power and influence of human beings. The progress the writer seems to want is socio- political, not technological.
- TazeTSchnitzel 7y ago
  Maybe the writer should be understood here as criticising the hype around “AI”. AI is hyped as somehow allowing us to transcend our current approaches and find new patterns in the data, but what it really does is make us dig our heels deeper into our existing methodologies.
- MaxBarraclough 7y ago
  > Why on earth would we expect AI descision making to display a lack of prejudice when human decision making is suffused with it. Particularly when we consider what we mean by prejudice, which is presumably something like Making a decision on grounds which we deem it important to ignore. This is a very complex concept. It's a function of society, and changes with society. It's not something with a rigorous definition. Obvious example: reasonable modern people know it's indefensible to make an engineering hiring decision on the grounds of ethnicity, regardless of whether there are any correlations associated with ethnicity. This is even enshrined in law in many countries. To make a decision on the grounds of someone's qualifications and job experience, however, does not count as prejudice. We should expect a machine learning system to act as a correlation-seeker (that is after all what it is designed to do), without a nuanced understanding of what prejudice means. We've seen this issue crop up in the context of an AI having a say in parole decisions. [0] also relevant discussion at [1] [0] https://www.forbes.com/sites/bernardmarr/2019/01/29/3-steps-to-tackle-the-problem-of-bias-in-artificial-intelligence/ https://www.forbes.com/sites/bernardmarr/2019/01/29/3-steps-... [1] https://news.ycombinator.com/item?id=13156153 https://news.ycombinator.com/item?id=13156153
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anjc 7y ago
> Search for a refrigerator or a pair of shoes and they will follow you around the web as machine learning systems “re-target” you while you move from place to place, even after you’ve bought the fridge or the shoes > ... > This is what makes machine learning so toxic. I'm saddened to learn about our toxic contributions to society and can't wait to hear about alternative mind-reading approaches for fridge recommendation in the next article. Words like 'conservative' and 'toxic' are misleading because they imply that there are better alternatives that are not being chosen. Far better are the terms by commenters here, 'descriptive' and so on. That the article is not written for machine learning practitioners makes it even more misleading.
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hprotagonist 7y ago
Mom: If all your friends jumped off a bridge, would you, too? ML Algorithm: yes!!
- mirekrusin 7y ago
  Maybe bridge was on fire?
- TulliusCicero 7y ago
  If all my friends are jumping off a bridge, it's probably bungee jumping. I mean they could have all simultaneously gone insane, or be infested by alien mind parasites, but going bungee jumping is the much more likely reason.
- marcosdumay 7y ago
  https://www.xkcd.com/1170/ https://www.xkcd.com/1170/ It's not always that a XKCD is such a good answer.
at_a_remove 7y ago
I am not a fan of this article. I have seen many critiques in this vein, this is just another car in the train. None of them have quite reached the point of confronting what is bothering many: What will we do when machine learning (or science, or anything, really) comes to a conclusion we find unpalatable, for whatever reason? It could be any conclusion, not just those conservatives dislike. Using myself as a target, what if we eventually have enough sampling data to show that people of Irish extraction are more prone to alcoholism, and people of Scottish extraction tend to be statistically more thrifty? (This suggests that I would be a cheap drunk). How will we cope with that? It is true, ML is prone to some black-boxiness, but it could be any statistical extraction. We might very well use other methods besides ML to show the correlates once suggested. Will we simply put in a hard override to get the answers we want to get, the answers we find comfortable? History shows we have seen whole governments subscribe to this idea before. Ignore the results, publish what pleases. I've no easy answers here, but my guess is that history will repeat itself.
- moduspol 7y ago
  > What will we do when machine learning (or science, or anything, really) comes to a conclusion we find unpalatable, for whatever reason? What makes you think it hasn't already happened? Numerous times? Biological differences between sexes? IQ differences between differentiable subsets of people? The gender equality paradox? We already know what will happen when findings are unpalatable. We'll think of ways to explain why they don't matter and can't possibly be right, or that we weren't measuring the right thing in the first place. And these are just the more obvious cases!
  - bsanr2 7y ago
    >or that we weren't measuring the right thing in the first place. Which has been true, actually. For example, human facial recognition systems that can't properly distinguish dark-skinned faces. What is your conclusion in those cases? That dark-skinned people don't have faces? All look the same? Aren't human? Or is it more likely that the system is flawed in some way? Is that flaw in line with existing biases? What then is the chance that the flaw is BECAUSE of our existing biases, in some way or fashion? From what I've read on HN, many people involved in developing ML technology seem to be overly concerned with their systems spitting out "uncomfortable truths", and less concerned with flaws in their data capture, training, or system design processes. We're putting the cart before the horse, and the giddiness with which it's being done is troubling in and of itself.
rsync 7y ago
I am open-minded to the idea that "Machine Learning" is conservative in the manner Doctorow describes. However, I do wish we would not use the word "conservative" as an epithet. I think it's quite likely that "conservative" is exactly what we should be looking for in algorithms and prediction engines. The fact that their properties are misused and infused with reactionary (not necessarily conservative) biases by humans should not make us attach morally negative properties to being conservative. FWIW, my conservatism leads me to be suspicious of employing these tools in the first place ...
buboard 7y ago
the opposite would be also described as terrifying: machine learning making up the future of humanity. I m not sure if the author's point is particularly important. ML learns from the past, it doesn't have enough personality or 'intent' to be a 'conservative'. On average it will keep doing what people did. Also, not all ML systems do, a RL system may be trained with another objective, e.g. radicalism.
robsinatra 7y ago
Cory Doctorow is the kind of person who locks his car doors when a black person walks by while tweeting about social justice and fairness of algorithms
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AndrewKemendo 7y ago
Nor is machine learning likely to produce a reliable method of inferring intention: it’s a bedrock of anthropology that intention is unknowable without dialogue. As Cliff Geertz points out in his seminal 1973 essay, “Thick Description,” you cannot distinguish a “wink” (which means something) from a “twitch” (a meaningless reflex) without asking the person you’re observing which one it was. I see this and other examples of "explainability" from time to time as proof why humans are not a "Black Box." However it rests on two faulty assumptions. 1. The explanation will be truthful 2. The explainer can always reliably describe the actual cause of their own actions For the purposes of theory, you could explain away #1. However a minute of introspection will make you realize that you would fail at #2 the vast majority of the time - using story telling and retroactive explanations to explain your behavior.
naveen99 7y ago
i was hoping for a roast of AI similar to when he beat the semantic web into a pulp last time... now I wonder if the semantic web messed him up too much in revenge.
naveen99 7y ago
Alpha zero is not conservative at chess or go. It doesn’t have to have seen a position before to evaluate it. You can always train a model to reject a class just as easily as you train it to accept a class. So train it to reject a common class and accept a mutant and it will function more like an evolutionary algorithm that protects against bad luck like bacteria with antibiotics. I am way more optimistic for AI I guess.
YeGoblynQueenne 7y ago
>> Empiricism-washing is the top ideological dirty trick of technocrats everywhere: they assert that the data “doesn’t lie,” and thus all policy prescriptions based on data can be divorced from “politics” and relegated to the realm of “evidence.” Well data doesn't lie. Because data doesn't _say_ anything. People interpret data and they do so according to their own inherent biases. And if the data is already biased (i.e. gathered according to peoples' biases) its interpretations end up far, far away from any objective truth.

308 points by macawfish 7y ago

solipsism 7y ago

Why is the HN title so different from the article's title?

grzm 7y ago
(a) The submitter changed the title when submitting. (b) A mod changed the title after it was submitted. (c) The site changed the article title after it was submitted. I suspect (b), given the baity-ness of the article title: "Our Neophobic, Conservative AI Overlords Want Everything to Stay the Same", in accordance with the guidelines: > "please use the original title, unless it is misleading or linkbait; don't editorialize." https://news.ycombinator.com/newsguidelines.html https://news.ycombinator.com/newsguidelines.html If you think a title has been changed inappropriately (or should be changed), you can let the mods know directly using the Contact link in the footer.
- lidHanteyk 7y ago
  Let the mods know publically, by posting comments. Don't trust the mods to be genuine in private conversations.
  - grzm 7y ago
    The mods don’t see every comment, so if the goal is to let the mods know, email is the most reliable way. Of course it’s up to them how to act on it. Just commenting isn’t going to improve anything, as they legitimately might not see it. It just increases the likelihood of inaction.
macawfish 7y ago
I took the quote from the article. The LA Review of Books Twitter account used the same quote when they shared this article there.

bordercases 7y ago

> Data analysis is as old as censuses of the tax collectors of antiquity — it’s as old as the Book of Numbers! — and it is unquestionably useful. But the idea that we should “treasure what we measure” and a reliance on unaccountable black boxes to tell us what we want and how to get it, has delivered to us automated systems of reaction and retreat in the guise of rationalism and progress. The question of what the technology does is important, but far more important is who it is doing it for and who it is doing it to. See "The Cult of Information" by Theodore Roszak.

intuitionist 7y ago
I think the idea of ML as “unaccountable black boxes” is a bit of a bait-and-switch from the problem being described. The problem is that ML has no political biases, it just minimizes some loss function on the data you give it. So it can’t correct for implicit biases in how that data was collected, or how the model’s outputs are used. If you fitted a decision tree to predict recidivism risk, it would be extremely easy to interpret. But if black men are rearrested more often in your dataset, then black men will likely have a higher predicted risk on average—no matter the causes of that feature of your dataset.
- Iolaum 7y ago
  Your example demonstrates a political bias in a dataset that can lead to a biased ML model. This is what some people mean when they say ML can be biased.
  - strken 7y ago
    If I slap my friend Tom once a day, train an ML model to detect which of my friends is going to be slapped next, and then find out it always predicts Tom, the model isn't biased against Tom: the model is correctly showing me my own anti-Tom bias. I don't get to stand there and blame the model when I'm the one doing the slapping.
    - pelario 7y ago

xpe 7y ago

Sure, many of the examples ring true. However, I would refine the claim "Machine learning is fundamentally conservative" to say "Reducing a distribution of predictions to only the most likely prediction is conservative."

jrumbut 7y ago
Also similarity is conservative. There are alternatives. For a product/content recommendation you could include the product that's maximally different, the second most similar, you could exclude products within the same category, you could choose randomly and weight by conversion rate. We need more creativity and experimentation here I believe.
- Pamar 7y ago
  * you could include the product that's maximally different, the second most similar, you could exclude products within the same category, you could choose randomly and weight by conversion rate* This is interesting. Has anyone tried that or knows of a website that uses this?
  - xpe 7y ago
    The exploration versus exploitation tradeoff is well studied in bandit algorithms and reinforcement learning in general.
  - jrumbut 7y ago
    I have personally implemented discarding the top-n (small n, like 3-5) most similar results in a recommendation system as well as adding a totally random item in the results but it was a small scale system. The top-n removal was a small but noticeable amount better. That was in part due to the specific content in the database as well as the audience, but it's a thing to try I believe.
- goatlover 7y ago
  How would Amazon determine what's maximally different from a tv or pair of shoes? Is there a consensus way that humans determine maximum difference?

yeetard 7y ago

checkmate liberals

hacknat 7y ago

This is a problem in almost every academic field right now. Peoples’ lack of sophistication with understanding the mathematical/philosophical constraints of their tools is incredibly scary. For example, people throw around the low labor productivity stat all the time to prove that no automation is happening, not realizing that GDP is the numerator of that stat. Well, GDP is a pretty terrible gauge of who is benefitting from technological innovation, as it is distributed incredibly unevenly, probably more so than income even. The problem with automation isn’t that it’s not happening, it’s that only a very small number of people are capturing the wealth that it is generating. Also, it is generating a small number of professional jobs, but mostly the jobs it is generating suck (Uber driver, AMZN warehouse worker, etc).

mnowicki 7y ago
Yang gets it
oars 7y ago
That's why we need to vote in Andrew Yang.
alexmingoia 7y ago
Likewise, looking only at nominal wage increases is a “pretty terrible” way of gauging who benefits from increased wealth. If improvement in productivity (automation) results in lower prices (or better or more goods for the same price), that increases the purchasing power of a worker’s earnings, even if their wage has not risen. In other words, a worker is richer if they can buy more and better stuff even without any rise in their wage. The fact that everyone can afford a smartphone, including those with jobs that “suck”, does not reconcile with “only a very small number of people are capturing the wealth that it is generating.“
- smackay 7y ago
  Sorry, but I see a lot of people, kids especially, with expensive smart phones because of status not because they can afford one.

api 7y ago

This headline alone seems like a shark jumping moment.

yarg 7y ago

And ... Tell me that you love me. NSFW: https://m.youtube.com/watch?v=KkJnd9rSAQ8 https://m.youtube.com/watch?v=KkJnd9rSAQ8

knzhou 7y ago

The article calls machine learning "conservative" because it only tells us what is, and not what should be. I don't think that's a useful framing. It's more accurate to say that, like all statistical techniques, machine learning is descriptive, not prescriptive. Not everything in the world has to be prescriptive.

avs733 7y ago
The article is not addressing what machine learning can and cannot do but how individuals and organizations conceptualize what machine learning can and cannot do. It, as the current top comment mentions, is a philosophical rather than technical argument. It doesn't matter if all machine learning can do is describe the current state of things of it is presumed by those interpreting the output to provide insight into some great and universal truth. Most science degree programs have a philosophy of science course as a degree requirement. As an engineering faculty member I have found huge resistance to and equivalent course becoming part of engineering or computer science degrees. Those degrees, instead, take an apparoch to scientific/technical knowledge that is less science and more religion. The comments here are a reminder why such courses have a place.
- abdullahkhalids 7y ago
  It is not just a philosophy course which is missing from software engineering degrees. Software has real impact on individuals and societies that use them. Therefore, software engineers must necessarily be taught about the interface of psychology/sociology with software and the ethics surrounding this interface. The world would be a better place for it. Please read this thread https://twitter.com/yonatanzunger/status/975545527973462016 https://twitter.com/yonatanzunger/status/975545527973462016 "computer science is a field which hasn't yet encountered consequences."
visarga 7y ago
We generally want ML to imitate us (or human generated data), thus we teach it to do so. But there are also generative ML (like GANs) and simulation based ML (like AlphaGo) which can be more creative. There is nothing stoping us from letting agents evolve in a complex environment and be creative. It's just not commercially useful to do that yet. Doctorow writes like he doesn't understand much math behind ML, yet has strong opinions on it. Every time a random number is involved in the training process (stochastic selection of mini batches, noise injection, epsilon-greedy action selection), a form of exploration is taking place, finding novel paths of solving the problem at hand. The mixing of noise with data is paradoxically useful, even necessary in learning.

azinman2 7y ago

When you have a dumb machine that simply solves for maximum likelihood, using past data to predict the future is what you’re going to get. Why is this surprising? I don’t understand what he’s arguing for, exactly. That we should have AGI all of the sudden to understand intent, and detangle causation vs correlation? I don’t think anyone in the machine learning community would argue against that, but the question is how. What’s new here?

inimino 7y ago
Did you think this or the other works mentioned are written for the machine learning community? They are written so the rest of society can understand what is happening despite mountains of hype to the contrary and the marketing of ML as magic.
onion2k 7y ago
I don't think Cory is writing for the machine learning community. Cory is trying to inform people that AI and ML aren't a magical solution to all our wasteful systems because in some cases we need to throw away the system and start again, and AI can't tell us that. It can only tell us the best way to run the current system. That's what he means when he says AI is conservative. It's not surprising at all if you understand AI but most non-tech people don't.
- azinman2 7y ago
  It has the same flavor generally of people who criticize something that’s hard but offer no solutions — a style that has become increasingly popular IMHO. This also isn’t a new angle for the non-informed audiences. It bears repeating for those audiences, but I’d wish he’d at least point to others who have already echoed such concerns.
  - onion2k 7y ago
    But... it's Cory Doctorow. He's not just some random writer; he's one of the best and most insightful technology commentators around.

reilly3000 7y ago

Conservative in this sense means something like 'resistant to deviation from established norms'. I think a lot of the headline-only readers take conservative to mean 'of the character of a specific political movement' which ironically seems more activistic than change resistant.

TeMPOraL 7y ago
Perhaps you could call it "integral controller" (like the I in "PID controller")? Because systems that have a memory behave like ones, and we are definitely in a feedback loop with those systems. And, from what I remember from my control theory classes, the integral part of a controller introduces lag, inertia, generally making the output more resistant to input changes. (Also note that the "non-conservative" Proportional and Derivative components in a PID by definition don't learn - they react to input and its change.)
- reilly3000 7y ago
  That's a pretty interesting corollary! I suppose I could extrapolate that in such a system, too much change over too little time would invite a strong conservative response / lag to future input. Our 20 year old millennium has seen a tremendous rate of change in both technology and social norms. Perhaps politics has some similar inertial dynamics.
TazeTSchnitzel 7y ago
But isn't “resistant to deviation from established norms” at least partly what conservatism means politically?
- dzamie 7y ago
  On paper, yes, but in practice, there is significant lag time between a new noem being established and conservatives giving up the old one in favor of the new normal. For example, being against gay marriage (as opposed to it being a non-issue) is still seen as the conservative opinion despite pro-gay-marriage being the plurality opinion for eight years and an outright majority for six[0]. Further, since economic deregulation is seen as the conservative stance, being in favor of abolishing the minimum wage labels someone as a conservative, despite it being nationally established for seven decades. [0] https://www.pewforum.org/fact-sheet/changing-attitudes-on-gay-marriage/ https://www.pewforum.org/fact-sheet/changing-attitudes-on-ga...

ALittleLight 7y ago

The author writes: >Machine learning systems are good at identifying cars that are similar to the cars they already know about. They’re also good at identifying faces that are similar to the faces they know about, which is why faces that are white and male are more reliably recognized by these systems — the systems are trained by the people who made them and the people in their circles. But this seems like an absurd claim. Surely it's more likely that darker skin reflects less light than paler skin and so features are harder to detect on darker skin people. This explains why facial recognition works better on white people than on black people. Likewise women are more likely to wear makeup than men are and therefore are more likely to have a visually different face which could cause problems for facial recognition. To assume that dark skinned people aren't involved in creating or testing facial recognition, or that women aren't, seems bizarre and vaguely racist/sexist. It's also a strange and I think poor assumption to think that the different accuracy rates for facial recognition on different demographics are due to a bunch of white dudes being too dumb to train their models on the faces of anyone but themselves. The author also writes about how recommendation systems are "conservative" but to me it actually seems the opposite. Recommendation systems get you to stuff that you haven't tried before. They take what they know about you and progress you to new content or products. For example, I used to listen to listen to a lot of country music on YouTube. Over time my recommendations evolved to include Irish folk music - which I quite like but would've never discovered without YouTube gradually testing and expanding my musical tastes.

di4na 7y ago
Do you have a harder time recognising black or women faces ? If you as human have no more problem, then your argument fail. Or explained otherwise: then don't use techniques that depends on lightning reflection. The fact you use techniques that needs high lightning reflection means that you only care about the result with white faces and/or only tested fitness with white faces.... which is the piece main argument.
- jevgeni 7y ago
  Boy, our eyes sure are racist for using light reflection. Also, obviously the difference between the dynamic range of cameras and our eyes play a major part in technical difficulties in multi-racial facial recognition as well?

deleted 7y ago

[deleted]

daenz 7y ago

He specifically used the example of predictive policing. I have a question: is it racist to send police patrolling areas of high crime, if those areas have a majority ethnic demographic? Should that information be discarded, and all places patrolled equally? Should they be patrolled less? It seems like there is no winning. You're either wasting resources, ignoring a problem, or being perceived as racist.

Reelin 7y ago
I suspect that there is indeed no winning, and that under such circumstances every possible course of action is likely to be perceived as racist by some number of people. This is because the population at large doesn't seem to be able to agree on what the word actually means (https://slatestarcodex.com/2017/06/21/against-murderism/ https://slatestarcodex.com/2017/06/21/against-murderism/). Regarding ML based predictive policing specifically, it seems like a spectacularly bad (but efficient and statistically effective!) idea to naively apply a machine learning based classification approach to such a problem.
ben_w 7y ago
It depends. Here are three different examples that would meet your description, with very different answers: 1) The X community has exactly the same, or lower, crime rates than the entire nation. However, nationwide anti-X sentiment means that despite this, most convicted criminals are from the X community. This makes it look like the regions where the X community live are high-crime regions. This is “accidentally racist”. Researchers know about this problem. 2) The X community is more prone to crime, and as this is purely an example, it just is and there’s no need to justify that. Extra police in this scenario is not racist, though I suspect anyone who jumps right in and assumes it to be true about the world might well be racist themselves. 3) A confounding variable, such as income or education, means that members of the X community are more likely to commit crimes than the general population, albeit it at the same rate as the equivalent income/education/confounding sunset of the general population. In this case, while it would not be racist to send in more police, it would totally be racist if politicians applied unequal effort to solve the confounding variable within the general population as compared specifically to group X.
7y ago

macawfish 7y ago

Here's the Molly Sauter essay that this article takes core inspiration from: https://reallifemag.com/instant-recall/ https://reallifemag.com/instant-recall/

jevgeni 7y ago

Doctorow is a smart guy, which is why the weasel words he uses seems disingenuous. For example "machine learning is fundamentally conservative, and it hates change". Mate, it doesn't hate anything. It's math.

yipbub 7y ago
He wasn't being literal...
- jevgeni 7y ago
  So? He is still attaching emotional value to a technology. You could say the same thing with "machine learning doesn't handle change robustly". Same meaning, different emotion.
  - kick 7y ago
    Math isn't technology, and like you said yourself, AI is just math. Attaching emotional value to math is done incredibly frequently for all sorts of reasons. Attaching emotional value to technology is also fine. "Machine learning is fundamentally conservative" gets the point across better than "machine learning doesn't handle change robustly," which is vague and doesn't give a person looking for something to read any reason why that would matter.
    - jmmcd 7y ago
      "doesn't handle change robustly" is a good point. Doctorow doesn't just lack a good headline, he fails to really make this point. Data bias and the context-free nature of text auto-suggest are worth discussing, though.
    - jevgeni 7y ago
      > Attaching emotional value to math is done incredibly frequently for all sorts of reasons. Writing alarmist articles for clicks being one of those reasons. > Attaching emotional value to technology is also fine. Why??? Doing this with other things would be ridiculous. For example, instead of "The poor are disproportionately affected by climate change" a journalist could write "The climate hates the poor". Which subtly plays down the human cause in all of this. > "Machine learning is fundamentally conservative" gets the point across better It demonstrably doesn't. If you just read this thread, you'll find enough people discussing whether "conservative" has a political meaning or just a "deviation from the norm" meaning.

kitsuac 7y ago

It isn't /fundamentally/ conservative, it is just typically programmed to choose the most conservative (highest probability) predictions. You could integrate a liberal aspect by fuzzing the decision process to choose from lower probability predictions. More creativity, and ability to escape local minima, but at some cost when dealing with 'typical' cases and when making particularly damaging mispredictions.

l0b0 7y ago
I think the point is rather that you can't get a more useful prediction by choosing a lower probability description unless you have AGI. Only an AGI could tell that you're not in the mood for "Hey" to be followed by "darling", and only a superhuman AGI could realistically compensate for human bias in data sets.
- kitsuac 7y ago
  Without AGI there are still cases when the lower probability prediction will be better, and will lead to escaping a local minima. I'd argue that the potential benefits of calibrating that axis dynamically exist with or without AGI.
  - ben_w 7y ago
    Are you describing the explore/exploit tradeoff or simulated annealing in this case?
goto11 7y ago
Fuzzing will just give you more random predictions, it doesn't remove any inherent biases in the training data.

jstewartmobile 7y ago

If I trained an AI to converse/analyze/flag using the western literary canon, it would probably end up racist/sexist/homophobic/anti-semitic/religious AF--just like Tay! I am truly grateful for this, for I believe that this is the only thing holding back our diversity/feminist/LGBTQ+/jewish/agnostic inclined silicon-valley elite from fully unleashing it upon us as a means of social control. Of course, it is only a matter of time before we build a large enough body of literature off of the new value systems... Some of us may live long enough to be thought-policed by something programmed--partially at least--by "Harry Potter," "Magic Mike," and "Twilight."

ramraj07 7y ago

While Cory is talking about a slightly different facet of conservatism, I found it quite ironic that he made a rather conservative statement himself: >Nor is machine learning likely to produce a reliable method of inferring intention: it’s a bedrock of anthropology that intention is unknowable without dialogue. As Cliff Geertz points out in his seminal 1973 essay, “Thick Description,” you cannot distinguish a “wink” (which means something) from a “twitch” (a meaningless reflex) without asking the person you’re observing which one it was. Was this mathematically proven? It's definitely an interesting statement, since a lot of "AI" systems try to predict intention and do a piss poor job of it, but to quote that the anthropological ancestors have proclaimed for eternity that a computer can never know even the slightest fraction of intention from just observation seems hypocritical.

petters 7y ago
Lots of these things have been written about AI. Kasparov wrote a decade ago that Poker would be out of reach from computers because bluffing was inherently human. That statement was easy to refute mathematically, but I don't know about Geertz. That seems more vague.
jmmcd 7y ago
This was one of the stupidest parts of a fairly weak essay. Of course you can distinguish a wink from a twitch without asking, otherwise we wouldn't use winks for surreptitious communication.
- robotbikes 7y ago
  As an anthropologist you are in theory studying humans from out of a shared cultural context and it is easy to argue that a machine which hasn't even the slightest semblance of a shared cultural context would not be capable of distinguishing a twitch from a wink with 100% reliability. Not all human gestures are universal either so even if you trained your AI to read faces it would likely be trained on a small subset of human cultures and would produce varying results. In regards to the greater question of knowing intention I'd agree that algorithms do a much poorer job of guessing my intention than me directly communicating it and I am annoyed that so much of our on-line communication is filtered by algorithmic social engagement that an encourages shallow interactions.

fastball 7y ago

Can we stop trying to place complex topics and people and positions onto a one-dimensional scale?

zozbot234 7y ago

And I for one welcome our neophobic, conservative AI overlords! /s

sgt101 7y ago

There are systems of algorithmic inference that are not conservative. Constraint satisfaction and logical deduction can create novel insights.

gyulai 7y ago
...also, as soon as you bring a network element or social element into it, it may no longer be this conservative/self-reinforcing thing. For example, if the algo behind spotify were to identify a "music taste clone" of yours somewhere in the world, they could present you with music you've never heard about that your clone has liked, and vice-versa. So you actually start discovering new stuff that you end up liking. Furthermore, there is a psychological element at play around mirroring back your own intelligence at you (see Eliza / Rogerian Psychotherapy) in a way that will lead you to new thought.

lowdose 7y ago

Conservative or is not yet full context aware?

loraa 7y ago

Logic over feelings.

krick 7y ago

Examples are good and on point, the conclusion is not. When he tries to frame it all in some grand political/quasi-philosophical manner, it becomes outright wrong and stupid, but I won't argue with that part, because it won't be useful to anyone. What I want to point out is that nothing he says should be attributed specifically to "machine learning". Machine learning is a set of techniques to make inferences from data automatically, but there is no implicit restriction on what the inferences should be. So machine learning is not "conservative" — almost all popular applications of it are. There is no inherent reason why an ML-algorithm should suggest the most similar videos to the ones you watched recently. The same way you can use (say) association learning to find most common item sets, you can use it to find the least common item sets with the given item, and recommend them instead. Or anything in between. But application designers usually choose the less creative option to recommend (understandably so) stuff similar to what you already got. Sometimes it's ok: if the most popular thing to ask somebody to sit on nowadays is "my face" it's only logical to advice that, I see nothing wrong with this application. But many internet shops indeed could benefit from considering what a user has already bought (from this very shop), because it isn't likely he will want to buy a second similar, but different TV anytime soon. Or, when recommending a movie, you could try to optimize for something different than "the most popular stuff watched by people that watched a lot of stuff you did watch" — which is a "safe" thing to recommend, but at the same time not really interesting. Of course, finding another sensible approach is a lot of work, but it doesn't mean there isn't one: maybe it could be "a movie with unusually high score given by somebody, who also highly rated a number of movies you rated higher than the average".

0x8BADF00D 7y ago
I felt the same way about this essay’s conclusion. It’s like he took all of the substance out of Ted Kaczynski’s arguments and repackaged it as a diatribe against ML. Except it’s not convincing in the slightest.
rytill 7y ago
Thank you. “Conservativeness” is an inductive bias that is simple, often helpful, and more often used, but not fundamental.
7y ago

jonnypotty 7y ago

Teach AI using our behaviour, AI learns our behaviour. A bit like our children. I'm genuinely confused as to the alternative. The objection seems to be based on the falasy that technological progress equals social or political "progress". Why on earth would we expect AI descision making to display a lack of prejudice when human decision making is suffused with it. The only people who expect technology to act like a benevolent god are the ones who have replaced their god with it. All technological progress does is to increase the power and influence of human beings. The progress the writer seems to want is socio- political, not technological.

TazeTSchnitzel 7y ago
Maybe the writer should be understood here as criticising the hype around “AI”. AI is hyped as somehow allowing us to transcend our current approaches and find new patterns in the data, but what it really does is make us dig our heels deeper into our existing methodologies.
MaxBarraclough 7y ago
> Why on earth would we expect AI descision making to display a lack of prejudice when human decision making is suffused with it. Particularly when we consider what we mean by prejudice, which is presumably something like Making a decision on grounds which we deem it important to ignore. This is a very complex concept. It's a function of society, and changes with society. It's not something with a rigorous definition. Obvious example: reasonable modern people know it's indefensible to make an engineering hiring decision on the grounds of ethnicity, regardless of whether there are any correlations associated with ethnicity. This is even enshrined in law in many countries. To make a decision on the grounds of someone's qualifications and job experience, however, does not count as prejudice. We should expect a machine learning system to act as a correlation-seeker (that is after all what it is designed to do), without a nuanced understanding of what prejudice means. We've seen this issue crop up in the context of an AI having a say in parole decisions. [0] also relevant discussion at [1] [0] https://www.forbes.com/sites/bernardmarr/2019/01/29/3-steps-to-tackle-the-problem-of-bias-in-artificial-intelligence/ https://www.forbes.com/sites/bernardmarr/2019/01/29/3-steps-... [1] https://news.ycombinator.com/item?id=13156153 https://news.ycombinator.com/item?id=13156153

deleted 7y ago

[deleted]

anjc 7y ago

> Search for a refrigerator or a pair of shoes and they will follow you around the web as machine learning systems “re-target” you while you move from place to place, even after you’ve bought the fridge or the shoes > ... > This is what makes machine learning so toxic. I'm saddened to learn about our toxic contributions to society and can't wait to hear about alternative mind-reading approaches for fridge recommendation in the next article. Words like 'conservative' and 'toxic' are misleading because they imply that there are better alternatives that are not being chosen. Far better are the terms by commenters here, 'descriptive' and so on. That the article is not written for machine learning practitioners makes it even more misleading.

deleted 7y ago

[deleted]

hprotagonist 7y ago

Mom: If all your friends jumped off a bridge, would you, too? ML Algorithm: yes!!

mirekrusin 7y ago
Maybe bridge was on fire?
TulliusCicero 7y ago
If all my friends are jumping off a bridge, it's probably bungee jumping. I mean they could have all simultaneously gone insane, or be infested by alien mind parasites, but going bungee jumping is the much more likely reason.
marcosdumay 7y ago
https://www.xkcd.com/1170/ https://www.xkcd.com/1170/ It's not always that a XKCD is such a good answer.

at_a_remove 7y ago

I am not a fan of this article. I have seen many critiques in this vein, this is just another car in the train. None of them have quite reached the point of confronting what is bothering many: What will we do when machine learning (or science, or anything, really) comes to a conclusion we find unpalatable, for whatever reason? It could be any conclusion, not just those conservatives dislike. Using myself as a target, what if we eventually have enough sampling data to show that people of Irish extraction are more prone to alcoholism, and people of Scottish extraction tend to be statistically more thrifty? (This suggests that I would be a cheap drunk). How will we cope with that? It is true, ML is prone to some black-boxiness, but it could be any statistical extraction. We might very well use other methods besides ML to show the correlates once suggested. Will we simply put in a hard override to get the answers we want to get, the answers we find comfortable? History shows we have seen whole governments subscribe to this idea before. Ignore the results, publish what pleases. I've no easy answers here, but my guess is that history will repeat itself.

moduspol 7y ago
> What will we do when machine learning (or science, or anything, really) comes to a conclusion we find unpalatable, for whatever reason? What makes you think it hasn't already happened? Numerous times? Biological differences between sexes? IQ differences between differentiable subsets of people? The gender equality paradox? We already know what will happen when findings are unpalatable. We'll think of ways to explain why they don't matter and can't possibly be right, or that we weren't measuring the right thing in the first place. And these are just the more obvious cases!
- bsanr2 7y ago
  >or that we weren't measuring the right thing in the first place. Which has been true, actually. For example, human facial recognition systems that can't properly distinguish dark-skinned faces. What is your conclusion in those cases? That dark-skinned people don't have faces? All look the same? Aren't human? Or is it more likely that the system is flawed in some way? Is that flaw in line with existing biases? What then is the chance that the flaw is BECAUSE of our existing biases, in some way or fashion? From what I've read on HN, many people involved in developing ML technology seem to be overly concerned with their systems spitting out "uncomfortable truths", and less concerned with flaws in their data capture, training, or system design processes. We're putting the cart before the horse, and the giddiness with which it's being done is troubling in and of itself.

rsync 7y ago

I am open-minded to the idea that "Machine Learning" is conservative in the manner Doctorow describes. However, I do wish we would not use the word "conservative" as an epithet. I think it's quite likely that "conservative" is exactly what we should be looking for in algorithms and prediction engines. The fact that their properties are misused and infused with reactionary (not necessarily conservative) biases by humans should not make us attach morally negative properties to being conservative. FWIW, my conservatism leads me to be suspicious of employing these tools in the first place ...

buboard 7y ago

the opposite would be also described as terrifying: machine learning making up the future of humanity. I m not sure if the author's point is particularly important. ML learns from the past, it doesn't have enough personality or 'intent' to be a 'conservative'. On average it will keep doing what people did. Also, not all ML systems do, a RL system may be trained with another objective, e.g. radicalism.

robsinatra 7y ago

Cory Doctorow is the kind of person who locks his car doors when a black person walks by while tweeting about social justice and fairness of algorithms

deleted 7y ago

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AndrewKemendo 7y ago

Nor is machine learning likely to produce a reliable method of inferring intention: it’s a bedrock of anthropology that intention is unknowable without dialogue. As Cliff Geertz points out in his seminal 1973 essay, “Thick Description,” you cannot distinguish a “wink” (which means something) from a “twitch” (a meaningless reflex) without asking the person you’re observing which one it was. I see this and other examples of "explainability" from time to time as proof why humans are not a "Black Box." However it rests on two faulty assumptions. 1. The explanation will be truthful 2. The explainer can always reliably describe the actual cause of their own actions For the purposes of theory, you could explain away #1. However a minute of introspection will make you realize that you would fail at #2 the vast majority of the time - using story telling and retroactive explanations to explain your behavior.

naveen99 7y ago

i was hoping for a roast of AI similar to when he beat the semantic web into a pulp last time... now I wonder if the semantic web messed him up too much in revenge.

naveen99 7y ago

Alpha zero is not conservative at chess or go. It doesn’t have to have seen a position before to evaluate it. You can always train a model to reject a class just as easily as you train it to accept a class. So train it to reject a common class and accept a mutant and it will function more like an evolutionary algorithm that protects against bad luck like bacteria with antibiotics. I am way more optimistic for AI I guess.

YeGoblynQueenne 7y ago

>> Empiricism-washing is the top ideological dirty trick of technocrats everywhere: they assert that the data “doesn’t lie,” and thus all policy prescriptions based on data can be divorced from “politics” and relegated to the realm of “evidence.” Well data doesn't lie. Because data doesn't _say_ anything. People interpret data and they do so according to their own inherent biases. And if the data is already biased (i.e. gathered according to peoples' biases) its interpretations end up far, far away from any objective truth.