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I'm increasingly concerned that the impact of ML is going to be limited. This sounds laughable at face value. And it is: ML has impacted my own life in a few wa

I'm increasingly concerned that the impact of ML is going to be limited. This sounds laughable at face value. And it is: ML has impacted my own life in a few ways, from being able to generate endless video game music (https://soundcloud.com/theshawwn/sets/ai-generated-videogame-music https://soundcloud.com/theshawwn/sets/ai-generated-videogame...) to... well. Thus my point: I can't think of ways it's seriously impacted my life, other than being an interesting challenge to pursue. As someone on the forefront of ML, you would expect me to be in a position to reap the benefits. It's possible I am incompetent. But I often wonder what we're doing, chasing gradients and batchnorms while training classifiers to generate photos of lemurs wearing suits. I try not to dwell on it too much, since I truly love the work for its own sake. But one must wonder what the endgame is. The models of consequence are locked up by companies and held behind an API. The rest are nothing more than interesting diversions. I've been reading some history of math and science, and it seems like many of the big discoveries were made from people pursuing the work for its own sake. Feynman loved physics long before physics became world-changing. But if physics never lead to the creation of the bomb, would it have been so prestigious? We seem to be lauding ML with the same accolades as physics during the postwar period. And I can't help but wonder when it will wear off. ML will be a fine tool for massive corporations, though, for endless reasons. But I was hoping for a more personal impact with the work. Something like, being able to enable a blind person to use a computer in a new way, or... something more than memes and amusement. Perhaps doing the work for its own sake is enough.

superbcarrot 6y ago
> I'm increasingly concerned that the impact of ML is going to be limited. Of course, just like any other technology. What else could be the case? I don't see this as a point of concern. Is it overhyped - yes (salespeople gonna sell); is it still useful in a number of applications - also yes.
- username90 6y ago
  I think the main problem is that people call it a neural network. They are nothing like neurons. Neurons are lifeforms in their own right and are as advanced as this guy: https://www.reddit.com/r/NatureIsFuckingLit/comments/jed2vd/vicious_microscopic_hunter_the_singlecell/ https://www.reddit.com/r/NatureIsFuckingLit/comments/jed2vd/... Now, what about neural networks? Well, you replace that guy with a one liner math function and call it a day. Now consider that you have billions of guys like that in your head, way more than any of our simplified neural networks, and it becomes very clear how far we are from getting anywhere. They are really important, they decide who to connect to, where and when to send signals etc. Neural networks doesn't model neurons, just the network hoping that the neuron wasn't important.
  - api 6y ago
    I studied biology and point this kind of thing out all the time. It usually gets dismissed by people who have not studied biology with a lot of hand waving. Everything you say is true, and more. There is another kind of cell in the brain that outnumbers neurons about ten to one. They’re called glial cells and they come in numerous forms. We used to think they were just support cells but more recently started to find ways they are involved in computation. Here is one link: https://en.m.wikipedia.org/wiki/Tripartite_synapse https://en.m.wikipedia.org/wiki/Tripartite_synapse The computational role they have is unclear so far (unless there is more recent stuff I am not aware of) but they are involved. We are nowhere near the human brain. I think it will take at least a few more orders of magnitude plus much additional understanding. GPT-3 only looks amazing to us because we are easily fooled by bullshit. It impresses us for the same reason millions now follow a cult called Qanon based on a series of vague shitposts. This stuff glitters to our brains like shiny objects for raccoons. What this stuff does show is that generating coherent and syntactically correct but meaningless language is much easier than our intuition would suggest: https://nbviewer.jupyter.org/gist/yoavg/d76121dfde2618422139 https://nbviewer.jupyter.org/gist/yoavg/d76121dfde2618422139 Those are extremely simple models and they already produce passable text. You could probably train it on a corpus of new age babble and create a cult with it. GPT-3 is just enormous compared to those models, so it’s not surprising to me that it bullshits very convincingly. Edit: forgot to mention the emerging subject of quantum biology. It’s looking increasingly plausible that quantum computation of some kind (probably very different from what we are building) happens in living systems. It would not shock me if it played a role in intelligence. The speed with which the brain can generalize suggests something capable of searching huge n-dimensional spaces with crazy efficiency. Keep in mind that the brain only consumes around 40-80 watts of power while doing this.
    - sedgjh23 6y ago
      You’ve made excellent points about why we shouldn’t be trying to emulate the human brain. I’m sure, collectively, we can come up with something better.
      - username90 6y ago
ganstyles 6y ago
My company has used ML to create synthetic cancer data to train classifiers to augment doctors/specialists who are looking for cancer. This work has greatly increased accuracy in diagnosis, saving lives. To say it's only for music generation or generating waifus is a bit unfair.
- sillysaurusx 6y ago
  That's actually wonderful to hear! Is there some way to assist with that work? Doing something with a human impact is appealing. (To be clear, my argument wasn't that ML isn't useful -- but rather that individual lone hackers are less likely to be using ML to achieve superman-type powers than I originally thought. Supermen do exist, but they are firmly in the ranks of DeepMind et al, and must pursue projects collectively rather than individually.)
  - yorwba 6y ago
    > individual lone hackers are less likely to be using ML to achieve superman-type powers than I originally thought For a single individual to have "superhuman" impact with ML, they need not only generic ML knowledge, but also specialized knowledge of some domain they want to impact. Actually, because ML has become so generic (just grab a pre-trained model, maybe fine-tune it, and push your data through it) a very shallow understanding of the fundamentals is probably enough, and in-depth domain knowledge much more important. That doesn't mean generic ML research isn't important, it's just that it has an average impact on everything, not a huge impact in one specific area. (I suspect many hobbyist ML projects are about generating entertaining content because everyone has experience with entertainment, even ML researchers.)
    - np_tedious 6y ago
      They also likely need a lot of training data and compute resources
    - counters 6y ago
      !00% nailed it. ML/AI are tools. As in any other exercise, tools can make it easier for beginners/amateurs to engage with a project, but they don't replace 10,000 hours of experience and deep domain experience and understanding. It's the master craftsmen and domain experts who will create the most value with these tools, but that may not be in obvious or clearly visible ways.
dgb23 6y ago
Much of current AI is concerned with optimization and automation. Not something we couldn’t do before, but faster and automated. This is exciting in the sense of being a force multiplier. But I agree with the sentiment that progress is often driven by intrinsic motivation.
ironchef 6y ago
I think it’s getting there... but I also think a lot of the impact are things we don’t see. Top of mind would be: * autonomous driving * language translation * salience mapping * fraud detection I guess I’m somewhat on the opposite side of the fence. I see it everywhere... although yes I think Big Bang things are probably a few years off
- username90 6y ago
  Autonomous driving isn't even ML, it uses ML to parse image feeds but the driving is human coded.
- Daho0n 6y ago
  >Big Bang things are probably a few years off As far as I have seen experts see autonomous driving as at least ten years away (unless you look at the sliding "next year" BS from Tesla) so I don't think we're only a few years off big changes because of ML. More like 10-15.
  - rakhodorkovsky 6y ago
    Could you point me to some of those expert takes on autonomous driving?
indigochill 6y ago
A girl I went to high school with went on to apply machine learning to identify human trafficking victims and ads for law enforcement. My perception of machine learning as a mere dabbler myself is that "machine learning" is just a sci-fi name for what's essentially applied statistics. In places where that is useful (e.g. clustering ads by feature similarity to highlight unclassified ads that appear similar to known trafficking ads), then machine learning is useful. It's not necessarily as one-size-fits-all as, say, networking or operating systems are, but in cases where you can identify a useful application of statistics, machine learning can be a useful tool.
- lupire 6y ago
  ML (neural nets) are useful because they automate much of parameter search. Instead of trying an SVM and then Random forests and then whatever, just throw all the data at a GPU and let it build an NN for you.
  - PartiallyTyped 6y ago
    NNs come with many caveats and lack the theoretical guarantees and support of SVMs and RFs. They should never be the first approach to solving a problem unless a) you have good reason to support the hypothesis that other models don't cut it such as a non stationary problem e.g. RL, or b) other methods can't scale to the difficulty of the problem. NNs are also very expensive to train compared to everything else.
  - hooande 6y ago
    It's a rare case when a neural net will give you materially different results than a random forest or an svm. ie, if an svm is 80% accurate, a well tuned neural net might be 83% accurate. sometimes that's a huge difference. in a domain like medical image classification, maybe not so much also you can just "throw all the data" at an svm or random forest, or any number of similar models. Automated parameter tuning can be convenient, but it's prone to overfitting and doesn't eliminate a lot of the actual work
- baxtr 6y ago
  > My perception of machine learning as a mere dabbler myself is that "machine learning" is just a sci-fi name for what's essentially applied statistics As a physicist I used to say that ML is simply non-linear tensor calculus. (I’m not sure if I’m right though)
WanderPanda 6y ago
To me this was google search response was mindblowing. I searched for it some time ago without the “springer” but I added it now, because some (good) reddit explanation is now the top result. But if you go to that springer page it is an endless technical document and google just found the perfect sentence out of it. Now THIS is a bicycle for the brain! If this is not amazing to you there is only one possible explanation for that (by the almighty himself): > The only thing I find more amazing than the rate of progress in AI is the rate in which we get accustomed to it - Ilya Sutskever [1] https://www.google.de/search?client=safari&hl=en-gb&biw=414&bih=622&sxsrf=ALeKk02R1oQ__HEAx3-tC4jbg8Pf8hjVEA%3A1612704394859&ei=iuofYNLvM5TzgQaS4YbwBQ&q=what%27s+the+difference+between+slam+and+visual+odometry+springer&oq=what%27s+the+difference+between+slam+and+visual+odometry+springer&gs_lcp=ChNtb2JpbGUtZ3dzLXdpei1zZXJwEAM6BAgAEEc6BQghEKABOggIIRAWEB0QHjoECB4QClCCO1jLSGCxSWgAcAF4AIABmgOIAYkQkgEHMi02LjEuMZgBAKABAcgBCMABAQ&sclient=mobile-gws-wiz-serp https://www.google.de/search?client=safari&hl=en-gb&biw=414&...
- fock 6y ago
  and yet the same machinery decided to translate the director of a movie as "direktor" in germany. I guess your quote applies there as well!
- sdflhasjd 6y ago
  I've seen similar infoboxes that are completely and outright wrong by taking excerpts out of context. For example, I searched for "How to do Y" The google infobox says "X" Clicking into the article to find the more explanation, and I read "X is never the right way to do Y, you should do Z instead" I've seen this happen enough that I literally cannot trust those snippets. A bit of an absurd, exaggerated example, Google search (or ask your Google Home) "Is the moon made of cheese"
  - WanderPanda 6y ago
    They seem to have fixed it. Google home answers properly. Sad, I was hoping for something funny :D
- Bakary 6y ago
n4r9 6y ago
I sympathise with your general point although as an aside I'm not sure this is accurate: > Feynman loved physics long before physics became world-changing. Feynman was an intensely practical person and learnt a lot about physics from e.g. fixing his neighbours' radios as a child. And radio is certainly something I'd class as "world-changing". He loved physics because of the things you could build and create, and did not enjoy abstraction or generality for its own sake. A better example for your argument might be Hardy, who explicitly stated that his love of number theory was partly due to its abstraction and uselessness. This was long before it had critical applications in cryptography.
bumbada 6y ago
You probably already know enough of machine learning and can study other topics in order to make ML more useful, for example: - Real marketing(people misunderstand marketing as sales and advertising) that is the study of people's needs -History, in particular History of inventors and inventions that really changed the world, like paper(also papyri and pergamine) Gutenberg press, crossing the Atlantic on a ship,electricity, bicycles and Ford cars, antibiotics, rockets, nuclear power, Internet. -Read books on innovation, like "The Innovator's Dilemma" that talks about most innovation having organic growth, looking so small at the beginning and then growing proportionally to itself. Almost everybody that cares about absolute things, like fame or fortune, ignoring it at first because the absolute value is so low. Once you do that you will literally "see the future". You will recognize patterns that happened in the past and are happening right now. And you will be able to invest or move to those areas with a great probability of success.
calebkaiser 6y ago
I also work in the field and while the work itself keeps me obsessed, similar to you, I keep a list of companies doing amazing things beyond "better ad personalization" so that I don't get a bit jaded. Some that might be interesting to you: PostEra - A medicinal chemistry platform that accelerates the compound development process in pharmaceuticals. They're running an open source moonshot for developing a compound for treating COVID, with tons of chemists around the world participating. Thorn - They use ML to identify child abuse content, both to help platforms filter it and to help law enforcement save children/arrest abusers. All of the healthcare startups (too many to list all), including Benevolent, Grail, Freenome, and more. Wildlife Protection Services - use ML to detect poachers in nature preserves around the world, and have already significantly increased the capture rate by rangers.
- pdimitar 6y ago
  Is there any open data that clearly demonstrates those companies have made a practical difference? Have their NNs been used at scale already?
  - calebkaiser 6y ago
    Yep, if you Google Thorn you'll find a bunch of material on how law enforcement uses them and related statistics. Several of the other companies (I admittedly haven't done a deep dive on all of them) have similarly accessible material.
- hooande 6y ago
  my issue is that none of these things represent new capacities that humans didn't have before. a spreadsheet will help you to identify and track child abuse content. you could do that using paper if you committed enough resources My hope for machine learning was that it would allow people to see patterns that could not be seen before. While this does happen, most practical problems are driven by a few very obvious indicators. ML can identify them with a high consistency and low cost. This is a useful tool (like a hammer) much more than a super power (like iron man's suit)
IdiocyInAction 6y ago
I think the applicability of ML, at least so far, is quite narrow. That's not to say that there are no applications; but rather, that it's not the universal AI tool that it is being portrayed as in the media. ML in practice seems to resemble a complex DSP-like step more than anything. It seems to be mostly used in classical DSP-like domains too (text, speech, images, etc.) It's a tool to handle complex, high-dimensional multi-media data. Though, models like GPT-3 and CLIP show some promise in being something beyond that. With the caveat of having billions of parameters...
- wombatmobile 6y ago
  I didn’t know how to search for this... I’m wondering if anyone has used GPT3 to power a bot that is a HN account?
smeeth 6y ago
>I can't think of ways it's seriously impacted my life, other than being an interesting challenge to pursue. What an odd framing. Are technologies only impactful if they result in a shiny new toy? Machine learning is a functional part of at least 50% of the tech I use daily. I typed this on my phone, so I unintentionally used machine learning (key-tap recognition) to reply to your comment about machine learning impact. Machine learning is a technology whose benefit is mostly in facilitating other technologies. In that way it’s more like the invention of plastic than the invention of the personal computer. Plastic has made other inventions lighter, more affordable, and cheaper. So too will ML, but I see where you’re coming from.
chiefalchemist 6y ago
> Thus my point: I can't think of ways it's seriously impacted my life, other than being an interesting challenge to pursue. Benefitted? Perhaps not. But you, me, we're all being impacted. The fact that it's not easily recognizable - intentionally, I might add - doesn't mean it isn't there. Truth be told, I'm approx half way through Zuboff's "The Age of Surveillance Capitalism." As for your hopes, she specifically makes mention of the fact that these tools are _not_ being used to solve big problems (e.g., poverty) but instead to harvest more and more data and exploit that as much as possible. Rest assured this imperative is by no means limited. https://www.publicaffairsbooks.com/titles/shoshana-zuboff/the-age-of-surveillance-capitalism/9781610395694/ https://www.publicaffairsbooks.com/titles/shoshana-zuboff/th... https://www.wnycstudios.org/podcasts/otm/segments/living-under-surveillance-capitalism-on-the-media https://www.wnycstudios.org/podcasts/otm/segments/living-und...
A12-B 6y ago
Machine Learning has literally partially solved protein folding. I used to be a denier too but there's no getting past this now.
dfilppi 6y ago
Perhaps ML is just a necessary building block to the traditional science fiction future.
aaron695 6y ago
There's a GPT-2 on HN which you've talked(replied) to. I guess, if a tree falls in the forest. But when there are hundreds of them, I feel like it will 'seriously impact'. They are exceedingly stupid.
- wombatmobile 6y ago
  Oh? Is there a GPT3 bot?
  - aaron695 6y ago
    GPT-3 is not very open. https://en.wikipedia.org/wiki/GPT-3 https://en.wikipedia.org/wiki/GPT-3 Related - https://liamp.substack.com/p/my-gpt-3-blog-got-26-thousand-visitors https://liamp.substack.com/p/my-gpt-3-blog-got-26-thousand-v...
jjcon 6y ago
> I'm increasingly concerned that the impact of ML is going to be limited. You say likely typing on a keyboard with ML predictive algorithms on it, or dictating with NLP speech to text. On a phone capable of recognizing your face, uploading photos to services that will recognize everything in them.
- username90 6y ago
  And that seems to be the limits of ML. We might eek out self driving cars, but I don't think we will get much more than that. It is pretty significant, but still limited compared to general purpose AI.
  - ChefboyOG 6y ago
    Predictive text and image classification are not the extent of ML's limits, even going by what is currently in production. Recommendation engines, ETA prediction, translation, drug discovery, medicinal chemistry, fraud detection—these are all areas where ML is already very important and present. Sure, it's not artificial general intelligence, but what technological invention in history would compare to the impact of AGI? That's sort of a weird bar.
  - jjcon 6y ago
    For the average person that is their greatest exposure - but were already seeing huge movements in medicine and defense and plenty of places where ML is not in average consumer use (the biggest applications are not for consumers). Add in your note on transportation and it is at the front of a huge section of the world economy. That’s all on track today - what will tomorrow’s innovations bring (the question asked by sci-fi)?
    - username90 6y ago
      Image recognition is just image recognition no matter where it is used.
      - jjcon 6y ago
blacklion 6y ago
Your life is heavily impacted by ML right now. Ask bank for loan? Your request is scored by ML-based system. Apply to some position via big HR agency? You CV is scored by ML system. Buy some tickets to flight (I understand, that it sound sad in 2021)? You go to airport, ML-based system recognize yur face and scans of your luggage and mark you (pray for this!) as harmless. Take some modern drug? It was selected for synthesis and tests by large ML system out of myriads other formulas (exactly what author of this essay says!). See this ad on Instagram or some page? ML-based AI decided to show it to you. And so on, and so on.
- deleted 6y ago
  [deleted]
laurent92 6y ago
ML has been key in finding the invaders of Congress. I’m more worried about ancestral issues about crime: The definition of what a « crime » is (hatecrime: verbally misgendering someone) and unequal application of law (one side being encouraged to commit $2bn damage, the other receiving condemnation of international presidents). It is just being leveraged to give more power to the powerful, enabling not the 1% but the 1‰.
YeGoblynQueenne 6y ago
>> As someone on the forefront of ML, you would expect me to be in a position to reap the benefits. Can you say how you are "someone at the forefront of ML"?
robotresearcher 6y ago
> I can't think of ways it's seriously impacted my life It’s there in a thousand little prosaic things, like trackpads, camera autofocus, credit applications, news feeds, movie recommendations, Amazon logistics optimization. You don’t feel it but it’s there, and the effects accumulate. It’s like robots: dishwashers and laundry washers are commonplace computer- and feedback-controlled mechatronics, ie. simple special-purpose robots. And almost everything you own was made partially with robots. But it doesn’t feel like the world’s full of robots.

superbcarrot 6y ago

> I'm increasingly concerned that the impact of ML is going to be limited. Of course, just like any other technology. What else could be the case? I don't see this as a point of concern. Is it overhyped - yes (salespeople gonna sell); is it still useful in a number of applications - also yes.

username90 6y ago
I think the main problem is that people call it a neural network. They are nothing like neurons. Neurons are lifeforms in their own right and are as advanced as this guy: https://www.reddit.com/r/NatureIsFuckingLit/comments/jed2vd/vicious_microscopic_hunter_the_singlecell/ https://www.reddit.com/r/NatureIsFuckingLit/comments/jed2vd/... Now, what about neural networks? Well, you replace that guy with a one liner math function and call it a day. Now consider that you have billions of guys like that in your head, way more than any of our simplified neural networks, and it becomes very clear how far we are from getting anywhere. They are really important, they decide who to connect to, where and when to send signals etc. Neural networks doesn't model neurons, just the network hoping that the neuron wasn't important.
- api 6y ago
  I studied biology and point this kind of thing out all the time. It usually gets dismissed by people who have not studied biology with a lot of hand waving. Everything you say is true, and more. There is another kind of cell in the brain that outnumbers neurons about ten to one. They’re called glial cells and they come in numerous forms. We used to think they were just support cells but more recently started to find ways they are involved in computation. Here is one link: https://en.m.wikipedia.org/wiki/Tripartite_synapse https://en.m.wikipedia.org/wiki/Tripartite_synapse The computational role they have is unclear so far (unless there is more recent stuff I am not aware of) but they are involved. We are nowhere near the human brain. I think it will take at least a few more orders of magnitude plus much additional understanding. GPT-3 only looks amazing to us because we are easily fooled by bullshit. It impresses us for the same reason millions now follow a cult called Qanon based on a series of vague shitposts. This stuff glitters to our brains like shiny objects for raccoons. What this stuff does show is that generating coherent and syntactically correct but meaningless language is much easier than our intuition would suggest: https://nbviewer.jupyter.org/gist/yoavg/d76121dfde2618422139 https://nbviewer.jupyter.org/gist/yoavg/d76121dfde2618422139 Those are extremely simple models and they already produce passable text. You could probably train it on a corpus of new age babble and create a cult with it. GPT-3 is just enormous compared to those models, so it’s not surprising to me that it bullshits very convincingly. Edit: forgot to mention the emerging subject of quantum biology. It’s looking increasingly plausible that quantum computation of some kind (probably very different from what we are building) happens in living systems. It would not shock me if it played a role in intelligence. The speed with which the brain can generalize suggests something capable of searching huge n-dimensional spaces with crazy efficiency. Keep in mind that the brain only consumes around 40-80 watts of power while doing this.
  - sedgjh23 6y ago
    You’ve made excellent points about why we shouldn’t be trying to emulate the human brain. I’m sure, collectively, we can come up with something better.
    - username90 6y ago

ganstyles 6y ago

My company has used ML to create synthetic cancer data to train classifiers to augment doctors/specialists who are looking for cancer. This work has greatly increased accuracy in diagnosis, saving lives. To say it's only for music generation or generating waifus is a bit unfair.

sillysaurusx 6y ago
That's actually wonderful to hear! Is there some way to assist with that work? Doing something with a human impact is appealing. (To be clear, my argument wasn't that ML isn't useful -- but rather that individual lone hackers are less likely to be using ML to achieve superman-type powers than I originally thought. Supermen do exist, but they are firmly in the ranks of DeepMind et al, and must pursue projects collectively rather than individually.)
- yorwba 6y ago
  > individual lone hackers are less likely to be using ML to achieve superman-type powers than I originally thought For a single individual to have "superhuman" impact with ML, they need not only generic ML knowledge, but also specialized knowledge of some domain they want to impact. Actually, because ML has become so generic (just grab a pre-trained model, maybe fine-tune it, and push your data through it) a very shallow understanding of the fundamentals is probably enough, and in-depth domain knowledge much more important. That doesn't mean generic ML research isn't important, it's just that it has an average impact on everything, not a huge impact in one specific area. (I suspect many hobbyist ML projects are about generating entertaining content because everyone has experience with entertainment, even ML researchers.)
  - np_tedious 6y ago
    They also likely need a lot of training data and compute resources
  - counters 6y ago
    !00% nailed it. ML/AI are tools. As in any other exercise, tools can make it easier for beginners/amateurs to engage with a project, but they don't replace 10,000 hours of experience and deep domain experience and understanding. It's the master craftsmen and domain experts who will create the most value with these tools, but that may not be in obvious or clearly visible ways.

dgb23 6y ago

Much of current AI is concerned with optimization and automation. Not something we couldn’t do before, but faster and automated. This is exciting in the sense of being a force multiplier. But I agree with the sentiment that progress is often driven by intrinsic motivation.

ironchef 6y ago

I think it’s getting there... but I also think a lot of the impact are things we don’t see. Top of mind would be: * autonomous driving * language translation * salience mapping * fraud detection I guess I’m somewhat on the opposite side of the fence. I see it everywhere... although yes I think Big Bang things are probably a few years off

username90 6y ago
Autonomous driving isn't even ML, it uses ML to parse image feeds but the driving is human coded.
Daho0n 6y ago
>Big Bang things are probably a few years off As far as I have seen experts see autonomous driving as at least ten years away (unless you look at the sliding "next year" BS from Tesla) so I don't think we're only a few years off big changes because of ML. More like 10-15.
- rakhodorkovsky 6y ago
  Could you point me to some of those expert takes on autonomous driving?

indigochill 6y ago

A girl I went to high school with went on to apply machine learning to identify human trafficking victims and ads for law enforcement. My perception of machine learning as a mere dabbler myself is that "machine learning" is just a sci-fi name for what's essentially applied statistics. In places where that is useful (e.g. clustering ads by feature similarity to highlight unclassified ads that appear similar to known trafficking ads), then machine learning is useful. It's not necessarily as one-size-fits-all as, say, networking or operating systems are, but in cases where you can identify a useful application of statistics, machine learning can be a useful tool.

lupire 6y ago
ML (neural nets) are useful because they automate much of parameter search. Instead of trying an SVM and then Random forests and then whatever, just throw all the data at a GPU and let it build an NN for you.
- PartiallyTyped 6y ago
  NNs come with many caveats and lack the theoretical guarantees and support of SVMs and RFs. They should never be the first approach to solving a problem unless a) you have good reason to support the hypothesis that other models don't cut it such as a non stationary problem e.g. RL, or b) other methods can't scale to the difficulty of the problem. NNs are also very expensive to train compared to everything else.
- hooande 6y ago
  It's a rare case when a neural net will give you materially different results than a random forest or an svm. ie, if an svm is 80% accurate, a well tuned neural net might be 83% accurate. sometimes that's a huge difference. in a domain like medical image classification, maybe not so much also you can just "throw all the data" at an svm or random forest, or any number of similar models. Automated parameter tuning can be convenient, but it's prone to overfitting and doesn't eliminate a lot of the actual work
baxtr 6y ago
> My perception of machine learning as a mere dabbler myself is that "machine learning" is just a sci-fi name for what's essentially applied statistics As a physicist I used to say that ML is simply non-linear tensor calculus. (I’m not sure if I’m right though)

WanderPanda 6y ago

To me this was google search response was mindblowing. I searched for it some time ago without the “springer” but I added it now, because some (good) reddit explanation is now the top result. But if you go to that springer page it is an endless technical document and google just found the perfect sentence out of it. Now THIS is a bicycle for the brain! If this is not amazing to you there is only one possible explanation for that (by the almighty himself): > The only thing I find more amazing than the rate of progress in AI is the rate in which we get accustomed to it - Ilya Sutskever [1] https://www.google.de/search?client=safari&hl=en-gb&biw=414&bih=622&sxsrf=ALeKk02R1oQ__HEAx3-tC4jbg8Pf8hjVEA%3A1612704394859&ei=iuofYNLvM5TzgQaS4YbwBQ&q=what%27s+the+difference+between+slam+and+visual+odometry+springer&oq=what%27s+the+difference+between+slam+and+visual+odometry+springer&gs_lcp=ChNtb2JpbGUtZ3dzLXdpei1zZXJwEAM6BAgAEEc6BQghEKABOggIIRAWEB0QHjoECB4QClCCO1jLSGCxSWgAcAF4AIABmgOIAYkQkgEHMi02LjEuMZgBAKABAcgBCMABAQ&sclient=mobile-gws-wiz-serp https://www.google.de/search?client=safari&hl=en-gb&biw=414&...

fock 6y ago
and yet the same machinery decided to translate the director of a movie as "direktor" in germany. I guess your quote applies there as well!
sdflhasjd 6y ago
I've seen similar infoboxes that are completely and outright wrong by taking excerpts out of context. For example, I searched for "How to do Y" The google infobox says "X" Clicking into the article to find the more explanation, and I read "X is never the right way to do Y, you should do Z instead" I've seen this happen enough that I literally cannot trust those snippets. A bit of an absurd, exaggerated example, Google search (or ask your Google Home) "Is the moon made of cheese"
- WanderPanda 6y ago
  They seem to have fixed it. Google home answers properly. Sad, I was hoping for something funny :D
Bakary 6y ago

n4r9 6y ago

I sympathise with your general point although as an aside I'm not sure this is accurate: > Feynman loved physics long before physics became world-changing. Feynman was an intensely practical person and learnt a lot about physics from e.g. fixing his neighbours' radios as a child. And radio is certainly something I'd class as "world-changing". He loved physics because of the things you could build and create, and did not enjoy abstraction or generality for its own sake. A better example for your argument might be Hardy, who explicitly stated that his love of number theory was partly due to its abstraction and uselessness. This was long before it had critical applications in cryptography.

bumbada 6y ago

You probably already know enough of machine learning and can study other topics in order to make ML more useful, for example: - Real marketing(people misunderstand marketing as sales and advertising) that is the study of people's needs -History, in particular History of inventors and inventions that really changed the world, like paper(also papyri and pergamine) Gutenberg press, crossing the Atlantic on a ship,electricity, bicycles and Ford cars, antibiotics, rockets, nuclear power, Internet. -Read books on innovation, like "The Innovator's Dilemma" that talks about most innovation having organic growth, looking so small at the beginning and then growing proportionally to itself. Almost everybody that cares about absolute things, like fame or fortune, ignoring it at first because the absolute value is so low. Once you do that you will literally "see the future". You will recognize patterns that happened in the past and are happening right now. And you will be able to invest or move to those areas with a great probability of success.

calebkaiser 6y ago

I also work in the field and while the work itself keeps me obsessed, similar to you, I keep a list of companies doing amazing things beyond "better ad personalization" so that I don't get a bit jaded. Some that might be interesting to you: PostEra - A medicinal chemistry platform that accelerates the compound development process in pharmaceuticals. They're running an open source moonshot for developing a compound for treating COVID, with tons of chemists around the world participating. Thorn - They use ML to identify child abuse content, both to help platforms filter it and to help law enforcement save children/arrest abusers. All of the healthcare startups (too many to list all), including Benevolent, Grail, Freenome, and more. Wildlife Protection Services - use ML to detect poachers in nature preserves around the world, and have already significantly increased the capture rate by rangers.

pdimitar 6y ago
Is there any open data that clearly demonstrates those companies have made a practical difference? Have their NNs been used at scale already?
- calebkaiser 6y ago
  Yep, if you Google Thorn you'll find a bunch of material on how law enforcement uses them and related statistics. Several of the other companies (I admittedly haven't done a deep dive on all of them) have similarly accessible material.
hooande 6y ago
my issue is that none of these things represent new capacities that humans didn't have before. a spreadsheet will help you to identify and track child abuse content. you could do that using paper if you committed enough resources My hope for machine learning was that it would allow people to see patterns that could not be seen before. While this does happen, most practical problems are driven by a few very obvious indicators. ML can identify them with a high consistency and low cost. This is a useful tool (like a hammer) much more than a super power (like iron man's suit)

IdiocyInAction 6y ago

I think the applicability of ML, at least so far, is quite narrow. That's not to say that there are no applications; but rather, that it's not the universal AI tool that it is being portrayed as in the media. ML in practice seems to resemble a complex DSP-like step more than anything. It seems to be mostly used in classical DSP-like domains too (text, speech, images, etc.) It's a tool to handle complex, high-dimensional multi-media data. Though, models like GPT-3 and CLIP show some promise in being something beyond that. With the caveat of having billions of parameters...

wombatmobile 6y ago
I didn’t know how to search for this... I’m wondering if anyone has used GPT3 to power a bot that is a HN account?

smeeth 6y ago

>I can't think of ways it's seriously impacted my life, other than being an interesting challenge to pursue. What an odd framing. Are technologies only impactful if they result in a shiny new toy? Machine learning is a functional part of at least 50% of the tech I use daily. I typed this on my phone, so I unintentionally used machine learning (key-tap recognition) to reply to your comment about machine learning impact. Machine learning is a technology whose benefit is mostly in facilitating other technologies. In that way it’s more like the invention of plastic than the invention of the personal computer. Plastic has made other inventions lighter, more affordable, and cheaper. So too will ML, but I see where you’re coming from.

chiefalchemist 6y ago

> Thus my point: I can't think of ways it's seriously impacted my life, other than being an interesting challenge to pursue. Benefitted? Perhaps not. But you, me, we're all being impacted. The fact that it's not easily recognizable - intentionally, I might add - doesn't mean it isn't there. Truth be told, I'm approx half way through Zuboff's "The Age of Surveillance Capitalism." As for your hopes, she specifically makes mention of the fact that these tools are _not_ being used to solve big problems (e.g., poverty) but instead to harvest more and more data and exploit that as much as possible. Rest assured this imperative is by no means limited. https://www.publicaffairsbooks.com/titles/shoshana-zuboff/the-age-of-surveillance-capitalism/9781610395694/ https://www.publicaffairsbooks.com/titles/shoshana-zuboff/th... https://www.wnycstudios.org/podcasts/otm/segments/living-under-surveillance-capitalism-on-the-media https://www.wnycstudios.org/podcasts/otm/segments/living-und...

A12-B 6y ago

Machine Learning has literally partially solved protein folding. I used to be a denier too but there's no getting past this now.

dfilppi 6y ago

Perhaps ML is just a necessary building block to the traditional science fiction future.

aaron695 6y ago

There's a GPT-2 on HN which you've talked(replied) to. I guess, if a tree falls in the forest. But when there are hundreds of them, I feel like it will 'seriously impact'. They are exceedingly stupid.

wombatmobile 6y ago
Oh? Is there a GPT3 bot?
- aaron695 6y ago
  GPT-3 is not very open. https://en.wikipedia.org/wiki/GPT-3 https://en.wikipedia.org/wiki/GPT-3 Related - https://liamp.substack.com/p/my-gpt-3-blog-got-26-thousand-visitors https://liamp.substack.com/p/my-gpt-3-blog-got-26-thousand-v...

jjcon 6y ago

> I'm increasingly concerned that the impact of ML is going to be limited. You say likely typing on a keyboard with ML predictive algorithms on it, or dictating with NLP speech to text. On a phone capable of recognizing your face, uploading photos to services that will recognize everything in them.

username90 6y ago
And that seems to be the limits of ML. We might eek out self driving cars, but I don't think we will get much more than that. It is pretty significant, but still limited compared to general purpose AI.
- ChefboyOG 6y ago
  Predictive text and image classification are not the extent of ML's limits, even going by what is currently in production. Recommendation engines, ETA prediction, translation, drug discovery, medicinal chemistry, fraud detection—these are all areas where ML is already very important and present. Sure, it's not artificial general intelligence, but what technological invention in history would compare to the impact of AGI? That's sort of a weird bar.
- jjcon 6y ago
  For the average person that is their greatest exposure - but were already seeing huge movements in medicine and defense and plenty of places where ML is not in average consumer use (the biggest applications are not for consumers). Add in your note on transportation and it is at the front of a huge section of the world economy. That’s all on track today - what will tomorrow’s innovations bring (the question asked by sci-fi)?
  - username90 6y ago
    Image recognition is just image recognition no matter where it is used.
    - jjcon 6y ago

blacklion 6y ago

Your life is heavily impacted by ML right now. Ask bank for loan? Your request is scored by ML-based system. Apply to some position via big HR agency? You CV is scored by ML system. Buy some tickets to flight (I understand, that it sound sad in 2021)? You go to airport, ML-based system recognize yur face and scans of your luggage and mark you (pray for this!) as harmless. Take some modern drug? It was selected for synthesis and tests by large ML system out of myriads other formulas (exactly what author of this essay says!). See this ad on Instagram or some page? ML-based AI decided to show it to you. And so on, and so on.

deleted 6y ago
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laurent92 6y ago

ML has been key in finding the invaders of Congress. I’m more worried about ancestral issues about crime: The definition of what a « crime » is (hatecrime: verbally misgendering someone) and unequal application of law (one side being encouraged to commit $2bn damage, the other receiving condemnation of international presidents). It is just being leveraged to give more power to the powerful, enabling not the 1% but the 1‰.

YeGoblynQueenne 6y ago

>> As someone on the forefront of ML, you would expect me to be in a position to reap the benefits. Can you say how you are "someone at the forefront of ML"?

robotresearcher 6y ago

> I can't think of ways it's seriously impacted my life It’s there in a thousand little prosaic things, like trackpads, camera autofocus, credit applications, news feeds, movie recommendations, Amazon logistics optimization. You don’t feel it but it’s there, and the effects accumulate. It’s like robots: dishwashers and laundry washers are commonplace computer- and feedback-controlled mechatronics, ie. simple special-purpose robots. And almost everything you own was made partially with robots. But it doesn’t feel like the world’s full of robots.