6 ms·

You're wrong. Machine Learning is driving all of the ad placement on major ad platforms, personalization on all top social/media apps, search ranking for google

by m_ke 7y ago

You're wrong. Machine Learning is driving all of the ad placement on major ad platforms, personalization on all top social/media apps, search ranking for google, translation, speech recognition, finance, fraud detection. In the next decade it will start taking over computer graphics, medicine, manufacturing, surveillance, hardware / software and every other aspect of our lives. EDIT: for people who are downvoting me, here are some examples of how machine learning is and will transform our lives: 1. 3D graphics: https://www.youtube.com/watch?v=FlgLxSLsYWQ https://www.youtube.com/watch?v=FlgLxSLsYWQ Andrew Price doesn't have a technical background but does a decent job summarizing some of the ways deep learning will transform computer graphics. Soon all of the mocap, character design and animation will be driven by deep learning systems. 2. Computer Architecture and Traditional Software: https://www.youtube.com/watch?v=TTpKWOuzOxc https://www.youtube.com/watch?v=TTpKWOuzOxc There's a ton of recent research showing that you can use machine learning to beat human crafted heuristics in hardware, scheduling, compiler design and query planning.

chubot 7y ago
Machine learning finally came of age in the 2010s and is now table stakes for every tech company, large and small. I would say it's table stakes for large companies in certain areas, but not small (see my other reply). Machine learning seems to be good at squeezing percentage points out of monopoly with a business that already works. That's very different than "table stakes for small companies". Small companies need optimize their product with fundamental changes. For example, in the early days of AirBNB there was a more inconsistent user experience, which you heard about in the media. I think they addressed those problems with policies, incentives, and some elbow grease (kicking bad users off the platform, etc.). Not machine learning. Machine learning doesn't help you expand into Europe or Asia, etc.
cm2012 7y ago
This guy is downvoted but he's right. A huge amount of Facebook's and Google's value growth comes from machine learning driving their various algorithms.
eitland 7y ago
> Machine Learning is driving all of the ad placement on major ad platforms, personalization on all top social/media apps, search ranking for google, Yep, and the results are hilarious or tragic depending on how you look at it: We keep getting ads for the thing we bought yesterday, ads for dating sites after we got married and had kids (continously, for ten years despite my utter lack of interest), search results keep getting worse[0], obvious spammers keep on spamming in social media (seriously, it seems a simple regex filter could have done a better job to reduce crypto scamming in replies on Twitter than whatever was there last time I checked.) [0]: some people will always claim it is because black hat SEO is so much worse, but that doesn't explain why Google sometimes can neither understand doublequotes nor the verbatim option anymore. That is not the result of black hat SEO but of sloppy maintenance, and I guess so is a number of other problems.
- m_ke 7y ago
  > We keep getting ads for the thing we bought yesterday, ads for dating sites after we got married and had kids (continously, for ten years despite my utter lack of interest), search results keep getting worse[0], obvious spammers keep on spamming in social media (seriously, it seems a simple regex filter could have done a better job to reduce crypto scamming in replies on Twitter than whatever was there last time I checked.) That's because in a lot of cases they're optimizing for the wrong metrics, as in maximizing their revenue instead of your utility. There's way more content on the web than there was in the early 2000s, most of it in form of "content marketing" and explicitly attempting to game the system. If you look at recent results from TREC, it's pretty clear that machine learning provides a large boost over the traditional retrieval systems, on any metric that you want to optimize.
  - OnlineGladiator 7y ago
    > That's because in a lot of cases they're optimizing for the wrong metrics, as in maximizing their revenue instead of your utility. What? How do they maximize their revenue by spending money on ads the users actually laugh about for how bad their targeting is? Based on what I know about machine learning - it almost always gives great short-term results, and it almost always fails to deliver the expected long-term results. What's worse is this comes with the weirdest most indecipherable bugs that pop up more and more over time. Unless you have a large enough database to show statistical errors that are negligible (something like 99.9999% precision or recall, depending on your metrics), you should assume it will break in ways you cannot possibly predict. And even then, you might be using the wrong training data without even realizing it. I'm not saying ML is bad, although I am saying it is ridiculously overhyped. I'm saying ML is still nascent enough nobody really knows how a lot of edge cases will shake out, simply because there are too many edge cases to test before putting it into production. It's not hard to find examples of ML algorithms gone wrong even for sites like Amazon. https://gizmodo.com/amazon-prime-day-glitch-let-people-buy-13-000-camera-g-1836487919 https://gizmodo.com/amazon-prime-day-glitch-let-people-buy-1...
ken 7y ago
ML is certainly valuable for speech recognition, translation, and fraud detection, but those are somewhat niche applications. It's a long way from "every tech company, large and small". As for ad placement, social media, and search ranking: is that actually effective? The one constant with ads and social media and web search, IME, is that it just never gets any better. From the Lycos/AltaVista days to Google there was a huge jump in search result quality, and then it plateaued. The ads I see online are still laughably bad. They're more professionally produced than 10 or 20 years ago, but they're still never for any category of product that I'd ever buy. Targeted and tracked advertising seems like a big scam.
- m_ke 7y ago
  > ML is certainly valuable for speech recognition, translation, and fraud detection, but those are somewhat niche applications. It's a long way from "every tech company, large and small". Those were just some of the most visible examples. OCR, image recognition, NLP and speech recognition alone are enough to revolutionize most data entry jobs and we'll see a ton of startups using them to do just that for every industry. There are countless other examples of machine learning applications, including drug discovery, radiology, production line quality assurance, etc. > The ads I see online are still laughably bad. Have ads ever been good? You see the ads that you're seeing because someone is paying a lot of money to put them there. Bloomberg spent 120 million on ads last month, ad platforms had to find eyeballs for them.
  - hakfoo 7y ago
    IMO, you can get a pretty good ad experience by sticking to content-targeted ads. If I'm looking at, for example, "how to programmatically control my model railway with a Raspberry Pi", there's a large number of easy to target, relevant products you can advertise against that. You don't need a gigabyte of creepy backstory on me to figure out "hey, show me ads for the products mentioned in the article, and there's a good chance I'll buy them because it's convenient." Yeah, Mike Bloomberg might be willing to pay $1 to show his ugly mug next to that page, but the ad from a modeler's supply shop, who would only have paid 95 cents, would have felt much more appropriate to the consumer. I know some ad networks try to measure quality via clickthrough rate, but I'm not sure it was weighted in a way to nerf this issue. Another problem is that a pure-content model doesn't extend to all types of site. There's no suitable product to advertise against "six die in New Year's party gone horribly wrong." and nobody wanted to run low-yield CPM branding ads, so they had to backfill wth retargeting and profile-based ads instead.
agumonkey 7y ago
Any link on ml use in medicine ?
ekianjo 7y ago
> In the next decade it will start taking over computer graphics, medicine, manufacturing, surveillance, hardware / software and every other aspect of our lives. The counter-example to your point is that deep learning maniacs were predicting self driving to be a done deal by 2020 and we now realize we are so far away from that goal. Machine learning will only work well for one-pony tricks problems that can be clearly isolated. Nothing like "every other aspect of our lives".
smsm42 7y ago
> Machine Learning is driving all of the ad placement on major ad platforms And the results are, as far as I can see, horrible. I mean yes, if I make a mistake to search for shoes on Google, half of the internet will be showing me shoe ads for the next 6 months (how many feet do you think I have? Do you think I buy shoes in dozens?) - but presenting it as the triumph of ML is IMHO an overreach. > personalization on all top social/media apps From what I see, the same apps struggle to not get sued because of the said personalization regularly pushes sex content on kids, triggers on snowflakes and election interference on potential voters. Or at least that's what I hear every time next round of censorship is introduced into the social media. Somehow I am still not seeing a cause for celebration here. > search ranking for google Same as above, plus one shouldn't use Google search anyway. Use DuckDuckGo. > translation, speech recognition OK, here it got pretty good results, though sometimes it is as good as a very drunk chimp who got into a dictionary store, but in many other times it's decent. You get this one. > finance, fraud detection As a consumer, haven't noticed it. 100% of fraud on my cards have been detected by me reviewing my credit card statements. 100% of fraud alerts by banks have been false positives. I do not begrudge that specifically, I'd better have false positives than more fraud, but not seeing much ML-driven progress there tbh. > In the next decade it will start taking over computer graphics, medicine, manufacturing, surveillance, hardware / software and every other aspect of our lives. Not sure what "computer graphics" means, medicine probably not, surveillance maybe, but that's exactly the opposite of what I'd want, software definitely not even close, for the rest I'm not even sure what you're talking about. > here's a ton of recent research showing that you can use machine learning to beat human crafted heuristics in hardware, scheduling, compiler design and query planning. I'll believe it when I see ML-driven code generator doing something useful without tight supervision by humans. I can believe ML doing specific heuristics better (heck, doing exactly that has been part of my job recently) but there's a huge difference between "figure out exactly how much sugar makes the specific cookie recipe taste the best" and "invent whole cookie recipe from scratch and bake the cookie". I am sure ML would be useful for the former, for the latter... I'll believe it when I see it.
- redisman 7y ago
  > fraud detection Having worked in this industry for a while I just want to point out that the vast majority of fraud detection is done for the merchant, not the customer.
ksec 7y ago
I think people are downvoting you because of the word you are wrong, might be I disagree would be a better phase. I do think in general Machine learning is only just started with respect to All industry, not just tech. There is so much shown what could be done, the next 10 years will be very interesting.

by m_ke 7y ago

chubot 7y ago

Machine learning finally came of age in the 2010s and is now table stakes for every tech company, large and small. I would say it's table stakes for large companies in certain areas, but not small (see my other reply). Machine learning seems to be good at squeezing percentage points out of monopoly with a business that already works. That's very different than "table stakes for small companies". Small companies need optimize their product with fundamental changes. For example, in the early days of AirBNB there was a more inconsistent user experience, which you heard about in the media. I think they addressed those problems with policies, incentives, and some elbow grease (kicking bad users off the platform, etc.). Not machine learning. Machine learning doesn't help you expand into Europe or Asia, etc.

cm2012 7y ago

This guy is downvoted but he's right. A huge amount of Facebook's and Google's value growth comes from machine learning driving their various algorithms.

eitland 7y ago

> Machine Learning is driving all of the ad placement on major ad platforms, personalization on all top social/media apps, search ranking for google, Yep, and the results are hilarious or tragic depending on how you look at it: We keep getting ads for the thing we bought yesterday, ads for dating sites after we got married and had kids (continously, for ten years despite my utter lack of interest), search results keep getting worse[0], obvious spammers keep on spamming in social media (seriously, it seems a simple regex filter could have done a better job to reduce crypto scamming in replies on Twitter than whatever was there last time I checked.) [0]: some people will always claim it is because black hat SEO is so much worse, but that doesn't explain why Google sometimes can neither understand doublequotes nor the verbatim option anymore. That is not the result of black hat SEO but of sloppy maintenance, and I guess so is a number of other problems.

m_ke 7y ago
> We keep getting ads for the thing we bought yesterday, ads for dating sites after we got married and had kids (continously, for ten years despite my utter lack of interest), search results keep getting worse[0], obvious spammers keep on spamming in social media (seriously, it seems a simple regex filter could have done a better job to reduce crypto scamming in replies on Twitter than whatever was there last time I checked.) That's because in a lot of cases they're optimizing for the wrong metrics, as in maximizing their revenue instead of your utility. There's way more content on the web than there was in the early 2000s, most of it in form of "content marketing" and explicitly attempting to game the system. If you look at recent results from TREC, it's pretty clear that machine learning provides a large boost over the traditional retrieval systems, on any metric that you want to optimize.
- OnlineGladiator 7y ago
  > That's because in a lot of cases they're optimizing for the wrong metrics, as in maximizing their revenue instead of your utility. What? How do they maximize their revenue by spending money on ads the users actually laugh about for how bad their targeting is? Based on what I know about machine learning - it almost always gives great short-term results, and it almost always fails to deliver the expected long-term results. What's worse is this comes with the weirdest most indecipherable bugs that pop up more and more over time. Unless you have a large enough database to show statistical errors that are negligible (something like 99.9999% precision or recall, depending on your metrics), you should assume it will break in ways you cannot possibly predict. And even then, you might be using the wrong training data without even realizing it. I'm not saying ML is bad, although I am saying it is ridiculously overhyped. I'm saying ML is still nascent enough nobody really knows how a lot of edge cases will shake out, simply because there are too many edge cases to test before putting it into production. It's not hard to find examples of ML algorithms gone wrong even for sites like Amazon. https://gizmodo.com/amazon-prime-day-glitch-let-people-buy-13-000-camera-g-1836487919 https://gizmodo.com/amazon-prime-day-glitch-let-people-buy-1...

ken 7y ago

ML is certainly valuable for speech recognition, translation, and fraud detection, but those are somewhat niche applications. It's a long way from "every tech company, large and small". As for ad placement, social media, and search ranking: is that actually effective? The one constant with ads and social media and web search, IME, is that it just never gets any better. From the Lycos/AltaVista days to Google there was a huge jump in search result quality, and then it plateaued. The ads I see online are still laughably bad. They're more professionally produced than 10 or 20 years ago, but they're still never for any category of product that I'd ever buy. Targeted and tracked advertising seems like a big scam.

m_ke 7y ago
> ML is certainly valuable for speech recognition, translation, and fraud detection, but those are somewhat niche applications. It's a long way from "every tech company, large and small". Those were just some of the most visible examples. OCR, image recognition, NLP and speech recognition alone are enough to revolutionize most data entry jobs and we'll see a ton of startups using them to do just that for every industry. There are countless other examples of machine learning applications, including drug discovery, radiology, production line quality assurance, etc. > The ads I see online are still laughably bad. Have ads ever been good? You see the ads that you're seeing because someone is paying a lot of money to put them there. Bloomberg spent 120 million on ads last month, ad platforms had to find eyeballs for them.
- hakfoo 7y ago
  IMO, you can get a pretty good ad experience by sticking to content-targeted ads. If I'm looking at, for example, "how to programmatically control my model railway with a Raspberry Pi", there's a large number of easy to target, relevant products you can advertise against that. You don't need a gigabyte of creepy backstory on me to figure out "hey, show me ads for the products mentioned in the article, and there's a good chance I'll buy them because it's convenient." Yeah, Mike Bloomberg might be willing to pay $1 to show his ugly mug next to that page, but the ad from a modeler's supply shop, who would only have paid 95 cents, would have felt much more appropriate to the consumer. I know some ad networks try to measure quality via clickthrough rate, but I'm not sure it was weighted in a way to nerf this issue. Another problem is that a pure-content model doesn't extend to all types of site. There's no suitable product to advertise against "six die in New Year's party gone horribly wrong." and nobody wanted to run low-yield CPM branding ads, so they had to backfill wth retargeting and profile-based ads instead.

agumonkey 7y ago

Any link on ml use in medicine ?

ekianjo 7y ago

> In the next decade it will start taking over computer graphics, medicine, manufacturing, surveillance, hardware / software and every other aspect of our lives. The counter-example to your point is that deep learning maniacs were predicting self driving to be a done deal by 2020 and we now realize we are so far away from that goal. Machine learning will only work well for one-pony tricks problems that can be clearly isolated. Nothing like "every other aspect of our lives".

smsm42 7y ago

> Machine Learning is driving all of the ad placement on major ad platforms And the results are, as far as I can see, horrible. I mean yes, if I make a mistake to search for shoes on Google, half of the internet will be showing me shoe ads for the next 6 months (how many feet do you think I have? Do you think I buy shoes in dozens?) - but presenting it as the triumph of ML is IMHO an overreach. > personalization on all top social/media apps From what I see, the same apps struggle to not get sued because of the said personalization regularly pushes sex content on kids, triggers on snowflakes and election interference on potential voters. Or at least that's what I hear every time next round of censorship is introduced into the social media. Somehow I am still not seeing a cause for celebration here. > search ranking for google Same as above, plus one shouldn't use Google search anyway. Use DuckDuckGo. > translation, speech recognition OK, here it got pretty good results, though sometimes it is as good as a very drunk chimp who got into a dictionary store, but in many other times it's decent. You get this one. > finance, fraud detection As a consumer, haven't noticed it. 100% of fraud on my cards have been detected by me reviewing my credit card statements. 100% of fraud alerts by banks have been false positives. I do not begrudge that specifically, I'd better have false positives than more fraud, but not seeing much ML-driven progress there tbh. > In the next decade it will start taking over computer graphics, medicine, manufacturing, surveillance, hardware / software and every other aspect of our lives. Not sure what "computer graphics" means, medicine probably not, surveillance maybe, but that's exactly the opposite of what I'd want, software definitely not even close, for the rest I'm not even sure what you're talking about. > here's a ton of recent research showing that you can use machine learning to beat human crafted heuristics in hardware, scheduling, compiler design and query planning. I'll believe it when I see ML-driven code generator doing something useful without tight supervision by humans. I can believe ML doing specific heuristics better (heck, doing exactly that has been part of my job recently) but there's a huge difference between "figure out exactly how much sugar makes the specific cookie recipe taste the best" and "invent whole cookie recipe from scratch and bake the cookie". I am sure ML would be useful for the former, for the latter... I'll believe it when I see it.

redisman 7y ago
> fraud detection Having worked in this industry for a while I just want to point out that the vast majority of fraud detection is done for the merchant, not the customer.

ksec 7y ago

I think people are downvoting you because of the word you are wrong, might be I disagree would be a better phase. I do think in general Machine learning is only just started with respect to All industry, not just tech. There is so much shown what could be done, the next 10 years will be very interesting.