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> 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.

> 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.

> 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-1...

Recommending something you bought before is probably a much better bet than showing you random items from their huge catalogue.

You can have a system that generates them a ton of cash while making mistakes in some cases, outliers are inevitable and feedback loops in recommender systems can lead to such issues. Amazon wouldn't deploy these systems if they didn't move the needle.

Nobody is saying the ML algorithm (or any algorithm) is expected to be perfect.

> Amazon wouldn't deploy these systems if they didn't move the needle.

This is an appeal to authority that Amazon executives are immune to making mistakes. They could have bad metrics. They could have bad incentives encouraging managers to make poor decisions (basically this describes everything wrong with Google today). They could be incompetent. They could be focused on short-term gains at the expense of long-term gains because it maximizes their personal net wealth and they can just jump ship in a few years.

Again, nobody is saying ML algorithms are worthless. I just don't believe (and have lots of reasons based on personal experience I won't go into) that they are 10% as useful as the industry wants you to believe.

I'm saying that they have systems in place to measure the impact of the code that they put in production on their bottom line and if those recommendations didn't move their profit margins in the right direction they wouldn't be using them.

I know what you're saying. I don't think you understand what I'm saying, which is those systems can be wrong or they might not be optimizing for the bottom line. Why didn't their systems catch that bug that was discounting expensive equipment for more than 90% that I linked in my previous post? People make mistakes. And for a frame of reference, Google's corporate policies definitely don't optimize for the bottom line, and it took a minimum of 5 years for it to affect them negatively (I'd guess closer to 10+).

Anyway, agree to disagree.

> I'm saying that they have systems in place to measure the impact of the code that they put in production on their bottom line

There's a story, and I think it was (re)posted here recently about a series of MBAs all optimizing the cost of the burger bums by removing seeds until there are three seeds neatly laid out at the top.

It might be measurable all the way but at some point it becomes ridiculous. For me that time was some months ago. For the rest of Internet they might manage to reduce the quality once or twice more before it becomes obvious.

This is my way of fighting back. By posting here and on my blog and getting upvoted a lot for pointing out what many can already feel. By letting people who read HN know that yes, that feeling they have that a lot of their ads are wasted because of bad targeting might very well be true.

That's definitely true, maximizing shareholder value is all about extracting as much value as possible from your customers, until it no longer works.

I'm against most forms of recommendations as well, that doesn't mean that they're not valuable to the businesses deploying them. In most cases the end users of these systems are not the real customers of these platforms, and they're there to serve the advertisers who keep these businesses running.

> Recommending something you bought before is probably a much better bet

Not if it's something I only buy once a year, for example. That's where "learning" part should come in. You don't need any learning to just parrot me back my inputs.

> Amazon wouldn't deploy these systems if they didn't move the needle.

I don't know about Amazon, but I've recently read on HN some articles strongly suggesting almost nobody is properly measuring the impact of ads, let alone the impact of "targeting". In most cases, people more or less just stuff money into ads budgets, because that's what you do, and they get customers - because people still need to buy things, regardless of any targeting - but the casual link between the former and the latter is not really very well established.

> in maximizing their revenue instead of your utility.

I am not sure how these are contradictory. My interest is buying things I want. Their interest is selling me things I'd buy. How showing dating site ads to a married person promotes any of those? Where the revenue would come from? Or do you mean it's ad agency revenue, not advertiser revenue? In that case we clearly have a case of agent problem.

We keep getting ads for the thing we bought yesterday

This does not demonstrate that the machine learning algorithms are doing a bad job. Quite the opposite really. See:

https://twitter.com/patio11/status/875629380105416705

2% of households returning their refrigerators and buying a new one seems pretty high, although I don't have any data to back this up. How many people reading this have made multiple (separate) refrigerator purchases in a week?

With arbitrary assumptions about statistics, you can "statistically prove" pretty much anything. Until the 2% figure is substantiated, it's all baloney.

There's also the fact that advertisers spend many millions of dollars buying these ads, so they have a pretty large financial incentive to do it well.

You are assuming there's a way to do it well and not well, but what if nobody actually knows how it is - doing it well? What if marketing is just given a budget of X million dollars to spend on advertising, but nobody there actually knows whether doing X or Y works, but they know everybody does X, so if they do X, there's no chance they'd be fired for that?

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.

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.

> 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.

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.

Because the fault here doesn't lie with ML, but with errors of judgment made by humans when setting up the targeting demographics for their ads.

There's still a lot of anti-data sentiment in the paid ad industry, where media buyers will guide themselves by what they think their customer looks like, and not what data tells them it is.

And until this is a fixed behavior, you'll keep on seeing untargeted ads.

I cannot recall ever buying anything from any ad targeted at me through a website I visit. Am I a unique snowflake in that the AI always guesses wrong for just me, or is AI based advertising simply a total failure?

I have bought many things from ads related to the media I am reading. For example, I read muscle car magazines, and buy parts/tools from the ads in those magazines. Back in the era of print mags, my company would do well placing ads for compilers next to relevant programming articles.

> 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".

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.

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.

Any link on ml use in medicine ?

> 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.

> 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.

OK, that makes sense. Hopefully there's some progress there.

Machine learning is interesting in computer graphics for denoising path traced images för example. There also some uses in game developmemt where graphicsl artefacts might be detected during automated test runs instead of having an army of QA people.

Pretty sure ML have shown promising results in cancer classification in images (such as xray, etc). Not sure how this will pan out but the limited scope seems ideal .