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Comment by ken | Hacker News Reader

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.

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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.

> 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.

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