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Comment by pgcj_poster | Hacker News Reader

I think the main difference between AI and traditional software is that the latter is actually useful in the real world.

The place where AI has had the most impact is image recognition—and that's cool, but that's a very small part of what we use computers for. As someone who spends almost every waking hour in front a computer, I don't think I use any software that employs AI for any useful purpose except maybe search engines—and I'm pretty sure that they were a lot better before they used AI. GPT-2 is neat, but there's literally no circumstance under which I would want to use GPT-2-generated text for anything except experiments or toys. And while I'm sure Google makes good use of AI for spying on people and recommending YouTube videos to turn my brother into a nazi, I'd much prefer if they didn't do that.

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I worked at an AI startup that pioneered modeling over inbound leads and Sales outcomes (most of this data is in Salesforce + Marketo/Pardot/Hubspot) and that application was hugely valuable for a few companies with massive inbound volume. Zendesk and New Relic were the poster child success stories, since they didn’t have to massively scale their sales development teams to call on every mom and pop / student exploring a trial. It highlighted the 5-10% of their volume that could actually yield real money very effectively.

Unfortunately, there’s not that many companies with that problem, and the market never materialized to the necessary degree and they sold the existing contracts and pivoted. That said, it was a home run for what it did.

If you find the right problem, AI can create massive value. The hard part is never the learning, it’s finding and framing the right problem.

Deep learning is mostly focused on image recognition, and classification on tabular data is way more common for smaller businesses.

Your activity online triggers 10s of AI models for every hour. AI is just not much of a consumer technology, but it is what the producers increasingly use.

GPT-2 is useful way way beyond text generation, it has very powerful embeddings useful for many other tasks. The text generation and cat picture classification is what pulled it into the mainstream: how you know about it, while not familiar with the particulars.

Stop putting the sole blame on recommendation algorithms for radicalization. Or paint everything with the same brush and start blaming Youtube too for radical thoughts on climate change, veganism, identity politics, etc.

I suspect your brother is not even a Nazi anyway, just has differing views on immigrants and black crime statistics. Let's hope some other Youtuber does not get recommended a video on how to punch a Nazi, as by your reasoning, people could not help themselves.

I guess it depends on how narrow one defines "AI". It sounds like you are defining it as "Deep Learning"... (maybe the article did as well, I didn't read it).

Busted! How did you find out about your brother? I thought we were acting in complete secrecy ...

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