I need to share with you an epiphany that occurred to me yesterday.
Have you heard of GPT-3? If not, I can tell you that it's a language model that has been showing up everywhere. Having been trained with a lot of data, it can generate text that people find impressive.
If you follow the hype, GPT-3 will revolutionize everything - people have been using it to generate plausible-looking creative fiction, pickup lines, SQL Queries that are sometimes wrong, trivia answers, tweets, and so on.
But you know what no one has generated yet, as far as I know? Something useful. Or even better: something that people always wanted but current technology cannot provide.
People are excited about GPT-3 because it promises to "just work" - you give it the right prompt and you get the right answer. This would solve all of those pesky problems associated with NLP such as "this search terms make no sense", "I hate knowledge bases", "That question has multiple answers", or "I don't want to manually write all possible answers for my system". But this is not what GPT-3 can do, because GPT-3 will not bend to your so-called facts and therefore will not do what you want. As Robert Dale puts it when talking about GPT-2: "driven as it is by information that is ultimately about language use, rather than directly about the real world, it roams untethered to the truth". In other words, people are excited about GPT-3 because they think it solves a different problem that the one it actually does.
If you want a chatbot to tell a patient that the solution to their depression is talking to a professional instead of GPT-3's suggestion that they should just go ahead and kill themselves, you need a way to constrain the system's output. This means that you still need to write the code that interprets the patient's problems, the code that chooses the right solution to that problem, and the code that says exactly what you want, no more and no less. And while turning structured data into human-readable sentences is a valid possible use for GPT-3, the amount of work required to constrain its output to an acceptable error level is comparable to the effort required to write smart templates that guarantee you'll generate exactly what you want.
And so, GPT-3 joins blockchain technology in being a solution searching for a problem. In fact, the parallels are kind of amazing: both technologies are hyped to the extreme, completely misunderstood by the general public, very expensive to run, and products based on them rarely make it out of the proof-of-concept stage.
I would like to leave you with two optimistic thoughts. First, I do think that it is only a matter of time before someone actually finds a good use for GPT-3. I predict it is going to be something marginal, with my best bet being something related to grammatical correctness. Abstractive summarization is also a good candidate, but my faith is lower because inserting unrelated facts is simultaneously what abstractive summarization tries to avoid and what GPT-3 does best.
And second, I want to let you know that there's a great business opportunity here. The blockchain craze reached the point where simply putting "blockchain" in your company name is enough to make your stock price rise by 289 percent. Therefore, if my prediction is correct then all you need to do is either name your own company "GPT-3" or invest in someone else doing it. Sure, their stock will probably tank once investors realize they invested for the wrong reasons, but by then you will have hopefully cashed out and moved on to something else.
Disclaimer: I am not an investment banker, this post does not constitute financial advice, I don't know why anyone would listen to me, and you shouldn't follow advice you find on random blog posts anyway.