Benedict Evans, whose post about leaving Twitter I featured last week, has written about AI tools such as ChatGPT from a product point of view.
He makes quite a few good points, not least that if you need ‘cheat sheets’ and guides on how to prompt LLMs effectively, then they’re not “natural language”.
Alexa and its imitators mostly failed to become much more than voice-activated speakers, clocks and light-switches, and the obvious reason they failed was that they only had half of the problem. The new machine learning meant that speech recognition and natural language processing were good enough to build a completely generalised and open input, but though you could ask anything, they could only actually answer 10 or 20 or 50 things, and each of those had to be built one by one, by hand, by someone at Amazon, Apple or Google. Alexa could only do cricket scores because someone at Amazon built a cricket scores module. Those answers were turned back into speech by machine learning, but the answers themselves had to be created by hand. Machine learning could do the input, but not the output.Source: Unbundling AI | Benedict Evans
LLMs solve this, theoretically, because, theoretically, you can now not just ask anything but get an answer to anything.
This is understandably intoxicating, but I think it brings us to two new problems - a science problem and a product problem. You can ask anything and the system will try to answer, but it might be wrong; and, even if it answers correctly, an answer might not be the right way to achieve your aim. That might be the bigger problem.
Right now, ChatGPT is very useful for writing code, brainstorming marketing ideas, producing rough drafts of text, and a few other things, but for a lot of other people it looks a bit like those PCs ads of the late 1970s that promised you could use it to organise recipes or balance your cheque book - it can do anything, but what?