The other day I was noticing how many things out there tout to be using AI to solve things today and that’s a great thing. That gave me flashbacks of when I started at Amazon, in 2004. Back then I was just finishing my Ph.D. in a machine learning area, dealing with feature extraction on graph-structured databases, keeping a “fond” eye for the future of the web, the Semantic Web, where computers would be able to interact with the web as well as humans and that would be the turning point for what we’ve imagined as AI back then.
But we didn’t call it AI. AI wasn’t seen as a very good term. We talked about expert systems, or sometimes we did mention machine learning. But AI evoked two negative reactions:
- From the general population, AI was like 2001’s HAL 9000 or the Terminator, a robot/computer that was going to kill us all.
- For the research, and especially professional population, AI was that dream that people had in the past that just didn’t work. But “expert systems” were showing some great results (i.e. very bounded applications for algorithms initially developed, or inspired by algorithms developed for this “AI” field).
That even caused some awkward relationships at work. My manager then was a former IBM guy that was working on the AI team at IBM. A lot of people around me disregarded some of the work that we were doing because it was based on the ideas of this “AI guy”, so it wasn’t going to work.
Fast-forward to today and AI is everywhere. And now things have changed in perspective:
- From the general population, the biggest fear is not that it will kill us all, but that it will get rid of all jobs. A way more sensible fear based on things that go beyond Hollywood.
- In the research field, there is still some reluctance to call AI, as we are not at the “AI” imaged in the 80s and 90s, the one that failed miserably. We are just on a little bit less specific “expert systems” (or really “expert systems” that can learn from different applications too, but still applied to specific applications).
- On the professional field, people want to say that they do AI to say that they are at the edge of R&D and they are not going to be one of those companies that are going to be replaced by other company’s AIs. Yes, just like humans are afraid that they are going to be replaced by AIs, companies are afraid of the same risk.
So I think we are at a more sensible place. I personally don’t like that people are calling now any machine learning system as AI, but maybe that just softens the expectation of where we are going, making us forget a little bit that goal of this human-like machine that can out-think us. I can live with that!