Next time you fly through a busy airport, think about the system which assigns planes to gates in realtime based on a large number of variable factors in order to maximize utilization and minimize waits. This is an expert system design in the 80's and which allowed a huge increase in the number of planes handled per day at the busiest airports.
Or when you drive your car, think about the lights-out factory that built-it, using robotics technologies developed in the 80's and 90's, and the freeways which largely operate without choke points again due to expert system models used by city planners.
These advances were just as revolutionary before, and people were just as excited about AI technologies eating the world. Still, it largely didn't happen. To continue the example of robotics, we don't have an equivalent of the Jetson's home robot Rosey. We can make a robot assemble a $50,000 car, but we can't get it to fold the laundry.
These rapid successes you see aren't literally "any problem from any field" -- it's specific problems chosen specifically for their likely ease in solving using current methods. DeepMind didn't decide to take on protein folding at random; they looked around and picked a problem that they thought they could solve. Don't expect them to have as much success on every problem they put their minds to.
No, machine learning is not trivially solving the hardest problems in every field. Not even close. In biomedicine, for example, protein folding is probably one of the easiest challenges. It's a hard problem, yes, but it's self-contained: given an amino acid sequence, predict the structure. Unlike, say, predicting the metabolism of a drug applied to a living system, which requires understanding an extremely dense network of existing metabolic pathways and their interdependencies on local cell function. There's no magic ML pixie dust that can make that hard problem go away.
Well, we can agree that world peace is off the table!
Beyond that, let's notice that expert systems did indeed change how airports and freeways work: They improved the areas where they solved problems. Deployment happened.
What we're seeing now is new classes of previously unsolvable problems falling. Deployment in medicine is known to be particularly hard, but not impossible. My read on the situation is that there have been a number of ML applications in the current round that have been kinda-successful 'in vitro' and failed in deployment. That doesn't mean that all deployments will fail.
Furthermore... Neil Lawrence points out that in most cases we change the world to fit new technologies. For example, mechanized tomato pickers suck, so we develop a more machine-resistant tomato. Cars break easily on dirt roads, so we pave half the planet. ML/AI somehow flips people's expectations of how technology works, and expect the algorithms to adapt to the world. This is almost certainly wrong.
"it's specific problems chosen specifically for their likely ease in solving using current methods. DeepMind didn't decide to take on protein folding at random; they looked around and picked a problem that they thought they could solve."
I'm actually not sure this is at all true. Protein folding is a long-standing grand challenge on which no current methods were working. My guess is that it was initially chosen for potential impact, and chased with more resources after some initial success.
Or when you drive your car, think about the lights-out factory that built-it, using robotics technologies developed in the 80's and 90's, and the freeways which largely operate without choke points again due to expert system models used by city planners.
These advances were just as revolutionary before, and people were just as excited about AI technologies eating the world. Still, it largely didn't happen. To continue the example of robotics, we don't have an equivalent of the Jetson's home robot Rosey. We can make a robot assemble a $50,000 car, but we can't get it to fold the laundry.
These rapid successes you see aren't literally "any problem from any field" -- it's specific problems chosen specifically for their likely ease in solving using current methods. DeepMind didn't decide to take on protein folding at random; they looked around and picked a problem that they thought they could solve. Don't expect them to have as much success on every problem they put their minds to.
No, machine learning is not trivially solving the hardest problems in every field. Not even close. In biomedicine, for example, protein folding is probably one of the easiest challenges. It's a hard problem, yes, but it's self-contained: given an amino acid sequence, predict the structure. Unlike, say, predicting the metabolism of a drug applied to a living system, which requires understanding an extremely dense network of existing metabolic pathways and their interdependencies on local cell function. There's no magic ML pixie dust that can make that hard problem go away.