But that doesn't mean that they'll solve more problems, or solve them better. It just means that if some expensive developers are competing on the market with a cheap GPU cluster, it's obvious who will win. At some point I imagine they will stop competing for solving the same problems, things are just not that mature yet.
I mean, it HAS to be accurate. But once it can replicate the accuracy of humans (which isn't 100% in many use cases), what I demand from an AI is that it removes the need for day to day maintenance.
This post focuses entirely on where AI research is going, and indeed we need the bread and butter of human intelligence to get us there; but from a business perspective, what I'm trying to do is outsource my non-core business units (and some core ones) to someplace called AI, that upgrades what companies used to do when they outsourced to India: a good enough alternative that becomes cheaper.
I think about robotics as an example of a problem domain that seems resistant to pure-learning approaches. I'm thinking about the whole perception-planning-execution problem, in which a map of the world, and a notion of a goal, are key modeling objects.
It's really hard to think about how a robotic system that doesn't maintain a map could work intelligently to accomplish navigation goals, for instance. A map seems to imply some kind of model. And if the world (the map) isn't static, the modeling problem seems to get even more urgent.
For navigation with less map input, see the work of Oussama Khatib. A single nav goal is not a problem. A sequence of nav goals is where the planner adds value.
Pure Sense-Plan-Act has long since been abandoned. Brooks' paper "Elephants Don't Play Chess" is a fun and easy read, and even though old, is still on the must-read list if you want to think about these problems.
These days the "three layer architecture" is common in some form: classic controllers close to the hardware, a reactive layer, and a deliberative layer. If you squint your eyes the typical ROS-based robot is more-or-less three-layer. The default nav stack has a reactive Khatib-style layer executing nav sub-goals dropped into it from a planner that does map searches.
Pure world-modelling is too slow and error-prone in the short term. Pure reactive is unwieldy with complex sequences of sub-goals.
