There is fortunately a good amount of money being spread across different AI techniques still. Not from every funding source, but the traditional ones are still following the usual model of hedging their bets somewhat conservatively. It's mostly industry, and closely industry-aligned nonprofits like OpenAI that are going 100% all-in on nothing but deep networks. But those kinds of groups have always been a bit short-term driven and susceptible to hype; in the '80s they were putting all their money into expert systems.
If you look at which AI projects the National Science Foundation is funding (or in Europe, the Horizon 2020 program), it's a lot more diverse. Even just in machine learning they're not putting all their eggs into the deep neural nets basket, with considerable funding going to the other major areas of ML (e.g. Bayesian methods). Symbolic methods have a decent amount of funding too, including some explicitly "cognitive systems" grants. Some other symbolic techniques are still funded but not as much by "AI" bodies, e.g. the logic-based branch of AI now gets a decent amount of its funding from the software engineering community, because verification is a big application of solver / theorem-prover techniques.
Attending this past year's AAAI in Phoenix was kind of funny in that respect. Maybe 95% of the consultants and recruiters there were solely interested in hiring people to tweak deep networks. But the scientific side of the conference didn't look quite like that.
If you look at which AI projects the National Science Foundation is funding (or in Europe, the Horizon 2020 program), it's a lot more diverse. Even just in machine learning they're not putting all their eggs into the deep neural nets basket, with considerable funding going to the other major areas of ML (e.g. Bayesian methods). Symbolic methods have a decent amount of funding too, including some explicitly "cognitive systems" grants. Some other symbolic techniques are still funded but not as much by "AI" bodies, e.g. the logic-based branch of AI now gets a decent amount of its funding from the software engineering community, because verification is a big application of solver / theorem-prover techniques.
Attending this past year's AAAI in Phoenix was kind of funny in that respect. Maybe 95% of the consultants and recruiters there were solely interested in hiring people to tweak deep networks. But the scientific side of the conference didn't look quite like that.