>> <a href="https://www.youtube.com/live/BY9KV8uCtj4?feature=share&t=1766">https://www.youtube.com/live/BY9KV8uCtj4?feature=share&t=176...</a><p>Here, Yann LeCun is saying that we don't have systems that can reason and plan.<p>That needs a qualifier: we don't have _statistical machine learning_ systems that can reason and plan. There are plenty of systems that can reason, and plenty of systems that can plan, and even some that can do both, but those systems are not statistical machine learning systems. Rather they are what is derisively dismissed as "Good Old-Fashioned, AI": they are classical, logic-based, symbolic systems, automated theorem provers and planners.<p>Reasoning, in particular, specifically deductive reasoning, is solved to a degree that cannot be surpassed: the Resolution principle is a sound and complete deductive reasoning system with a single inference rule that is easily run on a computer because of its simplicity, and because it is a single inference rule; while other sound and complete systems for deductive reasoning exist, they do not consist of a single rule and a human must be on hand to select the appropriate rule at each step of a proof. Or, they are just extremely expensive to run whereas Resolution, thanks to its One Simple Trick™ of unification can be executed efficiently.<p>As to planing, we have fast algorithms for planning today, as for all kinds of tree search, constraint optimisation, SAT-solving and the like, and those algorithms are routinely used in the industry; except of course they are no longer recognised as "artificial intelligence" <i>because</i> they are so common. The so-called "AI effect".<p>In any case, reasoning and planning, and many other tasks that were perfectly possible with classical AI are, for the time being, impossible with deep neural networks, as Yann LeCun (and not just anybody) says. We have regressed. In our passion to build ever better classifiers, we threw away the ability to reason.