1. The method of training in current use won't lead to general systems.
It doesn't matter how large your image set is you'll never learn all of language and motor behavior from it. (In realistic terms)
Multi-modal learning where an agent is interacting with an environment will be critical for general systems due to a common expectation that general systems will be able to explore and problem solve. The requirement to explore and problem solve needs to be built into training.
I go a bit further and believe that you need a fairly high-fidelity world in which physically modeled agents are given progressively more difficult tasks if you want to achieve this generalization.
As he notes at the end of the talk some variant of reinforcement learning will be key for this.
2. Disentangling representations will be useful for both generality and preventing catastrophic forgetting.
Current algorithms update all parameters by default which means that all knowledge is vulnerable to loss. If you can disentangle representations (and I disagree that this can't be done in pixel space) then you can selectively prevent updates to some parameters and better prevent accidental forgetting.
Concretely, mimicking the sparsity of activation seen in the brain through implementing lateral inhibition (and further exploring top-down attentional sparsification mechanisms) should be useful in this area. (some good papers on this in 2018!)
3. The ability for agents to learn from teachers and the ability to teach agents interactively will improve generality and learning speed.
Agents today are largely trained from scratch. Some are fine-tuned from other agents, but relatively few are instructed in the way you would a dog or a child. Cultural transmission of information underlies a lot of what we think of as modern human intelligence so it's likely that systems that can benefit from this method of skill acquisition will have an advantage.
A point not mentioned was the crucial distinction between knowledge and abilities which are innate to biological creatures (developed over evolutionary timescales) and knowledge gained during a single lifetime.
Right now we try to teach agents everything and don't give them the benefit of starting with evolved bodies, reflexes, and adapted perceptive abilities. So even before we get to the cultural transmission of knowledge through teaching I think we need to (in parallel) be developing base agents which are "evolved" with solutions to lower level problems built in so that the networks don't have to re-learn how to catch yourself when falling every single time.