The speaker discusses the need for researchers to change their mindset and recognize that accuracy improvements require significant team effort, as opposed to just having a great corpus of annotated data. These efforts include the feedback phase and asking what success looks like in terms of optimization functions.
In the future, the work of machine learning systems will be to better understand and encode the semantics of the world for more accurate interpretation of input values and transform them into desired outputs.
The feedback mechanism of the brain allows us to constantly hallucinate how the world should be based on our internal model which is constructed through the feedforward and feedback connections.
The reason people click on things on the internet is that it makes them laugh. Empirical evidence shows optimization works on most problems, although the neural network can't correctly identify a complicated image.
The episode discusses how algorithmic systems infer information from user behavior, including their patterns of watching videos, to categorize them and provide them with specific recommendations or demonetize them if they are deemed harmful content.
The podcast discusses the challenges of building an AI system that consumers will want to talk to for an extended period of time, like friends, as opposed to simply using for quick requests like checking the weather. They explore why it is difficult to imagine exactly what this system will look like in the future.