What was unusual about Al's talk was his focus on cooperation between computers and humans to allow both to solve harder problems than they might be able to otherwise.
Starting at 8:30 in the talk, Al describes this as a "virtuous cycle" of improvement using people's interactions with an application, allowing optimizations and features like like learning to rank, personalization, and recommendations that might not be possible otherwise.
Later, around 33:20, he elaborates, saying we need "hybrid, not artificial, intelligence." Al explains, "It sure seems a lot easier ... when computers aren't trying to replace people but to help us in what we do. Seems like an easier problem .... [to] extend the capabilities of people."
Al goes on to say the most progress on very challenging problems (e.g. image recognition, voice-to-text, personalized education) will come from combining several independent, massive data sets with a feedback loop from people interacting with the system. It is an "increasingly fluid partnership between people and computation" that will help both solve problems neither could solve on their own.
This being a Google Research talk, there was much else covered, including the usual list of research papers out of Google, solicitation of students and faculty, pumping of Google as the best place to access big data and do research on big data, and a list of research challenges. The most interesting of the research challenges were robust, high performance, transparent data migration in response to load in massive clusters, ultra-low power computing (e.g. powered only by ambient light), personalized education where computers learn and model the needs of their students, and getting outside researchers access to the big data they need to help build hybrid, not artificial, intelligence.
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