SRI started work with Calo, funded through DARPA, probably largest AI project in 1953. Focus on learning in the wild, the way a real human personal assistant would learn. Still quite a frontier. Biggest challenge is user experience, when things in the interface change, he’s out of control. Wants something that does the right thing every time. Calo was most major project before Siri. People who worked on Siri came out of Calo team.
sRI is very financially pragmatic about what they fund, must be relatively certain. We start with a market problem, not technology.
How important was natural speech? Global conversations with telephone ecosystem, most people don’t talk to a phone for service but Bill thought that wasn’t a future predictor. Is rare that customer expresses their intent in first sentence. W are learning what we want as we talk. One Question might lead to another.
Tempo, a smart calendar, offers insights into your daily life, predictive intent. Destiny is a travel company, bought by Nokia. Quantum game about math. Open question do we need verticals or can we use one? Different kinds of personal assistants. On kind is general, other is really about me.
There will be millions of assistants, but two layers. APIs to talk to each other? Th hard part is how to have this multi-term part conversation between all. Won’t stay mobile device centered, internet of things will help with simple intents that happen all the time. Which decisions are contingent on emotional responses or other timely options? Language, interacting, is not the future that is learning and understanding. Mobile will be center of interactions related to where, when…
Genderization is also a complex issue. Real world: not homogeneous, will be a variety in personas.
Personalization in retail, for example, can recommend, but puts you in a category. Difference between, and we need both little data as well as big.
Eliza, from 1960s, tried to emulate a psychologist. People really engaged, some went on for hours. Touring test of thinking machines… Some people want a human interaction, some dont.
What technologies need to improve before it’s conversational? Deep knowledge, represents fundamental things that are known to us (e.g., bank account), also context for human interactions and how we remember things. The way we deal with this now is through verticals, where context and intents are more established. Across market verticals not possible today. Shallow vs deep.
Tooling and analytics are useful to people buying them. Problems with deep knowledge, structuring the representations, and how it all works together.