Project Oxygen: A Retrospective

Larry Rudolph

What was it

Lessons

• Human-centric input: inherently ambiguous

• goal is to disambiguate

• chop into pieces with meaning

• Stepford Wives

input --> recog pieces --> reassembly --> output

Lessons

• information must flow across abstraction barriers

• app --> what is expected

n-best approximation --> app

Lessons

• State of the world is “stochastic”

• most of the time, stuff isn’t working correctly

Multi-modes compensate for errors

Lessons• Lots of similarities in diverse fields

• Bayesian inference widely used

• speech, vision, gestures, handwriting, location, congestion control

• Lots of app-specific optimizations

• real engineering work, not easily generalized

“Its all the same, but different”

Lessons

• Location (indoor) is key

• Hard to beat commodity items

• two heads better than one

Things done right (usually by accident)

• No one overall architecture

• No major standardization effort

• Lots of pairwise interactions

• Fewer constraints with state-of-the-art systems

• Professional videos

• very little cheating; real deadlines

Publications in subfield areas -- measurable progress

Version 2.0?

• “Eat our own sausage”

• lots of support staff for internal technology transfer

• speech recognition on every desk

• free-hand sketching --> powerpoint

• comprehension-lite versions

a’la speech builder

Brittleness

• up and running all the time (not just during demos)

• too many publish & subscribe mechanisms

must be able to measure

Scenario’s

• too much emphasis

• for every “wouldn’t it be cool” scenario, there exists a “nightmare” scenario

there is no “cool-meter” or cool-metric

Metric for Success of Oxygen?

• did it change the world? No

did it help the world change? Yes