Thursday, September 15, 2016

Deep learning, Doug Englebart and Jimmy Hendrix

The late great Doug Englebart did foundational work in many areas of computing and was particularly interested in the relationship between human intelligence and machine intelligence.

Even a not-so-smart machine can augment human productivity if even simple cognitive tasks can be handled by the machine. Reason being, machines are super fast. Super fast can compensate for "not-so-smart" in many useful domains. Simple totting up of figures, printing lots of copies of a report, shunt lots of data around, whatever.

How do you move a machine from "not-so-smart" to "smarter" for any given problem? The obvious way is to get the humans to do the hard thinking and come up with a smarter way. It is hard work because the humans have to be able to oscillate between smart thinking and thinking like not-so-smart machines because ultimately the smarts have to be fed to the not-so-smart machine in grindingly meticulous instructions written in computer-friendly (read "not-so-smart") programs. Simple language because machines can only grok simple language.

The not-so-obvious approach is to create a feedback loop where the machine can change its behavior over time by feeding outputs back into inputs. How to do that? Well, you got to start somewhere so get the human engineers to create feedback loops and teach them to the computer. You need to do that to get the thing going - to bootstrap it....

then stand back....

Things escalate pretty fast when you create feedback loops! If the result you get is a good one, it is likely to be *a lot* better than your previous best because feedback loops are exponential.

Englebart's insight was to recognize that the intelligent, purposeful creation of feedback loops can be a massive multiplier : both for human intellect at the species level, and at the level of machines. When it works, it can move the state of the art of any problem domain forward, not by a little bit, but by *a lot*.

A human example would be the invention of writing. All of a sudden knowledge could survive through generations and could spread exponentially better than it could by oral transmission.

The hope and expectation around Deep Learning is that it is basically a Doug Englebart Bootstrap for machine intelligence. A smart new feedback loop in which the machines can now do a vital machine intelligence step ("feature identification") that previously required humans. This can/should/will move things forward *a lot* relative to the last big brohuha around machine intelligence in the Eighties.

The debates about whether or not this is really "intelligence" or just "a smarter form of dumb" will rage on in parallel, perhaps forever.

Relevance to Jimmy Hendrix? See https://www.youtube.com/watch?v=JMyoT3kQMTg

1 comment:

Dave Pawson said...

Definitions please? Inputs? Outputs? I could agree with a system of hardware and software, but a pure software system?
When does iteration and adjustment become learning? A big grey area?
Interesting perspective Sean. Thanks.