Intelligence is a key concept in the quest for artificial intelligence, and more generally the singularity. Remarkably, relatively little work has gone into developing general, encompassing and theoretically founded definitions of intelligence for machines. This leaves us without a clear foundation for either theoretical research or developing empirical measures of progress. In this talk I outline the major perspectives on the nature of intelligence and some of the informal definitions that have been put forward. I then sketch the main ideas behind the universal intelligence measure. This is a formal definition of intelligence based on Hutter’s AIXI model of theoretically optimal machine intelligence. Based on this a number of researchers, including myself, are developing practical tests of machine intelligence. I describe some of the challenges faced when doing this and share some recent results from testing various artificial agents. Might we soon be able to measure our progress towards machine intelligence?
He is reviewing the computations per second from the best supercomputers by year. He notes that it is slightly curving upward on a log graph. It is projected that we have exaflops around 2018 and 100 exaflops by 2025.
But he wants to see machine intelligence against the year.
Will you use a human model of intelligence or a more ideal or normative model of intelligence
Human Ideal Internal niel block Aristotal Properties External Turing Will look Properties here
Theese are different questions not just different answers.
Look at ideal external because if it cures cancer then don’t care how it does it.
But if you cared about uploading then the Internal human measure would matter.
Work well in many different environments
cluster of cognitive abilities that enable success in different areas
be able to adapt and meet goals.
* property of an agent
* that interacts with its environment
* so as to successfully achieve goals
* across a wide range of environments
* Occam’s razor
formally defines with an equation. Summation across environments with occam razar multiple of success measure …
formally defined, captures essence of many informal definitions, orders imple agents correctly, upper limit is AIXI, continuously measure, non-anthropocentric
Algorithmic Intelligence Quotient (AIQ)
Testing against algorithms is placing them in the expected and correct order.
Q lambda and func approx result AIQ 477
Q lambda result AIQ 405
Q(0) result AIQ 303
Freq agent result AIQ 277
random result AIQ 0
Brian Wang is a Futurist Thought Leader and a popular Science blogger with 1 million readers per month. His blog Nextbigfuture.com is ranked #1 Science News Blog. It covers many disruptive technology and trends including Space, Robotics, Artificial Intelligence, Medicine, Anti-aging Biotechnology, and Nanotechnology.
Known for identifying cutting edge technologies, he is currently a Co-Founder of a startup and fundraiser for high potential early-stage companies. He is the Head of Research for Allocations for deep technology investments and an Angel Investor at Space Angels.
A frequent speaker at corporations, he has been a TEDx speaker, a Singularity University speaker and guest at numerous interviews for radio and podcasts. He is open to public speaking and advising engagements.