Accelerating Change 2005
Jacobstein, Lincoln, Norvig & Olshausen
Subscribe to Podcast
[runtime: 01:13:01, 33.4 mb, recorded 2005-09-17]
'The Prospects for AI'
http://itc.conversationsnetwork.org/shows/detail713.html
IT Conversations podcasts can be listened to on your computer. Simply click on the listen arrow button.
For convenient listening they can also be downloaded to an MP3 player.
If this podcast was indeed of interest, I encourage you to register as a guest at
http://www.conversationsnetwork.org/join/getguestreginfo/ to receive bi-weekly updates about new
releases.
Enjoy!
The Prospects for AI Panel takes the form of four individual presentations by the panellists followed by a
lengthy Q&A session.
Neil Jacobstein is first up, with a talk that ranges from a look at the design and build of a classic Frank
Lloyd Wright house to the innovative approach used to design new Lexus cars. Since the early 1980s
the systematic codification of knowledge in computer languages has enabled a wide range of useful
applications in industry and government. These applications may include the performance of complex
tasks but none has really exhibited general intelligence. However, for all the technical and cultural
limitations manifested by these applications, each has contributed incrementally to our ability to harness
the power of knowledge.
Jacobstein sees the future for AI becoming much brighter, thanks to the confluence of factors such as
advances in neurosciences, the advent of large scale ontologies and the semantic web, as well as the
emerging development of nanotechnology and molecular manufacturing, and the exponential increases
in computing hardware speed and memory. There still remains a need, however, for a systematic
approach to the cultural and organizational problems involved in the co-evolution of machines and
humans. To this end, Jacobstein ends his session by listing a set of ten rules that he believes provide
fertile ground for a successful implementation of complex AI projects.
Patrick Lincoln
Next up to the microphone is Patrick Lincoln. He believes that the primary purpose of AI is to augment
intelligence. Although the value of the IT portion of products, services, and the entire economy
increases steadily, this has come with an increasing reliance on automated computing systems. At the
same time, there is a decreasing visibility of the critical properties of these systems by both users and
designers. Lincoln posits a new law: Moore's Wall, which accepts that human capabilities are growing
but not fast enough to control fully what we build. It will become beneficial, therefore, to provide
designers and users tools and methods to enable them to understand and improve the trustworthiness
of complex digital systems.
Lincoln proposes that we should enable rapid analysis and understanding of the critical properties of
complex systems, even when the complex systems under study involve tight interactions with human
components. More importantly, we should do this before we align our interests strongly with automated
systems. Although recent rapid advances in automated reasoning make this plausible, it still requires a
greater and more focused effort to make it reality.
Peter Norvig proposes the slogan 'AI in the middle', meaning that AI technology becomes a mediator
between authors and readers. History has so far produced exactly one system in which trillions of facts
are transmitted to billions of learners: the system of publishing the written word. No other system comes
within a factor of a million of this performance benchmark. This in spite of the fact that the written word is
notoriously imprecise and ambiguous.
In the early days of AI, most work was on creating a new system of transmission - a new representation
language, and/or a new axiomization of a domain. Well-structured data was manipulated by sound
means. Although it will remain expensive to create knowledge in any formal language, AI can leverage
the work of millions of authors by understanding, classifying, prioritizing, translating, summarizing and
presenting the written word in an intelligent just-in-time basis to billions of potential readers.
Bruno Olshausen believes that, despite much effort in the engineering and mathematics community
over the past 40 years, there has been little progress emulating even the most elementary aspects of
intelligence. This lack of progress is especially striking, considering the fact that, in the past two
decades alone, we have seen a 1000-fold increase in computer power. The actual intelligence of
computers, on the other hand, has improved only moderately by comparison.
If we are to make progress in building truly intelligent systems, Olshausen says we need to turn our
efforts toward understanding how intelligence arises within the brain. Neuroscience has produced vast
amounts of data about the structure and function of neurons but what is missing is a theoretical
framework for linking these details to intelligence. Theoretical neuroscience seeks to bridge this gap by
constructing mathematical and computational models of the underlying neurobiological mechanisms
involved in perception, cognition, learning, and motor function.
Jacobstein, Lincoln, Norvig & Olshausen
Subscribe to Podcast
[runtime: 01:13:01, 33.4 mb, recorded 2005-09-17]
'The Prospects for AI'
http://itc.conversationsnetwork.org/shows/detail713.html
IT Conversations podcasts can be listened to on your computer. Simply click on the listen arrow button.
For convenient listening they can also be downloaded to an MP3 player.
If this podcast was indeed of interest, I encourage you to register as a guest at
http://www.conversationsnetwork.org/join/getguestreginfo/ to receive bi-weekly updates about new
releases.
Enjoy!
The Prospects for AI Panel takes the form of four individual presentations by the panellists followed by a
lengthy Q&A session.
Neil Jacobstein is first up, with a talk that ranges from a look at the design and build of a classic Frank
Lloyd Wright house to the innovative approach used to design new Lexus cars. Since the early 1980s
the systematic codification of knowledge in computer languages has enabled a wide range of useful
applications in industry and government. These applications may include the performance of complex
tasks but none has really exhibited general intelligence. However, for all the technical and cultural
limitations manifested by these applications, each has contributed incrementally to our ability to harness
the power of knowledge.
Jacobstein sees the future for AI becoming much brighter, thanks to the confluence of factors such as
advances in neurosciences, the advent of large scale ontologies and the semantic web, as well as the
emerging development of nanotechnology and molecular manufacturing, and the exponential increases
in computing hardware speed and memory. There still remains a need, however, for a systematic
approach to the cultural and organizational problems involved in the co-evolution of machines and
humans. To this end, Jacobstein ends his session by listing a set of ten rules that he believes provide
fertile ground for a successful implementation of complex AI projects.
Patrick Lincoln
Next up to the microphone is Patrick Lincoln. He believes that the primary purpose of AI is to augment
intelligence. Although the value of the IT portion of products, services, and the entire economy
increases steadily, this has come with an increasing reliance on automated computing systems. At the
same time, there is a decreasing visibility of the critical properties of these systems by both users and
designers. Lincoln posits a new law: Moore's Wall, which accepts that human capabilities are growing
but not fast enough to control fully what we build. It will become beneficial, therefore, to provide
designers and users tools and methods to enable them to understand and improve the trustworthiness
of complex digital systems.
Lincoln proposes that we should enable rapid analysis and understanding of the critical properties of
complex systems, even when the complex systems under study involve tight interactions with human
components. More importantly, we should do this before we align our interests strongly with automated
systems. Although recent rapid advances in automated reasoning make this plausible, it still requires a
greater and more focused effort to make it reality.
Peter Norvig proposes the slogan 'AI in the middle', meaning that AI technology becomes a mediator
between authors and readers. History has so far produced exactly one system in which trillions of facts
are transmitted to billions of learners: the system of publishing the written word. No other system comes
within a factor of a million of this performance benchmark. This in spite of the fact that the written word is
notoriously imprecise and ambiguous.
In the early days of AI, most work was on creating a new system of transmission - a new representation
language, and/or a new axiomization of a domain. Well-structured data was manipulated by sound
means. Although it will remain expensive to create knowledge in any formal language, AI can leverage
the work of millions of authors by understanding, classifying, prioritizing, translating, summarizing and
presenting the written word in an intelligent just-in-time basis to billions of potential readers.
Bruno Olshausen believes that, despite much effort in the engineering and mathematics community
over the past 40 years, there has been little progress emulating even the most elementary aspects of
intelligence. This lack of progress is especially striking, considering the fact that, in the past two
decades alone, we have seen a 1000-fold increase in computer power. The actual intelligence of
computers, on the other hand, has improved only moderately by comparison.
If we are to make progress in building truly intelligent systems, Olshausen says we need to turn our
efforts toward understanding how intelligence arises within the brain. Neuroscience has produced vast
amounts of data about the structure and function of neurons but what is missing is a theoretical
framework for linking these details to intelligence. Theoretical neuroscience seeks to bridge this gap by
constructing mathematical and computational models of the underlying neurobiological mechanisms
involved in perception, cognition, learning, and motor function.
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