Artificial Intelligence
What's the ground truth on artificial intelligence? In a new video, DARPA's, John Launchbury, the Director of DARPA's Information Innovation Office, attempts to demystify AI—what it can do, what it can't do, and where it is headed.
Through a discussion of the "three waves of AI" and the capabilities required for AI to reach its full potential, John Launchbury, the Director of DARPA's Information Innovation Office (I2O) provides analytical context to help understand the roles AI already has played, does play now, and could play in the future. Check out the video below.
The second wave systems are able to make more sense of the natural world than first wave systems. Self driving car technology is a key example of how these systems have become smarter than expert systems. Machine learning, using statistical methods have also led to the development of advanced computer vision and speech recognition systems. These systems can learn well, but do not have much in the way of reasoning capabilities according to Launchbury.
Neural nets, which are part of the second wave are really just, "spreadsheets on steroids," according to Launchbury. Even though the foundations of such networks are simple, they can be astoundingly powerful, as the recent advances in AI demonstrate.
"There are challenges with second wave," states Launchbury. "It's not a perfect picture." They are statistically significant, but individually unreliable. Microsoft's failure with the Tay.AI project is an example of problems with skewed training data.
"We need to move beyond the simple calculation we're seeing in a spreadsheet style calculation."
Now we are entering into the realm of the third wave Launchbury describes. "We need to move beyond the simple calculation we're seeing in a spreadsheet style calculation," he states. In the third wave of AI technology, contextual adaptation. In this model, the systems will, over time, build underlying explanatory models that will allow them to characterize real world phenomena.Related articles
In the examples Launchbury presents, the system can tell why it makes decisions, not just a statistical reasoning output. We don't know how some second wave systems come up with accurate results after churning through thousands of pieces of sample data, but in the third wave, the decisions will be clear. Launchbury also predicts that such systems will be able to abstract data to go beyond the base programming.Launchbury joined DARPA as a Program Manager in July 2014 and was named Director of the Information Innovation Office (I2O) in September 2015. In this role he develops Office strategy, staffs the Office, and works with I2O program managers to develop new programs and transition program products. Dr. Launchbury has been instrumental in formulating and implementing I2O research thrusts in programming languages, security, privacy and cryptography.
Before joining DARPA, Dr. Launchbury was chief scientist of Galois, Inc., which he founded in 1999 to address challenges in information assurance through the application of functional programming and formal methods. Under his leadership, the company experienced strong growth, successfully delivered on multiple contract awards and was recognized for thought leadership in high-assurance technology development.
Prior to founding Galois, Dr. Launchbury was a full professor in computer science and engineering at the Oregon Graduate Institute (OGI) School of Science and Engineering, which was subsequently incorporated into the Oregon Health and Science University. At OGI, he earned several awards for outstanding teaching and gained international recognition for his work on the analysis and semantics of programming languages, the Haskell programming language in particular.
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