Learning algorithms which improve how they learn, computers which define their own objectives and then do it, robots which learn from us like children do, its all not far off now.
Panelists:
https://www.youtube.com/watch?v=Di4p6MnN1Eo
This talk by Jordan Pederson suggests human level AGI is here within the year. And after we go expo.
Jordan Peterson’s Links:
Patreon: https://www.patreon.com/jordanbpeterson
Self Authoring: http://selfauthoring.com/
Jordan Peterson Website: http://jordanbpeterson.com/
Podcast: http://jordanbpeterson.com/jordan-b-p…
Reading List: http://jordanbpeterson.com/2017/03/gr…
Twitter: https://twitter.com/jordanbpeterson
Continue reading “Creators of future AI must not be ideologues” »
A glimpse at the coming AI researchers. (AI’s that do research).
A new type of artificial-intelligence-driven chemistry could revolutionise the way molecules are discovered, scientists claim.
In a new paper published today in the journal Nature, chemists from the University of Glasgow discuss how they have trained an artificially-intelligent organic chemical synthesis robot to automatically explore a very large number of chemical reactions.
Continue reading “Robot chemist discovers new molecules and reactions” »
Not long ago, getting a virus was about the worst thing computer users could expect in terms of system vulnerability. But in our current age of hyper-connectedness and the emerging Internet of Things, that’s no longer the case. With connectivity, a new principle has emerged, one of universal concern to those who work in the area of systems control, like João Hespanha, a professor in the departments of Electrical and Computer Engineering, and Mechanical Engineering at UC Santa Barbara. That law says, essentially, that the more complex and connected a system is, the more susceptible it is to disruptive cyber-attacks.
“It is about something much different than your regular computer virus,” Hespanha said. “It is more about cyber physical systems—systems in which computers are connected to physical elements. That could be robots, drones, smart appliances, or infrastructure systems such as those used to distribute energy and water.”
In a paper titled “Distributed Estimation of Power System Oscillation Modes under Attacks on GPS Clocks,” published this month in the journal IEEE Transactions on Instrumentation and Measurement, Hespanha and co-author Yongqiang Wang (a former UCSB postdoctoral research and now a faculty member at Clemson University) suggest a new method for protecting the increasingly complex and connected power grid from attack.
Nicholas Papernot discusses “Making Machine Learning Robust Against Adversarial Inputs” (cacm.acm.org/magazines/2018/7/229030), a Contributed Article in the July 2018 CACM.
Researchers have shown that it is possible to train artificial neural networks directly on an optical chip. The significant breakthrough demonstrates that an optical circuit can perform a critical function of an electronics-based artificial neural network and could lead to less expensive, faster and more energy efficient ways to perform complex tasks such as speech or image recognition.
“Using an optical chip to perform neural network computations more efficiently than is possible with digital computers could allow more complex problems to be solved,” said research team leader Shanhui Fan of Stanford University. “This would enhance the capability of artificial neural networks to perform tasks required for self-driving cars or to formulate an appropriate response to a spoken question, for example. It could also improve our lives in ways we can’t imagine now.”
An artificial neural network is a type of artificial intelligence that uses connected units to process information in a manner similar to the way the brain processes information. Using these networks to perform a complex task, for instance voice recognition, requires the critical step of training the algorithms to categorize inputs, such as different words.
Continue reading “Researchers move closer to completely optical artificial neural network” »
