Autonomous Weapons Research Paper

Tech Focus.

From: Dilin Anand (EFY) [mailto:[e-mail]]

Sent: Wednesday, August 17, 2016 10:28 AM

To: Rahul Chopra

Cc: Janani Gopalakrishnan

Subject: For Review: TF on autonomous weapons

Sir,

PFB story for review on autonomous weapons.

Target Audience: Technologists

Why Read: What’s new in enabling autonomous weapons

Key Takeaway: Technologies available and in development to build them

Should LAWs Be Outlawed: What are Autonomous Weapons up to?

By Dilin Anand and Saurabh Durgapal

EDI, a fighter jet based on the F/A-37 Talon, is a fictional lethal autonomous weapon (LAW) designed to carry out airborne attacks on enemies and come back home in time for dinner without any human intervention. Why? A machine pilot

If you remember, the day after Microsoft introduced an innocent ‘female’ artificial intelligence chat robot to Twitter, it transformed itself into an evil Hitler-loving character that spewed hate and swore at Twitterati who tried to speak to it. Why?

Neural networks like the one in the fictional Edi do not perform rule-based calculations. Instead, they learn by exposure to large data sets. These data sets could be easily powered by the big data generated from the Internet of Things.

As a result, the internal structure of the network that generates output can be opaque to the designers—a “black box.” Even more unsettling, for reasons that may not be entirely clear to AI researchers, the neural network sometimes can yield odd, counterintuitive results like the Twitter bot.

“A study of visual classification AIs using neural networks found that while the AIs were able to generally identify objects as well as humans, in some cases the AIs made confident identifications of objects that were not only incorrect, but that looked vastly different from the purported object to human eyes. The AIs interpreted images that to the human eye looked like static or abstract wavy lines as animals or other objects, and asserted greater than 99.6% confidence in their estimation,” explains Paul Scharre in Autonomous Weapons and Operational Risk, senior fellow and director of the 20YY Future

This February, 2016, the IEEE International Solid-State Circuits Conference in San Francisco, teams from MIT, Nvidia, and the Korea Advanced Institute of Science and Technology (KAIST) showed prototypes of chips designed to run artificial neural networks The KAIST design minimises data movement by bringing memory and processing closer together. It saves energy by both limiting data movement and by lightening the computational load. Kim’s group observed that 99 per cent of the numbers used in a key calculation require only 8 bits, so they were able to limit the resources devoted to it.

Nvidia also announced a new chip called the Tesla P100 that’s designed for a technique called deep learning used by Google software AlphaGo. They also unveiled a special computer for researchers involved in deep learning. It comes with eight P100 chips, memory, and storage. Models of this computer, known as the DGX-1, will also be sold for $129,000. It is being given to leading academic research groups, including ones at the University of California, Berkeley, Stanford, New York University, and