In his years-long career developing software for power grids, Stan McHann had never before heard the ominous noise that rang out last Wednesday. Standing in the middle of a utility command center, he flinched as a cyberattack tripped the breakers in all seven of the grid's low voltage substations, plunging the system into darkness. "I heard all the substations trip off and it was just like bam bam bam bam bam bam bam bam," McHann says. All you can do is say, OK, we have to start from scratch bringing the power back up. You just take a deep breath and dig in." Thankfully, what McHann experienced wasn't the first-ever blackout caused by a cyberattack in the United States. Instead, it was part of a live, week-long federal research exercise in which more than 100 grid and cybersecurity experts worked to restore power to an isolated, custom-built test grid. In doing so they faced not just blackout conditions and rough weather, but also a group of fellow researchers throwing a steady ...
This is a guest post. The views expressed here are solely those of the authors and do not represent positions of IEEE Spectrum or the IEEE. Light detection and ranging, or lidar, is a sensing technology based on laser light. It's similar to radar, but can have a higher resolution, since the wavelength of light is about 100,000 times smaller than radio wavelengths. For robots, this is very important: Since radar cannot accurately image small features, a robot equipped with only a radar module would have a hard time grasping a complex object.
Adversarial attacks against machine learning models are a rather hefty obstacle to our increasing reliance on these models. Due to this, provably robust (certified) machine learning models are a major topic of interest. Lipschitz continuous models present a promising approach to solving this problem. By leveraging the expressive power of a variant of neural networks which maintain low Lipschitz constants, we prove that three layer neural networks using the FullSort activation function are Universal Lipschitz function Approximators (ULAs). This both explains experimental results and paves the way for the creation of better certified models going forward. We conclude by presenting experimental results that suggest that ULAs are a not just a novelty, but a competitive approach to providing certified classifiers, using these results to motivate several potential topics of further research.
The Defense Advanced Research Projects Agency (DARPA) has awarded a contract of up to $4.7 million to BAE Systems to integrate machine learning (ML) into platforms deciphering radio frequency signals. Officials said the Controllable Hardware Integration for Machine-learning Enabled Real-time Adaptivity (CHIMERA) program provides a reconfigurable hardware platform for ML algorithm developers to make sense of radio frequency (RF) signals in increasingly crowded electromagnetic spectrum environments. "CHIMERA brings the flexibility of a software solution to hardware," said Dave Logan, vice president and general manager of Command, Control, Communications, Computers, Intelligence, Surveillance and Reconnaissance (C4ISR) Systems at BAE Systems. "Machine-learning is on the verge of revolutionizing signals intelligence technology, just as it has in other industries." CHIMERA will enable ML software development to adapt the hardware's Radio Frequency (RF) configuration in real-time to optimize mission performance, officials said, adding the capability has never before been available in a hardware solution, with the system providing multiple control surfaces for the user, enabling on-the-fly performance trade-offs maximizing sensitivity, selectivity and scalability depending on mission need.
Neural networks are taking the world of computing by storm. Researchers have used them to create machines that are learning a huge range of skills that had previously been the unique preserve of humans--object recognition, face recognition, natural language processing, machine translation. All these skills, and more, are now becoming routine for machines. So there is great interest in creating more capable neural networks that can push the boundaries of artificial intelligence even further. The focus of this work is in creating circuits that operate more like neurons, so-called neuromorphic chips.