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White Paper Machine Learning in Certified Systems

arXiv.org Artificial Intelligence

Machine Learning (ML) seems to be one of the most promising solution to automate partially or completely some of the complex tasks currently realized by humans, such as driving vehicles, recognizing voice, etc. It is also an opportunity to implement and embed new capabilities out of the reach of classical implementation techniques. However, ML techniques introduce new potential risks. Therefore, they have only been applied in systems where their benefits are considered worth the increase of risk. In practice, ML techniques raise multiple challenges that could prevent their use in systems submitted to certification constraints. But what are the actual challenges? Can they be overcome by selecting appropriate ML techniques, or by adopting new engineering or certification practices? These are some of the questions addressed by the ML Certification 3 Workgroup (WG) set-up by the Institut de Recherche Technologique Saint Exup\'ery de Toulouse (IRT), as part of the DEEL Project.


Towards a Framework for Certification of Reliable Autonomous Systems

arXiv.org Artificial Intelligence

The capability and spread of such systems have reached the point where they are beginning to touch much of everyday life. However, regulators grapple with how to deal with autonomous systems, for example how could we certify an Unmanned Aerial System for autonomous use in civilian airspace? We here analyse what is needed in order to provide verified reliable behaviour of an autonomous system, analyse what can be done as the state-of-the-art in automated verification, and propose a roadmap towards developing regulatory guidelines, including articulating challenges to researchers, to engineers, and to regulators. Case studies in seven distinct domains illustrate the article. Keywords: autonomous systems; certification; verification; Artificial Intelligence 1 Introduction Since the dawn of human history, humans have designed, implemented and adopted tools to make it easier to perform tasks, often improving efficiency, safety, or security.


The Future of Transportation

#artificialintelligence

Sengupta: Thank you so much for having me today. I'm really excited to be in San Francisco. I don't get to come here that often, which is strange because I live in Los Angeles, but I do like to come whenever I can. For my talk today, I'm going to talk about the future of transportation, specifically on the things that I worked on that I think are kind of the up and coming thing, the thing that I'm working on now and what's going to happen in the future. I think part of my career has always been about just doing fun and exciting new things and all my degrees are in aerospace engineering, ever since I was a little kid, I loved science fiction. I actually am a Star Trek person versus a Star Wars person, but I knew since I was a little kid that I wanted to be involved in the space program, so that's why I decided to go the aerospace engineering route and I wanted to build technology. I got my Ph.D. in plasma propulsion systems. Has anyone heard of the mission called Dawn that's out in the main asteroid belt? My Ph.D. research actually was developing the ion engine technology for that mission. It actually flew and got it to a pretty cool place out in the main asteroid belt looking at Vesta and Ceres. I did that for about five years and then I kind of felt like I had done everything I could possibly do on that front, from a research perspective. My management asked me if I wanted to work on the next mission to Mars. There's very few engineers in the space program who'd be like, "No, I'm just not interested in that." And they're like, "We want you to do the supersonic parachute for it."


Tackling Climate Change with Machine Learning

arXiv.org Artificial Intelligence

Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help. Here we describe how machine learning can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by machine learning, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the machine learning community to join the global effort against climate change.


Revisiting the Importance of Individual Units in CNNs via Ablation

arXiv.org Artificial Intelligence

We revisit the importance of the individual units in Convolutional Neural Networks (CNNs) for visual recognition. By conducting unit ablation experiments on CNNs trained on large scale image datasets, we demonstrate that, though ablating any individual unit does not hurt overall classification accuracy, it does lead to significant damage on the accuracy of specific classes. This result shows that an individual unit is specialized to encode information relevant to a subset of classes. We compute the correlation between the accuracy drop under unit ablation and various attributes of an individual unit such as class selectivity and weight L1 norm. We confirm that unit attributes such as class selectivity are a poor predictor for impact on overall accuracy as found previously in recent work \cite{morcos2018importance}. However, our results show that class selectivity along with other attributes are good predictors of the importance of one unit to individual classes. We evaluate the impact of random rotation, batch normalization, and dropout to the importance of units to specific classes. Our results show that units with high selectivity play an important role in network classification power at the individual class level. Understanding and interpreting the behavior of these units is necessary and meaningful.


Tech and the future of transportation: From here to there

ZDNet

Articles about technology and the future of transportation rarely used to get far without mentioning jet-packs: a staple of science fiction from the 1920s onwards, the jet pack became a reality in the 1960s in the shape of devices such as the Bell Rocket Belt. But despite many similar efforts, the skies over our cities remain stubbornly free of jet-pack-toting commuters.


From Energy To Transport To Healthcare, Here Are 8 Industries Being Disrupted By Elon Musk And His Companies

#artificialintelligence

Elon Musk is CEO of Tesla and SpaceX, has plans to colonize Mars, and thinks AI may turn humans into its pets. But beyond the hype and his enormous net worth and Twitter presence, here's how Musk's companies are actually taking on ... virtually every industry.


Catching Amazon's Eye

#artificialintelligence

Amazon's hunt for a second headquarters, after several months of publicity stunts and dangled perks from cities and regions vying to lure the e-commerce giant, has been narrowed to 20 options from 238 bids. The company, which is based in Seattle, plans to invest $5 billion in development and create up to 50,000 jobs wherever it builds its newest hub. With the kind of enthusiasm normally reserved for bids to host the Olympics, governors, mayors, business leaders and others have pulled together proposals promoting the potential of their cities and regions, sometimes going to outlandish lengths. These are some of the places that caught Amazon's attention. Schools: The caliber of local schools is impressive, including Harvard University, Boston University, the Massachusetts Institute of Technology and Tufts University.


SureFly, a New Air Taxi That Runs On Electricity--and Gasoline

IEEE Spectrum Robotics

Range anxiety, the bugaboo of all-electric driving, is even more frightening for all-electric flying, where running out of power has worse consequences than having to pull over to the side of the road. A solution now comes from Workhorse, an Ohio-based firm. It has a passenger-carrying air taxi, called the SureFly, which combines the company's expertise in partially automated operation, from its drone business, and in hybrid-electric propulsion, from its truck business. The craft's eight counter-rotating motors each drive a carbon-fiber rotor, and the power comes from a generator cranked by an internal-combustion engine. You can fly 110 kilometers (70 miles) on a tank, then refill in minutes.