We identify a class of over-parameterized deep neural networks with standard activation functions and cross-entropy loss which provably have no bad local valley, in the sense that from any point in parameter space there exists a continuous path on which the cross-entropy loss is non-increasing and gets arbitrarily close to zero. This implies that these networks have no sub-optimal strict local minima.

Initial DR studies mainly adopt model predictive control and thus require accurate models of the control problem (e.g., a customer behavior model), which are to a large extent uncertain for the EV scenario. Hence, model-free approaches, especially based on reinforcement learning (RL) are an attractive alternative. In this paper, we propose a new Markov decision process (MDP) formulation in the RL framework, to jointly coordinate a set of EV charging stations. State-of-the-art algorithms either focus on a single EV, or perform the control of an aggregate of EVs in multiple steps (e.g., aggregate load decisions in one step, then a step translating the aggregate decision to individual connected EVs). On the contrary, we propose an RL approach to jointly control the whole set of EVs at once. We contribute a new MDP formulation, with a scalable state representation that is independent of the number of EV charging stations. Further, we use a batch reinforcement learning algorithm, i.e., an instance of fitted Q-iteration, to learn the optimal charging policy. We analyze its performance using simulation experiments based on a real-world EV charging data. More specifically, we (i) explore the various settings in training the RL policy (e.g., duration of the period with training data), (ii) compare its performance to an oracle all-knowing benchmark (which provides an upper bound for performance, relying on information that is not available or at least imperfect in practice), (iii) analyze performance over time, over the course of a full year to evaluate possible performance fluctuations (e.g, across different seasons), and (iv) demonstrate the generalization capacity of a learned control policy to larger sets of charging stations.

A mixed group of manned and unmanned aerial vehicles is considered as a distributed system. A lattice of tasks which may be fulfilled by the system matches to it. An external multiplication operation is defined at the lattice, which defines correspondingly linear logic operations. Linear implication and tensor product are used to choose a system reconfiguration variant, i.e., to determine a new task executor choice. The task lattice structure (i.e., the system purpose) and the operation definitions largely define the choice. Thus, the choice is mainly the system purpose consequence. The suggested method is illustrated using an example of a mixed group control at forest fire compression. Keywords Multi-Agent Systems · Decision making · Mixed Group · Goal Lattice · Linear logic 1 Introduction At present, aviation surveillance systems in the emergency zone have received wide distribution [1]. Lately, unmanned aerial vehicles (UAV) are actively used in these surveillance systems.

Judging by the presentations at the 2018 Symposium on VLSI Technology, held in Honolulu this summer, the semiconductor industry has a challenge ahead of it: how to develop the special low-power hardware needed to support artificial intelligence-enabled networks. To meet society's needs for low-power-consumption machine learning (ML), "we do need to turn our attention to this new type of computing," said Naveen Verma, an associate professor of electrical engineering at Princeton University." While introducing intelligence into engineering systems has been what the semiconductor industry has been all about, Verma said machine learning represents a "quite distinct" inflection point. Accustomed as it is to fast-growing applications, machine learning is on a growth trajectory that Verma said is "unprecedented in our own industry" as ML algorithms have started to outperform human capabilities in a wide variety of fields. Faster GPUs driven by Moore's Law, and combining chips in packages by means of heterogenous computing, "won't be enough as we proceed into the future.

Machine Learning (ML) is not easy, but creating a good workflow which you can reproduce, revisit and deploy to production is even harder. There has been many advances towards creating a good platform or managing solution for ML. Note that this is not the Data Science (DS) Lifecycle, which is more complex and has many parts. The ML lifecycle exists inside the DS lifecycle. These packages are great, but not so easy to follow.

Darktrace differentiates itself in using advanced machine learning and mathematics developed at the University of Cambridge to identify abnormalities in a company's IT network that might be an attack. The company also aims to tackle the latest phenomenon of artificial intelligence-based cyber attacks with its own AI-based software. "We are starting to see early signs of very rudimentary machine learning being used in these (cyber) attacks ... we do estimate that in the next 18 months we will start seeing much sophisticated AI based attacks," Eagan said. Darktrace, co-headquartered in Britain's Cambridge and San Francisco, has customers including chipmaker Micron Technology Inc, international airports like London Gatwick Airport, and financial institutions such as AIG Inc.

For all the advances being made in the field, artificial intelligence still struggles when it comes to identifying hate speech. When he testified before Congress in April, Facebook CEO Mark Zuckerberg said it was "one of the hardest" problems. But, he went on, he was optimistic that "over a five- to 10-year period, we will have AI tools that can get into some of the linguistic nuances of different types of content to be more accurate in flagging things for our systems." For that to happen, however, humans will need first to define for ourselves what hate speech means--and that can be hard because it's constantly evolving and often dependent on context. "Hate speech can be tricky to detect since it is context and domain dependent. Trolls try to evade or even poison such [machine learning] classifiers," says Aylin Caliskan, a computer science researcher at George Washington University who studies how to fool artificial intelligence.

The graph represents a network of 4,158 Twitter users whose tweets in the requested range contained "#AR", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Wednesday, 26 September 2018 at 17:07 UTC. The requested start date was Wednesday, 26 September 2018 at 00:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 5,000. The tweets in the network were tweeted over the 1-day, 3-hour, 28-minute period from Monday, 24 September 2018 at 20:32 UTC to Wednesday, 26 September 2018 at 00:00 UTC.

Japan's police and military are to separately begin tests of artificial intelligence systems to predict crimes and the activities of suspicious vessels at sea, including the potential threats foreign ships may pose to Japanese territory. The National Police Agency is to request Y144 million (US$1.29 million) in its budget for 2019 to test the ability of artificial intelligence to forecast crimes like money laundering, terrorist attacks at major public events and incidents involving vehicles. The Mainichi newspaper said a system capable of predicting the likelihood and possible location of crimes would eventually be rolled out to police forces across the country "as soon as possible" to make efforts to avert criminal activity more effective. "From a security point of view, Japan is perhaps one of the least advanced nations in the world simply because we have a relatively low level of crime," said Morinosuke Kawaguchi, an innovation and technology consultant. "The US and the UK are ...

The U.S. could face heightened national security threats and lose its economic edge if the government doesn't step up its game when it comes to artificial intelligence, according to a pair of oversight lawmakers. Will Hurd, R-Texas, and Robin Kelly, D-Ill., on Tuesday published a report detailing the current state of the country's artificial intelligence ecosystem and offering recommendations for how government could steer and accomodate the technology's development in the years ahead. The report is based on a series of hearings examining the government's role in advancing AI hosted earlier this year by the House Oversight Subcommittee on Information Technology, on which Hurd chairs and Kelly serves as ranking member. "[Artificial intelligence] is a topic that's going to transcend and be important beyond this Congress," Hurd said Tuesday during a call with reporters. "I think this report [will] lay a foundation for future focus by Congress and other parts of the government."