An ambitious smart-city project spearheaded by Alphabet subsidiary Sidewalk Labs has run into local resistance, causing delays. The backstory: Waterfront Toronto, a development agency founded by the Canadian government, partnered with the Google sister company in October 2017 to create a futuristic neighborhood on the Toronto waterfront. Sidewalk Labs plans to fill the 12-acre plot with driverless shuttle buses, garbage-toting robots, and other gadgets to show how emerging technologies can improve city life. The problem: Sidewalk Labs' connection to Google and vague descriptions of its business model alarmed privacy advocates and urban planners from the start. Local pushback has increased since, causing a key supporter to resign from the project and delaying the release of its final development plan to spring 2019.

Some of China's smartest high school graduates have been recruited to study the manufacturing of AI weaponry to keep Beijing ahead of the war game. The Chinese teenagers are studying at Beijing Institute of Technology, a top university in the country specialising in engineering and national defence. The class, unveiled last month, comprises 31 students who are selected based on their academic achievements and their level of patriotism, according the school. AI weapons, called by some as'killer robots', generally mean automated weapons which select, engage and eliminate human targets without the involvement of other humans. It has been described as the third revolution in warfare - after gunpowder and nuclear arms - and has been a controversial topic due to the ethics behind them.

A worrying vulnerability in DJI drones gave hackers complete access to a user's account without them realizing it. Security researchers from Check Point in March discovered a flaw in DJI's cloud infrastructure that allowed attackers to take over users' accounts and access private data like drone logs with location data, maps, account information and photos or videos taken during flight. However, DJI said it patched the vulnerability in September. A worrying vulnerability in DJI drones gave hackers complete access to a user's account. Users fell prey to the attack by clicking on a malicious link shared through DJI Forum, an online forum the firm runs for user discussions about its products.

We present a deep learning framework for quantifying and propagating uncertainty in systems governed by non-linear differential equations using physics-informed neural networks. Specifically, we employ latent variable models to construct probabilistic representations for the system states, and put forth an adversarial inference procedure for training them on data, while constraining their predictions to satisfy given physical laws expressed by partial differential equations. Such physics-informed constraints provide a regularization mechanism for effectively training deep generative models as surrogates of physical systems in which the cost of data acquisition is high, and training data-sets are typically small. This provides a flexible framework for characterizing uncertainty in the outputs of physical systems due to randomness in their inputs or noise in their observations that entirely bypasses the need for repeatedly sampling expensive experiments or numerical simulators. We demonstrate the effectiveness of our approach through a series of examples involving uncertainty propagation in non-linear conservation laws, and the discovery of constitutive laws for flow through porous media directly from noisy data.

A colored graph is a directed graph in which either nodes or edges have been assigned colors that are not necessarily unique. Observability problems in such graphs are concerned with whether an agent observing the colors of edges or nodes traversed on a path in the graph can determine which node they are at currently or which nodes they have visited earlier in the path traversal. Previous research efforts have identified several different notions of observability as well as the associated properties of colored graphs for which those types of observability properties hold. This paper unifies the prior work into a common framework with several new analytic results about relationships between those notions and associated graph properties. The new framework provides an intuitive way to reason about the attainable path reconstruction accuracy as a function of lag and time spent observing, and identifies simple modifications that improve the observability properties of a given graph. This intuition is borne out in a series of numerical experiments. This work has implications for problems that can be described in terms of an agent traversing a colored graph, including the reconstruction of hidden states in a hidden Markov model (HMM).

DJI makes some of the most popular quadcopters on the market, but its products have repeatedly drawn scrutiny from the United States government over privacy and security concerns. Most recently, the Department of Defense in May banned the purchase of consumer drones made by a handful of vendors, including DJI. Now DJI has patched a problematic vulnerability in its cloud infrastructure that could have allowed an attacker to take over users' accounts and access private data like photos and videos taken during drone flights, a user's personal account information, and flight logs that include location data. A hacker could have even potentially accessed real-time drone location and a live camera feed during a flight. The security firm Check Point discovered the issue and reported it in March through DJI's bug bounty program.

A group of some of China's smartest students have been recruited straight from high school to begin training as the world's youngest AI weapons scientists. Local media reports: The 27 boys and four girls, all aged 18 and under, were selected for the four-year "experimental programme for intelligent weapons systems" at the Beijing Institute of Technology (BIT) from more than 5,000 candidates, the school said on its website. The BIT is one of the country's top weapons research institutes, and the launch of the new programme is evidence of the weight it places on the development of AI technology for military use. China is in competition with the United States and other nations in the race to develop deadly AI applications -- from nuclear submarines with self-learning chips to microscopic robots that can crawl into human blood vessels.

In China, enthusiasm for innovation in artificial intelligence starts at the highest levels. In his remarks to the 19th party congress, Xi Jinping called for China to "promote the deep integration of the internet, big data, and AI with the real economy." His 2018 new year's address saw two books on AI positioned on the bookshelf behind him, another indication of the extent of his interest. China's'rise' in AI – and potential emergence as an "AI superpower" – commands headlines, while the remarks of China's policy and business leaders indicate a keen awareness of continued challenges and shortcomings.[1] Innovation is at the core of Xi's strategy to advance the "China Dream" (中国梦 zhongguo meng) of national rejuvenation.

Two startups-- View Inc., which makes light-adjustable glass, and Zume Inc., which uses robots to make pizza--disclosed investments over the past week totaling a combined $1.5 billion from SoftBank's Saudi-backed Vision Fund. Late last month, Katerra Inc., an innovator in property construction, reached a tentative deal with the Saudi government to build up to 50,000 units of housing annually for the kingdom. That followed a $1 billion funding round led by the Vision Fund early this year that valued the Menlo Park, Calif., company at more than $3 billion. Meanwhile, negotiations continued in recent weeks for a deal in which Tokyo-based SoftBank would invest $15 billion to $20 billion to buy a majority stake in WeWork Cos. likely with Vision Fund money, according to people familiar with the discussions. A WeWork spokeswoman declined to comment.

Amazon employees plan to take CEO Jeff Bezos to task about the firm's controversial facial recognition software, Rekognition. The tech giant will host an all-staff meeting on Thursday and it's there that employees will flood executives with questions about Rekognition, as well as why Amazon continues work with immigration authorities, according to Recode. Pressure has been mounting for Amazon to cancel its contracts with ICE and law enforcement agents, which allow them to test out the facial recognition technology. Amazon employees plan to take CEO Jeff Bezos (pictured) to task at an all-hands meeting on Thursday about the firm's controversial facial recognition software, Rekognition Amazon lets employees submit their questions for Bezos and other executives beforehand using an online form. They then go through the list and decide on which questions to answer.