The vehicles will operate in the Jurong West region of Singapore, where the island's Nanyang Technological University (NTU) is situated. The buses will ferry up to 80 people between NTU and the neighboring "eco-business" hub CleanTech Park -- around a one-mile journey. The team behind the trial is also considering servicing a nearby train station, which would extend the route to around a 5-mile round trip. The vehicles will charge at depots and at bus stops via charging masts. It won't be the first trial of a full-sized autonomous bus.

When is it appropriate for police to conduct a face recognition search? To figure out who's who in a crowd of protesters? To monitor foot traffic in a high-crime neighborhood? To confirm the identity of a suspect -- or a witness -- caught on tape? According to a new report by Georgetown Law's Center on Privacy & Technology, these are questions very few police departments asked before widely deploying face recognition systems.

Elon Musk has done it again. The CEO of Tesla Motors and SpaceX has made an aggressive prediction for his technology, and paired it with an unreasonable deadline. In the past, he's promised electric cars for everyone and trips to Mars, and built a reputation for achieving those outrageous things, albeit far behind schedule. Now, Musk is pledging that by the end of 2017, he'll produce a Tesla that can drive itself from Los Angeles to New York City, no human needed. That timeline puts him years ahead of every other big player working on fully autonomous cars.

An AI judge has accurately predicted most verdicts of the European Court of Human Rights, and might soon be making important decisions about cases. Scientists built an artificially intelligence computer that was able to look at legal evidence as well as considering ethical questions to decide how a case should be decided. And it predicted those with 79 per cent accuracy, according to its creators. The algorithm looked at data sets made up 584 cases relating to torture and degrading treatment, fair trials and privacy. The computer was able to look through that information and make its own decision – which lined up with those made by Europe's most senior judges in almost every case.

"Too big to fail" strategy did not save banks from failing in financial crush in 2008. Recent news about content meets pipe by merging AT&T and Time warner or previous news Comcast was buying Timer warner that was not successful. Delta airlines almost bought southwest airlines that was blocked. It was not blocked when Delta bought northwest. JP Morgan Chase bought several Banks during financial crush and became one of the biggest financial institution ever. Continuous effort to grow bigger and making their stock price higher.

The rise of artificial intelligence (AI) and the growing maturity of cloud-based business software promise to dramatically change the role of the accountant over the next five to 10 years. Savvy professionals should already be reskilling themselves in anticipation of the shift in the market. The Finance Indaba is taking place today and tomorrow – it's the perfect platform for finance professionals to gear up and learn about the changing accounting world from leaders in the industry He says that the arrival of smart software bots, paired with the affordability of cloud-based business applications, will change the way that accountants work as vividly as the first spreadsheet and accounting software packages did. "Financial software is getting smarter, more affordable and easier to use, so more and more of the admin accountants typically do for the business is becoming automated," Cohen says. "What's more, intuitive software paired with AI and other new developments, could empower small business owners do more of the tasks they used to entrust to an accountant."

Takuya Onishi used a robotic arm to safely perform the delicate mission of capturing the Cygnus cargo ship upon its arrival at the International Space Station. Astronaut Onishi, 40, maneuvered the arm and attached the Cygnus to the Earth-facing port of the ISS Node-One module at around 8:30 p.m. Sunday Japan time. His fellow Japanese astronaut, Kimiya Yui, 46, performed a similar maneuver during his stint aboard the ISS in August 2015 when he captured Japan's Kounotori resupply craft. The Cygnus ship was developed by U.S. aerospace and defense company Orbital ATK Inc. Its approach to the ISS and docking employs the same technology as the Kounotori.

Artifical intelligence is set to transform the marketing and communications world even more than social media has, according to 55 percent of CMOs surveyed by Weber Shandwick across five markets. The agency's latest study examines current consumer knowledge and attitudes toward AI in the US, UK, Brazil, China and Canada. Of the 150 senior executives surveyed, 68 percent said their brand is currently selling, using or planning for business in the AI era. Moreover, nearly six in 10 believe that within the next five years, companies will need to compete in the AI space to succeed. Weber Shandwick also polled 2,100 consumers across the five markets, and found that Chinese consumers (31 percent) report having the strongest knowledge of AI, while UK consumers report the weakest (10 percent).

Vector representations of words have heralded a transformational approach to classical problems in NLP; the most popular example is word2vec. However, a single vector does not suffice to model the polysemous nature of many (frequent) words, i.e., words with multiple meanings. In this paper, we propose a three-fold approach for unsupervised polysemy modeling: (a) context representations, (b) sense induction and disambiguation and (c) lexeme (as a word and sense pair) representations. A key feature of our work is the finding that a sentence containing a target word is well represented by a low rank subspace, instead of a point in a vector space. We then show that the subspaces associated with a particular sense of the target word tend to intersect over a line (one-dimensional subspace), which we use to disambiguate senses using a clustering algorithm that harnesses the Grassmannian geometry of the representations. The disambiguation algorithm, which we call $K$-Grassmeans, leads to a procedure to label the different senses of the target word in the corpus -- yielding lexeme vector representations, all in an unsupervised manner starting from a large (Wikipedia) corpus in English. Apart from several prototypical target (word,sense) examples and a host of empirical studies to intuit and justify the various geometric representations, we validate our algorithms on standard sense induction and disambiguation datasets and present new state-of-the-art results.

Subspace clustering is the problem of partitioning unlabeled data points into a number of clusters so that data points within one cluster lie approximately on a low-dimensional linear subspace. In many practical scenarios, the dimensionality of data points to be clustered are compressed due to constraints of measurement, computation or privacy. In this paper, we study the theoretical properties of a popular subspace clustering algorithm named sparse subspace clustering (SSC) and establish formal success conditions of SSC on dimensionality-reduced data. Our analysis applies to the most general fully deterministic model where both underlying subspaces and data points within each subspace are deterministically positioned, and also a wide range of dimensionality reduction techniques (e.g., Gaussian random projection, uniform subsampling, sketching) that fall into a subspace embedding framework (Meng & Mahoney, 2013; Avron et al., 2014). Finally, we apply our analysis to a differentially private SSC algorithm and established both privacy and utility guarantees of the proposed method.