With leaders increasingly seeing artificial intelligence (AI) as helping to drive the next great economic expansion, a fear of missing out is spreading around the globe. Numerous nations have developed AI strategies to advance their capabilities, through investment, incentives, talent development, and risk management. As AI's importance to the next generation of technology grows, many leaders are worried that they will be left behind and not share in the gains. There is a growing realization of AI's importance, including its ability to provide competitive advantage and change work for the better. A majority of global early adopters say that AI technologies are especially important to their business success today--a belief that is increasing. A majority also say they are using AI technologies to move ahead of their competition, and that AI empowers their workforce. AI success depends on getting the execution right. Organizations often must excel at a wide range of practices to ensure AI success, including developing a strategy, pursuing the right use cases, building a data foundation, and cultivating a strong ability to experiment. These capabilities are critical now because, as AI becomes even easier to consume, the window for competitive differentiation will likely shrink. Early adopters from different countries display varying levels of AI maturity. Enthusiasm and experience vary among early adopters from different countries. Some are pursuing AI vigorously, while others are taking a more cautious approach.

Law firms and legal departments are not taking advantage of artificial intelligence and machine learning tools that could be helping them run more efficiently, according to a new Bloomberg Law survey. Not On Board: Some 54 percent of respondents to the survey said they don't use these tools, while only 23 percent did. Another 24 percent were unsure about whether they used AI and machine learning, which Molly Huie, Bloomberg Law's team leader for data analysis and surveys, said could...

The answers people give when asked to 'think of the unexpected' for everyday event scenarios appear to be more expected than unexpected. There are expected unexpected outcomes that closely adhere to the given information in a scenario, based on familiar disruptions and common plan-failures. There are also unexpected unexpected outcomes that are more inventive, that depart from given information, adding new concepts/actions. However, people seem to tend to conceive of the unexpected as the former more than the latter. Study 1 tests these proposals by analysing the object-concepts people mention in their reports of the unexpected and the agreement between their answers. Study 2 shows that object-choices are weakly influenced by recency, the order of sentences in the scenario. The implications of these results for ideas in philosophy, psychology and computing is discussed

For 600 years it has steadfastly refused to give up its secrets and has beaten some of the world's most brilliant brains, including Alan Turing. Experts variously claimed that the Voynich manuscript - known as the'world's most mysterious text' - contained codes, magic spells, alien messages and even communist propaganda. Eventually most agreed that it was either impossible to solve or else written in gibberish as an elaborate practical joke. But a linguistics expert from the University of Bristol has now cracked it - and it took him just two weeks. Dr Gerard Cheshire worked out that it was written in a dead language - proto-Romance - and then by studying symbols and their descriptions he deciphered the meaning of the letters and words.

An advanced research arm of the U.S. government's intelligence community is looking to develop AI capable of tracking people across a vast surveillance network. As reported by Nextgov, the Intelligence Advanced Research Projects Activity (IARPA) has put out a call for more information on developing an algorithm that can be trained to identify targets by visually analyzing swaths of security camera footage. The goal, says the request, is to be able to identify and track subjects across areas as large as six miles in an effort to reconstruct crime scenes, protect military operations, and monitor critical infrastructure facilities. To develop the technology, IARPA will collect nearly 1,000 hours of video surveillance from at least 20 camera networks and then, using that sample, test various algorithms effectiveness. The agency's interest in AI-based surveillance technology mirrors a broader movement from governments and intelligence communities around the globe, many of whom have ramped up efforts to develop and scale systems.

San Francisco could become the first large city to bar police from using facial recognition software. They tried a facial recognition system for a time, but sources in the department say they gave up on it because it wasn't much good. But what is significant about this legislation is the way the city has now singled-out facial recognition going forward. AARON PESKIN: Facial recognition technology is uniquely dangerous and oppressive. KASTE: That's the legislation's author, Supervisor Aaron Peskin, explaining yesterday why his legislation allows for other kinds of surveillance tech but not facial recognition.

Tech Data on Wednesday said it's partnerubg with Deep Instinct for its end-to-end deep learning framework for cybersecurity. The Deep Instinct platform provides enhanced protection by predicting harmful known and unknown cyberattacks, terminating execution and preventing any possible damage. Tech Data's Alex Ryals "We believe artificial intelligence (AI)-based deep learning tools are the next wave in advanced cyberprotection," said Alex Ryals, vice president of security solutions, Americas, at Tech Data. "Because these tools are autonomous and constantly learning, not only do they require fewer updates, but unlike typical machine […]

This paper considers the real-time detection of anomalies in high-dimensional systems. The goal is to detect anomalies quickly and accurately so that the appropriate countermeasures could be taken in time, before the system possibly gets harmed. We propose a sequential and multivariate anomaly detection method that scales well to high-dimensional datasets. The proposed method follows a nonparametric, i.e., data-driven, and semi-supervised approach, i.e., trains only on nominal data. Thus, it is applicable to a wide range of applications and data types. Thanks to its multivariate nature, it can quickly and accurately detect challenging anomalies, such as changes in the correlation structure and stealth low-rate cyberattacks. Its asymptotic optimality and computational complexity are comprehensively analyzed. In conjunction with the detection method, an effective technique for localizing the anomalous data dimensions is also proposed. We further extend the proposed detection and localization methods to a supervised setup where an additional anomaly dataset is available, and combine the proposed semi-supervised and supervised algorithms to obtain an online learning algorithm under the semi-supervised framework. The practical use of proposed algorithms are demonstrated in DDoS attack mitigation, and their performances are evaluated using a real IoT-botnet dataset and simulations.

Security Analysts that work in a `Security Operations Center' (SoC) play a major role in ensuring the security of the organization. The amount of background knowledge they have about the evolving and new attacks makes a significant difference in their ability to detect attacks. Open source threat intelligence sources, like text descriptions about cyber-attacks, can be stored in a structured fashion in a cybersecurity knowledge graph. A cybersecurity knowledge graph can be paramount in aiding a security analyst to detect cyber threats because it stores a vast range of cyber threat information in the form of semantic triples which can be queried. A semantic triple contains two cybersecurity entities with a relationship between them. In this work, we propose a system to create semantic triples over cybersecurity text, using deep learning approaches to extract possible relationships. We use the set of semantic triples generated through our system to assert in a cybersecurity knowledge graph. Security Analysts can retrieve this data from the knowledge graph, and use this information to form a decision about a cyber-attack.

Clean-label poisoning attacks inject innocuous looking (and "correctly" labeled) poison images into training data, causing a model to misclassify a targeted image after being trained on this data. We consider transferable poisoning attacks that succeed without access to the victim network's outputs, architecture, or (in some cases) training data. To achieve this, we propose a new "polytope attack" in which poison images are designed to surround the targeted image in feature space. We also demonstrate that using Dropout during poison creation helps to enhance transferability of this attack. We achieve transferable attack success rates of over 50% while poisoning only 1% of the training set.