Businesses are increasing the pace of investment in AI systems to defend against the next generation of cyberattacks, a new study from the Capgemini Research Institute has found. Two thirds (69%) of organizations acknowledge that they will not be able to respond to critical threats without AI. With the number of end-user devices, networks, and user interfaces growing as a result of advances in the cloud, IoT, 5G and conversational interface technologies, organizations face an urgent need to continually ramp up and improve their cybersecurity. AI-enabled cybersecurity is now an imperative: Over half (56%) of executives say their cybersecurity analysts are overwhelmed by the vast array of data points they need to monitor to detect and prevent intrusion. Accordingly, almost half (48%) said that budgets for AI in cybersecurity will increase in FY2020 by nearly a third (29%).

Despite recent doom-and-gloom anecdotal reporting, a nationwide survey of 1,001 workers in the United States (U.S.) finds that 70% have an upbeat attitude toward new workplace technologies involving artificial intelligence (AI), such as chatbots, robots and augmented reality. Only 5% say they dislike new technology for putting their jobs at risk today. In fact, 32% of U.S. respondents feel AI will have a positive impact on their job in the next five years, increasing from 26% today. Just 19% of those surveyed express fear that AI/bots could swallow their jobs within the next decade. These findings stem from new research by Genesys (www.genesys.com),

By bridging deep networks and physics, the programme of geometrization of deep networks was proposed as a framework for the interpretability of deep learning systems. Following this programme we can apply two key ideas of physics, the geometrization of physics and the least action principle, on deep networks and deliver a new picture of deep networks: deep networks as memory space of information, where the capacity, robustness and efficiency of the memory are closely related with the complexity, generalization and disentanglement of deep networks. The key components of this understanding include:(1) a Fisher metric based formulation of the network complexity; (2)the least action (complexity=action) principle on deep networks and (3)the geometry built on deep network configurations. We will show how this picture will bring us a new understanding of the interpretability of deep learning systems.

Something stood out of the ordinary during a speech by China's president, Xi Jinping, in January 2018. Behind Xi, on a bookshelf, were two books on artificial intelligence (AI). Why were those books there? Similar to 2015, when Russia "accidentally" aired designs for a new weapon, the placement of the books may not have been an accident. Was China sending a message?

CHIEF financial officers (CFOs) and their finance teams need to understand how to anticipate and respond to artificial intelligence (AI), as it will be a key frontier technology to grow Singapore's economy in the years ahead, CPA Australia said on Thursday. The accounting professional body also urged professionals in the sector to upskill, particularly in terms of data mining, extraction and faster interpretation of big data. To help them navigate this digital journey, CPA Australia has published a resource titled Charting the Future of Accountancy with AI, in collaboration with Singapore Management University's School of Accountancy. The practical guide looks at how AI will reshape the accounting and finance sector in the coming years, and what the profession can do to continue to operate alongside the evolving technology and their changing roles. It draws on insights from professional services firms Accenture, Deloitte, EY, KPMG and PwC, as well as the Singapore Management University.

Facial recognition could be used to replace swipe cards on public transport, the New South Wales government has suggested, but the opposition and digital rights groups say it would pose a risk to privacy. The transport minister, Andrew Constance, said on Tuesday he wanted commuters "in the not too distant future" to be able to board trains using only their faces, with no need for Opal cards, barriers or turnstiles. "I'm about to outline some concepts which may seem pretty crazy and far-fetched," he told the Sydney Institute on Tuesday. "But look at it this way – who would have thought in 1970 that you'd be able to use a handheld device to have a video conversation with someone on the other side of the world? "I want people to not think about their travel.

Since its inception in the 1980s, ID3 has become one of the most successful and widely used algorithms for learning decision trees. However, its theoretical properties remain poorly understood. In this work, we analyze the heuristic of growing a decision tree with ID3 for a limited number of iterations $t$ and given that nodes are split as in the case of exact information gain and probability computations. In several settings, we provide theoretical and empirical evidence that the TopDown variant of ID3, introduced by Kearns and Mansour (1996), produces trees with optimal or near-optimal test error among all trees with $t$ internal nodes. We prove optimality in the case of learning conjunctions under product distributions and learning read-once DNFs with 2 terms under the uniform distribition. Using efficient dynamic programming algorithms, we empirically show that TopDown generates trees that are near-optimal ($\sim \%1$ difference from optimal test error) in a large number of settings for learning read-once DNFs under product distributions.

Not all types of supervision signals are created equal: Different types of feedback have different costs and effects on learning. We show how self-regulation strategies that decide when to ask for which kind of feedback from a teacher (or from oneself) can be cast as a learning-to-learn problem leading to improved cost-aware sequence-to-sequence learning. In experiments on interactive neural machine translation, we find that the self-regulator discovers an $\epsilon$-greedy strategy for the optimal cost-quality trade-off by mixing different feedback types including corrections, error markups, and self-supervision. Furthermore, we demonstrate its robustness under domain shift and identify it as a promising alternative to active learning.

We present BRIGITTE, a new path-finding algorithm for 8-connected grids. It is based on the notion bridge that we define here, i.e., a high-level description of paths between all pairs of points from two convex regions that allows fast distance query and fast generation of the prefix and suffix of these paths. BRIGITTE uses a pre-processing step to first partition the map into convex regions and then compute a sufficient set of bridges between every pair of regions. Path-finding is then performed by looking up the regions of the source and target cells and then iterating over the bridges of the pair of regions to determine which one yields the shortest path. BRIGITTE competes favourably compared to CH-SG-R and Copp, although this currently comes at a price of an extensive pre-processing.

Recent work in bidirectional front-to-end heuristic search has led to the development of novel algorithms that have advanced the state of the art after many years without major developments. In particular, three different algorithms with very different behavior have been proposed: MM, NBS and GBFHS. In this paper we perform a theoretical comparison of these algorithms, defining lower and upper bounds for each of them and analyzing why a given algorithm displays beneficial characteristics that the others lack. With this information, we propose a simple and intuitive near-optimal algorithm to be used as a baseline for comparison in bidirectional front-to-end heuristic search.