Thanks to the exponential growth of malware, traditional heuristics-based detection regimes have been overwhelmed, leaving computers at risk. Machine learning approaches can help, but the bottleneck presented by the feature engineering step is a potential dealbreaker. The best path forward at this point is deep learning, says the CEO of Deep Instinct, which claims to have taken an early lead in the emerging field. Ten years ago, the cybersecurity industry faced a dilemma. The volume of malware was exploding, with tens of thousands of new types discovered every day.

President and Chief Executive Officer at Insight Enterprises, helping clients manage their business today and transform for the future. If 2020 taught us anything about cybersecurity, it's that strengthening corporate defenses against cyberattacks is increasingly dependent on managing user identities of those who access your network. We've gradually moved in that direction since the birth of the bring-your-own-device movement, which created the need to control access to business data from outside the four walls of the office. The rise of the cloud, edge computing and the Internet of Things (IoT) have led us further along the path, requiring new strategies for managing access to resources across increasingly heterogeneous technology environments. Then Covid-19 triggered a work-from-home stampede.

The various institutions of the EU aim to be the rule makers and standard bearers for artificial intelligence and associated technology ("AI"). One AI use case which has come under particular scrutiny is that of facial recognition. Since we last wrote on the subject, it has become increasingly clear that the European Commission will take a restrictive approach to the use of facial recognition technology, especially when such use is in public areas. Earlier this year in April, the European Commission led the way in this area suggesting a legal framework for the regulation of facial recognition and certain types of AI systems. The draft legislation (also explained in a press release here) looks to create "trustworthy AI" which protects the fundamental rights of citizens while strengthening AI investment and innovation across the EU. The measures would restrict the use of live facial recognition to a very narrow set of scenarios where this would be deemed essential from a public interest perspective; such as the search for missing children or the policing of terrorist incidents.

Machine learning techniques have contributed to progress in science and technology fields ranging from health care to high-energy physics. Now, machine learning is poised to help accelerate the development of stronger alloys, particularly stainless steels, for America's thermal power generation fleet. Stronger materials are key to producing energy efficiently, resulting in economic and decarbonization benefits. "The use of ultra-high-strength steels in power plants dates back to the 1950s and has benefited from gradual improvements in the materials over time," says Osman Mamun, a postdoctoral research associate at Pacific Northwest National Laboratory (PNNL). "If we can find ways to speed up improvements or create new materials, we could see enhanced efficiency in plants that also reduces the amount of carbon emitted into the atmosphere."

Kirke Everson is a principal in KPMG's Federal Advisory practice, focusing on technology enablement, intelligent automation, program management, process improvement, cyber security, risk management, and financial management. He currently serves as the government lead for Intelligent automation for KPMG in the U.S. Federal and state government leaders are witnessing the expansion of artificial intelligence all around them. From back-office automation, that can help reduce backlogged work, to cognitive platforms, that can identify and respond to natural language requests to better serve the public, AI and automation has become a driving force in addressing mission and business objectives. Based on the use cases they described, it's clear that agencies are making significant headway in putting AI to work. At the same time, there a variety of issues where government CIOs also need broader support. The issues they and their executive teams face, in many ways, are not that different from previous technology breakthroughs that tended to upend familiar work processes.

Machine learning, a technique used in the artificial intelligence (AI) software behind self-driving cars and digital assistants, now enables scientists to address key challenges to harvesting on Earth the fusion energy that powers the sun and stars. The technique recently empowered physicist Dan Boyer of the U.S. Department of Energy's (DOE) Princeton Plasma Physics Laboratory (PPPL) to develop fast and accurate predictions for advancing control of experiments in the National Spherical Torus Experiment-Upgrade (NSTX-U)--the flagship fusion facility at PPPL that is currently under repair. Such AI predictions could improve the ability of NSTX-U scientists to optimize the components of experiments that heat and shape the magnetically confined plasma that fuels fusion experiments. By optimizing the heating and shaping of the plasma scientists will be able to more effectively study key aspects of the development of burning plasmas--largely self-heating fusion reactions--that will be critical for ITER, the international experiment under construction in France, and future fusion reactors. "This is a step toward what we should do to optimize the actuators," said Boyer, author of a paper in Nuclear Fusion that describes the machine learning tactics.

We present a new second-order oracle bound for the expected risk of a weighted majority vote. The bound is based on a novel parametric form of the Chebyshev-Cantelli inequality (a.k.a.\ one-sided Chebyshev's), which is amenable to efficient minimization. The new form resolves the optimization challenge faced by prior oracle bounds based on the Chebyshev-Cantelli inequality, the C-bounds [Germain et al., 2015], and, at the same time, it improves on the oracle bound based on second order Markov's inequality introduced by Masegosa et al. [2020]. We also derive the PAC-Bayes-Bennett inequality, which we use for empirical estimation of the oracle bound. The PAC-Bayes-Bennett inequality improves on the PAC-Bayes-Bernstein inequality by Seldin et al. [2012]. We provide an empirical evaluation demonstrating that the new bounds can improve on the work by Masegosa et al. [2020]. Both the parametric form of the Chebyshev-Cantelli inequality and the PAC-Bayes-Bennett inequality may be of independent interest for the study of concentration of measure in other domains.

While work in fields of CSCW (Computer Supported Collaborative Work), Psychology and Social Sciences have progressed our understanding of team processes and their effect performance and effectiveness, current methods rely on observations or self-report, with little work directed towards studying team processes with quantifiable measures based on behavioral data. In this report we discuss work tackling this open problem with a focus on understanding individual differences and its effect on team adaptation, and further explore the effect of these factors on team performance as both an outcome and a process. We specifically discuss our contribution in terms of methods that augment survey data and behavioral data that allow us to gain more insight on team performance as well as develop a method to evaluate adaptation and performance across and within a group. To make this problem more tractable we chose to focus on specific types of environments, Alternate Reality Games (ARGs), and for several reasons. First, these types of games involve setups that are similar to a real-world setup, e.g., communication through slack or email. Second, they are more controllable than real environments allowing us to embed stimuli if needed. Lastly, they allow us to collect data needed to understand decisions and communications made through the entire duration of the experience, which makes team processes more transparent than otherwise possible. In this report we discuss the work we did so far and demonstrate the efficacy of the approach.

Breakthroughs in machine learning in the last decade have led to `digital intelligence', i.e. machine learning models capable of learning from vast amounts of labeled data to perform several digital tasks such as speech recognition, face recognition, machine translation and so on. The goal of this thesis is to make progress towards designing algorithms capable of `physical intelligence', i.e. building intelligent autonomous navigation agents capable of learning to perform complex navigation tasks in the physical world involving visual perception, natural language understanding, reasoning, planning, and sequential decision making. Despite several advances in classical navigation methods in the last few decades, current navigation agents struggle at long-term semantic navigation tasks. In the first part of the thesis, we discuss our work on short-term navigation using end-to-end reinforcement learning to tackle challenges such as obstacle avoidance, semantic perception, language grounding, and reasoning. In the second part, we present a new class of navigation methods based on modular learning and structured explicit map representations, which leverage the strengths of both classical and end-to-end learning methods, to tackle long-term navigation tasks. We show that these methods are able to effectively tackle challenges such as localization, mapping, long-term planning, exploration and learning semantic priors. These modular learning methods are capable of long-term spatial and semantic understanding and achieve state-of-the-art results on various navigation tasks.

Collabware, a leading provider of archival, discovery, and records management software, today announced its pledge of $75,000 in cash and resources to contribute to an international research project aimed at the further understanding and responsible development of artificial intelligence (AI) for trustworthy records and archiving practices. "InterPARES is a 20-year project developing theoretical and methodological frameworks for sustainable and responsible information management and we are now launching phase 5, which focuses on the application of Artificial Intelligence," says Luciana Duranti, Director of I Trust AI. "Being able to create a feedback loop between researchers and users and get support from an organization, like Collabware, that is implementing AI in their software and for their customers, allows us to get real world access to understand its impacts and challenges." The Collabware financial commitment will be distributed over five years and include the time of Collabware staff members with expertise in records analysis and data science as consultants and industry experts, as well as, their expenses for participation in research meetings. The AI Project was awarded a $2.5 Million partnership grant this month by the Government of Canada's Social Sciences and Humanities Research Council (SSHRC) and over $3 Million matching funds in cash and in kind by its more than 60 partner organizations in 26 countries and 5 continents. "Collabware has been harnessing the immense computing power and automation of machine learning and artificial intelligence in our software development for years," says Graham Sibley, CEO of Collabware.