The world of work is changing radically. Each of us, to a greater or lesser extent, has to come to terms with new forms of interaction, business and work flows. New actors have appeared on the scene, clad not in flesh and blood, but in circuits and transistors: Artificial Intelligence systems, which are increasingly present in the management of a company's personnel. The recent work, elaborated by the Global Legal Group Ltd. of London, entitled "AI, automatic learning & Big Data -- Third Edition", in which various situations are analyzed, in which this new cybernetic actor enters by force into the global market, appears very interesting. As stated in the introduction, more and more employers are relying on these automated systems to decide on recruitment, select curricula, issue disciplinary measures or make dismissals!

In 1958, the New York Times reported on a demonstration by the US Navy of Frank Rosenblatt's "perceptron" (a rudimentary precursor to today's deep neural networks): "The Navy revealed the embryo of an electronic computer today that it expects will be able to walk, talk, see, write, reproduce itself, and be conscious of its existence". This optimistic take was quickly followed by similar proclamations from AI pioneers, this time about the promise of logic-based "symbolic" AI. In 1960 Herbert Simon declared that, "Machines will be capable, within twenty years, of doing any work that a man can do". The following year, Claude Shannon echoed this prediction: "I confidently expect that within a matter of 10 or 15 years, something will emerge from the laboratory which is not too far from the robot of science fiction fame". And a few years later Marvin Minsky predicted that, "Within a generation...the problems of creating'artificial intelligence' will be substantially solved". John McCarthy promoted the term Artificial Intelligence with a wishful thinking that, 'Every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made to find how to make machines use language, form abstractions, and concepts, solve the kinds of problems now reserved for humans, and improve themselves.' AI was assumed to simulate human reasoning, giving the ability of a computer program to learn and think.

WASHINGTON -- Winners of the Department of Veterans Affairs 2020-2021 Artificial Intelligence Tech Sprint are six tech companies that created programs aimed at preventing Veteran suicide and improving their health care using the latest AI technology. VA's National Artificial Intelligence Institute competition encourages innovators to develop ways to improve services for Veterans. VA also gave $5,000 awards to JumpStartCSR for an app that integrates with physical therapy to prevent and treat injuries; HIVE Lab at George Washington University for a an app that helps Veterans manage conditions such as diabetes by personalizing treatments based on gut microbiome; and Ouva, LLC for a platform that helps clinicians better monitor vital signs and other health care issues for patients in isolation. The intent of the sprint is to match the private sector with Veterans, VA clinicians and other experts who mentor the companies to brainstorm solutions and new ideas over a three-month period. VA will further evaluate the best ideas and products to potentially adopt at pilot sites and then roll out nationwide.

Click to learn more about author Anne Hardy. Artificial intelligence (AI) is no longer the future – it's already in our homes, cars, and pockets. As technology expands its role in our lives, an important question has emerged: What level of trust can – and should – we place in these AI systems? Trust is the very question the European Union (EU) Commission has set out to answer under its newly proposed EU Artificial Intelligence Act. Margrethe Vestager, Executive Vice President of the European Commission for A Europe Fit for the Digital Age, stated that trust is a must with AI.

Humans progress faster when we learn from the experience of others, and scientists at the Defense Advanced Research Programs Agency, or DARPA, want to translate that to lifelong learning models for artificial intelligence. The research agency opened a new Artificial Intelligence Exploration Opportunity to fund work on "the technical domain of lifelong learning by agents"--AI systems--"that share their experience with each other," according to an announcement on SAM.gov. DARPA is offering up to $1 million per proposal under the Shared-Experience Lifelong Learning, or ShELL, program. "Lifelong learning is a relatively new area of machine learning research, in which agents continually learn as they encounter varying conditions and tasks while deployed in the field, acquiring experience and knowledge and improving performance on both novel and previous tasks," the funding announcement states. The notice details how this is different from traditional "train-then-deploy" machine learning, which tend to fail in three ways: While lifelong learning is not a new concept for AI research, the announcement notes current research has focused on learning patterns for individual systems, rather than "populations of LL agents that benefit from each other's experiences.

This paper surveys the area of Trust Metrics related to security for autonomous robotic systems. As the robotics industry undergoes a transformation from programmed, task oriented, systems to Artificial Intelligence-enabled learning, these autonomous systems become vulnerable to several security risks, making a security assessment of these systems of critical importance. Therefore, our focus is on a holistic approach for assessing system trust which requires incorporating system, hardware, software, cognitive robustness, and supplier level trust metrics into a unified model of trust. We set out to determine if there were already trust metrics that defined such a holistic system approach. While there are extensive writings related to various aspects of robotic systems such as, risk management, safety, security assurance and so on, each source only covered subsets of an overall system and did not consistently incorporate the relevant costs in their metrics. This paper attempts to put this prior work into perspective, and to show how it might be extended to develop useful system-level trust metrics for evaluating complex robotic (and other) systems.

Botnets are one of the online threats with the biggest presence, causing billionaire losses to global economies. Nowadays, the increasing number of devices connected to the Internet makes it necessary to analyze large amounts of network traffic data. In this work, we focus on increasing the performance on botnet traffic classification by selecting those features that further increase the detection rate. For this purpose we use two feature selection techniques, Information Gain and Gini Importance, which led to three pre-selected subsets of five, six and seven features. Then, we evaluate the three feature subsets along with three models, Decision Tree, Random Forest and k-Nearest Neighbors. To test the performance of the three feature vectors and the three models we generate two datasets based on the CTU-13 dataset, namely QB-CTU13 and EQB-CTU13. We measure the performance as the macro averaged F1 score over the computational time required to classify a sample. The results show that the highest performance is achieved by Decision Trees using a five feature set which obtained a mean F1 score of 85% classifying each sample in an average time of 0.78 microseconds.

We propose a Bi-Directional Manifold Alignment (BDMA) that learns a non-linear mapping between two manifolds by explicitly training it to be bijective. We demonstrate BDMA by training a model for a pair of languages rather than individual, directed source and target combinations, reducing the number of models by 50%. We show that models trained with BDMA in the "forward" (source to target) direction can successfully map words in the "reverse" (target to source) direction, yielding equivalent (or better) performance to standard unidirectional translation models where the source and target language is flipped. We also show how BDMA reduces the overall size of the model.

Cybersecurity is a very challenging topic of research nowadays, as digitalization increases the interaction of people, software and services on the Internet by means of technology devices and networks connected to it. The field is broad and has a lot of unexplored ground under numerous disciplines such as management, psychology, and data science. Its large disciplinary spectrum and many significant research topics generate a considerable amount of information, making it hard for us to find what we are looking for when researching a particular subject. This work proposes a new search engine for scientific publications which combines both information retrieval and reading comprehension algorithms to extract answers from a collection of domain-specific documents. The proposed solution although being applied to the context of cybersecurity exhibited great generalization capabilities and can be easily adapted to perform under other distinct knowledge domains.

Six agencies, including the U.S. Park Police and the FBI said they had used facial recognition on people who participated in protests after the killing of George Floyd by Minneapolis police officer in May 2020. The agencies said they only used it on people they suspected of breaking the law, according to the report. The U.S. Capitol Police used Clearview AI to conduct its investigation into the Jan. 6 attack on the Capitol. Customs and Border Protection and the State Department said they ran searches for Capitol rioters on their own databases at the request of other federal agencies.