Companies who have some success with their initial AI projects are seeking ways to accelerate adoption to deliver more value to the business. One researcher has defined an AI Adoption Maturity Model that presents a roadmap for accelerating AI adoption. The first stage of the six-step AI adoption maturity model is the digitization of work, turning work in the physical world into digital processes that can be tracked and recorded as data, suggests Dr. Michael Wu, chief AI strategist for PROS Holdings, providing AI-based software as a service for pricing optimization, with a focus on the airline industry. "This stage is all about getting the data, which is the raw material for AI," stated Wu, in an account from ZDNet. "If you are on the digital transformation bandwagon, good for you. You are already in Stage 1 of this maturity curve."

Top science advisers to President Joe Biden are calling for a new "bill of rights" to guard against powerful new artificial intelligence technology. The White House s Office of Science and Technology Policy on Friday launched a fact-finding mission to look at facial recognition and other biometric tools used to identify people or assess their emotional or mental states and character. Biden's chief science adviser, Eric Lander, and the deputy director for science and society, Alondra Nelson, also published an opinion piece in Wired magazine detailing the need to develop new safeguards against faulty and harmful uses of AI that can unfairly discriminate against people or violate their privacy. "Enumerating the rights is just a first step," they wrote. "What might we do to protect them? Possibilities include the federal government refusing to buy software or technology products that fail to respect these rights, requiring federal contractors to use technologies that adhere to this'bill of rights,' or adopting new laws and regulations to fill gaps."

Learn why security needs to be integrated into every AI solution to ensure a smooth digital transformation that's safe from vicious cyberattacks on all fronts. After spending years as a data scientist and as part of data and analytics teams within HPE, I'm happy to see data and analytics careers ascend with the rise of artificial intelligence in both the IT and business world. Personally, it's been an interesting career journey that has gone from developing data models and analytics solutions to protecting them as a security practitioner of HPE GreenLake Cloud Services team. AI is perhaps the most used or even over-used term of the decade. The notion of AI has been a theoretical concept (and a fascinating movie theme) for many years.

The director of the Department of Defense's artificial intelligence clearinghouse hopes a new initiative will help combatant commands better make use of the department's data. The new Artificial Intelligence and Data Accelerator, or AIDA, is housed within the Joint Artificial Intelligence Center. "We're just now discovering what is the state of our data. Everybody loves to say that the Department of Defense has all kinds of data. Most of it's crap," Lt. Gen. Michael Groen, director of the JAIC, said during a pre-recorded interview shared at the Billington Cybersecurity Summit.

Deepfake technology could soon give anybody with a computer or phone the power of a Hollywood special effects department. In the next several years, technologists predict we will all be able to create photo-realistic videos and sound recordings using software enabled by artificial intelligence. That means instead of using cameras and microphones, next-generation "synthetic media'' will be completely generated by computers. Bill Whitaker looks at the state of the art today and volunteers as a guinea pig in an amazing deepfake transformation in which he becomes 30 years younger. The story will be broadcast on the next edition of 60 Minutes, Sunday, October 10 at 7 p.m. ET/PT on CBS. Nina Schick, a London-based researcher and political consultant was advising world leaders on Russian disinformation and election security when she first came across deepfakes. They have only gotten better since then. "The incredible thing about deepfakes and synthetic media is the pace of acceleration when it comes to the technology," Schick says. "By five to seven years, we are basically looking at a trajectory where any single creator -- so a YouTuber, a TikToker -- will be able to create the same level of visual effects that is only accessible to the most well-resourced Hollywood studio today." "It is without a doubt one of the most important revolutions in the future of human communication and perception.

The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. This week, The European Parliament, the body responsible for adopting European Union (EU) legislation, passed a non-binding resolution calling for a ban on law enforcement use of facial recognition technology in public places. The resolution, which also proposes a moratorium on the deployment of predictive policing software, would restrict the use of remote biometric identification unless it's to fight "serious" crime, such as kidnapping and terrorism. The approach stands in contrast to that of U.S. agencies, which continue to embrace facial recognition even in light of studies showing the potential for ethnic, racial, and gender bias. A recent report from the U.S. Government Accountability Office found that 10 branches including the Departments of Agriculture, Commerce, Defense, and Homeland Security plan to expand their use of facial recognition between 2020 and 2023 as they implement as many as 17 different facial recognition systems.

False data injection attacks (FDIA) are a main category of cyber-attacks threatening the security of power systems. Contrary to the detection of these attacks, less attention has been paid to identifying the attacked units of the grid. To this end, this work jointly studies detecting and localizing the stealth FDIA in power grids. Exploiting the inherent graph topology of power systems as well as the spatial correlations of measurement data, this paper proposes an approach based on the graph neural network (GNN) to identify the presence and location of the FDIA. The proposed approach leverages the auto-regressive moving average (ARMA) type graph filters (GFs) which can better adapt to sharp changes in the spectral domain due to their rational type filter composition compared to the polynomial type GFs such as Chebyshev. To the best of our knowledge, this is the first work based on GNN that automatically detects and localizes FDIA in power systems. Extensive simulations and visualizations show that the proposed approach outperforms the available methods in both detection and localization of FDIA for different IEEE test systems. Thus, the targeted areas can be identified and preventive actions can be taken before the attack impacts the grid.

Human body motions can be captured as a high-dimensional continuous signal using motion sensor technologies. The resulting data can be surprisingly rich in information, even when captured from persons with limited mobility. In this work, we explore the use of limited upper-body motions, captured via motion sensors, as inputs to control a 7 degree-of-freedom assistive robotic arm. It is possible that even dense sensor signals lack the salient information and independence necessary for reliable high-dimensional robot control. As the human learns over time in the context of this limitation, intelligence on the robot can be leveraged to better identify key learning challenges, provide useful feedback, and support individuals until the challenges are managed. In this short paper, we examine two uninjured participants' data from an ongoing study, to extract preliminary results and share insights. We observe opportunities for robot intelligence to step in, including the identification of inconsistencies in time spent across all control dimensions, asymmetries in individual control dimensions, and user progress in learning. Machine reasoning about these situations may facilitate novel interface learning in the future.

Deep Convolutional Neural Networks (CNN) models are one of the most popular networks in deep learning. With their large fields of application in different areas, they are extensively used in both academia and industry. CNN-based models include several exciting implementations such as early breast cancer detection or detecting developmental delays in children (e.g., autism, speech disorders, etc.). However, previous studies demonstrate that these models are subject to various adversarial attacks. Interestingly, some adversarial examples could potentially still be effective against different unknown models. This particular property is known as adversarial transferability, and prior works slightly analyzed this characteristic in a very limited application domain. In this paper, we aim to demystify the transferability threats in computer networks by studying the possibility of transferring adversarial examples. In particular, we provide the first comprehensive study which assesses the robustness of CNN-based models for computer networks against adversarial transferability. In our experiments, we consider five different attacks: (1) the Iterative Fast Gradient Method (I-FGSM), (2) the Jacobian-based Saliency Map attack (JSMA), (3) the L-BFGS attack, (4) the Projected Gradient Descent attack (PGD), and (5) the DeepFool attack. These attacks are performed against two well-known datasets: the N-BaIoT dataset and the Domain Generating Algorithms (DGA) dataset. Our results show that the transferability happens in specific use cases where the adversary can easily compromise the victim's network with very few knowledge of the targeted model.

Deep neural networks have achieved impressive performance in many areas. Designing a fast and provable method for training neural networks is a fundamental question in machine learning. The classical training method requires paying $\Omega(mnd)$ cost for both forward computation and backward computation, where $m$ is the width of the neural network, and we are given $n$ training points in $d$-dimensional space. In this paper, we propose two novel preprocessing ideas to bypass this $\Omega(mnd)$ barrier: $\bullet$ First, by preprocessing the initial weights of the neural networks, we can train the neural network in $\widetilde{O}(m^{1-\Theta(1/d)} n d)$ cost per iteration. $\bullet$ Second, by preprocessing the input data points, we can train the neural network in $\widetilde{O} (m^{4/5} nd )$ cost per iteration. From the technical perspective, our result is a sophisticated combination of tools in different fields, greedy-type convergence analysis in optimization, sparsity observation in practical work, high-dimensional geometric search in data structure, concentration and anti-concentration in probability. Our results also provide theoretical insights for a large number of previously established fast training methods. In addition, our classical algorithm can be generalized to the Quantum computation model. Interestingly, we can get a similar sublinear cost per iteration but avoid preprocessing initial weights or input data points.