Gaussian processes (GPs) are non-linear probabilistic models popular in many applications. However, na\"ive GP realizations require quadratic memory to store the covariance matrix and cubic computation to perform inference or evaluate the likelihood function. These bottlenecks have driven much investment in the development of approximate GP alternatives that scale to the large data sizes common in modern data-driven applications. We present in this manuscript MuyGPs, a novel efficient GP hyperparameter estimation method. MuyGPs builds upon prior methods that take advantage of the nearest neighbors structure of the data, and uses leave-one-out cross-validation to optimize covariance (kernel) hyperparameters without realizing a possibly expensive likelihood. We describe our model and methods in detail, and compare our implementations against the state-of-the-art competitors in a benchmark spatial statistics problem. We show that our method outperforms all known competitors both in terms of time-to-solution and the root mean squared error of the predictions.

Tensors, which provide a powerful and flexible model for representing multi-attribute data and multi-way interactions, play an indispensable role in modern data science across various fields in science and engineering. A fundamental task is to faithfully recover the tensor from highly incomplete measurements in a statistically and computationally efficient manner. Harnessing the low-rank structure of tensors in the Tucker decomposition, this paper develops a scaled gradient descent (ScaledGD) algorithm to directly recover the tensor factors with tailored spectral initializations, and shows that it provably converges at a linear rate independent of the condition number of the ground truth tensor for two canonical problems -- tensor completion and tensor regression -- as soon as the sample size is above the order of $n^{3/2}$ ignoring other dependencies, where $n$ is the dimension of the tensor. This leads to an extremely scalable approach to low-rank tensor estimation compared with prior art, which suffers from at least one of the following drawbacks: extreme sensitivity to ill-conditioning, high per-iteration costs in terms of memory and computation, or poor sample complexity guarantees. To the best of our knowledge, ScaledGD is the first algorithm that achieves near-optimal statistical and computational complexities simultaneously for low-rank tensor completion with the Tucker decomposition. Our algorithm highlights the power of appropriate preconditioning in accelerating nonconvex statistical estimation, where the iteration-varying preconditioners promote desirable invariance properties of the trajectory with respect to the underlying symmetry in low-rank tensor factorization.

New computing technologies inspired by the brain promise fundamentally different ways to process information with extreme energy efficiency and the ability to handle the avalanche of unstructured and noisy data that we are generating at an ever-increasing rate. To realise this promise requires a brave and coordinated plan to bring together disparate research communities and to provide them with the funding, focus and support needed. We have done this in the past with digital technologies; we are in the process of doing it with quantum technologies; can we now do it for brain-inspired computing?

Recent advances in natural language processing and computer vision have led to AI models that interpret simple scenes at human levels. Yet, we do not have a complete understanding of how humans and AI models differ in their interpretation of more complex scenes. We created a dataset of complex scenes that contained human behaviors and social interactions. AI and humans had to describe the scenes with a sentence. We used a quantitative metric of similarity between scene descriptions of the AI/human and ground truth of five other human descriptions of each scene. Results show that the machine/human agreement scene descriptions are much lower than human/human agreement for our complex scenes. Using an experimental manipulation that occludes different spatial regions of the scenes, we assessed how machines and humans vary in utilizing regions of images to understand the scenes. Together, our results are a first step toward understanding how machines fall short of human visual reasoning with complex scenes depicting human behaviors.

PARIS--The government of French President Emmanuel Macron aims to deploy algorithms and other technology to monitor the web-browsing of terror suspects amid growing tensions over a group of retired generals who recently warned the country was sliding toward a civil war. On Wednesday, Prime Minister Jean Castex said the government plans to submit a bill to parliament seeking permanent authority to order telecommunications companies to monitor not just telephone data but also the full URLs of specific webpages their users visit in real time. Government algorithms would alert intelligence officials when certain criteria are met, such as an internet user visiting a specific sequence of pages. Mr. Macron has come under intense pressure to crack down on terrorism as well as Islamist separatism, an ideology his government says fuels attacks by radicalizing segments of France's Muslim minority. A middle-school teacher was beheaded in a terrorist attack in October, and on Friday an administrative police worker was stabbed to death in a terrorist attack on a police station.

Cybersecurity failure could be among the greatest challenges confronting the world in the next decade, according to the World Economic Forum's Global Risks Report 2021. As artificial intelligence (AI) becomes increasingly embedded worldwide, fresh questions arise about how to safeguard countries and systems against attacks. To deal with the vulnerabilities of AI, engineers and developers need to evaluate existing security methods, develop new tools and strategies, and formulate technical guidelines and standards, said Arndt Von Twickel, Technical Officer at Germany's Federal Office for Information Security (BSI), at a recent AI for Good webinar. So-called "connectionist AI" systems support safety-critical applications like autonomous driving, which is set to be allowed on United Kingdom roads this year. Despite reaching "superhuman" performance levels in complex tasks like manoeuvring a vehicle, AI systems can still make critical mistakes based on misunderstood inputs.

US automakers have outlined principles designed to encourage drivers to pay attention to the road while driving partially automated vehicles as political scrutiny of the technology intensifies following a series of fatal crashes. The proposals, published yesterday before a Senate subcommittee hearing on the future of automotive safety and technology, come days after two men using Tesla's Autopilot driver-assist system were killed in a crash near Houston. Executives with the Alliance for Automotive Innovation and Motor & Equipment Manufacturers Association, which represents at least 20 automakers including General Motors, Ford and Toyota, proposed that vehicles with auto-driving systems should include driver monitoring as standard equipment. Those systems could include cameras to make sure drivers are paying attention, and that those systems should be designed so they cannot be "disengaged or disabled". If drivers don't pay attention, car features should issue warnings or take corrective action such as disengaging the automated systems.

A new CEPS Task Force report proposes concrete policy measures and recommendations to help ease the adoption of AI in cybersecurity and address the security and reliability of AI systems. Artificial Intelligence (AI) is gradually being integrated into the fabric of business and widely deployed across specific applications use cases. Not all sectors are equally advanced, however: the information technology and telecommunications sector are the most advanced in terms of AI adoption, with the automotive falling just behind. According to a recent global survey that polled more than 4,500 technology decision-makers across different sectors, 45% of large companies and 29% of SMEs said they had adopted AI. In the cybersecurity sector, AI will become increasingly indispensable to manage cyber threats: indeed, the market is expected to grow at a Compound Annual Growth Rate (CAGR) of 23.6% from 2020 to 2027 and to reach $46.3 billion by 2027. At the same time, the adoption of AI is not without risks in itself: more than 60% of companies adopting AI recognise cybersecurity risks generated by AI as the most relevant ones.

The European Vision for AI event, held on 22 April 2021, provided an opportunity for the public to hear from members of the European artificial intelligence (AI) community and representatives from the European Commission and parliament. The morning-long session was organised by the VISION project partners in cooperation with four networks of AI centres of excellence (AI4Media, ELISE, TAILOR, Humane-AI-Net). These networks were launched within the European Union's Horizon 2020 Programme in September 2020 and are bringing together scientists across Europe. This event followed hot on the heels of the announcement from the European Commission regarding proposed new rules and actions for artificial intelligence. During the morning, the speakers provided some context and details around this and there was plenty of interesting discussion on potential paths forward for AI in Europe.

Enemy drone attack threats are a key part of the inspiration for newer kinds of laser weapons because they can incinerate drones without generating large amounts of explosive fragmentation. Moreover, newer lasers can scale attacks to align with the target and desired combat effect and, perhaps most of all, travel at the speed of light to destroy drones quickly, ideally before they are able to strike. Attacking drone swarms may be approaching for attack so quickly that kinetic responses such as interceptor missile fire control systems may be challenged in certain respects, depending upon the extent of artificial intelligence (AI)-enabled target recognition technology and computer automation. The question of scaling lasers to optimize power input for counter-drone strikes is addressed in a recent essay from May of last year called "Testing the Efficiency of Laser Technology to Destroy Rogue Drones," in the Security & Defense Quarterly from War Studies University. The essay describes innovative experimental methods of "incorporating a laser module and groups of optical lenses to focus the power in one point to carbonize any target."