Now that ARM has unveiled its first new chip architecture in a decade, it's ready to show the CPU designs that will take advantage of those improvements. The company has unveiled a host of new Cortex CPUs (and companion Mali GPUs) that it hopes will power laptops, other computers and wearables in addition to the next wave of smartphones. The flagship is the ARM Cortex-X2, a CPU core meant to scale from "premium" smartphones to laptops. It reportedly offers a 30 percent performance boost over current high-end Android phones, although ARM didn't provide more details. You'll also see gains for more mainstream uses.

The global pandemic has given us all an opportunity to pause for thought and take stock of what is and what is not important. More and more businesses are turning to AI to become more sustainable, smarter and to better react to changing market conditions, as well as to ensure health, safety and social impact of our planet. We need a future where you can do the things you love; live the life you deserve and take the time to grow with nature and nurture the things that inspire you to help others. From pandemic prevention and fighting cancer, to fighting hunger, wildlife conservation and boosting accessibility, this article will explore exactly how AI is doing well by doing good. AI use cases can help towards overall adaptation in preventing wildfires, diagnosing deadly diseases, mitigating risks posed in critical areas as well as predictive analysis and monitoring to make our planet more resilient in the near future.

We consider a rank-one symmetric matrix corrupted by additive noise. The rank-one matrix is formed by an $n$-component unknown vector on the sphere of radius $\sqrt{n}$, and we consider the problem of estimating this vector from the corrupted matrix in the high dimensional limit of $n$ large, by gradient descent for a quadratic cost function on the sphere. Explicit formulas for the whole time evolution of the overlap between the estimator and unknown vector, as well as the cost, are rigorously derived. In the long time limit we recover the well known spectral phase transition, as a function of the signal-to-noise ratio. The explicit formulas also allow to point out interesting transient features of the time evolution. Our analysis technique is based on recent progress in random matrix theory and uses local versions of the semi-circle law.

We propose a new graphical model inference procedure, called SG-PALM, for learning conditional dependency structure of high-dimensional tensor-variate data. Unlike most other tensor graphical models the proposed model is interpretable and computationally scalable to high dimension. Physical interpretability follows from the Sylvester generative (SG) model on which SG-PALM is based: the model is exact for any observation process that is a solution of a partial differential equation of Poisson type. Scalability follows from the fast proximal alternating linearized minimization (PALM) procedure that SG-PALM uses during training. We establish that SG-PALM converges linearly (i.e., geometric convergence rate) to a global optimum of its objective function. We demonstrate the scalability and accuracy of SG-PALM for an important but challenging climate prediction problem: spatio-temporal forecasting of solar flares from multimodal imaging data.

Intelligent behaviour in the physical world exhibits structure at multiple spatial and temporal scales. Although movements are ultimately executed at the level of instantaneous muscle tensions or joint torques, they must be selected to serve goals defined on much longer timescales, and in terms of relations that extend far beyond the body itself, ultimately involving coordination with other agents. Recent research in artificial intelligence has shown the promise of learning-based approaches to the respective problems of complex movement, longer-term planning and multi-agent coordination. However, there is limited research aimed at their integration. We study this problem by training teams of physically simulated humanoid avatars to play football in a realistic virtual environment. We develop a method that combines imitation learning, single- and multi-agent reinforcement learning and population-based training, and makes use of transferable representations of behaviour for decision making at different levels of abstraction. In a sequence of stages, players first learn to control a fully articulated body to perform realistic, human-like movements such as running and turning; they then acquire mid-level football skills such as dribbling and shooting; finally, they develop awareness of others and play as a team, bridging the gap between low-level motor control at a timescale of milliseconds, and coordinated goal-directed behaviour as a team at the timescale of tens of seconds. We investigate the emergence of behaviours at different levels of abstraction, as well as the representations that underlie these behaviours using several analysis techniques, including statistics from real-world sports analytics. Our work constitutes a complete demonstration of integrated decision-making at multiple scales in a physically embodied multi-agent setting. See project video at https://youtu.be/KHMwq9pv7mg.

Autonomous discovery and direct instruction are two extreme sources of learning in children, but educational sciences have shown that intermediate approaches such as assisted discovery or guided play resulted in better acquisition of skills. When turning to Artificial Intelligence, the above dichotomy is translated into the distinction between autonomous agents which learn in isolation and interactive learning agents which can be taught by social partners but generally lack autonomy. In between should stand teachable autonomous agents: agents learning from both internal and teaching signals to benefit from the higher efficiency of assisted discovery. Such agents could learn on their own in the real world, but non-expert users could drive their learning behavior towards their expectations. More fundamentally, combining both capabilities might also be a key step towards general intelligence. In this paper we elucidate obstacles along this research line. First, we build on a seminal work of Bruner to extract relevant features of the assisted discovery processes. Second, we describe current research on autotelic agents, i.e. agents equipped with forms of intrinsic motivations that enable them to represent, self-generate and pursue their own goals. We argue that autotelic capabilities are paving the way towards teachable and autonomous agents. Finally, we adopt a social learning perspective on tutoring interactions and we highlight some components that are currently missing to autotelic agents before they can be taught by ordinary people using natural pedagogy, and we provide a list of specific research questions that emerge from this perspective.

The detection of critical infrastructures is of high importance in several fields such as security, anomaly detection, land use planning and land use change detection. However, critical infrastructures detection in aerial and satellite images is still a challenge as each one has completely different size and requires different spacial resolution to be identified correctly. Heretofore, there are no special datasets for training critical infrastructures detectors. This paper presents a smart dataset as well as a resolution-independent critical infrastructure detection system. In particular, guided by the performance of the detection model, we built a dataset organized into two scales, small and large scale, and designed a two-stage deep learning detection of different scale critical infrastructures (DetDSCI) methodology in ortho-images. DetDSCI methodology first determines the input image zoom level using a classification model, then analyses the input image with the appropriate scale detection model. Our experiments show that DetDSCI methodology achieves up to 37,53% F1 improvement with respect to the baseline detector.

Stone Age humans were savvy hunters who devised long stone'runways' that could trap animals inside and make them easy prey to kill by the hundreds. These v-shaped structures, called'desert kites,' have been widely observed in the Middle East. Using laser scanning techniques, researchers in South Africa have confirmed these'desert kites' were used much further south in sub-Saharan Africa than previously believed. Found in Keimoes, South Africa, the desert kites are thousands of years newer than ones found in Israel and Syria and indicate a complex understanding of animal behavior and migratory patterns. Light Detection And Ranging, or LiDAR, technology illustrates where the stone walls of the desert kites' funnels were erected, guiding prey into a killing pit Desert kites were traps devised by Neolithic and Bronze Age hunter-gatherers to corner game like cattle, pig and deer.

Melissa Sassi is the Chief Penguin of IBM Hyper Protect Accelerator. Yes, she created her own penguin title! Melissa works with early stage entrepreneurs on digital and business transformation. Have you heard of Call for Code? It's an IBM initiative aimed at preparing for and responding to climate change via tech innovation.

Despite their impressive performance in NLP, self-attention networks were recently proved to be limited for processing formal languages with hierarchical structure, such as $\mathsf{Dyck}_k$, the language consisting of well-nested parentheses of $k$ types. This suggested that natural language can be approximated well with models that are too weak for formal languages, or that the role of hierarchy and recursion in natural language might be limited. We qualify this implication by proving that self-attention networks can process $\mathsf{Dyck}_{k, D}$, the subset of $\mathsf{Dyck}_{k}$ with depth bounded by $D$, which arguably better captures the bounded hierarchical structure of natural language. Specifically, we construct a hard-attention network with $D+1$ layers and $O(\log k)$ memory size (per token per layer) that recognizes $\mathsf{Dyck}_{k, D}$, and a soft-attention network with two layers and $O(\log k)$ memory size that generates $\mathsf{Dyck}_{k, D}$. Experiments show that self-attention networks trained on $\mathsf{Dyck}_{k, D}$ generalize to longer inputs with near-perfect accuracy, and also verify the theoretical memory advantage of self-attention networks over recurrent networks.