A group of researchers from Stanford have developed a way to combine processors and memory on multiple hybrid chips to allow AI to run on battery-powered devices such as smartphones and tablets. The team believes that all manner of battery-power electronics would be smarter if they could run AI algorithms. The problem is efforts to build AI-capable chips for mobile devices have run up against something known as the "memory wall." The memory wall is the name for the separation of data processing and memory chips that have to work together to meet the computational demands of AI. Computer scientist Subhasish Mitra says the transactions between processors and memory can consume 95 percent of the energy needed to perform machine learning and AI, severely limiting battery life.

-- Human beings can utilize multiple balance strategies, e.g. In this work, we propose a novel Nonlinear Model Predictive Control (NMPC) framework for robust locomotion, with the capabilities of step location adjustment, Center of Mass (CoM) height variation, and angular momentum adaptation. These features are realized by constraining the Zero Moment Point within the support polygon. By using the nonlinear inverted pendulum plus flywheel model, the effects of upper-body rotation and vertical height motion are considered. As a result, the NMPC is formulated as a quadratically constrained quadratic program problem, which is solved fast by sequential quadratic programming. Using this unified framework, robust walking patterns that exploit reactive stepping, body inclination, and CoM height variation are generated based on the state estimation. The adaptability for bipedal walking in multiple scenarios has been demonstrated through simulation studies. Humanoid robots have attracted much attention for their capabilities in accomplishing challenging tasks in real-world environments. With several decades passed, state-of-the-art robot platforms such as ASIMO [1], Atlas [2], W ALK-MAN [3], and CogIMon [4] have been developed for this purpose. However, due to the complex nonlinear dynamics of bipedal locomotion over the walking process, enhancing walking stability, which is among the prerequisites in making humanoids practical, still needs further studies. In this paper, inspired by the fact that human beings can make use of the redundant Degree of Freedom (DoF) and adopt various strategies, such as the ankle, hip, and stepping strategies, to realize balance recovery [5]-[7], we aim to develop a versatile and robust walking pattern generator which can integrate multiple balance strategies in a unified way. To generate the walking pattern in a time-efficient manner, simplified dynamic models have been proposed, among which the Linear Inverted Pendulum Model (LIPM) is widely used [8]. Using the LIPM, Kajita et al. proposed the preview control for Zero Moment Point (ZMP) tracking [9]. By adopting a Linear Quadratic Regulator (LQR) scheme, the ankle torque was adjusted to modulate the ZMP trajectory and Center of Mass (CoM) trajectory. Nevertheless, this strategy can neither modulate the step parameters nor take into consideration the feasibility constraints arisen from actuation limitations and environmental constraints. To overcome this drawback, Wieber et al. proposed a Model Predictive Control (MPC) algorithm to utilize the ankle strategy [10] and then extended it for adjusting step location [11].

We propose a reinforcement learning (RL) approach to model optimal exercise strategies for option-type products. We pursue the RL avenue in order to learn the optimal action-value function of the underlying stopping problem. In addition to retrieving the optimal Q-function at any time step, one can also price the contract at inception. We first discuss the standard setting with one exercise right, and later extend this framework to the case of multiple stopping opportunities in the presence of constraints. We propose to approximate the Q-function with a deep neural network, which does not require the specification of basis functions as in the least-squares Monte Carlo framework and is scalable to higher dimensions. We derive a lower bound on the option price obtained from the trained neural network and an upper bound from the dual formulation of the stopping problem, which can also be expressed in terms of the Q-function. Our methodology is illustrated with examples covering the pricing of swing options.

The field of artificial intelligence is moving at a staggering clip, with breakthroughs emerging in labs across MIT. Through the Undergraduate Research Opportunities Program (UROP), undergraduates get to join in. In two years, the MIT Quest for Intelligence has placed 329 students in research projects aimed at pushing the frontiers of computing and artificial intelligence, and using these tools to revolutionize how we study the brain, diagnose and treat disease, and search for new materials with mind-boggling properties. Rafael Gomez-Bombarelli, an assistant professor in the MIT Department of Materials Science and Engineering, has enlisted several Quest-funded undergraduates in his mission to discover new molecules and materials with the help of AI. "They bring a blue-sky open mind and a lot of energy," he says. "Through the Quest, we had the chance to connect with students from other majors who probably wouldn't have thought to reach out."

Feature attribution is widely used in interpretable machine learning to explain how influential each measured input feature value is for an output inference. However, measurements can be uncertain, and it is unclear how the awareness of input uncertainty can affect the trust in explanations. We propose and study two approaches to help users to manage their perception of uncertainty in a model explanation: 1) transparently show uncertainty in feature attributions to allow users to reflect on, and 2) suppress attribution to features with uncertain measurements and shift attribution to other features by regularizing with an uncertainty penalty. Through simulation experiments, qualitative interviews, and quantitative user evaluations, we identified the benefits of moderately suppressing attribution uncertainty, and concerns regarding showing attribution uncertainty. This work adds to the understanding of handling and communicating uncertainty for model interpretability.

Unsupervised clustering is a branch of machine learning that aims to categorize the data based on the self-similarity. In other word, data-points in the same group (called a cluster) are more similar to each other than to those in other groups. This task can be achieved by various algorithms (the well-known K-means or spectral clustering but also hierarchical clustering [1] or density-based clustering [2]) that differ significantly in their understanding of what constitutes a cluster and how to efficiently find them. In many cases, there exist models/functions, governed by a finite set of parameters, providing either physics or phenomenology correlations between input data. The presence of these models can in principle be used to characterize clusters (cluster characterization) because one can define a loss function to measure how well a point belongs to this cluster (cluster affiliation).

To reduce Operation and Maintenance (O&M) costs on offshore wind farms, wherein 80% of the O&M cost relates to deploying personnel, the offshore wind sector looks to robotics and Artificial Intelligence (AI) for solutions. Barriers to Beyond Visual Line of Sight (BVLOS) robotics include operational safety compliance and resilience, inhibiting the commercialization of autonomous services offshore. To address safety and resilience challenges we propose a symbiotic system; reflecting the lifecycle learning and co-evolution with knowledge sharing for mutual gain of robotic platforms and remote human operators. Our methodology enables the run-time verification of safety, reliability and resilience during autonomous missions. We synchronize digital models of the robot, environment and infrastructure and integrate front-end analytics and bidirectional communication for autonomous adaptive mission planning and situation reporting to a remote operator. A reliability ontology for the deployed robot, based on our holistic hierarchical-relational model, supports computationally efficient platform data analysis. We analyze the mission status and diagnostics of critical sub-systems within the robot to provide automatic updates to our run-time reliability ontology, enabling faults to be translated into failure modes for decision making during the mission. We demonstrate an asset inspection mission within a confined space and employ millimeter-wave sensing to enhance situational awareness to detect the presence of obscured personnel to mitigate risk. Our results demonstrate a symbiotic system provides an enhanced resilience capability to BVLOS missions. A symbiotic system addresses the operational challenges and reprioritization of autonomous mission objectives. This advances the technology required to achieve fully trustworthy autonomous systems.

We outline a perspective of an entirely new research branch in Earth and climate sciences, where deep neural networks and Earth system models are dismantled as individual methodological approaches and reassembled as learning, self-validating, and interpretable Earth system model-network hybrids. Following this path, we coin the term "Neural Earth System Modelling" (NESYM) and highlight the necessity of a transdisciplinary discussion platform, bringing together Earth and climate scientists, big data analysts, and AI experts. We examine the concurrent potential and pitfalls of Neural Earth System Modelling and discuss the open question whether artificial intelligence will not only infuse Earth system modelling, but ultimately render them obsolete.

However, most companies don't have the resources to implement sophisticated AI programs to stay secure and advance digital capabilities on their own. Irrespective of size, available budget, and in-house personnel, all energy companies must manage operations and security fundamentals to ensure they have visibility and monitoring across powerful digital tools to remain resilient and competitive. The achievement of that goal is much more likely in partnership with the right experts. MIT Technology Review Insights, in association with Siemens Energy, spoke to more than a dozen information technology (IT) and cybersecurity executives at oil and gas companies worldwide to gain insight about how AI is affecting their digital transformation and cybersecurity strategies in oil and gas operating environments. Energy sector organizations are presented with a major opportunity to deploy AI and build out a data strategy that optimizes production and uncovers new business models, as well as secure operational technology. Oil and gas companies are faced with unprecedented uncertainty--depressed oil and gas prices due to the coronavirus pandemic, a multiyear glut in the market, and the drive to go green--and many are making a rapid transition to digitalization as a matter of survival.

A universal fault-tolerant quantum computer that can solve efficiently problems such as integer factorization and unstructured database search requires millions of qubits with low error rates and long coherence times. While the experimental advancement towards realizing such devices will potentially take decades of research, noisy intermediate-scale quantum (NISQ) computers already exist. These computers are composed of hundreds of noisy qubits, i.e. qubits that are not error-corrected, and therefore perform imperfect operations in a limited coherence time. In the search for quantum advantage with these devices, algorithms have been proposed for applications in various disciplines spanning physics, machine learning, quantum chemistry and combinatorial optimization. The goal of such algorithms is to leverage the limited available resources to perform classically challenging tasks. In this review, we provide a thorough summary of NISQ computational paradigms and algorithms. We discuss the key structure of these algorithms, their limitations, and advantages. We additionally provide a comprehensive overview of various benchmarking and software tools useful for programming and testing NISQ devices.