While there are anemones in those images, it also seems that the system is recognizing a clownfish.

In this issue of Communications, as evidenced by the cover and lead article, we celebrate the latest recipients of the ACM A.M. Turing Award. Yoshua Bengio, Yann LeCun, and Geoffrey Hinton carried out pioneering work in deep learning that has touched all our lives. As Turing Laureates, they now join the eminent group of technology visionaries recognized with the world's highest distinction in computing. The Turing Award is one of a suite of professional honors ACM bestows annually to recognize technical achievements that have made significant contributions to our field. This month, I will have the pleasure of joining the awardees, ACM Fellows, and other luminaries in San Francisco for the ACM Awards Banquet.

We have all read market predictions describing billions of devices and the hundreds of billions dollars in profit that the Internet of Things (IoT) promises.a Security and the challenges it represents27 are often highlighted as major issues for IoT, alongside scalability and standardization. In 2017, FBI Director James Comey warned, during a senate hearing, of the threat represented by a botnet taking control of devices owned by unsuspecting users. Such a botnet can seize control of devices ranging from connected dishwashers,b to smart home cameras and connected toys, not only using them as a platform to launch cyber-attacks, but also potentially harvesting the data such devices collect. In addition to concerns about cyber-security, corporate usage of personal data has seen increased public scrutiny. A recent focus of concern has been connected home hubs (such as Amazon Alexa and Google Home).c

Columbia University is learning how to build and train self-aware neural networks, systems that can adapt and improve by using internal simulations and knowledge of their own structures. The University of California, Irvine, is studying the dual memory architecture of the hippocampus and cortex to replay relevant memories in the background, allowing the systems to become more adaptable and predictive while retaining previous learning. Tufts University is examining an intercellular regeneration mechanism observed in lower animals such as salamanders to create flexible robots capable of adapting to changes in their environment by altering their structures and functions on the fly. SRI International is developing methods to use environmental signals and their relevant context to represent goals in a fluid way rather than as discrete tasks, enabling AI agents to adapt their behavior on the go.

When Geoffrey Hinton started doing graduate student work on artificial intelligence at the University of Edinburgh in 1972, the idea that it could be achieved using neural networks that mimicked the human brain was in disrepute. Computer scientists Marvin Minsky and Seymour Papert had published a book in 1969 on Perceptrons, an early attempt at building a neural net, and it left people in the field with the impression that such devices were nonsense. "It didn't actually say that, but that's how the community interpreted the book," says Hinton who, along with Yoshua Bengio and Yann LeCun, will receive the 2018 ACM A.M. Turing award for their work that led deep neural networks to become an important component of today's computing. "People thought I was just completely crazy to be working on neural nets." Even in the 1980s, when Bengio and LeCun entered graduate school, neural nets were not seen as promising.

This review considers a problem in the development of mobile robot adhesion methods with vertical surfaces and the appropriate locomotion mechanism design. The evolution of adhesion methods for wall-climbing robots (based on friction, magnetic forces, air pressure, electrostatic adhesion, molecular forces, rheological properties of fluids and their combinations) and their locomotion principles (wheeled, tracked, walking, sliding framed and hybrid) is studied. Wall-climbing robots are classified according to the applications, adhesion methods and locomotion mechanisms. The advantages and disadvantages of various adhesion methods and locomotion mechanisms are analyzed in terms of mobility, noiselessness, autonomy and energy efficiency. Focus is placed on the physical and technical aspects of the adhesion methods and the possibility of combining adhesion and locomotion methods.

Hyperspectral imaging is a powerful technology that is plagued by large dimensionality. Herein, we explore a way to combat that hindrance via non-contiguous and contiguous (simpler to realize sensor) band grouping for dimensionality reduction. Our approach is different in the respect that it is flexible and it follows a well-studied process of visual clustering in high-dimensional spaces. Specifically, we extend the improved visual assessment of cluster tendency and clustering in ordered dissimilarity data unsupervised clustering algorithms for supervised hyperspectral learning. In addition, we propose a way to extract diverse features via the use of different proximity metrics (ways to measure the similarity between bands) and kernel functions. The discovered features are fused with $l_{\infty}$-norm multiple kernel learning. Experiments are conducted on two benchmark datasets and our results are compared to related work. These datasets indicate that contiguous or not is application specific, but heterogeneous features and kernels usually lead to performance gain.

Reduced models of large Markov decision processes accelerate planning by considering a subset of outcomes for each state-action pair. This reduction in reachable states leads to replanning when the agent encounters states without a precomputed action during plan execution. However, not all states are suitable for replanning. In the worst case, the agent may not be able to reach the goal from the newly encountered state. Agents should be better prepared to handle such risky situations and avoid replanning in risky states. Hence, we consider replanning in states that are unsafe for deliberation as a negative side effect of planning with reduced models. While the negative side effects can be minimized by always using the full model, this defeats the purpose of using reduced models. The challenge is to plan with reduced models, but somehow account for the possibility of encountering risky situations. An agent should thus only replan in states that the user has approved as safe for replanning. To that end, we propose planning using a portfolio of reduced models, a planning paradigm that minimizes the negative side effects of planning using reduced models by alternating between different outcome selection approaches. We empirically demonstrate the effectiveness of our approach on three domains: an electric vehicle charging domain using real-world data from a university campus and two benchmark planning problems.

Consider scene understanding problems such as predicting where a person is probably reaching, or inferring the pose of 3D objects from depth images, or inferring the probable street crossings of pedestrians at a busy intersection. This paper shows how to solve these problems using Approximate Bayesian Computation. The underlying generative models are built from realistic simulation software, wrapped in a Bayesian error model for the gap between simulation outputs and real data. The simulators are drawn from off-the-shelf computer graphics, video game, and traffic simulation code. The paper introduces two techniques for speeding up inference that can be used separately or in combination. The first is to train neural surrogates of the simulators, using a simple form of domain randomization to make the surrogates more robust to the gap between the simulation and reality. The second is to adaptively discretize the latent variables using a Tree-pyramid approach adapted from computer graphics. This paper also shows performance and accuracy measurements on real-world problems, establishing that it is feasible to solve these problems in real-time.

Recent advances in deep reinforcement learning (RL) have shown remarkable success on challenging tasks (Andrychowicz et al., 2018; Mnih et al., 2015; Silver et al., 2016). However, data efficiency and robustness to new contexts remain persistent challenges for deep RL algorithms, especially when the goal is for agents to learn practical tasks with limited supervision. Drawing inspiration from self-supervised "play" in human development (Gopnik et al., 1999; Settles, 2011), we introduce an agent that learns object-centric representations of its environment without supervision and subsequently harnesses these to learn policies efficiency and robustly. Our agent, which we call Curious Object-Based seaRch Agent (COBRA), brings together three key ingredients: (i) learning representations of the world in terms of objects, (ii) curiosity-driven exploration, and (iii) model based RL. The benefits of this synthesis are data efficiency and policy robustness. To put this into practice, we introduce the following technical contributions: - A method for learning action-conditioned dynamics over slot-structured object-centric representations that requires no supervision and is trained from raw pixels.