At a speech at the US Military Academy last weekend, President Donald Trump appeared lethargic and occasionally slurred words. He seemed to have difficulty lifting a glass of water to his lips, and apparently shuffled slowly down a ramp leading away from his dais. The images caught fire on social media and in the press. What might have been a nothingburger for any other elderly, overweight gentleman a day shy of his 74th birthday turned into a news peg--and for renewed demands that the president release results from a comprehensive physical examination, including a neurological assessment to determine if he had motor or cognitive disabilities. Well, here is a counterproposal: Nobody needs to see this president's exam results except the president and his doctors.

In February this year, the European Commission released a white paper entitled: On Artificial Intelligence – A European approach to excellence and trust. With the public consultation phase on this document now closed, CLAIRE (Confederation of Laboratories for Artificial Intelligence Research in Europe) have published their response, which largely endorses the EC plans. CLAIRE note that the plans and actions outlined in the EC white paper are closely aligned with their vision for European excellence in human-centred AI. One idea they believe has considerable potential is the concept of a CERN-inspired "lighthouse centre" that will bring together top researchers from across Europe and around the world. Holger Hoos, Professor of Machine Learning at Leiden University, The Netherlands, and Chairman of the Board of CLAIRE said "The white paper offers a compelling blueprint. Now, important details need to be filled in, for example on how to balance supporting excellence within the European AI ecosystem along with a broader network, whose members are of key importance for reaching critical mass and ensuring global impact."

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. A sizeable chunk of shoreline was spotted by boaters this week in Michigan's Muskegon Lake that could be the result of record water levels and erosion. The floating piece of vegetation was featured in an aerial drone video that shows a pontoon boat circling it. "I've lived my whole life in the Muskegon area, and I've never seen anything like it," said Joe Gee, the photographer who captured footage of the islet.

Among the intrinsic properties of a sand, the surface friction, the compressibility and the strength of individual grains, the particle shape and particle size distributions are known to play a crucial role in its macroscopic properties [1, 2, 3, 4]. Relative density and confining pressure are the most influent state variables for dry granular soils [5] and govern the mechanical behavior of the material to a large extent [6, 7, 8]. The relationship between the particle size distribution, PSD, and the mechanical behavior is not yet fully understood. On one hand, the effects of variations in the PSD are not independent from those produced by variations of other intrinsic properties or state parameters. For example, the state parameter ψ, proposed within the theoretical framework of the critical state of sands [5], helps to distinguish between the contractive or dilatant behavior exhibited by a sand upon triaxial compression. However the critical state line, and hence the value of ψ associated to given void ratio e, changes with the PSD [9]. As another example, there is a complex interplay between size and shape polydispersity, as shown by numerical modeling [10]. On the other hand, linking single quantities (maximum and minimum dry density, critical state void ratio, macroscopic friction angle, stiffness, etc.) to a PSD is not immediate, since the latter is a highly variable curve that is many times long-tailed and/or multi-modal. Descriptors derived from the PSD are not enough to anticipate macroscopic (void ratio, stiffness, friction angle) or microscopic features (average coordination number, fraction of non-contributing particles, etc.) obtained after a given process.

Recent evidence suggests that Golgi cells in the cerebellar granular layer are densely connected to each other with massive gap junctions. Here, we propose that the massive gap junctions between the Golgi cells contribute to the representational complexity of the granular layer of the cerebellum by inducing chaotic dynamics. We construct a model of cerebellar granular layer with diffusion coupling through gap junctions between the Golgi cells, and evaluate the representational capability of the network with the reservoir computing framework. First, we show that the chaotic dynamics induced by diffusion coupling results in complex output patterns containing a wide range of frequency components. Second, the long non-recursive time series of the reservoir represents the passage of time from an external input. These properties of the reservoir enable mapping different spatial inputs into different temporal patterns.

System identification is a key step for model-based control, estimator design, and output prediction. This work considers the offline identification of partially observed nonlinear systems. We empirically show that the certainty-equivalent approximation to expectation-maximization can be a reliable and scalable approach for high-dimensional deterministic systems, which are common in robotics. We formulate certainty-equivalent expectation-maximization as block coordinate-ascent, and provide an efficient implementation. The algorithm is tested on a simulated system of coupled Lorenz attractors, demonstrating its ability to identify high-dimensional systems that can be intractable for particle-based approaches. Our approach is also used to identify the dynamics of an aerobatic helicopter. By augmenting the state with unobserved fluid states, a model is learned that predicts the acceleration of the helicopter better than state-of-the-art approaches. The codebase for this work is available at https://github.com/sisl/CEEM.

Hierarchical DQN (h-DQN) is a two-level architecture of feedforward neural networks where the meta level selects goals and the lower level takes actions to achieve the goals. We show tasks that cannot be solved by h-DQN, exemplifying the limitation of this type of hierarchical framework (HF). We describe the recurrent hierarchical framework (RHF), generalizing architectures that use a recurrent neural network at the meta level. We analyze the expressiveness of HF and RHF using context-sensitive grammars. We show that RHF is more expressive than HF. We perform experiments comparing an implementation of RHF with two HF baselines; the results corroborate our theoretical findings.

Explainable AI, in the context of autonomous systems, like self driving cars, has drawn broad interests from researchers. Recent studies have found that providing explanations for an autonomous vehicle actions has many benefits, e.g., increase trust and acceptance, but put little emphasis on when an explanation is needed and how the content of explanation changes with context. In this work, we investigate which scenarios people need explanations and how the critical degree of explanation shifts with situations and driver types. Through a user experiment, we ask participants to evaluate how necessary an explanation is and measure the impact on their trust in the self driving cars in different contexts. We also present a self driving explanation dataset with first person explanations and associated measure of the necessity for 1103 video clips, augmenting the Berkeley Deep Drive Attention dataset. Additionally, we propose a learning based model that predicts how necessary an explanation for a given situation in real time, using camera data inputs. Our research reveals that driver types and context dictates whether or not an explanation is necessary and what is helpful for improved interaction and understanding.

Constraint Optimization Problems (COP) are often considered without sufficient knowledge on the boundaries of the objective variable to optimize. When available, tight boundaries are helpful to prune the search space or estimate problem characteristics. Finding close boundaries, that correctly under- and overestimate the optimum, is almost impossible without actually solving the COP. This paper introduces Bion, a novel approach for boundary estimation by learning from previously solved instances of the COP. Based on supervised machine learning, Bion is problem-specific and solver-independent and can be applied to any COP which is repeatedly solved with different data inputs. An experimental evaluation over seven realistic COPs shows that an estimation model can be trained to prune the objective variables' domains by over 80%. By evaluating the estimated boundaries with various COP solvers, we find that Bion improves the solving process for some problems, although the effect of closer bounds is generally problem-dependent.

Sarcasm is a form of communication in whichthe person states opposite of what he actually means. It is ambiguous in nature. In this paper, we propose using machine learning techniques with BERT and GloVe embeddings to detect sarcasm in tweets. The dataset is preprocessed before extracting the embeddings. The proposed model also uses the context in which the user is reacting to along with his actual response.