The European Parliament's proposal to create a new legal status for artificial intelligence (AI) and robots brought into focus the idea of electronic legal personhood. This discussion, however, is hugely controversial. While some scholars argue that the proposed status could contribute to the coherence of the legal system, others say that it is neither beneficial nor desirable. Notwithstanding this prospect, we conducted a survey (N=3315) to understand online users' perceptions of the legal personhood of AI and robots. We observed how the participants assigned responsibility, awareness, and punishment to AI, robots, humans, and various entities that could be held liable under existing doctrines. We also asked whether the participants thought that punishing electronic agents fulfills the same legal and social functions as human punishment. The results suggest that even though people do not assign any mental state to electronic agents and are not willing to grant AI and robots physical independence or assets, which are the prerequisites of criminal or civil liability, they do consider them responsible for their actions and worthy of punishment. The participants also did not think that punishment or liability of these entities would achieve the primary functions of punishment, leading to what we define as the punishment gap. Therefore, before we recognize electronic legal personhood, we must first discuss proper methods of satisfying the general population's demand for punishment.

Learning-based approaches often outperform hand-coded algorithmic solutions for many problems in robotics. However, learning long-horizon tasks on real robot hardware can be intractable, and transferring a learned policy from simulation to reality is still extremely challenging. We present a novel approach to model-free reinforcement learning that can leverage existing sub-optimal solutions as an algorithmic prior during training and deployment. During training, our gated fusion approach enables the prior to guide the initial stages of exploration, increasing sample-efficiency and enabling learning from sparse long-horizon reward signals. Importantly, the policy can learn to improve beyond the performance of the sub-optimal prior since the prior's influence is annealed gradually. During deployment, the policy's uncertainty provides a reliable strategy for transferring a simulation-trained policy to the real world by falling back to the prior controller in uncertain states. We show the efficacy of our Multiplicative Controller Fusion approach on the task of robot navigation and demonstrate safe transfer from simulation to the real world without any fine tuning. The code for this project is made publicly available at https://sites.google.com/view/mcf-nav/home.

Artificial intelligence (AI) will change the way we learn and work in the near future. Nearly 400 million workers globally will change their occupations in the next 10 years, and business schools are uniquely situated to respond to the shifts coming to the future of work. However, a recent study, "Implications of Artificial Intelligence on Business Schools and Lifelong Learning," shows that business schools remain cautious in adapting management education to address the changing needs of students, workers and organizations, writes Anne Trumbore in this opinion piece. Trumbore, one of the study's coauthors, is senior director of Wharton Online, a strategic digital learning initiative at the Wharton School of the University of Pennsylvania. In the past few weeks, COVID 19 has moved hundreds of millions of students around the globe from physical to online classes.

Artificial intelligence is the most electrifying and exciting technology in the business landscape. It has the potential to drive value across the business, particularly delivering enhanced customer experience, reducing cost and spurring revenue. From smarter products and services to better business decisions and optimized business processes, the technology can transform almost everything. However, before getting started with this disruptive technology businesses need to define their AI strategy effectively in order to boost revenue and accomplish business goals. They must ask themselves how do they create a utilitarian AI strategy to harness its power?

A study to prod an antimony nucleus (buried in the middle of this device) with magnetic fields became one with electric fields when a key wire melted a gap in it. An accidental innovation has given a dark-horse approach to quantum computing a boost. For decades, scientists have dreamed of using atomic nuclei embedded in silicon--the familiar stuff of microchips--as quantum bits, or qubits, in a superpowerful quantum computer, manipulating them with magnetic fields. Now, researchers in Australia have stumbled across a way to control such a nucleus with more-manageable electric fields, raising the prospect of controlling the qubits in much the same way as transistors in an ordinary microchip. "That's incredibly important," says Thaddeus Ladd, a research physicist at HRL Laboratories LLC., a private research company. "This could potentially change the game for nuclear qubits in silicon."

Established in 2013, 360DigiTMG is the training arm of Innodatatics Inc., USA, an IT services company that builds innovative solutions for core business problems. The institute is an accredited centre for Skim Bantuan Latihan (SBL) schemes by the Human Resources Development Fund (HRDF) under the Ministry of Human Resources, Malaysia. With headquarters in the United States and presence in India, Malaysia, East Asia, Australia, Middle East, the United Kingdom, and the Netherlands, 360DigiTMG adds a holistic, global market perspective to its curriculum.

The idea of slicing divergences has been proven to be successful when comparing two probability measures in various machine learning applications including generative modeling, and consists in computing the expected value of a `base divergence' between one-dimensional random projections of the two measures. However, the computational and statistical consequences of such a technique have not yet been well-established. In this paper, we aim at bridging this gap and derive some properties of sliced divergence functions. First, we show that slicing preserves the metric axioms and the weak continuity of the divergence, implying that the sliced divergence will share similar topological properties. We then precise the results in the case where the base divergence belongs to the class of integral probability metrics. On the other hand, we establish that, under mild conditions, the sample complexity of the sliced divergence does not depend on the dimension, even when the base divergence suffers from the curse of dimensionality. We finally apply our general results to the Wasserstein distance and Sinkhorn divergences, and illustrate our theory on both synthetic and real data experiments.

We introduce the exactCover global constraint dedicated to the exact cover problem, the goal of which is to select subsets such that each element of a given set belongs to exactly one selected subset. This NP-complete problem occurs in many applications, and we more particularly focus on a conceptual clustering application. We introduce three propagation algorithms for exactCover, called Basic, DL, and DL+: Basic ensures the same level of consistency as arc consistency on a classical decomposition of exactCover into binary constraints, without using any specific data structure; DL ensures the same level of consistency as Basic but uses Dancing Links to efficiently maintain the relation between elements and subsets; and DL+ is a stronger propagator which exploits an extra property to filter more values than DL. We also consider the case where the number of selected subsets is constrained to be equal to a given integer variable k, and we show that this may be achieved either by combining exactCover with existing constraints, or by designing a specific propagator that integrates algorithms designed for the NValues constraint. These different propagators are experimentally evaluated on conceptual clustering problems, and they are compared with state-of-the-art declarative approaches. In particular, we show that our global constraint is competitive with recent ILP and CP models for mono-criterion problems, and it has better scale-up properties for multi-criteria problems.

Human neck postures and movements need to be monitored, measured, quantified and analyzed, as a preventive measure in healthcare applications. Improper neck postures are an increasing source of neck musculoskeletal disorders, requiring therapy and rehabilitation. The motivation for the research presented in this paper was the need to develop a notification mechanism for improper neck usage. Kinematic data captured by sensors have limitations in accurately classifying the neck postures. Hence, we propose an integrated use of kinematic and kinetic data to efficiently classify neck postures. Using machine learning algorithms we obtained 100% accuracy in the predictive analysis of this data. The research analysis and discussions show that the kinetic data of the Hyoid muscles can accurately detect the neck posture given the corresponding kinematic data captured by the neck-band. The proposed robust platform for the integration of kinematic and kinetic data has enabled the design of a smart neck-band for the prevention of neck musculoskeletal disorders.

The segmentation and analysis of coronary arteries from intravascular optical coherence tomography (IVOCT) is an important aspect of diagnosing and managing coronary artery disease. However, automated, robust IVOCT image analysis tools are lacking. Current image processing methods are hindered by the time needed to generate these expert-labelled datasets and also the potential for bias during the analysis. Here we present a new deep learning method based on capsules to automatically produce lumen segmentations, built using a large IVOCT dataset of 12,011 images with ground-truth segmentations. This dataset contains images with both blood and light artefacts (22.8%), as well as noise from metallic (23.1%) and bioresorbable stents (2.5%). We trained our model on a dataset containing 9,608 images. We rigorously investigate design variations with respect to upsampling regimes and input selection and validate our deep learning model using 2,403 images. We show that our fully trained and optimized model achieves a mean Soft Dice Score of 97.11% (median of 98.2%), segments 200 IVOCT images in an acceptable timeframe of 12 seconds and outperforms current algorithms.