The following paper represents the beginning of a long and productive line of work on robust statistics in high dimensions. While robust statistics has long been studied, going back at least to Tukey,6 the recent revival centers on algorithmic questions that were largely unaddressed by the earlier statistical work. Robust statistics centers on the question of how to extract information from data that may have been corrupted in some way. The most common form of robustness, also considered here, is robust to outliers: some fraction of the data has been removed and replaced with arbitrary, erroneous points. A familiar instance of robust statistics is using the median instead of the mean, since the median is less sensitive to extreme points, while in contrast a single overly large value could completely skew the mean.

"The biology of mind bridges the sciences -- concerned with the natural world -- and the humanities -- concerned with the meaning of human experience." Welcome to Part 3 of this new series exploring artificial general intelligence (AGI). If you missed Part 1 or Part 2, check them out; part 1 covers what AGI is, and part 2 is a brief overview of cognitive science for AI folk. This week we will introduce important concepts in philosophy of mind that I think every computer scientist, AI/ML researcher, or AGI enthusiast should know. The concept of minds--their nature, their implementation, their applications, etc.--are of huge interests to AGI researchers, and even anyone remotely interested in AI; arguably, this is the entire job of an AI/AGI researcher: creating artificial minds (some are just more narrow then others).

The significant increase in world population and urbanisation has brought several important challenges, in particular regarding the sustainability, maintenance and planning of urban mobility. At the same time, the exponential increase of computing capability and of available sensor and location data have offered the potential for innovative solutions to these challenges. In this work, we focus on the challenge of traffic forecasting and review the recent development and application of graph neural networks (GNN) to this problem. GNNs are a class of deep learning methods that directly process the input as graph data. This leverages more directly the spatial dependencies of traffic data and makes use of the advantages of deep learning producing state-of-the-art results. We introduce and review the emerging topic of GNNs, including their most common variants, with a focus on its application to traffic forecasting. We address the different ways of modelling traffic forecasting as a (temporal) graph, the different approaches developed so far to combine the graph and temporal learning components, as well as current limitations and research opportunities.

The latest research from Market Research Intellect, the Global Artificial Intelligence in Precision Medicine Market Report, provides a comprehensive survey of geographic landscape, industry size, and business revenue estimates. In addition, the report highlights the challenges and forecasts that hinder market growth and expansion strategies adopted by leading companies. The statistical information provided in this report is based on major and minor surveys and studies in the Artificial Intelligence in Precision Medicine market, as well as media releases. This includes data provided through a professional team of well-known global participants to provide up-to-date information on the international market. Artificial Intelligence in Precision Medicine market competition landscape provides detailed information by competitor – company profile, company finance, revenue generation, market potential, research and development investments, new market initiatives, global presence, production sites and facilities, production capacity, company strengths and weaknesses, product release, product width The above data points are only relevant to the company's focus on the Artificial Intelligence in Precision Medicine market.

Progress within physical oceanography has been concurrent with the increasing sophistication of tools available for its study. The incorporation of machine learning (ML) techniques offers exciting possibilities for advancing the capacity and speed of established methods and also for making substantial and serendipitous discoveries. Beyond vast amounts of complex data ubiquitous in many modern scientific fields, the study of the ocean poses a combination of unique challenges that ML can help address. The observational data available is largely spatially sparse, limited to the surface, and with few time series spanning more than a handful of decades. Important timescales span seconds to millennia, with strong scale interactions and numerical modelling efforts complicated by details such as coastlines. This review covers the current scientific insight offered by applying ML and points to where there is imminent potential. We cover the main three branches of the field: observations, theory, and numerical modelling. Highlighting both challenges and opportunities, we discuss both the historical context and salient ML tools. We focus on the use of ML in situ sampling and satellite observations, and the extent to which ML applications can advance theoretical oceanographic exploration, as well as aid numerical simulations. Applications that are also covered include model error and bias correction and current and potential use within data assimilation. While not without risk, there is great interest in the potential benefits of oceanographic ML applications; this review caters to this interest within the research community.

Vision-based driver assistance systems is one of the rapidly growing research areas of ITS, due to various factors such as the increased level of safety requirements in automotive, computational power in embedded systems, and desire to get closer to autonomous driving. It is a cross disciplinary area encompassing specialised fields like computer vision, machine learning, robotic navigation, embedded systems, automotive electronics and safety critical software. In this paper, we survey the list of vision based advanced driver assistance systems with a consistent terminology and propose a taxonomy. We also propose an abstract model in an attempt to formalize a top-down view of application development to scale towards autonomous driving system.

To minimize the average of a set of log-convex functions, the stochastic Newton method iteratively updates its estimate using subsampled versions of the full objective's gradient and Hessian. We contextualize this optimization problem as sequential Bayesian inference on a latent state-space model with a discriminatively-specified observation process. Applying Bayesian filtering then yields a novel optimization algorithm that considers the entire history of gradients and Hessians when forming an update. We establish matrix-based conditions under which the effect of older observations diminishes over time, in a manner analogous to Polyak's heavy ball momentum. We illustrate various aspects of our approach with an example and review other relevant innovations for the stochastic Newton method.

Transformer based architectures are recently used for the task of answering questions over tables. In order to improve the accuracy on this task, specialized pre-training techniques have been developed and applied on millions of open-domain web tables. In this paper, we propose two novel approaches demonstrating that one can achieve superior performance on table QA task without even using any of these specialized pre-training techniques. The first model, called RCI interaction, leverages a transformer based architecture that independently classifies rows and columns to identify relevant cells. While this model yields extremely high accuracy at finding cell values on recent benchmarks, a second model we propose, called RCI representation, provides a significant efficiency advantage for online QA systems over tables by materializing embeddings for existing tables. Experiments on recent benchmarks prove that the proposed methods can effectively locate cell values on tables (up to ~98% Hit@1 accuracy on WikiSQL lookup questions). Also, the interaction model outperforms the state-of-the-art transformer based approaches, pre-trained on very large table corpora (TAPAS and TaBERT), achieving ~3.4% and ~18.86% additional precision improvement on the standard WikiSQL benchmark.

There have been increasing concerns about Artificial Intelligence (AI) due to its unfathomable potential power. To make AI address ethical challenges and shun undesirable outcomes, researchers proposed to develop socially responsible AI (SRAI). One of these approaches is causal learning (CL). We survey state-of-the-art methods of CL for SRAI. We begin by examining the seven CL tools to enhance the social responsibility of AI, then review how existing works have succeeded using these tools to tackle issues in developing SRAI such as fairness. The goal of this survey is to bring forefront the potentials and promises of CL for SRAI.

The Expectation-Maximization (EM) algorithm is one of the main algorithms in machine learning for estimation of model parameters [2][3][4]. For example, it is used to estimate mixing coefficients, means, and covariances in mixture models as shown in Figure 1. Its objective is to maximize the likelihood p(X θ) where X is a matrix of observed data and θ is a vector of model parameters. This is maximum likelihood estimation and in practice the log-likelihood ln p(X θ) is maximized. The model parameters that maximize this function are deemed to be the correct model parameters.