Demis Hassabis, co-founder and CEO, DeepMind, says: 'I'm delighted to expand our support of AI research at Oxford with the DeepMind Professorship of Artificial Intelligence. I look forward to seeing who the University appoints and where they decide to focus their research with the support of Oxford's world-class AI research community.'

Scientists have found that robotic vacuum cleaners could allow snoopers to remotely listen in to household conversations, despite not being fitted with microphones. US experts found they can perform a remote eavesdropping attack on a Xiaomi Roborock robot cleaner by remotely accessing its Lidar readings – which helps these cleaners to avoid bumping into furniture. Lidar is a method for measuring distances by illuminating the target with laser beams and measuring their reflection with a sensor. But Lidar can also capture sound signals by obtaining reflections off of objects in the home, like a rubbish bin, that vibrate due to nearby sound sources, such as a person talking. A hacker could repurpose a vacuum's Lidar sensor to sense acoustic signals in the environment, remotely harvest the Lidar data from the cloud and process the raw signal with deep learning techniques to extract audio information.

Data Science & Deep Learning for Business 20 Case Studies - Use Python to solve problems in Retail, Marketing, Product Recommendation, Customer Clustering, NLP, Forecasting & more! Created by Rajeev D. RatanPreview this Course - GET COUPON CODE Welcome to the course on Data Science & Deep Learning for Business 20 Case Studies! This course teaches you how Data Science & Deep Learning can be used to solve real-world business problems and how you can apply these techniques to 20 real-world case studies. Traditional Businesses are hiring Data Scientists in droves, and knowledge of how to apply these techniques in solving their problems will prove to be one of the most valuable skills in the next decade! What student reviews of this course are saying, "I'm only half way through this course, but i have to say WOW. It's so far, a lot better than my Business Analytics MSc I took at UCL. The content is explained better, it's broken down so simply.

Engineers are increasingly looking to successfully integrate AI into projects and applications while attempting to climb their own AI learning curve. To tackle AI, engineers start with wanting to understand what AI is and how it fits into their current workflow, which might not be as straightforward as it seems. A simple search of "What is AI?" yields millions of results on Google, with varying degrees of technical and relevant information. Most of the focus on AI leans heavily on the AI model, which drives engineers to quickly dive into the modeling aspect of AI. After a few starter projects, engineers quickly learn that AI is not just modeling, but rather a complete set of steps that includes data preparation, modeling, simulation and test, and deployment.

Training-serving skew: The offline numbers may look great, but what if your model will be evaluated on a different or broader set of examples than those found in the training set? This phenomenon, more generally referred to as "dataset shift" or "distribution shift", happens all the time in real-world situations. Models are trained on a curated set of examples, or clicks on top-ranked recommendations, or a specific geographical region, and then applied to every user or use case. Curiosities and anomalies in your training and testing data become genuine and sustained loss patterns. Bad individual errors: Models are often judged by their worst behavior --- a single egregious outcome can damage the faith that important stakeholders have in the model and even cause serious reputational harm to your business or institution.

DeepMind this week open-sourced Lab2D, a software system designed to support the creation of 2D environments for AI and machine learning research. The Alphabet subsidiary says that Lab2D was built with the needs of deep reinforcement learning researchers in mind, but that it can be useful beyond that particular subfield of machine learning. The DeepMind team behind Lab2D makes the case that 2D environments are inherently easier to understand than 3D ones at little loss of expressiveness. Even a game as simple as Pong, which essentially consists of three moving rectangles on a black background, can capture something fundamental about the real game of table tennis, the researchers assert. This abstraction ostensibly makes it easier to capture the essence of problems and concepts in AI. "Rich complexity along numerous dimensions can be studied in 2D just as readily as in 3D, if not more so … In addition, 2D worlds are significantly less resource-intensive to run, and typically do not require any specialized hardware (like GPUs) to attain reasonable performance," the researchers continued in their paper describing Lab2D. "2D worlds have been successfully used to study problems as diverse as social complexity, navigation, imperfect information, abstract reasoning, exploration, and many more."

This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence. Creating machines that have the general problem–solving capabilities of human brains has been the holy grain of artificial intelligence scientists for decades. Our current AI methods either require a huge amount of data, or a very large number of hand-coded rules, and they're only suitable for very narrow domains. AGI, on the other hand, should be able to perform multiple tasks with little data and specific instructions. While approaches to creating AGI have shifted and evolved over the decades, one thing has remained constant: The human brain is proof that general intelligence does exist.

We demonstrate that deep learning techniques can be used to predict motility induced phase separation (MIPS) in suspensions of active Brownian particles (ABPs) by creating a notion of phase at the particle level. Using a fully connected network in conjunction with a graph neural network we use individual particle features to predict to which phase a particle belongs. From this, we are able to compute the fraction of dilute particles to determine if the system is in the homogeneous dilute, dense, or coexistence region. Our predictions are compared against the MIPS binodal computed from simulation. The strong agreement between the two suggests that machine learning provides an effective way to determine the phase behavior of ABPs and could prove useful for determining more complex phase diagrams.

Graph Neural Networks (GNNs) have achieved tremendous success in various real-world applications due to their strong ability in graph representation learning. GNNs explore the graph structure and node features by aggregating and transforming information within node neighborhoods. However, through theoretical and empirical analysis, we reveal that the aggregation process of GNNs tends to destroy node similarity in the original feature space. There are many scenarios where node similarity plays a crucial role. Thus, it has motivated the proposed framework SimP-GCN that can effectively and efficiently preserve node similarity while exploiting graph structure. Specifically, to balance information from graph structure and node features, we propose a feature similarity preserving aggregation which adaptively integrates graph structure and node features. Furthermore, we employ self-supervised learning to explicitly capture the complex feature similarity and dissimilarity relations between nodes. We validate the effectiveness of SimP-GCN on seven benchmark datasets including three assortative and four disassorative graphs. The results demonstrate that SimP-GCN outperforms representative baselines. Further probe shows various advantages of the proposed framework. The implementation of SimP-GCN is available at \url{https://github.com/ChandlerBang/SimP-GCN}.

Clinical machine learning is increasingly multimodal, collected in both structured tabular formats and unstructured forms such as freetext. We propose a novel task of exploring fairness on a multimodal clinical dataset, adopting equalized odds for the downstream medical prediction tasks. To this end, we investigate a modality-agnostic fairness algorithm - equalized odds post processing - and compare it to a text-specific fairness algorithm: debiased clinical word embeddings. Despite the fact that debiased word embeddings do not explicitly address equalized odds of protected groups, we show that a text-specific approach to fairness may simultaneously achieve a good balance of performance and classical notions of fairness. We hope that our paper inspires future contributions at the critical intersection of clinical NLP and fairness. The full source code is available here: https://github.com/johntiger1/multimodal_fairness