Facebook rushed to pull down footage of the New Zealand mass shooter's video from its platform, but it didn't start doing so until after the live broadcast was done. In a new post, Facebook VP of Integrity Guy Rosen discussed the company's successes and shortcomings in addressing the situation, as well as its plans to prevent videos like that from spreading on the social network in the future. He explained that while the platform's AI can quickly detect videos containing suicidal or harmful acts, the shooter's stream didn't trigger it. To be able to train the matching AI to detect that specific type of content, the platform needs big volumes of training data. As Facebook explains, something like that is difficult to obtain as "these events are thankfully rare."

Tesla filed a lawsuit this week against four former employees for allegedly stealing trade secrets and providing them to a rival company. According to the complaint filed with the US district court for Northern California, the ex-Tesla workers gave confidential information to autonomous vehicle start-up Zoox. The documents allegedly allowed the company to accelerate the development of its technology by cribbing off of Tesla's proprietary work. According to Tesla, the four former employees violated the terms of their contracts by forwarding documents and other information from work email addresses to personal accounts. The files included inventory documents, company schematics and other proprietary pieces of information.

The age of artificial intelligence (AI) has arrived, and is transforming everything from healthcare to transportation to manufacturing. America has long been the global leader in this new era of AI, and is poised to maintain this leadership going forward. Realizing the full potential of AI for the Nation requires the combined efforts of industry, academia, and government. The Administration has been active in developing policies and implementing strategies that accelerate AI innovation in the U.S. for the benefit of the American people. These activities align with four main pillars of emphasis: AI for American Innovation, AI for American Industry, AI for the American Worker, and AI with American Values.

India has two contrast images when it comes to the healthcare sector. While one faction is able to avail the advancement of technologies in the healthcare space, another faction can at the most only avail the basic healthcare treatments. But this spectrum is altering now. Starting from making the basic healthcare services available to the needy, several innovative technologies are disrupting the space and taking them to an altogether different standard. The private healthcare sector in close association with startups has been seeing the innovation that has amazed everyone and spend the industry up. The feather in the hat that the sector could earn from its tech innovation is the implementation of Artificial Intelligence (AI) to treat patients suffering from Cancer.

Antoine Bruel, head of growth at Braincities and Céline Pluijm, key account manager at Wiidii share their thoughts on why France is fast-becoming a leader in establishing'AI for humanity', fresh from Hello Tomorrow… Artificial Intelligence (AI) is everywhere. Across industry verticals, it's being used to enable businesses and organisations to work smarter and faster than ever before. From automating repetitive transactions and manual tasks to powering customer support platforms, AI is transforming the way we work, live and interact with the world. According to PwC research, AI is estimated to provide $15.7 trillion in economic growth by 2030, creating opportunities for innovation on a global scale. AI, however, is as much a source of fascination as it is a cause for concern.

In this paper, we propose a distributed off-policy actor critic method to solve multi-agent reinforcement learning problems. Specifically, we assume that all agents keep local estimates of the global optimal policy parameter and update their local value function estimates independently. Then, we introduce an additional consensus step to let all the agents asymptotically achieve agreement on the global optimal policy function. The convergence analysis of the proposed algorithm is provided and the effectiveness of the proposed algorithm is validated using a distributed resource allocation example. Compared to relevant distributed actor critic methods, here the agents do not share information about their local tasks, but instead they coordinate to estimate the global policy function.

Factor graphs have recently gained increasing attention as a unified framework for representing and constructing algorithms for signal processing, estimation, and control. One capability that does not seem to be well explored within the factor graph tool kit is the ability to handle deterministic nonlinear transformations, such as those occurring in nonlinear filtering and smoothing problems, using tabulated message passing rules. In this contribution, we provide general forward (filtering) and backward (smoothing) approximate Gaussian message passing rules for deterministic nonlinear transformation nodes in arbitrary factor graphs fulfilling a Markov property, based on numerical quadrature procedures for the forward pass and a Rauch-Tung-Striebel-type approximation of the backward pass. These message passing rules can be employed for deriving many algorithms for solving nonlinear problems using factor graphs, as is illustrated by the proposition of a nonlinear modified Bryson-Frazier (MBF) smoother based on the presented message passing rules.

The speed and accuracy with which robots are able to interpret natural language is fundamental to realizing effective human-robot interaction. A great deal of attention has been paid to developing models and approximate inference algorithms that improve the efficiency of language understanding. However, existing methods still attempt to reason over a representation of the environment that is flat and unnecessarily detailed, which limits scalability. An open problem is then to develop methods capable of producing the most compact environment model sufficient for accurate and efficient natural language understanding. We propose a model that leverages environment-related information encoded within instructions to identify the subset of observations and perceptual classifiers necessary to perceive a succinct, instruction-specific environment representation. The framework uses three probabilistic graphical models trained from a corpus of annotated instructions to infer salient scene semantics, perceptual classifiers, and grounded symbols. Experimental results on two robots operating in different environments demonstrate that by exploiting the content and the structure of the instructions, our method learns compact environment representations that significantly improve the efficiency of natural language symbol grounding.

DTW calculates the similarity or alignment between two signals, subject to temporal warping. However, its computational complexity grows exponentially with the number of time-series. Although there have been algorithms developed that are linear in the number of time-series, they are generally quadratic in time-series length. The exception is generalized time warping (GTW), which has linear computational cost. Yet, it can only identify simple time warping functions. There is a need for a new fast, high-quality multisequence alignment algorithm. We introduce trainable time warping (TTW), whose complexity is linear in both the number and the length of time-series. TTW performs alignment in the continuous-time domain using a sinc convolutional kernel and a gradient-based optimization technique. We compare TTW and GTW on 85 UCR datasets in time-series averaging and classification. TTW outperforms GTW on 67.1% of the datasets for the averaging tasks, and 61.2% of the datasets for the classification tasks.

Reasoning is essential for the development of large knowledge graphs, especially for completion, which aims to infer new triples based on existing ones. Both rules and embeddings can be used for knowledge graph reasoning and they have their own advantages and difficulties. Rule-based reasoning is accurate and explainable but rule learning with searching over the graph always suffers from efficiency due to huge search space. Embedding-based reasoning is more scalable and efficient as the reasoning is conducted via computation between embeddings, but it has difficulty learning good representations for sparse entities because a good embedding relies heavily on data richness. Based on this observation, in this paper we explore how embedding and rule learning can be combined together and complement each other's difficulties with their advantages. We propose a novel framework IterE iteratively learning embeddings and rules, in which rules are learned from embeddings with proper pruning strategy and embeddings are learned from existing triples and new triples inferred by rules. Evaluations on embedding qualities of IterE show that rules help improve the quality of sparse entity embeddings and their link prediction results. We also evaluate the efficiency of rule learning and quality of rules from IterE compared with AMIE+, showing that IterE is capable of generating high quality rules more efficiently. Experiments show that iteratively learning embeddings and rules benefit each other during learning and prediction.