Most likely it's a clumsy attempt to throw disinformation shade at rivals.) Returning to the regulation point, an EU proposal -- the Digital Services Act -- is set to introduce some transparency requirements on large digital platforms, as part of a wider package of accountability measures. And asked about this Geurkink described the DSA as "a promising avenue for greater transparency". But she suggested the legislation needs to go further to tackle recommender systems like the YouTube AI. "I think that transparency around recommender systems specifically and also people having control over the input of their own data and then the output of recommendations is really important -- and is a place where the DSA is currently a bit sparse, so I think that's where we really need to dig in," she told us. One idea she voiced support for is having a "data access framework" baked into the law -- to enable vetted researchers to get more of the information they need to study powerful AI technologies -- i.e. rather than the law trying to come up with "a laundry list of all of the different pieces of transparency and information that should be applicable", as she put it.

First of all, I want to state for the record that I have never played a video game that involved violence or war. I think the last time I played a "video game" was Flight Simulator. As a result, I suspect some readers are much more familiar with intensive and fanciful warfare than I am. Still, recently, I've been part of discussions with the Department of Defense and organizations that advise, consult and criticize the DoD on the topic AI in warfare. It is a complicated issue to introduce AI ethics with the violence and killing of war.

The Biden administration has followed through on a Congressional mandate to create a National AI Research Resource Task Force. With top experts from the federal government, higher education, and private organizations, the task force is dedicated to strengthening America's foundation and spurring advances in artificial intelligence (AI). As a computer scientist, AI researcher and educator, co-director of the Stanford Institute for Human-Centered AI, and a major supporter of the bipartisan legislation that authorized this endeavor, I am honored to have accepted an offer to serve as a member of the task force. The time has never been more critical for us to come together and cement America's leadership in AI -- a technology that has the potential to drive innovation in every industry, from manufacturing and healthcare to transportation and defense. Like all technologies human civilization has built, AI is a tool that is as good or as bad as those who make and use it.

TBILISI, July 5 (Thomson Reuters Foundation) - (Editor's note: contains offensive language and terms of racial abuse) From scanning residents' faces to let them into their building to spotting police suspects in a crowd, the rise of facial recognition is accompanied by a growing chorus of concern about unethical uses of the technology. A report published on Monday by U.S.-based researchers showing that Russian facial recognition companies have built tools to detect a person's race has raised fears among digital rights groups, who describe the technology as "purpose-made for discrimination." Developer guides and code examples unearthed by video surveillance research firm IPVM show software advertised by four of Russia's biggest facial analytics firms can use artificial intelligence (AI) to classify faces based on their perceived ethnicity or race. There is no indication yet that Russian police have targeted minorities using the software developed by the firms - AxxonSoft, Tevian, VisionLabs and NtechLab - whose products are sold to authorities and businesses in the country and abroad. But Moscow-based AxxonSoft said the Thomson Reuters Foundation's enquiry prompted it to disable its ethnicity analytics feature, saying in an emailed response it was not interested "in promoting any technologies that could be a basis for ethnic segregation".

Late in 2019, the Pentagon chose Microsoft for a $10 billion contract called JEDI that aimed to use the cloud to modernize US military computing infrastructure. Tuesday, the agency ripped up that deal. The Pentagon said it will start over with a new contract that will seek technology from both Amazon and Microsoft, and that offers better support to data-intensive projects, such as enhancing military decisionmaking with artificial intelligence. The new contract will be called the Joint Warfighter Cloud Capability. It attempts to dodge a legal and political mess that had formed around JEDI. Microsoft competitors Amazon and Oracle both claimed in lawsuits that the award process had been skewed.

Due to their increasing spread, confidence in neural network predictions became more and more important. However, basic neural networks do not deliver certainty estimates or suffer from over or under confidence. Many researchers have been working on understanding and quantifying uncertainty in a neural network's prediction. As a result, different types and sources of uncertainty have been identified and a variety of approaches to measure and quantify uncertainty in neural networks have been proposed. This work gives a comprehensive overview of uncertainty estimation in neural networks, reviews recent advances in the field, highlights current challenges, and identifies potential research opportunities. It is intended to give anyone interested in uncertainty estimation in neural networks a broad overview and introduction, without presupposing prior knowledge in this field. A comprehensive introduction to the most crucial sources of uncertainty is given and their separation into reducible model uncertainty and not reducible data uncertainty is presented. The modeling of these uncertainties based on deterministic neural networks, Bayesian neural networks, ensemble of neural networks, and test-time data augmentation approaches is introduced and different branches of these fields as well as the latest developments are discussed. For a practical application, we discuss different measures of uncertainty, approaches for the calibration of neural networks and give an overview of existing baselines and implementations. Different examples from the wide spectrum of challenges in different fields give an idea of the needs and challenges regarding uncertainties in practical applications. Additionally, the practical limitations of current methods for mission- and safety-critical real world applications are discussed and an outlook on the next steps towards a broader usage of such methods is given.

Partition of unity networks (POU-Nets) have been shown capable of realizing algebraic convergence rates for regression and solution of PDEs, but require empirical tuning of training parameters. We enrich POU-Nets with a Gaussian noise model to obtain a probabilistic generalization amenable to gradient-based minimization of a maximum likelihood loss. The resulting architecture provides spatial representations of both noiseless and noisy data as Gaussian mixtures with closed form expressions for variance which provides an estimator of local error. The training process yields remarkably sharp partitions of input space based upon correlation of function values. This classification of training points is amenable to a hierarchical refinement strategy that significantly improves the localization of the regression, allowing for higher-order polynomial approximation to be utilized. The framework scales more favorably to large data sets as compared to Gaussian process regression and allows for spatially varying uncertainty, leveraging the expressive power of deep neural networks while bypassing expensive training associated with other probabilistic deep learning methods. Compared to standard deep neural networks, the framework demonstrates hp-convergence without the use of regularizers to tune the localization of partitions. We provide benchmarks quantifying performance in high/low-dimensions, demonstrating that convergence rates depend only on the latent dimension of data within high-dimensional space. Finally, we introduce a new open-source data set of PDE-based simulations of a semiconductor device and perform unsupervised extraction of a physically interpretable reduced-order basis.

Two-sided matching markets, such as Uber, Airbnb, stock markets, and dating apps, play a significant role in today's world. As a result, there is a tremendous and rising interest to design better mechanisms for two-sided matching. The seminal work of Gale and Shapley [14] introduced a simple mechanism for stable matching in two-sided markets--Deferred-acceptance (DA)--which has since has been applied in doctor-hospital matching [25], school choice [3, 22, 2], and the matching of cadets to their branches of military service [30, 29]. DA is stable, i.e., no pair of agents mutually prefer each other to their DA partners. On the other hand, DA is not strategy-proof (SP); that is, under fully general preferences, it is always possible that some agent can mis-report her preferences to obtain a better matching than she would receive under the DA mechanism.

Tropical cyclones can be of varied intensity and cause a huge loss of lives and property if the intensity is high enough. Therefore, the prediction of the intensity of tropical cyclones advance in time is of utmost importance. We propose a novel stacked bidirectional long short-term memory network (BiLSTM) based model architecture to predict the intensity of a tropical cyclone in terms of Maximum surface sustained wind speed (MSWS). The proposed model can predict MSWS well advance in time (up to 72 h) with very high accuracy. We have applied the model on tropical cyclones in the North Indian Ocean from 1982 to 2018 and checked its performance on two recent tropical cyclones, namely, Fani and Vayu. The model predicts MSWS (in knots) for the next 3, 12, 24, 36, 48, 60, and 72 hours with a mean absolute error of 1.52, 3.66, 5.88, 7.42, 8.96, 10.15, and 11.92, respectively.

Over the last few years there has been rapid research growth into eXplainable Artificial Intelligence (XAI) and the closely aligned Interpretable Machine Learning (IML). Drivers for this growth include recent legislative changes and increased investments by industry and governments, along with increased concern from the general public. People are affected by autonomous decisions every day and the public need to understand the decision-making process to accept the outcomes. However, the vast majority of the applications of XAI/IML are focused on providing low-level `narrow' explanations of how an individual decision was reached based on a particular datum. While important, these explanations rarely provide insights into an agent's: beliefs and motivations; hypotheses of other (human, animal or AI) agents' intentions; interpretation of external cultural expectations; or, processes used to generate its own explanation. Yet all of these factors, we propose, are essential to providing the explanatory depth that people require to accept and trust the AI's decision-making. This paper aims to define levels of explanation and describe how they can be integrated to create a human-aligned conversational explanation system. In so doing, this paper will survey current approaches and discuss the integration of different technologies to achieve these levels with Broad eXplainable Artificial Intelligence (Broad-XAI), and thereby move towards high-level `strong' explanations.