The algorithm selection problem is to choose the most suitable algorithm for solving a given problem instance and thus, it leverages the complementarity between different approaches that is present in many areas of AI. We report on the state of the art in algorithm selection, as defined by the Algorithm Selection Competition series 2015 to 2017. The results of these competitions show how the state of the art improved over the years. Although performance in some cases is very promising, there is still room for improvement in other cases. Finally, we provide insights into why some scenarios are hard, and pose challenges to the community on how to advance the current state of the art. Keywords: 1. Introduction Algorithm Selection, Meta-Learning, Competition Analysis In many areas of AI, there are different algorithms to solve the same type of problem. Often, these algorithms are complementary in the sense that one algorithm works well when others fail and vice versa. For example in propositional satisfiability solving (SAT), there are complete tree-based solvers aimed at structured, industrial-like problems, and local search solvers aimed at randomly generated problems. In many practical cases, the performance difference between algorithms can be very large, for example as shown by Xu et al. (2012) for SAT. Per-instance algorithm selection (Rice, 1976) is a way to leverage this complementarity between different algorithms.

In the context of tree-search stochastic planning algorithms where a generative model is available, we consider on-line planning algorithms building trees in order to recommend an action. We investigate the question of avoiding re-planning in subsequent decision steps by directly using sub-trees as action recommender. Firstly, we propose a method for open loop control via a new algorithm taking the decision of re-planning or not at each time step based on an analysis of the statistics of the sub-tree. Secondly, we show that the probability of selecting a suboptimal action at any depth of the tree can be upper bounded and converges towards zero. Moreover, this upper bound decays in a logarithmic way between subsequent depths. This leads to a distinction between node-wise optimality and state-wise optimality. Finally, we empirically demonstrate that our method achieves a compromise between loss of performance and computational gain.

Counterfactual learning from human bandit feedback describes a scenario where user feedback on the quality of outputs of a historic system is logged and used to improve a target system. We show how to apply this learning framework to neural semantic parsing. From a machine learning perspective, the key challenge lies in a proper reweighting of the estimator so as to avoid known degeneracies in counterfactual learning, while still being applicable to stochastic gradient optimization. To conduct experiments with human users, we devise an easy-to-use interface to collect human feedback on semantic parses. Our work is the first to show that semantic parsers can be improved significantly by counterfactual learning from logged human feedback data.

We address the problem of maintaining high voltage power transmission networks in security at all time, namely anticipating exceeding of thermal limit for eventual single line disconnection (whatever its cause may be) by running slow, but accurate, physical grid simulators. New conceptual frameworks are calling for a probabilistic risk-based security criterion. However, these approaches suffer from high requirements in terms of tractability. Here, we propose a new method to assess the risk. This method uses both machine learning techniques (artificial neural networks) and more standard simulators based on physical laws. More specifically we train neural networks to estimate the overall dangerousness of a grid state. A classical benchmark problem (manpower 118 buses test case) is used to show the strengths of the proposed method.

In this paper, we present a regression framework involving several machine learning models to estimate water parameters based on hyperspectral data. Measurements from a multi-sensor field campaign, conducted on the River Elbe, Germany, represent the benchmark dataset. It contains hyperspectral data and the five water parameters chlorophyll a, green algae, diatoms, CDOM and turbidity. We apply a PCA for the high-dimensional data as a possible preprocessing step. Then, we evaluate the performance of the regression framework with and without this preprocessing step. The regression results of the framework clearly reveal the potential of estimating water parameters based on hyperspectral data with machine learning. The proposed framework provides the basis for further investigations, such as adapting the framework to estimate water parameters of different inland waters.

Anthony Giddens is the former director of the London School of Economics and a member of the House of Lords Select Committee on Artificial Intelligence. LONDON -- In 1215, England adopted the Magna Carta to stop kings from abusing their power. Today, the new kings are big tech companies, and just like centuries ago, we need a charter to govern them. The digital revolution is the greatest dynamic force in the world today. It affects everything from the intimacies of everyday life to geopolitical struggles and has made the world become one in a way that was never possible before.

Facebook's reputation took a massive hit earlier this year when it was revealed that it had improperly provided user data to UK-based political consulting firm Cambridge Analytica. However, the damage appeared to be much greater in the other direction, as the controversial Cambridge Analytica announced Wednesday that it would cease operations and file for bankruptcy. Cambridge Analytica is shutting down. The firm with ties to Trump's campaign says the Facebook data scandal drove away business. The firm announced its closure in a statement on its website.

Two-thirds (66%) of senior IT decision-makers believe failure to adopt artificial intelligence (AI) will lead to a loss of competitiveness, research by global reviews and customer insights company Feefo has found. According to the report, 96% also felt that AI will have a positive effect on customer-engagement in their organisation, while 45% believed that personalisation is where the biggest gains will be made in improving customer experience. See also: AI: the next level of smart customer service? The findings were revealed in a survey of 100 senior IT decision-makers in the UK, covering their attitudes towards AI and its adoption in their respective organisations. As a result of these beliefs, 61% of respondents said they are using, or will use AI for customer-service analysis and intervention.

Cambridge Analytica, the firm that used data from millions of Facebook users without their knowledge, said Wednesday that it is "immediately ceasing all operations." The firm worked for President Trump's 2016 campaign. SCL Elections Ltd., Cambridge Analytica's U.K.-based parent firm, said the company had begun the process of filing for bankruptcy for Cambridge Analytica and some of its U.S. affiliates, and was moving to apply for insolvency in the U.K. "Over the past several months," the company said in a statement, "Cambridge Analytica has been the subject of numerous unfounded accusations and, despite the Company's efforts to correct the record, has been vilified for activities that are not only legal, but also widely accepted as a standard component of online advertising in both the political and commercial arenas." It also said it had "unwavering confidence that its employees have acted ethically and lawfully" after an internal investigation but that a barrage of negative media coverage drove away "virtually all" of Cambridge Analytica's customers and suppliers. In March, Britain's Channel 4 exposed Cambridge Analytica's stealthy means of supporting clients.

Here's how a data firm helped Donald Trump get elected as president. Cambridge Analytica's chief executive officer Alexander Nix gives an interview during the 2017 Web Summit in Lisbon on November 9, 2017. Cambridge Analytica, the political ad marketing firm that worked for President Trump and was involved in the misappropriation of 87 million Facebook users' data is closing its offices. The specialist in political predictions announced that bankruptcy proceedings will begin in the U.S., as well as insolvency proceedings in the U.K, where the firm has an office and where its parent company The SCL Group is based. "Over the past several months, Cambridge Analytica has been the subject of numerous unfounded accusations and, despite the Company's efforts to correct the record, has been vilified for activities that are not only legal, but also widely accepted as a standard component of online advertising in both the political and commercial arenas," the company said in a statement Wednesday.