The planning domain has experienced increased interest in the formal synthesis of decision-making policies. This formal synthesis typically entails finding a policy which satisfies formal specifications in the form of some well-defined logic, such as Linear Temporal Logic (LTL) or Computation Tree Logic (CTL), among others. While such logics are very powerful and expressive in their capacity to capture desirable agent behavior, their value is limited when deriving decision-making policies which satisfy certain types of asymptotic behavior. In particular, we are interested in specifying constraints on the steady-state behavior of an agent, which captures the proportion of time an agent spends in each state as it interacts for an indefinite period of time with its environment. This is sometimes called the average or expected behavior of the agent. In this paper, we explore the steady-state planning problem of deriving a decision-making policy for an agent such that constraints on its steady-state behavior are satisfied. A linear programming solution for the general case of multichain Markov Decision Processes (MDPs) is proposed and we prove that optimal solutions to the proposed programs yield stationary policies with rigorous guarantees of behavior.

We consider evaluating the causal effects of dynamic treatments, i.e. of multiple treatment sequences in various periods, based on double machine learning to control for observed, time-varying covariates in a data-driven way under a selection-on-observables assumption. To this end, we make use of so-called Neyman-orthogonal score functions, which imply the robustness of treatment effect estimation to moderate (local) misspecifications of the dynamic outcome and treatment models. This robustness property permits approximating outcome and treatment models by double machine learning even under high dimensional covariates and is combined with data splitting to prevent overfitting. In addition to effect estimation for the total population, we consider weighted estimation that permits assessing dynamic treatment effects in specific subgroups, e.g. among those treated in the first treatment period. We demonstrate that the estimators are asymptotically normal and $\sqrt{n}$-consistent under specific regularity conditions and investigate their finite sample properties in a simulation study. Finally, we apply the methods to the Job Corps study in order to assess different sequences of training programs under a large set of covariates.

The World Intellectual Property Organization's (WIPO) first report of a series called WIPO Technology Trends, an extensive study of patent applications and other scientific documents, offers clues to the next big thing in AI. Rather than treating'AI' as a single homogeneous discipline (see our guide to AI terminology), the WIPO report divides it into AI techniques, AI functional applications and AI application fields, offering a finer-grained analysis. AI techniques are advanced forms of statistical and mathematical models used in AI, including machine learning, logic programming, ontology engineering, probabilistic reasoning and fuzzy logic. Machine learning is included in more than one third of all identified inventions and represents 89 per cent of AI filings, the report finds. Between 2013 and 2016, filings related to deep learning rocketed by about 175 per cent.

As businesses of all sizes welcome a fearful and anxious workforce back to the office, they are simultaneously challenged with ensuring a safe work environment. The stark reality facing business owners still navigating the covid-19 pandemic is the diligence required to limit infectious spread. Corporations are taking note: plexiglass barriers, clearly marked walkways, and hand-sanitizing stations are now as commonplace as paper clips and ergonomic chairs. Although such measures can mitigate the risk of infection, management teams will be challenged to properly sanitize the workplace without jeopardizing human health or affecting employee productivity while also facing agency and government regulations. Many business owners are finding solutions by partnering with innovative organizations like J Ferg Global, an industry leader in infection control, risk mitigation, and revenue restoration.

In February of 2013, Eric Loomis was driving around in the small town of La Crosse in Wisconsin, US, when he was stopped by the police. The car he was driving turned out to have been involved in a shooting, and he was arrested. Eventually a court sentenced him to six years in prison. This might have been an uneventful case, had it not been for a piece of technology that had aided the judge in making the decision. They used COMPAS, an algorithm that determines the risk of a defendant becoming a recidivist.

Motivated by the EU's "Right To Be Forgotten" regulation, we initiate a study of statistical data deletion problems where users' data are accessible only for a limited period of time. This setting is formulated as an online supervised learning task with \textit{constant memory limit}. We propose a deletion-aware algorithm \texttt{FIFD-OLS} for the low dimensional case, and witness a catastrophic rank swinging phenomenon due to the data deletion operation, which leads to statistical inefficiency. As a remedy, we propose the \texttt{FIFD-Adaptive Ridge} algorithm with a novel online regularization scheme, that effectively offsets the uncertainty from deletion. In theory, we provide the cumulative regret upper bound for both online forgetting algorithms. In the experiment, we showed \texttt{FIFD-Adaptive Ridge} outperforms the ridge regression algorithm with fixed regularization level, and hopefully sheds some light on more complex statistical models.

IBM, Microsoft and Amazon all recently announced they are either halting or pausing facial recognition technology initiatives. IBM even launched the Notre Dame-IBM Tech Ethics Lab, "a'convening sandbox' for affiliated scholars and industry leaders to explore and evaluate ethical frameworks and ideas." In my view, the governance that will yield ethical artificial intelligence (AI) -- specifically, unbiased decisioning based on AI -- won't spring from an academic sandbox. AI governance is a board-level issue. Boards of directors should care about AI governance because AI technology makes decisions that profoundly affect everyone.

Slate has relationships with various online retailers. If you buy something through our links, Slate may earn an affiliate commission. We update links when possible, but note that deals can expire and all prices are subject to change. All prices were up to date at the time of publication. The simplest way to summarize the plot of Ready Player Two is to repeat the plot of its predecessor, Ready Player One, as they are largely the same.

Artificial Intelligence ("AI") is clearly on the horizon of the regulatory landscape. Alongside the use of technology to assist with navigating the regulatory process, regulators are now digitizing their enforcement efforts. The Canadian Securities Administrators ("CSA")1 have approached this challenge head-on. In 2018, the CSA put the capital markets on notice that they were strengthening their technological capabilities to assist in fighting securities misconduct.2 The CSA confirmed they would rely on AI technology to analyze large data sets, allowing them to detect misconduct faster and earlier, through the Market Analysis Platform ("MAP"), an automated centralized solution that the CSA believed could handle the size of the current market practices.

Tantech Holdings Ltd (NASDAQ: TANH) ("Tantech" or the "Company"), a clean energy company in China, today announced the launch by its subsidiary, Shangchi Automobile Co., Ltd. The Shangchi SC-100A follows the launch last month of the SC-120A model featuring unmanned, automatic sweeping. Shangchi Automobile's innovative driverless and autonomous street sweepers are designed for quieter operation and improved cleaning performance, with the ability to reduce or eliminate the 7 to 8 humans required for typical sweeper vehicle operation. Lidar-based, machine vision technology provides long-distance detection and obstacle identification, with sensors for short-distance obstacle detection and avoidance. This enables the driverless model to safely and accurately operate in common environments.