The large bulk of work in multiple testing has focused on specifying procedures that control the false discovery rate (FDR), with relatively less attention being paid to the corresponding Type II error known as the false non-discovery rate (FNR). A line of more recent work in multiple testing has begun to investigate the tradeoffs between the FDR and FNR and to provide lower bounds on the performance of procedures that depend on the model structure. Lacking thus far, however, has been a general approach to obtaining lower bounds for a broad class of models. This paper introduces an analysis strategy based on derandomization, illustrated by applications to various concrete models. Our main result is meta-theorem that gives a general recipe for obtaining lower bounds on the combination of FDR and FNR. We illustrate this meta-theorem by deriving explicit bounds for several models, including instances with dependence, scale-transformed alternatives, and non-Gaussian-like distributions. We provide numerical simulations of some of these lower bounds, and show a close relation to the actual performance of the Benjamini-Hochberg (BH) algorithm.

Microsoft's AI ethics committee helped craft internal Department of Defense contract policy, and G20 member nations wouldn't have passed AI ethics principles if it weren't for Japanese leadership. Published Tuesday, the UC Berkeley Center for Long-Term Cybersecurity (CLTC) case study examines how organizations are putting AI ethics principles into practice. Ethics principles are often vaguely phrased rules that can be challenging to translate into the daily practices of an engineer or other frontline worker. CLTC research fellow Jessica Cussins Newman told VentureBeat that many AI ethics and governance debates have focused more on what is needed, but less on the practices and policies necessary to implement goals enshrined in principles. The study focuses on OpenAI's rollout of GPT-2; the adoption of AI principles by OECD and G20; and the creation of the AI, Ethics, and Effects in Engineering and Research (AETHER) committee at Microsoft.

Artificial intelligence is still in its infancy. But it may well prove to be the most powerful technology ever invented. It has the potential to improve health, supercharge intellects, multiply productivity, save the environment and enhance both freedom and democracy. But as that intelligence continues to climb, the danger from using AI in an irresponsible way also brings the potential for AI to become a social and cultural H-bomb. It's a technology that can deprive us of our liberty, power autocracies and genocides, program our behavior, turn us into human machines and, ultimately, turn us into slaves.

Do you remember the 2018 Accident Case of Self Driving Uber Car? The car collided with a pedestrian and caused her death. Since then the scrutiny has raised at another level for the security of such autonomous vehicles. Many have claimed that rolling out self-driving cars in the road at this stage is extremely dangerous and criticized the autonomous tech development. However, considering a different angle from a general perspective, National Transportation Safety Board (NTSB) said, "Had the vehicle operator been attentive, she would likely have had sufficient time to detect and react to the crossing pedestrian to avoid the crash or mitigate the impact."

For the purpose of this narrative review, we searched PubMed and MEDLINE databases with no date restriction using search terms related to AI and medicine and cardiology subspecialties. Articles were reviewed and selected for inclusion on the basis of relevance. This article highlights that the role of ML in cardiovascular medicine is rapidly emerging, and mounting evidence indicates it will power the new tools that drive the field. Among other uses, AI has been deployed to interpret echocardiograms, to automatically identify heart rhythms from an ECG, to uniquely identify an individual using the ECG as a biometric signal, and to detect the presence of heart disease such as left ventricular dysfunction from the surface ECG.6x6Attia, Z.I., Kapa, S., Lopez-Jimenez, F. et al.

Scientists are working day and night to fight against Covid-19, which is a blessing and a curse. With different research being done all around the world, it's difficult to know which solutions deserve their time, money, and resources. Trying to speed the process along, scientists are forgoing the time-tested "peer review" model of printing findings in favor of so-called open science, which risks accuracy. It's a classic problem of trying to find the needle in the haystack, except the hay is coming from everyone and everywhere on Earth. Using a machine-learning algorithm trained on thousands of multidisciplinary research papers, a team of computer scientists and sociologists from Northwestern University have developed a scalable method to help scientists identify the most promising solutions for this global problem.

Security teams are dealing with a stream of warnings about failed login attempts, possible phishing emails and potential malware threats, among other challenges. There are concerns over authorized use -- who has permission to do what, when they will access it and why -- and issues around private data generated by staff and consumers. Around 26 percent of these alerts are false positives, according to Neustar. Some require no action, others are an easy fix and a small percentage actually require IT intervention. The result is hardly surprising: alert fatigue.

After decades spent discussing how and what to teach in the classrooms, the focus is now turning more to implementation, experts said at the World Innovation Summit for Education (WISE) conference in Doha, hosted by the Qatar Foundation on 19-21 November. Ministers and education experts discussed in Doha how to reap the benefits of the digital revolution as new challenges arise from teaching students across the world in the era of artificial intelligence. OECD countries spend on average 4.5% of their GDP on education. At the same time, education itself is transforming to adapt to a changing planet. The constant retooling of labour skills will be a central element of a European Commission paper on the future of the EU social pillar, to be published on 26 April, EURACTIV.com In an increasingly uncertain and unstable world, citizens are expected to become life-long learners in order to remain relevant for a fast-changing labour market that will be disrupted by machines.

This paper proposes a detailed and extensive comparison of the Trust Region Policy Optimization and DeepQ-Network with Normalized Advantage Functions with respect to other state of the art algorithms, namely Deep Deterministic Policy Gradient and Vanilla Policy Gradient. Comparisons demonstrate that the former have better performances then the latter when asking robotic arms to accomplish manipulation tasks such as reaching a random target pose and pick &placing an object. Both simulated and real-world experiments are provided. Simulation lets us show the procedures that we adopted to precisely estimate the algorithms hyper-parameters and to correctly design good policies. Real-world experiments let show that our polices, if correctly trained on simulation, can be transferred and executed in a real environment with almost no changes.

Vertex nomination is a lightly-supervised network information retrieval (IR) task in which vertices of interest in one graph are used to query a second graph to discover vertices of interest in the second graph. Similar to other IR tasks, the output of a vertex nomination scheme is a ranked list of the vertices in the second graph, with the heretofore unknown vertices of interest ideally concentrating at the top of the list. Vertex nomination schemes provide a useful suite of tools for efficiently mining complex networks for pertinent information. In this paper, we explore, both theoretically and practically, the dual roles of content (i.e., edge and vertex attributes) and context (i.e., network topology) in vertex nomination. We provide necessary and sufficient conditions under which vertex nomination schemes that leverage both content and context outperform schemes that leverage only content or context separately. While the joint utility of both content and context has been demonstrated empirically in the literature, the framework presented in this paper provides a novel theoretical basis for understanding the potential complementary roles of network features and topology.