Designing the future of healthcare could become everyone's responsibility. Making that happen will require a new education focus around artificial intelligence for healthcare professionals and patients, write Jane Rendall, UK manager of Sectra and Rachel Dunscombe, director for Tektology and CEO of the NHS Digital Academy. A crisis point could be on the horizon for NHS imaging disciplines. Rising demand and pervasive recruitment challenges mean there will be too few experts to go around based on current ways of working. We certainly don't want to reach that point, and to achieve that the health service will need to adopt artificial intelligence in new ways as an important mechanism in redesigning services.

If you use such social media websites as Facebook and Twitter, you may have come across posts flagged with warnings about misinformation. So far, most misinformation – flagged and unflagged – has been aimed at the general public. Imagine the possibility of misinformation – information that is false or misleading – in scientific and technical fields like cybersecurity, public safety and medicine. There is growing concern about misinformation spreading in these critical fields as a result of common biases and practices in publishing scientific literature, even in peer-reviewed research papers. As a graduate student and as faculty members doing research in cybersecurity, we studied a new avenue of misinformation in the scientific community.

An AI arms race is already underway. That's the reality we have to deal with," Maas told DW, speaking in a new DW documentary, "Future Wars -- and How to Prevent Them." "This is a race that cuts across the military and the civilian fields," said Amandeep Singh Gill, former chair of the United Nations group of governmental experts on lethal autonomous weapons. "This is a multi-trillion dollar question." This is apparent in a recent report from the United States' National Security Commission on Artificial Intelligence. It speaks of a "new warfighting paradigm" pitting "algorithms against algorithms," and urges massive investments "to continuously out-innovate potential adversaries." And you can see it in China's latest five-year plan, which places AI at the center of a relentless ramp-up in research and development, while the People's Liberation Army girds for a future of what it calls "intelligentized warfare." As Russian President Vladimir Putin put it as early as 2017, "whoever ...

The rapid advancement indicates that artificial intelligence is on its way to changing combat and that states will undoubtedly continue to build the automated weapons systems that AI will enable. Fremont, CA: The rapid acceleration in computing power, memory, big data, and high-speed communication is not only developing an innovation, investment, and application frenzy, but it is also increasing the quest for AI chips as AI, machine learning, and deep learning evolve further and move from concept to commercialization. This rapid advancement indicates that artificial intelligence is on its way to changing combat and that states will undoubtedly continue to build the automated weapons systems that AI will enable. When countries work together and individually to obtain a competitive advantage in research and technology, the weaponization of AI will become unavoidable. As a result, it's important to imagine what an algorithmic war of the future may look like because developing autonomous weapons systems is one thing, but employing them in algorithmic warfare against other states and humans is quite another.

Japan can't build a cutting-edge chip development and manufacturing base on its own, and must seek to cooperate with Taiwan Semiconductor Manufacturing Co. (TSMC), according to Akira Amari, a senior lawmaker from the ruling Liberal Democratic Party. Amari, a former economy minister who heads an LDP working group on semiconductor strategy, added that the government must be prepared to spend trillions of yen to keep up with the U.S. and Europe. Both have plans to pour money into the industry amid a global shortage of semiconductors, including advanced logic chips that are essential for everything from artificial intelligence to autonomous driving. "Unlike the purely domestic, independent way it was done in the past, I think we need to cooperate with overseas counterparts," Amari said in an interview in Tokyo on Monday. "The world's top logic chipmaker is TSMC, so we must think about how to cooperate with them."

Limited expert time is a key bottleneck in medical imaging. Due to advances in image classification, AI can now serve as decision-support for medical experts, with the potential for great gains in radiologist productivity and, by extension, public health. However, these gains are contingent on building and maintaining experts' trust in the AI agents. Explainable AI may build such trust by helping medical experts to understand the AI decision processes behind diagnostic judgements. Here we introduce and evaluate explanations based on Bayesian Teaching, a formal account of explanation rooted in the cognitive science of human learning. We find that medical experts exposed to explanations generated by Bayesian Teaching successfully predict the AI's diagnostic decisions and are more likely to certify the AI for cases when the AI is correct than when it is wrong, indicating appropriate trust. These results show that Explainable AI can be used to support human-AI collaboration in medical imaging.

Programmatic weak supervision creates models without hand-labeled training data by combining the outputs of noisy, user-written rules and other heuristic labelers. Existing frameworks make the restrictive assumption that labelers output a single class label. Enabling users to create partial labelers that output subsets of possible class labels would greatly expand the expressivity of programmatic weak supervision. We introduce this capability by defining a probabilistic generative model that can estimate the underlying accuracies of multiple noisy partial labelers without ground truth labels. We prove that this class of models is generically identifiable up to label swapping under mild conditions. We also show how to scale up learning to 100k examples in one minute, a 300X speed up compared to a naive implementation. We evaluate our framework on three text classification and six object classification tasks. On text tasks, adding partial labels increases average accuracy by 9.6 percentage points. On image tasks, we show that partial labels allow us to approach some zero-shot object classification problems with programmatic weak supervision by using class attributes as partial labelers. Our framework is able to achieve accuracy comparable to recent embedding-based zero-shot learning methods using only pre-trained attribute detectors

We present the Multiple Criteria Decision Analysis Methods Selection Software (MCDA-MSS). This decision support system helps analysts answering a recurring question in decision science: Which is the most suitable Multiple Criteria Decision Analysis method (or a subset of MCDA methods) that should be used for a given Decision-Making Problem (DMP)?. The MCDA-MSS includes guidance to lead decision-making processes and choose among an extensive collection (over 200) of MCDA methods. These are assessed according to an original comprehensive set of problem characteristics. The accounted features concern problem formulation, preference elicitation and types of preference information, desired features of a preference model, and construction of the decision recommendation. The applicability of the MCDA-MSS has been tested on several case studies. The MCDA-MSS includes the capabilities of (i) covering from very simple to very complex DMPs, (ii) offering recommendations for DMPs that do not match any method from the collection, (iii) helping analysts prioritize efforts for reducing gaps in the description of the DMPs, and (iv) unveiling methodological mistakes that occur in the selection of the methods. A community-wide initiative involving experts in MCDA methodology, analysts using these methods, and decision-makers receiving decision recommendations will contribute to expansion of the MCDA-MSS.

Policies are authoritative assets that are present in multiple domains to support decision-making. They describe what actions are allowed or recommended when domain entities and their attributes satisfy certain criteria. It is common to find policies that contain geographical rules, including distance and containment relationships among named locations. These locations' polygons can often be found encoded in geospatial datasets. We present an approach to transform data from geospatial datasets into Linked Data using the OWL, PROV-O, and GeoSPARQL standards, and to leverage this representation to support automated ontology-based policy decisions. We applied our approach to location-sensitive radio spectrum policies to identify relationships between radio transmitters coordinates and policy-regulated regions in Census.gov datasets. Using a policy evaluation pipeline that mixes OWL reasoning and GeoSPARQL, our approach implements the relevant geospatial relationships, according to a set of requirements elicited by radio spectrum domain experts.

This Overview, Design Concepts, and Details Protocol (ODD) provides a detailed description of an agent-based model (ABM) that was developed to simulate hospitalizations during the COVID-19 pandemic. Using the descriptions of submodels, provided parameters, and the links to data sources, modelers will be able to replicate the creation and results of this model.