Organizations across industries are turning to artificial intelligence (AI) in cybersecurity to protect their networks and relieve often …

The Government has said that artificial intelligence (AI) in GP practices will help manage patients in the elective care backlog. It today announced that new technology and innovation will allow the NHS to treat 30% more elective care patients by 2023/24. It added that NHS'come forward with a delivery plan for tackling the backlog'. In March, NHS England suggested that GPs could be asked to review hospital waiting lists for elective care to help prioritise and manage patients from the following month. Details were limited, but NHS England later told GPs that they must'jointly manage' patients stuck in the backlog of care caused by the Covid pandemic with hospitals. Meanwhile, Pulse revealed in June that NHSX and NHS England were considering the viability of a wider roll out of an artificial intelligence triage model based on that used by Babylon.

We propose solving the general Stochastic Shortest-Path Markov Decision Process (SSP MDP) as probabilistic inference. Furthermore, we discuss online and offline methods for planning under uncertainty. In an SSP MDP, the horizon is indefinite and unknown a priori. SSP MDPs generalize finite and infinite horizon MDPs and are widely used in the artificial intelligence community. Additionally, we highlight some of the differences between solving an MDP using dynamic programming approaches widely used in the artificial intelligence community and approaches used in the active inference community.

In cross-lingual language models, representations for many different languages live in the same space. Here, we investigate the linguistic and non-linguistic factors affecting sentence-level alignment in cross-lingual pretrained language models for 101 languages and 5,050 language pairs. Using BERT-based LaBSE and BiLSTM-based LASER as our models, and the Bible as our corpus, we compute a task-based measure of cross-lingual alignment in the form of bitext retrieval performance, as well as four intrinsic measures of vector space alignment and isomorphism. We then examine a range of linguistic, quasi-linguistic, and training-related features as potential predictors of these alignment metrics. The results of our analyses show that word order agreement and agreement in morphological complexity are two of the strongest linguistic predictors of cross-linguality. We also note in-family training data as a stronger predictor than language-specific training data across the board. We verify some of our linguistic findings by looking at the effect of morphological segmentation on English-Inuktitut alignment, in addition to examining the effect of word order agreement on isomorphism for 66 zero-shot language pairs from a different corpus. We make the data and code for our experiments publicly available.

Feature based local attribution methods are amongst the most prevalent in explainable artificial intelligence (XAI) literature. Going beyond standard correlation, recently, methods have been proposed that highlight what should be minimally sufficient to justify the classification of an input (viz. pertinent positives). While minimal sufficiency is an attractive property, the resulting explanations are often too sparse for a human to understand and evaluate the local behavior of the model, thus making it difficult to judge its overall quality. To overcome these limitations, we propose a novel method called Path-Sufficient Explanations Method (PSEM) that outputs a sequence of sufficient explanations for a given input of strictly decreasing size (or value) -- from original input to a minimally sufficient explanation -- which can be thought to trace the local boundary of the model in a smooth manner, thus providing better intuition about the local model behavior for the specific input. We validate these claims, both qualitatively and quantitatively, with experiments that show the benefit of PSEM across all three modalities (image, tabular and text). A user study depicts the strength of the method in communicating the local behavior, where (many) users are able to correctly determine the prediction made by a model.

The unprecedented success of deep learning (DL) makes it unchallenged when it comes to classification problems. However, it is well established that the current DL methodology produces universally unstable neural networks (NNs). The instability problem has caused an enormous research effort -- with a vast literature on so-called adversarial attacks -- yet there has been no solution to the problem. Our paper addresses why there has been no solution to the problem, as we prove the following mathematical paradox: any training procedure based on training neural networks for classification problems with a fixed architecture will yield neural networks that are either inaccurate or unstable (if accurate) -- despite the provable existence of both accurate and stable neural networks for the same classification problems. The key is that the stable and accurate neural networks must have variable dimensions depending on the input, in particular, variable dimensions is a necessary condition for stability. Our result points towards the paradox that accurate and stable neural networks exist, however, modern algorithms do not compute them. This yields the question: if the existence of neural networks with desirable properties can be proven, can one also find algorithms that compute them? There are cases in mathematics where provable existence implies computability, but will this be the case for neural networks? The contrary is true, as we demonstrate how neural networks can provably exist as approximate minimisers to standard optimisation problems with standard cost functions, however, no randomised algorithm can compute them with probability better than 1/2.

Tuya employee Ella Yuan demonstrates the company's facial recognition system at the Consumer Electronics Show in Las Vegas on January 9, 2019. Three US senators are calling for the Chinese Internet of Things platform operator to be added to a list of sanctioned Chinese companies, citing national security concerns.

Most of the public discourse around artificial intelligence (AI) policy focuses on one of two perspectives: how the government can support AI innovation, and how the government can deter its harmful or negligent use. Yet there can also be a role for government in making it easier to use AI beneficially--in this niche, the National Science Foundation (NSF) has found a way to contribute. Through a grant-making program called Fairness in Artificial Intelligence (FAI), the NSF is providing $20 million in funding to researchers working on difficult ethical problems in AI. The program, a collaboration with Amazon, has now funded 21 projects in its first two years, with an open call for applications in its third and final year. This is an important endeavor, furthering a trend of federal support for the responsible advancement of technology, and the NSF should continue this important line of funding for ethical AI.

The Pentagon is not the most inviting place for first-time visitors, and it was no different for Chris Lynch. When he rode the escalator out of the Pentagon metro station, Lynch was greeted by guard dogs and security personnel wearing body armor and toting machine guns. He lost cell service upon entering the building and was forced to run through more than a half mile of hallways to make his meeting in the office of the secretary of defense. He showed up late and out of breath, his hoodie and gym shoes soaked with sweat. It was a surreal experience, Lynch told me, and it marked the beginning of "the most delightful detour of my entire life." Lynch had just completed a 45-day posting in the United States Digital Service, an organization formed in 2014 to fill what many officials viewed as a crucial gap in the government's technology expertise. That year, the White House had launched HealthCare.gov to help enroll Americans in government health insurance, but it had been a technological debacle that almost derailed the Affordable Care Act. The website was so buggy that on its first day, only six people were able to sign up through the site. In response, and to prevent similar flops from occurring in the future, the White House created the USDS.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. U.S. forces shot down a pair of Iranian drones that attacked the Irbil airport in Kurdish-held northern Iraq late on 20th anniversary of Sept. 11. There were no injuries or damage, according to a spokesman for the U.S.-led coalition in Iraq. The U.S. counter-rocket, artillery and mortar system (C-RAM) engaged the two bomb-laden drones which were made in Iran, a separate U.S. official told Fox News.