In developed nations, there is a rich trove of data that the intelligence community can and does mine. Valuable information can be pulled from media reports, public financial information and social media posts. Websites track user activity, and smartphones are constantly gobbling up information about their users, from geolocations to search histories and more. By using artificial intelligence tools, analysts are able to make sense of this torrent of publicly available data and turn it into usable open-source intelligence, known as OSINT. But not every part of the world produces that vast torrent of data.

Aiming to close what it calls a "data divide," Microsoft on Tuesday announced a plan to make more data widely available so the benefits of artificial intelligence aren't confined to a few large companies. Why it matters: Machine learning has the potential to make governments and countries far more efficient but often requires an enormous amount of data, in addition to the necessary computing power. What they're saying: "Fully half of all of the data created, every day, on the internet, is flowing to only 100 companies," Microsoft President Brad Smith said in an interview. Smith said that those same companies, left unchecked, would be the beneficiaries of AI, while most others would likely fall behind: "Fundamentally, it's going to accrue to a handful of companies on the West Coast of the United States and the East Coast of China." Yes, but: While Microsoft is pledging to share data around issues like health and the environment, Microsoft and other companies are unlikely to share their most proprietary data sets, which will likely generate most of the profits in the AI era.

The hype around artificial intelligence is under the scanner as the technology has not made a big impact in the fight against COVID-19. Undoubtedly, AI has taken the central stage within various organisations to drive business growth, but its effectiveness in a wide range of use cases is yet again being questioned. This is because researchers have failed to bring anything on the table that could significantly help the world fight COVID-19. Today, the world needs AI more than ever to slow the spread of the deadly virus and, in turn, save thousands of lives. Has AI ultimately failed us all during the COVID-19 crisis? Lockdown of places have helped slow the community spread of the virus, but today, the consumer-driven economy is taking a huge hit.

Machine-learning systems can be duped or confounded by situations they haven't seen before. A self-driving car gets flummoxed by a scenario that a human driver could handle easily. An AI system laboriously trained to carry out one task (identifying cats, say) has to be taught all over again to do something else (identifying dogs). In the process, it's liable to lose some of the expertise it had in the original task. Computer scientists call this problem "catastrophic forgetting."

Much of the work undertaken by artificial intelligence involves a training process known as machine learning, where AI gets better at a task such as recognising a cat or mapping a route the more it does it. Now that same technique is being use to create new AI systems, without any human intervention. For years, engineers at Google have been working on a freakishly smart machine learning system known as the AutoML system (or automatic machine learning system), which is already capable of creating AI that outperforms anything we've made. Now, researchers have tweaked it to incorporate concepts of Darwinian evolution and shown it can build AI programs that continue to improve upon themselves faster than they would if humans were doing the coding. The new system is called AutoML-Zero, and although it may sound a little alarming, it could lead to the rapid development of smarter systems - for example, neural networked designed to more accurately mimic the human brain with multiple layers and weightings, something human coders have struggled with.

UK spies will need to use artificial intelligence (AI) to counter a range of threats, an intelligence report says. Adversaries are likely to use the technology for attacks in cyberspace and on the political system, and AI will be needed to detect and stop them. But AI is unlikely to predict who might be about to be involved in serious crimes, such as terrorism - and will not replace human judgement, it says. The report is based on unprecedented access to British intelligence. The Royal United Services Institute (Rusi) think tank also argues that the use of AI could give rise to new privacy and human-rights considerations, which will require new guidance.

Not so long ago, a method was discovered that successfully overcomes the catastrophic forgetting of neural networks. Although we know about the cases of using this method to preserve skills when adapting pre-trained networks to particular tasks, it has not yet obtained widespread distribution. In this paper, we would like to propose an alternative method of overcoming catastrophic forgetting based on the total absolute signal passing through each connection in the network. This method has a simple implementation and seems to us essentially close to the processes occurring in the brain of animals to preserve previously learned skills during subsequent learning. We hope that the ease of implementation of this method will serve its wide application.

Background: In the United States, 5.7 million patients are admitted annually to intensive care units (ICU), with costs exceeding $82 billion. Although close monitoring and dynamic assessment of patient acuity are key aspects of ICU care, both are limited by the time constraints imposed on healthcare providers. Methods: Using the University of Florida Health (UFH) Integrated Data Repository as Honest Broker, we created a database with electronic health records data from a retrospective study cohort of 38,749 adult patients admitted to ICU at UF Health between 06/01/2014 and 08/22/2019. This repository includes demographic information, comorbidities, vital signs, laboratory values, medications with date and timestamps, and diagnoses and procedure codes for all index admission encounters as well as encounters within 12 months prior to index admission and 12 months follow-up. We developed algorithms to identify acuity status of the patient every four hours during each ICU stay. Results: We had 383,193 encounters (121,800 unique patients) admitted to the hospital, and 51,073 encounters (38,749 unique patients) with at least one ICU stay that lasted more than four hours. These patients requiring ICU admission had longer median hospital stay (7 days vs. 1 day) and higher in-hospital mortality (9.6% vs. 0.4%) compared with those not admitted to the ICU. Among patients who were admitted to the ICU and expired during hospital admission, more deaths occurred in the ICU than on general hospital wards (7.4% vs. 0.8%, respectively). Conclusions: We developed phenotyping algorithms that determined patient acuity status every four hours while admitted to the ICU. This approach may be useful in developing prognostic and clinical decision-support tools to aid patients, caregivers, and providers in shared decision-making processes regarding resource use and escalation of care.

Modern causal inference methods allow machine learning to be used to weaken parametric modeling assumptions. However, the use of machine learning may result in bias and incorrect inferences due to overfitting. Cross-fit estimators have been proposed to eliminate this bias and yield better statistical properties. We conducted a simulation study to assess the performance of several different estimators for the average causal effect (ACE). The data generating mechanisms for the simulated treatment and outcome included log-transforms, polynomial terms, and discontinuities. We compared singly-robust estimators (g-computation, inverse probability weighting) and doubly-robust estimators (augmented inverse probability weighting, targeted maximum likelihood estimation). Nuisance functions were estimated with parametric models and ensemble machine learning, separately. We further assessed cross-fit doubly-robust estimators. With correctly specified parametric models, all of the estimators were unbiased and confidence intervals achieved nominal coverage. When used with machine learning, the cross-fit estimators substantially outperformed all of the other estimators in terms of bias, variance, and confidence interval coverage. Due to the difficulty of properly specifying parametric models in high dimensional data, doubly-robust estimators with ensemble learning and cross-fitting may be the preferred approach for estimation of the ACE in most epidemiologic studies. However, these approaches may require larger sample sizes to avoid finite-sample issues.

Normalization is an important and vastly investigated technique in deep learning. However, its role for Ordinary Differential Equation based networks (neural ODEs) is still poorly understood. This paper investigates how different normalization techniques affect the performance of neural ODEs. Particularly, we show that it is possible to achieve 93% accuracy in the CIFAR-10 classification task, and to the best of our knowledge, this is the highest reported accuracy among neural ODEs tested on this problem.