The medical industry is sitting on a huge trove of data, but in many cases it can be a challenge to realize the value of it because that data is unstructured and in disparate places. Today, a startup called Mendel, which has built an AI platform both to ingest and bring order to that body of information, is announcing $18 million in funding to continue its growth and to build out what it describes as a "clinical data marketplace" for people not just to organize, but also to share and exchange that data for research purposes. It's also going to be using the funding to hire more talent -- technical and support -- for its two offices, in San Jose, CA and Cairo, Egypt. The Series A round is being led by DCM, with OliveTree and MTVLP, and previous backers Launch Capital, SOSV, Bootstrap Labs and Chairman of UCSF Health Hub Mark Goldstein also participating. The funding comes on the heels of what Mendel says is a surge of interest among research and pharmaceutical companies in sourcing better data to gain a better understanding of longer-term patient care and progress, in particular across wider groups of users, not just at a time when it has been more challenging to observe people and run trials, but in light of the understanding that using AI to leverage much bigger data sets can produce better insights.

From a ship that has been designed to tell you what maintenance it needs and when, to an intelligent journey planner for global goods transport. The three universities in Zuid-Holland are abuzz with AI research in the field of ports and maritime. "The big challenges facing ports are accessibility, sustainability and finding the right employees," says Rudy Negenborn, Professor of Multi-Machine Operations & Logistics in Delft. "In a busy port, you have to optimise your planning to avoid delays, congestion and unnecessary emissions. This doesn't just require solutions to technical challenges: a solution can only be implemented if an organisation wants and has the right infrastructure for this."

In brief Machines are like humans – they're lazy. When given the chance to take the easy route to complete an easy task, they will. Academics at the University of Washington found that algorithms trained to diagnose COVID-19 from chest X-rays often look at secondary features, such as a patient's age, rather than focusing on the images themselves – something known as shortcut learning. "A physician would generally expect a finding of COVID-19 from an X-ray to be based on specific patterns in the image that reflect disease processes," said Alex DeGrave, a medical science student at the American university and co-author of a paper published this week in Nature Intelligence. "But, rather than relying on those patterns, a system using shortcut learning might, for example, judge that someone is elderly and, thus, infer that they are more likely to have the disease because it is more common in older patients. The shortcut is not wrong per se, but the association is unexpected and not transparent. And, that could lead to an inappropriate diagnosis."

As humans, it seems we are putting too much trust in AI, and business leaders are disinterested in ensuring that the systems they use are ethical and responsible. When humans administer cardiopulmonary resuscitation (CPR), they get fatigued relatively quickly, affecting the quality of CPR they can deliver. LUCAS 3 is a mechanical system that can administer high-quality CPR consistently without a break. South Central Ambulance Service, an NHS ambulance service in the UK for four counties, is the first to take LUCAS 3 onboard its vehicles [E&T Editorial staff, The Institution of Engineering and Technology]. The system uses wireless Bluetooth connectivity, allowing it to configure the compression rate, depth, and alerts specific to an organisation's resuscitation guidelines.

It's the sort of thing that can almost pass for background noise these days: over the past week, a number of publications tentatively declared, based on a UN report from the Libyan civil war, that killer robots may have hunted down humans autonomously for the first time. As one headline put it: "The Age of Autonomous Killer Robots May Already Be Here." As you might guess, it's a hard question to answer. The new coverage has sparked a debate among experts that goes to the heart of our problems confronting the rise of autonomous robots in war. Some said the stories were wrongheaded and sensational, while others suggested there was a nugget of truth to the discussion.

Reinforcement learning competitions have formed the basis for standard research benchmarks, galvanized advances in the state-of-the-art, and shaped the direction of the field. Despite this, a majority of challenges suffer from the same fundamental problems: participant solutions to the posed challenge are usually domain-specific, biased to maximally exploit compute resources, and not guaranteed to be reproducible. In this paper, we present a new framework of competition design that promotes the development of algorithms that overcome these barriers. We propose four central mechanisms for achieving this end: submission retraining, domain randomization, desemantization through domain obfuscation, and the limitation of competition compute and environment-sample budget. To demonstrate the efficacy of this design, we proposed, organized, and ran the MineRL 2020 Competition on Sample-Efficient Reinforcement Learning. In this work, we describe the organizational outcomes of the competition and show that the resulting participant submissions are reproducible, non-specific to the competition environment, and sample/resource efficient, despite the difficult competition task.

Traditional automated theorem provers have relied on manually tuned heuristics to guide how they perform proof search. Recently, however, there has been a surge of interest in the design of learning mechanisms that can be integrated into theorem provers to improve their performance automatically. In this work, we introduce TRAIL, a deep learning-based approach to theorem proving that characterizes core elements of saturation-based theorem proving within a neural framework. TRAIL leverages (a) an effective graph neural network for representing logical formulas, (b) a novel neural representation of the state of a saturation-based theorem prover in terms of processed clauses and available actions, and (c) a novel representation of the inference selection process as an attention-based action policy. We show through a systematic analysis that these components allow TRAIL to significantly outperform previous reinforcement learning-based theorem provers on two standard benchmark datasets (up to 36% more theorems proved). In addition, to the best of our knowledge, TRAIL is the first reinforcement learning-based approach to exceed the performance of a state-of-the-art traditional theorem prover on a standard theorem proving benchmark (solving up to 17% more problems).

Blockwise missing data occurs frequently when we integrate multisource or multimodality data where different sources or modalities contain complementary information. In this paper, we consider a high-dimensional linear regression model with blockwise missing covariates and a partially observed response variable. Under this semi-supervised framework, we propose a computationally efficient estimator for the regression coefficient vector based on carefully constructed unbiased estimating equations and a multiple blockwise imputation procedure, and obtain its rates of convergence. Furthermore, building upon an innovative semi-supervised projected estimating equation technique that intrinsically achieves bias-correction of the initial estimator, we propose nearly unbiased estimators for the individual regression coefficients that are asymptotically normally distributed under mild conditions. By carefully analyzing these debiased estimators, asymptotically valid confidence intervals and statistical tests about each regression coefficient are constructed. Numerical studies and application analysis of the Alzheimer's Disease Neuroimaging Initiative data show that the proposed method performs better and benefits more from unsupervised samples than existing methods.

Enzolytics, Inc. (ENZC) is making a powerful move up the charts in recent days since a brief dip below the $0.10 mark. ENZC is a major league runner and powerhouse stock; over the past few months ENZC has seen a legendary run to recent highs of 0.958 per share as it completes the historic merger between BioClonetics and Enzolytics; the new biotech is getting noticed as its technology for producing fully human monoclonal antibodies is currently being employed to produce anti-SARS-CoV-2 (CoronaVirus) monoclonal antibodies for treating COVID-19. With each day of progression of the Coronavirus pandemic, the dire need for multiple active therapeutics becomes more evident. ENZC is a pioneer in using monoclonal antibodies for treating COVID-19. ENZC has partnered with Intel to publish a white paper titled, "Optimizing Empathetic A.I. to Cure Deadly Diseases," highlighting Intel's Artificial Intelligence Analytic tools and Enzolytic's innovative approach and groundbreaking contributions to create universal, durable, and broadly effective treatment targeting all virus variants.

Most of us take it for granted that we can read another person's emotions through subtleties such as body language, yet this is a real struggle for many others. Researchers at Stanford University modified Google's augmented reality glasses to read emotions in others and notify the wearer. The glasses detect someone's mood through their eye contact, facial expressions and body language, and then tell the wearer what emotions it's picking up. "Emotion AI taps into the individual," explains Zabeth Venter, CEO and co-founder of Averly. "If you think about facial recognition, which is a kind of emotion AI, I can pick up if you like what I'm saying by whether your smile is a smirk or a real genuine smile."