The first Industrial Revolution used steam and water to mechanize production. The second, the Technological Revolution, offered standardization and industrialization. The third capitalized on electronics and information technology to automate production. Now a fourth Industrial Revolution, our modern Digital Age, is building on the third; expanding exponentially, it is disrupting and transforming our lives, while evolving too fast for governance, ethics and management to keep pace. Most high school graduates have been exposed to information technology through personal computers, word processing software and their phones. Nonetheless, the digital divide separates the tech savvy from the tech illiterate, driven by disparities in access to technology for pre-K to 12 students based on where they live and socioeconomic realities.

Artificial intelligence systems take inputs in the form of visuals, audios, texts, etc. As a result, filtering, handling, and detecting malicious inputs and behaviours have become more complicated. Cybersecurity is one of the top priorities of companies worldwide. The increase in the number of AI Security papers from just 617 in 2018 to over 1500 in 2020 (an increase of almost 143% as per an Adversa report) is a testament to the growing importance of cybersecurity. Microsoft has recently announced the release of Counterfit – a tool to test the security of AI systems – as an open-source project.

Federated Learning (FL) is an emerging learning scheme that allows different distributed clients to train deep neural networks together without data sharing. Neural networks have become popular due to their unprecedented success. To the best of our knowledge, the theoretical guarantees of FL concerning neural networks with explicit forms and multi-step updates are unexplored. Nevertheless, training analysis of neural networks in FL is non-trivial for two reasons: first, the objective loss function we are optimizing is non-smooth and non-convex, and second, we are even not updating in the gradient direction. Existing convergence results for gradient descent-based methods heavily rely on the fact that the gradient direction is used for updating. This paper presents a new class of convergence analysis for FL, Federated Learning Neural Tangent Kernel (FL-NTK), which corresponds to overparamterized ReLU neural networks trained by gradient descent in FL and is inspired by the analysis in Neural Tangent Kernel (NTK). Theoretically, FL-NTK converges to a global-optimal solution at a linear rate with properly tuned learning parameters. Furthermore, with proper distributional assumptions, FL-NTK can also achieve good generalization.

As machine learning (ML) models find wide-spread application, there is a growing demand for interpretability: access to tools that help people see why the model made its decision. There are still many obstacles towards achieving this goal though, particularly in the context of deep learning. These obstacles stem from the scale of modern deep networks, as well as the complexity of even defining and assessing the (often context-dependent) desiderata of interpretability. Existing work on deep network interpretability has largely approached this problem from two perspectives. The first one seeks to uncover the concepts associated with specific neurons in the network, for example through visualization [Yos 15] or semantic labeling [Bau 17].

Societal biases resonate in the retrieved contents of information retrieval (IR) systems, resulting in reinforcing existing stereotypes. Approaching this issue requires established measures of fairness in respect to the representation of various social groups in retrieval results, as well as methods to mitigate such biases, particularly in the light of the advances in deep ranking models. In this work, we first provide a novel framework to measure the fairness in the retrieved text contents of ranking models. Introducing a ranker-agnostic measurement, the framework also enables the disentanglement of the effect on fairness of collection from that of rankers. To mitigate these biases, we propose AdvBert, a ranking model achieved by adapting adversarial bias mitigation for IR, which jointly learns to predict relevance and remove protected attributes. We conduct experiments on two passage retrieval collections (MSMARCO Passage Re-ranking and TREC Deep Learning 2019 Passage Re-ranking), which we extend by fairness annotations of a selected subset of queries regarding gender attributes. Our results on the MSMARCO benchmark show that, (1) all ranking models are less fair in comparison with ranker-agnostic baselines, and (2) the fairness of Bert rankers significantly improves when using the proposed AdvBert models. Lastly, we investigate the trade-off between fairness and utility, showing that we can maintain the significant improvements in fairness without any significant loss in utility.

It still remains to be seen whether the sci-fi genre is correct and artificial intelligence will one day rise up against the human race, but in the meantime, AI just might save your life. An algorithm developed by the Mayo Clinic can significantly increase the number of cases of low ejection fraction caught in its earliest stages, when it's still most treatable, according to a study published this month in Nature Medicine. The condition, in which the heart is unable to pump enough blood from its chamber with each contraction, is associated with cardiomyopathy and heart failure and is often symptomless in its early stages. Traditionally, the only way to diagnose low ejection fraction is with the use of an echocardiogram, a time-consuming and expensive cardiac ultrasound. The Mayo Clinic's AI algorithm, however, can screen for low ejection fraction in a standard 12-lead electrocardiogram (EKG) reading, which is a much faster and more readily available tool. In the study, more than 22,600 patients received an EKG as part of their usual primary care checkups, then were randomly assigned to have their results analyzed by the AI or by a physician as usual.

With NASA aiming to get humans to Mars by 2030, the idea of a long-term settlement on the Red Planet is getting closer to reality and scientists are working on innovated ways to power these habitats. Researchers in the Netherlands propose using massive kites to harness high Martian winds that would transformed into energy for colonists. The kite is attached by cable to a spindle. Similar kites are being developed to harness wind power on Earth, but these would be much larger, with a surface area of 530 square feet. Wind turbines and batteries are too heavy to bring to Mars via rocket, and the planet doesn't get enough sunlight to consider solar power.

Federal investigators said Monday they were able to glean some insights into what might have happened after a fire erupted from a Tesla crash that killed two people in the Houston area in April and destroyed the vehicle's data recorder. . The National Transportation Safety Board released preliminary findings from its probe into the crash, which raised speculation about whether the vehicle's partially self-driving system, Autopilot, was to blame. The speculation stemmed from local authorities saying they were nearly positive that no one was behind the wheel when the vehicle crashed. The NTSB, in its preliminary report, said video footage from the vehicle owner's home security system showed him getting behind the wheel of the Tesla Model S and then slowly exiting the driveway. The vehicle traveled about 550 feet "before departing the road on a curve, driving over the curb, and hitting a drainage culvert, a raised manhole and a tree," according to the NTSB.

Because COVID-19 made it difficult for consumers to venture out and run their usual errands, FIs needed to find other ways to provide their services. The only way for them to really keep up with the speedy digitization was through the implementation of AI systems. To further discuss all things AI, PaymentsJournal sat down with Sudhir Jha, Mastercard SVP and head of Brighterion, and Tim Sloane, VP of Payments Innovation at Mercator Advisory Group. Jha believes that there were two fundamentally big changes that occurred in banking during the pandemic: the environment began constantly shifting, and person-to-person interactions were abruptly limited. "Every week, every month, there were different ways that we were trying to react to the pandemic," explained Jha.

NASA's OSIRIS-REx mission will leave asteroid Bennu today and begin its 1.4 billion mile, two year long journey back to the Earth, the space agency confirmed. OSIRIS-REx (the Origins, Spectral Interpretation, Resource Identification, Security, Regolith Explorer) was the first NASA mission to visit a near-Earth asteroid, survey the surface, and collect a sample to deliver to Earth. The spaceship was sent to study Bennu, an asteroid around the size of the Empire State Building and 200 million miles away, between the orbit of Earth and Mars. OSIRIS-REx gathered 2.1 ounces (60 grams) of rock and dust during its land and grab mission to the surface of the giant space rock, filling its storage compartment. It will begin its long journey home at 21:00 BST (16:00 EDT), with a live broadcast from NASA sharing the moment it fires its thrusters to push away from Bennu's orbit. If all goes to plan, OSIRIS-REx will orbit the sun twice, travelling 1.4 billion miles as it lines up with Earth, returning its samples in Utah on September 24, 2023.