Coming off its best quarter ever, SoftBank is on the hunt for its next billion dollar IPO. Having funded 29 of the 657 unicorns in the world, according to CB Insights, the Japanese telecom giant has been on a shopping spree, looking for promising new AI startups to bet big on. At Collision's tech conference held online last month, I had a chance to talk with the CEOs of SoftBank's newest portfolio companies, Standard Cognition and Forward. Here's how the two San Francisco startups are leveraging artificial intelligence to help gain market dominance in the post-pandemic world. In 2017, a group of machine learning engineers at the SEC became obsessed with computers that could see better than humans and ditched their jobs to join Y Combinator to build the computer vision company of their dreams.

While there are many free-to-play titles these days, it seems like most high-profile games don't give players a way to try them out without paying the full price up front. That's not the case for Resident Evil Village, although an odd time-locked system has made it frustrating for fans to dive into the game before it's released next week. The good news is that Capcom has relaxed its policy a bit. The final demo will unlock tonight on PlayStation, Xbox, Steam and Stadia, and players can get a 60 minute taste of the game -- complete with towering vampire ladies -- at any point over the next eight days. On Friday, NASA announced it plans to transition the rotorcraft to an operational role once it completes its remaining test flights.

Spoken dialogue is the most natural way for people to interact with complex autonomous agents such as robots. Future Army operational environments will require technology that allows artificial intelligent agents to understand and carry out commands and interact with them as teammates. Researchers from the U.S. Army Combat Capabilities Development Command, known as DEVCOM, Army Research Laboratory and the University of Southern California's Institute for Creative Technologies, a Department of Defense-sponsored University Affiliated Research Center, created an approach to flexibly interpret and respond to Soldier intent derived from spoken dialogue with autonomous systems. This technology is currently the primary component for dialogue processing for the lab's Joint Understanding and Dialogue Interface, or JUDI, system, a prototype that enables bi-directional conversational interactions between Soldiers and autonomous systems. "We employed a statistical classification technique for enabling conversational AI using state-of-the-art natural language understanding and dialogue management technologies," said Army researcher Dr. Felix Gervits.

The Indiana Transportation Department (INDOT) is applying machine learning (ML) to project bundling to maximize cost savings and reduce the time it takes to create bundles from a week to minutes. Transportation engineers typically group infrastructure construction projects into bundles to gain efficiencies in contracting and project management, minimize infrastructure disruption and achieve economies of scale. Bundles are created from similar projects – bridge projects, freeway lighting installations or safety improvements – or from a variety of types of work in a specific location. Bundling is complex, manual, subjective process, with engineers usually working from maps and spreadsheets. INDOT has done manual project bundling for about five years and used that data to test benefits of ML-enabled bundling, in which an algorithm that had been trained on historic data makes bundling suggestions.

Russia seems to actively explore innovative technologies. Now, the country wants to implement the means of artificial intelligence for pilotless aircraft. For this matter, Russia plans on establishing a special department for studying the technology. The move to use AI was unveiled by the Russian Ministry of Defense during the visit to Sukhoi's design bureau. The delegation included the First Deputy Chairman of the Military Industrial Commission (MIC) Andrey Yelchaninov and other members of the MIC.

The proposed legislation means that companies will no longer be free in the safety assessment of their AI products and will have to observe strict …

LOS ALAMOS, N.M., April 22, 2021--A new machine-learning model that generates realistic seismic waveforms will reduce manual labor and improve earthquake detection, according to a study published recently in JGR Solid Earth. "To verify the e?cacy of our generative model, we applied it to seismic?eld data collected in Oklahoma," said Youzuo Lin, a computational scientist in Los Alamos National Laboratory's Geophysics group and principal investigator of the project. "Through a sequence of qualitative and quantitative tests and benchmarks, we saw that our model can generate high-quality synthetic waveforms and improve machine learning-based earthquake detection algorithms." Quickly and accurately detecting earthquakes can be a challenging task. Visual detection done by people has long been considered the gold standard, but requires intensive manual labor that scales poorly to large data sets.

AI Researcher, Cognitive Technologist Inventor - AI Thinking, Think Chain Innovator - AIOT, XAI, Autonomous Cars, IIOT Founder Fisheyebox Spatial Computing Savant, Transformative Leader, Industry X.0 Practitioner Applied to vehicles, cybersecurity takes on an important role - Systems & Components that govern safety must be protected from harmful attacks, unauthorised access, damage or anything else that might interfere with safety functions. Increasingly, today's vehicles feature driver assistance #technology, such as forward collision warning, automatic emergency braking and vehicle safety communications. In the future, the deployment of driver assistance technologies may result in avoiding crashes altogether, particularly crashes attributed to human drivers' choices. A multi-layered approach to vehicle cybersecurity reduces the possibility of a successful vehicle cyber-attack and mitigates the potential consequences of a successful intrusion.

Machine learning-based technologies are playing a substantial role in the response to the COVID-19 pandemic. Experts are using machine learning to study the virus, test potential treatments, diagnose individuals, analyze the public health impacts, and more. Below, we describe some of the leading efforts and identify data protection and ethical issues related to machine learning and COVID-19, with a particular focus on apps directed to health care professionals that leverage audio-visual data, text analysis, chatbots, and sensors. "Machine Intelligence (MI) is rapidly becoming an important approach across biomedical discovery, clinical research, medical diagnostics/devices, and precision medicine. Such tools can uncover new possibilities for researchers, physicians, and patients, allowing them to make more informed decisions and achieve better outcomes. When deployed in healthcare settings, these approaches have the potential to enhance efficiency and effectiveness of the health research and care ecosystem, and ultimately improve quality of patient care." Now – with the development of the pandemic resulting from the spread of the coronavirus (COVID-19), medical providers, institutions, and commercial developers are all considering whether and how to apply machine learning to confront the threat of this current crisis.

To assess generalization, machine learning scientists typically either (i) bound the generalization gap and then (after training) plug in the empirical risk to obtain a bound on the true risk; or (ii) validate empirically on holdout data. However, (i) typically yields vacuous guarantees for overparameterized models. Furthermore, (ii) shrinks the training set and its guarantee erodes with each re-use of the holdout set. In this paper, we introduce a method that leverages unlabeled data to produce generalization bounds. After augmenting our (labeled) training set with randomly labeled fresh examples, we train in the standard fashion. Whenever classifiers achieve low error on clean data and high error on noisy data, our bound provides a tight upper bound on the true risk. We prove that our bound is valid for 0-1 empirical risk minimization and with linear classifiers trained by gradient descent. Our approach is especially useful in conjunction with deep learning due to the early learning phenomenon whereby networks fit true labels before noisy labels but requires one intuitive assumption. Empirically, on canonical computer vision and NLP tasks, our bound provides non-vacuous generalization guarantees that track actual performance closely. This work provides practitioners with an option for certifying the generalization of deep nets even when unseen labeled data is unavailable and provides theoretical insights into the relationship between random label noise and generalization.