Altoida Inc. today announced a $6.3 million round of venture capital financing to bring its FDA-cleared and CE Mark-approved medical device and brain health data platform to patients, physicians and researchers around the globe. Led by a team of esteemed neuroscientists, physicians and computer scientists, Altoida uses digital biomarkers to drive better clinical outcomes for brain disease. The Series A round was led by M Ventures, the corporate venture capital arm of the science and technology company Merck KGaA, Darmstadt, Germany, with participation from Grey Sky Venture Partners, VI Partners AG, Alpana Ventures, and FYRFLY Venture Partners. The new capital will be used to further expand Altoida's global presence with an immediate focus on commercialization activities in the US and EU markets. "Altoida is at the forefront of a new era to leverage Artificial Intelligence and Machine Learning to assess brain health," said Alexander Hoffmann, Principal, New Businesses at M Ventures.

There is a rise in the number of natural disasters happening all over the world. According to the National Oceanic and Atmospheric Administration, there were 16 natural disasters in 2017. The cost of all the damages is in the billions. The amount of destruction they cause is devastating and it has left many of us wondering what more can be done. Unfortunately, we don't have control over what nature decides to do but we can work on improving our emergency management services.

Fuel optimization of diesel and petrol vehicles within industrial fleets is critical for mitigating costs and reducing emissions. This objective is achievable by acting on fuel-related factors, such as the driving behaviour style. In this study, we developed an Explainable Boosting Machine (EBM) model to predict fuel consumption of different types of industrial vehicles, using real-world data collected from 2020 to 2021. This Machine Learning model also explains the relationship between the input factors and fuel consumption, quantifying the individual contribution of each one of them. The explanations provided by the model are compared with domain knowledge in order to see if they are aligned. The results show that the 70% of the categories associated to the fuel-factors are similar to the previous literature. With the EBM algorithm, we estimate that optimizing driving behaviour decreases fuel consumption between 12% and 15% in a large fleet (more than 1000 vehicles).

The full range of activity in a temporal network is captured in its edge activity data -- time series encoding the tie strengths or on-off dynamics of each edge in the network. However, in many practical applications, edge-level data are unavailable, and the network analyses must rely instead on node activity data which aggregates the edge-activity data and thus is less informative. This raises the question: Is it possible to use the static network to recover the richer edge activities from the node activities? Here we show that recovery is possible, often with a surprising degree of accuracy given how much information is lost, and that the recovered data are useful for subsequent network analysis tasks. Recovery is more difficult when network density increases, either topologically or dynamically, but exploiting dynamical and topological sparsity enables effective solutions to the recovery problem. We formally characterize the difficulty of the recovery problem both theoretically and empirically, proving the conditions under which recovery errors can be bounded and showing that, even when these conditions are not met, good quality solutions can still be derived. Effective recovery carries both promise and peril, as it enables deeper scientific study of complex systems but in the context of social systems also raises privacy concerns when social information can be aggregated across multiple data sources.

Artificial intelligence (AI) and machine learning (ML) are now part of our everyday lives, and this includes cybersecurity. In the right hands, AI/ML can identify vulnerabilities and reduce incident response time. But in cybercriminals' hands, they can create significant harm. Here are seven positive and seven negative ways AI/ML is impacting cybersecurity. AI/MI is used in network traffic analysis, intrusion detection systems, intrusion prevention systems, secure access service edge, user and entity behavior analytics, and most technology domains described in Gartner's Impact Radar for Security.

These are some of the outcomes that AI developers fear will come from their work, according to a new report issued today by the Deloitte AI Institute and the U.S. Chamber of Commerce. Titled "Investing in trustworthy AI," the 82-page report from Deloitte and the Chamber Technology Engagement Center sought to identify the concerns that technology experts have when it comes to the adoption of AI, as well as highlight the impact that government investment in AI can have on the emerging technology. Algorithmic bias and a lack of humans in decision loops are concerns for about two-thirds of the 250 people who participated in the survey. Another 60% identified "rogue or unanticipated behavior" of autonomous agents as a threat, while 56% said the lack of explainability of algorithms was a concern. "Perceived, and actual, discrimination by AI systems undermines the confidence individuals have in whether they are being given a fair opportunity when AI is involved," the report stated.

The energy consumption from crypto mining has been increasingly exponentially with the increasing adoption of crypto. This increasing becoming of concern as it should be. Large parts of the world suffer from energy deprivation due to unaffordability and inadequate energy generation. At the same time climate change goals will require the world to reduce net emission much of which is produced from electricity generation. Supporting the world's growth and generating the and while reducing emissions when large populations suffer from energy deficiency is a very difficult issue requires trillions is capital over the coming 2 decades.

A highway is closed due to snow and ice in Houston, Texas on Feb. 15, 2021. Up to 2.5 million ... [ ] customers were without power as the state's power generation capacity was impacted by an ongoing winter storm brought by Arctic blast. A new weather tech startup says it has created a new artificial intelligence (AI)-powered weather radar and satellite network to take on big weather. Climavision, which has $100 million in private equity funding, has created a high-resolution weather radar and satellite network that combines lower altitude, proprietary data with machine learning and AI technology. Chris Goode, CEO of Climavision, says the new sensing network will fill the coverage gaps in the existing NOAA and NWS systems across the US.

NASA's Perseverance rover has officially started its search for ancient life on Mars and the US space agency will share the initial findings on Wednesday. According to a NASA statement, the $2.7 billion rover is using its seven-foot mechanical arm to analyze Martian rocks with X-rays and ultraviolet light. This allows the rover to'zoom in for closeups' of tiny segments of rock that may show signs of microbial activity in the past. Known as PIXL (Planetary Instrument for X-ray Lithochemistry), the X-ray instrument on the arm'delivered unexpectedly strong science results' while it was still being tested, a period that lasted 90 sols (Martian days), according to Abigail Allwood, PIXL's principal investigator at NASA JPL. NASA's Perseverance rover has started its search for ancient life on Mars. A news conference will be held on Wednesday at 1 p.m. EST to discuss the results PIXL, one of seven instruments aboard NASA's Perseverance Mars rover, is equipped with light diodes circling its opening to take pictures of rock targets in the dark'We got our best-ever composition analysis of Martian dust before it even looked at rock,' Allwood said.

NASA's Perseverance Mars rover has begun its hunt for evidence of ancient microbial life. The spacecraft, which landed on the red planet in February, has tested an array of instruments on its 7-foot robotic arm. In a Monday release, NASA said that Perseverance had commenced its probe of Martian rocks and sediment, testing detectors and capturing its first science readings. The rover will use X-rays and ultraviolet light to examine rocks in addition to zooming for "closeups" of surfaces. PIXL, one of seven instruments aboard NASA's Perseverance Mars rover, is equipped with light diodes circling its opening to take pictures of rock targets in the dark.