WATCH THIS: A group of autonomous military robots navigate through an underground power plant

Daily Mail - Science & tech

Scientists convened on an unfinished underground power plant in Elma, Washington to test a group of autonomous military robots in a simulated disaster scenario. The scientists weren't taking part in an experiment but a competition sponsored by the Defense Advanced Research Projects Agency (DARPA), as part of its efforts to develop a range of autonomous robots to fill a variety of military roles. The winning team came from NASA's Jet Propulsion Laboratory, a 60 person crew that oversaw a group of 12 robots they'd programmed through an initiative called Collaborative SubTerranean Autonomous Robots (CoSTAR). 'The goal is to develop software for our robots that lets them decide how to proceed as they face new surprises,' JPL's Ali Agha said. 'These robots are highly autonomous and for the most part make decisions without human intervention.'


Second coronavirus case of unknown origin confirmed in California; Oregon confirms first 'community spread' case

FOX News

Trump calls out politicization of outbreak; reaction and analysis on'The Five.' A second coronavirus case of unknown origin was confirmed in the state of California on Friday, after a Santa Clara County resident reportedly tested positive for the disease. Meanwhile, state officials in Oregon confirmed the first "community spread" case of the virus. The Centers for Disease Control and Prevention (CDC) said that officials are "aware of a second possible instance of community spread of COVID-19 in California," and that the patient has tested positive for the virus and is considered a presumptive positive case, The Associated Press reported. Oregon Health Authority (OHA) officials said Friday that the state's case was "presumptive," as it hadn't yet been confirmed by the CDC, Fox 12 Oregon reported.


Decision Trees Overfitting and Pruning

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In this video, we will discuss practical considerations in designing a decision tree model. We will discuss how to overcome overfitting in decision trees, three ways to prune the tree, and how to handle missing attributes and continuous values. This channel is part of CSEdu4All, an educational initiative that aims to make computer science education accessible to all! We believe that everyone has the right to good education, and geographical and political boundaries should not be a barrier to obtaining knowledge and information. We hope that you will join and support us in this endeavor!


Big data in IBD: big progress for clinical practice

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Precision medicine holds great promise to improve the landscape of IBD course of care for an individual patient, providing the most beneficial therapy while minimising the risk. The ultimate goals of precision medicine include stratifying patients based on disease subtypes and severity, disease progression and treatment response using personal and clinical data coupled with molecular profiling data of patients.1 2 IBD, with its two main subtypes, Crohn's disease (CD) and UC, is a complex inflammatory disease with a wide range of contributing factors including host genetics, immune system, environmental exposures and the gut microbiome.3–5 The inherent complexity of the disease introduces a large number of confounding factors, which stand in the way of accurate diagnosis and precision medicine.6 The term'big data' is generally referred to as large volume of rapidly produced data from variable sources, known as the three'V's (volume, velocity and variety).7 Over the past decades, the production and availability of data that could inform healthcare has increased remarkably mainly due to technological advancements and falling costs of data generation.


This Bengaluru startup aims to help firms become data smart and intelligent using AI and ML

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Chethan KR and Ashish Koushik were working together at MSys Technologies, an IT services firm, where they helped large product companies and enterprises with digital transformation. They realised that though these companies aligned towards automation, cloud analytics, and machine learning, they were not ready for artificial intelligence (AI). Some of the challenges the duo saw in getting companies adopt AI or data driven decision making was the huge volume of data, siloed teams within organisations, legacy old infrastructure, and skill-gap for right data technology talents. AI being the forefront for future and to bridge the current skill gaps in data science, Chethan and Ashish started SynctacticAI in February 2019. It is an end-to-end platform that handles the entire data life cycle management of a company, and helps build smarter businesses at scale.


AI Company Says: Keep Your Eyes on the Road, We're Watching

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San Diego's Lytx, the maker of DriveCam video monitoring technology for commercial truck fleets, has expanded its machine vision and artificial intelligence capabilities to detect when drivers are looking at cellphones on the road. The company's latest update to its in-cab camera technology recognizes when a driver is distracted by a mobile device or other behaviors. That triggers the camera to issue a warning and start recording video, which can be shared with fleet managers through an online portal. Others video telematics companies also have products that can detect cellphone use in the cab of commercial vehicles. But Lytx says its artificial intelligence technology has been developed using millions of miles of video data from its library collected over many years.


Danone, Microsoft join forces for AI accelerator: 'The success of the food revolution will depend on data'

#artificialintelligence

The AI Factory for AgriFood is the third class of the Microsoft AI factory, which aims to support start-ups specialising in artificial intelligence. The programme is organised around five major economic and societal challenges: health, environment & energy, transportation, financial services and agrifood. The AI Factory for Agrifood aims to accelerate the digital transformation of the food sector by helping start-ups to continue their development in artificial intelligence and cloud computing. Specifically, Danone and Microsoft said they hope to encourage projects serving regenerative agriculture (soil health, animal welfare, support for farmers), sustainable food, waste minimisation and optimisation of supply chains. The accelerator will take a'full ecosystem' approach, reflecting Danone's'One Planet, One Health' vision, which links human and planetary health across the venue chain.



Army researchers enhance AI critical to Soldier-machine teamwork

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Artificial intelligence possesses the capacity to achieve incredible results, but cannot always work alone. Researchers identified two key components in successful human-machine collaboration that may enhance how the U.S. Army will fight in the future. To achieve dominance in what is known as multi-domain operations, warfighters will need a layered intelligence, surveillance and reconnaissance, or ISR, network that maintains a functional relationship between autonomous sensors, human intelligence and friendly special operations forces. Multi-domain operations, known as MDO, is a joint warfighting concept that foresees conflict occurring in multiple domains: land, air, sea, cyber and space. The concept has many nuances, but basically describes how the Army, as part of the joint force, will solve the problem of layered standoff in all domains.


Artificial intelligence can scan doctors' notes to distinguish between types of back pain

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About 80 percent of adults experience lower back pain in their lifetime; it is the most common cause of job-related disability. Many argue that prescribing opioids for lower back pain contributed to the opioid crisis; thus, determining the quality of lower back pain in clinical practice could provide an effective tool not only to improve the management of lower back pain but also to curb unnecessary opioid prescriptions. Acute and chronic lower back pain are different conditions with different treatments. However, they are coded in electronic health records with the same code and can be differentiated only by retrospective reviews of the patient's chart, which includes the review of clinical notes. The single code for two different conditions prevents appropriate billing and therapy recommendations, including different return-to-work scenarios.