HOUSTON--(BUSINESS WIRE)--inContext.ai, a developer of healthcare-centric information extraction software and AI-powered clinical applications, announced an award of $225K in National Science Foundation (NSF) funding. The inContext.ai platform improves physicians interactions with complex and counter-intuitive software by augmenting and assisting the physicians workflow, decreasing the possibility of medical errors and compromised care while reducing the frustrations that lead to physician burnout.

80% of diagnostic information is in the form of free-text reports, rendering it difficult to access and make actionable. said Dr. Grzeszczuk, CEO of inContext.ai. Our goal is to make dark clinical data which is buried in diagnostic reports, accessible and actionable anytime, anywhere. The generous support from the NSF will enable us to work in collaboration with our partners at leading academic institutions to improve the accuracy of our existing tools and develop new ones.

NSF is proud to support the technology of the future by thinking beyond incremental developments and funding the most creative, impactful ideas across all markets and areas of science and engineering, said Andrea Belz, Division Director of the Division of Industrial Innovation and Partnerships at NSF. With the support of our research funds, any deep technology startup or small business can guide basic science into meaningful solutions that address tremendous needs.

inContext.ai is eager to fulfill Phase I requirements and is excited to be in contention for the $1M Phase II follow-on funding, said Dr. Grzeszczuk.

The proposal was submitted in collaboration with Lucille Packard Childrens Hospital at Stanford University School of Medicine (Palo Alto, CA) and Texas Childrens Hospital (Houston, TX).

About inContext.ai

Based in Houston TX, with offices in Los Angeles, CA and Vancouver, BC, inContext.ai develops cognitive technology and tools that streamline delivery of patient care and decrease medical errors by helping clinicians focus on what they do best: caring for patients. Implementation of inContext.ais Deep Artificial Intelligence (AI) tools supporting Natural Language Processing (NLP) tasks including Question Answering (QA), Reading Comprehension (RC), and Natural Language Inference (NLI), which seamlessly synchronize with EHR systems, means healthcare providers remain in compliance with medical/legal/regulatory responsibilities, significantly improve cost, quality, and access to care. For more information, please visit http://www.incontext.ai

About the National Science Foundation's Small Business Programs: Americas Seed Fund powered by NSF awards $200 million annually to startups and small businesses, transforming scientific discovery into products and services with commercial and societal impact. Startups working across almost all areas of science and technology can receive up to $1.75 million to support research and development (R&D), helping de-risk technology for commercial success. Americas Seed Fund is congressionally mandated through the Small Business Innovation Research (SBIR) program. The NSF is an independent federal agency with a budget of about $8.1 billion that supports fundamental research and education across all fields of science and engineering.

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inContext.ai Awarded $225K by the National Science Foundation to Develop Artificial Intelligence Tools for Healthcare - Business Wire

Trusted Business Insights answers what are the scenarios for growth and recovery and whether there will be any lasting structural impact from the unfolding crisis for the Artificial Intelligence in Agriculture market.

Trusted Business Insights presents an updated and Latest Study on Artificial Intelligence in Agriculture Market 2019-2026. The report contains market predictions related to market size, revenue, production, CAGR, Consumption, gross margin, price, and other substantial factors. While emphasizing the key driving and restraining forces for this market, the report also offers a complete study of the future trends and developments of the market.The report further elaborates on the micro and macroeconomic aspects including the socio-political landscape that is anticipated to shape the demand of the Artificial Intelligence in Agriculture market during the forecast period (2019-2029).It also examines the role of the leading market players involved in the industry including their corporate overview, financial summary, and SWOT analysis.

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Industry Insights, Market Size, CAGR, High-Level Analysis: Artificial Intelligence in Agriculture Market

The global artificial intelligence in agriculture market size was valued at USD 608.9 million in 2018 and is anticipated to register a CAGR of 25.4% from 2019 to 2025. Artificial intelligence techniques for farming help increase productivity and yield. Therefore, agribusiness corporations adopt artificial intelligence technologies in terms of predictive analytics-based solutions. AI-based applications and techniques help control pests, yield healthier crops, monitor the soil, and improve agriculture-related tasks in the entire food supply chain. Artificial intelligence is increasingly being adopted in the agriculture industry for the improvement of harvest quality and accuracy since it helps analyze farm data.The global population is expected to reach 9.8 billion by 2050, according to the UN. Rapidly growing population drives the need for bringing AI in the agriculture industry. Limited arable land availability and need for increased food production for food security drive the need for a green revolution fueled by artificial intelligence, Internet of Things (IoT), and big data. AI-enabled applications cater to several areas in the agriculture industry, such as predictive and recommendation analytics, identifying plant diseases, detecting pest infestations, and soil monitoring.

Artificial intelligence solutions comprising robots, drones, and ground-based wireless sensors are increasingly being deployed in the agriculture industry. For instance, in November 2017, Microsoft collaborated with the International Crop Research Institute for the Semi-Arid Tropics (ICRISAT) to build an AI Sowing App. Furthermore, NatureFresh Farms, a U.S.-based tech company, is working on developing an AI technology to analyze plant information at scale to produce accurate harvest and yield forecasts. This AI technology predicts how long the blossom would take to ripen by using an artificial intelligence algorithm.Furthermore, automation in agriculture helps allocate resources such as fertilizers and water, determine the optimal date for crop sowing, and detect weeds, thereby driving the demand for artificial intelligence solutions. It also helps recommend how many seeds must be sowed by using historical long-term weather reports, production data, commodity pricing predictions, and seed information, among other inputs. The multiple benefits of artificial intelligence-based applications encourage several tech giants and start-ups to develop IoT-enabled devices for the deployment of AI applications for agriculture on a large scale.

Component Insights of Artificial Intelligence in Agriculture Market

The market is classified on the basis of component into hardware, software, and service. The software segment is expected to have a significant market share among components. Major players such as IBM, Microsoft, and Deere & Company offer AI-based solutions for the agriculture industry. AI-based software boost crop productivity and yield by implementing prediction-based analytics and computer vision.Moreover, increasing penetration of predictive analytics-based software boosts the growth of the software segment. Prominent predictive analytics-based software include Watson Decision Platform by IBM Corporation, AI Sowing App by Microsoft, and See and Spray pesticide and herbicide distribution systems by Deere & Company. These artificial intelligence solutions help farmers determine the optimal dates for crop sowing; detect crop diseases; monitor crop yield; and determine the required amount of land, fertilizers, water, and pesticides. Advantages of AI-enabled software for potential application areas, such as precision farming and drone analytics, further drives the growth of the software segment in the AI in agriculture market.

Application Insights of Artificial Intelligence in Agriculture Market

On the basis of application, the market has been classified into precision farming, drone analytics, agriculture robots, livestock monitoring, and others. The precision farming segment is expected to account for a significant market share over the projected period. Precision farming is one of the fastest-growing AI-enabled applications in agriculture. It helps farmers minimize costs and optimize resources effectively.Precision farming uses AI for data collection, interpretation, and analysis of digital data. For instance, GPS-equipped combine harvesters deploy artificial intelligence to track the harvest yield for field variability analysis, such as differences in water, soil makeup, or fungus, to produce georeferenced data. The analysis and predictions enable farmers to customize fertilizers or pesticides accordingly. Agriculture robots controlled by an AI system combine artificial intelligence, field sensors, and data analytics and can be effectively used for a variety of applications. These robots are efficient harvesting systems as they have the ability to weed and hoe. Increasing adoption of artificial intelligence in agriculture and new developments in robotics drive the agriculture robots segment.

Technology Insights of Artificial Intelligence in Agriculture Market

By way of technology, the market is segmented into machine learning and deep learning, predictive analytics, and computer vision. Several agribusiness corporations adopt predictive analytics to deploy artificial intelligence. For instance, AgEagle Aerial Systems Inc.; Microsoft; and Granular, Inc.; have worked on a prediction-based analytics technology to develop AI-enabled solutions and platforms for farming and agriculture.

The significant challenges faced by the agriculture industry are pesticide control, weed management, irrigation and drainage management, weather tracking, and crop disease infestations. Predictive analysis helps farmers analyze and address these challenges with the use of image analysis and neural networks. Furthermore, drone-enabled agricultural solutions have been introduced to support predictive analytics. For instance, AgEagle Aerial Systems Inc., focused on using artificial intelligence to enhance crop yield production, offers drone analytical solutions for the identification of concerned areas in crop fields and irrigation management. Since predictive analytics provides more efficiency in agricultural applications, the segment is expected to witness a steady CAGR over the forecast period. Moreover, by applying machine learning to sensor data, farm management systems are evolving into real artificial intelligence systems, increasing the scope of production improvement. Therefore, the machine learning and deep learning segment is also expected to witness growth.

Regional Insights of Artificial Intelligence in Agriculture Market

The market in North America accounted for a share of more than 35.0% in 2018, owing to the leading industrial automation industry and adoption of artificial intelligence solutions in the region. North America is characterized by improved purchasing power of the population, continuous investments in automation, considerable investments in IIoT, and increasing focus from governments on in-house AI equipment production. The market also benefits from the presence of numerous agricultural technology providers exploring artificial intelligence solutions, including IBM Corporation; Deere & Company; Microsoft; Granular, Inc.; and The Climate Corporation.The Asia Pacific market is expected to demonstrate the highest CAGR over the forecast period. Its growth is attributed to increasing adoption of artificial intelligence technologies in agriculture. Emerging economies such as India and China are leveraging the adoption of artificial intelligence solutions such as remote monitoring technology and predictive analysis in the food industry. Furthermore, the rising demand to create smart cities in these economies is encouraging agribusiness companies to adopt AI-leveraging solutions and services.

Market Share Insights of Artificial Intelligence in Agriculture Market

Key industry participants in the market include IBM Corporation; Microsoft; Deere & Company; AgEagle Aerial Systems Inc.; The Climate Corporation; Granular, Inc.; Descartes Labs, Inc.; Prospera Technologies; Taranis; aWhere Inc.; GAMAYA; ec2ce; PrecisionHawk; VineView; and Tule Technologies Inc.Vendors providing artificial intelligence solutions for agriculture focus on increasing their customer base to gain a competitive edge in the market by adopting several strategic initiatives such as collaborations, acquisitions, mergers, and partnerships. For instance, in May 2019, Deere & Company partnered with Cultivating New Frontiers in Agriculture (CNFA), an international agricultural development organization to increase productivity and income for smallholder farmers by implementing mechanization in the agriculture industry. In October 2018, The Climate Corporation collaborated with three agriculture-tech companies, SoilOptix; AgCon Aerial Corp.; and A&L Canada Laboratories Inc., to deliver new capabilities for farmers and expand its digital agriculture platform, Climate FieldView.

Segmentations, Sub Segmentations, CAGR, & High-Level Analysis overview of Artificial Intelligence in Agriculture Market Research ReportThis report provides forecasts for revenue growth at the global, regional, and country levels and analyses of the latest industry trends in each of the sub-segments from 2014 to 2025. For the purpose of this study, this market research report has segmented the global artificial intelligence in agriculture market report based on component, technology, application, and region:

Component Outlook (Revenue, USD Million, 2019 2030)

Hardware

Software

Service

Technology Outlook (Revenue, USD Million, 2019 2030)

Machine Learning & Deep Learning

Predictive Analytics

Computer Vision

Application Outlook (Revenue, USD Million, 2019 2030)

Precision Farming

Drone Analytics

Agriculture Robots

Livestock Monitoring

Others

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Artificial Intelligence in Agriculture Market Analysis Of Global Trends, Demand And Competition 2020-2028 - Cole of Duty

The concept of artificial intelligence (AI) is certainly nothing new. In fact, this term was first defined by the Oxford Dictionary as far back as 1955.

At the time, artificial intelligence was nothing more than a theoretical concept that was being examined within laboratory settings. This was largely due to the fact that computing power lagged far behind human imagination. However, we are now living within a world that has become rife with these advanced digital systems. The online casino industry is beginning to capitalise upon such advancements and this brings up an important question. What can players expect in the near future and how will the presence of AI transform their overall experiences? Let us first look at why artificial intelligence is being employed by virtual casinos before highlighting what the future may have in store.

Why AI and Why Now?

Online casinos fully appreciate the fact that they are facing stiff competition from similar digital providers. As geographic borders will not often come into play, it is important for developers to market to as large of an audience as possible. This is when the role of AI can serve to massively benefit their operations. Artificial intelligence offers several distinct advantages such as:

Players will remain engaged for longer periods of time and as a result, even novices can quickly become loyal customers. This is obviously beneficial for online casinos that hope to enjoy higher retention rates. However, users will also be able to leverage the variety of gaming options at their disposal.

A Bright Digital Future

It is a well-known fact that many professional casino reviews focus upon features such as rewards programmes, VIP packages, the variety of available games, and which software developers are associated with a specific website. This is quite important, as well-known providers such as Microgaming and NetEnt are one step ahead of the curve in regards to the technology that they are deploying. Artificial intelligence is only one example of what is now being incorporated into what they have to offer. This is also why players who are looking to enjoy a truly immersive experience should opt for larger online portals.

We also need to appreciate that artificial intelligence is becoming even more advanced on a monthly basis. While these systems will never be able to replace organic human interactions, they are undoubtedly set to transform the entire online casino sector. This is great news for anyone who does not have the time to travel to a brick-and-mortar establishment. Of course, the wide selection of available games will also prove beneficial in terms of variety. The bottom line is that AI is set to make waves throughout the digital landscape and we should expect to read even more about this innovative technology in the coming years.

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How Artificial Intelligence is Transforming the Online Casino Industry - Wales247

This week L.A. Mayor Eric Garcetti made a very big deal about new thermal imaging cameras installed at LAX that will detect if travelers are running fevers, a big indication that a person might be infected with COVID-19. You'll probably be seeing those cameras showing up in many more places beyond just the airport.

The cameras are just the start: how about wearing a $300 ring that is supposed to warn you several days in advance of getting sick that you're likely infected with COVID........the NBA is offering these rings to all of its players returning to finish their season. There's also artificial intelligence algorithms that will predict where the next COVID outbreak will happen.

Bryan Walsh is the Future Correspondent for Axios covering emerging technology & trends.

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Rings that can tell you if you're infected, artificial intelligence that can predict the next outbreak: does coronavirus detection tech actually work?...

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Advantages of Artificial Intelligence | Top 7 Most Useful ...

Artificial intelligence (AI) brings with it a promise of genuine human-to-machine interaction. When machines become intelligent, they can understand requests, connect data points and draw conclusions. They can reason, observe and plan. Consider:

Clearly, were not talking about robotic butlers. This isnt a Hollywood movie. But we are at a new level of cognition in the artificial intelligence field that has grown to be truly useful in our lives.

We get it, though. Youre still confused about how all these topics AI, machine learning and deep learning relate. Youre not alone. And we want to help.

In this article well explore the basic components of artificial intelligence and describe how various technologies have combined to help machines become more intelligent.

So where did AI come from? Well, it didnt leap from single-player chess games straight into self-driving cars. The field has a long history rooted in military science and statistics, with contributions from philosophy, psychology, math and cognitive science. Artificial intelligence originally set out to make computers more useful and more capable of independent reasoning.

Most historians trace the birth of AI to a Dartmouth research project in 1956 that explored topics like problem solving and symbolic methods. In the 1960s, the US Department of Defense took interest in this type of work and increased the focus on training computers to mimic human reasoning.

For example, the Defense Advanced Research Projects Agency (DARPA) completed street mapping projects in the 1970s. And DARPA produced intelligent personal assistants in 2003, long before Google, Amazon or Microsoft tackled similar projects.

This work paved the way for the automation and formal reasoning that we see in computers today.

As a whole, artificial intelligence contains many subfields, including:

While machine learning is based on the idea that machines should be able to learn and adapt through experience, AI refers to a broader idea where machines can execute tasks "smartly."

Artificial Intelligence applies machine learning, deep learning and other techniques to solve actual problems.

With AI, you can ask a machine questions out loud and get answers about sales, inventory, customer retention, fraud detection and much more. The computer can also discover information that you never thought to ask. It will offer a narrative summary of your data and suggest other ways to analyze it. It will also share information related to previous questions from you or anyone else who asked similar questions. Youll get the answers on a screen or just conversationally.

How will this play out in the real world? In health care, treatment effectiveness can be more quickly determined. In retail, add-on items can be more quickly suggested. In finance, fraud can be prevented instead of just detected. And so much more.

In each of these examples, the machine understands what information is needed, looks at relationships between all the variables, formulates an answer and automatically communicates it to you with options for follow-up queries.

We have decades of artificial intelligence research to thank for where we are today. And we have decades of intelligent human-to-machine interactions to come.

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Artificial intelligence, machine learning, deep learning ...

NEW YORK, June 24, 2020 /PRNewswire/ -- Deloitte today announced the launch of the Deloitte AI Institute, a center that focuses on artificial intelligence (AI) research, eminence and applied innovation across industries. The Institute will bring together the brightest minds in the field of AI to apply cutting-edge research to help address a wide spectrum of relevant AI use cases.

"The Deloitte AI Institute is being established to advance the conversation and development of AI for enterprises," said Nitin Mittal, AI co-leader and principal, Deloitte Consulting LLP. "Our goal is to blend Deloitte's deep experience in applied AI with a robust network of some of the most intelligent AI minds in the world to challenge the status quo. Through the power of this center, we aim to deliver impactful and game-changing research; and innovation to help our clients lead in the 'Age of With,' a world where humans work side-by-side with machines."

The Institute's network will consist of top industry thought leaders and academic luminaries; start-ups; research and development groups; entrepreneurs; investors; and innovators. This network of specialists and research, combined with Deloitte's depth of applied AI knowledge and understanding of pain points across industries and sectors whether it is identifyinguse cases, understanding industry specific ecosystems, scaling from AI proof-of-concepts or securing AI systems can help organizations transform quickly with AI.

"With our unique experience, investments in AI and work with top organizations, we believe the Deloitte AI Institute can ignite ground-breaking applied AI solutions for enterprises," saidBeena Ammanath, executive director of Deloitte AI Institute, Deloitte Consulting LLP. "Further, to help enterprises advance with AI, we will aim to help organizations remain distinctively human in a technology-driven world."

In addition, as enterprises continue to navigate the complexity of AI ethics, the Deloitte AI Institute will collaborate with leading ethicists, thought leaders and organizations to raise awareness and provide services.

"In today's world, the benefits of AI enable greater outcomes for organizations than ever before. However, ethical safeguards must be put into place to help protect reputation and future performance," said Irfan Saif, Deloitte Risk & Financial Advisory principal, Deloitte & Touche LLP and Deloitte AI co-leader. "With AI ethics, the Institute aims to help organizations achieve a positive future by bringing together top stakeholders from all sectors of society to discuss and co-design effective policies and frameworks, such as Deloitte's Trustworthy AI framework, for governing AI."

To learn more about the Deloitte AI Institute and its catalogue of research and articles, such as the bi-annual State of AI in the Enterprise study and Trustworthy AI framework, visit our website.

About DeloitteDeloitte provides industry-leading audit, consulting, tax and advisory services to many of the world's most admired brands, including nearly 90% of the Fortune 500 and more than 7,000 private companies.Our people work across the industry sectors that drive and shape today's marketplace delivering measurable and lasting results that help reinforce public trust in our capital markets, inspire clients to see challenges as opportunities to transform and thrive, and help lead the way toward a stronger economy and a healthy society. Deloitte is proud to be part of the largest global professional services network serving our clients in the markets that are most important to them.Now celebrating 175 years of service, our network of member firms spans more than 150 countries and territories. Learn how Deloitte's more than 312,000 people worldwidemake an impact that matters atwww.deloitte.com.

Deloitte refers to one or more of Deloitte Touche Tohmatsu Limited, a UK private company limited by guarantee ("DTTL"), its network of member firms, and their related entities. DTTL and each of its member firms are legally separate and independent entities. DTTL (also referred to as "Deloitte Global") does not provide services to clients. In the United States, Deloitte refers to one or more of the US member firms of DTTL, their related entities that operate using the "Deloitte" name in the United States and their respective affiliates. Certain services may not be available to attest clients under the rules and regulations of public accounting. Please see http://www.deloitte.com/aboutto learn more about our global network of member firms.

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Deloitte Launches the Deloitte AI Institute to Advance Applied Artificial Intelligence Innovation and Research for the Enterprise - PRNewswire

An important aspect of treating patients with conditions like diabetes and heart disease is helping them stay healthy outside of the hospital before they to return to the doctors office with further complications.

But reaching the most vulnerable patients at the right time often has more to do with probabilities than clinical assessments. Artificial intelligence (AI) has the potential to help clinicians tackle these types of problems, by analyzing large datasets to identify the patients that would benefit most from preventative measures. However, leveraging AI has often required health care organizations to hire their own data scientists or settle for one-size-fits-all solutions that arent optimized for their patients.

Now the startup ClosedLoop.ai is helping health care organizations tap into the power of AI with a flexible analytics solution that lets hospitals quickly plug their data into machine learning models and get actionable results.

The platform is being used to help hospitals determine which patients are most likely to miss appointments, acquire infections like sepsis, benefit from periodic check ups, and more. Health insurers, in turn, are using ClosedLoop to make population-level predictions around things like patient readmissions and the onset or progression of chronic diseases.

We built a health care data science platform that can take in whatever data an organization has, quickly build models that are specific to [their patients], and deploy those models, says ClosedLoop co-founder and Chief Technology Officer Dave DeCaprio 94. Being able to take somebodys data the way it lives in their system and convert that into a model that can be readily used is still a problem that requires a lot of [health care] domain knowledge, and thats a lot of what we bring to the table.

In light of the Covid-19 pandemic, ClosedLoop has also created a model that helps organizations identify the most vulnerable people in their region and prepare for patient surges. The open source tool, called the C-19 Index, has been used to connect high-risk patients with local resources and helped health care systems create risk scores for tens of millions of people overall.

The index is just the latest way that ClosedLoop is accelerating the health care industrys adoption of AI to improve patient health, a goal DeCaprio has worked toward for the better part of his career.

Designing a strategy

After working as a software engineer for several private companies through the internet boom of the early 2000s, DeCaprio was looking to make a career change when he came across a project focused on genome annotation at the Broad Institute of MIT and Harvard.

The project was DeCaprios first professional exposure to the power of artificial intelligence. It blossomed into a six year stint at the Broad, after which he continued exploring the intersection of big data and health care.

After a year in health care, I realized it was going to be really hard to do anything else, DeCaprio says. Im not going to be able to get excited about selling ads on the internet or anything like that. Once you start dealing with human health, that other stuff just feels insignificant.

In the course of his work, DeCaprio began noticing problems with the ways machine learning and other statistical techniques were making their way into health care, notably in the fact that predictive models were being applied without regard for hospitals patient populations.

Someone would say, I know how to predict diabetes or I know how to predict readmissions, and theyd sell a model, DeCaprio says. I knew that wasnt going to work, because the reason readmissions happen in a low-income population of New York City is very different from the reason readmissions happen in a retirement community in Florida. The important thing wasnt to build one magic model but to build a system that can quickly take somebodys data and train a model thats specific for their problems.

With that approach in mind, DeCaprio joined forces with former co-worker and serial entrepreneur Andrew Eye, and started ClosedLoop in 2017. The startups first project involved creating models that predicted patient health outcomes for the Medical Home Network (MHN), a not-for-profit hospital collaboration focused on improving care for Medicaid recipients in Chicago.

As the founders created their modeling platform, they had to address many of the most common obstacles that have slowed health cares adoption of AI solutions.

Often the first problems startups run into is making their algorithms work with each health care systems data. Hospitals vary in the type of data they collect on patients and the way they store that information in their system. Hospitals even store the same types of data in vastly different ways.

DeCaprio credits his teams knowledge of the health care space with helping them craft a solution that allows customers to upload raw data sets into ClosedLoops platform and create things like patient risk scores with a few clicks.

Another limitation of AI in health care has been the difficulty of understanding how models get to results. With ClosedLoops models, users can see the biggest factors contributing to each prediction, giving them more confidence in each output.

Overall, to become ingrained in customers operations, the founders knew their analytics platform needed to give simple, actionable insights. That has translated into a system that generates lists, risk scores, and rankings that care managers can use when deciding which interventions are most urgent for which patients.

When someone walks into the hospital, its already too late [to avoid costly treatments] in many cases, DeCaprio says. Most of your best opportunities to lower the cost of care come by keeping them out of the hospital in the first place.

Customers like health insurers also use ClosedLoops platform to predict broader trends in disease risk, emergency room over-utilization, and fraud.

Stepping up for Covid-19

In March, ClosedLoop began exploring ways its platform could help hospitals prepare for and respond to Covid-19. The efforts culminated in a company hackathon over the weekend of March 16. By Monday, ClosedLoop had an open source model on GitHub that assigned Covid-19 risk scores to Medicare patients. By that Friday, it had been used to make predictions on more than 2 million patients.

Today, the model works with all patients, not just those on Medicare, and it has been used to assess the vulnerability of communities around the country. Care organizations have used the model to project patient surges and help individuals at the highest risk understand what they can do to prevent infection.

Some of it is just reaching out to people who are socially isolated to see if theres something they can do, DeCaprio says. Someone who is 85 years old and shut in may not know theres a community based organization that will deliver them groceries.

For DeCaprio, bringing the predictive power of AI to health care has been a rewarding, if humbling, experience.

The magnitude of the problems are so large that no matter what impact you have, you dont feel like youve moved the needle enough, he says. At the same time, every time an organization says, This is the primary tool our care managers have been using to figure out who to reach out to, it feels great.

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Bringing the predictive power of artificial intelligence to health care - MIT News

BAE Systems Australia is also providing the unmanned flight vehicle management component of Boeing Australias Loyal Wingman Advanced Development Program that is being developed with the RAAF and DST Group.

In a three-decade R&D program, BAE Systems Australia worked with the University of Sydneys Australian Centre for Field Robotics during the 1990s to develop robotics and autonomous control technologies, says Mr Yelland.

It explored GPS-free navigation and decentralised data fusion technologies for swarms of unmanned vehicles, much of which was subsequently adopted by mining giant Rio Tinto for use on remote mine sites in Australia.

Rheinmetall Mission Master operating autonomously as a battlefield ambulance carrying two casualties.

Now, as the industry lead for land autonomy in Defences new, Brisbane-based Trusted Autonomous Systems CRC, BAE Systems Australia is working with the Australian Army and DSTs Land Division to develop a technology road map to tackle this most challenging of operational environments.

Last year it successfully demonstrated autonomously controlled M113 armoured personnel carriers.

Rheinmetall Defence Australia isnt part of this CRC but its German parent has been researching autonomous vehicles for two decades.

The Australian Armys new Boxer 8x8 combat reconnaissance vehicles will be manufactured at the companys new Military Vehicle Centre of Excellence (MILVEHCOE) at Redbank, Queensland.

Having the hardware moving safely alongside and amongst an infantry combat team as a member of the team, that is one of the key differentiators were working on here.

Gary Stewart, Rheinmetall Defence Australia MD

There, Rheinmetall Defence Australia engineers are leading 60 researchers across Australia in a two-year, $12 million robotics and autonomy R&D program in partnership with DST, CSIROs Data61, QUT and RMIT as well as with German and Canadian researchers.

When we stood up the R&D program we wanted to make sure we had the best and brightest, Gary Stewart, managing director of Rheinmetall Defence Australia, told The Australian Financial Review.

RMIT is one of the worlds leading video game development hubs and so an expert in machine learning; QUT has world-leading expertise in artificial vision how robots interpret the world around them; and CSIROs Data61 is a global leader in Artificial Intelligence.

The company already has two Canadian wheeled Mission Master robotic vehicles in-country and will receive a lightweight German Wiesel Wingman tracked armoured vehicle later this year.

The aim, says Mr Stewart, is to start demonstrating genuinely transformational autonomous vehicle technologies to the Australian Army in 2020, COVID-19 permitting.

The vehicle wont be controlled by a member of the infantry team its supporting it will be completely autonomous.

The Nulka missile decoy system at work.

Having the hardware moving safely alongside and amongst an infantry combat team as a member of the team, that is one of the key differentiators were working on here, he said.

This culminates in the vehicle driving and making its own decisions, interpreting the infantry hand signals, watching how the soldiers move and then determining the appropriate way it should behave as a member of that team.

Rheinmetall Defence Australias R&D is all company funded, says Mr Stewart. But we are looking for co-investment opportunities.

For defence giant Lockheed Martin Australia, R&D is its lifeblood, according to Dr Tony Lindsay, Director of the companys Melbourne-based STELaRLab.

The company is now investing locally in a portfolio of advanced technologies, including quantum science, hypersonics, space systems and AI.

It is also the Combat Systems Integrator (CSI) for the Navys new Attack-class submarines and this month announced eight contracts worth a combined $600,000 with the Universities of Adelaide, South Australia, Tasmania and Melbourne and three local high-technology firms to prepare white papers on the development of novel and emerging combat system technologies.

This follows $900,000-worth of similar contracts awarded last year and funded under its submarine contract.

Lockheed Martin is working with the white paper authors on longer-term research based on these. Topics include underwater communications, dynamic computing resource allocation, and AI-enabled novel operational concepts associated with the use of uninhabited and autonomous systems by a submarine.

The main focus of Lockheed Martin Australias Australian industrial participation program is R&D and it has funded some 32 export-focused R&D projects since 2012, the company says.

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Artificial intelligence is where our natural talents lie - The Australian Financial Review

STR in Isaac Sim with dolly.

Building cars at BMW involves the handling of millions of parts flowing into a factory from more than 4,500 supplier sites. One factor increasing the factory logistics challenge for the BMW Group is the customizability offered by the company. With an average of 100 different options available, this translates into 99% of customer orders being unique to each customer.

Ultimately, the sheer volume of possible configurations became a challenge to BMW Group production in three fundamental areascomputing, logistics planning, and data analysis, said Jurgen Maidl, senior vice president of logistics for the BMW Group.

Nvidia Isaac robotics platform working in sync with robot.To better handle logistics within its factories, BMW now uses four types of material handling robots and a smart transport robot. These robots were developed using Nvidias Isaac robotics platform.

According to Nvidia, its Isaac software development kit provides these robots with neural networks capable of addressing perception, segmentation, and human pose estimation to perceive their environment, detect objects, navigate autonomously and move objects. These robots are trained both on real and synthetic data using Nvidia graphics processing units (GPUs) to render ray-traced machine parts in a variety of lighting and occlusion conditions to augment real data.

Hanns Huber of BMWHanns Huber, with BMW Groups Communications Production Network, explained how the five different types of robots the company outfitted with Nvidias technology support factory logistics in production operations.

He said that, after delivery to the plant, parts are transported to the assembly line in containers of various sizes. Stationary SplitBots take full plastic boxes from the pallet in the incoming goods area and place them on a conveyor system that transports the boxes to a warehouse. The SplitBot also makes sure the containers are lined up correctly for automated storage. Using Nvidias artificial intelligence, the SplitBot can detect and process up to 450 different containers.

Mobile PlaceBots unload tugger trains and place boxes loaded with goods on a shelf. These robots use Nvidias image recognition system to classify the small load carriers and determine the ideal grip point from the combined input of sensors, cameras, and artificial intelligence. These technologies allow the PlaceBots to move autonomously in a predetermined area.

Another logistics robot, the PickBot has a robotic arm that it uses to collect various small parts from supply racks. Like the SplitBots, the PickBot uses Nvidias AI technology to calculate the right grip point.

The robotic manipulation arm of the SortBot takes empty boxes and puts them on a palette to be sent back to supplier area. These SortBots are deployed in series production to stack empty containers on pallets before they re-enter circulation.

BMWs autonomous Smart Transport Robots (STRs) can identify obstacles such as forklift trucks, as well as humans, to more accurately and quickly suggest alternative routes as needed. They can also learn from the environment and apply different responses to people and objects.

Huber noted that all of these robots have been developed by BMW in the past five years, with most being deployed and tested in BMW factories since 2019. The robots are trialed during our development process at various BMW plants in Germany, as well at our logistics laboratory in Munich, he explained.

The STR was developed by BMWs logistics innovation team together with Fraunhofer Institute Dortmund, Huber added.

STR Robot in BMW facility.BMWs work with Nvidia on this project began in 2019. A BMW Group team of engineers worked on implementing the Nvidia technology with the robots, Huber said. The complete implementation was done in-house at BMW. Two teams from both sidesBMW Group and Nvidiaworked closely to customize and adapt a suitable solution. The first STR with Nvidia technology was deployed as a proof-of-technology in our logistics laboratory in Munich in May 2020. The first productive test will go live by the forth quarter of 2020.

Nvidia notes that the real and synthetic data generated during the testing of these robots are used to train deep neural networks on Nvidias DGX AI infrastructure development systems. The robots are then continuously tested in Nvidias Isaac Sim simulators for navigation and manipulation development, operating on Nvidias Omniverse platform, where multiple BMW Group personnel in different geographies can all work in one simulated environment.

Huber said that, incorporating Nvidias AI technology into BMWs robots allows BMW to optimize our robotics and material flow, as well as take simulations in the planning process to a new level.

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BMW Outfits Robots with Artificial Intelligence - Automation World