Artificial intelligencecouldplay adecisiverole in stopping the Covid-19 pandemic. To give the technology a push, the MIT-IBM Watson AI Lab is funding 10 projects at MIT aimed atadvancing AIs transformative potential for society. The research will target the immediate public health and economic challenges of this moment. But it could havealasting impact on how we evaluate and respond to risk long after the crisis has passed. The 10 research projects are highlightedbelow.

Early detection of sepsis in Covid-19 patients

Sepsis is a deadly complication of Covid-19, the disease caused by the new coronavirus SARS-CoV-2. About 10 percent of Covid-19 patients get sick with sepsis within a week of showing symptoms, but only about half survive.

Identifying patients at risk for sepsis can lead to earlier, more aggressive treatment and a better chance of survival. Early detection can also help hospitals prioritize intensive-care resources for their sickest patients. In a project led by MIT ProfessorDaniela Rus, researchers will develop a machine learning system to analyze images of patients white blood cells for signs of an activated immune response against sepsis.

Designing proteins to block SARS-CoV-2

Proteins are the basic building blocks of life, and with AI, researchers can explore and manipulate their structures to address longstanding problems. Take perishable food: The MIT-IBM Watson AI Labrecently used AIto discover that a silk protein made by honeybees could double as a coating for quick-to-rot foods to extend their shelf life.

In a related project led by MIT professorsBenedetto MarelliandMarkus Buehler, researchers will enlist the protein-folding method used in their honeybee-silk discovery to try to defeat the new coronavirus. Their goal is to design proteins able to block the virus from binding to human cells, and to synthesize and test their unique protein creations in the lab.

Saving lives while restarting the U.S. economy

Some states are reopening for business even as questions remain about how to protect those most vulnerable to the coronavirus. In a project led by MIT professorsDaron Acemoglu,Simon JohnsonandAsu Ozdaglarwill model the effects of targeted lockdowns on the economy and public health.

In arecent working paperco-authored by Acemoglu,Victor Chernozhukov, Ivan Werning, and Michael Whinston,MIT economists analyzed the relative risk of infection, hospitalization, and death for different age groups. When they compared uniform lockdown policies against those targeted to protect seniors, they found that a targeted approach could save more lives. Building on this work, researchers will consider how antigen tests and contact tracing apps can further reduce public health risks.

Which materials make the best face masks?

Massachusetts and six other states have ordered residents to wear face masks in public to limit the spread of coronavirus. But apart from the coveted N95 mask, which traps 95 percent of airborne particles 300 nanometers or larger, the effectiveness of many masks remains unclear due to a lack of standardized methods to evaluate them.

In a project led by MIT Associate ProfessorLydia Bourouiba, researchers are developing a rigorous set of methods to measure how well homemade and medical-grade masks do at blocking the tiny droplets of saliva and mucus expelled during normal breathing, coughs, or sneezes. The researchers will test materials worn alone and together, and in a variety of configurations and environmental conditions. Their methods and measurements will determine howwell materials protect mask wearers and the people around them.

Treating Covid-19 with repurposed drugs

As Covid-19s global death toll mounts, researchers are racing to find a cure among already-approved drugs. Machine learning can expedite screening by letting researchers quickly predict if promising candidates can hit their target.

In a project led by MIT Assistant ProfessorRafael Gomez-Bombarelli, researchers will represent molecules in three dimensions to see if this added spatial information can help to identify drugs most likely to be effective against the disease. They will use NASAs Ames and U.S. Department of Energys NSERC supercomputers to further speed the screening process.

A privacy-first approach to automated contact tracing

Smartphone data can help limit the spread of Covid-19 by identifying people who have come into contact with someone infected with the virus, and thus may have caught the infection themselves. But automated contact tracing also carries serious privacy risks.

Incollaborationwith MIT Lincoln Laboratory and others, MIT researchersRonald RivestandDaniel Weitznerwill use encrypted Bluetooth data to ensure personally identifiable information remains anonymous and secure.

Overcoming manufacturing and supply hurdles to provide global access to a coronavirus vaccine

A vaccine against SARS-CoV-2 would be a crucial turning point in the fight against Covid-19. Yet, its potential impact will be determined by the ability to rapidly and equitably distribute billions of doses globally.This is an unprecedented challenge in biomanufacturing.

In a project led by MIT professorsAnthony SinskeyandStacy Springs, researchers will build data-driven statistical models to evaluate tradeoffs in scaling the manufacture and supply of vaccine candidates. Questions include how much production capacity will need to be added, the impact of centralized versus distributed operations, and how to design strategies forfair vaccine distribution. The goal is to give decision-makers the evidenceneededto cost-effectivelyachieveglobalaccess.

Leveraging electronic medical records to find a treatment for Covid-19

Developed as a treatment for Ebola, the anti-viral drug remdesivir is now in clinical trials in the United States as a treatment for Covid-19. Similar efforts to repurpose already-approved drugs to treat or prevent the disease are underway.

In a project led by MIT professorsRoy Welschand Stan Finkelstein, researchers will use statistics, machine learning, and simulated clinical drug trials to find and test already-approved drugs as potential therapeutics against Covid-19. Researchers will sift through millions of electronic health records and medical claims for signals indicating that drugs used to fight chronic conditions like hypertension, diabetes, and gastric influx might also work against Covid-19 and other diseases.

Finding better ways to treat Covid-19 patients on ventilators

Troubled breathing from acute respiratory distress syndrome is one of the complications that brings Covid-19 patients to the ICU. There, life-saving machines help patients breathe by mechanically pumping oxygen into the lungs. But even as towns and cities lower their Covid-19 infections through social distancing, there remains a national shortage of mechanical ventilators and serious health risks of ventilation itself.

In collaboration with IBM researchers Zach Shahn and Daby Sow, MIT researchersLi-Wei LehmanandRoger Markwill develop an AI tool to help doctors find better ventilator settings for Covid-19 patients and decide how long to keep them on a machine. Shortened ventilator use can limit lung damage while freeing up machines for others.To build their models, researchers will draw on data from intensive-care patients with acute respiratory distress syndrome, as well as Covid-19 patients at a local Boston hospital.

Returning to normal via targeted lockdowns, personalized treatments, and mass testing

In a few short months, Covid-19 has devastated towns and cities around the world. Researchers are now piecing together the data to understand how government policies can limit new infections and deaths and how targeted policies might protect the most vulnerable.

In a project led by MIT ProfessorDimitris Bertsimas, researchers will study the effects of lockdowns and other measures meant to reduce new infections and deaths and prevent the health-care system from being swamped. In a second phase of the project, they will develop machine learning models to predict how vulnerable a given patient is to Covid-19, and what personalized treatments might be most effective. They will also develop an inexpensive, spectroscopy-based test for Covid-19 that can deliver results in minutes and pave the way for mass testing. The project will draw on clinical data from four hospitals in the United States and Europe, including Codogno Hospital, which reported Italys first infection.

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Marshaling artificial intelligence in the fight against Covid-19 - MIT News

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Last year, officials at the Montour school district in western Pennsylvania approached band director Cyndi Mancini with an idea: How about using artificial intelligence to teach music?

Mancini was skeptical.

As soon as I heard AI, I had this panic, she said. All I thought about were these crazy robots that can think for themselves.

There were no robots. Just a web application that uses AI to build original instrumental tracks from a library of prerecorded samples after a user selects a few parameters.

Equipped with Chromebooks, Mancinis students could program mood and genre, manipulate the tempo or key, mute sections, and switch instrument kits with a couple of clicks. And just like that, an original piece is produced instantly.

The AI programdesigned for use by anyone who needs cheap background tunes for media contentenabled Mancini to teach in ways not possible before: Students in an elective course who do not play instruments or read sheet music were now creating their own compositions. For the musically inclined students, Mancini said the software allowed for an even deeper fusion of computer and humantheyd create a track and play over it, combining AI-generated rhythms with live instrumentation.

For me, music is an emotional experience. I know what I put into my playing and teaching of music. For that emotion to come out of an algorithm, I couldn't wrap my head around it at first. How can a computer replicate that? she said. But it can. Im a convert.

While Montour is embracing AI technology with a full-blown bear hug, most school districts are notat least not yet. Some are dabbling with applications. Others arent using AI at all.

And still other educators cant say if their districts are using AI, oftentimes because theyre not familiar enough with the technology to recognize it.

Whether that changes with the nationwide distance learning experiment that happened this spring is still to be seen.

This much, however, is clear: School budgets are going to be devastated from the economic onslaught wrought by the virus, and strapped-for-cash districts could delay tech acquisitions other than the devices and hotspots students need to go online as they prioritize necessities. Still lingering are serious questions about privacy, data bias, and just how effective AI solutions are for education.

The 3,000-student Montour district, in the suburbs of Pittsburgh, is using AI inside and outside the classroom.

The district teaches courses focused on artificial intelligence, ranging from ethics to robotics. It partners with universities and technology companies working on the cutting edge of AI. Theres even a 4-foot tall autonomous robot, a boxy machine that looks like a filing cabinet on wheels, zooming around the hallways of its elementary school delivering packages.

And on the districts backend IT infrastructure, there are dashboards and programs powered by AI providing educators with real-time data about each student, producing metrics that monitor progress and even forecast future success.

When we come back to school next year after the coronavirus, were going to have data on every single kid from their remote learning experience, said Justin Aglio, the director of academic achievement and district innovation at Montour. Not your traditional A,B,C data, either.

Districts, already inundated with trying to keep up, might also shy away from AI tools in the immediate future while teachers and staff adjust to a new digital ecosystem already pushing the boundaries for many.

Its not even on our radar right now, said Andrew McDaniel, the principal of Southwood High School in central Indiana, when asked if hes considering incorporating some of the most basic forms of AI, such as Alexa voice devices, into classrooms. A lot of teachers are looking at what they know works now and sticking to that. Theyre not going to mess around with much that goes beyond that.

Increasingly, though, voice-activated devices such as Alexa, Siri, and Google Home are being used as teaching assistants in classes. Schools are turning to smart thermostats to save money on energy costs and using AI programs to monitor their computer networks. AI is helping districts identify students who are at risk of dropping out, and math tutors and automated essay-scoring systems that have been used for decades now feature more sophisticated AI software than they did in the past.

Until recently, though, most of those tools have relied on simpler AI algorithms that work on a basis of preset rules and conditions.

But a new age of AI-based ed-tech tools are emerging using machine-learning techniques to discover patterns and identify relationships that are not part of their original programming. These systems consistently learn from data collected every time theyre in use and more truly mirror human intelligence.

Ed-tech vendors are pitching advanced statistical AI tools as a way to provide greater personalized learning, tailoring curriculum to a students strengths and weaknesses. Researchers say it is unlikely advanced AI will transform K-12 education, but it can have a positive impact in areas like adaptive instruction, automated essay scoring and feedback, language learning, and online curriculum-recommendation engines.

Most of the startups pioneering education solutions with this type of AI arent yet in a position to offer their products on a mass scale in the United States. Thats because highly accurate advanced AI systems require access to massive data sets to populate and train the machine-learning algorithm to make reliable predictions. Those algorithms must also have access to high-quality data to avoid reinforcing racial, gender, and other biases.

Bill Salak, the chief technology officer for Brainly, an AI-based content generator and homework assistant that uses machine learning, said his company has traditionally worked directly with students, not districts. Now, however, Brainly is diving into more advanced statistical models for its AI to allow for even deeper personalization, and it is planning to eventually start creating products that could go into the classroom.

Salak said that all AI-based technology vendors face an uphill climb because school districts are consistently underfunded, and if theyre going to spend money on a tech tool, it has to be proven to be effective and contributing to academic goals.

The education systems prioritize things that will help them meet their goals, and not many outcomes relate to teaching with new tech, he said. Even if the teacher may see a huge amount of value in something, at the end of the day, that teacher has to have a certain percentage of their kids meeting certain competency standards.

April DeGennaro, a teacher in the gifted program at Peeples Elementary in Fayetteville, Ga., knows firsthand what its like for district administrators to buy into the idea of using AI-tech tools but not backing up that commitment with funding.

DeGennaro runs a lab where students focus on robotics, and her 4th graders use an AI-based robot called Cozmo. Shaped like a mini bulldozer that can fit in your palm, Cozmo uses facial recognition and a so-called emotion engine, allowing it to react to different situations with a humanlike personality by showing a range of emotions, from happy or sad to bored and grumpy. Because of COVID-19-related school closures, the AI robots currently arent being used.

But under normal circumstances, up to four students can use one of the robots at a time with an iPad, coding it to carry out different tasks. At $150 each, DeGennaro said the robots amount to a low-cost investment, but shes had to find her own funding for all seven Cozmo robots in her class.

DeGennaro raised money online, where she got parents to chip in to buy robots. Shes also made it clear to those that know her: For Christmas, for an end-of-the-year gift, or whenever you want to buy Mrs. D a present, buy a robot.

School districts may like things, DeGennaro said, but that doesnt mean they're going to fund them.

At the Saddle Mountain Unified School District in Arizona, a new policy allowing high school teachers to use Alexa or Google Home went into effect this year after a group of district officials and teachers walked through several STEM schools in the Phoenix area and saw the devices being used in classrooms.

Joel Wisser, the technology integration specialist for the 2,300-student district, said teachers walked away impressed, and several decided to incorporate the devices into their daily classroom activities. The district didnt pay for the devices, however. Instead, teachers had to bring their own, and Wisser said he doesnt expect that to change.

One history teacher uses his Alexa as a mini-assistant: reminding him when to return papers to students, answering student and teacher inquiries, providing a Jeopardy-style quiz game, or even playing music set from a time period the class is studying to add ambience to a lesson.

Its really just a personal assistant, a helper, for him. His eyesight is not great. He has a 46-inch computer monitor and hes not a fast typer, said Wisser. Being able to talk to a device is much more efficient for him, so hes not spending time at a keyboard typing in the words 'ancient Greek music.

Everyone didnt welcome the devices at first. The districts technology director, for one, was hesitant because the Alexa was going to be tapped into the districts network, and he wasnt going to have complete control over it, Wisser said.

The voice-activated speakers are also at the center of an ongoing privacy debate since they can record conversations. Wisser said there hadnt been any pushback from parents so far, and class conversations were not recorded.

Christina Gardner-McCune, the director of the Engaging Learning Labs at the University of Florida, said parents, students, and teachers have concerns about what kind of data an Alexa device is collecting in the classroom and what is it doing on the districts network while there. Even though the recording function on an Alexa can be turned off, Gardner-McCune said some districts dont want anything to do with them.

A lot of districts are not allowing those devices in the classroom even though they could have some educational purposes, said Gardner-McCune, who is also a steering committee co-chair of the AI for K-12 Initiative, a national working group of teachers and AI experts focused on jump-starting discussion on how to incorporate AI learning into school curricula.

It will take more time and use of AI devices and tech tools in classrooms before districts become increasingly comfortable with them on a larger scale, she said. And more research is needed showing the benefits of advanced AI systems before districts are willing to pony up for them: For major school districts, said Gardner-McCune, its going to come down to how does it affect test scores.

Back in the Montour district, band director and teacher Mancini said her apprehension about the AI music program vanished when she became familiar with the web application and realized there wasnt going to be a robot in the middle of my room. One of her favorite class exercises using the AI music program involved muting the background music on a movie cliplike the scene where the ship is sinking in Titanicand letting students rework the general vibe by adding their own music.

Music education has been so traditionally taught one way. We play instruments or sing or learn music theory. This is so far from traditional, and Im glad I did it because it was so much fun when I got into it, she said. As teachers, we just need to not be afraid of technology.

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Artificial Intelligence in K-12: The Right Mix for Learning or a Bad Idea? - Education Week

The common thread across all AI technologies is the ability to impart human-like decision-making capabilities into applications and systems.

Artificial intelligence (AI) refers to thesimulation of human intelligence in systems programmed to think like humans andmimic their actions. AI includes a broad range of technologies, including cognitive computing, deep learning, expert systems, machine learning, natural language processing, and IBM Watson.

The common thread across these areas, and allof AI, for that matter, is the ability to impart human-like decision-makingcapabilities into applications and systems. Experts predict AI will be rapidlyadopted because they believe it will be a disruptive technology acrossmany industries.

There already are many examples of the impactAI has in a variety of fields, including:

AI is a very broad field with manysubcategories. Each is aimed at particular application areas and uses specifictechnologies for those application areas. They include

Cognitive computing is the use of computerizedmodels to simulate the human thought process in complex situations where theanswers may be ambiguous and uncertain. It mimics how humans learn, think, and adapt,enabling a wide range of real-time insights and actions.

For example, cognitive computing is being usedto aid human resources with hiring decisions, help doctors make diagnoses and treatment decisionsby using the data relating to a patients case to make suggestions withconfidence levels assigned to them, and improve call center customer experience.

Cognitive computing enables such applicationsusing several technologies, including:

Deep learning is a subset of machine learningin artificial intelligence (AI) that has networks capable of learningunsupervised from unstructured or unlabeled data. Deep learning systems notonly think, but keep learning and self-directing as new data flows in.

Deep learning can play a role in a range ofreal-time, interactive applications, including speech recognition, visual recognition, and machine translation.It accomplishes this using several techniques and technologies including:

An expert system that uses artificialintelligence techniques and databases of expert knowledge to offer advice ormake decisions. In particular, expert systems emulate the decision-makingability of a human expert. Expert systems are designed to solve complexproblems by reasoning through bodies of knowledge, represented mainly asif-then rules rather than through conventional procedural code.

A key attribute of expert systems is that theyautomate many tasks and work interactively with external information (e.g., atext message, an event log, a verbal question or answer, and more). Applicationareas for expert systems include use as:

Machine learning is an application ofartificial intelligence that provides systems the ability to automaticallylearn and improve from experience without being explicitly programmed. Machinelearning uses structured data that has a single, direct input for each fieldused. In general, machine learning makes use of clean data, that is easy towork with, and for which there are no nuances to it. (In contrast, deeplearning uses unstructured data.)

Machine learning is best when there aremassive volumes of structured data that would take years for a human operatorto process. It can efficiently classify information, predicting outcomes basedon previous behavior and performance, and organizing information together basedon key variables. General applications areas include:

Natural language processing (NLP) makes use oflinguistics and artificial intelligence to improve interactions betweencomputers and humans. In many applications, NLP is used to helpsolve a problem, answer a question, or direct a person to an appropriateresource based on the spoken word.

To achieve such results, NLP-bases systemsmake use of some core technologies and deliver essential capabilities,including:

IBM Watson is an artificial intelligence platform that helps businessespredict and shape future outcomes, automate complex processes, and optimizeemployee productivity. It is widely known from its first use case as a questionand answer computer system used in a series of matches against humans on the TVshow Jeopardy!

Today, IBM Watson technology delivers acompetitive advantage to businesses by using AI to unlock the value of data innew, profound ways, giving every member of a business the power of AI. IBMWatson consists of a suite of pre-built applications and tools to givebusinesses insights to predict and shape outcomes and infuse intelligence intoyour workflows. Implementations of IBM Watson include:

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Exploring Artificial Intelligence Variants and Their Uses - RTInsights

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Artificial intelligence technology can tell doctors when a scan reveals a tumor, can help the military distinguish between a truck and a school bus as a target, and can answer a high volume of college students questions.

Sectors of our economy such as the military, health care, and higher education are much further along than the K-12 system in incorporating artificial intelligence systems and machine learning into their operations. And many of those useseven when they are not specifically for educationcan spark ideas for applications in K-12 that may be more pertinent than ever imagined.

With the coronavirus upending traditional ways of delivering education, AI technologieswhich are designed to model human intelligence and solve complex problemsmay be able to help with logistical challenges such as busing and classroom social distancing, provide support to overwhelmed teachers, and glean new information about remote learning.

AI techniques and systems are like the internal combustion engineyou can use them to power a lot of different things, said David Danks, a professor of philosophy and psychology at Carnegie Mellon University in Pittsburgh, who studies cognitive science, machine learning, and how AI affects people. The exact same thing can be used to predict whether someone has cancer, or whether students understand a concept, or to classify somebody as a bad guy you want to go after.

Of course, there are lots of potential trouble spots when thinking about the role of AI in K-12 education. Artificial intelligence learns from the data that are fed into it, and if that input includes bad data or data applied incorrectly, poor or biased decisions may result. At the same time, the use of AI in K-12 raises very serious data-privacy concerns because such technologies would likely be used to personalize education or make important decisions for individual students.

But even with those concerns, AI advocates say other sectors are already offering lessons learned for how the technologies could be used in K-12 for teaching and learning and the management of schools. That is especially the case with the military, health-care, and higher education fields.

Here is a look at what K-12 educators, policymakers, and planners could learn from those three sectors:

Nearly every military in the world believes that advances in AI will play a critical role in shaping the future of military power. But there are big disagreements about what is possible and what is wise.

SimulationsMilitary leaders are using AI simulations to assess military tactics and determine the likely outcome of strategic plans. Plugging different variables into these scenarioseverything from weather predictions to the timing of attacks and estimating troop numberscan show how outcomes might change. Also, soldiers can get important practice in simulated real-world settings with low risk.

> K-12 Applications: AI-powered simulations could be useful for planning purposes for everything from scheduling to determining the most effective models for social distancing when students return to their school buildings amid the COVID-19 outbreak. Some companies are already using simulations to train educators on successful techniques to help students with social-emotional learning, trauma, and mental-health issues.

MaintenanceTanks, airplanes, submarines, trucksall that military equipment needs to be maintained to keep troops safe and operations running smoothly. Some high-tech AI systems can predict when parts need replacing before they break or when systems need tuneup. Artificial intelligence has helped the military optimize in-flight refueling of jets to make the dangerous technique safer and more efficient.

> K-12 Applications: School districts also rely on a lot of equipmentthink buses, computers, air conditioning systems, and more. AI-powered smart programs are already being used in some schools to fine-tune building operations, lower energy costs, and manage maintenance and repairs.

LogisticsThe backbone of the military revolves around logistics and supply-chain management. How to get equipment and personnel from point A to point B most efficiently and cost effectively is something that AI systems are tackling for the military.

> K-12 Applications: The uses are widespread: AI systems could optimize scheduling, the distribution of laptops, cafeteria operations, and bus routes. In fact, the Boston school district has saved more than $5 million using a high-tech AI system that streamlined bus routes.

At its core, AI is really about using big data to be able to help predict what will happen so we can show up at the right time with the right solution.

ScanningArtificially intelligent technologies can analyze radiology and CT scans looking for abnormalities. Programs can quickly sift through images much faster than humans and identify patterns based on vast data. These techniques can identify tumors and health issues and suggest treatments, which are then reviewed by medical professionals.

> K-12 Applications: Programs powered by artificial intelligence could do a better job identifying student risk factors and recommending earlier and more targeted academic or mental-health interventions. The goal isnt to replace teacher decisions but to save teachers time and to amplify their own expertise. Using big data and AI to spot patterns might be applied to other situations, such as taking student temperatures to check for COVID-19 before they enter school buildings or being able to target outbreaks more quickly.

PersonalizationAccess to massive amounts of digital medical data and the use of AI to analyze it are making it easier to personalize medical treatments for patients. AI can predict how someones current health behaviors are likely to affect their future health outcomes. High-tech systems can design much more sophisticated drug and treatment strategies tailored to an individual patients biology or type of cancer, for example.

> K-12 Applications: Many education companies already talk about being able to help personalize the learning experience for students, but this is still just an emerging effort in most places. Some K-12 programs are using artificial intelligence to collect data on student behavior and academic engagement and then guide students through suggested individualized lessons. CENTURY Tech, a London-based company, for example, uses an AI platform that tracks student interactions and behavior patterns and academic performance to create personalized learning paths.

TrainingArtificial-intelligence-powered programs are being used to train medical professionals in many ways. AI company Kognito, for example, uses its health simulations to help doctors and nurses practice discussing and interacting with patients around sensitive topics like obesity, mental health, and suicide. Through conversations with virtual humans, medical practitioners can practice and model effective techniques.

> K-12 Applications: Kognito has a version of its product that is designed for educators, training them to lead conversations with students around social-emotional learning and mental health, using research-based language and techniques. An expanded version of this technology could be applied in other areas. About 15,000 K-12 schools currently have access to Kognito simulations.

Cost SavingsEarly medical intervention, making sure patients adhere to treatment, and supply-chain management are all ways that AI can affect the bottom line in various aspects of health care.

> K-12 Applications: The same goes for schools and districts. AI-powered programs could predict what supplies are needed and where with more accuracy, analyze budget trends, and identify spending patterns in areas ripe for savings, especially given that K-12 budgets are likely to be slashed significantly as the economy struggles through COVID-19.

Theres not an obvious wall between higher education and K-12 [around uses for AI].

Remote LearningWhat if teachers could have more information in real time about whether their students grasp concepts or are struggling when learning online? Whitehill is exploring the idea of an AI-based program that uses a video camera to take many small snapshots of students as they learn remotely to analyze their facial reactions. Such a program would provide teachers with real-time feedback on students cognitive and emotional states. (But that program is also just the kind of technological approach that would prompt intense criticism from student-data-privacy advocates.)

Virtual Teachers AssistantWhen Georgia Tech interactive-computing professor Ashok Goel was having a hard time answering all the questions coming from the hundreds of students in his online computer science class, he created an artificially intelligent tutor he dubbed Jill Watson. She was able to answer many of the students more routine questions, freeing up time for Goel to do higher-level work. Since that first experiment, Watson is now used in 17 online classes, Goel said, covering more than a thousand person-hours of work. Goel, who is also the chief scientist for C21U, a company developing innovative uses for AI, is now working to adapt Watson for high school and middle school teachers. And with remote learning, he believes the AI teaching assistant could also be used to help answer parents questions as they support students at home.

Essay GradingColleges and universities are already using this approach to some degree, and this latest version of that technology is moving into the K-12 education space. Automated AI essay graders have been around for some time, but the makers of the software say the AI features now available are much more sophisticated evaluators of student writing than what were available years ago. They can judge hundreds of features in a written piece, everything from spelling and grammar to sentence structure. (Lots of concerns remain that these programs can be biased, can fail to interpret creativity correctly, and can be gamed by students writing to the algorithm.) Though some states are using these types of programs to grade essays on their standardized state tests, theyre yet to be widely adopted on a district and school level.

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What Soldiers, Doctors, and Professors Can Teach Us About Artificial Intelligence During COVID-19 - Education Week

Now independent from previous owner Micro Focus International PLC, SUSE is out to make its presence more deeply felt with developers and innovators. Its biggest competitors, Red Hat Inc. and Microsoft Corp., have developed impressively broad, varied portfolios. Can SUSEpull any tricks from its Linux-distro hat interesting enough to compete for the attention of leading-edge, developer-driven IT departments?

Even amid the COVID-19 pandemic, SUSEis busily engaging with its community, according toMelissa Di Donato, chief executive officer of SUSE.Open source is developing a community that often times does not sit together. And now were really trying to engage with that community as much as possible to keep innovation alive, to keep collaboration alive, Di Donato said.

SUSE will collaborate and integrate with its developer community in 2020, as well as sharpen its focus on Linux use cases at the edge, such as autonomous driving, Di Donato added.

Di Donatospoke withStu Miniman, host of theCUBE, SiliconANGLE Medias livestreaming studio, during the SUSECON Digital event. They discussed how to drive engagement in open-source communities and how SUSEis infusing its portfolio with artificial intelligence, edge technology and more. (* Disclosure below.)

SUSEhas recently opened up a community to developers with content around Linux, DevOps, containers, Kubernetes, microservices and more. It has also introduced the SUSECloud Application Platform Developer Sandbox.

We wanted to make it easy for these developers to benefit from the best practicesthat evolved from the cloud-native application deliverythat we offer every day to customers and now for free to our developers, Di Donato said.You can expect SUSE to enter new markets like powering autonomous vehicles with safety-certified Linux and other really innovative technologies.

For example, SUSEiscarving out fresh terrain through its partnership with ElectrobitWireless Communications Oy, aleading providerof embedded software solutions for automotive. The two companies will be working on the use of safety-certified Linux in self-driving cars. Also, next quarter the company will announce a solution for simplifying the integration of AI building blocks into software.

Heres the complete video interview, part of SiliconANGLEs and theCUBEs coverage of the SUSECON Digital event. (* Disclosure: TheCUBE is a paid media partner for SUSECON Digital. Neither SUSE, the sponsor for theCUBEs event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Show your support for our mission with our one-click subscription to our YouTube channel (below). The more subscribers we have, the more YouTube will suggest relevant enterprise and emerging technology content to you. Thanks!

Support our mission: >>>>>> SUBSCRIBE NOW >>>>>> to our YouTube channel.

Wed also like to tell you about our mission and how you can help us fulfill it. SiliconANGLE Media Inc.s business model is based on the intrinsic value of the content, not advertising. Unlike many online publications, we dont have a paywall or run banner advertising, because we want to keep our journalism open, without influence or the need to chase traffic.The journalism, reporting and commentary onSiliconANGLE along with live, unscripted video from our Silicon Valley studio and globe-trotting video teams attheCUBE take a lot of hard work, time and money. Keeping the quality high requires the support of sponsors who are aligned with our vision of ad-free journalism content.

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SUSE infuses portfolio with artificial intelligence and edge technology - SiliconANGLE

According to a recent study, humans and artificial intelligence systems can perform better when both of them work together to tackle problems. The research was done by Eric Horvitz Chief scientist Microsoft, Ece Kamar the Microsoft Research principal researcher, and Bryan Wilder, a student at Harvard University and Microsoft Research intern.

It seems that Eric Horvitz first published the research paper. He was hired as Microsoft principal researcher back in the year 1993 and the company named him Microsoft Chief Scientist officer during March. He led the companys research programs from the year 2017 to 2020. The research paper was published earlier this month and it studies the performance of artificial intelligence teams and humans operating together on two PC vision projects namely breast cancer metastasis recognition and Galaxy categorization. With this proposed approach, the artificial intelligence (AI) model evaluates which tasks humans can perform best and what type of tasks AI systems can handle better.

In this approach, the learning procedure is developed to merge human contributions and machine predictions. The artificial intelligence systems work to tackle problems that can be difficult for humans while humans focus on solving issues that can be tough for AI systems to figure out. Basically, AI system predictions generated with lower accuracy levels are routed to human teams in this system. According to researchers, combined training of human and artificial intelligence systems can enhance the galaxy classification model for us. It can improve the performance of Galaxy Zoo with a 21 to 73% decrease in loss. This system can also deliver an up to 20% better performance for CAMELYON16.

The research paper states that the performance of machine learning in segregation overcomes the circumstances where human skills can add integral context, although human teams have their own restrictions including systematic biases. Researchers stated in the paper that they have developed methods focused on training the AI learning model to supplement human strengths. It also accounts for the expense of inquiring an expert. Human and AI system teamwork can take various forms but the researchers focused on settings where machines would decide which instances required human absorption and then merging human and machine judgments.

Horvitz, during the year 2007, worked on a policy to examine when human assistants should interfere in consumer conversations with computerized receptionist systems. The researchers also stated in the paper, Learning to Complement Humans, that they see opportunities of studying extra aspects of human-machine cooperation across various settings. While studying a different type of teamwork, Open Artificial Intelligence research experts have looked at machine assistants operating together in games such as hide and seek, and Quake 3.

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Humans And Artificial Intelligence Systems Perform Better Together: Microsoft Chief Scientist Eric Horvitz - Digital Information World

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Machine Learning and Artificial Intelligence in Healthcare Market 2020 Driving Forces, Future Growth, Top Key Players, Industry Share, Regional...

By DOE/Oak Ridge National LaboratoryMay 19, 2020

Using ORNLs Summit supercomputer, scientists can comb through millions of medical journal articles looking for possible connections among FDA-approved drug therapies and known COVID-19 symptoms. Credit: Dasha Herrmannova/Oak Ridge National Laboratory, U.S. Dept. of Energy

Scientists have tapped the immense power of the Summit supercomputer at Oak Ridge National Laboratory to comb through millions of medical journal articles to identify potential vaccines, drugs, and effective measures that could suppress or stop the spread of COVID-19.

A team comprising researchers from ORNL and Georgia Tech are using artificial intelligence methods designed to unearth relevant information from about 18 million available research documents. They looked for connections among 84 billion concepts and cross-referenced keywords associated with COVID-19 such as high fever, dry cough, and shortness of breath with existing medical solutions.

Our goal is to assist doctors and researchers ability to identify information about drug therapies that are already approved by the U.S. Federal Drug Administration, said ORNLs Ramakrishnan Ramki Kannan.

A massive subset of 6 million documents dated between 2010 and 2015 took 80 minutes, and the entire 18 million will take less than a day to run on Summit. Results will be shared with medical researchers for feedback, which will inform adjustments to improve future calculations.

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Artificial Intelligence Equipped Supercomputer Mining for COVID-19 Connections in 18 Million Research Documents - SciTechDaily