Category Archives: Artificial Intelligence
Getting Girls Into the Artificial Intelligence Pipeline – Medium
Closing the Imagination Gap for GirlsWhy this is a critical step for creating an equitable future
The term artificial intelligence (AI) was coined 64 years ago at a scholarly conference. The AI field hasnt remained the theoretical province of computer scientists and mathematicians; it now is a pervasive part of everyday life. With a technology this powerful, it is critical to include the perspectives of all women, including those from underrepresented communities.
AI applications based on algorithms are found in robotics, machine learning, natural language processing, machine vision, speech recognition and more. These applications are found in homes, vehicles and myriad other aspects of daily life. Examples include facial recognition; robots helping older people live more independently at home; autonomous vehicles; smart watches; and drone safety systems.
AI applications must be able to reach conclusions and offer information. Some require the capacity to sense emotions in order to relate to people.
Today, women are making their way into AI and leading the way for more girls to enter AI careers. Theyre helping this burgeoning industry progress and innovate in ways that otherwise might not be possible. In essence, adding women to the teams creating components of AI fundamentally changes the suitability and functionality of a product or service by eliminating biases and better reflecting the needs of a wider group of users.
Taniya Mishra is director of Artificial Intelligence Research and lead speech scientist at Affectiva, which originated at MIT. The companys technology calibrates peoples speech patterns to recognize emotions.
Mishra offers some concrete examples of machine learning algorithms.
Algorithms are a set of rules logic or a set of instructions that you can give to a machine in order to get it to accomplish a goal to make it behave like a human being, Mishra says. It could be any goal. It could be lifting a block from one place to another. It could be understanding human emotion. All of these could be the goals for designing a machine learning algorithm.
The basic algorithm recipe tells the computer when to do x, then when to do y and then z. For this process to work right, the programmer must give the right instructions. For it to be inclusive, the programmer must think of the entire humanity of users, Mishra notes.
When it comes to diversity, AI benefits from including women and other underrepresented people. These voices must be included when writing instructions or algorithms to power machine learning or other elements of AI. The data gathered to support AI must also come from diverse groups of people, if the resulting algorithm is going to fully meet its potential.
For example, a small homogenous group designing a facial recognition program for a large heterogeneous group will miss the target if data about a variety of faces from the larger group is not represented. In other words, the algorithm is only as bias-free as the sources of data and the data sets.
To be effective, creators of AI-related applications need to be as diverse as the people using them.
Eighteen-year-old Betelhem Dessie is founder and chief executive officer of iCog-Anyone Can Code in Ethiopia. She also co-founded Solve IT, which provides technical resources to develop local solutions for community problems.
As different AI tools were being developed, I observed a lack of contributions from people of color and women, Dessie notes. The solution, I thought, was having early childhood tech education but also inspiring girls who are already in the workforce to pursue these types of career paths. The most rewarding part of my work is inspiring others particularly women and girls to pursue careers in technology.
But gender and diversity issues remain.
A 2019 article written by Kari Paul for The Guardian states the lack of diversity in the AI field has reached a moment of reckoning, according to findings by a New York University research center. The survey of more than 150 studies and reports, published by AI Now Institute, found that diversity disaster has contributed to flawed systems that perpetuate gender and racial biases, Paul writes.
One remedy is educating girls including girls of color sooner and more widely about the field and making appropriate educational opportunities and career guidance accessible to them early on.
Mastery of complex subjects is required, so girls must continue building on their basic math and science education, and intensify their focus as early as seventh grade. High school and certainly college may be too late to capture their interest so they can acquire the needed foundation.
Girls interested in AI will need to write code, algorithms and source data sets. Beyond that, they will need to understand and eliminate bias in data sets, as well as in applications designed to serve humanity.
Along with a rigorous early academic foundation, girls must develop social and emotional learning skills to help fuel their careers. These skills will prove beneficial whether they are leading a team or a company or programming soft skills into a robot.
A proven method for inspiring girls is to bring female role models working in AI into your classroom. Give girls a chance to ask these experts questions about their careers and personal stories. One way to start your search for experts is to inquire at universities and businesses from your local community; network with those professionals to build your sources.
Girls visions for the future are boosted when theyre introduced to female role models who demonstrate rewarding careers in the AI field and show that girls can excel in this arena.
As women enter the profession and assume leadership roles, society is seeing the advantages of perspectives they bring to AI systems.
For example, Mishra builds new systems that enhance peoples lives and give them a positive experience of interacting with technology. AI is ingrained into every aspect of our lives now and will be even more so in the future, says Mishra. Her advice to girls is to dream big: ambition is attractive and inspires those around you.
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Getting Girls Into the Artificial Intelligence Pipeline - Medium
Artificial Intelligence Can’t Deal With Chaos, But Teaching It Physics Could Help – ScienceAlert
While artificial intelligence systems continue to make huge strides forward, they're still not particularly good at dealing with chaos or unpredictability. Now researchers think they have found a way to fix this, by teaching AI about physics.
To be more specific, teaching them about the Hamiltonian function, which gives the AI information about the entirety of a dynamic system: all the energy contained within it, both kinetic and potential.
Neural networks, designed to loosely mimic the human brain as a complex, carefully weighted type of AI, then have a 'bigger picture' view of what's happening, and that could open up possibilities for getting AI to tackle harder and harder problems.
"The Hamiltonian is really the special sauce that gives neural networks the ability to learn order and chaos," says physicist John Lindner, from North Carolina State University.
"With the Hamiltonian, the neural network understands underlying dynamics in a way that a conventional network cannot. This is a first step toward physics-savvy neural networks that could help us solve hard problems."
The researchers compare the introduction of the Hamiltonian function to a swinging pendulum it's giving AI information about how fast the pendulum is swinging and its path of travel, rather than just showing AI a snapshot of the pendulum at one point in time.
If neural networks understand the Hamiltonian flow so where the pendulum is, in this analogy, where it might be going, and the energy it has then they are better able to manage the introduction of chaos into order, the new study found.
Not only that, but they can also be built to be more efficient: better able to forecast dynamic, unpredictable outcomes without huge numbers of extra neural nodes. It helps AI to quickly get a more complete understanding of how the world actually works.
A representation of the Hamiltonian flow, with rainbow colours coding a fourth dimension. (North Carolina State University)
To test their newly improved AI neural network, the researchers put it up against a commonly used benchmark called the Hnon-Heiles model, initially created to model the movement of a star around a sun.
The Hamiltonian neural network successfully passed the test, correctly predicting the dynamics of the system in states of order and of chaos.
This improved AI could be used in all kinds of areas, from diagnosing medical conditions to piloting autonomous drones.
We've already seen AI simulate space, diagnose medical problems, upgrade movies and develop new drugs, and the technology is, relatively speaking, just getting started there's lots more on the way. These new findings should help with that.
"If chaos is a nonlinear 'super power', enabling deterministic dynamics to be practically unpredictable, then the Hamiltonian is a neural network 'secret sauce', a special ingredient that enables learning and forecasting order and chaos," write the researchers in their published paper.
The research has been published in Physical Review E.
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Artificial Intelligence Can't Deal With Chaos, But Teaching It Physics Could Help - ScienceAlert
Artificial intelligence is on the rise – Independent Australia
New developments and opportunities are opening up in artificial intelligence, says Paul Budde.
I RECENTLY followed a "lunch box lecture", organised by the University of Sydney.In thetalk, Professor Zdenka Kuncic explored the very topical issue of artificial intelligence.
The world is infatuated with artificial intelligence (AI), and understandably so, given its super-human ability to find patterns in big data as we all notice when using Google, Facebook, Amazon, eBay and so on. But the so-called general intelligence that humans possess remains elusive forAI.
Interestingly, Professor Kuncic approached this topic from a physics perspective. By viewing the brains neural network as a physical hardware system, rather than the algorithm-based software as for example AI-based research used insocial media.
Her approach reveals clues that suggest the underlying nature of intelligence is physical.
Basically, what this means is that a software-based system will require ongoing input from software specialists to make updates based on new developments.Her approach, however, is to look at a physical system based on nanotechnology and use these networks as self-learning systems, where human intervention is no longer required.
Imagine the implications of the communications technologies that are on the horizon, where basically billions of sensors and devices will be connected to networks.
The data from these devices need to be processed in real-time and dynamic decisions will have to be made without human intervention. The driverless car is, of course, a classic example of such an application.
The technology needed to make such a system work will have to be based on edge technology in the device out there in the field. It is not going to work in any scaled-up situation if the data from these devices will first have to be sent to the cloud for processing.
Nano networks are a possible solution for such situations. A nanonetwork or nanoscale network is a set of interconnected nanomachines (devices a few hundred nanometers or a few micrometres at most in size), which at the moment can perform only very simple tasks such as computing, data storing, sensing and actuation.
However, Professor Kuncik expects that new developments will see expanded capabilities of single nanomachines both in terms of complexity and range of operation by allowing them to coordinate, share and fuse information.
Professor Kuncik concentrates, in her work, on electromagnetics for communication in the nanoscale.
This is commonly defined as the 'transmission and reception of electromagnetic radiation from components based on novel nanomaterials'.
Professor Kuncik mentioned this technology was still in its infancy. She was very upbeat about the future, based on the results of recent research and international collaboration. Advancements in carbon and molecular electronics have opened the door to a new generation of electronic nanoscale components such as nanobatteries, nanoscale energy harvesting systems, nano-memories, logical circuitry in the nanoscale and even nano-antennas.
From a communication perspective, the unique properties observed in nanomaterials will decide on the specific bandwidths for the emission of electromagnetic radiation, the time lag of the emission, or the magnitude of the emitted power forinput energy.
The researchers are looking at the output of these nanonetworks rather than the input. The process is analogue rather than digital. In other words, the potential output provides a range of possible choices, rather than one (digital) outcome.
The trick is to understand what choices are made in a nanonetwork and why.
There are two main alternatives for electromagnetic communication in the nanoscale the one as pursued by Professor Kuncik the other one being based on molecular communication.
Nanotechnology could have an enormous impact on for example the future of 5G. If nanotechnology can be included in the various Internet of Things (IoT) sensors and devices than this will open an enormous amount of new applications.
It has been experimentally demonstrated that is possible to receive and demodulate an electromagnetic wave by means of a nano radio.
Second, graphene-based nano-antennas have been analysed as potential electromagnetic radiators in the terahertz band.
Once these technologies are further developed and commercialised, we can see a revolution in edge-computing.
Paul Buddeis an Independent Australia columnist and managing director ofPaul Budde Consulting, an independent telecommunications research and consultancy organisation. You can follow Paul on Twitter@PaulBudde.
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Artificial intelligence is on the rise - Independent Australia
Powerful optical sorting technologies, artificial intelligence and robotics reduce contamination for recycling operations – Recycling Product News
"With the combination of technologies in the new AUTOSORT, we are able to see a different kind of chemical (makeup) of the same material," explains Atienza. For example, for different PET bottles and trays, "It's all PET for NIR, but combined with the other technologies, we are able to see the difference.
"We are able to identify and sort, for example, different kinds of PET, such as trays, bottles, etc., which is very important for recyclers, because the quality of the end product can change when you have a lot of trays on the PET line. We definitely see more and more PET trays on the market," which he points to as one of the drivers for the development of this technology. Key to taking material detection to the next level is DEEP LAISER, which is an optional add-on to the system.
According to TOMRA, the DEEP LAISER is one of the first fully integrated systems of its kind, and stands out for its compactness and flexible range of uses, with object recognition that enables a deeper sorting sharpness. This includes the use of Artificial Intelligence (AI) via Deep Learningwhich TOMRA first introduced in 2019.
In basic terms, FLYING BEAM's NIR/VIS (Visual Spectrum) technology makes an overall determination of what kind of plastic and colour is on the belt, and SHARP EYE looks more closely at the chemical composition, to give a deeper sort, differentiating between different types of PET, for example. When you add on the DEEP LAISER component, it detects material that other optical sorters cannot detect.
"It is a laser combined with an artificially intelligent camera, using our IOR software," explains Atienza. "With DEEP LAISER we are sorting material on the conveyor that the FLYING BEAM and SHARP EYE cannot detect, such as black plastic or glass." He says it detects and sorts these materials out as an impurity, and that due to the IOR, the system sees, and is able to learn about, a very wide range of different types of material, detecting chemical composition, shapes and colour.
"With our AUTOSORT technology we can see types of materials that many other technologies cannot see. For example, our machine can differentiate LDPE, (low density polyethylene) from linear LLDPE. It's mostly the same material, because it is all LDPE, but they have differences, and we are able to see it, even at 6m/second."
Another example is LDPE and HDPE film, he says. "With films, you have low-density and high-density polyethylene, and we can see that difference, because our sensor has very high detection sensitivity."
In addition to its vastly increased sorting capacity overall, the new AUTOSORT ValveBlock geometry system is also 25% more powerful, with a reduction in air consumption of about 15%, compared to the previous version. This contributes to lower operating costs, including less electrical consumption, and notably this new generation AUTOSORT also auto calibrates every millisecond for optimal operation through changing conditions.
"This is very important," says Atienza, especially because NIR is very sensitive to temperature. "The auto calibration every single millisecond allows us to operate efficiently through a wide range of temperatures during a day," he says.
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Powerful optical sorting technologies, artificial intelligence and robotics reduce contamination for recycling operations - Recycling Product News
Artificial intelligence helping NASA design the new Artemis moon suit – SYFY WIRE
Last fall, NASA unveiled the new suits that Artemis astronauts will wear when they take humanitys first steps on the lunar surface for the first time since way back in 1972. The look of theA7LB pressure suit variants that accompanied those earlierastronauts to the Moon, and later to Skylab, has since gone on to signify for many the definitive, iconic symbol of humanitys most ambitiously-realized space dreams.
With Artemis 2024 launch target approaching, NASAs original Moon suit could soon be supplanted in the minds of a new generation of space dreamers with the xEMU, the first ground-up suit made for exploring the lunar landscape since Apollo 17s Eugene Cernan and Harrison Schmitt took humanitys last Moon walk (to date). Unlike those suits, the xEMUs design is getting an assist from a source of "brain" power that simply wasnt available back then: artificial intelligence.
Specifically, AI is reportedly crunching numbers behind the scenes to help engineer support components for the new, more versatile life support system thatll be equipped to the xEMU (Extravehicular Mobility Unit) suit. WIRED reports that NASA is using AI to assist the new suits life support system in carrying out its more vital functions while streamlining its weight, component size, and tolerances for load-bearing pressure, temperature, and the other physical demands that a trip to the Moon (and back) imposes.
Recruiting AI isnt just about speed though speed is definitely one of the perks to meeting NASAs ambitious 2024 timeline and all that lies beyond. The machines iterative process is 100 or 1,000 times more than we could do on our own, and it comes up with a solution that is ideally optimized within our constraints, Jesse Craft, a senior design engineer at a Texas-based contractor working on the upgraded version of the xEMU suit, told WIRED.
But in some instances, AI even raises the bar for quality, as Craft also noted. Were using AI to inspire design, he explained. We have biases for right angles, flat surfaces, and round dimensions things youd expect from human design. But AI challenges your biases and allows you to see new solutions you didnt see before.
So far, NASA is relying on AI only to design physical brackets and supports for the life support system itself in other words, not the kind of stuff that might spell life or death in the event of failure. But that approach is already paying off by cutting mass without sacrificing strength, yielding component weight reductions of up to 50 percent, according to the report.
Even at 1/6 the gravity that astronauts experience back on Earth, that kind of small weight savings here and there can add up to make a big difference on the Moon. And even a slight slimming down cant hurt the xEMUs chances at perhaps becoming a new standard bearer in space fashion, as Artemis captivates a new generation with its sights set on the stars.
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Artificial intelligence helping NASA design the new Artemis moon suit - SYFY WIRE
La Sierra University to Deploy OMNIQ’s Artificial Intelligence Cloud Based Access Control Parking and Security System – GlobeNewswire
SALT LAKE CITY, June 22, 2020 (GLOBE NEWSWIRE) -- OMNIQ, Inc. (OTCQB:OMQS) (OMNIQor the Company), announces that it has received a follow-on order to upgrade the access control and parking system at La Sierra University in Riverside, California with the new PERCS system the Company acquired with its acquisition of the assets of Eyepax USA.
Based on OMNIQs AI-Machine Vision technology, PERCS improves safety and provides a wide range of operational benefits, including: seamless account management for users, increased efficiency and time management for operators, as well as enhanced revenue generation and elevated customer satisfaction levels. The system incorporates parking access and revenue enforcement capabilities within a single platform, using Machine Vision vehicle recognition technology, allowing the administrator to manage lanes and track revenue from one web portal, using a dashboard for the monitoring of all activity and transactions for visitors and transient parkers. PERCS enables the virtual management of permitting, citations, occupancy and access control, enhancing efficiencies and safety for customers including municipalities, universities, medical centers and public parking operations across the U.S.
Shai Lustgarten, CEO of OMNIQ commented: This order follows the recently announced selection of PERCS by the city of San Mateo, California and the selection of our Quest Shield solution by the Talmudic Academy in Baltimore, demonstrating growing market recognition of the benefits of our AI-Machine Vision based technology. Were pleased to have this opportunity to work with La Sierra University. We believe the Universitys decision to install the PERCS platform demonstrates their confidence in our capabilities and their recognition of the value added by our state-of-the-art solutions for permitting and enforcement, access control and automated parking. Our Machine Vision technology is implemented for public safety on school campuses, parking management and control as well as in sensitive areas for homeland security purposes.
We are excited about the momentum we are seeing in the public safety/smart city market, which we believe represents a significant potential for growth in revenue and profitability. Our solutions provide value in a variety of applications and we remain focused on continuing to innovate and leverage our AI-Machine Vision capabilities to meet the changing demands of our growing Public Safety, Automation of Parking and Supply Chain verticals and our broad customer base including many Fortune 500 customers, Mr. Lustgarten concluded.
About OMNIQ, Corp.OMNIQ Corp. (OMQS) provides computerized and machine vision image processing solutions that use patented and proprietary AI technology to deliver data collection, real time surveillance and monitoring for supply chain management, homeland security, public safety, traffic & parking management and access control applications. The technology and services provided by the Company help clients move people, assets and data safely and securely through airports, warehouses, schools, national borders, and many other applications and environments. OMNIQs customers include government agencies and leading Fortune 500 companies from several sectors, including manufacturing, retail, distribution, food and beverage, transportation and logistics, healthcare, and oil, gas, and chemicals. Since 2014, annual revenues have grown to more than $50 million from clients in the USA and abroad. The Company currently addresses several billion-dollar markets, including the Global Safe City market, forecast to grow to $29 billion by 2022, and the Ticketless Safe Parking market, forecast to grow to $5.2 billion by 2023.
Information about Forward-Looking StatementsSafe Harbor Statement under the Private Securities Litigation Reform Act of 1995. Statements in this press release relating to plans, strategies, economic performance and trends, projections of results of specific activities or investments, and other statements that are not descriptions of historical facts may be forward-looking statements within the meaning of the Private Securities Litigation Reform Act of 1995, Section 27A of the Securities Act of 1933 and Section 21E of the Securities Exchange Act of 1934. This release contains forward-looking statements that include information relating to future events and future financial and operating performance. The words anticipate, may, would, will, expect, estimate, can, believe, potential and similar expressions and variations thereof are intended to identify forward-looking statements. Forward-looking statements should not be read as a guarantee of future performance or results, and will not necessarily be accurate indications of the times at, or by, which that performance or those results will be achieved. Forward-looking statements are based on information available at the time they are made and/or managements good faith belief as of that time with respect to future events, and are subject to risks and uncertainties that could cause actual performance or results to differ materially from those expressed in or suggested by the forward-looking statements. Important factors that could cause these differences include, but are not limited to: fluctuations in demand for the Companys products particularly during the current health crisis , the introduction of new products, the Companys ability to maintain customer and strategic business relationships, the impact of competitive products and pricing, growth in targeted markets, the adequacy of the Companys liquidity and financial strength to support its growth, the Companys ability to manage credit and debt structures from vendors, debt holders and secured lenders, the Companys ability to successfully integrate its acquisitions, and other information that may be detailed from time-to-time in OMNIQ Corp.s filings with the United States Securities and Exchange Commission. Examples of such forward looking statements in this release include, among others, statements regarding revenue growth, driving sales, operational and financial initiatives, cost reduction and profitability, and simplification of operations. For a more detailed description of the risk factors and uncertainties affecting OMNIQ Corp., please refer to the Companys recent Securities and Exchange Commission filings, which are available at http://www.sec.gov. OMNIQ Corp. undertakes no obligation to publicly update or revise any forward-looking statements, whether as a result of new information, future events or otherwise, unless otherwise required by law.
Investor Contact: John Nesbett/Jen BelodeauIMS Investor Relations203.972.9200jnesbett@institutionalms.com
5 Reasons Artificial Intelligence Will Improve Greenhouse Production – Greenhouse Grower
Artificial intelligence (AI) involves using computers to do things that traditionally require human intelligence. This means creating algorithms to classify, analyze, and draw predictions from data. It also involves acting on data, learning from new data, and improving over time.
Thats the definition of AI, at least. But what does it actually mean for greenhouse growers?
According to Gursel Karacor, Senior Data Scientist at Grodan, a supplier of sustainable stone wool growing media solutions for the horticulture market, greenhouses will, to a large extent, be autonomous in the near future.
My mission is the realization of autonomous greenhouses through the use of all this data with state-of-the-art machine learning methodologies, Karacor says. I want to realize this goal step-by-step in five years.
Click here to learn more about why AI will change the way you work, for the better.
Gursel Karacor is a Senior Data Scientist with Grodan. See all author stories here.
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5 Reasons Artificial Intelligence Will Improve Greenhouse Production - Greenhouse Grower
Coronavirus tests the value of artificial intelligence in medicine – The Star Online
Dr Albert Hsiao and his colleagues at the UC San Diego health system in the United States had been working for 18 months on an artificial intelligence (AI) program designed to help doctors identify pneumonia on a chest X-ray. When the coronavirus hit the United States, they decided to see what it could do.
The researchers quickly deployed their program, which dots X-ray images with spots of colour where there may be lung damage or other signs of pneumonia. It has now been applied to more than 6,000 chest X-rays, and its providing some value in diagnosis, said Dr Hsiao, the director of UCSDs augmented imaging and artificial intelligence data analytics laboratory.
His team is one of several around the country that has pushed AI programs into the Covid-19 crisis to perform tasks like deciding which patients face the greatest risk of complications and which can be safely channeled into lower-intensity care.
The machine-learning programs scroll through millions of pieces of data to detect patterns that may be hard for clinicians to discern.
Yet few of the algorithms have been rigorously tested against standard procedures.
So while they often appear helpful, rolling out the programs in the midst of a pandemic could be confusing to doctors and dangerous for patients, some AI experts warn.
AI is being used for things that are questionable right now, said Dr Eric Topol, director of the Scripps Research Translational Institute and author of several books on health IT.
Topol singled out a system created by Epic, a major vendor of electronic health records software, that predicts which coronavirus patients may become critically ill. Using the tool before it has been validated is pandemic exceptionalism, he said.
Epic said the companys model had been validated with data from more than 16,000 hospitalised Covid-19 patients in 21 healthcare organizations.
No research on the tool has been published for independent researchers to assess, but in any case, it was developed to help clinicians make treatment decisions and is not a substitute for their judgment, said James Hickman, a software developer on Epics cognitive computing team.
Others see the Covid-19 crisis as an opportunity to learn about the value of AI tools.My intuition is its a little bit of the good, bad and ugly, said Eric Perakslis, a data science fellow at Duke University and former chief information officer at the Food and Drug Administration. Research in this setting is important.
Nearly US$2bil (RM8.5bil) poured into companies touting advancements in healthcare AI in 2019.
Investments in the first quarter of 2020 totalled US$635mil (RM2.7bil), up from US$155mil (RM663mil) in the first quarter of 2019, according to digital health technology funder Rock Health.
At least three healthcare AI technology companies have made funding deals specific to the Covid-19 crisis, including Vida Diagnostics, an AI-powered lung-imaging analysis company, according to Rock Health.
Overall, AIs implementation in everyday clinical care is less common than hype over the technology would suggest. Yet the coronavirus has inspired some hospital systems to accelerate promising applications.
UCSD sped up its AI imaging project, rolling it out in only two weeks.
Dr Hsiaos project, with research funding from Amazon Web Services, the University of California and the National Science Foundation, runs every chest X-ray taken at its hospital through an AI algorithm.
While no data on the implementation has been published yet, doctors report that the tool influences their clinical decision- making about a third of the time, said Dr Christopher Longhurst, UCSD Healths chief information officer.
The results to date are very encouraging, and were not seeing any unintended consequences, he said.
Anecdotally, were feeling like its helpful, not hurtful.
AI has advanced further in imaging than in other areas of clinical medicine because radiological images have tons of data for algorithms to process, and more data makes the programs more effective, Longhurst said.
But while AI specialists have tried to get AI to do things like predict sepsis and acute respiratory distress researchers at Johns Hopkins University recently won a National Science Foundation grant to use it to predict heart damage in Covid-19 patients it has been easier to plug it into less risky areas such as hospital logistics.
In New York City, two major hospital systems are using AI-enabled algorithms to help them decide when and how patients should move into another phase of care or be sent home.
At Mount Sinai Health System, an artificial intelligence algorithm pinpoints which patients might be ready to be discharged from the hospital within 72 hours, said Robbie Freeman, vice president of clinical innovation at Mount Sinai.
Freeman described the AIs suggestion as a conversation starter, meant to help clinicians working on patient cases decide what to do. AI isnt making the decisions.
NYU Langone Health has developed a similar AI model. It predicts whether a Covid-19 patient entering the hospital will suffer adverse events within the next four days, said Dr Yindalon Aphinyanaphongs, who leads NYU Langones predictive analytics team.
The model will be run in a four- to six-week trial with patients randomised into two groups: one whose doctors will receive the alerts, and another whose doctors will not.
The algorithm should help doctors generate a list of things that may predict whether patients are at risk for complications after theyre admitted to the hospital, Aphinyanaphongs said.
Some health systems are leery of rolling out a technology that requires clinical validation in the middle of a pandemic. Others say they didnt need AI to deal with the coronavirus.
Stanford Health Care is not using AI to manage hospitalised patients with Covid-19, said Ron Li, the centres medical informatics director for AI clinical integration.
The San Francisco Bay Area hasnt seen the expected surge of patients who would have provided the mass of data needed to make sure AI works on a population, he said.
Outside the hospital, AI-enabled risk factor modelling is being used to help health systems track patients who arent infected with the coronavirus but might be susceptible to complications if they contract Covid-19.
At Scripps Health, clinicians are stratifying patients to assess their risk of getting Covid-19 and experiencing severe symptoms using a risk-scoring model that considers factors like age, chronic conditions and recent hospital visits.
When a patient scores seven or higher, a triage nurse reaches out with information about the coronavirus and may schedule an appointment.
Though emergencies provide unique opportunities to try out advanced tools, its essential for health systems to ensure doctors are comfortable with them, and to use the tools cautiously, with extensive testing and validation, Topol said.
When people are in the heat of battle and overstretched, it would be great to have an algorithm to support them, he said.
We just have to make sure the algorithm and the AI tool isnt misleading, because lives are at stake here. Kaiser Health News/Los Angeles Times/Tribune News Service
(Kaiser Health News (KHN) is a US national health policy news service. It is an editorially independent programme of the Henry J. Kaiser Family Foundation.)
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Coronavirus tests the value of artificial intelligence in medicine - The Star Online
Do you wash your hands correctly? Artificial intelligence will check it out – Emergency-Live
A monitor with artificial intelligence will ensure the correct way to wash your hands. this idea comes from Japan. The importance of washing hands cannot be underestimated, and not because of the coronavirus, but for our hygiene and for others.
It is the Japanese giant Fujitsu Ltd. that developed this method to push people in hands cleaning. This technology will be for healthcare professionals, hotels and the food industry.
This kind of technology has been developed before the pandemic at the request of many Japanese companies interested in implementing hygiene regulations. This way of monitoring is able to recognize complex hand movements and detect when people do not use soap or do not clean properly, following the six-step protocol indicated by the Ministry of Health.
Genta Suzuki, a senior researcher at the group declared, the food and healthcare professionals we have tested the technology on are eager to use the system as soon as possible, but we still dont know when it will be ready for the market. In order to train that artificial intelligence machine,Fujitsu developers have created 2,000 handwashing models using different soaps and sinks.
SOURCE
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Do you wash your hands correctly? Artificial intelligence will check it out - Emergency-Live
Microsoft CTO Kevin Scott Believes Artificial Intelligence Will Help Reprogram The American Dream – Forbes
Is artificial intelligence a key ingredient to inspire rural children to become entrepreneurs?
Microsoft Chief Technology Officer Kevin Scott rise to his current post is about as unlikely as you will find. He grew up in Gladys, Virginia, a town of a few hundred people. He loved his family and his hometown to such an extent that he did not aspire to leave. He caught the technology bug in the 1970s by chance, and that passion would provide a ticket to bigger places that he did not initially seek.
The issue was one of opportunity. In his formative years, jobs were decreasing in places like Gladys just as they were increasing dramatically in tech hubs like Silicon Valley. After pursuing a PhD in computer science at the University of Virginia, he left in 2003 prior to completing his dissertation to join Google. He would rise to become a Senior Engineering Director there. He left Google for LinkedIn in 2011. He would eventually rise to become the Senior Vice President of Engineering & Operations at LinkedIn. From LinkedIn he joined Microsoft three and a half years ago as CTO. He is deeply satisfied with the course of his career and its trajectory, but part of him laments that it took him so far from his roots and the hometown that he loves.
As he reflected further on this conundrum, he put his thoughts to paper and published the book, Reprogramming the American Dream in April, co-authored by Greg Shaw. As he noted in a conversation I recently had with him, Silicon Valley is a perfectly wonderful place, but we should be able to create opportunity and prosperity everywhere, not just in these coastal urban innovation centers.
Scott believes that machine learning and artificial intelligence will be key ingredients to aiding an entrepreneurial rise in smaller towns across the United States. These advances will place less of a burden on companies to hire employees in the small towns, as some technical development will be conducted by the bots. He also hopes that as some of these businesses blossom, more kids will be inspired to start their own businesses powered by technology, creating a virtuous cycle of sorts.
The biggest impediment to this dream boils down to more basic elements, however. There is just no way that you can reasonably educate your kids and attract and retain really great employees to these jobs and to even run the businesses themselves unless you have good broadband connectivity in all of these places, notes Scott. 25 million people in the United States do not have adequate access to broadband. 19 million of those are in these rural communities. So that is something we definitely have to fix. Scott also says that there must be redoubled efforts for venture capitalists to invest in businesses in non-traditional towns and cities. He highlights the work that Steve Case has done with his Rise of the Rest Seed Fund through Revolution Capital.
Scott underscores that venture capital is not enough. It will require a private public partnership. I think we could choose to say that we want to pick one of these big, hairy, audacious goals that AI technologies and machine learning could help reach and pour a little bit of our national wealth into this in a coordinated way, says Scott. [We can] create a great collaboration between private companies, the academy and the government to solve a big problem for the public good like, potentially, ubiquitous high quality, low-cost health care. We could do something that is even better than the Apollo program.
Some might think that artificial intelligence is too esoteric and complicated to teach to children so that they are fluent enough to leverage the technology of the future. Scott argues otherwise. He says, If we can harness this ability that we have to teach each other, we can certainly teach machines how to solve problems, which makes programming or harnessing a computer's power even more accessible than it has ever been and certainly a thing and a set of skills that are absolutely approachable for even very young kids.
Scott and his wife have created the Scott Foundation, which helps create opportunities for children to achieve self-sufficiency and lifelong success. Not so surprisingly, Scott believe technology is a major ingredient of that future success, as well. His day job and his foundation work are sources of optimism. At a time when many lament that the rise of artificial intelligence will eliminate many jobs, Scott believes those losses will be more than offset by those new businesses created in all corners of the United States leveraging AI and other technical advances.
Peter Highis President ofMetis Strategy, abusiness and IT advisory firm. His has written two bestselling books, moderates theTechnovationpodcast series, and speaks at conferences around the world. Follow himon Twitter@PeterAHigh.
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Microsoft CTO Kevin Scott Believes Artificial Intelligence Will Help Reprogram The American Dream - Forbes