Category Archives: Ai

Analysing big data to optimise assets and leverage predictive algorithms and artificial intelligence (AI) to boost efficiency and enhance capacity will be essential skills for the tech-driven future.

Its time for recruiting teams to leverage the power of AI software to help them source, filter, and hire the best applications. The winners of this category at the HR Vendors of the Year 2021 provide the best tools that are designed to simplify and make mainstream the process of recruitment, providing HR professionals a game-changing way to hire top talent from any field.

Gold: SHLhttps://www.shl.com/This email address is being protected from spambots. You need JavaScript enabled to view it.

Silver: The Talent Gameshttps://thetalentgames.com/This email address is being protected from spambots. You need JavaScript enabled to view it.

Bronze: TALENTCLOUD AIhttps://www.talentcloud.ai/ This email address is being protected from spambots. You need JavaScript enabled to view it.

Gold: SHLhttps://www.shl.com/

Silver: PeopleStronghttps://www.peoplestrong.com/sg/This email address is being protected from spambots. You need JavaScript enabled to view it.

Bronze: The Talent Gameshttps://thetalentgames.com/This email address is being protected from spambots. You need JavaScript enabled to view it.

To find out more about the awards or to participate in the 2022 edition:

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Best AI recruitment tools in Malaysia and Singapore - Human Resources Online

Autonomous driving technology company Pony.ai has introduced its 6th generation autonomous driving (AD) system, with leading-edge sensors, NVIDIA DRIVE computing platform solutions, and styling and design features for L4 automotive-grade mass production fleets. The first model equipped with this system, Toyota S-AM, a seven-seat hybrid electric platform for autonomous mobility (earlier post), will start road testing in China this year and be deployed within Pony.ais public-facing robotaxi operations in the first half of 2023.

Pony.ai next-generation autonomous driving system on Toyota Sienna Autono-MaaS (S-AM) platform. (

Pony.ai started to develop the self-driving capabilities for the S-AM China model together with Toyota as early as 2019. Pony.ai believes this highly customized S-AMwith its dual redundancy systemwill provide better functionality and control performance for Level 4 AD development. In addition, as an MPV model, the S-AM has a flexible riding configuration to meet numerous family and professional usage scenarios.

The sensor solution, comprising 23 sensors, includes four solid-state LiDARs on the roof, covering a 360 field of view; three near-range LiDARs on the body of the vehicle, covering the blind spots of the roof LiDARs; four millimeter-wave radars located at the corners of the roof; one long-range forward-facing millimeter-wave radar, and 11 cameras deployed around the roof and body of the vehicle (in a combination of wide angle, super wide angle, middle and long-range, and traffic light detection cameras).

The central mechanical LiDAR has been replaced with solid-state LiDARs. The self-developed traffic light camera has a resolution rate 1.5 times that of the previous generation. Coupled with its in-house Sensor Fusion technology, Pony.ai will significantly reduce the cost associated with the solution by utilizing mass-produced, automotive-grade sensors.

Pony.ai is also introducing its next-generation mass-production autonomous computing unit, using the NVIDIA DRIVE Orin. This builds on Pony.ai and NVIDIAs relationship, which dates back to 2017 when the company first adopted the NVIDIA DRIVE platform.

The Pony.ai next-generation autonomous computing platform, built on NVIDIA DRIVE Orin. NVIDIA DRIVE Orin achieves 254 TOPS (trillion operations per second) of performance, and includes comprehensive CUDA and NVIDIA deep learning accelerator (NVDLA) toolchain support.

Pony.ais autonomous computing unit features low latency, high performance, and high reliability. The company is one of the first in the autonomous vehicle industry to create a product portfolio featuring multiple configurations with one or more DRIVE Orin processors and automotive-grade NVIDIA Ampere architecture GPUs. This enables scalable deployment across self-driving trucks and robotaxis, and accelerates Pony.ais future of a robust, mass-production platform for autonomous vehicles.

Compared with the previous generation computing platform, the new generation is expected to have greater than a 30% increase in computing power, at least 30% less weight, and more than a 30% reduction in cost.

The new generation system has complete redundancy in place, which maximizes safety and enables the vehicle to pull over or stop safely in case of an emergency.

In the S-AM model, Pony.ai is also unveiling a concept design for the new AD system, making the sensor suite more integrated and aesthetically pleasing while also more effective for mass production.

Among other design features, this concept design includes rooftop signaling units which have a horizontal lighting unit on the front and three vertically placed lighting stripes on the rear, to give the vehicle an elegant futuristic touch. By using different colors and lighting combinations, the rooftop signaling units can provide external communication and interaction, demonstrating the robotaxis status and intentions.

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Pony.ai introduces 6th generation autonomous driving system design; road-testing in Toyota S-AM this year - Green Car Congress

For the first time ever, scientists have shown that artificial intelligence (AI) could lead to better ways to predict the onset and course of cardiovascular disease.

A new AI-based system developed by scientists at University of Utah Health (Salt Lake City, Utah, USA) mines Electronic Health Records (EHRs) and assesses the combined effects of various risk factors to better predict the onset and outcomes of heart disease. The researchers developed unique computational tools to precisely measure the synergistic effects of existing medical conditions on the heart and blood vessels.

The researchers say this comprehensive approach could help physicians foresee, prevent, or treat serious heart problems, perhaps even before a patient is aware of the underlying condition. Although the study only focused on cardiovascular disease, the researchers believe it could have far broader implications. In fact, they suggest that these findings could eventually lead to a new era of personalized, preventive medicine. Doctors would proactively contact patients to alert them to potential ailments and what can be done to alleviate the problem.

Current methods for calculating the combined effects of various risk factors - such as demographics and medical history - on cardiovascular disease are often imprecise and subjective. As a result, these methods fail to identify certain interactions that could have profound effects on the health of the heart and blood vessels. To more accurately measure how these interactions, also known as comorbidities, influence health, the researchers used machine learning software to sort through more than 1.6 million EHRs after names and other identifying information were deleted.

These electronic records, which document everything that happens to a patient, including lab tests, diagnoses, medication usage, and medical procedures, helped the researchers identify the comorbidities most likely to aggravate a particular medical condition such as cardiovascular disease. In their current study, the researchers used a form of AI called probabilistic graphical networks (PGM) to calculate how any combination of these comorbidities could influence the risks associated with heart transplants, congenital heart disease, or sinoatrial node dysfunction (SND, a disruption or failure of the hearts natural pacemaker).

Among adults, the researchers found that individuals who had a prior diagnosis of cardiomyopathy (disease of the heart muscle) were at 86 times higher risk of needing a heart transplant than those who didnt. They also found that those who had viral myocarditis had about a 60 times higher risk of requiring a heart transplant. In addition the usage of milrinone, a vasodilating drug used to treat heart failure, pushed the transplant risk 175 times. This was the strongest individual predictor of heart transplant. In some instances, the combined risk was even greater. For instance, among patients who had cardiomyopathy and were taking milrinone, the risk of needing a heart transplant was 405 times higher than it was for those whose hearts were healthier.

Comorbidities had a significantly different influence on the transplant risk among children, according to the researchers. Overall, the risk of pediatric heart transplant ranged from 17 to 102 times higher than children who didnt have pre-existing heart conditions, depending on the underlying diagnosis. The researchers also examined influences that a mothers health during pregnancy had on her children. Women who had high blood pressure during pregnancy were about twice as likely to give birth to infants who had congenital heart and circulatory problems. Children with Down syndrome had about three times greater risk of having a heart anomaly. Infants who had Fontan surgery, a procedure that corrects a congenital blood flow defect in the heart, were about 20 times more likely to develop SND heart rate dysfunction than those who didnt need the surgery. The researchers also detected important demographic differences. For instance, a Hispanic patient with atrial fibrillation (rapid heartbeat) had twice the risk of SND compared with Blacks and Whites, who had similar medical histories.

We can turn to AI to help refine the risk for virtually every medical diagnosis, said Martin Tristani-Firouzi, M.D. the studys corresponding author and a pediatric cardiologist at U of U Health. The risk of cancer, the risk of thyroid surgery, the risk of diabetesany medical term you can imagine.

This novel technology demonstrates that we can estimate the risk for medical complications with precision and can even determine medicines that are better for individual patients, said Josh Bonkowsky, M.D. Ph.D., Director of the Primary Childrens Center for Personalized Medicine, who believes this research could lead to development of a practical clinical tool for patient care.

Related Links:University of Utah Health

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AI Predicts Cardiovascular Disease Before Patient Becomes Aware of Underlying Condition - HospiMedica

Humans have been obsessed with immortality since time immemorial. While immortality continues to be a distant dream, science has taken strides in getting humans close to immortality--by increasing the life expectancy and making sure people feel younger, longer. But the secret to immortality lies in understanding the genomics of an individual, observing not just how to treat them for a disease but preventing the disease from happening at the first place.

Precision medicine is an area that's focused on getting you custom-made medicine based on not just your medical history but also your family background, work history, living situation etc. This ensures that for the same problem two different individuals with widely different backgrounds are not given the same one-size-fits-all medicine--ensuring you respond to medication better and there's no guess work in getting you the right treatment.

This is where artificial intelligence comes to the rescue. AI's use in bespoke medicines can truly revolutionize the healthcare industry and that's why in today's lead story, we delve into this topic and explain the role AI really plays in the future of medicine.

Do share your thoughts.

RegardsVarun AggarwalEditor, ETCIOvarun.aggarwal@timesinternet.in

Would you taste these bespoke medicines made with the help of AI?

Researchers and experts have been using Artificial Intelligence in precision medicine to study and better understand genomics of an individual. This study and understanding helps to predict if you are going to have cancer or diabetes in the later years, basis which the right treatment can be given to prevent the disease.

Precision medicine goes deep in the DNA to look for the driver of a disease, what caused it, what drugs will the DNA respond to. This helps healthcare providers alter and tailor the treatments they offer.Read more..

AI at par with specialists in diagnosing prostate cancer: Study

Fifteen of the algorithms were selected to have their performance measured against diagnoses made by specialist uropathologists and general pathologists.Read More

IBM sells its Watson healthcare assets to Francisco Partners

Watson was one of IBM's highest-profile initiatives in recent years and a big bet on the growing healthcare sector. IBM currently has a market value of $108 billion, way behind its Cloud-computing rivals like Amazon and Microsoft. IBM Watson was one of the "strategic imperatives" under former CEO Ginni Rometty. Read more

Google AI tools bring back women in science to the fore

Google has developed new machine learning (ML) tools for use by curators at the Smithsonian in the US to help uncover and highlight many roles women have played in science over more than 174 years of history. Read more

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AI is bringing science fiction to life - ETCIO.com

A rise in financial crime since the COVID-19 pandemic and the burden of increasingly complex compliance requirements mean that many older anti money-laundering (AML) systems are not coping well.

Artificial intelligence is emerging as a potential game-changer. With its ability to rapidly process and spot anomalies in large volumes of data, AI could provide AML technologies with an upgrade and could unlock billions of dollars in value globally.

Many believe that, in order to reap the full benefit of an AI transition, organizations need high-quality data. But using dirty data as an excuse to shy away from AI to combat financial crime could prove riskier in the long run. Regardless of data quality, there may be potential benefits from an AI transformation.

What you believe to be dirty data might not be as dirty as you think. Anomalies like duplicate entries, misspelled or alternatively spelled words and names, punctuation errors, and some types of incomplete or outdated data that trip up older systems tend to not be a problem for AI. Advanced AI solutions are programmed to analyze data from multiple angles and sources, so they can produce meaningful results even in the presence of anomalies and exceptions.

For example, a person making a transfer could write their full first name one time and only their initials the next time. While less advanced systems would flag this as an anomaly, AI can corroborate the two entries and understand that they apply to the same person. The same goes for numerous other data discrepancies that could be keeping you away from AI but are not as problematic as you first thought.

The average bank processes thousands of transactions from multiple sources each second, so the scope for data error is staggering. Its no wonder that organizations worry about dirty data. An AI transformation can be done in phases that allow lessons learned in one area to be applied in other areas in the future.

Isolating SWIFT data from the masses of other data can be an ideal place to start. An advanced AI-based system can rapidly analyze terabytes of data to reveal suspicious transaction profiles and unlock hidden insights. Implementing AI in this one area alone could save significant compliance costs, reduce human error and free up hours of personnel time.

If your organization is truly plagued by dirty data, then any rules-based AML system will be compromised. Just living with dirty data is, therefore, a dangerous option, so cleaning up your data as much as possible should be a priority. Adopting an unbiased or intuitive AI system may offer value from the get-go even as your data cleanup continues in the background.

New AI technologies can bypass many of the dirty data issues that would trip up the legacy systems. This means that you dont need to wait until youve done a thorough data-cleansing to start protecting yourself against would-be money launderers and other criminals.

Idan Keret is chief customer officer for ThetaRay.

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Dirty data is not an AI-for-AML dealbreaker - BAI Banking Strategies

The paradigm shift in physical security from a reactionary and defensive proposition to a more proactive stance has characterized the migration of advanced analytics into almost every platform. Security end-user demanding systems that are faster and more intelligent, and at the same time cost-efficient and better suited for integrated solutions, are looking for more than technology that detects and deters. They now require systems that can digest vast amounts of data, then process autonomously monitored responses at lightning speeds. Upping the preemptive ante is a crucial step in the growth of intelligent physical security systems. And it is currently moving beyond the ubiquitous use of video surveillance analytics to other sensory devices at the edge and controlling access into and across the interior of a facility.

This improvement in security operations at the enterprise level is also addressing the convergence of physical and cybersecurity threats while easing the migration into a more defined digital world. As stated in a recent Security Industry Association (SIA) report: Security will move beyond video surveillance and access control with features such as autonomous reporting, monitoring and response. Autonomous security systems will communicate with each other and with people and will act on their own to collect more information and trigger complex safety protocols. Security technology will operate with predictive intelligence and will be deeply integrated with building systems, including HVAC, lighting, elevators and fire alarm and suppression. Remote monitoring capabilities will be the norm and this interconnectivity will bring the Internet of Things (IoT), 5G edge sensors, mobile devices, body-worn cameras, robots, drones, contextual conversational AI and augmented reality together to provide frictionless access, risk analysis, and predictive behaviors for proactive responses with real-time machine intelligence.

For example, says Sam Joseph, co-founder and chief executive officer of Hakimo, whose company develops software for the physical security industry powered by artificial intelligence (AI), suppose you work at Google or any big enterprise and you have offices in San Francisco and in New York, and suppose you are in the San Francisco office, or somewhere on the west coast, logging into your email using single sign-on or any other standard techniques. If someone uses your badge or a cloned badge of yours in New York, these two pieces of information are stored in completely separate systems. No one will notice that there is no system connecting the two, and a (security breach) as obvious as this goes completely undetected today.

Joseph, like many technologists who have made their way into the physical security industry because they see a sector that is moving forward despite itself, contends that physical security systems have lagged behind cybersecurity advancements for the previous two decades because many systems operators are overwhelmed with incoming data and constant alerts that distract more than inform and that is more than most humans can manage.

This was a problem that cybersecurity faced in the 2000s. Fifteen, twenty years ago when cybersecurity systems started generating a lot of alerts, there was no way a human analyst or a human operator could monitor them all effectively, Joseph continues, pointing out that the cybersecurity industry quickly developed tools like Security Information and Event Management (SIEM) and Orchestration, Automation and Response (SOAR) software to simplify the data tsunami. Physical security has reached that point only now. And one reason convergence is getting delayed is that cybersecurity is way ahead in terms of tools and techniques. Physical security is still lagging behind. Our vision is to elevate physical security quickly to cybersecurity levels so that cases like the previous examples can be easily resolved.

The Hakimo software streamlines the workflow of a global security operations center (GSOC) by freeing up time for operators and by surfacing security threats that would have gone unnoticed previously. The AI algorithms in the Hakimo software initially serviced video surveillance platforms, but working closely with security end-users, the need to rectify the GSOC logjams and improve security accountability of access control systems moved the solution in another direction.

Security is not just about video. There are access control that can (track) patterns that every employee (exhibits) and so on. There are many (applications) beyond video for which we could use AI to detect anomalies, detect vulnerabilities and anomalous events. Even though we started out with video, there has been a natural progression to our product today based on customer feedback and based on our expertise in other industries like cybersecurity where AI has done important things, says Joseph.

His teams software application with its data analytics algorithms can also analyze alarms across time and diagnose faulty hardware, such as door sensors and sensors. Pointing out anomalies in cardholder behavior is a crucial tool for access control accountability. The software can point out impossible travel (the same card being used at multiple locations within a short duration which is physically impossible), unusual time or location of usage.

The real world is complicated (when working with physical security). Of course, somebody from the cybersecurity world can argue that cybersecurity is also complicated, but one of the big challenges that we face with physical security are the (disparate) systems. Just take cameras, for example. Every time there's an access control event, we look at the corresponding video and analyze what's happening. We still run into customers that have 10% analog cameras in their fleet. Then there are other customers that might have advanced five-megapixel or even better cameras. Some might have standard VGA cameras from five years ago or ten years ago, admits Joseph. So, one challenge that we constantly run into is how can we build algorithms that can handle high-resolution footage from a five-megapixel camera, as well as a 320p or 480p or a small resolution camera.

That's just resolution. That's just one dimension. You have solutions like this across other dimensions, he continues. It could be the kind of door. You might have a glass door, a wooden door. In different lighting conditions, the camera might be outside facing the door or inside facing outside. And if there is sun outside with a lot of glare coming in. So, just with cameras, there are plenty of challenges. Now you add one more dimension, where you have different access control systems. C-CURE does things in one way, Lenel does things in another way. Pro-Watch just has a slightly different look and S2 has a completely different architecture. Building something that works with different kinds of systems and different kinds of real-world environments is also the biggest challenge, but it has also become our biggest strength.

Those strengths have been more than tested over the last 24 months with the lingering COVID crisis that has staggered office time for workers and challenged employers to provide an extra measure when it comes to duty of care. The mindset of what an access control system is and what it should do has been turned on its head. For Joseph, the present environment has been a motivating element for a changing technology segment.

COVID was a significant change for the physical security departments within enterprises because everyone started turning to physical security and asking, How many people are there in the building today? What's our occupancy right now? That data was always there in your Lenel database or in your C-CURE systems, but nobody cared (to leverage) it. This crisis has shown, in some sense, the value that the data sitting in these systems have in general for security, health and safety. It also showed how difficult it is to do something extremely basic, Joseph says. We literally have talked to customers who were running reports daily in Lenel, exporting into a spreadsheet, and then copy-pasting the data into a different spreadsheet and before finally building graphs on their own tools to show how building utilization is changing across time.

Joseph continues that it is all about the software now. And when he and his company talk about software, it is an AI-driven solution.

We put zero hardware in the field. We just take in the existing cameras, existing access control systems and use our algorithms. It shows how software has become much more powerful and important than hardware when integrating a system. Even if you have a suboptimal camera or suboptimal hardware in the field, superior software can make up for that. And when I say software, I'm using software as a general term that includes AI, he adds.

Given the steady migration of AI into the physical security space, where does Joseph see the evolution of AI-based access control solutions going five or ten years down the road?

It's a very general point, but one clear trend is the (declining) number of security guards; there's a huge labor shortage. While the number of guards is going down, the number of sensors in the industry is growing exponentially. So, there's a real need in the industry to monitor all these sensors and monitor all these cameras. We see monitoring emerging as the largest sector within the physical security industry. AI is desperately needed because we literally don't have enough humans to look at all these cameras, concludes Joseph. If you don't have anyone looking at it, it just becomes a forensic tool, which it has historically been in most cases. Analyzing video at scale, analyzing events at scale will be the primary use case for AI.

About the Author:Steve Lasky is a 34-year veteran of the security industry and an award-winning journalist. He is the editorial director of theEndeavor Business MediaSecurity Group, which includes magazinesSecurity Technology Executive,Security BusinessandLocksmith Ledger Internationaland top-rated webportalSecurityInfoWatch.com.Steve can be reached atslasky@endeavorb2b.com.

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AI is leveraging advanced analytics for physical security operations - SecurityInfoWatch

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Applying data science to predict unrest. AI that can anticipate the next variant of COVID-19s structure. Reducing carbon emissions from planes using algorithms. Thats a few of the headlines in AI this week, which ran the gamut from the dour (how AI might prevent the next attack on the U.S. Capitol) to the uplifting (making air travel greener). Its caveated optimism, but nonetheless a breath of fresh air in a community thats becoming increasingly cynical about the technologys potential to do good.

Wired first reported that a researcher at the University of North Carolina ran simulations using AI systems, including Alphabet-owned DeepMinds AlphaFold and the University of Washingtons RoseTTAFold, to predict the protein structure of the Omicron variant of COVID-19. Ford managed to predict one structure that was pretty much right an impressive feat, given that he arrived at his conclusions before scientists were able to map Omicrons structure properly.

AI promises to expedite certain processes in drug discovery and virology, for example identifying compounds to treat conditions for which medications remain elusive. But as Sriram Subramaniam, a professor at the University of British Columbia who studied Omicron samples, told The Register, having access to a real sample still beats algorithmic models. AI still cant predict things like the strength of new virus variants binding to host cells, for instance, or the infectiousness of those variants.

Could AI perhaps predict events like the January 6 attack on the U.S. Capitol? A piece in The Washington Post this week investigates the premise. While the consensus is mixed, some researchers believe that algorithms can serve as early indicators of violence in regions ahead of major political conflicts.

Unrest prediction, also known as conflict prediction, is a burgeoning field in academia and industry. It and its practitioners, such as the University of Central Floridas CoupCast, aim to design systems that consider variables (e.g., the role of a leader encouraging a mob, long-term democratic history) to determine whether, for example, electoral violence might occur.

Those who are bullish about the technology say that its already revealed surprising insights, like the fact that social media conflict is an unreliable indicator of real-world unrest. But others caution that its little better than chance in terms of accuracy and that it could be used to justify crackdowns on peaceful protests.

Actors react, Roudabeh Kishi, director of innovation at the nonprofit Armed Conflict Location & Event Data Project, a group engaged in conflict prediction research, told The Post. If people are shifting their tactics, a model trained on historical data will miss it.

The global aviation industry produces around 2% of all human-generated carbon dioxide emissions. If they were a country, all the airlines in the industry some of which run thousands of nearly-empty flights to keep valuable airport slots would rank among the top ten in the world.

Like other greenhouse gases, carbon dioxide drives climate change, leading to extreme weather, larger wildfires, disease from smog and air pollution, food supply disruptions, and other effects. In an effort to combat this, some airlines, including Air France, Norwegian, Malaysia Airlines, Cebu Pacific, Go Air, and Atlas Air, are turning to algorithms trained on data from billions of flights to identify emissions-reducing opportunities. Openairlines SkyBreathe the system recently adopted by Air France can reportedly reduce total fuel consumption by up to 5%.

Other startups, like Flyways, are creating AI-powered platforms that attempt to optimize aircraft routing, giving suggestions on how and where to fly planes. During a six-month pilot program at Alaska Airlines, Flyways claims to have shaved off five minutes from flights and saved 480-thousand gallons of jet fuel on average.

Some critics argue that airlines arent going far enough; they call for a phase-out of short-haul flights in Europe, among other footprint-reducing measures. But considering the long road ahead to meaningfully cutting the worlds carbon output, every bit helps.

If you went a teeny bit slower, you were on time, you had a gate, and because you went a teeny bit slower the airplane actually burned less fuel, that might be a win/win combination for both the guest and the operation and sustainability impact, Diana Birkett Rakow, senior VP of sustainability at Alaska Airlines, told ABC News.

For AI coverage, send news tips toKyle Wiggers and be sure to subscribe to the AI Weekly newsletterand bookmark our AI channel,The Machine.

Thank

AMPs for reading,

Kyle Wiggers

AI Staff Writer

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AI Weekly: What can AI tell us about social unrest, virus structures, and carbon emissions? - VentureBeat

Leading U.S. health, educational and research organizations have come together with the creation of a group aimed at accelerating artificial intelligence innovation and adoption in healthcare, Microsoft announced Thursday.

The Artificial Intelligence Industry Innovation Coalition (AI3C) will leverage the collective brainpower, experience and expertise of the Brookings Institution, Cleveland Clinic, Duke Health, Intermountain Healthcare, Microsoft, Novant Health, Plug and Play, Providence, UC San Diego, andUniversity of Virginia.

From diagnosing and treating disease to addressing disparities in care, overcoming social barriers, speeding research and reducing physician burnout, artificial intelligence has the power to overhaul healthcare, according to leaders of the new coalition.

To get there, AI3C is honing in on some core goals. Those include showcasing new AI innovations, gathering industry-specific information on how AI is used, developing best practices for responsible AI implementation and preparing students for careers in AI and data science.

Meeting the urgent need for new health technologies requires diverse partners coming together across sectors, Ashley Llorens, vice president and managing director for Microsoft Research and Incubations, said in a news release announcing the coalition. With perspectives from AI practitioners, healthcare professionals and the research community, the AI3C can guide collaborative projects that accelerate the translation of frontier technologies from research to solution development, to implementation.

The coalition will also be seeking to resolve significant economic and industrial challenges, improve peoples digital skills and employability, transform the workforce in a way that reduces clinical fatigue, and improve data access and privacy.

AIs transformational power extends to other industries, like financial services. AI3C inception follows the creation of another Microsoft-affiliated AI coalition focused on that sector in December 2020.

The group will also develop white papers to engage the broader healthcare community in advancing AI innovation and application, and host quarterly meetings and events to provide updates on progress. It will provide more details on the coalitions programs and how its making an impact later this year.

AI in healthcare provides us powerful tools to address systemic challenges by identifying true root causes, Shamyla Lando, chief technology officer at Duke Health, said in a statement noting the organizations commitment to eliminating inequities in clinical care. AI will empower us to redesign the care delivery system at the population level and I look forward to creating health equity for our generation and future generations.

Photo: MF3d, Getty Images

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New coalition aims to accelerate responsible AI innovation to transform healthcare - MedCity News

A pivotal step in every employment journey involves a jobseeker learning about the requirements of a potential job to assess their own interest and qualifications. Interfaces for job boards work in some situations, but what if there was a better way for candidates to learn about a position?

Zammo.ais founder and CEO, Alex Farr, is encouraged that customers like Seattle Airport and OurAbility are using Zammo to make information more accessible. Now, he is eager to scale that success by enabling any company with a job board and make it easy for any person with a disability to use their voice to discover and apply for jobs.

On Zammos journey to apply AI in accessibility scenarios, they met Khadija Bari, a student coordinator at VISIONS Services for the Blind. She opened a whole new avenue of thought with a powerful question: Im visually impaired. With [Zammos] voice platform now, I just need to talk to my home assistant, and I can get the information I need to help the participants that I serve. We do it with so many other products, why not start doing it with jobs?

That question led Zammo to explore how their solution could benefit the recruitment industry. Zammos intention is to learn from their previous insights, research and ultimately to produce accessible interfaces for various online job boards, enabling people with disabilities to get informational details about positions, align their skills, and complete an application easily. This project will leverage Natural Language Processing and Voice to create an interface that will give customers the ability to browse semi-structured data on various job search websites. Zammos platform combines Azure Bot Service, Azure Communication Service, Azure Cognitive Search, and leveraging newly released Azure Semantic Search to provide better search results given its understanding of the linguistic content of search terms.

To ensure their end solution addresses the real need of people with disabilities, Zammo is partnering with Open Inclusion, an inclusive insight, design and innovation consultancy. They help organizations unlock value by asking, understanding, and considering the needs of people who move, sense, think or feel differently. Zammos project matches Open Inclusions purpose making the world more inclusive and leveraging technology to solve current needs by reducing frictions in new ways.

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How AI is being used to improve disability employment - Microsoft Accessibility Blog - Microsoft

Nvidias latest game-ready driver includes a tool that could let you improve the image quality of games that your graphics card can easily run, alongside optimizations for the new God of War PC port. The tech is called Deep Learning Dynamic Super Resolution, or DLDSR, and Nvidia says you can use it to make most games look sharper by running them at a higher resolution than your monitor natively supports.

DLDSR builds on Nvidias Dynamic Super Resolution tech, which has been around for years. Essentially, regular old DSR renders a game at a higher resolution than your monitor can handle and then downscales it to your monitors native resolution. This leads to an image with better sharpness but usually comes with a dip in performance (you are asking your GPU to do more work, after all). So, for instance, if you had a graphics card capable of running a game at 4K but only had a 1440p monitor, you could use DSR to get a boost in clarity.

DLDSR takes the same concept and incorporates AI that can also work to enhance the image. According to Nvidia, this means you can upscale less (and therefore lose less performance) while still getting similar image quality improvements. In real numbers, Nvidia claims youll get image quality similar to running at four times the resolution using DSR with only 2.25 times the resolution with DLDSR. Nvidia gives an example using 2017s Prey: Digital Deluxe running on a 1080p monitor: 4x DSR runs at 108 FPS, while 2.25x DLDSR is getting 143 FPS, only two frames per second slower than running at native 1080p.

Of course, you may want to take those impressive results with a grain of salt, as Nvidias obviously going to want to show one of the best-case examples. In the real world, you may get different results with different games, both in terms of FPS and what settings you have to run DLDSR in to get it looking crisp. Given its wider game support, though, youll probably be able to play around with it using one of your favorite older titles though you still will need an RTX card, and they arent exactly easy to get right now.

This isnt the first time Nvidias used deep learning to improve image quality and performance its gotten a lot of praise for its Deep Learning Super Sampling, or DLSS, system. However, DLSS needs to be specifically supported by the game, and the list of games you can use it with is relatively small (though, as of today, it includes the God of War).

AMD, Nvidias graphics card competitor, has also announced tech to improve performance and graphics on a wide array of games. It calls its approach Radeon Super Resolution, and while it doesnt use exactly the same methods as DLSS or DLDSR (AMD has its own upscaling tech called virtual super resolution), its aiming towards the same goal.

If you want to try out Nvidias DLDSR, update to the latest driver, then open up Nvidia Control Panel app. Go to Manage 3D Settings, click the DSR - Factors drop-down, and select one of the DL Scaling options.

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Nvidias AI-powered scaling makes old games look better without a huge performance hit - The Verge