Category Archives: Ai

November 28, 2021 At the Radiological Society of North Americas (RSNA) 107th Scientific Assembly and Annual Meeting, Nuance Communications Inc. launched the Nuance Precision Imaging Networ, an AI-powered cloud platform that delivers patient-specific data and insights from diagnostic imaging into existing clinical and administrative workflows across the healthcare ecosystem. Leveraging the scale of Nuances PowerScribe and PowerShare diagnostic imaging solutions, the Nuance Precision Imaging Network connects imaging stakeholders and facilitates the use of AI to inform precision diagnostics and therapeutics, increase physician efficiency, and lower overall healthcare costs.

The importance of diagnostic imaging is reflected in a study published by the Journal of the American Medical Association, which found that more than 80% of all hospital and health system visits included at least one imaging study. As a result, hospitals and health systems today spend an estimated $65 billion each year on diagnostic imaging related to more than 23,000 different conditions. By enabling the secure and efficient sharing of AI-enhanced diagnostic imaging information, the Nuance Precision Imaging Network augments clinical decision making, facilitates earlier detection and treatment of diseases when the chance for positive patient outcomes is greater, and enhances interpretation and workflow efficiencies for a wide array of physicians.

Radiology and other diagnostic imaging stakeholders are seeking enterprise-wide platforms that enable us to bridge medical information silos and expand cloud-based access to imaging data to improve patient care, physician efficiency, and costs. The Nuance Precision Imaging Network is a systematic platform that integrates best-in-class AI-powered diagnostic models, task automation, clinical decision support, and patient-centered care team communication within familiar radiology and other clinical workflows. Using Nuances trusted cloud-based radiology solutions, the network helps radiologists and other physicians manage growing workloads while enabling them to apply the full potential of diagnostic imaging in patient care, said Dr. Ryan Lee, chair of the department of radiology at Einstein Healthcare Network.

Nuance recognizes that almost every patient story starts with a diagnostic image. The Nuance Precision Imaging Network is the only nationwide patient-centered diagnostic imaging platform that seamlessly delivers AI-generated patient information into the full array of clinical and administrative workflows across provider, payer, and life science use cases, said Peter Durlach, chief strategy officer, Nuance. By leveraging Nuances unmatched scale in diagnostic imaging built up over the past 25 years and a robust partner ecosystem, the Nuance Precision Imaging Network establishes a common cloud-based framework that enables all stakeholders to apply rapid advances in imaging AI to improve clinical outcomes, financial performance and efficiency across the entire patient journey from screening through follow-up.

The Nuance Precision Imaging Network maximizes the effectiveness, efficiency, and value of diagnostic imaging by delivering meaningful, AI-generated, patient-centered data and insights to all key imaging stakeholders including:

The Nuance Precision Imaging Network leverages best in class AI models from an array of partners and seamlessly integrates with existing picture archival communication systems (PACS), vendor neutral archives (VNA) and electronic health record (EHR) systems to maximize the value of existing healthcare IT infrastructure. It is available for customers to preview at the RSNA 2021 Annual Meeting Nov. 28 Dec. 2, 2021. Those interested in learning more about the Precision Imaging Network should visit booth #3300 or contact us.

For more information: http://www.nuance.com

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Nuance Launches AI-powered Precision Imaging Network to Improve Clinical and Financial Outcomes Across the Care Continuum - Imaging Technology News

Hear from CIOs, CTOs, and other C-level and senior execs on data and AI strategies at the Future of Work Summit this January 12, 2022. Learn more

Its not often the world of semiconductors is turned on its head. Its clear that a similar transformation is occurring as a superabundance of start-ups takes on the challenge of low-power neural nets.

These start-ups are trying to move neural network-based machine learning from the cloud data center to embedded systems in the field to whats now called the edge. Making chips work in this new world will require new ways of setting up neurals, designing memory paths, and compiling to hardware.

Establishing this new formula will challenge the brightest heads in electrical engineering. But the push has begun for edge AI. Its spawned myriad startups, including Axelera.AI, Deep Vision, EdgeQ, Hailo, Sima.ai, and many more.

Driving this, according to analyst firm ABI Research, is the need for local data processing, low latency, and avoidance of repeated calls to AI chips back on the cloud. The firm also cites better data privacy as an impetus. Its all seen as an opening for upstarts in an edge AI chipset market that ABI estimates will grow to $28 billion in 2026, for a compound annual growth rate (CAGR) of 28.4% from 2021 to 2026.

That growth will require designs that move beyond bellwether AI apps, like those that recognize images of cats and dogs, created in power-rich cloud data centers. That quest to expand use cases should bring pause to optimists.

Making the chips is one thing, but getting them to work across many different neural network types is another. We are not there yet, said Marian Verhelst, a circuits and systems researcher at Katholieke Universiteit Leuven and the Imec tech hub in Belgium, as well as a member of the TinyML Foundation, who spoke with VentureBeat.

Still, its a really cool time to be active in this new domain, adds Verhelst, who is also an advisor to Netherlands-based Axelera.AI. The company recently gained $12 million in seed funding from security infrastructure provider Bitfury to pursue Edge AI chips.

What matters when it comes to designing this new chip generation? Chip designers and their customers alike now need to explore the question. In an interview, Verhelst outlined the pressing points as she saw them:

These matters drive design decisions at Axelera AI. The company is preparing to go to market with an accelerator chip centered around analog in-memory processing, transformer neural nets, and data flow architecture while consuming less than 10 watts.

We put together the in-memory computing, which is a new paradigm in technology, and we merge this with a data flow architecture, which gives a lot of flexibility in a small footprint, with small power consumption, said Axelera cofounder and CEO Fabrizio Del Maffeo, who emphasized that this is an accelerator that can work with an agnostic assortment of CPUs.

Del Maffeo cites vision systems, smart cities, manufacturing, drones, and retail as targets for Edge AI efforts.

The competition to forge a solution in edge AI is tough, but entrepreneurs like Del Maffeo and engineers like Verhelst will enthusiastically accept the challenge.

Its a very interesting time for hardware, chips, designers, and startups, Verhelst said. For the first time in a couple of decades, hardware really starts to be at the center of attention again.

No doubt, its interesting to be there when a new IC architecture is born.

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The shape of edge AI to come - VentureBeat

Hear from CIOs, CTOs, and other C-level and senior execs on data and AI strategies at the Future of Work Summit this January 12, 2022. Learn more

AI thrives on data. The more data it can access, and the more accurate and contextual that data is, the better the results will be.

The problem is that the data volumes currently being generated by the global digital footprint are so vast that it would take literally millions, if not billions, of data scientists to crunch it all and it still would not happen fast enough to make a meaningful impact on AI-driven processes.

This is why many organizations are turning to AI to help scrub the data that is needed by AI to function properly.

According to Dells 2021 Global Data Protection Index, the average enterprise is now managing ten times more data compared to five years ago, with the global load skyrocketing from just 1.45 petabytes in 2016 to 14.6 petabytes today. With data being generated in the datacenter, the cloud, the edge, and on connected devices around the world, we can expect this upward trend to continue well into the future.

In this environment, any organization that isnt leveraging data to its full potential is literally throwing money out the window. So going forward, the question is not whether to integrate AI into data management solutions, but how.

AI brings unique capabilities to each step of the data management process, not just by virtue of its capability to sift through massive volumes looking for salient bits and bytes, but by the way it can adapt to changing environments and shifting data flows. For instance, according to David Mariani, founder of, and chief technology officer at AtScale, just in the area of data preparation, AI can automate key functions like matching, tagging, joining, and annotating. From there, it is adept at checking data quality and improving integrity before scanning volumes to identify trends and patterns that otherwise would go unnoticed. All of this is particularly useful when the data is unstructured.

One of the most data-intensive industries is health care, with medical research generating a good share of the load. Small wonder, then, that clinical research organizations (CROs) are at the forefront of AI-driven data management, according to Anju Life Sciences Software. For one thing, its important that data sets are not overlooked or simply discarded, since doing so can throw off the results of extremely important research.

Machine learning is already proving its worth in optimizing data collection and management, often preserving the validity of data sets that would normally be rejected due to collection errors or faulty documentation. This, in turn, produces greater insight into the results of trial efforts and drives greater ROI for the entire process.

Still, many organizations are just getting their new master data management (MDM) suites up and running, making it unlikely they will replace them with new intelligent versions any time soon. Fortunately, they dont have to. According to Open Logic Systems, new classes of intelligent MDM boosters are hitting the channel, giving organizations the ability to integrate AI into existing platforms to support everything from data creation and analysis to process automation, rules enforcement, and workflow integration. Many of these tasks are trivial and repetitive, which frees up data managers time for higher-level analysis and interpretation.

This trend toward deploying AI to manage the data it needs to perform other duties in the digital enterprise will change the nature of work for data scientists and other knowledge workers. People will no longer be tasked with doing the work they do now and instead will focus on monitoring the results of AI-driven processes and then making changes should they veer from defined objectives.

More than anything, however, AI-driven data management will speed up the pace of business dramatically. Data is king in the digital universe, and kings dont like to wait.

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AI will soon oversee its own data management - VentureBeat

SINGAPORE - Media OutReach - 29 November 2021 - Established online learning platform Superstar Teacher is partnering with AI-centred EdTech startup Cerebry to launch a new Artificial Intelligence (AI) Learning Mode to complement their existing library of online lessons. Students can now benefit from tailored questions catered to enhancing their strengths and improving on their shortcomings thanks to the help of AI technology.

Superstar Teacher's online course library is built around topical concepts found in MOE syllabuses and commonly tested exam questions. Questions generated through this new Learning Mode will complement existing lessons while ensuring that students have access to a variety of challenging exercises.

Individualised Learning Powered by Artificial Intelligence

The questions (and solutions) generated by Superstar Teacher's AI Learning Mode are automatically generated on the fly, building upon the child's performance in previous lessons. This means that no two students will receive the same question from the AI system. Additionally, the endless possibility of different questions keeps a student engaged and away from boredom. The system will likewise generate intuitive hints and step-by-step solutions corresponding to the question posed to ensure that the student fully understands the process of getting to the correct answer.

Catering to the uniqueness of each child's learning needs, the AI system has the functionality to create personalised study schedules using diagnostic testing and data-driven insights, focusing on concepts the student appears to be weaker at to remedy all knowledge gaps and attain subject mastery.

Efficient and Engaging Revision Plans

If the AI system deems that the child does not fully understand a particular concept, they will be brought back to the specific part of the lesson covering this topic in the online course library. From there, the child will be equipped to review their knowledge in particular areas they may have trouble comprehending.

Unlike a classroom setting where it would be impossible for a teacher to create a personalised and highly precise study plan for every student, the AI Learning Mode's real-time feedback enables individual students to work on their knowledge gaps and efficiently target areas of uncertainty with relevant practices. This also ensures that time will be spent effectively. After revision, they will be prompted to return to the AI Learning mode and complete questions to test their understanding. Students will only be able to progress to higher-level topics once they have displayed mastery in foundational topics.

AI-Human Synergy in Optimising Learning

With the advancement of AI technologies, there are growing concerns and fears that artificial intelligence will eventually replace human educators. However, Superstar Teacher recognises the importance of the interactive element in human teaching and aims to provide a holistic learning environment driven by both AI learning and human experts. This synergy between self-directed AI-enhanced learning and on-demand feedback from qualified mentors will optimise the learning experience for students.

Interested parents can sign their child up for a free trial to experience these features.

Conceptualised in 2011, Superstar Teacher is committed to creating an excellent learning experience for students through quality strategy-based teaching aligned with Singapore's MOE syllabuses. Its online learning platform is equipped with innovative features and aims at keeping students engaged in the pursuit for academic excellence. For more information, visit http://www.superstarteacher.com.sg.

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Superstar Teacher Uses the Latest in AI to Bring Online Learning to the Next Level in the Age of EdTech - Taiwan News

After Clearview AI scraped billions of photos from the public web from websites including Instagram, Venmo and LinkedIn to create a facial recognition tool for law enforcement authorities, many concerns were raised about the company and its norm-breaking tool. Beyond the privacy implications and legality of what Clearview AI had done, there were questions about whether the tool worked as advertised: Could the company actually find one particular persons face out of a database of billions?

Clearview AIs app was in the hands of law enforcement agencies for years before its accuracy was tested by an impartial third party. Now, after two rounds of federal testing in the last month, the accuracy of the tool is no longer a prime concern.

In results announced on Monday, Clearview, which is based in New York, placed among the top 10 out of nearly 100 facial recognition vendors in a federal test intended to reveal which tools are best at finding the right face while looking through photos of millions of people. Clearview performed less well in another version of the test, which simulates using facial recognition for providing access to buildings, such as verifying that someone is an employee.

Were pleased, said Clearviews chief executive, Hoan Ton-That. It reflects our actual-use case.

The company also performed well last month in a test called a one-to-one test of its ability to match two different photos of the same person, simulating the facial verification that people use to unlock their smartphones.

The positive results have been a shot in the arm for the sales team, Mr. Ton-That said.

The National Institute of Standards and Technology has been administering Face Recognition Vendor Tests for two decades. Since those tests began, the report notes, face recognition has undergone an industrial revolution, with algorithms increasingly tolerant of poorly illuminated and other low-quality images, and poorly posed subjects.

Clearview made an impressive debut on the charts for investigative, or one-to-many, searches, but the top performers were SenseTime, a Chinese company, and Cubox, from South Korea. In 2019, the Commerce Department blacklisted SenseTime and 27 other Chinese entities because their products were implicated in Chinas campaign against Uyghurs and other Muslim minorities. Axios has reported that the designation was later changed to Beijing SenseTime, limiting the effects of the blacklisting.

Accuracy aside, questions remain about the legality of Clearviews tool. The authorities in Canada and in Australia have said Clearview broke their laws by failing to get the consent of citizens whose photos are included in the database, and the company is fighting lawsuits over privacy in Illinois and Vermont.

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Clearview AI Does Well in Another Round of Facial Recognition Accuracy Tests - The New York Times

Artificial intelligence has made great advances in many industries since its inception. While talking about the ever-changing travel industry is also taking advantage of AI to transform the way it operates. At present, travel companies highly leverage AI-powered tools and solutions for diverse processes from travel planning to landing at the destination. AI has been incorporated into many areas of the travel and hospitality industry making the lives of travellers across the globe easier.

AI Assistants for Booking: AI assistants and Intelligent chatbots have taken over the place of travel agents allowing travellers to book their flights and accommodation using the internet. These AI chatbots are deployed in social media sites to offer users a more personalized booking experience. The travel reservation giants such as Skyscanner, Booking.com and Expedia are using such operations.Robots for Customer Services: Robots are used to filter customer services in the travel industry, by eliminating the need for human agents. They are being used to know the information or try to find gates in a busy terminal. Hotels are also using robots for this very reason to assist the customers. The latest report also says that it is soon expected to replace robots for the checking process by 2030.AI Forecasting: These days the AI technology can help you in finding the best price at the right time and also provides a heads up on the fight prices too. Some giant companies have also made this feature available to their users. Hopper is one such example that can be used by travellers to avail this opportunity.

Identify Insights: As huge amounts of data are generated every single second, there is a need for travel companies to intercept this data to understand and gain insights. And so, most travel companies are leveraging AI to sort through these vast data sets accurately, which is otherwise a daunting task for human beings.

Sentiment Analysis: Social media is booming like anything. Airlines and hotels are leveraging sentiment analysis combined with AI to identify the sentiment of travellers through social media and how it relates to the journey of the travellers. This technology is being used to deliver a better customer experience to the travel partners.Room Mapping: Most of the hotels are using AI for room mapping to track dynamic prices for the same room across multiple suppliers. AI can also help in predicting dynamic prices for a specific room, which can get a precise idea for when and how long the prices are likely to remain the cheapest as they can.

Smart Baggage Handling: Airports handle millions of bags each year, so most airports are leveraging AI for handling airport baggage systems. They can also be automated with robotics and AI in the future by handling the luggage smartly. They have already implemented AI solutions in some of the airports with pilot projects.

Personalized Travel Planning: Travel planning is likely to transform with an increase in mobile applications that can provide personalized end-to-end travel planning. These applications may include new capabilities such as tracking passengers health by integrating with wearable technologies and recommending safe travel zones. AI can provide better customer satisfaction for travel planning.

Augmented Reality Apps: Artificial Intelligence led to AR which can enhance peoples perceptions of the environment when viewed through dedicated devices or smartphones. Most of the travellers step towards AR-enabled travel apps seems like a no-brainer. They all use AR apps to improve the overall visitor experience with interactivity and instantly accessible information.Online Reputation Management: With the help of artificial intelligence, companies can now easily monitor their customer reviews. AI enables the companies to know about social media comments, services and other mentions about the brand. Artificial intelligence can view customer reviews and comments that would not be possible for a human being. By using this technology, hotels and airlines can easily find out the negative feedback of the customers and respond accordingly.

The Future of AI in the Travel IndustryAI Technology in the travel industry has steadily plunged and transformed the way we travel today. There are many potential ways passengers and travel companies can utilize AI to make the journey an efficient and smooth one while maintaining customer satisfaction at a higher level. From a consumer viewpoint, AI makes it easier for users to find the relevant information, make informed decisions, customize deals and give better customer experiences. And from the business point of view, it can improve the efficiency and reduce the cost of the travel industry.

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Analytics Insight is an influential platform dedicated to insights, trends, and opinions from the world of data-driven technologies. It monitors developments, recognition, and achievements made by Artificial Intelligence, Big Data and Analytics companies across the globe.

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10 AI Innovations that are Revolutionizing the Travel Industry - Analytics Insight

By Harvard John A. Paulson School of Engineering and Applied SciencesNovember 28, 2021

Lily Xu. Credit: Eliza Grinnell/Harvard SEAS

Lily Xu knew from a young age how much the environment and conservation mattered to her.

By 9 years old, shed already decided to eat vegetarian because, as she put it, I didnt want to hurt animals.

Xu grew up believing her passions would always be separate from her professional interest in computer science. Then she became a graduate student in Milind Tambes Teamcore Lab, and everything changed.

Xu is now doing award-winning research into using machine learning and artificial intelligence to help conservation and anti-poaching efforts around the world. Her recent paper, Learning, Optimization, and Planning Under Uncertainty for Wildlife Conservation, won the 2021 INFORMS Doing Good with Good OR Student Paper Competition.

From our earliest conversations, it was crystal clear that Lily was very passionate about sustainability, conservation, and the environment, said Tambe, the Gordon McKay Professor of Computer Science at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS). This was also the reason our wavelengths matched and I went out of my way to recruit her and ensure she joined my group.

In the Teamcore Lab, Xu helped develop Protection Assistant for Wildlife Security (PAWS), an artificial intelligence system that interfaces with a database used by park rangers to record observations of illegal poaching and predict which areas are likely to be poaching hotspots. The system makes it easier for rangers to choose the best locations to patrol.

Lily Xu poses at the entrance to Srepok Wildlife Sanctuary in Cambodia. Credit: Lily Xu

In 2019, Xu and the Teamcore Lab partnered with the Srepok Wildlife Sanctuary in Cambodia to test the efficacy of PAWS. At the time, the sanctuary only had 72 rangers to patrol an area slightly larger than the state of Rhode Island.

Our work with Cambodia was the most intensive collaboration with a park that weve had, said Xu. We had several months of meetings, and our interactions with them and the feedback they were giving us about the process really shaped the design of our algorithms.

Xu played a lead role in implementing field tests of the PAWS program. Through Tambe, Xu and her lab mates, Srepoks rangers greatly increased the number of poachers snares they removed throughout the sanctuary.

Lily has led and taken PAWS from a small research concept to a globally impactful research effort leading to removal of thousands of lethal animal snares, saving endangered wildlife globally, said Tambe. Lily has led a global effort that has made the PAWS software available worldwide to hundreds of national parks. This is true global impact, aiming to save endangered wildlife around the world.

Lily Xu patrols Srepok Wildlife Sanctuary in Cambodia. Credit: Lily Xu

Xu has always loved nature, but didnt get to experience much of it while growing up in the Maryland suburbs of Washington, D.C. Once she got to Dartmouth College as an undergraduate in 2014, she finally got to immerse herself in the outdoors.

I went hiking and camping for the first time as part of my freshman orientation trip, just absolutely fell in love with it, and then spent as much time as I could outdoors, she said. That made me even more attuned to how precious the natural environment is, and how much I care about doing my part to preserve it.

She eventually began to help organize Dartmouths first-year trip and took on leadership roles with the schools sophomore trip and canoe club. Xu didnt want to just experience nature, she wanted others to care about it too.

Thats continued at Harvard, where shes mentored four students since the summer of 2020, and been part of several mentorship teams.

I care a lot about mentorship in all capacities, whether thats bringing people out of their comfort zone, encouraging them to explore the outdoors and realize that this is a place for them, Xu said. The outdoors community is traditionally wealthy and traditionally white. Im neither of those things, and I really want to encourage other people and show them that this can be their space too. Similarly, from a computer science standpoint, this is a field that is traditionally male-dominated, and especially in AI research, its traditionally people in the western world.

Xu has published multiple award-winning publications through her work on PAWS. A paper presented at the 35th Association for the Advancement of Artificial Intelligence, Dual-Mandate Patrols: Multi-Armed Bandits for Green Security, was named a Best Paper Award Runner-Up as a top-six paper out of nearly 1,700 accepted papers, while another publication, Enhancing Poaching Predictions for Under-Resourced Wildlife Conservation Parks Using Remote Sensing Imagery, won the Best Lightning Paper Award at the Machine Learning for Development Workshop at the 34th Conference on Neural Information Processing Systems in 2020.

Xu is working to address those disparities as a member of Mechanism Design for Social Good (MD4SG), a multi-school, multi-disciplinary research initiative that organizes working groups and colloquium series to address the needs of underserved and marginalized communities all over the world. Xu joined MD4SG in 2020 as co-organizer for the groups environmental working group, and this past March became a co-organizer for the entire organization.

I thought, Oh this sounds like a phenomenal opportunity, because I dont really know of a strong community of computational researchers who are working in environmental challenges, and I would love to help foster a community, Xu said. Our working group, for example, has really been able to bring in people from all around the world.

Shes fantastic to work with in all of these areas, said Bryan Wilder, PhD 21, a former Teamcore lab member and member of the MD4SG leadership team. She has the combination of being incredibly engaged and energetic and really making things happen, while also just being a kind person to work with.

For Xu, research is about more than just publishing its all about building relationships and fostering community engagement.

We are researchers that are not just trying to get your data sets, publish a paper and then just walk away, said Xu. We are here for the long run. We are committed. We want to achieve conservation results as much as we want to achieve academic publication.

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Computer Conservation: Lily Xu Uses Artificial Intelligence To Stop Poaching Around the World - SciTechDaily

Hear from CIOs, CTOs, and other C-level and senior execs on data and AI strategies at the Future of Work Summit this January 12, 2022. Learn more

Artificial intelligence isnt tomorrows technology its already here. Now too is the legislation proposing to regulate it.

Earlier this year, the European Union outlined its proposed artificial intelligence legislation and gathered feedback from hundreds of companies and organizations. The European Commission closed the consultation period in August, and next comes further debate in the European Parliament.

As well as banning some uses outright (facial recognition for identification in public spaces and social scoring, for instance), its focus is on regulation and review, especially for AI systems deemed high risk those used in education or employment decisions, say.

Any company with a software product deemed high risk will require a Conformit Europenne (CE) badge to enter the market. The product must be designed to be overseen by humans, avoid automation bias, and be accurate to a level proportionate to its use.

Some are concerned about the knock-on effects of this. They argue that it could stifle European innovation as talent is lured to regions where restrictions arent as strict such as the US. And the anticipated compliance costs high-risk AI products will incur in the region perhaps as much as 400,000 ($452,000) for high risk systems, according to one US think tank could prevent initial investment too.

So the argument goes. But I embrace the legislation and the risk-based approach the EU has taken.

Why should I care? I live in the UK, and my company, Healx, which uses AI to help discover new treatment opportunities for rare diseases, is based in Cambridge.

This autumn, the UK published its own national AI strategy, which has been designed to keep regulation at a minimum, according to a minister. But no tech company can afford to ignore what goes on in the EU.

EU General Data Protection Regulation (GDPR) laws required just about every company with a website either side of the Atlantic to react and adapt to them when they were rolled out in 2016. It would be naive to think that any company with an international outlook wont run up against these proposed rules too. If you want to do business in Europe, you will still have to adhere to them from outside it.

And for areas like health, this is incredibly important. The use of artificial intelligence in healthcare will almost inevitably fall under the high risk label. And rightly so: Decisions that affect patient outcomes change lives.

Mistakes at the very start of this new era could damage public perception irrevocably. We already know how well-intentioned AI healthcare initiatives can end up perpetuating structural racism, for instance. Left unchecked, they will continue to.

Thats why the legislations focus on reducing bias in AI, and setting a gold standard for building public trust, is vital for the industry. If an AI system is fed patient data that does not accurately represent a target group(women and minority groups are often underrepresented in clinical trials), the results can be skewed.

That damages trust, and trust is crucial in healthcare. A lack of trust limits effectiveness. Thats part of the reason such large swathes of people in the West are still declining to get vaccinated against COVID. The problems thats causing are plain to see.

AI breakthroughs will mean nothing if patients are suspicious of a diagnosis or therapy produced by an algorithm, or dont understand how conclusions have been drawn. Both result in a damaging lack of trust.

In 2019, Harvard Business Review found that patients were wary of medical AI even when it was shown to out-perform doctors, simply because we believe our health issues to be unique. We cant begin to shift that perception without trust.

Artificial intelligence has proven its potential to revolutionize healthcare, saving lives en route to becoming an estimated $200 billion industry by 2030.

The next step wont just be to build on these breakthroughs but to build trust so that they can be implemented safely, without disregarding vulnerable groups, and with clear transparency, so worried individuals can understand how a decision has been made.

This is something that will always, and should always, be monitored. Thats why we should all take notice of the spirit of the EUs proposed AI legislation, and embrace it, wherever we operate.

Tim Guilliams is a co-founder and CEO of drug discovery startup Healx.

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Europes AI laws will cost companies a small fortune but the payoff is trust - VentureBeat

Bias AI-based hiring tools may affect many people, especially those needing jobs. Right now, many individuals across the globe are having a hard time making a living because of the ongoing COVID-19 pandemic.

(Photo : Photo by OLIVIER DOULIERY/AFP via Getty Images)A man wearing a face mask walks past a sign "Now Hiring" in front of a store amid the coronavirus pandemic on May 14, 2020 in Arlington, Virginia. - Another 3 million people filed initial unemployment claims last week on a seasonally adjusted basis, according to the Department of Labor. (Photo by Olivier DOULIERY / AFP)

On the other hand, many companies were left with no choice but to lay off some of their employees to maintain their business. This issue could get worse if the hiring instrument, which uses artificial intelligence, is biased when picking applications.

This is why New York City decided to pass a bill specifically addressing the rising bias AI-based tool problem.

According toPC Mag's latest report, the new bill was passed on Nov. 10. Bill de Blasio, the current New York City Mayor, still hasn't signed this new act.

(Photo : Photo by Joe Raedle/Getty Images)MIAMI, FL - MARCH 10: A Now Hiring sign is seen as the Bureau of Labor Statistics reports that nonfarm payrolls increased by 235,000 in February and the unemployment rate was 4.7 percent in the first full month of President Donald Trump's term on March 10, 2017 in Miami, Florida.

Also Read:AI for Skin Disease Detection Only Works Accurately on White Skin Tone, Study Says

The NYC mayor still has until Dec. 10 to allow or veto the new bill. If ever he decided to sign it, the bill would become a law, which would take effect on January 1, 2023.

Thanks to this long time frame, hiring companies and agencies still have more than a year to prepare their AI tools. They have a lot of time to meet the bill's standards.

"This bill would require that a biased audit be conducted on an automated employment decision tool prior to the use of said tool," explained the New York City Council via its official legislation report.

NYCC added that companies using AI-based hiring tools need to notify applicants if they are going to use the technology during the employee's evaluation or assessment.

Those who would not be able to meet the bill's standards would be subjected to a civil penalty. You can view this link to see more details.

Right now, new AI technologies are being developed by various experts across the globe. Because of this, artificial intelligence skills are now considered essential as technology further grows.

Deloitte, a global consultancy and auditing firm, explained that the majority of companies and businesses now prefer individuals who understand and know how to use artificial intelligence.

In other news, experts claimed that super-intelligent AI is becoming hard to control. On the other hand, NASA's new AI training program is recruiting people to test the technology.

For more news updates about AIs and other similar technologies, always keep your tabs open here at TechTimes.

Related Article:Current And Upcoming Technology Trends For 2021 And Beyond

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Bias AI-Based Hiring Tools Face NYC's New Bill! Agencies Need to Provide This to Continue Using the Tech - Tech Times

LAGUNA HILLS, Calif.--(BUSINESS WIRE)--BrainChip Holdings Ltd (ASX: BRN), (OTCQX: BCHPY) a leading provider of ultra-low power high performance artificial intelligence technology and the worlds first commercial producer of neuromorphic AI chips and IP, today announced that MegaChips, a pioneer in the ASIC industry, has licensed BrainChip Akida IP to enhance and grow its technology positioning for next-generation, Edge-based AI solutions.

A multibillion-dollar global fabless semiconductor company based in Japan, MegaChips provides chip solutions that fulfill various requirements, including low power consumption, cost and time to market, while achieving breakthrough functions and performance by fusing knowledge of Large Scale Integrations and applications for problems in device development. By partnering with BrainChip, MegaChips is able to quickly and easily maintain its industry innovator status by supplying solutions and applications that leverage the Akida revolutionary technology in markets such as automotive, IoT, cameras, gaming and industrial robotics.

As a trusted and loyal partner to market leaders, we deliver the technology and expertise they need to ensure products are uniquely designed for their customers and engineered for ultimate performance, said Tetsuo Hikawa, President and CEO of MegaChips. Working with BrainChip and incorporating their Akida technology into our ASIC solutions service, we are better able to handle the development and support processes needed to design and manufacture integrated circuits and systems on chips that can take advantage of AI at the Edge.

BrainChips Akida technology brings artificial intelligence to the edge in a way that existing technologies are not capable. The solution is high-performance, small, ultra-low power and enables a wide array of edge capabilities. Due to its flexibility and scalability, the Akida (NSoC) and intellectual property can be used in applications including Smart Home, Smart Health, Smart City and Smart Transportation. These applications include but are not limited to home automation and remote controls, industrial IoT, robotics, security cameras, sensors, unmanned aircraft, autonomous vehicles, medical instruments, object detection, sound detection, odor and taste detection, gesture control and cybersecurity.

The MegaChips and BrainChip partnership furthers both companys missions to push boundaries and offer unprecedented products, said Rob Telson, BrainChip VP of Worldwide Sales and Marketing. By providing Akidas on-chip learning and ultra-low power Edge AI capabilities as an integrated technology in MegaChips ASIC solutions, we are able to deliver a cascading array of benefits to cutting-edge products that not only ensure power efficiency without compromising outcomes but can run autonomously for incremental learning without the need to go back and forth to the cloud. This is an exciting collaboration from both a business perspective as well as from an industry-altering aspect.

About MegaChips Corporation

MegaChips Corporation (1st section of the TSE (Tokyo Stock Exchange): 6875) was established in 1990 as the first innovative fabless semiconductor company in Japan. MegaChips exploits expertise in analog and digital technology and globally provides SoCs and solutions that are crucial for advancing technology innovation. MegaChips focuses in the growth areas of automotive and industrial equipment, such as 5G communications infrastructure and Factory Automation. http://www.megachips.com

About BrainChip Holdings Ltd (ASX: BRN, OTCQX: BCHPY)

BrainChip is a global technology company that is producing a groundbreaking neuromorphic processor that brings artificial intelligence to the edge in a way that is beyond the capabilities of other products. The chip is high performance, small, ultra-low power and enables a wide array of edge capabilities that include on-chip training, learning and inference. The event-based neural network processor is inspired by the spiking nature of the human brain and is implemented in an industry standard digital process. By mimicking brain processing BrainChip has pioneered a processing architecture, called Akida, which is both scalable and flexible to address the requirements in edge devices. At the edge, sensor inputs are analyzed at the point of acquisition rather than through transmission via the cloud to a data center. Akida is designed to provide a complete ultra-low power and fast AI Edge Network for vision, audio, olfactory and smart transducer applications. The reduction in system latency provides faster response and a more power efficient system that can reduce the large carbon footprint of data centers.

Additional information is available at https://www.brainchipinc.com

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BrainChip Partners with MegaChips to Develop Next-Generation Edge-Based AI Solutions - Business Wire