Category Archives: Artificial Intelligence

In the fall of 2019, Google told the world it had reached quantum supremacy.

It was a significant scientific milestone that some compared to the first flight at Kitty Hawk. Harnessing the mysterious powers of quantum mechanics, Google had built a computer that needed only three minutes and 20 seconds to perform a calculation that normal computers couldnt complete in 10,000 years.

But more than two years after Googles announcement, the world is still waiting for a quantum computer that actually does something useful. And it will most likely wait much longer. The world is also waiting for self-driving cars, flying cars, advanced artificial intelligence and brain implants that will let you control your computing devices using nothing but your thoughts.

Silicon Valleys hype machine has long been accused of churning ahead of reality. But in recent years, the tech industrys critics have noticed that its biggest promises the ideas that really could change the world seem further and further on the horizon. The great wealth generated by the industry in recent years has generally been thanks to ideas, like the iPhone and mobile apps, that arrived years ago.

Have the big thinkers of tech lost their mojo?

The answer, those big thinkers are quick to respond, is absolutely not. But the projects they are tackling are far more difficult than building a new app or disrupting another aging industry. And if you look around, the tools that have helped you cope with almost two years of a pandemic the home computers, the videoconferencing services and Wi-Fi, even the technology that aided researchers in the development of vaccines have shown the industry hasnt exactly lost a step.

Imagine the economic impact of the pandemic had there not been the infrastructure the hardware and the software that allowed so many white-collar workers to work from home and so many other parts of the economy to be conducted in a digitally mediated way, said Margaret OMara, a professor at the University of Washington who specializes in the history of Silicon Valley.

As for the next big thing, the big thinkers say, give it time. Take quantum computing. Jake Taylor, who oversaw quantum computing efforts for the White House and is now chief science officer at the quantum start-up Riverlane, said building a quantum computer might be the most difficult task ever undertaken. This is a machine that defies the physics of everyday life.

A quantum computer relies on the strange ways that some objects behave at the subatomic level or when exposed to extreme cold, like metal chilled to nearly 460 degrees below zero. If scientists merely try to read information from these quantum systems, they tend to break.

While building a quantum computer, Dr. Taylor said, you are constantly working against the fundamental tendency of nature.

The most important tech advances of the past few decades the microchip, the internet, the mouse-driven computer, the smartphone were not defying physics. And they were allowed to gestate for years, even decades, inside government agencies and corporate research labs before ultimately reaching mass adoption.

The age of mobile and cloud computing has created so many new business opportunities, Dr. OMara said. But now there are trickier problems.

Still, the loudest voices in Silicon Valley often discuss those trickier problems as if they were just another smartphone app. That can inflate expectations.

People who arent experts who understand the challenges may have been misled by the hype, said Raquel Urtasun, a University of Toronto professor who helped oversee the development of self-driving cars at Uber and is now chief executive of the self-driving start-up Waabi.

Technologies like self-driving cars and artificial intelligence do not face the same physical obstacles as quantum computing. But just as researchers do not yet know how to build a viable quantum computer, they do not yet know how to design a car that can safely drive itself in any situation or a machine that can do anything the human brain can do.

Even a technology like augmented reality eyeglasses that can layer digital images onto what you see in the real world will require years of additional research and engineering before it is perfected.

Andrew Bosworth, vice president at Meta, formerly Facebook, said that building these lightweight eyeglasses was akin to creating the first mouse-driven personal computers in the 1970s (the mouse itself was invented in 1964). Companies like Meta must design an entirely new way of using computers, before stuffing all its pieces into a tiny package.

Over the past two decades, companies like Facebook have built and deployed new technologies at a speed that never seemed possible before. But as Mr. Bosworth said, these were predominantly software technologies built solely with bits pieces of digital information.

Building new kinds of hardware working with physical atoms is a far more difficult task. As an industry, we have almost forgotten what this is like, Mr. Bosworth said, calling the creation of augmented reality glasses a once-in-a-lifetime project.

Technologists like Mr. Bosworth believe they will eventually overcome those obstacles and they are more open about how difficult it will be. But thats not always the case. And when an industry has seeped into every part of daily life, it can be hard to separate hand-waving from realism especially when it is huge companies like Google and well-known personalities like Elon Musk drawing that attention.

Many in Silicon Valley believe that hand-waving is an important part of pushing technologies into the mainstream. The hype helps attract the money and the talent and the belief needed to build the technology.

If the outcome is desirable and it is technically possible then its OK if were off by three years or five years or whatever, said Aaron Levie, chief executive of the Silicon Valley company Box. You want entrepreneurs to be optimistic to have a little bit of that Steve Jobs reality-distortion field, which helped to persuade people to buy into his big ideas.

The hype is also a way for entrepreneurs to generate interest among the public. Even if new technologies can be built, there is no guarantee that people and businesses will want them and adopt them and pay for them. They need coaxing. And maybe more patience than most people inside and outside the tech industry will admit.

When we hear about a new technology, it takes less than 10 minutes for our brains to imagine what it can do. We instantly compress all of the compounding infrastructure and innovation needed to get to that point, Mr. Levie said. That is the cognitive dissonance we are dealing with.

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Why Is Silicon Valley Still Waiting for the Next Big Thing? - The New York Times

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Global Marketing Automation Market Report 2021-2026 - Integration of Artificial Intelligence (AI) is Anticipated to Drive the Market -...

January 24, 2022 - Neurobiologists at Harvard University have entered a joint research agreement with NTT Research, Inc., a division of NTT, to study animal neuro-responses with the hope of informing future artificial intelligence systems. The five-year research project, launched in the fall of 2021, enables researchers at the two organizations to collaboratively study how animals maintain behavioral flexibility, specifically in the task of navigation. Greater understanding of how this challenge is approached in biology may eventually enable the design of new computing machines with similar capabilities. The agreement was coordinated by Harvard Office of Technology Development.

The principal investigator is Venkatesh Murthy, PhD, the Raymond Leo Erikson Life Sciences Professor of Molecular and Cellular Biology at Harvard and the Paul J. Finnegan Family Director of the Center for Brain Science. Murthys counterpart at NTT Research for the joint project is Physics & Informatics (PHI) Lab Research Scientist Gautam Reddy, PhD, who was previously an Independent Post-Doctoral Fellow at Harvards NSF-Simons Center for Mathematical and Statistical Analysis of Biology.

This joint research aims to better elucidate how animals maintain the ability to respond appropriately to a wide variety of complex real-world scenarios. The investigators expect the results from one aspect of the research to be a source of new, biologically inspired ideas for artificial reinforcement learning systems that rely on representation learning. Such ideas have played a major role in recent advances in artificial intelligence. Results from another aspect of the research should provide a quantitative understanding of how animals track trails, as well as identify the basic elements of general behavioral strategies that perform flexibly and reliably in the real world. Murthys lab has a long track record in experimental and computational neurobiology. Expertise relevant to the joint research includes the ability to record from or image many individual neurons in the brain while an animal performs behavioral tasks. This technical expertise will enable the research team to understand what computations are performed by biological neural networks when an animal is navigating in a complex world.

Murthy and Reddy have previously worked together on understanding the computational principles behind olfaction. Their focus was on how the smell receptors in the nose respond to blends of odorous compounds. During his time at Harvards NSF-Simons Center for Mathematical Biology, Reddy worked on the theory behind how animals track scent trails and on developing a computational framework to explain how evolution optimizes organisms.

I am delighted to continue this line of inquiry with Dr. Reddy through the NTT Research PHI Lab, Murthy said. The brain is an example of an extremely efficient computational device, and plenty of phenomena within it remain unexplored and unexplained. We believe the results of these investigations in neurobiology will reveal basic understandings and prove useful in the field of artificial intelligence.

Efficient computation is at the heart of quantum computing and neuroscience. Inspired by neuroscience, recent advances in machine learning have recently begun to change how we process data, said NTTs PHI Lab Director Yoshihisa Yamamoto, PhD. This joint research project could provide a rich source of animal-inspired algorithms that generalize across various research domains within NTT and inspire truly novel interdisciplinary ideas.

Adapted from a press release by NTT Research.

Originally posted here:

Collaboration with NTT Research to advance computational neurobiology - Harvard Office of Technology Development

We have accepted the use of artificial intelligence (AI) in complex processes from health care to our daily use of social media often without critical investigation, until it is too late. The use of AI is inescapable in our modern society, and it may perpetuate discrimination without its users being aware of any prejudice. When health-care providers rely on biased technology, there are real and harmful impacts.

This became clear recently when a study showed that pulse oximeters which measure the amount of oxygen in the blood and have been an essential tool for clinical management of COVID-19 are less accurate on people with darker skin than lighter skin. The findings resulted in a sweeping racial bias review now underway, in an attempt to create international standards for testing medical devices.

There are examples in health care, business, government and everyday life where biased algorithms have led to problems, like sexist searches and racist predictions of an offenders likelihood of re-offending.

AI is often assumed to be more objective than humans. In reality, however, AI algorithms make decisions based on human-annotated data, which can be biased and exclusionary. Current research on bias in AI focuses mainly on gender and race. But what about age-related bias can AI be ageist?

In 2021, the World Health Organization released a global report on aging, which called for urgent action to combat ageism because of its widespread impacts on health and well-being.

Ageism is defined as a process of systematic stereotyping of and discrimination against people because they are old. It can be explicit or implicit, and can take the form of negative attitudes, discriminatory activities, or institutional practices.

The pervasiveness of ageism has been brought to the forefront throughout the COVID-19 pandemic. Older adults have been labelled as burdens to societies, and in some jurisdictions, age has been used as the sole criterion for lifesaving treatments.

Digital ageism exists when age-based bias and discrimination are created or supported by technology. A recent report indicates that a digital world of more than 2.5 quintillion bytes of data is produced each day. Yet even though older adults are using technology in greater numbers and benefiting from that use they continue to be the age cohort least likely to have access to a computer and the internet.

Read more: Online arts programming improves quality of life for isolated seniors

Digital ageism can arise when ageist attitudes influence technology design, or when ageism makes it more difficult for older adults to access and enjoy the full benefits of digital technologies.

There are several intertwined cycles of injustice where technological, individual and social biases interact to produce, reinforce and contribute to digital ageism.

Barriers to technological access can exclude older adults from the research, design and development process of digital technologies. Their absence in technology design and development may also be rationalized with the ageist belief that older adults are incapable of using technology. As such, older adults and their perspectives are rarely involved in the development of AI and related policies, funding and support services.

The unique experiences and needs of older adults are overlooked, despite age being a more powerful predictor of technology use than other demographic characteristics including race and gender.

AI is trained by data, and the absence of older adults could reproduce or even amplify the above ageist assumptions in its output. Many AI technologies are focused on a stereotypical image of an older adult in poor health a narrow segment of the population that ignores healthy aging. This creates a negative feedback loop that not only discourages older adults from using AI, but also results in further data loss from these demographics that would improve AI accuracy.

Even when older adults are included in large datasets, they are often grouped according to arbitrary divisions by developers. For example, older adults may be defined as everyone aged 50 and older, despite younger age cohorts being divided into narrower age ranges. As a result, older adults and their needs can become invisible to AI systems.

In this way, AI systems reinforce inequality and magnify societal exclusion for sections of the population, creating a digital underclass primarily made up of older, poor, racialized and marginalized groups.

We must understand the risks and harms associated with age-related biases as more older adults turn to technology.

The first step is for researchers and developers to acknowledge the existence of digital ageism alongside other forms of algorithmic biases, such as racism and sexism. They need to direct efforts towards identifying and measuring it. The next step is to develop safeguards for AI systems to mitigate ageist outcomes.

There is currently very little training, auditing or oversight of AI-driven activities from a regulatory or legal perspective. For instance, Canadas current AI regulatory regime is sorely lacking.

This presents a challenge, but also an opportunity to include ageism alongside other forms of biases and discrimination in need of excision. To combat digital ageism, older adults must be included in a meaningful and collaborative way in designing new technologies.

With bias in AI now recognized as a critical problem in need of urgent action, it is time to consider the experience of digital ageism for older adults, and understand how growing old in an increasingly digital world may reinforce social inequalities, exclusion and marginalization.

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Artificial intelligence can discriminate on the basis of race and gender, and also age - The Conversation CA

King Abdullah University of Science and Technology: Faculty Positions: Center for Teaching and Learning

Location

King Abdullah University of Science and Technology, Thuwal, Saudi Arabia

Deadline

Feb 28, 2022 at 11:59 PM Eastern Time

Description

The Center for Teaching and Learning at KAUST seeks to appoint one or more teaching stream faculty members in the field of artificial intelligence. Such a faculty member will teach in the underlying methodology of machine learning, and modern AI, as well as its application in software, using modern tools like TensorFlow and Pytorch. The faculty member will educate students in how to use these algorithms and software to implement advanced machine learning and AI methods on modern computing platforms, including graphical processor units (GPUs). The principal teaching will be on neural networks, for applications in image and natural language processing, but also in other areas, like medicine and geoscience. While the faculty member need not be an expert in all of these application areas, he/she should have deep enough understanding of the underlying methodology to adapt to a diverse set of applications.

The teaching responsibilities will come in several forms. The faculty member may teach up to one class each semester within a KAUST academic program, like Computer Science. Additionally, the faculty member will help lead small workshops at KAUST on AI training for a wide audience of scientists and engineers, for people who hope to apply the technology, but need not wish to become experts. Finally, KAUST is seeking to expand its exposure to the Saudi community outside the KAUST campus. AI training and development of micro-credentials will be performed for short periods in Saudi cities like Riyadh, accessible to a wide audience of technical people, as well as business leaders who hope to learn about what can be achieved with AI, but who do not seek to become experts themselves. These teaching opportunities outside of KAUST are meant to address the need for AI training throughout the Kingdom, and will help KAUST meet its expanded mission to help upskill a broad segment of the Saudi community. The faculty member will help design these training opportunities, and with KAUST colleagues will assist in their delivery. In this context, there may be opportunities to perform on-site training for employees at major Saudi companies.

For a teaching stream faculty member, it is anticipated that one would typically teach 2 to 3 classes per semester. However, the individual who fills the role described here will typically teach one class per semester. Therefore, the remaining time commitment is meant to address the development and implementation of AI workshops at KAUST, as well as the aforementioned training opportunities planned for Saudi cities like in Riyadh, and possibly targeted training for Saudi companies.

This teaching stream faculty position is full-time, over the 12 month calendar year, with vacation periods consistent with all KAUST faculty. The summer period will be a particularly important time for developing and executing the teaching to be performed outside KAUST.

Qualifications

We welcome candidates with a PhD in Computer Scienceor related areas, with a strong background in Artificial Intelligence and Data Science.

Application Instructions

To apply for this position, please complete the interfolio application form and upload the following materials:

Link:

Teaching Stream Faculty in Artificial Intelligence job with KING ABDULLAH UNIVERSITY OF SCIENCE & TECHNOLOGY | 278533 - Times Higher Education...

Rhinolophus rouxi, which inhabits parts of South Asia, was identified as a likely but undetected betacoronavirus host by the study authors. Credit: Brock and Sherri Fenton

Daniel Becker, an assistant professor of biology in the University of Oklahomas Dodge Family College of Arts and Sciences, has been leading a proactive modeling study over the last year and a half to identify bat species that are likely to carry betacoronaviruses, including but not limited to SARS-like viruses.

The study Optimizing predictive models to prioritize viral discovery in zoonotic reservoirs, which was published by Lancet Microbe, was guided by Becker; Greg Albery, a postdoctoral fellow at Georgetown Universitys Bansal Lab; and Colin J. Carlson, an assistant research professor at Georgetowns Center for Global Health Science and Security.

It also included collaborators from the University of Idaho, Louisiana State University, University of California Berkeley, Colorado State University, Pacific Lutheran University, Icahn School of Medicine at Mount Sinai, University of Glasgow, Universit de Montral, University of Toronto, Ghent University, University College Dublin, Cary Institute of Ecosystem Studies, and the American Museum of Natural History.

Becker and colleagues study is part of the broader efforts of an international research team called the Verena Consortium (viralemergence.org), which works to predict which viruses could infect humans, which animals host them, and where they could emerge. Albery and Carlson were co-founders of the consortium in 2020, with Becker as a founding member.

Despite global investments in disease surveillance, it remains difficult to identify and monitor wildlife reservoirs of viruses that could someday infect humans. Statistical models are increasingly being used to prioritize which wildlife species to sample in the field, but the predictions being generated from any one model can be highly uncertain. Scientists also rarely track the success or failure of their predictions after they make them, making it hard to learn and make better models in the future. Together, these limitations mean that there is high uncertainty in which models may be best suited to the task.

In this study, researchers used bat hosts of betacoronaviruses, a large group of viruses that includes those responsible for SARS and COVID-19, as a case study for how to dynamically use data to compare and validate these predictive models of likely reservoir hosts. The study is the first to prove that machine learning models can optimize wildlife sampling for undiscovered viruses and illustrates how these models are best implemented through a dynamic process of prediction, data collection, validation and updating.

In the first quarter of 2020, researchers trained eight different statistical models that predicted which kinds of animals could host betacoronaviruses. Over more than a year, the team then tracked discovery of 40 new bat hosts of betacoronaviruses to validate initial predictions and dynamically update their models. The researchers found that models harnessing data on bat ecology and evolution performed extremely well at predicting new hosts of betacoronaviruses. In contrast, cutting-edge models from network science that used high-level mathematics but less biological data performed roughly as well or worse than expected at random.

Importantly, their revised models predicted over 400 bat species globally that could be undetected hosts of betacoronaviruses, including not only in southeast Asia but also in sub-Saharan Africa and the Western Hemisphere. Although 21 species of horseshoe bats (in the Rhinolophusgenus) are known to be hosts of SARS-like viruses, researchers found at least two-fourths of plausible betacoronavirus reservoirs in this bat genus might still be undetected.

One of the most important things our study gives us is a data-driven shortlist of which bat species should be studied further, said Becker, who adds that his team is now working with field biologists and museums to put their predictions to use. After identifying these likely hosts, the next step is then to invest in monitoring to understand where and when betacoronaviruses are likely to spill over.

Becker added that although the origins of SARS-CoV-2 remain uncertain, the spillover of other viruses from bats has been triggered by forms of habitat disturbance, such as agriculture or urbanization.

Bats conservation is therefore an important part of public health, and our study shows that learning more about the ecology of these animals can help us better predict future spillover events, he said.

For more on this research, see Shall We Play a Game? Researchers Use AI To Search for the Next COVID/SARS-Like Virus.

Reference: Optimising predictive models to prioritise viral discovery in zoonotic reservoirs by Daniel J Becker, PhD; Gregory F Albery, PhD; Anna R Sjodin, PhD; Timothe Poisot, PhD; Laura M Bergner, PhD; Binqi Chen; Lily E Cohen, MPhil; Tad A Dallas, PhD; Evan A Eskew, PhD; Anna C Fagre, DVM; Maxwell J Farrell, PhD; Sarah Guth, BA; Barbara A Han, PhD; Nancy B Simmons, PhD; Michiel Stock, PhD; Emma C Teeling, PhD and Colin J Carlson, PhD, 10 January 2022, The Lancet Microbe.DOI: 10.1016/S2666-5247(21)00245-7

Originally posted here:

Artificial Intelligence Used To Search for the Next SARS-COV-2 - SciTechDaily

Kantar, the worlds leading data-driven analytics and brand consulting company, today announces the acquisition of all software intellectual property (IP) and the development team from MindIT, the Bologna, Italy-based artificial intelligence (AI) company.

MindIT, a spin-out from Bologna University, is an award-winning machine learning algorithms and AI specialist whose solutions have formed part of Kantars Trade Optimisation Revenue Managementoffer since 2018. The team will join Kantars Trade Optimisation SaaS business to advance their vision for an integrated, end-to-end Revenue Growth Management (RGM) platform.

CPG/FMCG companies use RGM tools to make faster and smarter planning decisions to optimise their trade spend and maximise value realisation. For CPG/FMCG brands, trade spending typically accounts for 25% of annual revenue, making it their second biggest expense after cost of goods sold.

Kantars Trade Optimisation Revenue Management solution is one of the top three RGM platforms globally, managing billions of dollars of clients trade spend. MindIT offers excellent analytics capabilities which, along with the existing Trade Optimisation offer, bridges the gap between insights and execution, giving clients the capabilities they need to deliver optimal revenue management.

Acquiring MindITs award winning AI engine along with its outstanding UX positions Kantar as a market leader in offering an end-to-end RGM solution.

Commenting, Cedric Guyot, Executive Managing Director, Trade Optimisation, Kantar, said: We know that RGM is a top priority for CPG/FMCG companies; with many currently going through the painful journey to digitalise their revenue management processes. Kantars vision for a world-leading end-to-end RGM platform is being co-designed with our top clients. The integration of MindITs capabilities is a crucial step in delivering against this vision and gives Kantar full control of the R&D roadmap for the Trade Optimisation portfolio of solutions.

A number of Kantar clients already use MindIT in combination with Trade Optimisation, facilitating:

Alessio Bonfietti, CEO, MindIT Solutions, added: Since our companys founding in 2017, we have been focused on applying AI to some of the consumer goods industrys biggest challenges. In joining Kantars Trade Optimisation team, one of the worlds largest Revenue Management SaaS vendors, we get to apply our vision at scale and help consumer goods manufacturers wherever they are in their transformation journey.

Commercial terms were not disclosed.

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MindIT Artificial Intelligence team joins Kantar to advance vision for consumer goods revenue management solutions - Kantar UK Insights

When combined with RadNets existing DeepHealth mammography AI division, the two acquisitions provide RadNet with the basis for future offerings for widespread cancer screening programs for the three most prevalent cancers (breast, prostate and lung)

Aidences AI for chest and lung CT scanning is currently used by customers in seven European countries

Aidences leading product is pending FDA approval for use in the United States

With customers in 20 countries worldwide, Quantibs solutions for prostate and brain MRI already have FDA 510(k) clearance in the United States and CE mark in Europe

Dr. Gregory Sorensen, President of RadNets DeepHealth division, will assume leadership responsibility for all of RadNets AI initiatives, including those within Aidence and Quantib

LOS ANGELES, Jan. 24, 2022 (GLOBE NEWSWIRE) -- RadNet, Inc. (NASDAQ: RDNT), a national leader in providing high-quality, cost-effective, fixed-site outpatient diagnostic imaging services through a network of 350 owned and operated outpatient imaging centers, today reported that it has acquired two unrelated Dutch technology companies, Aidence Holding B.V., (Aidence), a leading radiology artificial intelligence (AI) company focusing on clinical solutions for pulmonary nodule management and lung cancer screening and Quantib B.V., (Quantib), a leading radiology AI and machine learning company focusing on clinical solutions for prostate cancer and neurodegeneration.

Founded in 2015 and based in Amsterdam, Netherlands, Aidence is developing and deploying AI clinical applications to empower interpreting medical images and improving patient outcomes. Aidences first commercialized product, Veye Lung Nodules, is an AI-based solution for lung nodule detection and management. This product is CE marked in Europe, where it has a leading position for lung cancer AI screening tools. Aidences solution analyzes thousands of CT scans each week, with customers in seven European countries including France, the Netherlands and the United Kingdom (UK). In 2020, Aidence received an AI Award to help the UKs National Health Service improve lung cancer prognosis, and is playing a leading role in large-scale deployments of regional lung cancer screening programs. Aidences Veye solution was submitted in December for FDA 510(k) clearance in the United States. Upon successful clearance, Aidences solution would be available for use in the United States.

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Founded in 2012 and based in Rotterdam, Netherlands, Quantib has multiple AI-based solutions with both CE mark and FDA 510(k) clearance, including Quantib Prostate for analysis of prostate MR images and Quantib Brain and Quantib ND to quantify brain abnormalities on MRI. Quantib has customers in more than 20 countries worldwide, including the United States. All of Quantibs solutions are deployed through Quantibs AI Node platform which allows for efficient workflow integration and more accelerated regulatory clearance of future products. Quantib Prostate summarizes multiparametric MRI results into an AI heat map, which highlights areas of concern, enabling for faster and more accurate diagnosis of prostate disease. Currently, approximately one in every eight men is being diagnosed with prostate cancer in his lifetime, and according to the American Cancer Society estimates, there will be 268,490 new cases of prostate cancer in the United States in 2022. In addition to Quantib Prostate, Quantib Brain and Quantib Brain ND, Quantib is in advanced development of an AI algorithm for MRI of the breast, which could be complementary to Deep Healths solutions for mammography.

Aidence and Quantib will join RadNets AI division, formed after the earlier acquisition of DeepHealth in 2020, which to date has focused on breast cancer screening and detection. The acquisitions of Aidence and Quantib will further enable RadNets leadership in the development and deployment of AI to improve the care and health of patients.

Dr. Howard Berger, Chairman and Chief Executive Officer of RadNet, noted, We remain convinced that artificial intelligence will have a transforming impact on diagnostic imaging and the field of radiology. We are very pleased to expand our portfolio of AI software into two other cancer screening domains. With the addition of Aidence and Quantib, we will now have effective screening solutions for the three most prevalent cancers. We believe that large population health screening will play an important role for health insurers, health systems and large employer groups in the near future. As the largest owner of diagnostic imaging centers in the United States, RadNet has relationships that can serve to make large-scale screening programs, similar to what mammography is for breast cancer screening, a reality.

Dr. Berger continued, As we have explained in the past, the benefit of cancer screening for population health is evident, driving improved patient outcomes while lowering costs. Specifically, the data showing the benefit of lung cancer screening with chest CT is robust. While RadNet performs more than 100,000 chest CT scans per year, lung cancer screening is dramatically underutilized, and even more so now that screening guidelines have been expanded to include over 14 million people in the US. Though annual lung cancer screening with low dose CT is recommended for high-risk populations by the US Preventative Services Task Force, too few patients are following the screening guidelines. Furthermore, we believe that lung screening will play an important role for those who suffered from COVID-19 and who may have a requirement to monitor longer-term issues with their lungs. We believe the amount of chest CTs could significantly increase if high-risk patients and patients with long-term COVID-19 effects have access to low-cost, effective screening programs that we believe Aidences solutions can facilitate.

Prostate cancer remains another major cause of morbidity and mortality, and MRI has been shown to have a critical role in the diagnosis and management of prostate cancer. While prostate MRI is a growing area of our overall MRI business, the opportunity to create a lower-cost, more accurate service offering to Medicare and private payors allows for a conversation about creating large-scale screening programs for appropriately-qualified male patient populations, akin to how mammography is utilized today to detect and manage breast disease in women. Quantibs Prostate solutions further these objectives. Furthermore, Quantibs commercialized products for brain MRI will be important tools for our business and could have an impact with monitoring Alzheimers patients, particularly those who will undergo some of the newer drug and treatment therapies being developed in the marketplace today, Dr. Berger stated.

Mark-Jan Harte, co-founder and CEO of Aidence added, "The Aidence team, my co-founder, Jeroen van Duffelen and I are enthusiastic about joining forces with the RadNet experts. RadNet is a leader in medical imaging and is committed to furthering the use of AI in radiology. Together, we will accelerate our growth and innovation pipeline to serve clinicians with automated and integrated AI solutions for oncology. Our vision is that data is key to improving the prevention, management and treatment of disease. As an outgrowth of operating 350 facilities in some of the busiest and most populous U.S. markets and performing close to nine million exams per year, RadNets database of images and radiologist reports is one of the largest and most diverse we have identified. I see unprecedented opportunities to further scale adoption, leveraging RadNets capabilities.

Arthur Post Uiterweer, CEO of Quantib noted, "We are thrilled to join the RadNet family. Quantib aims to enable more accurate and efficient clinical decision-making. Being part of RadNet enables us to take a major step towards distributing our solutions and making a much greater impact on patient health and outcomes. We believe our AI Node technology and substantial clinical experience from serving our customers can improve the rate at which future AI innovations are shared across RadNets hundreds of locations and the radiology industry at large.

Dr. Berger concluded, We are excited to add the Aidence and Quantib teams to our AI family. The addition of Aidence and Quantib to our already world-class AI efforts will accelerate the transformation of our business.

Conference Call

Dr. Howard Berger, President and CEO of RadNet, Inc., Dr. Gregory Sorensen, President of DeepHealth and head of RadNets AI Division, Mark-Jan Harte, Chief Executive Officer of Aidence and Arthur Post Uiterweer, Chief Executive Officer of Quantib, will host a conference call to discuss RadNets Artificial Intelligence strategy on Thursday, January 27th, 2022 at 8:00 a.m. Pacific Time (11:00 a.m. Eastern Time).

Conference Call Details:

Date: Thursday, January 27, 2022Time: 11:00 a.m. Eastern TimeDial In-Number: 888-254-3590International Dial-In Number: 929-477-0448

It is recommended that participants dial in approximately 5 to 10 minutes prior to the start of the call. There will also be simultaneous and archived webcasts available at https://viavid.webcasts.com/starthere.jsp?ei=1526026&tp_key=150580c62fAn archived replay of the call will also be available and can be accessed by dialing 844-512-2921 from the U.S., or 412-317-6671 for international callers, and using the passcode 558728.

Forward Looking Statements

This press release contains forward-looking statements within the meaning of the safe harbor provisions of the U.S. Private Securities Litigation Reform Act of 1995. Forward-looking statements are expressions of our current beliefs, expectations and assumptions regarding the future of our business, future plans and strategies, projections, and anticipated future conditions, events and trends. Forward-looking statements can generally be identified by words such as: anticipate, intend, plan, goal, seek, believe, project, estimate, expect, strategy, future, likely, may, should, will and similar references to future periods. Forward-looking statements in this press release include, among others, statements or inferences we make regarding:

Whether Aidences and Quantibs existing or any future products will receive European CE and U.S, FDA510(k) clearance or other regulatory clearance and/or approval necessary for commercialization;

Whether Aidences and Quantibs existing and any future solutions will prove effective, and whether RadNets development and deployment of AI solutions will prove effective for improving the care and health of patients.

Expected market acceptance for Aidences and Quantibs products and the willingness of customers to use or continue to use Aidences and Quantibs products in the future.

Aidences, Quantibs and RadNets ability to develop, maintain and increase their market positions in a competitive environment.

Economic benefits and costs savings anticipated to be derived from AI products and solutions, as well as anticipated importance of, and impact of AI solutions, to RadNets future business operations.

Forward-looking statements are neither historical facts nor assurances of future performance. Because forward-looking statements relate to the future, they are inherently subject to uncertainties, risks and changes in circumstances that are difficult to predict and many of which are outside of our control. Our actual results and financial condition may differ materially from those indicated in the forward-looking statements. Therefore, you should not place undue reliance on any of these forward-looking statements. Important factors that could cause our actual results and financial condition to differ materially from those indicated or implied in the forward-looking statements include, those factors, identified in the Annual Report on Form 10-K, Quarterly Report on Form 10-Q and other reports that RadNet, Inc files from time to time with the Securities and Exchange Commission.

Any forward-looking statement contained in this press release is based on information currently available to us and speaks only as of the date on which it is made. We undertake no obligation to publicly update any forward-looking statement, whether written or oral, that we may make from time to time, whether as a result of changed circumstances, new information, future developments or otherwise, except as required by applicable law.

About RadNet, Inc.

RadNet, Inc. is the leading national provider of freestanding, fixed-site diagnostic imaging services and related information technology solutions (including artificial intelligence) in the United States based on the number of locations and annual imaging revenue. RadNet has a network of 350 owned and/or operated outpatient imaging centers. RadNet's markets include California, Maryland, Delaware, New Jersey, New York, Florida and Arizona. Together with affiliated radiologists, and inclusive of full-time and per diem employees and technicians, RadNet has a total of approximately 9,000 employees. For more information, visit http://www.radnet.com.

CONTACTS:

RadNet, IncMark Stolper, 310-445-2800Executive Vice President and Chief Financial Officer

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RadNet Completes the acquisitions of Aidence Holding B.V. and Quantib B.V. to Address Opportunities in Lung and Prostate Cancer Diagnosis and...

At GE, Artificial Intelligence (AI) development is primarily focused on connecting minds and industrial machines to enable intelligent and user-friendly products and services that move, cure and power the world. GE Research spearheads this charter via the invention and deployment of AI solutions that can execute on industrial devices, at the edge or in the cloud.

AIresearch is practiced as a multidisciplinary exercise at GE, where insights from data-driven machine learning is fused with domain-specific knowledge drawn from areas such as materials, physics, biology and design engineering, to amplify the quality as well as causal-veracity of the predictions derivedwhat we call hybrid AI. We are creating state-of-the-art perception and reasoning capabilities for our AI technology to observe and understand contextual meaning, to improve the performance and life of our assets, industrial systems and human health. We are developing continuous learning systems that teach or learn from other assets or agents and learn from real and virtual experiences to understand and improve behavior.

Some key challenges we tackle include a lack of sufficient labels needed for traditional supervised learning approaches, the need to ingest and link multiple data modalities, and the need to build AI solutions that are interpretable due to safety-related regulatory requirements.

State-of-the-art capabilities in computer vision, machine learning, knowledge representation, reasoning and human system interactions are used to robustly monitor, assess and predict the performance and health of assetsinformation that, when coupled with uncertainty quantification and assurance, provides the information needed to multi-objectively optimize customer-specific metrics.

Examples of customer outcomes enhanced by AI products include reduced downtime on assets through AI-driven proactive intervention (for e.g., airline delays and cancellation), increased throughput (for e.g., optimal control of wind turbine settings to maximize farm output), or reduced costs (for e.g., optimal power plant operation to minimize fuel costs). GE Research is developing and integrating artificial intelligence in healthcare by working to incorporate the technologyinto every aspect of the patient journey (for e.g., improved disease diagnosis, augmenting doctors and clinicians by increasing workflow efficiencies to save precious time).In addition to asset-awareness and management, active AI research areas include Computer Vision, automation, autonomy, User Experience, Augmented Reality and Robotics.

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Artificial Intelligence | GE Research

Scientists, students, and community members came together last month to discuss the promise and pitfalls of artificial intelligence at MITs Computer Science and Artificial Intelligence Laboratory (CSAIL) for the fourth TEDxMIT event held at MIT.

Attendees were entertained and challenged as they explored the good and bad of computing, explained CSAIL Director Professor Daniela Rus, who organized the event with John Werner, an MIT fellow and managing director of Link Ventures; MIT sophomore Lucy Zhao; and grad student Jessica Karaguesian. As you listen to the talks today, Rus told the audience, consider how our world is made better by AI, and also our intrinsic responsibilities for ensuring that the technology is deployed for the greater good.

Rus mentioned some new capabilities that could be enabled by AI: an automated personal assistant that could monitor your sleep phases and wake you at the optimal time, as well as on-body sensors that monitor everything from your posture to your digestive system. Intelligent assistance can help empower and augment our lives. But these intriguing possibilities should only be pursued if we can simultaneously resolve the challenges that these technologies bring, said Rus.

The next speaker, CSAIL principal investigator and professor of electrical engineering and computer science Manolis Kellis, started off by suggesting what sounded like an unattainable goal using AI to put an end to evolution as we know it. Looking at it from a computer science perspective, he said, what we call evolution is basically a brute force search. Youre just exploring all of the search space, creating billions of copies of every one of your programs, and just letting them fight against each other. This is just brutal. And its also completely slow. It took us billions of years to get here. Might it be possible, he asked, to speed up evolution and make it less messy?

The answer, Kellis said, is that we can do better, and that were already doing better: Were not killing people like Sparta used to, throwing the weaklings off the mountain. We are truly saving diversity.

Knowledge, moreover, is now being widely shared, passed on horizontally through accessible information sources, he noted, rather than vertically, from parent to offspring. I would like to argue that competition in the human species has been replaced by collaboration. Despite having a fixed cognitive hardware, we have software upgrades that are enabled by culture, by the 20 years that our children spend in school to fill their brains with everything that humanity has learned, regardless of which family came up with it. This is the secret of our great acceleration the fact that human advancement in recent centuries has vastly out-clipped evolutions sluggish pace.

The next step, Kellis said, is to harness insights about evolution in order to combat an individuals genetic susceptibility to disease. Our current approach is simply insufficient, he added. Were treating manifestations of disease, not the causes of disease. A key element in his labs ambitious strategy to transform medicine is to identify the causal pathways through which genetic predisposition manifests. Its only by understanding these pathways that we can truly manipulate disease causation and reverse the disease circuitry.

Kellis was followed by Aleksander Madry, MIT professor of electrical engineering and computer science and CSAIL principal investigator, who told the crowd, progress in AI is happening, and its happening fast. Computer programs can routinely beat humans in games like chess, poker, and Go. So should we be worried about AI surpassing humans?

Madry, for one, is not afraid or at least not yet. And some of that reassurance stems from research that has led him to the following conclusion: Despite its considerable success, AI, especially in the form of machine learning, is lazy. Think about being lazy as this kind of smart student who doesnt really want to study for an exam. Instead, what he does is just study all the past years exams and just look for patterns. Instead of trying to actually learn, he just tries to pass the test. And this is exactly the same way in which current AI is lazy.

A machine-learning model might recognize grazing sheep, for instance, simply by picking out pictures that have green grass in them. If a model is trained to identify fish from photos of anglers proudly displaying their catches, Madry explained, the model figures out that if theres a human holding something in the picture, I will just classify it as a fish. The consequences can be more serious for an AI model intended to pick out malignant tumors. If the model is trained on images containing rulers that indicate the size of tumors, the model may end up selecting only those photos that have rulers in them.

This leads to Madrys biggest concerns about AI in its present form. AI is beating us now, he noted. But the way it does it [involves] a little bit of cheating. He fears that we will apply AI in some way in which this mismatch between what the model actually does versus what we think it does will have some catastrophic consequences. People relying on AI, especially in potentially life-or-death situations, need to be much more mindful of its current limitations, Madry cautioned.

There were 10 speakers altogether, and the last to take the stage was MIT associate professor of electrical engineering and computer science and CSAIL principal investigator Marzyeh Ghassemi, who laid out her vision for how AI could best contribute to general health and well-being. But in order for that to happen, its models must be trained on accurate, diverse, and unbiased medical data.

Its important to focus on the data, Ghassemi stressed, because these models are learning from us. Since our data is human-generated a neural network is learning how to practice from a doctor. But doctors are human, and humans make mistakes. And if a human makes a mistake, and we train an AI from that, the AI will, too. Garbage in, garbage out. But its not like the garbage is distributed equally.

She pointed out that many subgroups receive worse care from medical practitioners, and members of these subgroups die from certain conditions at disproportionately high rates. This is an area, Ghassemi said, where AI can actually help. This is something we can fix. Her group is developing machine-learning models that are robust, private, and fair. Whats holding them back is neither algorithms nor GPUs. Its data. Once we collect reliable data from diverse sources, Ghassemi added, we might start reaping the benefits that AI can bring to the realm of health care.

In addition to CSAIL speakers, there were talks from members across MITs Institute for Data,Systems, and Society; the MIT Mobility Initiative; the MIT Media Lab; and the SENSEableCity Lab.

The proceedings concluded on that hopeful note. Rus and Werner then thanked everyone for coming. Please continue to reflect about the good and bad of computing, Rus urged. And we look forward to seeing you back here in May for the next TEDxMIT event.

The exact theme of the spring 2022 gathering will have something to do with superpowers. But if Decembers mind-bending presentations were any indication the May offering is almost certain to give its attendees plenty to think about. And maybe provide the inspiration for a startup or two.

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The promise and pitfalls of artificial intelligence explored at TEDxMIT event - MIT News