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Dublin, May 27, 2022 (GLOBE NEWSWIRE) -- The "Global Artificial Intelligence in Space Growth Opportunities" report has been added to ResearchAndMarkets.com's offering.

The multiple NewSpace start-ups entering the space industry as downstream services providers have created a fragmented market with increasing competition. Services providers are evolving their capabilities, including AI, to differentiate themselves.

AI-enabled space services will become an industry-wide trend, particularly in the downstream and satellite operations areas. The competition is slowly developing in the market and will increase in the next 5 years.

If you are an AI developer or interested in understanding how ICT capabilities such as AI, this study will help you get started with your research.

The study provides an assessment of the state of artificial intelligence (AI) deployment in the global space industry. The analysis covers key segments of the space industry where AI deployment could add value and explores the potential impact of the growing NewSpace economy. The research lists important satellite constellations and discusses their influence on the need for suitable AI capabilities.

Key Issues Addressed:

What are the key satellite constellations slated for launch up to 2040?

What are the drivers and restraints that will impact deployment of AI in the space industry?

Which segments of the space industry will gain value from AI capabilities?

What are the growth opportunities in the space industry for ICT market participants that specialize in AI?

Key Topics Covered:

1. Strategic Imperatives

Why is it Increasingly Difficult to Grow?

The Impact of the Top 3 Strategic Imperatives on Artificial Intelligence (AI) in the Space Industry

Growth Opportunities Fuel the Growth Pipeline Engine

2. Growth Opportunity Analysis

Growth Drivers

Growth Restraints

Satellite Constellations

AI in Automated Constellation Operations

AI in Space Situational Awareness Capabilities

AI in Satellite Data Processing

AI in Deep Space Missions

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3. Growth Opportunity Universe - AI in Space

Growth Opportunity 1: Customized AI Solutions for NewSpace Missions

Growth Opportunity 2: Customized AI Solutions for Deep Space Missions

Growth Opportunity 3: Customized AI Solutions for Downstream Services

For more information about this report visit https://www.researchandmarkets.com/r/j5aw7t

About ResearchAndMarkets.comResearchAndMarkets.com is the world's leading source for international market research reports and market data. We provide you with the latest data on international and regional markets, key industries, the top companies, new products and the latest trends.

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Growth Opportunities for Artificial Intelligence in the Global Space Industry: Customized AI Solutions for NewSpace Missions, Deep Space Missions and...

The avenue to adopting Artificial intelligence (AI) isnt always straightforward. Business leaders must collect the appropriate data, identify the right technologies for their firm, and teach their employees how to construct and enhance AI models. Even if leaders have identified the ideal AI for their company and have properly on boarded it, theres still a chance they wont receive what they want or need from it.

Artificial intelligence (AI) has found its route into practically every field, and its popularity is only growing. AI can enhance productivity and deliver valuable insights to corporate executives when used effectively. However, many leaders are confused about how to employ technology effectively, and a misguided AI program might do more damage than good.

Best practices must be followed to ensure that AI benefits rather than destroys the organization. Here are three pitfalls to avoid when using AI to achieve business objectives.

Not having the proper team size

Most firms are aware that AI solutions are robust, but many overlook the complexity they entail. AI implementations need an adequately sized crew to keep the algorithms in top form. As a result, many corporations choose to outsource AI development projects or extend their AI development teams using on-demand staffing services.

Also Read: Three Potent Ways Artificial Intelligence Can Assist With Pricing

Failure to retain AI effectiveness

To be a successful solution over time, AI will require involvement. For example, if AI fails or corporate objectives shift, AI procedures must also shift. If nothing is done or proper intervention is not implemented, AI advice may obstruct or contradict corporate goals.

Take, for example, AI-based pricing systems. If the AI system is not put up to adapt to market changes, its efficacy will suffer. To put it another way, the AI system must make adjustments to the current market as the source data changes.

The performance of the sales staff is one approach to assessing AI efficacy. Effective sales teams want to follow price suggestions that help them meet their objectives. Therefore, they should be willing to have their performance evaluated based on how well they use AI that delivers value. Profit margin and revenue are two common pricing-related KPIs. KPI tracking may also reveal which sales teams or team individuals use AI. If the recommendations are not helping them meet their KPIs, its time to step in.

To reduce the strain on AI users, interventions should be scalable and repeatable through highly automated procedures. The intervention should consist of two parts: examining the AI systems inputs and confirming that its output is as intended. Each of these activities should be done on a regular basis throughout the year.

Also Read: Artificial Intelligence and its Impact on The Future of Recruitment

Ignoring the architectural fit

Despite the urge to get started with AI, it can be challenging to reap the benefits that organizations want if they lack the proper data infrastructure, leading to a slew of errors.

Before contemplating AI, a business must be able to acquire, store, and process data in order to get value from it. If they dont, firms risk employing inexperienced analytics, making teams more vulnerable to a variety of mistakes.

Check Out The NewEnterprisetalk Podcast.For more such updates follow us on Google NewsEnterprisetalk News.

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Three Key Artificial Intelligence Adoption Pitfalls to Avoid in 2022 and Beyond - EnterpriseTalk

Using machine learning along with data from wave tank experiments, MIT engineers have found a way to model how waves break. With this, you could simulate waves to help design structures better, more efficiently, and without huge safety factors, says Themis Sapsis. Credit: iStockphoto

The new models predictions should help researchers improve ocean climate simulations and hone the design of offshore structures.

Waves break once they swell to a critical height, before cresting and crashing into a shower of droplets and bubbles. These waves can be as big as a surfers point break and as small as a gentle ripple rolling to shore. For decades, the dynamics of how and when a wave breaks have been too complex for scientists to predict.

Now, MIT engineers have found a new method for modeling how waves break. The researchers tweaked equations that have previously been used to predict wave behavior using machine learning and data from wave-tank tests. Engineers frequently use such equations to help them design robust offshore platforms and structures. But until now, the equations have not been able to capture the complexity of breaking waves.

The researchers discovered that the modified model predicted how and when waves would break more accurately. The model, for example, assessed a waves steepness shortly before breaking, as well as its energy and frequency after breaking, more accurately than traditional wave equations.

Their results, published recently in the journal Nature Communications, will help scientists understand how a breaking wave affects the water around it. Knowing precisely how these waves interact can help hone the design of offshore structures. It can also improve predictions for how the ocean interacts with the atmosphere. Having better estimates of how waves break can help scientists predict, for instance, how much carbon dioxide and other atmospheric gases the ocean can absorb.

Wave breaking is what puts air into the ocean, says study author Themis Sapsis, an associate professor of mechanical and ocean engineering and an affiliate of the Institute for Data, Systems, and Society at MIT. It may sound like a detail, but if you multiply its effect over the area of the entire ocean, wave breaking starts becoming fundamentally important to climate prediction.

The studys co-authors include lead author and MIT postdoc Debbie Eeltink, Hubert Branger, and Christopher Luneau of Aix-Marseille University, Amin Chabchoub of Kyoto University, Jerome Kasparian of the University of Geneva, and T.S. van den Bremer of Delft University of Technology.

To predict the dynamics of a breaking wave, scientists typically take one of two approaches: They either attempt to precisely simulate the wave at the scale of individual molecules of water and air, or they run experiments to try and characterize waves with actual measurements. The first approach is computationally expensive and difficult to simulate even over a small area; the second requires a huge amount of time to run enough experiments to yield statistically significant results.

The MIT team instead borrowed pieces from both approaches to develop a more efficient and accurate model using machine learning. The researchers started with a set of equations that is considered the standard description of wave behavior. They aimed to improve the model by training the model on data of breaking waves from actual experiments.

We had a simple model that doesnt capture wave breaking, and then we had the truth, meaning experiments that involve wave breaking, Eeltink explains. Then we wanted to use machine learning to learn the difference between the two.

The researchers obtained wave breaking data by running experiments in a 40-meter-long tank. The tank was fitted at one end with a paddle which the team used to initiate each wave. The team set the paddle to produce a breaking wave in the middle of the tank. Gauges along the length of the tank measured the waters height as waves propagated down the tank.

It takes a lot of time to run these experiments, Eeltink says. Between each experiment, you have to wait for the water to completely calm down before you launch the next experiment, otherwise they influence each other.

In all, the team ran about 250 experiments, the data from which they used to train a type of machine-learning algorithm known as a neural network. Specifically, the algorithm is trained to compare the real waves in experiments with the predicted waves in the simple model, and based on any differences between the two, the algorithm tunes the model to fit reality.

After training the algorithm on their experimental data, the team introduced the model to entirely new data in this case, measurements from two independent experiments, each run at separate wave tanks with different dimensions. In these tests, they found the updated model made more accurate predictions than the simple, untrained model, for instance making better estimates of a breaking waves steepness.

The new model also captured an essential property of breaking waves known as the downshift, in which the frequency of a wave is shifted to a lower value. The speed of a wave depends on its frequency. For ocean waves, lower frequencies move faster than higher frequencies. Therefore, after the downshift, the wave will move faster. The new model predicts the change in frequency, before and after each breaking wave, which could be especially relevant in preparing for coastal storms.

When you want to forecast when high waves of a swell would reach a harbor, and you want to leave the harbor before those waves arrive, then if you get the wave frequency wrong, then the speed at which the waves are approaching is wrong, Eeltink says.

The teams updated wave model is in the form of an open-source code that others could potentially use, for instance in climate simulations of the oceans potential to absorb carbon dioxide and other atmospheric gases. The code can also be worked into simulated tests of offshore platforms and coastal structures.

The number one purpose of this model is to predict what a wave will do, Sapsis says. If you dont model wave breaking right, it would have tremendous implications for how structures behave. With this, you could simulate waves to help design structures better, more efficiently, and without huge safety factors.

Reference: Nonlinear wave evolution with data-driven breaking by D. Eeltink, H. Branger, C. Luneau, Y. He, A. Chabchoub, J. Kasparian, T. S. van den Bremer & T. P. Sapsis, 29 April 2022, Nature Communications.DOI: 10.1038/s41467-022-30025-z

This research is supported, in part, by the Swiss National Science Foundation, and by the U.S. Office of Naval Research.

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MIT Engineers Use Artificial Intelligence To Capture the Complexity of Breaking Waves - SciTechDaily

Leptospirosis, a disease that dogs can get from drinking water contaminated with Leptospira bacteria, can cause kidney failure, liver disease and severe bleeding into the lungs. Early detection of the disease is crucial and may mean the difference between life and death.

Veterinarians and researchers at the University of California, Davis, School of Veterinary Medicine have discovered a technique to predict leptospirosis in dogs through the use of artificial intelligence. After many months of testing various models, the team has developed one that outperformed traditional testing methods and provided accurate early detection of the disease. The groundbreaking discovery was published in Journal of Veterinary Diagnostic Investigation.

Traditional testing for Leptospira lacks sensitivity early in the disease process, said lead author Krystle Reagan, a board-certified internal medicine specialist and assistant professor focusing on infectious diseases. Detection also can take more than two weeks because of the need to demonstrate a rise in the level of antibodies in a blood sample. Our AI model eliminates those two roadblocks to a swift and accurate diagnosis.

The research involved historical data of patients at the UC Davis Veterinary Medical Teaching Hospital that had been tested for leptospirosis. Routinely collected blood work from these 413 dogs was used to train an AI prediction model. Over the next year, the hospital treated an additional 53 dogs with suspected leptospirosis. The model correctly identified all nine dogs that were positive for leptospirosis (100% sensitivity). The model also correctly identified approximately 90% of the 44 dogs that were ultimately leptospirosis negative.

The goal for the model is for it to become an online resource for veterinarians to enter patient data and receive a timely prediction.

AI-based, clinical decision making is going to be the future for many aspects of veterinary medicine, said School of Veterinary Medicine Dean Mark Stetter. I am thrilled to see UC Davis veterinarians and scientists leading that charge. We are committed to putting resources behind AI ventures and look forward to partnering with researchers, philanthropists, and industry to advance this science.

Leptospirosis is a life-threatening zoonotic disease, meaning it can transfer from animals to humans. As the disease is also difficult to diagnose in people, Reagan hopes the technology behind this groundbreaking detection model has translational ability into human medicine.

My hope is this technology will be able to recognize cases of leptospirosis in near real time, giving clinicians and owners important information about the disease process and prognosis, said Reagan. As we move forward, we hope to apply AI methods to improve our ability to quickly diagnose other types of infections.

Reagan is a founding member of the schools Artificial Intelligence in Veterinary Medicine Interest Group comprising veterinarians promoting the use of AI in the profession. This research was done in collaboration with members of UC Davis Center for Data Science and Artificial Intelligence Research, led by professor of mathematics Thomas Strohmer. He and his students were involved in the algorithm building. The center strives to bring together world-renowned experts from many fields of study with top data science and AI researchers to advance data science foundations, methods, and applications.

Reagans group is actively pursuing AI for prediction of outcome for other types of infections, including a prediction model for antimicrobial resistant infections, which is a growing problem in veterinary and human medicine. Previously, the group developed an AI algorithm to predict Addisons disease with an accuracy rate greater than 99%.

Other authors include Shaofeng Deng, Junda Sheng, Jamie Sebastian, Zhe Wang, Sara N. Huebner, Louise A. Wenke, Sarah R. Michalak and Jane E. Sykes. Funding support comes from the National Science Foundation.

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Using Artificial Intelligence to Predict Life-Threatening Bacterial Disease in Dogs - University of California, Davis

Artificial intelligence is revolutionizing the iGaming industry

As the number of players that play for real money on a PC or mobile device grows, security measures are needed to provide a secure environment. AI has shown to be a great way for operators to protect player privacy while processing payments in the safest way possible.

Artificial intelligence is becoming more prevalent in many areas of daily life, including the online gaming industry. To give a better experience for gamers, both land-based and online gaming have progressed and used cutting-edge technologies. Players now have a safer and more realistic way to enjoy the same games they would find in a conventional casino thanks to the advent of artificial intelligence in online casinos.

To give the most latest games and services, the online gaming industry makes use of curated space and smart algorithms. When you visit a website, such as a betting site, algorithms use the information you provide to predict what you desire.

AI, which is essentially a computer system that replicates human intelligence while making decisions, is at the heart of many algorithms. We examine the impact of artificial intelligence (AI) on online casinos and how it may provide players with a better and safer way to play games from major game developers from the comfort of their own homes.

The use of artificial intelligence (AI) increases the online safety of players.

As the number of players playing for real money on a PC or mobile device grows, security measures are needed to provide a secure environment. AI has shown to be a great way for operators to protect player privacy while processing payments in the most secure way possible. To provide a safe atmosphere for making bets, the greatest websites will employ strong AI technology.

SSL encryption is one example of AI in action. In online gambling settings, this is one of the most crucial cybersecurity considerations. Its a security feature that helps secure sensitive data during transaction processing. It prevents account hacking and fraud by keeping information out of the hands of third parties.

Bettors want to know that their funds and account information are always safe. Websites may give amazing levels of security with the use of contemporary technology, avoiding the chance of banking information or credit card data being leaked to hackers or criminals.

Members want a personalized experience while playing online games, and AI can help with that. AI will gather information from players to determine which games they play the most, how much they wager, and even how frequently a site is visited. These particulars are then used to create projections. When you join your account, operators may then customise your online gaming experience by proposing specific games.

Artificial intelligence improves the ability of websites to detect cheaters and fraudsters. When AI software is used to capture behavioural patterns of members, the data may be used to determine if somebody is cheating when playing games. While AI has a favourable impact on cheating, technology does have a drawback. Gamblers can also employ AI systems to get over detection measures in place at a casino.

The capacity to discern specific patterns that can identify players who are cheating or attempting to influence game results has consequences for online casinos. Those who are found to be engaging in unfair play may have their accounts suspended pending an investigation. While cheating is impossible while playing video games, it is possible when playing table games or live casino games that are not supervised by a random number generator.

Artificial intelligence is paving the path for a more secure and enjoyable online gaming experience. This technology is certain to transform the way we play in the future, with enhanced experiences, tailored suggestions, greater security measures, and the ability to aid in the prevention of gambling problems. The casino industry is always changing, and artificial intelligence will have an impact on both how we play online gambling and how casino games are created.

edited and proofread by nikita sharma

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Artificial Intelligence Is Revolutionizing The IGaming Industry In India In 2022 - Inventiva

As artificial intelligence (AI) becomes increasingly embeddedinto products, services, and business decisions, state and locallawmakers have been considering and passing a range of lawsaddressing AI. These vary from laws that promote AI to moreregulatory approaches that impose obligations on AI in specificareas. In a development that parallels the evolution of privacylaws, states and localities have moved ahead with initiatives ontheir own. However, unlike in privacy, where a set of legislativeapproaches has been debated for years, approaches to dealing withAI have been far more varied and scattershot. This kind of apatchwork approach, if it continues, may create issues withmanaging regulatory compliance for many uses of AI acrossjurisdictions.

States and Localities Are Beginning to Move Forward with aPiecemeal Approach to AI

In 2021, five jurisdictions Alabama, Colorado, Illinois,Mississippi, and New York City enacted legislationspecifically directed at the use of AI. Their approaches varied,from creating bodies to study the impact of AI to regulating theuse of AI in contexts where governments have been concerned aboutincreased risk of harm to individuals.

Some of these laws have focused on promoting AI. For instance,Alabama's law establishes a council to review and advise theGovernor, the legislature, and other interested parties on the useand development of advanced technology and AI in the state. TheMississippi law implements a mandatory K-12 curriculum thatincludes instruction in AI.

Conversely, some laws are more regulatory and skeptical of AI.For example, Illinois has adopted two AI laws onethatdevelopsa task force to study the impactof emerging technologies, including AI, on the future of work andanother thatmandatesnotice, consent, and reportingobligations for employers that use AI in hiring. Under existingIllinois law, an employer that asks applicants to record videointerviews and uses an AI analysis must: (1) notify the applicantthat AI may be used to analyze the applicant's video interviewand consider the applicant's fitness for the position; (2)provide each applicant with information explaining how the AI worksand what general types of characteristics the AI uses to evaluateapplicants; and (3) obtain consent from the applicant. The law alsolimits the sharing of the videos and extends to applicants a rightto delete the videos. A 2021 amendment imposes reportingrequirements on an employer that relies solely upon an AI analysisof a video interview to determine whether an applicant will beselected for an in-person interview. The state Department ofCommerce and Economic Opportunity is required to annually analyzecertain demographic data reported and report to the Governor andGeneral Assembly whether the data discloses a racial bias in theuse of AI.

Colorado's law takes a sectoral approach,prohibitinginsurers from using anyinformation sources as well as any algorithms or predictive modelsin a way that produces unfair discrimination. Unfair discriminationincludes "the use of one or more external consumer data andinformation sources, as well as algorithms or predictive modelsusing external consumer data and information sources, that have acorrelation to race, color, national or ethnic origin, religion,sex, sexual orientation, disability, gender identity, or genderexpression, and that use results in a disproportionately negativeoutcome for such classification or classifications, which negativeoutcome exceeds the reasonable correlation to the underlyinginsurance practice, including losses and costs forunderwriting." This law comes in addition to Colorado'scomprehensive privacy law, theColorado Privacy Act, set to go into effect onJuly 1, 2023, which provides consumers with a right to opt out ofthe processing of their personal data for purposes of targetedadvertising, the sale of personal data, or automated profiling infurtherance of decisions that produce legal or similarlysignificant effects.

In late 2021, New York City notablyenacteda specific algorithmicaccountability law, becoming the first jurisdiction in the UnitedStates to require algorithms used by employers in hiring orpromotion to be audited for bias. New York City's law bars AIhiring systems that do not pass annual audits checking for race- orgender-based discrimination. The bill would require the developersof such AI tools to disclose the job qualifications andcharacteristics that will be used by the tool and would provideemployment candidates the option of choosing an alternative processfor employers to review their application. The law imposes fines onemployers or employment agencies of up to $1,500 per violation.

California's Privacy Regulations May Also TargetAI

California's California Privacy Protection Agency (CPPA), the new agency charged with rulemakingand enforcement authority over the California Privacy Rights Act(CPRA), is expected to issue regulations governing AI by 2023. Thestatute specifically addresses a consumer's right to understandand opt out of automating decision-making technologies such as AIand machine learning. In particular, the agency is charged with"[i]ssuing regulations governing access and opt-out rightswith respect to businesses' use of automated decisionmakingtechnology, including profiling and requiring businesses'response to access requests to include meaningful information aboutthe logic involved in those decisionmaking processes, as well as adescription of the likely outcome of the process with respect tothe consumer."

In September 2021, the CPPAreleasedan Invitation for PreliminaryComments on Proposed Rulemaking (Invitation) and accepted commentsthrough November 8, 2021. The Invitation to comment issued by theCPPA asked four questions regarding interpretation of theagency's automated decision-making rulemaking authority:

While the statute calls for final rules to be adopted by July2022, at a February 17 CPPA board meeting, Executive DirectorAshkan Soltani announced that draft regulations will be delayed. Aswe've previouslydiscussed, this effort in California toregulate certain automated decision-making processes may open thedoor to greater regulation of AI and should be watchedclosely.

Even as the federal governmentlooks more closelyat AI, some states andlocalities appear to be poised to jump ahead. Indeed, many otherstates continue to debate AI proposals in 2022. Companiesdeveloping and deploying AI should continue to monitor this area asthe regulatory landscape develops.

2022 Wiley Rein LLP

The content of this article is intended to provide a generalguide to the subject matter. Specialist advice should be soughtabout your specific circumstances.

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States And Localities Begin To Focus On Use Of Artificial Intelligence - New Technology - United States - Mondaq

BROSSARD, Quebec, May 26, 2022 (GLOBE NEWSWIRE) -- Diagnos Inc. (DIAGNOS, the Corporation or we) (TSX Venture: ADK) (OTCQB: DGNOF) a leader in early detection of critical health issues through the use of its FLAIRE platform based on Artificial Intelligence (AI), is pleased to announce, in partnership with the Centre hospitalier de lUniversit de Montral (CHUM), the launch of the testing phase for autonomous artificial intelligence solutions dedicated to diabetic retinopathy screening.

It is with great pleasure that DIAGNOS and the CHUM are launching the clinical study which will make it possible to establish, with precision, the level of performance of the autonomous algorithms utilized for the identification of the disease and its classification by level of severity, with patients suffering from diabetic retinopathy.

This phase marks the beginning of the final step of this partnership aiming for the automated detection of diabetic retinopathy, which began in June 2018, says Mr. Andr Larente, President of DIAGNOS. "This stage of testing and performance analysis was approved by the CHUM, following the positive results obtained over the past few months by CHUM clinicians as part of the rigorous independent evaluation process applied to analyze the performance of the classification algorithms by level of severity run in autonomous mode.

This project is well aligned with the CHUM's desire to improve accessibility of its services to the patients through partnerships that nurture the development and integration of innovative solutions. said Dr. Fabrice Brunet, President and CEO of the CHUM.

The retinal fundus images of more than 600 diabetic patients from the CHUM's endocrinology Department will be analyzed by the NeoRetina artificial intelligence algorithm of deep learning from DIAGNOS in collaboration with the CHUM's ophthalmology Department. This solution makes possible the identification of lesions caused by diabetic retinopathy and the classification of the evolution of the disease by level of severity. Performed in a double-blind comparison mode, this test will allow the CHUM professionals to precisely establish the level of performance and precision of the NeoRetina autonomous algorithms.

As for the benefits provided by this automated screening technology, the Head of the endocrinology Department at the CHUM, Dr. Andre Boucher, is delighted with the increased volume of screenings that can be performed: Diabetic retinopathy affects a good number of diabetic patients. However, considering that in the initial stages of the disease, patients are generally asymptomatic, and symptoms often only appear at the more advanced stages of the disease, this means of independent screening, easily carried out at the time of the annual examination of the patients, will allow us to reduce the number of complications that can lead to blindness.

Dr. Salim Lahoud, Head of the CHUM's ophthalmology Department, agrees with his colleague and affirms that the screening for diabetic retinopathy carried out using the artificial intelligence solutions developed by DIAGNOS will contribute to prioritizing and improving the speed of patient care by the ophthalmologists of his department. In addition to being accurate, efficient, and swift, DIAGNOS' artificial intelligence screening solutions permit a significant reduction of the costs and the early identification of pathologies. Diabetes being the second leading cause of blindness in Canada, the integration of these solutions into medical follow-up programs, such as those offered to diabetic patients, will allow numerous patients to preserve their sight!

About Centre hospitalier de lUniversit de Montral (CHUM)The Centre hospitalier de lUniversit de Montral is an innovative hospital devoted to serving patients. It provides the highest quality specialized and ultraspecialized care to patients and the general public all over Qubec. Through its unique expertise and innovations, its aim is to improve the health of the adult and aging population. As the Universit de Montral hospital, CHUM is dedicated to care, research, teaching, health promotion, and the assessment of technology and health intervention methods in order to continually improve the quality of care and the health of the population. Since fall 2017, patients and their families have been able to enjoy a renewed hospital experience at CHUM's new facilities.

Additional information is available at: http://www.chumontreal.qc.ca

About DIAGNOSDIAGNOS is a publicly traded Canadian corporation dedicated to early detection of critical health problems based on its FLAIRE Artificial Intelligence (AI) platform. FLAIRE allows for quick modifying and developing of applications such as CARA (Computer Assisted Retina Analysis). CARAs image enhancement algorithms provide sharper, clearer and easier-to-analyze retinal images. CARA is a cost-effective tool for real-time screening of large volumes of patients. CARA has been cleared for commercialization by the following regulators: Health Canada, the FDA (USA), CE (Europe), COFEPRIS (Mexico) and Saudi FDA (Saudi Arabia).

Additional information is available at http://www.diagnos.com and http://www.sedar.com

This news release contains forward-looking information. There can be no assurance that forward-looking information will prove to be accurate, as actual results and future events could differ materially from those anticipated in these statements. DIAGNOS disclaims any intention or obligation to publicly update or revise any forward-looking information, whether as a result of new information, future events or otherwise. The forward-looking information contained in this news release is expressly qualified by this cautionary statement.

Neither the TSX Venture Exchange nor its Regulation Services Provider (as that term is defined in the policies of the TSX Venture Exchange) accepts responsibility for the adequacy or accuracy of this release.

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DIAGNOS and the CHUM are launching the testing phase of autonomous artificial intelligence solutions dedicated to diabetic retinopathy screening -...

Even as rapid improvements in artificial intelligence have led to speculation over significant changes in the health care landscape, the adoption of AI in health care has been minimal. A 2020 survey by Brookings, for example, found that less than 1 percent of job postings in health care required AI-related skills.

The Abdul Latif Jameel Clinic for Machine Learning in Health (Jameel Clinic), a research center within the MIT Schwarzman College of Computing, recently hosted the MITxMGB AI Cures Conference in an effort to accelerate the adoption of clinical AI tools by creating new opportunities for collaboration between researchers and physicians focused on improving care for diverse patient populations.

Once virtual, the AI Cures Conference returned to in-person attendance at MITs Samberg Conference Center on the morning of April 25, welcoming over 300 attendees primarily made up of researchers and physicians from MIT and Mass General Brigham (MGB).

MIT President L. Rafael Reif began the event by welcoming attendees and speaking to the transformative capacity of artificial intelligence and its ability to detect, in a dark river of swirling data, the brilliant patterns of meaning that we could never see otherwise. MGBs president and CEO Anne Klibanski followed up by lauding the joint partnership between the two institutions and noting that the collaboration could have a real impact on patients lives and help to eliminate some of the barriers to information-sharing.

Domestically, about $20 million in subcontract work currently takes place between MIT and MGB. MGBs chief academic officer and AI Cures co-chair Ravi Thadhani thinks that five times that amount would be necessary in order to do more transformative work. We could certainly be doing more, Thadhani said. The conference just scratched the surface of a relationship between a leading university and a leading health-care system.

MIT Professor and AI Cures Co-Chair Regina Barzilay echoed similar sentiments during the conference. If were going to take 30 years to take all the algorithms and translate them into patient care, well be losing patient lives, she said. I hope the main impact of this conference is finding a way to translate it into a clinical setting to benefit patients.

This years event featured 25 speakers and two panels, with many of the speakers addressing the obstacles facing the mainstream deployment of AI in clinical settings, from fairness and clinical validation to regulatory hurdles and translation issues using AI tools.

On the speaker list, of note was the appearance of Amir Khan, a senior fellow from the U.S. Food and Drug Administration (FDA), who fielded a number of questions from curious researchers and clinicians on the FDAs ongoing efforts and challenges in regulating AI in health care.

The conference also covered many of the impressive advancements AI made in the past several years: Lecia Sequist, a lung cancer oncologist from MGB, spoke about her collaborative work with MGB radiologist Florian Fintelmann and Barzilay to develop an AI algorithm that could detect lung cancer up to six years in advance. MIT Professor Dina Katabi presented with MGBs doctors Ipsit Vahia and Aleksandar Videnovic on an AI device that could detect the presence of Parkinsons disease simply by monitoring a persons breathing patterns while asleep. It is an honor to collaborate with Professor Katabi, Videnovic said during the presentation.

MIT Assistant Professor Marzyeh Ghassemi, whose presentation concerned designing machine learning processes for more equitable health systems, found the longer-range perspectives shared by the speakers during the first panel on AI changing clinical science compelling.

What I really liked about that panel was the emphasis on how relevant technology and AI has become in clinical science, Ghassemi says. You heard some panel members [Eliezer Van Allen, Najat Khan, Isaac Kohane, Peter Szolovits] say that they used to be the only person at a conference from their university that was focused on AI and ML [machine learning], and now were in a space where we have a miniature conference with posters just with people from MIT.

The 88 posters accepted to AI Cures were on display for attendees to peruse during the lunch break. The presented research spanned different areas of focus from clinical AI and AI for biology to AI-powered systems and others.

I was really impressed with the breadth of work going on in this space, Collin Stultz, a professor at MIT, says. Stultz also spoke at AI Cures, focusing primarily on the risks of interpretability and explainability when using AI tools in a clinical setting, using cardiovascular care as an example of showing how algorithms could potentially mislead clinicians with grave consequences for patients.

There are a growing number of failures in this space where companies or algorithms strive to be the most accurate, but do not take into consideration how the clinician views the algorithm and their likelihood of using it, Stultz said. This is about what the patient deserves and how the clinician is able to explain and justify their decision-making to the patient.

Phil Sharp, MIT Institute Professor and chair of the advisory board for Jameel Clinic, found the conference energizing and thought that the in-person interactions were crucial to gaining insight and motivation, unmatched by many conferences that are still being hosted virtually.

The broad participation by students and leaders and members of the community indicate that theres an awareness that this is a tremendous opportunity and a tremendous need, Sharp says. He pointed out that AI and machine learning are being used to predict the structures of almost everything from protein structures to drug efficacy. It says to young people, watch out, there might be a machine revolution coming.

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Can artificial intelligence overcome the challenges of the health care system? - MIT News

The age of artificial intelligence (A.I.) is finally upon us. Consumer applications of A.I., in particular, have come a long way, leading to more accurate search results for online shoppers, allowing apps and websites to make more personalized recommendations, and enabling voice-activated digital assistants to better understand us.

As impressive as these uses of A.I. are, they only hint at how this game-changing technology will be applied in business. Because the goal of business A.I. is to help the companies that drive our global economy learn from their data to become vastly more resilient, adaptive, and innovative.

We all know there is tremendous potential value in data, which continues to grow exponentially. In fact, the world is creating 2.5 quintillion bytes of data every day (that's 2.5 followed by 18 zeros). To harness that potential, companies need A.I. to make sense of the data, and hybrid cloud computing platforms that can distribute it across organizations.

The economic opportunity behind these technologies is enormous, given that business is only about 10 percent of the way to realizing A.I.'s full potential. Fortunately, we are making steady progress, with the number of organizations poised to integrate A.I. into their business processes and workflows growing rapidly. A recent IBM study showed that more than a third of the companies surveyed were using some form of A.I. to save time and streamline operations.

Take the challenge of demographic shifts. A.I., in conjunction with hybrid cloud, is helping many companies automate certain routine business activities, and move people to higher-value work. In manufacturing, a factory floor operator can now rely on A.I. to detect defects that are invisible to the human eye. In health care, A.I.-enabled virtual agents can handle millions of calls at once. In the energy sector, autonomous robots can use cloud and A.I. to analyze data at the edge to improve equipment uptime and prevent power outages. Another example: IBM is helping McDonald's launch an automated order-taking drive-thru experience that benefits both customers and restaurant crews.

Then there is the massive challenge of cybersecurity. The inherent business value of data makes it a prime target for hackers. But with about a half-million unfilled cybersecurity jobs in the U.S. alone, security teams are stretched dangerously thin. Most data breaches today take an average of 287 days to detect and contain. That is clearly unacceptable. With A.I.'s ability to analyze threat information at scale, we can help reduce that timeline to a few days or even hours.

A.I. is not only making businesses smarter, stronger, and safer; it is also accelerating scientific discovery. A.I. can speed the ingestion of scientific papers and the extraction of knowledge by 1,000x compared with human experts. At the height of the global pandemic, IBM adapted our cloud-based A.I. platform to comb through thousands of scientific papers about the coronavirus. We then shared relevant data with fellow members of the Covid-19 High Performance Computing Consortium to speed up drug design.

As these use cases show, for business A.I. to be effective it must also be trustworthy and explainable. It is one thing to rely on an A.I. application to order dinner for us. It is quite another to have it drive a car or make potentially life-or-death recommendations about a course of medical treatment.

For this reason, technology companies must be clear about who trains their A.I. systems, what data is used in that training, and, most important, what went into their algorithm's recommendations. Developing responsible, ethical A.I. requires that we remove any potential for human bias to influence this process.

We must also recognize that the purpose of A.I. systems is to augment--not replace--human intelligence. Throughout history, the introduction of new technologies has led to sea changes in the way businesses create value while eliminating burdensome and repetitive tasks for humans. These include everything from windmills to the printing press to the steam engine and factory robotics. This is how progress happens. Artificial intelligence will create even greater progress, but only if it is deployed responsibly.

Businesses have the potential to usher in a new and unprecedented era of greater productivity, faster insights, better decision-making, and enhanced employee and customer experiences through the combination of A.I. and hybrid cloud. Given the enthusiasm of our clients for these transformative technologies, the business A.I. spring can't come soon enough.

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Why Artificial Intelligence Creates an Unprecedented Era of Opportunity in the Near Future - Inc.

SAN JOSE, Calif. (KRON) Security cameras are everywhere, but artificial intelligence is changing the way they are being used. KRON4 tested one such system at a grocery store in San Jose to see how A.I. is preventing shoplifting.

Picture this scenario, someone walks into Lunardis Market on Meridian Street.

They decide to take home a nice bottle of Merlot only they also decide not to pay. But before they can walk out the door, the store manager steps in and stops them.

The would-be thief is stopped in their tracks, thanks to state-of-the-art technology. Store director Rick Sanchez says theyve been using A.I. to prevent shoplifters for six months.

Sanchez says there hasnt been a time the A.I. incorrectly spotted a would-be thief.

The A.I. is able to detect thieves based on body gestures.

After those gestures are detected, it alerts a manager in real-time on their phone.

Sanchez says gets 10 to 12 alerts in real-time everyday.

It tells us basically on the camera, what aisle they are on, Sanchez said. We dont have to run anywhere we just go right to the aisle they are at.

Sanchez says certain merchandise is always coming up missing, including meat items, deli cheese items, stealing a lot of medicines, liquor, beer, and wine.

Now because of A.I., Lunardis has been able stop some their expensive cheese and wine from flying off the shelves.

Because of the loss the retailer is incurring, its driving prices up for everyone else, said Veesion, a surveillance company based in France, Country Manager Hiren Mowji. Were hoping that by controlling lost from shoplifting in retail stores around the world, we can protect pricing for other consumers.

Mowji said the technology does not detect, race, gender, or the size of the person. The technology is being used in over 1,000 hardware, liquor, and groceries stores globally and planning to expand.

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Artificial intelligence used to stop shoplifting - KRON4