Category Archives: Artificial Intelligence

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.

More here:

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.

The rest is here:

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.

Read the original here:

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.

Go here to read the rest:

Artificial intelligence used to stop shoplifting - KRON4

Artificial intelligence is not like us. For all of AIs diverse applications, human intelligence is not at risk of losing its most distinctive characteristics to its artificial creations.

Yet, when AI applications are brought to bear on matters of national security, they are often subjected to an anthropomorphizing tendency that inappropriately associates human intellectual abilities with AI-enabled machines. A rigorous AI military education should recognize that this anthropomorphizing is irrational and problematic, reflecting a poor understanding of both human and artificial intelligence. The most effective way to mitigate this anthropomorphic bias is through engagement with the study of human cognition cognitive science.

This article explores the benefits of using cognitive science as part of an AI education in Western military organizations. Tasked with educating and training personnel on AI, military organizations should convey not only that anthropomorphic bias exists, but also that it can be overcome to allow better understanding and development of AI-enabled systems. This improved understanding would aid both the perceived trustworthiness of AI systems by human operators and the research and development of artificially intelligent military technology.

For military personnel, having a basic understanding of human intelligence allows them to properly frame and interpret the results of AI demonstrations, grasp the current natures of AI systems and their possible trajectories, and interact with AI systems in ways that are grounded in a deep appreciation for human and artificial capabilities.

Artificial Intelligence in Military Affairs

AIs importance for military affairs is the subject of increasing focus by national security experts. Harbingers of A New Revolution in Military Affairs are out in force, detailing the myriad ways in which AI systems will change the conduct of wars and how militaries are structured. From microservices such as unmanned vehicles conducting reconnaissance patrols to swarms of lethal autonomous drones and even spying machines, AI is presented as a comprehensive, game-changing technology.

As the importance of AI for national security becomes increasingly apparent, so too does the need for rigorous education and training for the military personnel who will interact with this technology. Recent years have seen an uptick in commentary on this subject, including in War on the Rocks. Mick Ryans Intellectual Preparation for War, Joe Chapas Trust and Tech, and Connor McLemore and Charles Clarks The Devil You Know, to name a few, each emphasize the importance of education and trust in AI in military organizations.

Because war and other military activities are fundamentally human endeavors, requiring the execution of any number of tasks on and off the battlefield, the uses of AI in military affairs will be expected to fill these roles at least as well as humans could. So long as AI applications are designed to fill characteristically human military roles ranging from arguably simpler tasks like target recognition to more sophisticated tasks like determining the intentions of actors the dominant standard used to evaluate their successes or failures will be the ways in which humans execute these tasks.

But this sets up a challenge for military education: how exactly should AIs be designed, evaluated, and perceived during operation if they are meant to replace, or even accompany, humans? Addressing this challenge means identifying anthropomorphic bias in AI.

Anthropomorphizing AI

Identifying the tendency to anthropomorphize AI in military affairs is not a novel observation. U.S. Navy Commander Edgar Jatho and Naval Postgraduate School researcher Joshua A. Kroll argue that AI is often too fragile to fight. Using the example of an automated target recognition system, they write that to describe such a system as engaging in recognition effectively anthropomorphizes algorithmic systems that simply interpret and repeat known patterns.

But the act of human recognition involves distinct cognitive steps occurring in coordination with one another, including visual processing and memory. A person can even choose to reason about the contents of an image in a way that has no direct relationship to the image itself yet makes sense for the purpose of target recognition. The result is a reliable judgment of what is seen even in novel scenarios.

An AI target recognition system, in contrast, depends heavily on its existing data or programming which may be inadequate for recognizing targets in novel scenarios. This system does not work to process images and recognize targets within them like humans. Anthropomorphizing this system means oversimplifying the complex act of recognition and overestimating the capabilities of AI target recognition systems.

By framing and defining AI as a counterpart to human intelligence as a technology designed to do what humans have typically done themselves concrete examples of AI are measured by [their] ability to replicate human mental skills, as De Spiegeleire, Maas, and Sweijs put it.

Commercial examples abound. AI applications like IBMs Watson, Apples SIRI, and Microsofts Cortana each excel in natural language processing and voice responsiveness, capabilities which we measure against human language processing and communication.

Even in military modernization discourse, the Go-playing AI AlphaGo caught the attention of high-level Peoples Liberation Army officials when it defeated professional Go player Lee Sedol in 2016. AlphaGos victories were viewed by some Chinese officials as a turning point that demonstrated the potential of AI to engage in complex analyses and strategizing comparable to that required to wage war, as Elsa Kania notes in a report on AI and Chinese military power.

But, like the attributes projected on to the AI target recognition system, some Chinese officials imposed an oversimplified version of wartime strategies and tactics (and the human cognition they arise from) on to AlphaGos performance. One strategist in fact noted that Go and warfare are quite similar.

Just as concerningly, the fact that AlphaGo was anthropomorphized by commentators in both China and America means that the tendency to oversimplify human cognition and overestimate AI is cross-cultural.

The ease with which human abilities are projected on to AI systems like AlphaGo is described succinctly by AI researcher Eliezer Yudkowsky: Anthropomorphic bias can be classed as insidious: it takes place with no deliberate intent, without conscious realization, and in the face of apparent knowledge. Without realizing it, individuals in and out of military affairs ascribe human-like significance to demonstrations of AI systems. Western militaries should take note.

For military personnel who are in training for the operation or development of AI-enabled military technology, recognizing this anthropomorphic bias and overcoming it is critical. This is best done through an engagement with cognitive science.

The Relevance of Cognitive Science

The anthropomorphizing of AI in military affairs does not mean that AI is always given high marks. It is now clich for some commentators to contrast human creativity with the fundamental brittleness of machine learning approaches to AI, with an often frank recognition of the narrowness of machine intelligence. This cautious commentary on AI may lead one to think that the overestimation of AI in military affairs is not a pervasive problem. But so long as the dominant standard by which we measure AI is human abilities, merely acknowledging that humans are creative is not enough to mitigate unhealthy anthropomorphizing of AI.

Even commentary on AI-enabled military technology that acknowledges AIs shortcomings fails to identify the need for an AI education to be grounded in cognitive science.

For example, Emma Salisbury writes in War on the Rocks that existing AI systems rely heavily on brute force processing power, yet fail to interpret data and determine whether they are actually meaningful. Such AI systems are prone to serious errors, particularly when they are moved outside their narrowly defined domain of operation.

Such shortcomings reveal, as Joe Chapa writes on AI education in the military, that an important element in a persons ability to trust technology is learning to recognize a fault or a failure. So, human operators ought to be able to identify when AIs are working as intended, and when they are not, in the interest of trust.

Some high-profile voices in AI research echo these lines of thought and suggest that the cognitive science of human beings should be consulted to carve out a path for improvement in AI. Gary Marcus is one such voice, pointing out that just as humans can think, learn, and create because of their innate biological components, so too do AIs like AlphaGo excel in narrow domains because of their innate components, richly specific to tasks like playing Go.

Moving from narrow to general AI the distinction between an AI capable of only target recognition and an AI capable of reasoning about targets within scenarios requires a deep look into human cognition.

The results of AI demonstrations like the performance of an AI-enabled target recognition system are data. Just like the results of human demonstrations, these data must be interpreted. The core problem with anthropomorphizing AI is that even cautious commentary on AI-enabled military technology hides the need for a theory of intelligence. To interpret AI demonstrations, theories that borrow heavily from the best example of intelligence available human intelligence are needed.

The relevance of cognitive science for an AI military education goes well beyond revealing contrasts between AI systems and human cognition. Understanding the fundamental structure of the human mind provides a baseline account from which artificially intelligent military technology may be designed and evaluated. It possesses implications for the narrow and general distinction in AI, the limited utility of human-machine confrontations, and the developmental trajectories of existing AI systems.

The key for military personnel is being able to frame and interpret AI demonstrations in ways that can be trusted for both operation and research and development. Cognitive science provides the framework for doing just that.

Lessons for an AI Military Education

It is important that an AI military education not be pre-planned in such detail as to stifle innovative thought. Some lessons for such an education, however, are readily apparent using cognitive science.

First, we need to reconsider narrow and general AI. The distinction between narrow and general AI is a distraction far from dispelling the unhealthy anthropomorphizing of AI within military affairs, it merely tempers expectations without engendering a deeper understanding of the technology.

The anthropomorphizing of AI stems from a poor understanding of the human mind. This poor understanding is often the implicit framework through which the person interprets AI. Part of this poor understanding is taking a reasonable line of thought that the human mind should be studied by dividing it up into separate capabilities, like language processing and transferring it to the study and use of AI.

The problem, however, is that these separate capabilities of the human mind do not represent the fullest understanding of human intelligence. Human cognition is more than these capabilities acting in isolation.

Much of AI development thus proceeds under the banner of engineering, as an endeavor not to re-create the human mind in artificial ways but to perform specialized tasks, like recognizing targets. A military strategist may point out that AI systems do not need to be human-like in the general sense, but rather that Western militaries need specialized systems which can be narrow yet reliable during operation.

This is a serious mistake for the long-term development of AI-enabled military technology. Not only is the narrow and general distinction a poor way of interpreting existing AI systems, but it clouds their trajectories as well. The fragility of existing AIs, especially deep-learning systems, may persist so long as a fuller understanding of human cognition is absent from their development. For this reason (among others), Gary Marcus points out that deep learning is hitting a wall.

An AI military education would not avoid this distinction but incorporate a cognitive science perspective on it that allows personnel in training to re-think inaccurate assumptions about AI.

Human-Machine Confrontations Are Poor Indicators of Intelligence

Second, pitting AIs against exceptional humans in domains like Chess and Go are considered indicators of AIs progress in commercial domains. The U.S. Defense Advanced Research Projects Agency participated in this trend by pitting Heron Systems F-16 AI against a skilled Air Force F-16 pilot in simulated dogfighting trials. The goals were to demonstrate AIs ability to learn fighter maneuvers while earning the respect of a human pilot.

These confrontations do reveal something: some AIs really do excel in certain, narrow domains. But anthropomorphizings insidious influence lurks just beneath the surface: there are sharp limits to the utility of human-machine confrontations if the goals are to gauge the progress of AIs or gain insight into the nature of wartime tactics and strategies.

The idea of training an AI to confront a veteran-level human in a clear-cut scenario is like training humans to communicate like bees by learning the waggle dance. It can be done, and some humans may dance like bees quite well with practice, but what is the actual utility of this training? It does not tell humans anything about the mental life of bees, nor does it gain insight into the nature of communication. At best, any lessons learned from the experience will be tangential to the actual dance and advanced better through other means.

The lesson here is not that human-machine confrontations are worthless. However, whereas private firms may benefit from commercializing AI by pitting AlphaGo against Lee Sedol or Deep Blue against Garry Kasparov, the benefits for militaries may be less substantial. Cognitive science keeps the individual grounded in an appreciation for the limited utility without losing sight of its benefits.

Human-Machine Teaming Is an Imperfect Solution

Human-machine teaming may be considered one solution to the problems of anthropomorphizing AI. To be clear, it is worth pursuing as a means of offloading some human responsibility to AIs.

But the problem of trust, perceived and actual, surfaces once again. Machines designed to take on responsibilities previously underpinned by the human intellect will need to overcome hurdles already discussed to become reliable and trustworthy for human operators understanding the human element still matters.

Be Ambitious but Stay Humble

Understanding AI is not a straightforward matter. Perhaps it should not come as a surprise that a technology with the name artificial intelligence conjures up comparisons to its natural counterpart. For military affairs, where the stakes in effectively implementing AI are far higher than for commercial applications, ambition grounded in an appreciation for human cognition is critical for AI education and training. Part of a baseline literacy in AI within militaries needs to include some level of engagement with cognitive science.

Even granting that existing AI approaches are not intended to be like human cognition, both anthropomorphizing and the misunderstandings about human intelligence it carries are prevalent enough across diverse audiences to merit explicit attention for an AI military education. Certain lessons from cognitive science are poised to be the tools with which this is done.

Vincent J. Carchidi is a Master of Political Science from Villanova University specializing in the intersection of technology and international affairs, with an interdisciplinary background in cognitive science. Some of his work has been published in AI & Society and the Human Rights Review.

Image: Joint Artificial Intelligence Center blog

View original post here:

Is Artificial Intelligence Made in Humanity's Image? Lessons for an AI Military Education - War on the Rocks

CAMBRIDGE, Mass.--(BUSINESS WIRE)--Iterative Scopes, a pioneer in precision medicine technologies for gastroenterology, announced today that its artificial intelligence platforms will be featured in three abstract presentations at the upcoming Digestive Disease Week 2022 (DDW 2022). The meeting will take place virtually and onsite at the San Diego Convention Center in San Diego, CA, from May 21 to May 24.

Experts in inflammatory bowel disease (IBD) and artificial intelligence (AI) will present two abstracts discussing data on the companys endoscopic scoring algorithms in ulcerative colitis (UC), a condition included in the umbrella of IBD, developed in collaboration with Eli Lilly and Company.

The data are drawn from an innovative partnership between Iterative Scopes and Lilly, focusing on studying the effectiveness of machine learning (ML) models to automatically score endoscopic disease severity in UC. Progress in IBD research is hindered by variability in the human interpretation of endoscopic severity. This unique ML approach incorporates novel methods of interpreting and integrating visual data into the assessment of clinical trial endoscopic endpoints. This data has the potential to be considered as a substitute to human central readers, which may reduce clinical trial costs and accelerate IBD research.

Aasma Shaukat, MD, MPH, Robert M. and Mary H. Glickman Professor of Medicine and Gastroenterology at NYU Grossman School of Medicine, and a leader of the Iterative Scopes advisory board, will present the first publicly available registration trial data on SKOUT, the companys automated polyp detection algorithm for colorectal cancer screening, in a plenary session on Late-Breaking Clinical Science Abstracts. The plenary sessions at DDW are the forum for highlighting some of the years best research abstracts as determined by the conference organizers. In her discussion, Dr. Shaukat will highlight results of a multicentered, randomized clinical trial in the US assessing whether SKOUT is superior to standard colonoscopy in increasing the adenomas per colonoscopy.

SKOUT has a pending 510(k) and is not available for sale in the United States. SKOUT received its CE Mark certification in 2021.

We founded Iterative Scopes four years ago to change the trajectory of GI drug development and clinical care, and we are extremely excited to share results of Iterative Scopes work in applying cutting edge, computational approaches towards achieving this goal, said Jonathan Ng, MBBS, the founder and CEO of Iterative Scopes. We are excited to share our work with the clinical community at DDW, through these presentations and the other events surrounding DDW.

Iterative Scopes Presentations at DDW 2022:

Endoscopic Scoring Solutions in Ulcerative Colitis

Title: Can a single central reader provide a reliable ground truth (GT) for training a machine learning (ML) model that predicts endoscopic disease activity in ulcerative colitis (UC)?Date & Time: May 21, 5:15-5:30 PM PDTSession Type: Research ForumPresenter: Klaus Gottlieb, MD, JD (Senior Medical Fellow, Lilly)Presentation No: 278Location: Room: 23 - San Diego Convention Center

Title: Development of a novel ulcerative colitis (UC) endoscopic activity prediction model using machine learning (ML)Date & Time; May 23, 12:30-1:30 PM PDTSession Type: PosterPresenter: David T. Rubin, MD (Joseph B. Kirsner Professor of Medicine and Chief, Section of Gastroenterology, Hepatology and Nutrition, UChicago Medicine and Chair of Iterative Scopes Advisory Board)Presentation No: Mo 1639Location: Poster Hall - San Diego Convention Center

SKOUT, Polyp Detection in Colonoscopy

Title: Increased Adenoma Detection with the use of a novel computer aided detection device, SKOUTTM: Results of a multicenter randomized clinical trial in the USDate & Time: May 24, 8:15-8:30 AM PDTSession Type: PlenaryPresenter: Aasma Shaukat, MD, MPH (Robert M and Mary H. Glickman Professor of Medicine and Gastroenterology, Department of Medicine, NYU Grossman School of Medicine and a leader of Iterative Scopes Advisory Board)Presentation No: 5095Location: Room 3 - San Diego Convention Center

Iterative Scopes was founded in 2017 as a spin out of the Massachusetts Institute of Technology (MIT) by Dr. Ng, a physician-entrepreneur, who developed the companys foundational concepts while he was in school at MIT and Harvard. In December 2021, the company and its investors closed a $150 million Series B financing, which attracted a roster of A-list venture capitalists, big pharmaceutical companies venture arms, and individual leaders in healthcare.

About Iterative Scopes

Iterative Scopes is a pioneer in the application of artificial intelligence-based precision medicine for gastroenterology with the aim of helping to optimize clinical trials investigating treatment of inflammatory bowel disease (IBD). The technology is also designed to potentially enhance colorectal cancer screenings. Its powerful, proprietary artificial intelligence and computer vision technologies have the potential to improve the accuracy and consistency of endoscopy readings. Iterative Scopes is initially applying these advances to impact polyp detection for colorectal cancer screenings and working to standardize disease severity characterization for inflammatory bowel disease. Longer term, the company plans to establish more meaningful endpoints for GI diseases, which may be better predictors of therapeutic response and disease outcomes. Spun out of MIT in 2017, the company is based in Cambridge, Massachusetts.

About Digestive Disease Week (DDW)

Digestive Disease Week (DDW) is the largest international gathering of physicians, researchers and academics in the fields of gastroenterology, hepatology, endoscopy and gastrointestinal surgery. Jointly sponsored by the American Association for the Study of Liver Diseases (AASLD), the American Gastroenterological Association (AGA) Institute, the American Society for Gastrointestinal Endoscopy (ASGE) and the Society for Surgery of the Alimentary Tract (SSAT), DDW is an in-person and virtual meeting from May 21-24, 2022. The meeting showcases more than 5,000 abstracts and hundreds of lectures on the latest advances in GI research, medicine and technology. More information can be found at http://www.ddw.org.

Go here to see the original:

Iterative Scopes to Present Three Abstracts on Artificial Intelligence Applications for GI Endoscopy at DDW 2022 - Business Wire

EDINBURGH, Scotland--(BUSINESS WIRE)--Today, Metanomic (https://www.metanomic.net/) announces it has acquired Intoolab A.I (https://www.intoolab.com/) , a Bayesian Network Artificial Intelligence company, to expand and to improve data analysis and A.I across video games and Web3. This acquisition augments Metanomics current game economy infrastructure that allows developers to build, simulate and run balanced game economies and core gameplay loops in a live, real-time environment.

The addition of artificial intelligence helps developers create better experiences through intelligent insight from player behaviour in run-time. As a part of the acquisition the existing solution will be rebranded to Thunderstruck and extend the capabilities of the Metanomic Engine.

A more intelligent approach to video game development and Web3 analytics

Thunderstruck is based on a new approach to artificial intelligence that is not based on off the shelf machine learning algorithms but a powerful combination of Deep Learning and Dynamic Bayesian Networks. The solution has already been successfully proven in Healthcare and the technology is now being applied initially to video games and Web3 in a number of core applications -

Web3 and metaverse are the next big thing that will affect our everyday lives in so many dimensions and we couldnt be at a better place and time with our technology, to take the next step and introduce Dynamic Bayesian Networks to this untapped and exciting new market, said Nikos Tzagkarakis, CEO and Co-founder of IntooLab who joins Metanomic as their incoming Chief AI Officer.

We knew from the first time we met Theo and the rest of the team, that joining the incredible Metanomic powerhouse is our best bet to be part of an unmatched platform for Web3, game economies and the metaverse.

The incredible traction weve seen across both the traditional games industry, web3, and play-and-earn games for the beta of our economy platform already puts us in a strong position to help developers build more fun and engaging experiences, Theo Priestley, CEO of Metanomic said.

"With the acquisition of an artificial intelligence platform we can deliver more power to developers through real-time player insight as well as the most advanced game economy solution in the industry.

With the acquisition also comes the commitment to continue to build the companys development hub in Greece, supporting the local startup community and tech economy.

Thunderstruck is available for trial and partner integration from today. The Metanomic Engine is a complete, free to use game economy design and run-time solution for game developers across Web3 and traditional games and is currently in a closed beta. To sign up for the waiting list email hello@metanomic.net

About Metanomic

Metanomic is the first and only complete real-time economy as a service platform for developers. Built by professional economists and game designers, the platform utilizes patented algorithms to easily deploy plug and play, interoperable and infinitely scalable game and creator economies ready for web3, metaverse, and play-and-earn games. The platform allows developers to easily and quickly build a fully-scalable and configurable play-and-earn economy that features asset creation engines and fully balanced player markets.

About Intoolab

Founded in 2017, Intoolab has always been technology-first, building the first production ready Dynamic Bayesian Networks intelligence that can be a milestone neurosymbolic step towards A.I. frameworks that understand the human concepts structurally and not just correlationally.

Tzager is the first Bayesian Inference A.I. framework, built for biomedical research, drug discovery, and personalized medicine with the main features being Pathway Simulations, Predictor Research/Models, and Literature Intelligence. Tzager is not just another deep learning algorithm trained to solve very specific problems, but an intelligent system with its own framework.

More here:

Metanomic Acquires Intoolab, Developers of the First Bayesian Network Artificial Intelligence Engine - Business Wire

While the healthcare industry has faced unprecedented operational constraints in recent years, including limited physical capacity, vulnerable patients, loss of revenue, and shortages of staff, it has also reaped opportunities to adapt and excel.

Health systems and hospitals have been especially primed to rapidly adopt digital transformation and technology initiatives to predict and manage optimal scheduling, staffing, and patient flow.

With the support of AI-based analytics tools, health systems can better use the assets in which they have already invested. By utilizing critical resources like operating rooms, infusion clinics, and inpatient bed units effectively, they can improve financial performance, relieve burden on staff, and treat higher volumes of patients in shorter periods of time. Many healthcare organizations have already deployed analytics to achieve these results, quickly seeing a large return on a relatively small investment in implementation.

With their upcoming Transform Hospital Operations Summit, hosted in partnership with Beckers, healthcare analytics expert LeanTaaS will share these providers journeys, results, and stories. Driven by a focus on deploying AI to achieve better return on investment, the two-day program will connect over 1,000 attendees with health system executives, technology leaders, and industry experts to discuss how hospitals across the U.S. use AI and predictive and prescriptive analytics tools to solve critical challenges arising from case backlogs, provider burnout and staffing shortages, and increased patient wait times.

Summit attendees will learn about success stories from C-suite hospital and health system leaders who have transformed operations and unlocked revenue by using AI and machine learning solutions. These sessions will encompass a wide range of perspectives on this topic, including the strategies of breaking through operational barriers with partnerships, the urgency behind implementing high-powered AI, the best practices for scaling disruptive new technology at scale, the potential for AI to revolutionize the healthcare industry, and more.

Primary speakers include Dr. Patrick McGill, EVP, Chief Transformation Officer at Community Health Network; Dr. Douglas Flora, Executive Medical Director of Oncology Services at St. Elizabeth Healthcare; and Dr Eric Eskiolu, Executive Vice President, Chief Medical and Scientific Officer and Co-Director of the Institute of Innovation and Artificial Intelligence at Novant Health.

Were excited to speak at Transform and share our experiences with AI and analytics, but just as importantly, about how were building a culture that supports transformation through a commitment to clinical excellence, workforce development, and process improvement, shared Aaron Miri, Senior Vice President and Chief Digital and Information Officer and Amy Huveldt, VP of Performance Excellence, both of Baptist Health and who will also be primary speakers.

Further speakers include healthcare leaders and experts from Cone Health, Mount Nittany Medical Center, Multicare, UCHealth, University of Utah Health, Vanderbilt-Ingram Cancer Center, and Yale New Haven Health. These sessions will feature healthcare executives highlighting the results they have achieved by leveraging AI in their operations, including increasing surgical case length accuracy by 4%; reducing infusion patient wait times by 30%; and decreasing inpatient time-to-admit by 16%, despite an 18% increase in COVID-19 census. Attendees can build a hospital operations summit schedule based on interest and specialty, choosing from three Learning Tracks: Perioperative, Infusion Centers, and Inpatient Beds.

As the healthcare industry continues to grapple with lingering effects of the pandemic, its no secret that health systems need to do more with less while also prioritizing the valuable time and wellbeing of staff. Were looking forward to our June Transform event, as it will hone in on critical healthcare issues and how AI can support hardworking hospital leaders, said Mohan Giridharadas, LeanTaaS founder and CEO. This event will provide all attendees with the resources needed to compete and thrive by using smarter capacity management decisions every single day.

Transform registration is free for all attendees. To register and learn more about the sessions and speakers that will be featured at the summit, view the conference agenda here.

Read more:

Virtual hospital operations summit to focus on role of Artificial Intelligence in achieving ROI for system wide impact - Becker's Hospital Review

Artificial intelligence is being tipped to be the UAE's most important industry over the next 10 years, with universities urged to step up efforts to prepare the next generation of high-tech workers.

The fast-rising sector was ranked ahead of construction, electronics, aerospace, robotics, design engineering and IT and cybersecurity in a poll of technology and engineering employees in the Emirates.

The UAE government is driving forwards with ambitious plans to establish itself as a global AI hub.

In 2017, the country appointed Omar Al Olama as its first Minister of State for Artificial Intelligence and later adopted the National Artificial Intelligence Strategy 2031 to promote the growth of the cutting-edge technology.

The Mohamed bin Zayed University of Artificial Intelligence in Abu Dhabi was established in 2019 to develop the skills of top talent from across the world to lead workplaces of the future.

.

The survey, commissioned by the UK-based Institution of Engineering and Technology (IET), and carried out by YouGov, polled 325 employers and employees in the UAE in December 2021 and January 2022.

Julian Young, IET president, said artificial intelligence would most certainly continue to grow in prominence.

Alongside that, I would almost add everything to do with digitalisation. Everything in the future in a highly advanced technological community, will be about digitalisation and getting computers to do far more work for us," said Mr Young.

So if one has a skilled workforce in this field, one would be able to make a profitable company and a profitable organisation and be a truly global player.

I'm not surprised to see that these are the skill sets that are required in three years' time, that these are the skill sets required in 10 years' time.

Sir Julian Young, President of the Institution of Engineering and Technology, said artificial intelligence would most certainly continue to grow in prominence. Photo: the Institution of Engineering and Technology

"If you pick up artificial intelligence and then think about the type of courses that people are undertaking. Do we need courses in artificial intelligence? Yes, of course. But in the more traditional areas of engineering, mechanical, electrical, electronic aerospace, there needs to be a digital component."

He said all of the traditional industries need digital and software and computer science inputs to be able to make the best of their workforce.

Ian Mercer, head of international operations for the Institution of Engineering and Technology, said universities could use the findings to ensure their courses run parallel to the immediate and future demands of the economy.

"If I were an academician, then I would be thinking, 'if that's where the where the industry is going to go then the courses that we're going to offer to students probably need to be ramped up to be where the need is going to be'," Mr Mercer said.

"At the end of the day, universities want jobs to be available for the people that they put through the system.

"If you look at the the ambitions of the UAE government, they want to become a tech hub of the world."

He said that in a post-oil and gas economy, technology may be one of the main workforce providers in the region.

A graduation ceremony at the Mohamed bin Zayed University of Artificial Intelligence in Abu Dhabi. All photos: Khushnum Bhandari / The National

The UAE is continuing to explore ways in which artificial intelligence can be used to boost business, make government departments more agile and efficient, and support health services.

Artificial intelligence could soon be used to tailor UAE government employees working hours to their own personal productivity.

The initiative, which is being studied by the Federal Authority for Government Human Resources, is one of a host of practical applications for AI in everyday life.

In March, 41 business leaders who took a three-month course at Mohamed bin Zayed University for Artificial Intelligence, celebrated their graduation.

The course aimed to support UAE government and business sectors. Participants were required to complete 12 rigorous weeks of coursework, lectures and collaborative project work.

Dr Jamal Al Kaabi, undersecretary at the Department of Health in Abu Dhabi, joined the programme after the Covid-19 pandemic made him realise the potential of artificial intelligence.

He believes wearable technology and AI could be crucial in providing home services and follow-up care for the elderly.

Updated: May 20, 2022, 5:20 AM

See more here:

Artificial intelligence to be UAE's top sector over next decade, survey finds - The National

Background: Artificial intelligence (AI) for gastric cancer diagnosis has been discussed in recent years. The role of AI in early gastric cancer is more important than in advanced gastric cancer since early gastric cancer is not easily identified in clinical practice. However, to our knowledge, past syntheses appear to have limited focus on the populations with early gastric cancer. Objective: The purpose of this study is to evaluate the diagnostic accuracy of AI in the diagnosis of early gastric cancer from endoscopic images. Methods: We conducted a systematic review from database inception to June 2020 of all studies assessing the performance of AI in the endoscopic diagnosis of early gastric cancer. Studies not concerning early gastric cancer were excluded. The outcome of interest was the diagnostic accuracy (comprising sensitivity, specificity, and accuracy) of AI systems. Study quality was assessed on the basis of the revised Quality Assessment of Diagnostic Accuracy Studies. Meta-analysis was primarily based on a bivariate mixed-effects model. A summary receiver operating curve and a hierarchical summary receiver operating curve were constructed, and the area under the curve was computed. Results: We analyzed 12 retrospective case control studies (n=11,685) in which AI identified early gastric cancer from endoscopic images. The pooled sensitivity and specificity of AI for early gastric cancer diagnosis were 0.86 (95% CI 0.75-0.92) and 0.90 (95% CI 0.84-0.93), respectively. The area under the curve was 0.94. Sensitivity analysis of studies using support vector machines and narrow-band imaging demonstrated more consistent results. Conclusions: For early gastric cancer, to our knowledge, this was the first synthesis study on the use of endoscopic images in AI in diagnosis. AI may support the diagnosis of early gastric cancer. However, the collocation of imaging techniques and optimal algorithms remain unclear. Competing models of AI for the diagnosis of early gastric cancer are worthy of future investigation. Trial Registration: PROSPERO CRD42020193223; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=193223

Read more from the original source:

The Accuracy of Artificial Intelligence in the Endoscopic Diagnosis of Early Gastric Cancer: Pooled Analysis Study - Newswise