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Category Archives: Machine Learning

Machine Learning: The Future of Predicting Health Outcomes in Aging Canadians – Medriva

Healthcare as we know it is being transformed by artificial intelligence (AI) and machine learning. A research team from the University of Alberta is pioneering this transformation by using machine learning programs to predict the future mental and physical health of aging Canadians. The project, which utilizes data from the Canadian Longitudinal Study on Aging (CLSA), focuses on over 30,000 Canadians between the ages of 45 and 85.

The research team has developed a unique biological age index using machine learning models, which allows them to assess the health of individuals more accurately than ever before. This index is not just about chronological age. Instead, it provides a holistic view of an individuals health by considering various health-related, lifestyle, socio-economic, and other data. The biological age index gives a more accurate reflection of an individuals overall health status, providing critical insights for personalized care plans.

In addition to the biological age index, the team has also developed a program that can accurately predict the onset of depression within three years. Depression is a common but serious condition that can significantly impact the quality of life, especially for the aging population. Early detection and intervention are critical, and this machine learning model could potentially revolutionize mental health care by allowing for early, proactive interventions.

These machine learning models are not yet ready for real-world implementation. However, they signify a significant shift towards individualized care tailored to each patients unique health profile. The ultimate aim is to contribute to healthy aging, benefiting not just Albertans but all Canadians. These models could potentially transform patient care by providing clinicians, patients, and people with lived experience with valuable insights into potential health outcomes.

This groundbreaking research is funded by various organizations, including the Canada Research Chairs program, Alberta Innovates, Mental Health Foundation, Mitacs Accelerate program, and others. The researchers plan to refine these models further, involving clinicians, patients, and individuals with lived experience in the process. The goal is to demonstrate the potential benefits of these models and pave the way for their eventual implementation in healthcare settings.

AI and machine learning have immense potential in the healthcare sector. The ability to process and interpret multi-modal data can lead to more personalized patient care. They can also save time for researchers analyzing clinical trial results. However, as with any transformative technology, there are challenges. For AI and machine learning to work effectively, the quality of data fed into these models needs to be high. There is also a need for technologies that help patients manage their health. In addition, the ethical and regulatory aspects of AI use in healthcare need careful consideration.

As the University of Alberta continues to lead in the intersection of machine learning, health, energy, and indigenous initiatives in health and humanities, the future of healthcare looks promising. The ability of machine learning to predict future health conditions in aging Canadians is just the beginning. As these models are refined and tested further, they could significantly contribute to the development of a healthier future for all.

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Machine Learning: The Future of Predicting Health Outcomes in Aging Canadians - Medriva

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What is AI and Machine Learning? – GovernmentCIO Media & Research

Catch up on how artificial intelligence technologies work for the benefit of public service.

Artificial intelligence and machine learning are poised to transform government operations. In this edition of our ABCs of Fed IT series, we unpack the difference between AI and machine learning. Federal leaders highlight how they are thinking about the technology and what it means to have trustworthy and accurate data to power these solutions.

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What is AI and Machine Learning? - GovernmentCIO Media & Research

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Weekly AiThority Roundup: Biggest Machine Learning, Robotic And Automation Updates – AiThority

This is your AI Weekly Roundup. We are covering the top updates from around the world. The updates will feature state-of-the-art capabilities inartificial intelligence (AI),Machine Learning, Robotic Process Automation, Fintech, and human-system interactions. We cover the role of AI Daily Roundup and its application in various industries and daily lives.

As the technology landscape evolves, Dell emerges in 2023 with a host of transformative developments, marking its continued impact on the world of computing and innovation. Dell, a stalwart in the tech industry, starts the year 2023 with a flurry of groundbreaking news stories, offering a glimpse into the companys strategic moves and technological advancements that are set to shape the future of computing.

Skylo, the global leader in non-terrestrial networks, announced that it will interconnect its NTN satellite network with FocusPoints PULSE platform enabling FocusPoints IoT monitoring and emergency escalation service.

Ansysannounced that Ansys AVxcelerate Sensors will be accessible within NVIDIA DRIVE Sim,a scenario-based AV simulator powered by NVIDIA Omniverse, a platform for developingUniversal Scene Description (OpenUSD)applications for industrialdigitalization.

Intel CorpandDigitalBridge Group, a global investment firm announced the formation of Articul8 AI, Inc. (Articul8), an independent company offering enterprise customers a full-stack, vertically-optimized and secure generativeartificial intelligence(GenAI) software platform.

Cerence Inc.AI for a world in motion, announced it is collaborating with Microsoft to deliver an evolved in-vehicleuser experiencethat combines Cerences extensiveautomotive technologyportfolio and professional services with the innovative technology and intelligence of Microsoft Azure AI Services.

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Weekly AiThority Roundup: Biggest Machine Learning, Robotic And Automation Updates - AiThority

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Unlocking the Potential of Acceleration Data in Disease Diagnosis – Medriva

Unlocking the Potential of Acceleration Data in Disease Diagnosis

Advancements in technology have paved the way for innovative approaches to disease diagnosis, particularly in the realm of gait-related diseases such as peripheral artery disease (PAD). Traditional methods for diagnosing cardiovascular diseases, such as PAD, have proven to be inadequate in identifying individuals at risk, often resulting in late-stage diagnoses. This has necessitated the development of more accurate, cost-effective, and convenient diagnostic tools.

A recent study introduces a promising framework for processing acceleration data collected from reflective markers and wearable accelerometers. This data is key to diagnosing diseases affecting gait, including PAD. The framework shows impressive accuracy in distinguishing PAD patients from non-PAD controls using raw marker data. Although accuracy is slightly reduced when using data from a wearable accelerometer, the results remain promising.

Machine learning models have been proposed to overcome the limitations of current diagnostic methods. However, these models often require significant time, resources, and expertise. The new framework addresses these challenges by utilizing existing data and wearable accelerometers to gather detailed gait parameters outside laboratory settings.

One of the key advantages of this approach is the potential for data availability and consistency. With wearable accelerometers, data can be collected in a variety of real-world settings, providing a more accurate picture of an individuals gait. This could lead to earlier detection and treatment of PAD, and potentially other gait-related diseases.

Further advancements in technology have led to the development of self-powered gait analysis systems (SGAS) based on a triboelectric nanogenerator (TENG). These systems comprise a sensing module, a charging module, a data acquisition and processing module, and an Internet of Things (IoT) platform. They use specialized sensing units positioned at the forefoot and heel to generate synchronized signals for real-time step count and step speed monitoring. The data is then wirelessly transmitted to an IoT platform for analysis, storage, and visualization, offering a comprehensive solution for motion monitoring and gait analysis.

Aside from gait analysis, recent studies have also explored the use of eye movement patterns to diagnose neurodegenerative disorders such as Alzheimers disease, mild cognitive impairment, and Parkinsons disease. An algorithm has been developed to automatically identify these patterns, with significantly different saccade and pursuit characteristics observed in the patient groups compared to controls. This showcases the potential of non-invasive eye tracking devices to record eye motion and gaze location across different tasks, further contributing to early and accurate disease detection.

With the advent of smartwatch-smartphone technology, home-based monitoring of patients with gait-related diseases has become a realistic possibility. This technology can be used to process acceleration data, helping to diagnose diseases affecting gait. This approach offers a low-cost, convenient tool for diagnosing PAD and other gait-related diseases, marking a significant step forward in the field of disease diagnosis and management.

In conclusion, the use of acceleration data, machine learning, and wearable technology offers a promising pathway for the early detection and diagnosis of PAD and potentially other gait-related diseases. As we continue to push the boundaries of technology and harness the power of data, we can look forward to a new era of healthcare that is more proactive, personalized, and effective.

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Unlocking the Potential of Acceleration Data in Disease Diagnosis - Medriva

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New study: Countless AI experts doesnt know what to think on AI risk – Vox.com

In 2016, researchers at AI Impacts, a project that aims to improve understanding of advanced AI development, released a survey of machine learning researchers. They were asked when they expected the development of AI systems that are comparable to humans along many dimensions, as well as whether to expect good or bad results from such an achievement.

The headline finding: The median respondent gave a 5 percent chance of human-level AI leading to outcomes that were extremely bad, e.g. human extinction. That means half of researchers gave a higher estimate than 5 percent saying they considered it overwhelmingly likely that powerful AI would lead to human extinction and half gave a lower one. (The other half, obviously, believed the chance was negligible.)

If true, that would be unprecedented. In what other field do moderate, middle-of-the-road researchers claim that the development of a more powerful technology one they are directly working on has a 5 percent chance of ending human life on Earth forever?

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In 2016 before ChatGPT and AlphaFold the result seemed much likelier to be a fluke than anything else. But in the eight years since then, as AI systems have gone from nearly useless to inconveniently good at writing college-level essays, and as companies have poured billions of dollars into efforts to build a true superintelligent AI system, what once seemed like a far-fetched possibility now seems to be on the horizon.

So when AI Impacts released their follow-up survey this week, the headline result that between 37.8% and 51.4% of respondents gave at least a 10% chance to advanced AI leading to outcomes as bad as human extinction didnt strike me as a fluke or a surveying error. Its probably an accurate reflection of where the field is at.

Their results challenge many of the prevailing narratives about AI extinction risk. The researchers surveyed dont subdivide neatly into doomsaying pessimists and insistent optimists. Many people, the survey found, who have high probabilities of bad outcomes also have high probabilities of good outcomes. And human extinction does seem to be a possibility that the majority of researchers take seriously: 57.8 percent of respondents said they thought extremely bad outcomes such as human extinction were at least 5 percent likely.

This visually striking figure from the paper shows how respondents think about what to expect if high-level machine intelligence is developed: Most consider both extremely good outcomes and extremely bad outcomes probable.

As for what to do about it, there experts seem to disagree even more than they do about whether theres a problem in the first place.

The 2016 AI impacts survey was immediately controversial. In 2016, barely anyone was talking about the risk of catastrophe from powerful AI. Could it really be that mainstream researchers rated it plausible? Had the researchers conducting the survey who were themselves concerned about human extinction resulting from artificial intelligence biased their results somehow?

The survey authors had systematically reached out to all researchers who published at the 2015 NIPS and ICML conferences (two of the premier venues for peer-reviewed research in machine learning, and managed to get responses from roughly a fifth of them. They asked a wide range of questions about progress in machine learning and got a wide range of answers: Really, aside from the eye-popping human extinction answers, the most notable result was how much ML experts disagreed with one another. (Which is hardly unusual in the sciences.)

But one could reasonably be skeptical. Maybe there were experts who simply hadnt thought very hard about their human extinction answer. And maybe the people who were most optimistic about AI hadnt bothered to answer the survey.

When AI Impacts reran the survey in 2022, again contacting thousands of researchers who published at top machine learning conferences, their results were about the same. The median probability of an extremely bad, e.g., human extinction outcome was 5 percent.

That median obscures some fierce disagreement. In fact, 48 percent of respondents gave at least a 10 percent chance of an extremely bad outcome, while 25 percent gave a 0 percent chance. Responding to criticism of the 2016 survey, the team asked for more detail: how likely did respondents think it was that AI would lead to human extinction or similarly permanent and severe disempowerment of the human species? Depending on how they asked the question, this got results between 5 percent and 10 percent.

In 2023, in order to reduce and measure the impact of framing effects (different answers based on how the question is phrased), many of the key questions on the survey were asked of different respondents with different framings. But again, the answers to the question about human extinction were broadly consistent in the 5-10 percent range no matter how the question was asked.

The fact the 2022 and 2023 surveys found results so similar to the 2016 result makes it hard to believe that the 2016 result was a fluke. And while in 2016 critics could correctly complain that most ML researchers had not seriously considered the issue of existential risk, by 2023 the question of whether powerful AI systems will kill us all had gone mainstream. Its hard to imagine that many peer-reviewed machine learning researchers were answering a question theyd never considered before.

I think the most reasonable reading of this survey is that ML researchers, like the rest of us, are radically unsure about whether to expect the development of powerful AI systems to be an amazing thing for the world or a catastrophic one.

Nor do they agree on what to do about it. Responses varied enormously on questions about whether slowing down AI would make good outcomes for humanity more likely. While a large majority of respondents wanted more resources and attention to go into AI safety research, many of the same respondents didnt think that working on AI alignment was unusually valuable compared to working on other open problems in machine learning.

In a situation with lots of uncertainty like about the consequences of a technology like superintelligent AI, which doesnt yet exist theres a natural tendency to want to look to experts for answers. Thats reasonable. But in a case like AI, its important to keep in mind that even the most well-regarded machine learning researchers disagree with one another and are radically uncertain about where all of us are headed.

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How Machine Learning is Transforming the Financial Industry – Medium

The financial industry has always relied heavily on using data to model risks, identify opportunities, and optimize decisions. Today, machine learning is taking financial data science to new levels analyzing massive datasets, uncovering subtle patterns, and powerfully predicting future outcomes. These AI-powered models are being woven into countless processes in banking, insurance, trading firms, and more.

In this article, well explore some of the most impactful applications of machine learning across the financial sector and why this technology represents a breakthrough in capabilities compared to traditional statistical methods. Well also consider some promising directions this transformation might take in the years to come.

Banks lose billions each year to payment fraud despite their best efforts to stop it. The volume and variety of transactions make spotting criminals in the act like finding a needle in a haystack. Fortunately, machine learning algorithms have an uncanny knack for finding needles.

By analyzing past payment data like timestamps, locations, devices, and more, unsupervised learning models can define a normal pattern of legitimate behavior for each customer. When a new payment strays too far from that norm, the algorithms flag it for review. This enables banks to catch many more fraudulent payments while minimizing false alarms that frustrate legitimate customers.

Whats most impressive is that these models continually monitors customers and adapt to their evolving behaviors over time. So banks can keep account security tight without compromising convenience for most payments. Unsupervised learning stops fraud in real-time behind the scenes without customers ever knowing.

Evaluating loan applications requires careful analysis of employment details, financial statements, credit reports, property values, and more to estimate risks and repayment capacity. This complex process is time-consuming, subjective, and inconsistent when done manually.

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How Machine Learning is Transforming the Financial Industry - Medium

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