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Artificial Intelligence: A Shifting Paradigm in Cardio-Cerebrovascular Medicine

#artificialintelligence

The future of healthcare is an organic blend of technology, innovation, and human connection. As artificial intelligence (AI) is gradually becoming a go-to technology in healthcare to improve efficiency and outcomes, we must understand our limitations. We should realize that our goal is not only to provide faster and more efficient care, but also to deliver an integrated solution to ensure that the care is fair and not biased to a group of sub-population. In this context, the field of cardio-cerebrovascular diseases, which encompasses a wide range of conditions—from heart failure to stroke—has made some advances to provide assistive tools to care providers. This article aimed to provide an overall thematic review of recent development focusing on various AI applications in cardio-cerebrovascular diseases to identify gaps and potential areas of improvement. If well designed, technological engines have the potential to improve healthcare access and equitability while reducing overall costs, diagnostic errors, and disparity in a system that affects patients and providers and strives for efficiency.


A primer on digital twin technology

#artificialintelligence

In the 21st century, the concept of a twin need not be confined to fraternal or identical--a twin can be digital too. Digital twins have caught the eyes of some of the biggest companies in the world--Amazon and Nvidia, for instance, both made announcements about new digital-twin initiatives within the last month--as well as those of specialists like infrastructure engineering software company Bentley Systems. The concept started gaining traction at the beginning of the century, and picked up steam in the early 2010s when the rise of IoT made digital twins more feasible. As of 2020, it was estimated to be a $3.1 billion market, per Markets and Markets, and projected to grow into a $48.2 billion industry by 2026. So...what is a digital twin?



Explain Yourself - A Primer on ML Interpretability & Explainability

#artificialintelligence

The project to define what the late Marvin Minsky refers to as a suitcase word -- words that have so much packed inside them, making it difficult for us to unpack and understand this embedded intricacy in its entirety -- has not been without its fair share of challenges. The term does not have a single agreed-upon definition, with the dimensions of description shifting from optimization or efficient search space exploration to rationality and the ability to adapt to uncertain environments, depending on which expert you ask. The confusion becomes more salient when one hears news of machines achieving super-human performance in activities like Chess or Go -- traditional stand-ins for high intellectual aptitude -- but fail miserably in tasks like grabbing objects or moving across uneven terrain, which most of us do without thinking. But, several themes do emerge when we try to corner the concept. Our ability to explain why we do what we do makes a fair number of appearances in the list of definitions proposed by multiple disciplines.


China's 'New Generation' AI-Brain Project – Analysis

#artificialintelligence

China is pursuing what its leaders call a "first-mover advantage" in artificial intelligence (AI), facilitated by a state-backed plan to achieve breakthroughs by modeling human cognition. While not unique to China, the research warrants concern since it raises the bar on AI safety, leverages ongoing U.S. research, and exposes U.S. deficiencies in tracking foreign technological threats. The article begins with a review of the statutory basis for China's AI-brain program, examines related scholarship, and analyzes the supporting science. China's advantages are discussed along with the implications of this brain-inspired research. Recommendations to address our concerns are offered in conclusion. All claims are based on primary Chinese data.1 Analysts familiar with China's technical development programs understand that in China things happen by plan, and that China is not reticent about announcing these plans. On July 8, 2017 China's State Council released its "New Generation AI Development Plan"2 to advance Chinese artificial intelligence in three stages, at the end of which, in 2030, China would lead the world in AI theory, technology, and applications.3


A Brief Overview of Methods to Explain AI (XAI)

#artificialintelligence

I know this topic has been discussed many times. But I recently gave some talks on interpretability (for SCAI and France Innovation) and thought it would be good to include some of my work in this article. The importance of explainability for the decision-making process in machine learning doesn't need to be proved any longer. Users are demanding more explanations, and although there are no uniform and strict definitions of interpretability and explainability, the number of scientific papers explaining artificial intelligence (or XAI) is growing exponentially. As you may know, there are two ways to design an interpretable machine learning process.


A Brief Overview of Methods to Explain AI (XAI)

#artificialintelligence

I know this topic has been discussed many times. But I recently gave some talks on interpretability (for SCAI and France Innovation) and thought it would be good to include some of my work in this article. The importance of explainability for the decision-making process in machine learning doesn't need to be proved any longer. Users are demanding more explanations, and although there are no uniform and strict definitions of interpretability and explainability, the number of scientific papers explaining artificial intelligence (or XAI) is growing exponentially. As you may know, there are two ways to design an interpretable machine learning process.


A Brief Overview of Methods to Explain AI (XAI)

#artificialintelligence

I know this topic has been discussed many times. But I recently gave some talks on interpretability (for SCAI and France Innovation) and thought it would be good to include some of my work in this article. The importance of explainability for the decision-making process in machine learning doesn't need to be proved any longer. Users are demanding more explanations, and although there are no uniform and strict definitions of interpretability and explainability, the number of scientific papers explaining artificial intelligence (or XAI) is growing exponentially. As you may know, there are two ways to design an interpretable machine learning process.


Future Vision & Direction of AI Part II: Scaling AI Whilst Preventing a Big Brother World & Solving The Curse of the Modern Data Scientist

#artificialintelligence

Venture Capitalists are hoping to find the next superstar tech unicorn, AI startup founders dreaming of creating the next unicorn, and corporates adopting AI need to consider their data growth strategy in order to be able to scale their AI-enabled services or products. The past decade has been one of explosive growth in digital data and AI capabilities across the digital media and e-commerce space. And it is no accident that the strongest AI capabilities reside in the Tech majors. The author argues that there will be no AI winter in the 2020s as there was in 1974 and 1987 as the internet (social media and e-commerce) are so dependent upon AI capabilities and so too with being the Metaverse, and the era of 5G enabled Edge Computing with the Internet of Things (IoT). Furthermore, the following infographics illustrate how many people globally use social media and hence how central these channels have become to the everyday lives of people. Likewise, the size of the e-commerce market is vast. Although the era of standalone 5G networks may enable a window of opportunity for a new wave of consumer-facing applications in the business to consumer (B2C) in relation to e-commerce and perhaps even new digital media platforms that may challenge the current incumbents, after all the arrival of 4G provided a window for the likes of Airbnb, Uber, and leading social media platforms such as Facebook, Instagram, etc. to scale.


The Road Ahead for Augmented Reality

Communications of the ACM

Automotive head-up displays (HUDs), systems that transparently project critical vehicle information into the driver's field of vision, were developed originally for military aviation use, with the origin of the name stemming from a pilot being able to view information with his or her head positioned "up" and looking forward, rather than positioned "down" to look at the cockpit gauges and instruments. The HUD projects and superimposes data in the pilot's natural field of view (FOV), providing the added benefit of eliminating the pilot's need to refocus when switching between the outside view and the instruments, which can impact reaction time, efficiency, and safety, particularly in combat situations. In cars, the main concern is distracted driving, or the act of taking the driver's attention away from the road. According to the National Highway Transportation Safety Administration, distracted driving claimed 3,142 lives in 2019, the most recent year for which statistics have been published. Looking away from the road for even five seconds at a speed of 55 mph is the equivalent of driving the length of a football field with one's eyes closed.