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This past Thursday I held a public lecture, together with my long-time friend Ivan Bianchi, on the topic of Art and Artificial Intelligence. The event was organized by the "Galileo Festival" in Padova, for the Week of Innovation.Ivan is a professor of Contemporary Art at the University of Padova. We have known each other since we were two year olds, as our mothers were friends. We took very different career paths but we both ended up in academic and research jobs in Padova, and we have been able to take part together in several events where art and science are at the focus. Giving a lecture together is twice as fun!

The event took place in the historic "Sala Rossini" of Caff Pedrocchi (see above), in the town center, and was streamed live for online participants. We were a bit surprised to see that the hall was full of attendees, but in retrospect I think the venue, the timing, and the general organization were all playing their part to maximize the attention that the event received.Given that people are usually more interested in Art than in scientific topics I left to Ivan the better part of the hour we had, and took upon myself the task of introducing the topic, and to walk the audience through a discussion of what really is it that we talk about when we discuss Artificial Intelligence. I helped myself a bit with some material I had used earlier this year when I was invited at the Accademia dei Lincei (by its vice-president Giorgio Parisi, who a week ago won the Nobel prize in Physics!) - I will not repeat a summary of the discussion here as I did it in this other post already(which, amazingly, has already collected over 134000 page views...)

At the end of my half hour, in order to throw a bridge to the following discussion centered on art, I showed and discussed a video which showed how deep learning techniques are used to complete unfinished symphonies and works by classical music giants (Beethoven, Mahler, Schubert) - you can find the relevant material and a video at this link.

Ivan discussed how artificial intelligence is used in contemporary art nowadays. He touched on how artificial intelligence-powered instruments can be used as artistic objects (the shown case was a robotic arm which took the center stage of the Biennale 2019 in Venice) creating a performance of which they are the authors, or as support tools to produce artwork (such as robots that can sculpt marble figures and leave the artist only the final touch), or as the true subjects of the artistic production, such as a robot that creates paintings with acrylic paint on canvas. I will not go into the details of his explanation of the various trends and ideas, but you can certainly listen to the lecture in the linked video below (however, it is in Italian, unfortunately):

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Tommaso Dorigo (see hispersonal web page here) is an experimental particle physicist who works for theINFNand the University of Padova, and collaborates with theCMS experimentat the CERN LHC. He coordinates theMODE Collaboration, a group of physicists and computer scientists from eight institutions in Europe and the US who aim to enable end-to-end optimization of detector design with differentiable programming. Dorigo is an editor of the journalsReviews in PhysicsandPhysics Open. In 2016 Dorigo published the book "Anomaly! Collider Physics and the Quest for New Phenomena at Fermilab", an insider view of the sociology of big particle physics experiments. You canget a copy of the book on Amazon, or contact him to get a free pdf copy if you have limited financial means.

Read more here:
Art And Artificial Intelligence: An Odd Couple? - Science 2.0

Brent Leland, founder and president of High G, will present Artificial Intelligence Fear or Opportunity Thursday, Oct. 21.

The program is being offered by the Professional Womens Connection Ripon/Green Lake chapter. Networking will begin at 5:30 p.m. and will be followed by dinner and presentation at 6.

The event will take place in the upstairs banquet area of Roadhouse Pizza, 102 Watson St.

What is artificial intelligience? The dictionary defines it as the theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.

Learn how when combined with other emerging technologies, AI can deliver innovative solutions that transform businesses, disrupt markets and leapfrog the competition.

Leland will introduce the topic and begin to answer the questions that many companies are starting to ask: What is all the hype around AI? Is it relevant yet? What are the fundamentals we need to understand? How can we leverage AI with other disruptive technologies (IoT, Automation, AR/VR, etc.) to create new business models or to optimize our internal processes and capabilities? Where do we start?

Leland is the founder of High G, a boutique consulting firm focused on innovative and technology-enabled growth strategies and chaired the advisory board of Advancing AI Wisconsin.

Prior to his consulting career, Leland was the CIO of Trek Bicycle and earlier in his career held various finance, supply chain, engineering and IT roles for Spectrum Brands (formerly Rayovac), Hewlett-Packard, Loral and General Dynamics.

He holds an master of business arts degree from Stanford and a bachelor of science degree in aerospace engineering from the University of Florida.

Hes also an avid home-brewer and serves on the advisory board of Insight Brewing in Minneapolis.

Reservations must be made by Tuesday, Oct. 19 at noon and may be done by registering at https://pwcwi.clubexpress.com.

The dinner will consist of a soup, salad and assorted sandwich buffet with a cash bar.

Dietary requests should be sent to cbornick@vizance.com.

Member price is $15, while non-member cost is $20. Payment may be made online or upon arrival. Reservations made, but not honored, will be invoiced the cost of dinner selection.

Professional Womens Connection is a networking group that provides educational opportunities for area business and professional women, focusing on professional growth, personal development and the enhancement of leadership skills.

It is not a fundraising organization. The money for the annual scholarship comes from member dues, enabling current members to give back to the next generation of professional women.

Those interested in joining Professional Womens Connection may attend as a guest prior to joining the organization. Applications to join Professional Womens Connection are available through membership chair Cassie Bornick at pwc.ripon.greenlake@gmail.com and also will be available at the meeting.

Originally posted here:
Artificial intelligence is the topic of Oct. 21 Professional Women's Connection program - Ripon Commonwealth Press

The Fundamental Flaw in Artificial Intelligence & Who Is Leading the AI Race? Artificial Human Intelligence vs. Real Machine Intelligence

Artificial intelligence is impacting every single aspect of our future, but it has a fundamental flaw that needs to be addressed.

The fundamental flaw of artificial intelligence is that it requires a skilled workforce. Apple is currently leading the race of artificial intelligence by acquiring 29 AI startups since 2010.

Success in creating effective AI, could be the biggest event in the history of our civilization. Or the worst. We just don't know. So we cannot know if we will be infinitely helped by AI, or ignored by it and side-lined, or conceivably destroyed by it.

Stephen Hawking

Source: Reuters

Artificial intelligence is reduced to the following definitions:

1:a branch of computer science dealing with the simulation of intelligent behavior in computers; the capability of a machine to imitate intelligent human behavior;

2: an area of computer science that deals with giving machines the ability to seem like they have human intelligence;

3:the ability of a digitalcomputeror computer-controlledrobotto perform tasks commonly associated with intelligent beings; systems endowed with theintellectualprocesses characteristic of humans, such as the ability to reason, discover meaning, generalize, or learn from past experience;

4: system that perceives its environment and takes actions that maximize its chance of achieving its goals;

5: machines that mimic cognitive functions that humans associate with thehuman mind, such as learning and problem solving.

Source: Deloitte

The purpose of artificial intelligence isto enable computers and machines to perform intellectual taskssuch as problem solving, decision making, perception, and understanding human communication.

In fact, today's AI is not copying human brains, mind, intelligence, cognition, or behavior. It is all about advanced hardware, software and dataware, information processing technology, big data collection, big computing power. As it is rightly noted at the Financial Times Future Forum The Impact of Artificial Intelligence on Business and Society:Machines will outperform us not by copying us but by harnessing the combination of colossal quantities of data, massive processing power and remarkable algorithms.

They are advanced data-processing systems: weak or narrow AI applications, neural networks, machine learning, deep learning, multiple linear regression, RFM modeling, cognitive computing, predictive intelligence/analytics, language models, or knowledge graphs. Be it cognitive APIs (face, speech, text etc.),the Microsoft Azure AI platform, web searches or self-driving transportation, GPT-3-4-5 or BERT, Microsoft' KG, Google's KG orDiffbot, training their knowledge graph on the entire internet, encoding entities like people, places and objects into nodes, connected to other entities via edges.

Source: DZone

Today's"AI is meaningless" and "often just a fancy name for a computer program", software patches, like bug fixes, to legacy software or big databases to improve their functionality,security, usability, orperformance.

Such machines are not yet self-aware and they cannot understand context, especially in language. Operationally, too, they are limited by the historical data from which they learn, and restricted to functioning within set parameters.

Lucy Colback

Todays artificial intelligence (AI) is limited. It still hasa long way to go.

Artificial intelligence can be duped by scenarios it has never seen before.

With AI playing an increasingly major role in modern software and services, each major tech firm is battling to develop robust machine-learning technology for use in-house and to sell to the public via cloud services.

However most of the tech companies are still struggling to unlock the real power of artificial intelligence.

Today's artificial intelligence is at best narrow.Narrow artificial intelligence is what we see all around us in computers today -- intelligent systems that have been taught or have learned how to carry out specific tasks without being explicitly programmed how to do so.

Acording to CB Insights, artificial intelligence companies are a prime acquisition target for companies looking to leverage AI tech without building it from scratch. In the race for AI, this is who's leading the charge.

The usual suspects are leading the race for AI: tech giants like Facebook, Amazon, Microsoft, Google, and Apple (FAMGA) have all been aggressively acquiring AI startups for the last decade.

Among FAMGA, Apple leads the way. With 29 total AI acquisitions since 2010, the company has made nearly twice as many acquisitions as second-place Google (the frontrunner from 2012 to 2016), with 15 acquisitions.

Apple and Google are followed by Microsoft with 13 acquisitions, Facebook with 12, and Amazon with 7.

Source: CB Insights

Apples AI acquisition spree, which has helped it overtake Google in recent years, has been essential to the development of new iPhone features. For example, FaceID, the technology that allows users to unlock their iPhones by looking at them, stems from Apples M&A movesin chips and computer vision, including the acquisition of AI companyRealFace.

In fact, many of FAMGAs prominent products and services such as Apples Siri or Googles contributions to healthcare through DeepMind came out ofacquisitions of AI companies.

Other top acquirers include major tech players like Intel, Salesforce, Twitter, and IBM.

Source: Analytics Steps

Artificial Intelligence with robotics is poised to change our world from top to bottom, promising to help solve some of the worlds most pressing problems, from healthcare to economics to global crisis predictions and timely responses.

But while adopting and integrating and implementing AI technologies, as aDeloitte reportsays, around 94% of the enterprises face potential problems.

This article is not about the AI problems, such as the lack of technical know-how, data acquisition and storage, transfer learning, expensive workforce, ethical or legal challenges, big data addiction, computation speed, black box, narrow specialization, myths & expectations and risks, cognitive biases, or price factor. It is not our subject to discuss why small and mid-sized organizations struggle to adopt costly AI technologies, while big firms like Facebook, Apple, Microsoft, Google, Amazon, IBM allocate a separate budget for acquiring AI startups.

Instead, we focus on the AI itself, as the biggest issue, with its three fundamental problems looking for fundamental solutions in terms of Real Human-Machine Intelligence, as briefed below.

First, it is about AI philosophy, or rather lack of any philosophy, and blindly relying on observations and empirical data or statistics, its processes, algorithms, and inductive inferences, needing a large volume of big data as the fuel to train the model for the special tasks of the classifications and the predictions in very specific cases.

Second, today's AI is not a scientific AI that agrees with the rules, principles, and method of science. Todays AI is failing to deal with reality and its causality and mentality strictly following a scientific method of inquiry depending upon the reciprocal interaction of generalizations (hypothesis, laws, theories, and models) and observable/experimental data. Most ML models tuned and tweaked to best perform in labs fail to work in real settings of the real world at a wide range of different AI applications, from image recognition to natural language processing (NLP) to disease prediction due to data shift, under-specification or something else. The process used to build most ML models today cannot tell which models will work in the real world and which ones wont.

Third, extremeanthropomorphism in today's AI/ML/DL, "attributing distinctively human-like feelings, mental states, and behavioral characteristics to inanimate objects, animals, religious figures, the environment, and technological artifacts (from computational artifacts to robots)". Anthropomorphism permeates AI R & TD & D & D, making the very language of computer scientists, designers, and programmers, as "machine learning", which is not any human-like learning, "neural networks", which are not any biological neural networks, or "artificial intelligence", which is not any human-like intelligence. What entails the whole gamut of humanitarian issues, like AI ethics and morality, responsibility and trust, etc.

As a result, its trends are chaotic, sporadic and unsystematic, as theGartner Hype Cycle for Artificial Intelligence 2021demonstrates.

Source: Gartner

In consequence, there is no common definition of AI, and each one sees AI in its own way, mostly marked by an extreme anthropomorphism replacing real machine intelligence (RMI) with artificial human intelligence (AHI).

Source: Econolytics

Generally, there are two groups of ML/AI researchers, AI specialists and ML generalists.

Most AI folks are narrow specialists, 99.999%, involved with different aspects of the Artificial Human Intelligence (AHI), where AI is about programming human brains/mind/intelligence/behavior in computing machines or robots.

Artificial Human Intelligence (AHI) is sometimes defined as the ability of a machine to perform cognitive functions we associate with human minds, such as perceiving, reasoning, learning, interacting with the environment, problem solving, and even exercising creativity.

The EC High-Level Expert Group on artificial intelligence has formulated its own specific behaviorist definition.

Artificial intelligence (AI) refers to systems that display intelligent behaviour by analysing their environment and taking actions with some degree of autonomy to achieve specific goals

Artificial intelligence (AI) refers to systems designed by humans that, given a complex goal, act in the physical or digital world by perceiving their environment, interpreting the collected structured or unstructured data, reasoning on the knowledge derived from this data and deciding the best action(s) to take (according to predefined parameters) to achieve the given goal. AI systems can also be designed to learn to adapt their behaviour by analysing how the environment is affected by their previous actions''.

In all, the AHI is fragmented as in:

Very few of MI/AI researchers (or generalists), 00.0001%, know that Real MI is about programming reality models and causal algorithms in computing machines or robots.

The first group lives on the anthropomorphic idea of AHI of ML, DL and NNs, dubbed as a narrow, weak, strong or general, superhuman or superintelligent AI, or Fake AI simply. Its machine learning models are built on the principle of statisticalinduction: inferring patterns from specific observations, doing statistical generalization from observations or acquiring knowledge from experience.

This inductive approach is useful for building tools for specific tasks on well-defined inputs; analyzing satellite imagery, recommending movies, and detecting cancerous cells, for example. But induction is incapable of the general-purpose knowledge creation exemplified by the human mind. Humans develop general theories about the world, often about things of which weve had no direct experience.

Whereas induction implies that you can only know what you observe, many of our best ideas dont come from experience. Indeed, if they did, we could never solve novel problems, or create novel things. Instead, we explain the inside of stars, bacteria, and electric fields; we create computers, build cities, and change nature feats of human creativity and explanation, not mere statistical correlation and prediction.

The second advances a true and real AI, which is programming general theories about the world, instead of cognitive functions and human actions, dubbed as the real-world AI, or Transdisciplinary AI, the Trans-AI simply.

To summarize the hardest ever problem, the philosophical and scientific definitions of AI are of two polar types, subjective, human-dependent, and anthropomorphic vs. objective, scientific and reality-related.

So, we have a critical distinction, AHI vs. Real AI, and should choose and follow the true way.

Todays narrow AI advances are due to the computing brute force: the rise of big data combined with the emergence of powerful graphics processing units (GPUs) for complex computations and the re-emergence of a decades-old AI computation modelthe compute-hungry machine deep learning. Its proponents are now looking for a new equation for future AI innovation, that includes the advent of small data, more efficient deep learning models, deep reasoning, new AI hardware, such as neuromorphic chips or quantum computers, and progress toward unsupervised self-learning and transfer learning.

Ultimately, researchers hope to create future AI systems that do more than mimic human thought patterns like reasoning and perceptionthey see it performing an entirely new type of thinking. While this might not happen in the very next wave of AI innovation, its in the sights of AI thought leaders.

Considering the existential value of AI Science and Technology, we must be absolutely honest and perfectly fair here.

Todays AI is hardly any real and true AI, if you automate the statistical generalization from observations, with data pattern matching, statistical correlations, and interpolations (predictions), as the AI4EU is promoting.

Todays AI is narrow. Applying trained models to new challenges requires an immense amount of new data training, and time. We need AI that combines different forms of knowledge, unpacks causal relationships, and learns new things on its own.

Such a defective AI can only compute what it observes being fed with its training data, for very special tasks on well-defined inputs: blindly text translating, analyzing satellite imagery, recommending movies, or detecting cancerous cells, for example. By the very design it is incapable of general-purpose knowledge creation, where the beauty of intelligence is sitting.

Their machine learning models are built on the principle ofinduction: inferring patterns from specific observations or acquiring knowledge from experience, focused on big-data the more observations, the better the model. They have to feed their statistical algorithm millions of labelled pictures of cats, or millions of games of chess to reach the best prediction accuracy.

As the article,The False Philosophy Plaguing AI,wisely noted:

In fact, most of science involves the search for theories which explain the observed by the unobserved. We explain apples falling with gravitational fields, mountains with continental drift, disease transmission with germs. Meanwhile, current AI systems are constrained by what they observe, entirely unable to theorize about the unknown.

Again, no big data can lead you to a general principle, law, theory, or fundamental knowledge. That is the damnation of induction, be it mathematical or logical or experimental.

Due to lack of a deep conceptual foundation, todays AI is closely associated with its logical consequences,AI will automate entirety and remove people out of work,AI is totally a science-fiction based technology, orRobots will command the world?It is misrepresented as thetop five myths about Artificial Intelligence:

That means we need the true, real and scientific AI, not AHI, as the Real-World Machine Intelligence and Learning, or the Trans-AI, simulating and modeling reality, physically, mental or virtual, with its causality and mentality, as reflected in the real superintelligence (RSI).

Last not last, the transdisciplinary technology is S. Hawkings called effective and human-friendly AI and what the Googles founder is dreaming aboutAI would be the ultimate version of Google. The ultimate search engine would understand everything on the web. It would understand exactly what you wanted, and it would give you the right thing. Larry Page

Our approach to artificial intelligence is fundamentally wrong by not training and developing a skilled workforce capable of handling AI. Weve thought about AI the wrong way by focusing on algorithms instead of finding solutions to make AI better and unbiased.

Artificial intelligence has to be optimized based on human preferences so that it solves real problems. Apple is currently leading the race but it's a very competitive battle. American and Chinese tech companies are ahead of European tech companies when it comes to artificial intelligence.

A lot of work will need to be done to avoid the negative consequences of artificial intelligence especially with the adventof artificial superintelligence. The sooner we begin regulating artificial intelligence, the better equipped we will be to mitigate and manage the dark side of artificial intelligence.

Transdisciplinary artificial intelligence as a responsible global man-machine intelligence has all potential to help solve several problems related to AI and consequently improve the lives of billions.

Originally posted here:
The Fundamental Flaw in Artificial Intelligence & Who Is Leading the AI Race? Artificial Human Intelligence vs. Real Machine Intelligence - BBN...

Software as a Service, or SaaS, is a concept that is familiar to many. Long-time Photoshop users will recall when Adobe stopped selling its product and instead shifted to a subscriber model. Netflix and Disney+ are essentially Movies as a Service, particularly at a time when ownership of physical media is losing ground to media streaming. Artificial Intelligence as a Service (AIaaS) has been growing in market adoption in recent years, but the uninitiated might be asking: what exactly is it?

In a nutshell, AIaaS is what happens when a company develops and licenses use of an AI to another company, most often to solve a very specific problem. For example, Bill owns a company that sells hotdogs through his e-commerce site. While Bill offers a free returns policy for dissatisfied customers, he lacks the time to provide decent customer support, and rarely replies to emails. Separately, a software developer has created a chatbot that can handle most customer inquiries using natural language processing, and often solve the issue or answer a question before human intervention is even required. For a monthly fee, the chatbot is licensed to the hotdog vendor, and implemented on his website. Now, the bot is solving 80% of customer issues, leaving Bill with the time to respond to the remaining 20%. But Bill is still too preoccupied making hotdogs, so he subscribes to a service like Flowrite, that uses AI to intelligently write his emails on the fly.

AI is also being put in service to analyze large sets of data and make predictions, streamline information storage, or even detect fraudulent activity. Amazons personal recommendation engine, an AI powered by machine learning, is now available as a licensed service to other retailers, video stream platforms, and even the finance industry. Googles suite of AI services range from natural language processing, handwriting recognition, to real-time captioning and translation. IBMs groundbreaking AI, Watson, is now being deployed to fight financial crimes, target advertisements based on real-time weather analysis, and analyze data to help hospitals make treatment judgements.

Also read: AI-Enabled Payments: A Q&A with Tradeshift

Also read: How Quantum Computing Will Transform AI

Machine learning AIs improve with time, usage, and development. Some, like YouTubes recommendation engine, have become so sophisticated that it sometimes feels like we have entire television stations tailored perfectly to our interests. Others, like language model AI GPT-3, produce entire volumes of text that are nearly indistinguishable from an authentic human source.

Microsoft has even put GPT-3 to use to translate conversational language into a working computer code, potentially opening up a new frontier in how software can be written in the future, and giving coding novices a fighting chance. Microsoft has also partnered with NVIDIA to create a new natural language generation model, three times as powerful as GPT-3. Improvements in language recognition and generation have obvious carryover benefits for the future development of chatbots, home assistants, and document generation as well.

Industrial giant Siemens has announced they are integrating Googles AIaaS solutions to streamline and analyze data, and predict, for instance, the rate of wear-and-tear of machinery on their factory floor. This could reduce maintenance costs, improve the scheduling of routine inspections, and prevent unexpected equipment failures.

AIaaS is a rapidly growing field, and there will be many more niches discovered that it can fill for years to come.

Read next: Top 5 Benefits of AI in Banking and Finance

See the article here:
What is Artificial Intelligence as a Service (AIaaS)? | ITBE - IT Business Edge

Artificial intelligence is transforming industries around the world and health care is no exception. A recent Mayo Clinic study found that AI-enhanced electrocardiograms (ECGs) have the potential to save lives by speeding diagnosis and treatment in patients with heart failure who are seen in the emergency room.

The lead author of the study is Demilade Demi Adedinsewo, a noninvasive cardiologist at the Mayo Clinic who is actively integrating the latest AI advancements into cardiac care and drawing largely on her learning experience with MIT Professional Education.

Identifying AI opportunities in health care

A dedicated practitioner, Adedinsewo is a Mayo Clinic Florida Women's Health Scholar and director of research for the Cardiovascular Disease Fellowship program. Her clinical research interests include cardiovascular disease prevention, women's heart health, cardiovascular health disparities, and the use of digital tools in cardiovascular disease management.

Adedinsewos interest in AI emerged toward the end of her cardiology fellowship, when she began learning about its potential to transform the field of health care. I started to wonder how we could leverage AI tools in my field to enhance health equity and alleviate cardiovascular care disparities, she says.

During her fellowship at the Mayo Clinic, Adedinsewo began looking at how AI could be used with ECGs to improve clinical care. To determine the effectiveness of the approach, the team retroactively used deep learning to analyze ECG results from patients with shortness of breath. They then compared the results with the current standard of care a blood test analysis to determine if the AI enhancement improved the diagnosis of cardiomyopathy, a condition where the heart is unable to adequately pump blood to the rest of the body. While she understood the clinical implications of the research, she found the AI components challenging.

Even though I have a medical degree and a masters degree in public health, those credentials arent really sufficient to work in this space, Adedinsewo says. I began looking for an opportunity to learn more about AI so that I could speak the language, bridge the gap, and bring those game-changing tools to my field.

Bridging the gap at MIT

Adedinsewos desire to bring together advanced data science and clinical care led her to MIT Professional Education, where she recently completed the Professional Certificate Program in Machine Learning & AI. To date, she has completed nine courses, including AI Strategies and Roadmap.

All of the courses were great, Adedinsewo says. I especially appreciated how the faculty, like professors Regina Barzilay, Tommi Jaakkola, and Stefanie Jegelka, provided practical examples from health care and nonhealth care fields to illustrate what we were learning.

Adedinsewos goals align closely with those of Barzilay, the AI lead for the MIT Jameel Clinic for Machine Learning in Health. There are so many areas of health care that canbenefit from AI, Barzilay says. Its exciting to see practitioners like Demijoin the conversation and help identify new ideas for high-impact AIsolutions.

Adedinsewo also valued the opportunity to work and learn within the greater MIT community alongside accomplished peers from around the world, explaining that she learned different things from each person. It was great to get different perspectives from course participants who deploy AI in other industries, she says.

Putting knowledge into action

Armed with her updated AI toolkit, Adedinsewo was able to make meaningful contributions to Mayo Clinics research. The team successfully completed and published their ECG project in August 2020, with promising results. In analyzing the ECGs of about 1,600 patients, the AI-enhanced method was both faster and more effective outperforming the standard blood tests with a performance measure (AUC) of 0.89 versus 0.80. This improvement could enhance health outcomes by improving diagnostic accuracy and increasing the speed with which patients receive appropriate care.

But the benefits of Adedinsewos MIT experience go beyond a single project. Adedinsewo says that the tools and strategies she acquired have helped her communicate the complexities of her work more effectively, extending its reach and impact. I feel more equipped to explain the research and AI strategies in general to my clinical colleagues. Now, people reach out to me to ask, I want to work on this project. Can I use AI to answer this question? she said.

Looking to the AI-powered future

Whats next for Adedinsewos research? Taking AI mainstream within the field of cardiology. While AI tools are not currently widely used in evaluating Mayo Clinic patients, she believes they hold the potential to have a significant positive impact on clinical care.

These tools are still in the research phase, Adedinsewo says. But Im hoping that within the next several months or years we can start to do more implementation research to see how well they improve care and outcomes for cardiac patients over time.

Bhaskar Pant, executive director of MIT Professional Education, says We at MIT Professional Education feel particularly gratified that we are able to provide practitioner-oriented insights and tools in machine learning and AI from expert MIT faculty to frontline health researchers such as Dr. Demi Adedinsewo, who are working on ways to enhance markedly clinical care and health outcomes in cardiac and other patient populations. This is also very much in keeping with MITs mission of 'working with others for the betterment of humankind!'

Excerpt from:
Putting artificial intelligence at the heart of health care with help from MIT - MIT News