Posts Tagged “artificial intelligence”
The Mayo Clinic is set to implement an AI-powered patient triage and prediction platform developed by Israeli company Diagnostic Robotics at the American academic medical organization’s headquarters in Rochester, Minnesota to help reduce physician burden and optimize emergency room visits. The Mayo Clinic also has main campuses in Phoenix, Arizona, and Jacksonville, Florida.
The new collaboration, first publicized two weeks ago, will allow the Mayo Clinic’s emergency medicine department to make better informed, quicker decisions on patient care while reducing strain on medical teams.
Gush Dan Neighborhoods: Average COVID-19 associated symptoms region map. City municipal regions with at least 30 responders and neighborhoods with at least 10 responders are shown. Each region is colored by a category defined by the average symptoms ratio, calculated by averaging the reported symptoms rate by responders in that city or neighborhood. Green – low symptoms rate, red – high symptoms rate. Image: Weizmann Institute
Diagnostic Robotics was founded in 2017 by Jonathan Amir, who serves as CEO, AI expert Dr. Kira Radinsky, the former director of data science and Israel chief scientist for eBay, who serves as chairman and CTO, and Professor Moshe Shoham, a founder of Israeli company Mazor Robotics acquired by medical technology firm Medtronic in 2018. The trio set out to develop a human–machine hybrid AI diagnostic system that could help alleviate strained health budgets and workforces by helping physicians, healthcare providers and insurers with patient navigation while providing improved risk-prediction capabilities for clinical decision-making.
The system uses artificial intelligence, trained on data from millions of Electronic Health Records, some 27 million patient visits, and billions of data points from the US and Israel, as well as a simple questionnaire to perform clinical intake of patients in emergency rooms, urgent care clinics, and even patients from home. The medical teams can review the self-reported condition, suggest differential diagnoses, and issue a hospitalization risk score for the patient to supplement the physician decision-making process in real-time, Diagnostic Robotics described in the announcement.
The company raised $24 million in Series A funding in November.
The triage service itself is a personalized system guiding the patients through their journey in the medical ecosystem, analyzing their medical history and current medical case using NLP technologies, with generic ability to integrate with multiple sensory output data, Dr. Radinsky and Amir previously explained to NoCamels in April.
“Our mission at Diagnostic Robotics is to improve patients’ experience and support healthcare providers by creating seamless, data-driven interactions that reduce administrative burdens and curb the costs of care,” said Amir in a company statement dated June 18.
“We are excited to collaborate with Mayo Clinic and implement our triage platform, this collaboration reflects the synergy between our technological vision and Mayo Clinic’s cutting-edge medical expertise,” he added.
It’s been a long time since McDonald’s made a major acquisition. Twenty years actually, and their acquisition of Isreali-tech startup Dynamic Yield may be a shrewd move to buy the proprietary AI technology and keep it from their competitor’s reach.
What follows was excerpted and originally reported by NoCamels.com.
McDonald’s is set to acquire Israeli company Dynamic Yield, a market leader in customer personalization and decision logic technology, the two companies announced on Monday.
The financial terms of the deal were not disclosed but TechCrunch reported that “a source with knowledge” said the agreement was valued at over $300 million and is McDonald’s largest acquisition in 20 years.
Founded in 2011 by Israeli entrepreneurs Liad Agmon and Omri Mendellevich, the New York-headquartered company’s AI-powered omnichannel personalization engine helps product managers, and engineers build personalization campaigns that deliver individualized experiences at every customer touchpoint (online, mobile apps, email, kiosks, IoT, and call centers).
Dynamic Yield says its platform’s data management capabilities “provide for a unified view of the customer, allowing the rapid and scalable creation of highly targeted digital interactions. The company has over 300 clients that have included IKEA, URBN Brands, and Stitch Fix.
McDonald’s said in a statement that it will use Dynamic Yield’s technology “to provide an even more personalized customer experience by varying outdoor digital Drive Thru menu displays to show food based on time of day, weather, current restaurant traffic and trending menu items.” The tech can also suggest and display additional items based on customer current selections.
“Dynamic Yield’s ability to meet McDonald’s customer needs, coupled with their commitment to grow capabilities around ever-changing consumer trends and evolving marketing technologies, allows for the continued advancement and elevation of the McDonald’s customer experience with technology and innovation,” the fast-food giant said in the statement.
Steve Easterbrook, president and CEO of McDonald’s Corporation, said “technology is a critical element of our Velocity Growth Plan, enhancing the experience for our customers by providing greater convenience on their terms. With this acquisition, we’re expanding both our ability to increase the role technology and data will play in our future and the speed with which we’ll be able to implement our vision of creating more personalized experiences for our customers.
Agmon, who serves as Dynamic Yield’s CEO said: “We started Dynamic Yield seven years ago with the premise that customer-centric brands must make personalization a core activity. We’re thrilled to be joining an iconic global brand such as McDonald’s and are excited to innovate in ways that have a real impact on people’s daily lives.”
According to the agreement, Dynamic Yield will remain a stand-alone company and employees will continue to operate out of its offices across the world, including Berlin, Singapore, Moscow, Paris, London, NY, and Tel Aviv. Dynamic Yield will also continue to serve their current, and attract future, clients.
McDonald’s said upon the completion of the deal, it will become sole owner of Dynamic Yield, and will continue to invest in the company’s “core personalization product and world-class teams.”
Dynamic Yield previously raised some $83 million from investors such as Viola Growth, an Israeli-based technology growth capital fund, Innovation Endeavors, Bessemer Venture Partners, Vertex Ventures Israel, and Union Tech Ventures.
A new technological breakthrough is using AI and facial analysis to make it easier to diagnose genetic disorders. DeepGestalt is a deep learning technology created by a team of Israeli and American researchers and computer scientists for the FDNA company based in Boston. The company specializes in building AI-based, next-generation phenotyping (NGP) technologies to “capture, structure and analyze complex human physiological data to produce actionable genomic insights.”
Portions of this article were originally reported in NoCamels.com
DeepGestalt uses novel facial analysis to study photographs of faces and help doctors narrow down the possibilities. While some genetic disorders are easy to diagnose based on facial features, with over 7,000 distinct rare diseases affecting some 350 million people globally, according to the World Health Organization, it can also take years – and dozens of doctor’s appointments – to identify a syndrome.
“With today’s workflow, it can mean about six years for a diagnosis. If you have data in the first year, you can improve a child’s life tremendously. It is very frustrating for a family not to know the diagnosis,” Yaron Gurovich, Chief Technology Officer at FDNA and an Israeli expert in computer vision, tells NoCamels. “Even if you don’t have a cure, to know what to expect, to know what you’re dealing with helps you manage tomorrow.”
DeepGestalt — a combination of the words ‘deep’ for deep learning and the German word ‘gestalt’ which is a pattern of physical phenomena — is a novel facial analysis framework that highlights the facial phenotypes of hundreds of diseases and genetic variations.
According to the Rare Disease Day organization, 1 in 20 people will live with a rare disease at some point in their life. And while this number is high, there is no cure for the majority of rare diseases and many go undiagnosed.
“For years, we’ve relied solely on the ability of medical professionals to identify genetically linked disease. We’ve finally reached a reality where this work can be augmented by AI, and we’re on track to continue developing leading AI frameworks using clinical notes, medical images, and video and voice recordings to further enhance phenotyping in the years to come,” Dekel Gelbman, CEO of FDNA, said in a statement.
DeepGestalt’s neural network is trained on a dataset of over 150,000 patients, curated through Face2Gene, a community-driven phenotyping platform. The researchers trained DeepGestalt on 17,000 images and watched as it correctly labeled more than 200 genetic syndromes.
In another test, the artificial intelligence technology sifted through another 502 photographs to identify potential genetic disorders.
DeepGestalt provided the correct answer 91 percent of the time.
Indeed, FDNA, a leader in artificial intelligence and precision medicine, in collaboration with a team of scientists and researchers, published a milestone study earlier this year, entitled “Identifying Facial Phenotypes of Genetic Disorders Using Deep Learning” in the peer-reviewed journal Nature Medicine.
Israeli startup Aidoc, a developer of artificial intelligence (AI)-powered software that analyzes medical images, announced on Wednesday that it received Food and Drug Administration (FDA) clearance for a solution that flags cases of Pulmonary Embolism (PE) in chest scans for radiologists.
Portions of this article were originally reported in NoCamels.com
Aidoc has CE (Conformité Européenne) marking for the identification and triage of pulmonary embolism (PE) in CT pulmonary angiograms, and FDA approval to scan images for brain hemorrhages.
The latest approval came a month after Aidoc secured $27 million in a Series B round led by Square Peg Capital. Founded in 2016 by Guy Reiner, Elad Walach, and Michael Braginsky, the company has raised some $40 million to date.
Aidoc’s technology assists radiologists in expediting problem-spot detection through specific parameters such as neuron-concentration, fluid-flow, and bone-density in the brain, spine, abdomen, and chest. Aidoc says its solutions cut the time from scan to diagnosis for some patients from hours to under five minutes, speeding up treatment and improving prognosis.
“What really excites us about this clearance is that it paves the way towards scalable product expansion,” Walach, who serves as Aidoc CEO, said in a statement. “We strive to provide our customers with comprehensive end-to-end solutions and have put a lot of effort in developing a scalable AI platform.”
Walach said the company has eight more solutions in active clinical trials.
In the recent article by Abigail Klein Leichman titled “Could robots replace psychologists, politicians and poets?” published by Israel30c.com, Leichman concludes that AI will never develop a mind that can solve problems. Yet many neuroscientists, computer scientists, and those on the front lines of neural networks, machine learning, and all-things artificial intelligence believe they already have evidence that computers will develop true learning capabilities and some already have.
For the purpose of this essay, I’ll combine machine learning, neural networks, and artificial intelligence into the artificial intelligence monolith. In fact, today there is little difference between three other than their labels and the baggage that each label carries.
This debate has been around since humans started asking important existential questions. For most, free will is a given. We just must have it because by most accounts we are free to make decisions or choices save for governmental, religious or cultural restrictions and taboos. Even many theists advocate for free will. Christianity is predicated on it, that God gave us all free will so that we are free to accept the Christian god but don’t have to. Yet aside from these authorities it certainly seems that we have free choice, that we are presented with options and make a decision. Such decisions can be as simple as choosing which flavor of ice cream or as consequential as to whom to marry or what philosophy or politic to endorse.
Philosopher and neuroscientist Sam Harris says not true, and he believes he can prove it. In his best selling book, Free Will, Harris presents several scientific studies conducted over two decades that seem to confirm that free will is a delusion. The studies all conclude that the unconscious brain is what makes each and every decision, then it sends that information (and the conclusion) to our conscious mind where we then go through the motions of deciding something that was already decided, usually about a second before we began our conscious deliberation, sometimes a bit longer or shorter depending on the complexity of what is being considered. Yes. All of this has been measured and the data is quite unambiguous.
So how does this impact artificial intelligence and computers’ ability to think, solve problems, even give psychological advice and direction? If Harris is right, the question itself is misguided, arrogant and flat out wrong, for all the assumptions from which it hinges are incorrect. I must say, this is one of those rare times when new science tends to contradict the prevailing reality of existence, not to mention it’s counterintuitive if not downright unpleasant to consider, which is where I’m at right now.
That said, more on the fascinating topic of artificial intelligence to come, I promise.
In this segment, we’ll take a look at the practical applications of artificial intelligence (AI) today and what is right around the corner.
Recall the days on the evening news when reporters would interview a trader on the floor of the New York Stock Exchange to cover the impact of a trade war, a real war, or more the more disappointing earnings report from a blue chip company? Remember all those white middle-aged men in the middle of the pit gyrating most of every imaginable histrionic while shouting in desperation in secret Wall Street code and hand signals resembling gang signs with perspiration spraying everywhere?
You may not have realized it but those days are gone. Wall Street today is basically a working museum. The trading pits are gone. The only piece of the good old days that remains is the opening and closing bells which still does air on TV on occasion. The pit trader’s job has been lost to automation. Computer algorithms now do that job, and more efficiently too. But now that all (or most) human emotion has been jettisoned from the trade when unexpected events happen it’s all up to the computers to determine the best moves to make, all of which have been decided beforehand.
The same thing is true when you order up an Uber. The nearest pool of drivers is automatically petitioned. And the driver the gets the bid simply is verified of the task via a computer algorithm. Pickup and destination logistics, as well as price, are all determined by computers. As I write this, Uber has a new what I’ll call the “drunk algorithm” to determine the likelihood of a customer of having a few too many when ordering an Uber. The algorithm looks for, among other things, common typing mistakes, language used, location, and who knows what else, to determine their mental state and recommend an Uber driver more experience with unsober customers.
Advanced online marketing techniques use data analytics and other Big data to find predictive correlations between a consumers marital status and their likelihood to drink beer, and on what day, and what kind of beer. Men living in Nevada who have been recently divorced (say for less than two years) are more likely to buy a six-pack of beer on a Thursday, for instance. Knowing this, a Miller Lite advertisement will appear between 2 and 3 pm when they hit CNN.com to check the latest news.
These are but just a few examples of where AI is today. Imagine where it will be in five years! The only thing that can stop it is consumer insistence upon greater controls over their privacy concerns. But don’t hold your breath on that one. Most of us have already decided, albeit unwittingly, that the conveniences of the digital age outweigh the costs of giving up a bit of our privacy. In other words, we have traded away some of our privacy for its exchange value. And this is something we do a lot more than we would like to admit. You may recoil to the idea of having a microchip inserted into your person right now, but in five years you may find yourself opting into such a voluntary problem. Why? Because you may no longer need to remember your wallet, your keys, your passport, credit cards, rewards cards, pin numbers, or passwords. Pretty convenient, huh?
In this segment on artificial intelligence (AI), part four, we’ll look at the augmented human being, part human, part machine. And don’t laugh. Computer or robotic-assisted devices are being used to augment the human condition right now. For but one example, see my previous story on exoskeleton technology.
Another slick piece of wearables allows legally blind people to read newspaper and magazines, or product labels in a grocery store, even the money they take out of their pocket to pay the cashier, using artificial visualization technology.
As neuroscientists unleash the mysteries and power of the human brain while, at the same time, AI researchers build programs that get smart and smarter, even to the point where they become autonomous learners, human anatomy and robotics, along with AI software, will converge into human/machine hybrids, some of which will have more human characteristics than others. In other words, if we live long enough, say twenty more years, we may actually meet Mr. Spock, or a reasonable facsimile thereof.
Some of my academic friends who are working on this exciting future are not as enthusiastic as you would think. Many fear that the ethics will not keep pace with the technology, that we will create, arguably, a new species whose rights and freedoms will not comport with our justice system as it is today. Others are concerned about the economic value of people in an age where machines and computers will do almost all of the work. What are we going to do with 5 billion in surplus labor for which there will never be a job? Without income potential yet still constantly need to consume goods and services, how will the contribute to the betterment of our species and our world? There are no good answers for any of this yet. But there certainly are many grave concerns over them and many others.
But with all the ethical, economic and social concerns over AI, what most scientists are most anxious over is the notion of singularity. Singularity, as it pertains to AI, is the moment in the future whereby computers will become not only smarter than humans (and their programmers) but autonomous as well. If you haven’t guessed by now, they’re talking about the master/slave relationship between man and machine flipping. How this would exactly happen, nobody really knows. The anxiety of such a time is difficult to imagine. But it’s almost definitely only a few decades away. And while I may be naive, if a bunch of Mr. Spocks started running our world, it’s hard to imagine how that wouldn’t be an improvement.
As Climate Changes becomes more difficult to deny with each passing day — 95+ percent of scientists are already convinced — severe weather is becoming commonplace. Even in areas where it is common, it’s becoming more extreme and costly, both in terms of lives lost as well as the negative impact on the economy. In response to the growing urgency of the problem, Google Tel Avis has just rolled out an artificial intelligence (AI)-powered flood forecasting for flood stricken areas of India.
The remainder of this text was originally published by NoCamels.com
Google is harnessing artificial intelligence tech to create forecasting models that can better predict when and where floods will occur, and it has partnered with India’s Central Water Commission (CWC) to roll out early warnings in Google Search in the subcontinent, Google VP of engineering and the managing director of Google’s R&D Center in Tel Aviv, Yossi Matias, announced this week.
Deadly floods are common in some parts of India, especially during monsoon season, which runs from July to September every year. In August, India’s southern state of Kerala experienced the worst flood in the region in nearly 100 years, with over 400 killed and more than a million people displaced. A number of other areas in India have seen more devastating floods over the past decade, with death tolls running into the thousands. The 2004 tsunami is still the worst water-related natural disaster to have occurred in the country, with over 10,000 lives claimed in India alone.
“Floods are devastating natural disasters worldwide — it’s estimated that every year, 250 million people around the world are affected by floods, also costing billions of dollars in damages,” Matias wrote in a blog post. Existing warning systems can be inaccurate and uninformative while being wholly unavailable in some areas, “resulting in far too many people being underprepared and unaware before a flood happens,” he added
Google is now “using AI and significant computational power to create better forecasting models that predict when and where floods will occur, and incorporating that information into Google Public Alerts,” to help improve preparedness for impending floods, he wrote.
The tech giant feeds a number of elements – past events, river readings, elevation calculations – into its models to generate maps and “run up to hundreds of thousands of simulations in each location,” Matias explained.
“With this information, we’ve created river flood forecasting models that can more accurately predict not only when and where a flood might occur, but the severity of the event as well,” he said.
The partnership with India’s CWC was first announced in June by the agency. Under the terms of the agreement, the CWC would use “state-of-the-art advances made by Google in the field of Artificial Intelligence, Machine Learning and geospatial mapping for effective management of water resources particularly in the field of flood forecasting and dissemination of flood-related information to the masses widely using the dissemination platforms developed by Google.”
The CWC said in a statement that until recently, it was disseminating flood levels with maximum lead time of one day, but the cooperation with Google would allow for a lead time of up to three days.
The collaborative arrangement, the CWC said, is likely to save millions of rupees “which otherwise would have to be spent by the government on acquiring high-resolution DEM [digital elevation models], high-end computational resources and developing dissemination platforms widely used by the masses.”
This story was originally reported by NoCamels.com.
Habana Labs, an Israeli startup that develops AI processors, announced on Thursday that it has secured $75 million in a Series B funding led by Intel Capital, with participation from WRV Capital, Bessemer Venture Partners, Battery Ventures and existing investors.
Since it was founded in 2016, Habana Labs has raised a total of $120 million.
The new funding will go toward continued growth, including next-generation processors, sales, and customer support, said Habana Labs CEO David Dahan.
Related Story: Artificial Intelligence: Part 1.
“We are excited to invest in a dynamic team with a proven track record in the industry,” said Wendell Brooks, Senior Vice President of Intel Corporation and President of Intel Capital. “Habana Labs’ innovation and execution on their vision will help drive the next evolution of Artificial Intelligence.”
“AI brings a once-in-a-lifetime opportunity to create a significant wave of new semiconductor companies, and venture firms are heavily investing in AI-focused chip startups”, said Lip-Bu Tan, Founding Partner of WRV Capital, a leading international venture firm focusing on semiconductors and related hardware, systems, and software. “Among all AI semiconductor startups, Habana Labs is the first, and still the only one, which introduced a production-ready AI processor. We are delighted to partner with Intel in backing Habana Labs’ products and its extraordinary team.”
To say I was “blown away” by a recent editorial in NoCamels.com by Yaniv Garty, General Manager of Intel Israel, is a frustatingly cliche due to the poverty of English usage as it exists today. And it wasn’t Garty’s predictions of what the world could look like by 2025 that captured and downright agitated my imagination (in a way I enjoyed). Sure, his IT prophecies are all plausible among numerous pundits, evangelists, and visionaries. Nope. It wasn’t that.
It was the data, specifically the vast quantities of data being generated, even right now. Consider these three incredible facts:
- Of all the data created since the beginning of civilization, 90 percent of it has been generated in the last 2 years.
- By 2025, total data will reach 163 zettabytes. You probably never heard of a zettabyte, and you may want to pause before you attempt to digest it. 163 zettabytes is 1,000 Billion terabytes. Even with the comparison, I still find it incomprehensible.
- Only 1 percent of all data has been accessed in any meaningful way.
Garty, who is charged with growing Intel’s hardware for IT ecosystem of the future, has a lot to think about, namely…
Artificial Intelligence (AI), and how it can begin to mine the 99 percent for, among other things, greater insights and predictive measures. Intel already has its eyes on the medical field with aspirations to provide tailor-made solutions for each patient, perhaps and beyond, like unique biological and genetic characteristics.
Another good example is the interface between data and transportation: The potential of saving lives by lowering the number of accidents made possible with autonomous driving is incredible. But to reduce accidents we need a combination of technologies working together – from computer vision to end-computing, mapping, cloud, and of course AI. All these, in turn, require a systematic change in the way the industry views data-focused computing and technology.
My personal take is that the IT ecosystem of the future will more and more resemble the different executive and subordinate functions of the human brain with neuroscientists and computer scientists conspiring to construct the greatest monster even seen: one giant decentralized and interdependent mega-brain.
In the next segment of this series, we will consider the moral and religious implications of this almost godlike monstrosity.