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Artificial intelligence is for the birds – University of California

Bird species usually are counted twice a year by wildlife surveyors: once during the breeding season and again during theChristmas Bird Count.

New technology, however, is increasing the accuracy of bird population studies. A team of UC Merced researchers is developing a model to recognize bird calls.

Recording devices called AudioMoths have been placed across bird habitat in Sonoma County. During two-week periods, the device wakes up every 10 minutes and records one minute of sound.Department of Computer Science and Engineeringprofessor Shawn NewsamandElectrical Engineering and Computer Sciencegraduate student Shrishail Shree Baligar are using artificial intelligence (AI) to detect bird calls in the recordings.

Their model can detect 45 different species so far, and will be used to produce maps of where, when and how many species are present.

Newsam and several colleagues joined up to explore the idea with two recently funded awards.

I love working on multidisciplinary problems. It charges me, Newsam said. He and Baligar are working with geography professor Matthew Clark from Sonoma State University, and Leo Salas, a quantitative ecologist at Point Blue Conservation Science, a nonprofit focused on climate-smart conservation.

Im excited to apply AI for the benefit of the Earth, Newsam said. Being able to passively detect and map species distributions using low-cost audio recording devices allows a range of down-stream research by domain scientists.

The Soundscapes to Landscapes (S2L): Monitoring Animal Biodiversity from Space Using Citizen Scientists program is supported by $1.1 million over three years through NASAs Citizen Science for Earth Systems program. It uses citizen scientists to deploy the AudioMoths. Other birders knowledgeable in bird calls will annotate a subset of the recordings, which serve as the training data for the AI models.

This summer, Newsam also received a $90,000, one-year AI for Earth Innovation grant from Global Wildlife Conservation in partnership with Microsoft. The nonprofit relies on research to work with local communities to address the root causes of threats to wildlife.

Newsams is one of only five projects funded out of 135 applications. The grant supports AI projects that can scale quickly. The research will benefit many other projects because it is open source.

For Newsam, there are many questions about processing the data, and many technical challenges. The recordings have biophony, geophony and anthrophony noise, and the bird calls are often faint. Some species have different calls for different communications: warning calls, mating calls and others. Which one should the AI focus on?

Birds often modify their calls by changing frequency, for example, if other birds are also calling, Newsam said. I am learning a lot about bird calls.

Baligar hears the calls as something more than just bird communication.

I like to think of birds as musical instruments, he said. All the violins are orange crowned warblers, but no two violins are the same. A bird song plays different notes, and every bird likes to play a song differently every time.

Each AudioMoth gathers about 2,000 minutes of data per site. So far, the team has more than 500,000 minute-long recordings more than 8,000 hours of data from over 600 locations and terrabytes of data to manage.

However, training the AI model requires a lot of annotated data.

Deep learning is data hungry, Baligar said. The more data the better. On average, we have just 650 training clips per bird species, which is not a lot.

Newsam, who co-founded the Spatial Analysis Research Center (SpARC) at UC Merced, is an expert in image analysis and understanding.

Image and audio are sensorily very different but in the end, it is just data data that we are turning into information through several processes, he said.

Baligar did not set out to study sound or bird calls when he was a masters student. He was more interested in time-series questions. Now, audio over time is the focus of his dissertation, and potentially the basis for a company he hopes to launch after graduation.

Computer science and environmental science are two of UC Merceds growing number of strengths, saidprofessor Josh Viers, director of theCenter for Information Technology Research in the Interest of Societyat UC Merced.

Professor Newsams research is indicative of the progress UC Merced has made in attracting top talent and solving important global problems, Viers said. Shawn is a leader in developing computer science tools that interpret and integrate massive amounts of information, from Earth imagery to sound recordings, and his research is pushing the envelope on innovation in sustainability and technology. It is really exciting to see this example of artificial intelligence used to benefit wildlife conservation efforts.

Future work for the team includes trying to identify individual birds and be able to track them over their range.

If we can overcome some of the modeling challenges, Newsam said, we might be able to replace satellites with much more fine-scaled information about all kinds of wildlife.

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Intermountain uses Artificial Intelligence to reduce costs and improve outcomes – ABC 4

(INTERMOUNTAIN HEALTHCARE) Intermountain Healthcare has been on a mission to reduce surgery costs while improving patient care. Theyve been able to discover millions of dollars in savings through artificial intelligence.

David Skarda, MD, Intermountain Healthcares medical director for Center for Value-Based Surgery, has been named to the nations class of Top 25 Innovators for 2020 by Modern Healthcare magazine for his innovative work to reduce healthcare costs and improve patient outcomes.

Dr. Skarda is helping to establish a surgical care process model that changes the way Intermountain analyzes and codes surgeries to create better outcomes and lower costs for patients. This new state-of-the-art tool uses artificial intelligence (AI) to analyze data, claims, and anything associated with the cost of care from 30 days before to 90 days after surgery.

So far, the tool is being used for two procedures across the Intermountain system and its already projected to save more than $8 million during the fiscal year. The savings are expected to increase as the technology is applied to other surgical procedures.

In the past, most health systems would save money by cutting out devices or procedures that cost the most, said Dr. Skarda. By analyzing total medical costs over 120 days we get a clearer picture of what gives us the best surgical outcomes, which also tends to lower the total cost of care.

Dr. Raymond Price is the Vice Chairman of the Department of Surgery at Intermountain Healthcare.he also performs a lot of gallbladder surgeries and recently found a glitch in a computer system had ordered certain pre-surgery tests that were unnecessary.

There were many blood tests that were actually being drawn by my patients. The AI system identified and asked why are you one of the highest ones ordering these blood tests and I said I didnt know I was ordering those blood tests. We looked at the system and we really sat down and reorganized how we did my pre-operative patients so we could really get rid of the waste in the system and be a much better value for the patient, said Dr. Price.

Looking at the total cost of surgery, and not just what happens in the operating room, gives clinicians the information needed to improve care, said Dr. Skarda.

An example is a knee replacement, which is a common procedure. The AI system analyzes the cost of the knee replacement device but also looks at any medications, imaging, physical therapy, and complications over the 120-day period. If a device is slightly more expensive but leads to fewer complications, and quicker recovery, the system recognizes it as a better value even though the initial cost is more expensive.

Trying to find these results using only electronic medical records would be impossible but combining claims data and the AI system makes the information useful to caregivers.

Intermountain surgeons now receive a report card that shows where they can reduce costs and how other physicians in their field are improving outcomes. This helps doctors make better decisions because it gives them the necessary data to prove what works.

That information can easily be shared to help hospital systems around the world improve the way they give care.

To see the complete Modern Healthcare 2020 innovators list, click here.

One of the first uses for the AI system began in May for gallbladder surgeries. While this doesnt include a major device like hip and knee replacement there is still key information the AI system finds for cost savings and better outcomes. Doctor Ray Price says doctors are always willing to change their process if the information is there to prove what works best.

By analyzing cost information 90 days after surgery we see what steps and procedures lead to fewer complications, said Dr. Price. We have found just because something is more expensive doesnt mean its going to be better for the patient. When we have the numbers to prove what leads to better outcomes we end up lowering the total cost of care.

The AI system can also help correct inefficiencies in the system before, during, and after surgeries. For example, Dr. Price found a glitch in a computer system had ordered certain pre-surgery tests, that were no longer a part of the process or were not needed. When compared to other surgeons it stood out because no one else had the glitch which was quickly corrected.

According to Dr. Price, this system allows doctors to compare themselves to other surgeons and learn different steps that can help their patients. Better outcomes for patients with fewer complications means the total cost of surgery naturally goes down. This type of innovation is at the center of Intermountains value-based care model which aims to find the best way for patients to remain healthy at the lowest cost to them.

For more information about Intermountain Healthcare, visit their website.

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Applications of Artificial Intelligence Driven Kiosks – BBN Times

AI-driven kiosks powered with human intelligence and self-learning capabilities are making your local kiosks look obsolete.

Kiosks have evolved thanks to our insatiable expectations and demands from technology. The usage ofinteractive kiosks dates back to 1977. Then in 1985, the first successful network of interactive kiosks was deployed, followed by the first commercial interactive kiosk with a network connection in 1991. So its pretty obvious that todays digital kiosks are super smart, right? Or are they?

Most kiosks today are still not smart enough to show their true potential as they lack adequate human-like intelligence and self-learning capabilities.

Remember Chris Tucker saying this to Jackie Chan in the movie Rush Hour? Thats kind of what a frustrated you want to tell a smart kiosk sometimes! Heres why. Kiosks are deployed as a replacement for human representatives. Hence you expect them to exhibit intelligence that a human representative would possess. But most current kiosks have limited intelligence making even a smart kiosk quite dumb. For instance, even a kiosk with avoice user interface (VUI)might not be able to differentiate words in nuanced speech.Consider two words: don't know and dunno. Both words mean the same. But a kiosk with limited intelligence would not identify both the words as the same. It would interpret both the words differently and get confused. This often impacts the flow of operations, and kiosks in such a situation will not be able to provide an appropriate response. Thus, limited intelligence in kiosks can end up as weak interactions with users.

Learning is always a painful process - Lucy

Another movie, another gem that seems to apply to smart kiosks perfectly. Self-learning is now essential for machines as well. Without self-learning capabilities, a kiosk would require frequent updates for embracing every new change and training. This would not only prove to be inconvenient for users but also costly for organizations and governments that have deployed them.

Many of todays smart kiosks are not truly AI-driven. Such kiosks cannot process minute variations of expected inputs from the user. E.g. suppose there is a mall kiosk with gesture recognition capabilities thats trained to recognize a finger pointed towards a direction to show us a list of restaurants in that direction. Without self-learning capabilities, such a kiosk would not be able to understand a gesture that involves two or three fingers instead of one.

Now imagine the world with AI-driven kiosks. These kiosks will not only exhibit human intelligence but also have self-learning capabilities. With the help of machine learning algorithms, digital kiosks can learn with the help ofvarious learning algorithms. They will continuously keep on learning from variations they come across in every interaction with users. This will help them to expand their intelligence and surpass their own limitations.

Subset technologies of AI such as natural language processing and computer vision enable touchless kiosks to understand and extract voice and gestures even from nuanced backgrounds. ML algorithms have the potential to reduce background noise which can help gesture control interfaces to determine gestures and provide responses to users accurately. Self-learning AI capabilities also make it simple to scale AI-driven kiosks. Making the question even more pertinent with all these capabilities available today, why is your kiosk so dumb?

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Artificial intelligence solutions built in India can serve the world – The Indian Express

Updated: October 8, 2020 8:43:31 am

Written by Abhishek Singh

The RAISE 2020 summit (Responsible AI for Social Empowerment) has brought issues around artificial intelligence (AI) to the centre of policy discussions. Countries across the world are making efforts to be part of the AI-led digital economy, which is estimated to contribute around $15.7 trillion to the global economy by 2030. India, with its AI for All strategy, a vast pool of AI-trained workforce and an emerging startup ecosystem, has a unique opportunity to be a major contributor to AI-driven solutions that can revolutionise healthcare, agriculture, manufacturing, education and skilling.

AI is the branch of computer science concerned with developing machines that can complete tasks that typically require human intelligence. With the explosion of available data expansion of computing capacity, the world is witnessing rapid advancements in AI, machine learning and deep learning, transforming almost all sectors of the economy.

India has a large young population that is skilled and eager to adopt AI. The country has been ranked second on the Stanford AI Vibrancy Index primarily on account of its large AI-trained workforce. Our leading technology institutes like the IITs, IIITs and NITs have the potential to be the cradle of AI researchers and startups. Indias startups are innovating and developing solutions with AI across education, health, financial services and other domains to solve societal problems.

Machine Learning-based deep-learning algorithms in AI can give insights to healthcare providers in predicting future events for patients. It can also aid in the early detection and prevention of diseases by capturing the vitals of patients. A Bengaluru based start-up has developed a non-invasive, AI-enabled technology to screen for early signs of breast cancer. Similarly, hospitals in Tamil Nadu are using Machine Learning algorithms to detect diabetic retinopathy and help address the challenge of shortage of eye doctors. For the COVID-19 response, an AI-enabled Chatbot was used by MyGov for ensuring communications. Similarly, the Indian Council of Medical Research (ICMR) deployed the Watson Assistant on its portal to respond to specific queries of frontline staff and data entry operators from various testing and diagnostic facilities across the country on COVID-19. AI-based applications have helped biopharmaceutical companies to significantly shorten the preclinical drug identification and design process from several years to a few days or months. This intervention has been used by pharmaceutical companies to identify possible pharmaceutical therapies to help combat the spread of COVID19 by repurposing drugs.

Opinion | An AI future set to take over post-Covid world

AI-based solutions on water management, crop insurance and pest control are also being developed. Technologies like image recognition, drones, and automated intelligent monitoring of irrigation systems can help farmers kill weeds more effectively, harvest better crops and ensure higher yields. Voice-based products with strong vernacular language support can help make accurate information more accessible to farmers. A pilot project taken up in three districts Bhopal, Rajkot and Nanded has developed an AI-based decision support platform combined with weather sensing technology to give farm level advisories about weather forecasts and soil moisture information to help farmers make decisions regarding water and crop management. ICRISAT has developed an AI-power sowing app, which utilises weather models and data on local crop yield and rainfall to more accurately predict and advise local farmers on when they should plant their seeds. This has led to an increase in yield from 10 to 30 per cent for farmers. AI-based systems can also help is establishing partnerships with financial institutions with a strong rural presence to provide farmers with access to credit.

An AI-based flood forecasting model that has been implemented in Bihar is now being expanded to cover the whole of India to ensure that around 200 million people across 2,50,000 square kilometres get alerts and warnings 48 hours earlier about impending floods. These alerts are given in nine languages and are localised to specific areas and villages with adequate use of infographics and maps to ensure that it reaches all.

The Central Board of Secondary Education has integrated AI in the school curriculum to ensure that students passing out have the basic knowledge and skills of data science, machine learning and artificial intelligence. The Ministry of Electronics and Information Technology (MeitY) had launched a Responsible AI for Youth programme this year in April, wherein more than 11,000 students from government schools completed the basic course in AI.

As AI works for digital inclusion in India, it will have a ripple effect on economic growth and prosperity. Analysts predict that AI can help add up to $957 billion to the Indian economy by 2035. The opportunity for AI in India is colossal, as is the scope for its implementation. By 2025, data and AI can add over $500 billion and almost 20 million jobs to the Indian economy.

Opinion | Automation and AI in a changing business landscape

Indias AI for All strategy focuses on responsible AI, building AI solutions at scale with an intent to make India the AI garage of the world a trusted nation to which the world can outsource AI-related work. AI solutions built in India will serve the world.

AI derives strength from data. To this end, the government is in the process of putting in place a strong legal framework governing the data of Indians. The legislation stems from a desire to become a highly secure and ethical AI powerhouse. India wants to build a data-rich and a data-driven society as data, through AI, which offers limitless opportunities to improve society, empower individuals and increase the ease of doing business.

The RAISE 2020 summit has brought together global experts to create a roadmap for responsible AI an action plan that can help create replicable models with a strong foundation of ethics built-in. With the participation of more than 72,000 people from 145 countries, RAISE 2020 has become the true global platform for the exchange of ideas and thoughts for creating a robust AI roadmap for the world.

This article first appeared in the print edition on October 8, 2020 under the title Making AI work for India. The writer is president and CEO, NeGD, CEO MyGov and MD and CEO, Digital India Corporation.

The Indian Express is now on Telegram. Click here to join our channel (@indianexpress) and stay updated with the latest headlines

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Artificial Intelligence Powers 8X Conversion Growth by Taking the Guesswork out of Successful Digital Marketing – PR Web

Marketers are realizing 8x growth in revenue per conversion, 350% growth in ad engagement and more than 60% savings in cost per click armed with predictions and insights from an AI specially designed to dramatically boost marketing ROI.

AUSTIN, Texas (PRWEB) October 08, 2020

Innovative marketers and agencies are now using a first of its kind artificial intelligence (AI) solution to instantly predict the success of digital ads and dramatically boost ad results.

Junction AI, an Austin, Texas and Brisbane, Australia based start-up, has developed a proprietary AI platform, which predicts the best ad creative for Google Ads, Facebook Ads and website promotions all before deploying live.

Launched in beta late last year and now rolling out globally, the solution is already having a big impact. Marketers are realizing 8x growth in revenue per conversion, 350% growth in ad engagement and more than 60% savings in cost per click.

Until now, marketers had to spend significant time and money testing and optimizing digital ads over days, weeks and months to find out what works, said Junction AI CEO/Cofounder Vance Reavie.

This means that up to 60% of ad spend underperforms or is outright wasted. Every dollar that underperforms is a customer lost.

How does it work?The Junction AI platform harnesses a brands Google and Facebook ad data to create an AI Audience Model.

The platform uses a combination of AI techniques including image analysis, natural language processes, semantic reasoning and neural nets to understand how digital advertising is perceived by the brands audience.

Predictions are generated by running as many as 75 million tests within seconds. These predictions are relevant and applicable to a marketers audience because tests are run on a model using a brands actual audience data, rather than general population models or pooled data.

With Junction AI, brands and agencies can run unlimited tests to predict ad success and optimize every element of the ad creative to see if their ad will convert before having to spend real dollars.

Sometimes changing a word or a graphic can mean the difference between success and failure, he said. Weve generated insights on what to include and exclude, ranging from product models and features to locations and colors.

Matt Jenkins, Managing Director of leading Toronto based agency Other has been testing the AI platform since late 2019 and has seen strong results, with one client growing conversions 8x on Google Search Ads.

Our team needs to be ahead of the curve in using data to improve results for our clients, says Matt.

Introducing a practical and easily accessible AI tool offers an opportunity to change the way we work as marketers.

A $330B Market Growing Rapidly Digital advertising continues to grow rapidly with $330 billion spent last year. There is significant scrutiny on digital ad budget growth at the moment, with clients demanding smarter spend.

The volume of ad metrics and data is vast, and its a job uniquely suited to AI. No marketer has the time and budget to analyze millions of data combinations for insights, but an AI can crunch the data in seconds.

The use of AI deep learning makes this a perfect use case, says Vance. No human or team can run 75 million tests in seconds to uncover relationships in the data that may power the next successful ad.

Our challenge was to make it easy, make it on demand and deliver real time insights without the need to hire a data engineer or analyst.

Its like equipping everyone on your team with their own personal AI data scientist at their fingertips 24/7!

About Junction AIJunction AIs proprietary Advertising Intelligence platform is a first of its kind solution that puts the power of an AI in the hands of every marketer to take the guesswork out of successful digital advertising. Our real time and on demand insights on ad creative power massive results for digital marketing.

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Automation And Artificial Intelligence Revolutionize The Cannabis Industry – Benzinga

This article was originally published on Cannabis & Tech Today, and appears here with permission.

To some,artificial intelligence(AI) may be categorized next to the likes of Bitcoin and blockchain: its just another techie buzz word.

For others, artificial intelligence could conjure up images of sentient robots hellbent on world domination.

While artificial intelligence, in some ways, can be those things, what it is in practice often looks much different.

The artificial intelligence that many people talk about today can also be referred to as machine learning, or the process by which software takes in data, learns patterns, and makes whatever adjustments it needs to make to achieve its goal.

The goal in our case?

Maximizing cannabis yields and sales.

Every industry is being shaken up by AI these days, explained Brad Newkirk, strategic leader atLumiGrow, a smart lighting company working to develop AI solutions for cannabis cultivators. Cannabis is no different, except that its newness creates more opportunities.

LumiGrow lighting at Copper state Farms

Cultivators should look to autonomous growing techniques to improve their margin, Newkirk continued. In the retail space, Ive seen interactive experiences make product education easy and fun increasing the likelihood of a sale. The medical market already fully embraces AI technologies as they explore relationships between treatment techniques and patient outcomes.

While, like Newkirk mentioned, AI has some applications withinretailand the medical field, its most common implementation, at the moment, is in cultivation.

[Automation] brings environmental control, fertigation, and irrigation accuracy, which transfers into high quality, uniform crops, explained Justin Jacobs, AST Field Tech atArgus Control, which provides automated control systems for cannabis horticulture.

The point that Jacob makes highlights two important benefits of automation and AI technologies: efficiency and accuracy.

Having this amount of consistent control and accuracy naturally leads to a more consistent product, which is important in an industry with regularly shifting regulations.

At the same time, this technology also makes the entire operation run more efficiently by seamlessly and automatically making adjustments and reducing labor.

As explained by Adam Klaasmeyer, co-founder ofCEAD.ai, an AI solutions company for the cannabis industry, Automation reduces the requirements for labor, but also extends the abilities of existing employees by empowering them with new tools and data.

By using water and energy more efficiently, as well as labor, automation and AI also make facilities moresustainable.

Our data shows that smart lighting can reduce energy usage while keeping yields at optimal performance, Newkirk claims.

And dont think that artificial intelligence and automation being applied to cannabis is a new idea.

In fact, nearly every single horticulture industry has incorporated these technologies.

Automation has been a proven part of the agricultural industry for decades, Jacobs continued.

Newkirk referred to cannabis as just another crop.

He continued on to say, The truth is that AI applications for cannabis will be similar to other wholesale or medicinal crops its just that the research is in its infancy It will take time for AI to synchronize with specific plant genetics but honestly, this is the same for all agricultural products The only difference is that cannabis technology may be developing quicker due to the fact that its just so profitable in comparison.

Argus provides automated control systems for horticulture, aquaculture, and related biotechnology industries.

It starts with data.

The software learns the patterns of the plants (hence machine learning) over a period of time.

Then, its able to automatically adjust things like temperature, moisture content, and a number of other environmental aspects.

Klaasmeyer explained this idea of predictive analysis:

Predictive analysis makes assumptions based on human experience that future results will follow patterns from the past. Currently, predictive analysis is limited by the volume, time, and cost constraints of human data analysts. An AI system is able to make assumptions, test, and learn autonomously.

By learning this data patterns, the AI can not only adjust climate, but also can predict things such as harvest yields and potential issues, such as disease or pest outbreaks.

Newkirk explained how LumiGrows smart sensors can recognize airborne disease before plants are affected, giving cultivators the opportunity to take defensive action.

In fact, the three industry leaders we spoke to all highlighted different problems theyve encountered while implementing this technology for clients.

Jacobs specifically mentioned the learning curve to bring growers up to speed to show the value automation can bring to their facilities, to show them they can trust the control system.

In the case of Klaasmeyer and CEAD.ai, one major challenge in implementing AI is getting access to quality data to inform the system and correctly define the problem statements before training the model.

Newkirk highlighted a number of challenges hes faced: The most common challenge I see when using AI is getting your new technology to work together with your older, dumb, systems Another challenge is choosing which technology is right for you.

These technologies are in no way quick fixes.

Cultivators must take the time to learn the new system, and on the other end, the system will take time to learn the data patterns of the cultivation facility.

According to Jacobs, Automation is a long term investment that will pay off harvest after harvest.

While some aspects may still be in a state of infancy, this is the future of cultivation.

I think of the future of AI and horticulture as Automation with a human touch, Newkirk expressed. Growers will be given new technologies to make their jobs easier and their business more reliable We arent making growers irrelevant, were just making them super-growers.

And for those of you who still think AI will lead to robot overlords, Newkirk has some words of comfort.

Everyone mostly thinks AI means self-aware computers, but mostly thats still science fiction. Theres no need to worry your grow equipment is plotting to take over the world not yet, at least.

Images courtesy of Argus and LumiGrow.

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Thomas J. Fuchs, DSc, Named Dean of Artificial Intelligence and Human Health and Co-Director of the Hasso Plattner Institute for Digital Health at…

Newswise (New York, NY October 7, 2020) Thomas J. Fuchs, DSc, a prominent scientist in the groundbreaking field of computational pathologythe use of artificial intelligence to analyze images of tissue samples to identify disease and predict outcomehas been appointed Co-Director of the Hasso Plattner Institute for Digital Health at Mount Sinai, Dean of Artificial Intelligence (AI) and Human Health, and Professor of Computational Pathology and Computer Science in the Department of Pathology at the Icahn School of Medicine at Mount Sinai. In his new role, he will lead the next generation of scientists and clinicians to use machine learning and other forms of artificial intelligence to develop novel diagnostics and treatments for acute and chronic disease.

Dr. Fuchs has advanced the field of precision medicine through his contributions to artificial intelligence in pathology, helping the health care industry better understand and fight cancer. His expertise will enhance Mount Sinais continued efforts to use digital health to train future medical leaders and improve care for our patients, said Dennis S. Charney, MD, Anne and Joel Ehrenkranz Dean, Icahn School of Medicine at Mount Sinai, and President for Academic Affairs, Mount Sinai Health System. By building on existing AI and health initiatives, like the Mount Sinai Digital and Artificial Intelligence-Enabled Pathology Center of Excellence, Dr. Fuchss guidance, along with shared knowledge and academic excellence from our team of researchers and clinicians, will help revolutionize health care and science, nationally and globally.

Dr. Fuchss trailblazing work includes developing novel methods for analysis of digital microscopy slides to better understand genetic mutations and their influence on changes in tissues. He has been recognized for developing large-scale systems for mapping the pathology, origins, and progress of cancer. This breakthrough was achieved by building a high-performance compute cluster to train deep data networks at petabyte scale.

Mount Sinai is at the forefront of digital health in medicine with an exceptionally talented team driving innovation forward. I am tremendously excited to join them in expanding initiatives and efforts to advance artificial intelligence in human health; the honor of leading this task is utterly humbling, said Dr. Fuchs. Together, we will weave a fabric of AI services that help nurses, physicians, and hospital leadership to make personalized decisions for every patient. The key goals are to help especially vulnerable populations, improve treatment for all, and use AI to democratize health care throughout New York and across the globe.

His vision for Mount Sinai is to further revolutionize medical practice by pushing the boundaries of AI with the ultimate goal of transforming the quality of life and human health for people all over the globe. That vision includes transforming pathologythe study of causes and effects of a disease or injuryfrom a qualitative to a quantitative science, and empowering more doctors and medical students to use their talent for good by joining the novel field.

Dr. Fuchs will focus on developing a new system and code for machine learning; large-scale research models and computation; more effectively using data to apply to real-world clinical settings; and continuing to expand the use of computational pathology in treatments through collaboration.

He will co-lead the Hasso Plattner Institute for Digital Health at Mount Sinai, established in 2019 by the Mount Sinai Health System and the Hasso Plattner Institute with generous philanthropic support from the Hasso Plattner Foundation.

Dr. Fuchs has made key contributions in AI for cancer diagnosis which will be significant as we work to save lives, prevent disease, and improve the health of patients using artificial intelligence in real-time analysis of comprehensive health data from electronic health records, genetic information, and mobile sensor technologies, said Erwin P. Bottinger, MD, Co-Director of the Hasso Plattner Institute for Digital Health at Mount Sinai and Professor of Digital Health-Personalized Medicine, Hasso Plattner Institute, University of Potsdam, Germany. As Dr. Fuchs and I collaborate to advance artificial intelligence and machine learning in health care, the institute will continue to be a force in creating progressive digital health services.

Before joining Mount Sinai, Dr. Fuchs was Director of the Warren Alpert Center for Digital and Computational Pathology at Memorial Sloan Kettering Cancer Center (MSK) and Associate Professor at Weill Cornell Graduate School for Medical Sciences. At MSK he led a laboratory focused on computational pathology and medical machine learning. Dr. Fuchs co-founded Paige.AI in 2017 and led its initial growth to the leading AI company in pathology. He is a former research technologist at NASAs Jet Propulsion Laboratory and visiting scientist at the California Institute of Technology. Dr. Fuchs holds a Doctor of Sciences from ETH Zurich in Machine Learning and a MS in Technical Mathematics from Graz Technical University in Austria.

We are very pleased to welcome Thomas to our faculty, said Eric Nestler, MD, PhD, Nash Family Professor of Neuroscience, Director of The Friedman Brain Institute, and Dean for Academic and Scientific Affairs, Icahn School of Medicine at Mount Sinai. His vast knowledge in data science, machine learning, and artificial intelligence will significantly move Mount Sinai forward as a world leader in health care.

About the Mount Sinai Health SystemThe Mount Sinai Health System is New York City's largest academic medical system, encompassing eight hospitals, a leading medical school, and a vast network of ambulatory practices throughout the greater New York region. Mount Sinai is a national and international source of unrivaled education, translational research and discovery, and collaborative clinical leadership ensuring that we deliver the highest quality carefrom prevention to treatment of the most serious and complex human diseases. The Health System includes more than 7,200 physicians and features a robust and continually expanding network of multispecialty services, including more than 400 ambulatory practice locations throughout the five boroughs of New York City, Westchester, and Long Island. The Mount Sinai Hospital is ranked No. 14 on U.S. News & World Reports Honor Roll of the Top 20 Best Hospitals in the country and the Icahn School of Medicine as one of the Top 20 Best Medical Schools in the country. Mount Sinai Health System hospitals are consistently ranked regionally by specialty by U.S. News & World Report.

For more information, visit https://www.mountsinai.org or find Mount Sinai on Facebook, Twitter and YouTube.

To learn more about Dr. Thomas Fuchs and the Hasso Plattner Institute for Digital Health at Mount Sinai, watch the short videohere.

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How Is Artificial Intelligence Revolutionising Our Global Healthcare? – Blog – The Island Now

Meta: Artificial intelligence is reinventing everything we once knew about healthcare. Find out how this technology could save millions of lives in the future.

Artificial intelligence is one of the most promising technologies in the world. From robots in manufacturing to self-driving cars, the possibilities of AI are pretty much endless. However, its only been in recent years that people are now looking to artificial intelligence to improve global healthcare too.

In fact, this brilliant technology could be the key to saving millions of lives across the world. And so, in our article today, we are going to be looking at how AI is currently being used in global healthcare and how its revolutionizing everything we once knew about the world of medicine. Keep reading to find out more.

Robotic Surgery

Surgery is a notoriously high-risk procedure because of human error. For example, surgeons often have to create tiny incisions and sometimes they make mistakes, no matter how steady their hand. However, artificial intelligence might be able to correct this problem and ensure more people have a successful surgery.

Scientists are currently developing AI-assisted surgeries, where robots can guide the surgeons instruments and create minuscule incisions. Whats more, this technology is predicted to add $40 billion to the market value of the healthcare industry, attracting all kinds of philanthropic investors like Tej Kohli. AI-assisted surgeries could also reduce the amount of time that patients spend in recovery because they are facilitating non-invasive treatments, so people dont have to stay in the hospital for as long.

Virtual Nurses

Virtual nursing assistants are also being developed. They have the potential to save the healthcare industry $20 billion annually. Not only are AI nursing assistants able to direct people where exactly they need to go, but they also can monitor and interact with patients 24/7. This could facilitate communication between patients and their care providers, too. As these virtual nurses can provide quick answers to key questions, they might be able to prevent hospital readmissions or unnecessary visits, thereby saving healthcare organizations money on superfluous expenses.

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Clinical Diagnosis & Judgement

Precious time can often be wasted as healthcare professionals scramble to discover what is wrong with their patients. But with the help of artificial intelligence, this may no longer be an issue. Though this technology is still premature, AI clinical diagnosis and judgement have the potential to detect cancers faster than humans. Using deep-learning, AI could also analyse medical records and genetic information from the NHS database to draw helpful conclusions.

Automate Administration

Bureaucracy can sometimes lead to delays in patient care and cost healthcare organizations more money. Fortunately, artificial intelligence can provide a solution to this problem through automation. For example, administrative tasks could be completed in a fraction of the time, allowing professionals to focus more on imperative tasks. AI can also mine big data on medical papers, saving people from reading and analyzing them. The process of prescribing medication could also be quickened.

These are some of the main ways that artificial intelligence is revolutionizing our global healthcare. Who knows what else this brilliant technology will be capable of in the future?

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Penn researchers get $3.2 million grant to use artificial intelligence for improving heart transplants – PhillyVoice.com

A team of researchers at Penn Medicine are turning to artificial intelligence as a diagnostic tool to improve outcomes for patients who receive heart transplants.

Each year, more than 2,000 heart transplants are performed in the United States, but the immune systems of their recipients reject as many as 30% to 40% of these organs.

A new grant from the National Institutes of Health will support research into the use of artificial intelligence to better detect the risk of rejection and the immune mechanisms that underlie it. The $3.2 million grant will be shared over four years by Penn Medicine, Case Western Reserve University, Cleveland Clinic and Cedars-Sinai Medical Center.

When a patient's immune system recognizes a donor heart as a foreign object, the organ can become damaged and eventually rejected.

The current grading standard for such damage has poor diagnostic accuracy, leaving patients vulnerable to receiving too much or too little treatment.

With the grant funding, researchers will use AI to analyze cardiac biopsy tissue images and better distinguish between rejection grades. They hope the analysis will also detect patterns of immune cells that reveal the mechanism of rejection.

With improved diagnostic accuracy, researchers believe they may be able to spot serious rejection earlier on, reduce rates of infection, and prevent complications of immune-suppressing drugs.

By improving identification of rejection mechanisms, clinicians may be able to better target medications and predict long-term outcomes, reducing the need for frequent heart biopsies.

The research team will compare the relative performance of the AI analysis with human pathologists to see how computer-aided tissue diagnostics can serve as a decision support tool.

This research is focused on a critical component of heart transplantation improving patient outcomes," said Kenneth B. Marguiles, principle investigator and professor of cardiovascular medicine at Penn. "Unfortunately, the number of patients with end-stage heart failure is increasing. But research like this is another step in the right direction for improving survival and quality of life for heart failure patients."

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IBM Watson Advertising Expands Suite, Makes Artificial Intelligence The Backbone 10/08/2020 – MediaPost Communications

IBMWatson Advertising is building a suite of solutions using artificial intelligence as the foundation.

The suite, based on privacy measures, leverages first-party data to increase adoptimization and move the industry into cookieless targeting. The strategy also uses intelligent chatbots that connect brands and consumers.

Artificial intelligence is becoming the backbone ofonline advertising, says Sheri Bachstein, global head of Watson Advertising and The Weather Company. The industry is feeling a lot of pressure as targeting pixels disappear and privacylegislation increases, she says, suggesting that AI has moved from a buzzword to supporting brands.

Change requires education. Eight to 10 years ago, the industry underwent a majortransformation with programmatic based on automation. It took time for marketers to learn about the technology and for companies to adopt it, with trial and error -- but most importantly, it tookpatience.

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AI isnt about automation, but rather augmenting the human process, Bachstein said. Its about being predictive. The cookie can only tell you whathappened in the past. AI can tell you what happened in the past, present the insights, and tell you what you can gain in the future.

The expanded suite of cookieless offerings includeExtensions forIBMWatson Advertising Accelerator, IBMWatson Advertising Attribution, and IBMWatson Advertising Predictive Audiences.

The attributionproduct uses machine learning, for example, to help to determine when campaigns yield performance results.

IBM made the announcement with a series of partners that were willing to combinetheir data to help brands and publishers achieve these results. Partners include Xandr/AT&T, Magnite, Nielsen, MediaMath, LiveRamp and Beeswax.

Rand Harbert, executive vice president andchief agency, sales and marketing officer at State Farm, acknowledged using IBMs AI products through The Weather Channel, an approach that helps the company use data to create experiences withconsumers in the moment.

The new capabilities are focused on privacy and designed to allow brands to reach consumers while considering their customers privacy.

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