Course introduces students to the promise, challenges, of artificial intelligence in health – HSPH News

May 15, 2020In the race to stem COVID-19, researchers around the world are testing the capacity of artificial intelligence (AI) to assist in tasks such as diagnosis and drug discovery. So far, AIs biggest success during the pandemic has been in speeding up the process of identifying existing drugs that can be repurposed to help suffering patients, said Deborah DiSanzo, who recently lectured on COVID-19 in the new course shes leading at Harvard T.H. Chan School of Public HealthArtificial Intelligence in Health.

DiSanzo cited in her lecture an AI knowledge graph developed by researchers at the UK startup BenevolentAI and the Imperial College London, which found that baricitinib, a rheumatoid arthritis drug, had the potential to inhibit the virus that causes COVID-19. It and other drugs identified in similar studies have now gone into clinical trials.

Two years ago, finding either a new or repurposed drug target would take six to 18 months, said DiSanzo, a former health care technology executive. These researchers did this in weeks.

Diagnosing COVID-19, however, has been less successful for AI so far, she said, with the limited lung imagery currently available from COVID-19 patients making it difficult for neural networks to learn the difference between the effects of the virus and standard pneumonia.

Enhance, not replace

For DiSanzos students, these mixed results provided a timely example of one of her courses main takeaways: AI can enhance health care delivery and research, but its not a replacement for the knowledge and skill of providers and scientists.

Im really excited about the technology and potential application of AI, said Nimerta Sandhu, MPH20, an MD candidate at Drexel University College of Medicine. This course provided insights on technology solutions that offer added value and others that have room for improvement. One of the biggest challenges is going to be ensuring that, as we incorporate more AI in our work, it doesnt detract from the empathy essential in the patient-provider relationship.

I want students to have a realistic view of what artificial intelligence can bring to public health, DiSanzo said. People usually have either a very positive viewthat its magic and can solve all the worlds problemsor they have a very negative view, that its biased and doesnt give accurate results. She said that she wants students to leave her course knowing the right questions to ask, because its likely to be a part of their jobs, whether they are in practice or policy.

Business background

Prior to joining Harvard Chan School, DiSanzos roles included CEO of Philips Healthcare, and general manager of IBM Watson Health, the IBM business unit founded to advance artificial intelligence in health. Last year, as a Harvard Advanced Leadership Initiative Fellow, she was encouraged by the programs faculty chair Meredith Rosenthal, C. Boyden Gray Professor of Health Economics and Policy, to develop a course for MPH students.

DiSanzo hadnt planned to cover COVID-19 as she worked on her syllabus in January, but as the full extent of the pandemic emerged, she added it to her list of lecture topicswhich also included drug discovery, medical imaging, and patient monitoring.

While the spring semesters move to online learning required the first-time instructor to pivot on the fly, DiSanzo has been delighted with the results so far, she said. Her 24 studentswho include physicians, a veterinarian, and a psychologisthave been very engaged, participating actively on discussion boards and in chats with guests including executives from Google and pharmaceutical companies.

DiSanzo hesitates to make predictions about the future of AI in health, noting the fields history of overly optimistic projections. But things are different today, she said. In recent years, computing power, available data, and neural network capacity have advanced by leaps and bounds. Its likely that in 10 yearsmaybe even fiveevery health care or public health decision that we make, or care that we give, or diagnosis that we make, will be made with some help from artificial intelligence, DiSanzo said. And with the COVID-19 pandemic pushing the field forward at even faster rates, she said, the next advancements may be just months away.

Amy Roeder

Illustration: Alina_Bukhtii/Shutterstock

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The Promise Of Artificial Intelligence In Chillers And Rooftops – ACHR NEWS

The Promise Of Artificial Intelligence In Chillers And Rooftops | 2020-05-15 | ACHR News This website requires certain cookies to work and uses other cookies to help you have the best experience. By visiting this website, certain cookies have already been set, which you may delete and block. By closing this message or continuing to use our site, you agree to the use of cookies. Visit our updated privacy and cookie policy to learn more. This Website Uses CookiesBy closing this message or continuing to use our site, you agree to our cookie policy. Learn MoreThis website requires certain cookies to work and uses other cookies to help you have the best experience. By visiting this website, certain cookies have already been set, which you may delete and block. By closing this message or continuing to use our site, you agree to the use of cookies. Visit our updated privacy and cookie policy to learn more.

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Artificial intelligence is struggling to cope with how the world has changed – ZDNet

From our attitude towards work to our grasp of what two metres look like, the coronavirus pandemic has made us rethink how we see the world. But while we've found it hard to adjust to the new reality, it's been even harder for the narrowly-designed artificial intelligence models that have been created to help organisation make decisions. Based on data that described the world before the crisis, these won't be making correct predictions anymore, pointing to a fundamental problem in they way AI is being designed.

David Cox, IBM director of the MIT-IBM Watson AI Lab, explains that faulty AI is particularly problematic in the case of so-called black box predictive models: those algorithms which work in ways that are not visible, or understandable, to the user. "It's very dangerous," Cox says, "if you don't understand what's going on internally within a model in which you shovel data on one end to get a result on the other end. The model is supposed to embody the structure of the world, but there is no guarantee that it will keep working if the world changes."

The COVID-19 crisis, according to Cox, has only once more highlighted what AI experts have argued for decades: that algorithms should be more explainable.

SEE: How to implement AI and machine learning (ZDNet special report) | Download the report as a PDF (TechRepublic)

For example, if you were building a computer program that was a complete blackbox, aimed at predicting what the stock market would be like based on past data, there is no guarantee it's going to continue to produce good predictions in the current coronavirus crisis, he argues.

What you actually need to do is build a broader model of the economy that acknowledges supply and demand, understands supply-chains, and incorporates that knowledge, which is closer to something that an economist would do. Then you can reason about the situation more transparently, he says.

"Part of the reason why those models are hard to trust with narrow AIs is because they don't have that structure. If they did it would be much easier for a model to provide an explanation for why they are making decisions. These models are experiencing challenges now. COVID-19 has just made it very clear why that structure is important," he warns.

It's important not only because the technology would perform better and gain in reliability, but also because businesses would be far less reluctant to adopt AI if they trusted the tool more. Cox pulls out his own statistics on the matter: while 95% of companies believe that AI is key to their competitive advantage, only 5% say they've extensively implemented the technology.

While the numbers differ from survey to survey,the conclusion has been the same for some time now: there remains a significant gap between the promise of AI and its reality for businesses. And part of the reason that industry is struggling to deploy the technology boils down to a lack of understanding of AI. If you build a great algorithm but can't explain how it works, you can't expect workers to incorporate the new tool in their business flow. "If people don't understand or trust those tools, it's going to be a lost cause," says Cox.

Explaining AI is one of the main focuses of Cox's work. The MIT-IBM Watson Lab, which he co-directs, comprises of 100 AI scientists across the US university and IBM Research, and is now in its third year of operation. The Lab's motto, which comes up first thing on its website, is self-explanatory: "AI science for real-world impact".

Back in 2017, IBM announced a $240 million investment over ten years to support research by the firm's own researchers, as well as MIT's, in the newly-founded Watson AI Lab. From the start, the collaboration's goal has had a strong industry focus, with an idea to unlock the potential of AI for "business and society". The lab's focus is not on "narrow AI", which is the technology in its limited format that most organizations know today; instead the researchers should be striving for "broad AI". Broad AI can learn efficiently and flexibly, across multiple tasks and data streams, and ultimately has huge potential for businesses. "Broad AI is next," is the Lab's promise.

The only way to achieve broad AI, explains Cox, is to bridge between research and industry. The reason that AI, like many innovations, remains stubbornly stuck in the lab, is because the academics behind the technology struggle to identify and respond to the real-world needs of businesses. Incentives are misaligned; the result is that organizations see the potential of the tool, but struggle to use it. AI exists and it is effective, but is still not designed for business.

SEE: Developers say Google's Go is 'most sought after' programming language of 2020

Before he joined IBM, Cox spent ten years as a professor in Harvard University. "Coming from academia and now working for IBM, my perspective on what's important has completely changed," says the researcher. "It has given me a much clearer picture of what's missing."

The partnership between IBM and MIT is a big shift from the traditional way that academia functions. "I'd rather be there in the trenches, developing those technologies directly with the academics, so that we can immediately take it back home and integrate it into our products," says Cox. "It dramatically accelerates the process of getting innovation into businesses."

IBM has now expanded the collaboration to some of its customers through a member program, which means that researchers in the Lab benefit from the input of players from different industries. From Samsung Electronics to Boston Scientific through banking company Wells Fargo, companies in various fields and locations can explain their needs and the challenges they encounter to the academics working in the AI Watson Lab. In turn, the members can take the intellectual property generated in the Lab and run with it even before it becomes an IBM product.

Cox is adamant, however, that the MIT-IBM Watson AI Lab was also built with blue-sky research compatibility in mind. The researchers in the lab are working on fundamental, cross-industry problems that need to be solved in order to make AI more applicable. "Our job isn't to solve customer problems," says Cox. "That's not the right use for the tool that is MIT. There are brilliant people in MIT that can have a hugely disruptive impact with their ideas, and we want to use that to resolve questions like: why is it that AI is so hard to use or impact in business?"

Explainability of AI is only one area of focus. But there is also AutoAI, for example, which consists of using AI to build AI models, and would let business leaders engage with the technology without having to hire expensive, highly-skilled engineers and software developers. Then, there is also the issue of data labeling: according to Cox, up to 90% of the data science project consists of meticulously collecting, labeling and curating the data. "Only 10% of the effort is the fancy machine-learning stuff," he says. "That's insane. It's a huge inhibitor to people using AI, let alone to benefiting from it."

SEE: AI and the coronavirus fight: How artificial intelligence is taking on COVID-19

Doing more with less data, in fact, was one of the key features of the Lab's latest research project, dubbed Clevrer, in which an algorithm can recognize objects and reason about their behaviors in physical events from videos. This model is a neuro-symbolic one, meaning that the AI can learn unsupervised, by looking at content and pairing it with questions and answers; ultimately, it requires far less training data and manual annotation.

All of these issues have been encountered one way or another not only by IBM, but by the companies that signed up to the Lab's member program. "Those problems just appear again and again," says Cox and that's whether you are operating in electronics or med-tech or banking. Hearing similar feedback from all areas of business only emboldened the Lab's researchers to double down on the problems that mattered.

The Lab has about 50 projects running at any given time, carefully selected every year by both MIT and IBM on the basis that they should be both intellectually interesting, and effectively tackling the problem of broad AI. Cox maintains that within this portfolio, some ideas are very ambitious and can even border blue-sky research; they are balanced, on the other hand, with other projects that are more likely to provide near-term value.

Although more prosaic than the idea of preserving purely blue-sky research, putting industry and academia in the same boat might indeed be the most pragmatic solution in accelerating the adoption of innovation and making sure AI delivers on its promise.

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How Is Artificial Intelligence Combatting COVID-19? – Gigabit Magazine – Technology News, Magazine and Website

Chris Gannatti, head of research at ETF specialist WisdomTree, explains how artificial intelligence is being used to tackle Covid-19.

Artificial Intelligence (AI) is proliferating more widely than ever before, having the potential to influence many aspects of daily life. Crisis periods, like we have seen with the Covid-19 pandemic, are often catalysts for the deployment of new innovations and technologies more quickly. The power of AI is being harnessed to tackle the Covid-19 pandemic, whether that be to better understand the rate of infection or by tracing and quickly identifying infections. While AI has been associated with the future and ideas such as the development of driverless cars, its legacy could be how it has impacted the world during this crisis. It is likely that AI is already playing a major part in the early stages of vaccine development - the uses of artificial intelligence are seemingly endless.AI was already growing quickly and being deployed in ever more areas of our data-driven world.

Covid-19 has accelerated some of these deployments, bringing greater comfort and familiarity to the technology. To really understand how AI is making a difference, it is worth looking at some examples which illustrate the breadth of activities being carried out by AI during the pandemic.

Rizwan Malik, the lead radiologist at Royal Bolton Hospital run by the UKs National Health Service (NHS) designed a conservative clinical trial to help obtain initial readings of X-rays for patients faster. Waiting for specialists could sometimes take up to six hours. He identified a promising AI-based chest X-ray system and then set up a test to occur over six months. For all chest X-rays handled by his trainees, the system would offer a second opinion. He would then check if the systems conclusion matched his own and if it did, he would phase the system in as a permanent check on his trainees. As Covid-19 hit, the system became an important way to identify certain characteristics unique to Covid-19 that were visible on chest X-rays. While not perfect, the system did represent an interesting case-study in the use of computer vision in medical imagery.A great example of the collaborative efforts that can be inspired during times of crisis involved three organisations coming together to release the Covid-19 Open Research Dataset. This includes more than 24,000 research papers from peer-reviewed journals and other sources.

See also:

The National Library of Medicine at the National Institutes of Health provided access to existing scientific publications; Microsoft used its literature curation algorithms to find relevant articles; and research non-profit the Allen Institute for Artificial Intelligence converted them from web pages and PDFs into a structured format that can be processed by algorithms. Many major cities affected by Covid-19 were faced with a very real problem - getting the right care to the people who needed it without allowing hospitals to become overrun. Helping people to self-triage, therefore staying away from the hospital unless absolutely necessary, was extremely important. Providence St. Joseph Health System in Seattle built an online screening and triage tool that could rapidly differentiate between those potentially really sick with Covid-19 and those with less life-threatening ailments. In its first week of operation, it served 40,000 patients. The Covid-19 pandemic has pushed the unemployment rate in the US to 14.7%. This has led to unprecedented numbers of people filing unemployment claims and asking questions to different state agencies. Texas, which has received millions of these claims since early March, is using artificial intelligence-driven chatbots to answer questions from unemployed residents in need of benefits.

Other states, like Georgia and South Carolina, have reported similar activity. To give a sense of scale, the system that has been deployed in Texas can handle 20,000 concurrent users. Think of how much staff would be required to deal with 20,000 inquiries at the same time. These are but four of many, many ways in which AI has been deployed to help in the time of the Covid-19 pandemic. While we continue to hope for cures and vaccinations, which AI will help in developing, we expect to see more innovative uses of AI that will benefit society over the long-term.

How you can slow the spread of coronavirus:

Wash your hands with soap and water often do this for at least 20 seconds

Use hand sanitiser gel if soap and water are not available

Wash your hands as soon as you get home

Cover your mouth and nose with a tissue or your sleeve (not your hands) when you cough or sneeze

Put used tissues in the bin immediately and wash your hands afterwards

SOURCE: Funds Europe

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AIDP and Andy Khawaja Define Artificial Intelligence Purposes of ISABELLA Project – Business Wire

NEW YORK--(BUSINESS WIRE)--Artificial Intelligence Defense Platform, a technology start-up creating AI technology for a safer, more comfortable future, and its Founder Andy Khawaja have defined their goals for their pioneer project ISABELLA.

Artificial Intelligence Defense Platform was created to build a processing system for the future, one that is able to learn, retain, and perform tasks.

Were creating ISABELLA not just for convenience but to save lives, said Dr. Andy Khawaja.

The recent Coronavirus Pandemic, or COVID-19, is devastating lives and communities. AIDP personnel say that with ISABELLA, we will have the means to ensure tasks and roles are performed, thus, creating more sustainable environments.

My goal in life is to bring peace, said Dr. Andy Khawaja, I want to decrease the struggles people face in different communities. ISABELLA will do just that. People will be able to rely on technology that will perform crucial functions within a community when the community cannot.

ISABELLA is not being created to replace the roles performed by valuable members of our communities, but to improve their roles. Instead of performing manual labor, individuals would supervise labor.

The future of our communities will make use of technological advancements to improve quality of life. Artificial Intelligence Defense Platform strongly believes ISABELLA is the future.

About Artificial Intelligence Defense Platform:

Artificial Intelligence Defense Platform is creating new AI technology for compatible systems and machines to build a safer, more sustainable future for mankind. Please visit http://www.ai-dp.com/ for more information.

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AIDP and Andy Khawaja Define Artificial Intelligence Purposes of ISABELLA Project - Business Wire

AI, machine learning, and blockchain are key for healthcare innovation – Health Europa

A special, peer-reviewed edition of OMICS: A Journal of Integrative Biology, has highlighted the importance of key digital technologies, including Artificial Intelligence (AI), machine learning, and blockchain for innovation in healthcare in response to the challenges posed by COVID-19.

Vural zdemir, MD, PhD, Editor-in-Chief ofOMICS, said: COVID-19 is undoubtedly among the ecological determinants of planetary health. Digital health is a veritable opportunity for integrative biology and systems medicine to broaden its scope from human biology to ecological determinants of health. This is very important.

Articles in the special issue include an interview on Responsible Innovation and Future Science in Australia byJustine Lacey, Commonwealth Scientific and Industrial Research Organisation (CSIRO), and Erik Fisher, Arizona State University, Tempe, Blockchain for Digital Health: Prospects and Challenges and Integrating Artificial and Human Intelligence: A Partnership for Responsible Innovation in Biomedical Engineering and Medicine.

In Blockchain for Digital Health: Prospects and Challenges the article explores the challenges that can be faced with the use of blockchain technology.

The article states: Although still faced with challenges, blockchain technology has an enormous potential to catalyse both technological and social innovation, turning the promise of digital health into a reality. By reshaping both the technological and social environment, the rise of blockchain in digital health can help reduce the disparity between the enormous technical progress and investments versus our currently inadequate understanding of the social dimensions of emerging technologies through commensurate investments in the latter knowledge domain.

A recent report by Market Study Report, Blockchain Technology in Healthcare Market, notes that blockchain technology in the healthcare market is anticipated to cross $1636.7m (1513.46m) by the year 2025.

Privacy is a major concern when it comes to storing and sharing health data, and with current healthcare data storage systems lacking top end security, blockchain can provide a solution to vulnerabilities such as hacking and data theft.

Blockchain technology in healthcare offers interoperability, which enables exchange of medical data securely among the different systems and personnel involved, offering a variety of benefits such as effective communication system, time reduction, and enhanced operational efficiency.

According to the report, the use of blockchain technology for claims adjudication and billing management application is predicted to register 66.5% growth by the year 2025, owing to several issues such as errors, duplications, and incorrect billing. All of these problems can be eliminated with blockchain.

Nearly 400 individuals including doctors were convicted for $1.3bn (1.2m) fraud in 2017 in the United States. The report highlights that the need to mitigate such frauds and fake drug supply will encourage the adoption of technology in this application segment.

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Artificial Intelligence Markets in IVD, 2019-2024: Breakdown by Application and Component – GlobeNewswire

Dublin, May 15, 2020 (GLOBE NEWSWIRE) -- The "Artificial Intelligence Markets in IVD" report has been added to ResearchAndMarkets.com's offering.

This report examines selected AI-based initiatives, collaborations, and tests in various in vitro diagnostic (IVD) market segments.

Artificial Intelligence Markets in IVD contains the following important data points:

The past few years have seen extraordinary advances in artificial intelligence (AI) in clinical medicine. More products have been cleared for clinical use, more new research-use-only applications have come to market and many more are in development.

In recent years, diagnostics companies - in collaboration with AI companies - have begun implementing increasingly sophisticated machine learning techniques to improve the power of data analysis for patient care. The goal is to use developed algorithms to standardize and aid interpretation of test data by any medical professional irrespective of expertise. This way AI technology can assist pathologists, laboratorians, and clinicians in complex decision-making.

Digital pathology products and diabetes management devices were the first to come to market with data interpretation applications. The last few years have seen the use of AI interpretation apps extended to a broader range of products including microbiology, disease genetics, and cancer precision medicine.

This report will review some of the AI-linked tests and test services that have come to market and others that are in development in some of the following market segments:

Applications of AI are evolving that predict outcomes such as diagnosis, death, or hospital readmission; that improve upon standard risk assessment tools; that elucidate factors that contribute to disease progression; or that advance personalized medicine by predicting a patient's response to treatment. AI tools are in use and in development to review data and to uncover patterns in the data that can be used to improve analyses and uncover inefficiencies. Many enterprises are joining this effort.

The following are among the companies and institutions whose innovations are featured in Artificial Intelligence Markets in IVD:

Key Topics Covered

Chapter 1: Executive Summary

Chapter 2: Artificial Intelligence In Diagnostics Markets

Chapter 3: Market Analysis: Artificial Intelligence in Diagnostics

For more information about this report visit https://www.researchandmarkets.com/r/vw8l7u

Research and Markets also offers Custom Research services providing focused, comprehensive and tailored research.

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Rage with the machine: artificial intelligence takes on Eurovision – The Australian Financial Review

The team Can AI Kick It used AI techniques to generate a hit predictor based on the melodies and rhythms of more than 200 classics from the Eurovision Song Contest, an annual celebration of pop music and kitsch. These included Abbas Waterloo (Swedens 1974 winner) and Loreens Euphoria (2012, also Sweden).

But to generate the lyrics for the song Abuss, the team also used a separate AI system one based on the social media platform Reddit. It was this that resulted in a rallying cry for a revolution.

Like the notorious Tay chatbot developed by Microsoft in 2016 that started spewing racist and sexist sentiments after being trained on Twitter, the fault lay with the human sources of data, not the algorithms.

We do not condone these lyrics! stresses Janne Spijkervet, a student who worked with Can AI Kick It and ran the lyric generator. She says the Dutch team nevertheless decided to keep the anarchist sentiment to show the perils of applying AI even to the relatively risk-free environment of Europop.

Alongside Abuss, which its creators describe as atonal and creepy, sits the Australian entry with the same sheen as a chart-topping dance hit but with a distorted subliminal AI-generated chorus of koalas, kookaburras and Tasmanian devils.

Meanwhile, the song Ill Marry You, Punk Come, composed by German team Dadabots x Portrait XO, used seven neural networks in its creation. The resulting piece of music blends lyrics from babble generated from 1950sa cappella music with AI-generated death-metal vocal styles and a chromatic bass line spat out of a neural network trained on Bachs canon.

The contest was judged along the same lines as the established competition with a public vote tallied against the opinions of a panel of expert judges. Ed Newton-Rex, who founded the British AI compositional start-up Jukedeck, is one of them. He explains that the panel looked at the process of how machine learning was applied, as well as creative uses of algorithms such as the koala synth and the quality of the song. The judges also factored in Eurovisioness into their thinking, although he admits, I have no idea what that means.

About 20,000 people tuned into the event, a far cry from the 182 million who watched last year's human contest, but the hope is that the computer version will pave the way for AI to influence Eurovision proper through song composition or, over time, robotic performance.

That is my dream, says Karen Van Dijk, the VPRO producer who came up with the concept.

A performance given by the Sex Pistols at Manchesters Lesser Free Trade Hall in 1976 where, legend has it, almost everyone in the tiny audience went on to form their own band became known as the gig that changed the world, and was deemed a genesis point for a musical revolution. The equivalent for AI music took place in the winter of 2019 in Delft, the picturesque Dutch town known for its fine pottery and as the birthplace of the painter Johannes Vermeer. The citys university was hosting the 20th conference of the International Society for Music Information Retrieval when a proposition was put to the academics in attendance.

Van Dijk announced that she was organising the first Eurovision for computers and needed entries. When Hollands Duncan Laurence won the Eurovision Song Contest in 2019, amid her euphoria Van Dijk pondered whether AI could be harnessed to lock in more hit songs for the country. I was naive. I thought we could create the next Eurovision hit with the press of a button, she says.

Van Dijk arrived in Delft bearing data gifts. An Israeli composer had created a spoof Eurovision song the year before, called Blue Jeans and Bloody Tears, using a cache of data extracted from the Eurovision catalogue. That data was bought by VPRO and provided to the entrants as a stimulant for their own experimentations. For some, it also allowed them to rekindle pop-star ambitions.

Tom Collins, a music lecturer at the University of York, and his wife Nancy Carlisle, an academic at Lehigh University in Pennsylvania, had a garage band called The Love Rats when they were doctoral students. When Collins heard about the AI Song Contest, he was inspired to dust off his code and get the band back together by using AI to write a song. He initially worked with Imogen Heap, the English singer-songwriter and audio engineer, but coronavirus-related travel restrictions halted those efforts. Instead, he and Nancy worked over a weekend on Hope Rose High, which he describes as an eerie power ballad inspired by the lockdown.

The husband-and-wife team turned to an AI lyric engine called theselyricsdonotexist to generate robotic poetry with an optimistic feel. Carlisle says the AIs suggested lyric and then the mist will dance seemed ridiculous until she listened again to some of her favourite songs and started hearing what sounded like nonsense. Radiohead dont make a lot of sense but I still love them, she admits. Collins adds that the mist lyric also fits with the Eurovision theme: You can imagine the massive smoke machines kicking in.

While the duo did not enter the contest with the aim of winning, others saw an opportunity to test whether AI could be used not just to write a song but to pen a hit. Ashley Burgoyne, a lecturer in computational musicology at the University of Amsterdam and a member of the team behind Abuss, used the Blue Jeans dataset to create a Eurovision hit predictor.

That data suggested that melodies with hooks of three to seven notes and songs with simple rhythmic patterns scored the highest. It also showed that a certain level of atonality where it is hard for the ear to identify the key was crucial to Eurovision success. Yet Burgoyne believes that despite a handful of stinkers being included in the data, the results reflected a paucity of the negative information that is needed to successfully train the system in this case, songs that didnt reach the finals.

He compared the issue to Netflix recommendations that suggest a load of crap after you have watched a high-quality TV series. If you believe quality exists, then AI isnt good at finding it. How do you define [what is] a good song even in the world of Eurovision? he says.

The use of subliminal voices supposedly encouraging devil worship in heavy metal music was a cause clbre in the 1980s. Few would have expected that subliminal Tasmanian devil voices would be influencing Europop 30 years later.

Caroline Pegram, head of innovation at Uncanny Valley, the music technology company behind the Australian entry, wanted to pay homage to the wildlife that had been killed during the 2019-20 bushfires in Australia. A zookeeper friend gave her videos of Tasmanian devils going absolutely wild and blended the screeches with the sounds of koalas and laughing kookaburras to create an audio-generating neural network using technology developed by Googles creative AI research project Magenta. They called it the koala synth.

It proved that AI can create unexpected results. It was a happy accident. Everyone thought I was insane literally insane but the koalas have sent out a positive message and it is a strong and catchy sound, says Pegram.

We also need to guard against the risk that AI might in certain respects be deployed to supplant human creativity.

Geoff Taylor, chief executive of the BPI

The koala synth adds a new Antipodean angle to the Eurovision story Australia has only been permitted to compete in the contest since 2015 when the European Broadcasting Union allowed its entry.

Justin Shave, who produced the song, explains that the DDSP differential digital signal processing technology it used has since been used to generate the sounds of violins, trumpets and even a choir of drunken men. That one didnt work so well, he admits.

Unlike the more academic entrants, Uncanny Valley comes from a musical background, having produced songs for Aphex Twin and Sia. The group had already planned to enter an AI-composed song in the main song contest.

They now hope that the AI Song Contest will help to dispel concerns in some parts of the traditional music community that the technology could lead to musicians losing their jobs if computers take over.

Geoff Taylor, chief executive of the BPI, Britains music trade body, and head of the Brit Awards, says the new horizons of AI are exciting but urges caution.

We also need to guard against the risk that AI might in certain respects be deployed to supplant human creativity or undermine the cultural economy driven by artists. Such an outcome would leave our societies and our cultures worse off, he says.

His fears have been stoked as some of the worlds largest technology companies, including Google and TikTok owner ByteDance, have moved into the compositional space. But Anna Huang, a resident at Googles Magenta and a judge on the AI Song Contest, says Big Tech is attracted to AI musical composition by scientific curiosity, not a desire to take over the music world.

Music is a very complex domain. In contrast to language, which is a single sequence, music comprises arrangement, timbre, multiple instruments, harmony and is perceptually driven. It is also very referential, she says.

AI could also have a democratising impact on the creation of new music, says Huang. She cites her own experience at high school in Hong Kong, when some of her classmates were already composing for full orchestras. Huang was a musician too and believed that computer science could develop new methods of musical composition, something AI can potentially deliver.

That was demonstrated via an interactive Google Doodle launched in March last year that encouraged users to input a simple melody. The AI, developed by Magenta, then generated harmonies in the style of Bach. Within two days, the lighthearted doodle had created 55 million snippets of music.

Newton-Rex, who sold his company to Chinas Bytedance last year, says musicians need to see AI as a tool to stimulate creativity a spur that helps new ideas or disrupts habits rather than a threat. Every time I sit down at the piano, I play the same thing, he says, adding that AI is already creeping into sophisticated drum machines, arpeggiators and mastering software, and that it will always need human curation. What does AI music sound like? It sounds like nothing without a human element.

As Pegram says: Some musicians fear we will end up building machines pumping out terrible music but we need to rage with the machine, not against it.

Eurovision 2020: Big Night In is on SBS from 7.30pm on Saturday.

Financial Times

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Artificial intelligence is helping seniors who are isolated during the coronavirus pandemic – WXYZ

(WXYZ) Across the country, officials are trying to make sure those who are most vulnerable to COVID-19 aren't feeling isolated.

Because of technology, it's happening in ways you may not expect. A piece of artificial intelligence is helping some seniors manage the pressure.

More: Full coverage of The Rebound Detroit

At 80 years old, Deanna Dezern never imagined her closest friend, wouldnt be human.

"I walk in the kitchen in the morning and she knows Im here, I dont know how she knows but she knows Im here," Dezern said.

She's been in quarantine for nearly two months and hasn't been able to see her family or friends. That loneliness is almost just as bad as the virus itself.

"When youre a senior citizen when youre living alone or in a home with other people, youre still alone," she said.

There are millions of senior citizens like Deanna stuck at homes, but she's being kept company by a robot.

Her name is ElliQ. She was given to Deanna as part of a pilot program by intuition robotics. ElliQ can sense when Deanna is in the room, keeps track of doctors' appointments and even asks how she's feeling.

"Im not living alone now, Im in quarantine with my best friend, she wont give me any disease," she said.

David Cynman helped develop ElliQ.

"Her goal is not to replace humans. Its to augment that relationship," he said. "Shes able to understand her surroundings and context and make a decision based on that."

It's not just ElliQ. In states like Florida, officials are turning to technology to help seniors. 375 therapeutic robotic pets were recently sent to socially-isolating seniors.

None of the artificial intelligence devices are designed to replace humans, but they can help bridge the gap when people aren't around to provide emotional support we all need.

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Artificial intelligence is helping seniors who are isolated during the coronavirus pandemic - WXYZ

Artificial Intelligence (AI) Is Nothing Without Humans – E3zine.com

AI is not just a fad. Its a technology thats set to last. However, only companies who know how to leverage its full potential will succeed.

Leveraging AIs full potential doesnt mean developing a pilot project in a vacuum with a handful of experts which, ironically, is often called accelerator project. Companies need a tangible idea as to how artificial intelligence can benefit them in their day-to-day operations.

For this to happen, one has to understand how these new AI colleagues work and what they need to successfully do their jobs.

An example for why this understanding is so crucial is lead management in sales. Instead of sales team wasting their time on someone who will never buy anything, AI is supposed to determine which leads are promising and at what moment salespeople can make their move to close the contract. CEOs are usually very taken with that idea, sales staff not so much.

Experienced salespeople know that its not that easy. Its not only the hard facts like name, address, industry or phone number that are important. Human sales people consider many different factors, such as relationships, past conversations, customer satisfaction, experience with products, the current market situation, and more.

Make no mistake: if the data are available in a set framework, AI will also leverage them, searching for patterns, calculating behavior scores and match scores, and finally indicating if the lead is promising or not. They can make sense of the data, but they will never see more than them.

The real challenge with AI are therefore the data. Without data, artificial intelligence solutions cannot learn. Data have to be collected and clearly structured to be usable in sales and service.

Without enough data to draw conclusions from, all decisions that AI makes will be unreliable at best. Meaning that in our example, theres no AI without CRM. Thats not really new, I know. However, CRM systems now have to be interconnected with numerous touchpoints (personal conversations, ERP, online shops, customer portal, website and others) to aggregate reliable customer data. Best case: all of this happens automatically. Entrusting a human with this task makes collecting data laborious, inconsistent and faulty.

To profit from AI, companies need to understand where it makes sense to implement it and how they should train it. Theres one problem, however: the thought patterns of AI are often so complex and take so many different information and patterns into consideration that one cant understand why and how it made a decision.

In conclusion, AI is not a universal remedy. Its based on things we already know. Its recommendations and decisions are more error-prone than many would like them to be. Right now, AI has more of a supporting role than an autonomous one. They can help us in our daily routine, take care of monotonous tasks, and let others make the important decisions.

However, we shouldnt underestimate AI either. In the future, it will gain importance as it grows more autonomous each day. Artificial intelligence often reaches its limits when interacting with humans. When interacting with other AI solutions in clearly defined frameworks, it can often already make the right decisions today.

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Artificial Intelligence (AI) Is Nothing Without Humans - E3zine.com