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Symposium on the Future of AI: Will AIs Ever Be One of Us? – USC Viterbi | School of Engineering – USC Viterbi School of Engineering

March 8, 2021 | Author: admin

The USC AI Futures Symposium took place January 2021

We all interact with AI systems, but they dont interact with people in the same way that we interact with one another. They know very little, do very little, and learn very little compared to humans.

Imagine a future where AI systems are much more knowledgeable about the world, quickly learn to adapt to our preferences, and can be trusted with critical tasks that go beyond just entertainment or shopping. When will we be able to communicate with AI systems in the same ways that we do with other people?

On January 12-13, 2021, researchers gathered at a virtual USC AI Futures Symposium to share their insights on these topics.

Yolanda Gil, Chair of the 2021 USC AI Futures Symposium

We were very excited that over 300 people from all around the world joined us to learn about USC research in human-AI interaction, which is quite a remarkable number of participants for a virtual meeting and attests to the stature of USC in AI research, said Yolanda Gil, Director for Major Strategic AI and Data Science Initiatives at USCs Information Sciences Institute (ISI) and research professor of computer science, who chaired the Symposium.

L-R: Yannis Yortsos, Cyrus Shahabi, and Craig Knoblock

Yannis Yortsos, Dean of the USC Viterbi School of Engineering, presented in the opening session. AI is a strategic priority for USC, endorsed by our new President Carol Folt and Provost Chip Zukoski, he emphasized. The very rich interdisciplinary environment of USC is ideally suited for its development across all disciplines and all endeavors.

This sentiment was echoed by Cyrus Shahabi, Chair of the USC Department of Computer Science, who noted that human-AI interaction is a major AI strength at USC and will be the theme of the new department home at the Allen and Charlotte Ginsburg Human-Centered Computation Building. Following that, Craig Knoblock, Keston Executive Director of the USC ISI and research professor of computer science, highlighted the innovative research directions being pursued by USC researchers in this area, including commonsense reasoning, cultural adaptation of dialogue, misinformation, knowledge-rich learning, and responsible decision making.

L-R: Ralph Weischedel, Emilio Ferrara, Craig Knoblock, Wael AbdAlmageed, Ram Nevatia, and Liz Boschee

A session in the symposium was devoted to new research on AI systems that incorporate extensive knowledge about the world. The discussion was led by Ralph Weischedel, research team leader at ISI, and the speakers were Emilio Ferrara, associate professor of communication and computer science and research team leader at ISI; Craig Knoblock; Wael AbdAlmageed, research associate professor; Ram Nevatia, professor of computer science; and Liz Boschee, director at ISI Boston.

Though humans easily acquire common sense from an early age, it has been a notoriously challenging skill for AI to fully grasp. USC is a pioneer in AI research which extracts this type of knowledge from documents, images, and special sources like Wikipedia. While there have been significant advances in extracting individual statements, integrating all those statements into a usable knowledge source and using them to improve the performance of AI systems remains a major challenge.

Furthermore, new insights on detecting and handling misinformation were presented in this session. On social media platforms, its essential to distinguish bots from humans as to alert people to misinformation. Multimedia forensics can give reassurance of the integrity and authenticity of images and videos we see online that may be impersonating others (also known as deepfakes). Sometimes, AI systems simply misunderstand information, and qualifying the origins of any given data can help determine if it can be trusted.

L-R: Sven Koenig, Kristina Lerman, Fred Morstatter, Shri Narayanan, Keith Burghardt, and Bistra Dilkina

Not long after USC computer science emeritus professor George Bekey published a pioneering book on Robot Ethics in 2011, we can already see that ethics concerns have gradually taken center stage in the world of AI research.

This discussion was led by Sven Koenig, professor of computer science, and the USC experts who spoke on this topic were Kristina Lerman, research professor of computer science and research team leader at ISI; Fred Morstatter, research assistant professor of computer science and research lead at ISI; Shri Narayanan, professor of electrical and computer engineering and research director at ISI; Keith Burghardt, computer scientist at ISI; and Bistra Dilkina, associate professor of computer science.

New techniques were presented for identifying and measuring biases in data and understanding their sources in order to mitigate them. Algorithms for ranking, which are used in product recommendations and for search, have long been known to have biases due to popularity or positioning, and new findings show that they can be addressed by reducing instability under different conditions. AI systems can also help reduce biases in media by measuring factors such as on-screen time and speaking time for various demographic groups which expose opportunities to improve equality and diversity.

Inclusiveness and equity are also central topics in AI ethics. Inclusive design of AI systems leads to more equitable experiences that take into account individual differences, particularly in children at different developmental stages. Cultural differences can be identified by observing communications among large groups, with promising results based on analyses of open online collaborative projects from different countries.

Acting responsibly often involves consideration of resource limitations and maximizing impact. Several novel AI techniques were found to be effective for large-scale optimization and machine learning, particularly when learning is informed by downstream decision processes.

L-R: Yan Liu, Pedro Szekely, Greg Van Steeg, Aram Galstyan, Xiang Ren, Jose-Luis Ambite

Though growing at an unprecedented rate, AI systems have a long way to go before they can be on par with human abilities to learning continuously and independently. This session was led by Yan Liu, professor of computer science, and the speakers who addressed this topic included Pedro Szekely, research associate professor in computer science and research director at ISI; Greg Van Steeg, research associate professor in computer science and research lead at ISI; Aram Galstyan, director of Artificial Intelligence Division at ISI and research associate professor in computer science; Xiang Ren, assistant professor of computer science and research lead at ISI; and Jose-Luis Ambite, research associate professor in computer science and research team leader at ISI.

The world is constantly changing, which means our knowledge needs to be up to speed. New research on self-supervised machine learning is opening new doors to using data that has not been augmented with labels, which is what most machine learning approaches require. Studying the effects on continuous learning shows that AI systems that can learn to automatically refresh their beliefs about the world. Another strategy that has been shown to be very effective is distributed federated learning approach, which is used when data is collected but not publicly accessible, as is the case with health and other sensitive data.

As powerful as AI can be, they still need our guidance. New approaches that combine machine learning with human predictions have led to improved forecasting of trends and events. Incorporating human explanations and rationale have also improved machine learning significantly.

L-R: Pedro Szekely, Jon May, Mayank Kejriwal, Jay Pujara, Yolanda Gil, and Marjorie Freedman

USC has a long tradition of tackling problems of societal importance through human-AI interaction. A session was devoted to highlight this work, and was chaired by Pedro Szekely. This session included speakers Jon May, research assistant professor in computer science and research lead at ISI; Mayank Kejriwal, research assistant professor of industrial and systems engineering and research lead at ISI; Jay Pujara, research assistant professor in computer science and research lead at ISI; Yolanda Gil; and Marjorie Freedman, research team leader at ISI.

In times of natural disasters, victims and local first responders communicate in their own languages, which creates barriers for aid coming NGOs and other international organizations. AI technologies that are able to quickly create automated translators for regional languages are radically changing the ability to support emergency response and humanitarian aid.

Protecting runaway children from forced prostitution is often difficult due to worldwide commercial sex trafficking through online advertising and transactions that are difficult to track. Fortunately, significant progress is being made through AI technologies which automatically identify and crawl suspicious sites to detect patterns and trends, thereby generating leads and evidence for law enforcement.

Under-resourced entrepreneurs cannot easily develop solid business plans because of the difficulties in getting comprehensive knowledge about prospective competitors, customers, and existing technologies most relevant to their idea. Statistically, over 50% of new businesses fail within the first 5 years. A special customized market intelligence can be delivered through AI systems that automatically integrates years of data for hundreds of thousands of companies collected from webpages, patent filings, spreadsheets, social media, and regulatory filings with government agencies.

Furthermore, AI scientists could accelerate research by automatically analyzing the wealth of data that is now available in the sciences. AI algorithms for systematic search lead to better solutions by quickly exposing any inconsistencies in findings. AI techniques also reduce the time to create new models for complex systems. AI systems can assist and ultimately collaborate with scientists to accelerate discoveries.

Lastly, privacy and security can be significantly enhanced with AI approaches. For example, social engineering attackers (known as spear-phishing) can be deterred if the cost of an attack is heightened when they are engaged by AI systems instead of by the intended humans. Many other AI approaches are being applied to secure financial transactions, detect and fix code vulnerabilities, and predicting and detecting attacks.

L-R: Elizabeth Churchill, Antonio Damasio, Prem Natarajan

Two keynote speakers from the industry discussed significant challenges in human-AI interaction. Elizabeth Churchill, Director of User Experience at Google and an expert in the design of interactive technologies, made the provocative statement that systems create the conditions under which humans make errors, but the nature of those errors cannot be anticipated because every person is different. Prem Natarajan, Vice President of Amazons Alexa, talked about adapting AI systems by teaching them new concepts when the need arises during interactions.

A third keynote speaker was Antonio Damasio, USC Professor of Neuroscience, Psychology, and Philosophy, who discussed consciousness in terms of how behavior is governed by emotional states (e.g., fear) grounded in feelings which are directly linked to biological states (e.g., pain, hunger). He posed the intriguing question of whether consciousness will be relevant for the design of artificial intelligence systems grounded in physical sensors.

Maja Mataric

The closing session of the symposium featured Maja Mataric, USC Interim Vice President of Research and professor of computer science. She encouraged the AI community to consider the use of AI for major societal challenges. She said that AI must be embraced by major institutions and their operations all the way down: how were distributing vaccines, how were communicating about mask-wearing, and how were approaching equity.

As Mataric succinctly stated, AI is not for the futureAI is now.

For more information about the event, including videos of the sessions, click here.

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7 Bitcoin And Cryptocurrency Accounts To Follow On Twitter – Yahoo Finance

March 8, 2021 | Author: admin

Bloomberg

(Bloomberg) -- Long before Credit Suisse Group AG was forced to wind down a $10 billion group of funds it ran with financier Lex Greensill, there were plenty of red flags.Executives at the bank knew early on that a large portion of the assets in the funds were tied to Sanjeev Gupta, a Greensill client whose borrowings were at the center of a 2018 scandal at rival asset manager GAM Holding AG. They were also aware that a lot of the insurance coverage the funds relied on depended on a single insurer, according to a report. Credit Suisse even conducted a probe last year of its funds that detected potential conflicts of interest, yet failed to prevent their collapse months later.On Friday, the bank finally pulled the plug and said it would liquidate the strategy, a group of supply chain finance funds for which Greensill had provided the assets and which had been held up as a success story. The funds, which have about $3.7 billion in cash and equivalents, will start returning most of that next week, leaving about two-thirds of investor money tied up in securities whose value may be uncertain.The decision caps a dramatic week that started when Credit Suisse froze the funds after a major insurer for its securities refused to provide coverage on new notes. The move sent shock waves across the globe, prompted Greensill Capital to seek a buyer for its operations, and forced rival GAM Holding AG to shutter a similar strategy. For Credit Suisse and its new Chief Executive Officer Thomas Gottstein, its arguably the most damaging reputational hit after an already difficult first year in charge.While the financial toll on the bank may be limited, fund investors are left with about $7 billion locked up in a product that was presented as a relatively safe but higher-yielding alternative to money markets.The Greensill-linked funds were one of the fastest-growing strategies at Credit Suisses asset management unit, attracting money from yield-starved investors in a region that had for years had to contend with negative interest rates. The bank started the first of the funds in 2017, but they really took off in 2019, the year rival asset manager GAM finished winding down a group of bond funds that had invested a large chunk of their money in securities tied to Greensill and one of his early clients, Guptas GFG Alliance.The Credit Suisse funds, too, were heavily exposed to Gupta early on. As the bank ramped up the strategy, the flagship supply-chain finance fund had about a third of its $1.1 billion in assets in notes linked to Guptas GFG Alliance companies or his customers as of April 2018, according to a filing.Credit Suisse executives were aware but denied at the time that it was an outsized risk, according to people familiar with the matter. They argued that most of the loans were to customers of Gupta and not directly to GFG companies, the people said, asking not to be identified because the information is private.Over time, the proportion of loans linked to GFG and customers appeared to decrease, while new counterparties popped up in fund disclosures that packaged loans to multiple borrowers -- making it harder to determine who the ultimate counterparty is. Many of the vehicles were named after roads and landmarks around Lex Greensills hometown in Australia.The executives in charge of the fund also knew that much of the insurance coverage they relied on to make the funds look safe was dependent on just a single insurer, according to the Wall Street Journal. They considered requiring the funds to secure coverage from a broader set of insurers, with no single firm providing more than 20% of the coverage, but never put the policy in place, the newspaper said.A spokesman for Credit Suisse declined to comment.Greensill, meanwhile, was looking for new ways to fuel the growth of his trade finance empires after the collapse of the GAM funds removed a major buyer of his assets. In 2019, SoftBank Group Corp. stepped in, injecting almost $1.5 billion through its Vision Fund to become Greensills largest backer. It also made a big investment in the Credit Suisse supply chain finance funds, putting in hundreds of millions of dollars, though the exact timing isnt clear.Over the course of 2019, the flagship fund more than doubled in size, but soon questions arose about the intricate relationship between Greensill and SoftBank that fueled the growth. The funds had an unusual structure in that they used a warehousing agreement to buy the assets from Greensill Capital, with no Credit Suisse fund manager doing extensive due diligence on them. Within the broad framework set by the funds, the seller of the assets -- Greensill -- basically decided what the funds would buy.Credit Suisse started an internal probe that found, among other things, that the funds had extended large amounts of financings to other companies backed by SoftBanks Vision Fund, creating the impression that SoftBank was using them and its sway over Greensill to prop up its other investments. SoftBank pulled its fund investment -- some $700 million -- and Credit Suisse overhauled the fund guidelines to limit exposure to a single borrower.Neither Gottstein nor Eric Varvel, the head of the asset management unit, or Lara Warner, the head of risk and compliance, appeared to see a need for deeper changes. The bank reiterated it had confidence in the control structure at the asset management unit.Credit Suisses review didnt mention at the time that Greensill had also extended financing to another of his backers, General Atlantic. The private equity firm had invested $250 million in Greensill Capital in 2018. The following year, Greensill made a $350 million loan to General Atlantic, using money from the Credit Suisse funds, according to the Wall Street Journal. The loan is currently being refinanced, said a person familiar with the matter.A spokeswoman for General Atlantic declined to comment.Shortly after the Credit Suisse probe concluded, more red flags popped up. In Germany, regulator BaFin was looking into a small Bremen-based lender that Greensill had bought and propped up with money from the SoftBank injection. Greensill was using the bank effectively to warehouse assets he sourced, but BaFin was worried that too many of the those assets were linked to Guptas GFG -- a risk that the Credit Suisses managers, for their part, had brushed off earlier.SoftBank, meanwhile, was quietly starting to write off its investment in a stunning reversal from a bet it had made only a year earlier. By the end of last year, it had substantially written down the stake, and its considering dropping the valuation close to zero, people familiar with the matter said earlier this month.Credit Suisse, however, was highlighting the success of the funds to investors. Varvel, the head of asset management, listed them in a Dec. 15 presentation as an example of the innovative and higher-margin fixed-income offerings that the bank was planning to focus on.By that time, Greensill already knew that a little-known Australian insurer called Bond and Credit Company had decided not to renew policies covering $4.6 billion in corporate loans his firm had sourced. The policies were due to lapse on March 1, prompting a last-ditch effort from the supply-chain firm to take the insurer to court in Australia. That day, a judge in Sydney struck down Greensills injunction, triggering the series of events that have since reverberated around the world.Credit Suisse didnt know until very recently that the insurance was about to lapse, according to a person with knowledge of the matter.In an update to investors Tuesday, Credit Suisse said that several factors cumulatively led to the decision to freeze the funds, and that it was looking for ways to return cash holdings. But in a twist that may complicate the liquidation of the remainder, it also said that Greensills German Bank was one of the insured parties and plays a role in the claims process, and that bank was just shuttered by BaFin.Many of the assets in the funds have protection to make them more appealing to investors seeking an alternative to money market funds. Yet the second-biggest of them, the High Income Fund, doesnt use insurance. Its also the fund with the least liquidity, with less than 20% of the net assets in cash.Credit Suisse has said it wasnt aware of any evidence suggesting financial irregularities with the papers issued by Greensill or by the underlying companies. The bank still hasnt commented on how many of the assets in the funds are tied to Guptas GFG Alliance.For more articles like this, please visit us at bloomberg.comSubscribe now to stay ahead with the most trusted business news source.2021 Bloomberg L.P.

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The Price Average Is the Line in the Sand for Bitcoin Bulls, Analyst Says – CoinDesk – CoinDesk

March 8, 2021 | Author: admin

While bitcoin can suffer deeper drawdowns because of traditional market instability, its broader bullish trend would remain valid as long as historically strong chart support is held intact.

The 21-week SMA (Simple Moving Average) is the level to defend for the bulls, trader and technical analyst Michal van de Poppe told CoinDesk. The bias remains bullish as long as the SMA support is intact.

An SMA is an arithmetic moving average calculated by adding recent prices and dividing the tally by the number of periods. SMAs are trend-following, lagging indicators and often act as support and resistance levels.

The 21-week SMA acted as a price floor during the previous bull market, as seen below.

The cryptocurrency repeatedly found dip demand (marked by arrows) around the 21-week SMA throughout the rally from $300 to $19,783 seen in the October 2015-December 2017 period.

If history is a guide, deeper pullbacks, if any, could run out of steam around the 21-week SMA this year. The technical line is now located at $32,240, while bitcoin is changing hands near $46,500.

A continued rise in the U.S. Treasury yields could push the dollar higher, sending bitcoin toward the SMA support.

One cannot rule out that possibility as Federal Reserve Chairman Jerome Powell defied expectations on Thursday by expressing little concern regarding the recent spike in yields. That has left the doors open for a further rally in yields and an extension of last weeks risk aversion trades.

The dollar strengthened, while bitcoin and stocks fell in the seven days to Feb. 28, as the U.S. 10-year Treasury yield surged to a 12-month high of 1.6% and investors priced in higher odds of an early unwinding of the Federal Reserves stimulus.

The yield remains elevated near 1.6% at press time, and the dollar index is hovering at a three-month high of 92.00. Also, European stocks and the U.S. stock futures are flashing red.

Both bitcoin and stocks may find some relief later Friday if the U.S. nonfarm payrolls data due at 13:30 UTC paints a gloomy picture of the labor market and sends yields lower.

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Massive morph to the mainstream: Citi call fires bitcoin price – The Australian Financial Review

March 8, 2021 | Author: admin

Further, as US tech giants such as PayPal under its peer-to-peer Venmo business and Square enable bitcoin transactions, it was arguably moving closer to fulfilling one of the key roles of money in exchanging labour for future purchasing power in a single unit of account.

Bitcoins supporters argue its clearer role in the future of money means the price which topped $US50,400 on Monday is less relevant to its emerging use cases over the medium term.

Citi also made headlines by claiming bitcoin could uproot the US dollar as the primary means of payment between global importers and exporters: A focus on global reach and neutrality could see bitcoin become an international trade currency, Citi says.

This would take advantage of bitcoins decentralised and borderless design, its lack of foreign exchange exposure, its speed and cost advantage in moving money, the security of its payments, and its traceability.

If Citi is right about bitcoins ascent it may flatten banks lucrative foreign exchange fees and could do even more damage to pure-play discount rivals or middlemen such as Western Union, OFX Group, Transferwise and Travelex.

Blue-chip tech giant Facebook has already cottoned on to the possibility of creating its own blockchain-based digital currency named Diem as an exchange mechanism to eliminate overseas transfer fees and the spreads charged on currency exchanges to send money internationally.

In theory, a person in the US could transmit savings to Asia if the sender and receiver both had free Diem wallets via Facebook accounts. The recipients Diem could be exchanged into local currency with lower fees as the middleman in the process was cut out, alongside the ability to charge a mark-up on spot FX rates.

The idea is also applicable to tourists who could use a decentralised cryptocurrency or one backed by a basket of currencies (as in Facebooks Diem) to pay for goods or services abroad without paying a mark-up to middlemen often dealing in cash over-the-counter at airports, for example.

If central banks launch digital currencies of their own and allow citizens to hold accounts directly, this could accelerate a future where travel between Australia and New Zealand no longer required exchanging one fiat currency for another.

Central bank fiat-backed digital currencies and direct-to-consumer bank accounts could also disintermediate retail banks fee streams in other ways and would meet fierce resistance from the banking establishment.

Citi says bitcoin could especially appeal to the public and private sector in emerging nations where currencies were vulnerable to extreme devaluation if governments were considered uncreditworthy. It cited Africas largest economy, Nigeria, as an example where importers were forced to pay far more for US dollars in 2020 after its economy was rocked by low oil prices and COVID-19.

Other petrodollar economies across Africa, Latin America, and the Middle East can still only trade oil in US dollars as a result of deals done in exchange for US security and largesse after President Richard Nixon unpegged the dollar from gold in 1971.

Citi concluded there were obstacles ahead in the crypto space around regulation, custody, environmental concerns, insurance and cybersecurity, but the opportunities outweighed the risks to mean crypto was near a tipping point into joining the mainstream.

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SSM Health innovates kidney care with predictive analytics and machine learning – Healthcare IT News

March 6, 2021 | Author: admin

SSM Health, a nonprofit with $8 billion in revenue, provides its communities with high-quality care for vulnerable populations. One of the most vulnerable populations is made up of patients with kidney disease.

THE PROBLEM

Kidney disease is complex because 90% of people with the disease do not know they have it until they need dialysis or a transplant. There is little disease education or preventive efforts in the initial stages, making chronic kidney disease expensive to treat. Patients typically wind up receiving lower outcomes and lower quality of life than physicians would like to see.

CKD and end-stage renal disease patients manage 15-20 medications daily and have multiple comorbid conditions, complicating treatment.

"Patients with kidney disease make up under 5% of our patient population, but account for more than 20% of our total costs," said Carter Dredge, chief transformation officer at SSM Health. "We needed the focus and expertise that our partner Strive Health delivers through predictive analytics and the care team to better support our most at-risk population.

"Across the broad primary care base, providers are seeing patients with a range of health concerns, and CKD often involves just five to 10 patients in their panel," he continued. "During each visit, PCPs have limited time to meet these complex needs, and CKD symptoms are subtle. Often, patients were under-diagnosed for advanced CKD."

SSM Health needed a focused solution that helpedpredict the best time to engage patients to optimize the patient experience, improve outcomes and lower costs.

"At SSM Health, as our core clinical teams build the main programs that encompass all our patients and interventions across multiple populations, partnering with Strive Health has delivered focused care for a particularly complex condition that connects to the larger innovation pipeline, aiding the move to more risk-based contracts by helping build the required care coordination and analytics programs for more specific patient cohorts," Dredge said.

PROPOSAL

Analytics can offer diagnostic assistance and guide treatment decisions. Combining data from several sources, including claims, clinical data, live feeds from health exchanges, dialysis machines and demographic information for social determinants of health, algorithms can predict adverse events, including kidney failure during a given time frame or a cardiology event.

"The program we developed with Strive Health delivers comprehensive clinical services for CKD and ESRD patients that significantly improve quality of care and outcomes while lowering the total cost of care for patients," Dredge said. "Thirty-three algorithms assist with treating CKD, including one that can predict CKD progression to ESRD with 95% accuracy."

Carter Dredge, SSM Health

Strive Health's technology and full clinical model bring a focused approach to care, he added.

"We are intervening with the right patients at the right time," he explained. "Our care team can see when a patient is progressing more rapidly toward kidney failure and can take the time to fully educate and coach the patient through making the best renal replacement therapy option for them, whether this is home dialysis, in-center dialysis, preemptive transplant or conservative care."

MARKETPLACE

There are various vendors of predictive analytics technology on the market today. Some of these vendors include Alteryx, Anodot, Domo, Gainsight, IBM, Infer, Microsoft, Qrvey, RapidMiner, SAP, SAS Institute, Sisense and Strive Health.

MEETING THE CHALLENGE

"Strive Health's CareMultiplier platform, powered by proprietary machine learning algorithms, makes sense of massive amounts of data, cuts through the noiseand allows our clinicians to focus on doing what only they can do, deliver high-touch patient care," Dredge explained.

"Our clinical teams use predictive analytics in their day-to-day care," he continued. "Each patient receives an overall risk score that serves as a starting point for treatment and flows through our clinical care systems. As we engage our members, our team then uses focused initiatives developed through the analytics to be more proactive in their care."

As an example, SSM Health has a patient cohort called Planned Starts. Strive's technology has identified them as progressing toward dialysis in the next six to 12 months. These analytics allow clinicians to deploy focused interventions and care plans to help prevent these patients from "crashing" into dialysis.

RESULTS

"Strive Health brings economies of scale, regionalization and nationalization to a fragmented kidney care process," Dredge reported. "The program was launched in June 2020, during the COVID-19 pandemic. While the pandemic impacted most in the country, the first four months of data are promising, showing a more than 20% reduction in acute utilization for both CKD and ESRD populations and a more than 25% reduction in emergency department utilization for both CKD and ESRD populations."

Several patients have benefited from this approach, including one female patient who was predicted to have a 57% chance of kidney failure within two years. After more than a year of "watch and wait," the patient avoided a crash into dialysis through a high-touch care team coordinating between her nephrologist and primary care physician. They addressed her concerns and engaged her in appropriate treatment.

"Separately, a 36-year-old patient had acquired 16 hospital stays in two years with frequent readmissions and declining health," Dredge recalled. "This patient has since had only one emergency department visit and zero readmissions, reducing inpatient days by about 14 times her previous usage."

ADVICE FOR OTHERS

"As health systems move into population health and value-based contracts, analytics are needed to identify patient populations and follow them through their care journey," Dredge advised. "When selecting a partner, ensure there is alignment on goals and metrics.

"Understand what the healthcare organization should own versus accomplish with a partner," he continued. "Controlling all aspects of care through internal resources can stifle innovation. SSM Health's transformation team recognizes that a partner delivering an external, dedicated focus with tight integration and collaboration can speed innovation and raise all involved together for a better experience."

This leads to a virtuous cycle of innovation where the more successful one is at making progress, the faster they can go, he added.

"SSM Health turned to a partner so it could dedicate its efforts to what the health system does well, which is providing quality care to its communities," he concluded. "The partnership applied a dedicated focus to informing care that is innovating kidney care."

Twitter:@SiwickiHealthITEmail the writer:bsiwicki@himss.orgHealthcare IT News is a HIMSS Media publication.

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Revolution by Artificial Intelligence, Machine Learning and Deep Learning in the healthcare industry – Times Now

March 6, 2021 | Author: admin

Revolution by Artificial Intelligence, Machine Learning and Deep Learning in the healthcare industry | Photo Credits: Pixabay 

New Delhi: Medical science has come a long way from what it was back in the 80s and 90s. With the new era and technology, the healthcare industry is at its best times and with the growing demand is an unstoppable industry to look forward to. Read on to know what is really building on!

The population ageing, changing patient expectations, a shift in lifestyle choices, and the never-ending cycle of innovation are a few of the implications of an ageing population. As per the joint report with the European Unions EIT Health, by 2050, one in four people in Europe and North America will be over the age of 65this means the health systems will have to deal with more patients with complex needs. Managing such patients is expensive and requires systems to shift from an episodic care-based philosophy to one that is much more proactive and focused on long-term care management. What will come as an aid to the industry are artificial intelligence (AI), machine learning (ML) and deep learning (DL) as they will help revolutionize healthcare and address some of the major challenges.

So, what is AI? It is the capability of a computer program to perform tasks or reasoning processes that we usually associate with intelligence in a human being. AI can lead to better care outcomes and improve the productivity and efficiency of care delivery. It can also improve the day-to-day life of healthcare practitioners, letting them spend more time looking after patients and in so doing, raise staff morale and improve retention. It can even get life-saving treatments to market faster. Artificial Intelligence in Healthcare is expected to grow from $2.1 billion to $36.1 billion by 2025, displaying a CAGR of 50.2% over the span.

What really is ML? ML is the study of computer algorithms that improve automatically through experience. It is seen as a part of artificial intelligence. Machine learning (AI) has indeed played a key role in many areas of health care, including the development of new medical procedures, the handling of patient data and records, and the treatment of chronic diseases. It is understood that hospitals, clinics, and other healthcare organizations around the globe are gradually beginning to recognize the need for digitization and integration within administrative processes. In recent years, scientists and scholars have joined the field of cancer diagnosis and treatment. One future approach is combining cognitive computation with genomic tumour sequencing. This uses machine learning to build diagnostics and clinical therapies. For example, The da Vinci robot helps surgeons to conduct procedures at a level of precision. These robotic hands are more precise and reliable than human hands. Computer vision and machine learning are used to classify the body parts of humans.

Moving to DL, here is what it really means and how it is helping the healthcare industry. Deep learning is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised. When it comes to healthcare, in a recent book published by Dr Eric Topol entitled Deep Medicine, the cardiologist and geneticist emphasize how deep learning in healthcare could restore the care in healthcare. Aidoc, for example, has developed algorithms that expedite patient diagnosis and treatment within the radiology profession. The company has received several accreditations and approvals from the Food and Drug Administration, the European Union CE and the Therapeutic Goods of Australia (TGA) for its specialized algorithms. These algorithms include intracranial haemorrhage, pulmonary embolism and cervical-spine fracture and allow for the system to prioritize those patients that are in most need of medical care. This targeted form of AI and deep learning helps the overburdened radiologist by flagging items that are of concern and thereby allows the healthcare professional to direct patients with greater control and efficiency. It also reduces admin by integrating into workflows and improving access to relevant patient information.

AI is now top-of-mind for healthcare decision-makers, governments, investors and innovators, and the European Union itself. An increasing number of governments have set out aspirations for AI in healthcare, in countries as diverse as Finland, Germany, the United Kingdom, Israel, China, and the United States and many are investing heavily in AI-related research. The private sector continues to play a significant role, with venture capital (VC) funding for the top 50 firms in healthcare-related AI reaching $8.5 billion, and big tech firms, startups, pharmaceutical and medical-devices firms and health insurers, all engaging with the nascent AI healthcare ecosystem.

We are in the very early days of our understanding of AI and its full potential in healthcare, in particular with regards to the impact of AI on personalization. Nevertheless, interviewees and survey respondents conclude that over time we could expect to see three phases of scaling AI in healthcare, looking at solutions already available and the pipeline of ideas.

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Revolution by Artificial Intelligence, Machine Learning and Deep Learning in the healthcare industry - Times Now

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Explainable Machine Learning, Model Transparency, and the Right to Explanation Machine Learning Times – The Predictive Analytics Times

March 6, 2021 | Author: admin

Check out this topical video from Predictive Analytics World founder Eric Siegel:

A computer can keep you in jail, or deny you a job, a loan, insurance coverage, or housing and yet you cannot face your accuser. The predictive models generated by machine learning to drive these weighty decisions are generally kept locked up as a secret, unavailable for audit, inspection, or interrogation. The video above covers explainable machine learning and the loudly-advocated machine learning standards transparency and the right to explanation. Eric discusses why these standards generally are not met and overviews the policy hurdles and technical challenges that are holding us back.

About the Author

Eric Siegel, Ph.D.,is a leading consultant and former Columbia University professor who makes machine learning understandable and captivating. He is the founder of thePredictive Analytics WorldandDeep Learning Worldconference series, which have served more than 17,000 attendees since 2009, the instructor of the acclaimed online courseMachine Learning Leadership and Practice End-to-End Mastery, a popular speaker whos been commissioned formore than 110 keynote addresses, and executive editor ofThe Machine Learning Times. He authored the bestsellingPredictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, which has been used in courses at more than 35 universities, and he won teaching awards when he was a professor at Columbia University, where he sangeducational songsto his students. Eric also publishesop-eds on analytics and social justice. Follow him at@predictanalytic.

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Raytheon Applies AI, Machine Learning to Defense Tech Modeling, Simulation – Executive Mosaic Media

March 6, 2021 | Author: admin

Raytheon MDC video wall

Raytheon Technologies has infused emerging technology into the companys modeling and simulation process as part of efforts to help defense customers visualize the performance of weapons systems before they make procurement decisions.

The company said Wednesday an engineering team within its missiles and defense business used artificial intelligence and machine learning to put a Standard Missile-3 Block IIA missile through digital testing ahead of a flight test demonstration conducted by the U.S. Navy and the Missile Defense Agency in November last year.

Engineers collected and integrated data from previous SM-3 interceptor tests into a model as part of the process.

AI and machine learning are critical to the companys modeling and simulation framework, Bob Fitzpatrick, vice president of requirements and capabilities at Raytheon Missiles & Defense.

Every new flight test and every new experiment looks a little different than the last one and that means theres a lot of rapid learning happening for the company and its customers, Fitzpatrick added.

Wes Kremer, president of Raytheon Missiles & Defense, noted that modeling and simulation could help the warfighter understand the potential of current and future platforms to gain an edge in battlefield missions.

The company received a $722.4 million contract from MDA in late October to provide SM-3 engineering and technical support to U.S. and international defense customers.

Potomac Officers Club will host its 2020 Industrial Space Defense Summit on March 23 and its 3rd Annual Artificial Intelligence Summit on March 30. To register for these virtual summits and view other upcoming events, visit the Potomac Officers Club Events page.

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Raytheon Applies AI, Machine Learning to Defense Tech Modeling, Simulation - Executive Mosaic Media

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Home Lending Pal leverages AI and machine learning to efficiently solve lenders’ and borrowers’ problems – HousingWire

March 6, 2021 | Author: admin

To be successful, lenders have to rely on mortgage lead generation to find potential borrowers. But generating leads isnt an easy process, especially if businesses depend on older methods to produce results.

Home Lending Pal (HLP) has an anonymous marketplace that uses conversational intelligence, machine learning and blockchain to help first-time homebuyers through the home research and lending process. HLP helps banks, credit unions and non-bank lenders lead with transparency by automating and digitizing the process.

Our AI captures data at scale, reduces human capital, and improves efficiency and effectiveness to signal high-quality leads that will close fast, said Steven Better, Home Lending Pal COO & co-founder.

Its no secret the housing market is hot right now. Even though home prices are on the rise, low interest rates make purchasing a home an attractive option. And while this is great news for borrowers, lenders have to work hard to stand out among the competition and produce leads that will end in closings.

Some mortgage professionals are generating leads that are only being paired based on cost-per-lead budget instead of how well they fit into mortgage overlays. On top of this, most vetting of the lead is being done by the lender, which often requires the borrower to submit the same information multiple times.

Mortgage businesses need to lead with a digital-first experience that delights customers even on third party marketplaces. Home Lending Pals marketplace leverages artificial intelligence, data science and machine learning to automate manual tasks and to create a better experience. By integrating digital platforms, borrowers can get personalized insight with little to no human interaction.

With our solution, potential borrowers gain deeper insight into the possible outcome of a mortgage application on a home without a negative impact, a significant time commitment, sales pressure or potential embarrassment, said Bryan Young, Home Lending Pal CEO and co-founder.

Home Lending Pals AI-powered mortgage advisor simulates underwriting to determine mortgage approval odds, makes affordability recommendations, and solves borrowers and lenders problems. The pairs are based on mortgage overlays and allow potential borrowers to select which lenders they would like to work with based on service quality and approval likelihood, not just marketing spend.

Unlike other solutions, HLPs direct-to-consumer focus allows it to build, validate and attest consumer financial and credit information before connecting them to mortgage lenders. Borrowers can research privately before submitting all documents needed for underwriting electronically through the HLP platform, said Joey Barrow, Chief Mortgage Officer at Home Lending Pal.

Home Lending Pal removes human biases to provide transparent and objective information that advocates for the borrower in the lending process.

Bryan Young, CEO & Co-Founder

For over 15 years, Bryan Young has led global strategies and tactical solutions for the likes of the 2012 DNC and President Barack Obama, Microsoft, Zillow and other companies across the B2B and B2C sector.

Steven Better, COO & Co-Founder

Steven Better oversees the day to day operations of the company and the development of AI models.

Joey Barrow, Chief Knowledge Officer

Joey Barrow is a Presidents Club level mortgage broker with more than 20 years of industry experience.

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Home Lending Pal leverages AI and machine learning to efficiently solve lenders' and borrowers' problems - HousingWire

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Companion Raises $8M Seed Round to Use Machine Learning and Computer Vision to Talk to Dogs – PRNewswire

March 6, 2021 | Author: admin

SAN FRANCISCO, March 3, 2021 /PRNewswire/ --Today,Companion announces $8M in Seed funding to transform how we train, engage and communicate with our dogs. The funding comes from leading institutional investors and VCs along with some of the largest pet companies and charities in the world. Participating investors include IA Ventures, Tuesday Capital, frog Design, Companion Fund, backed by Mars Petcare, Michelson Found Animals Foundation, Wheelhouse Partners, PETStock and Central Garden & Pet. The funding will be used to continue rolling out the device and coaching service to early adopters. You can sign up for early access here.

Companion makes it easier than ever for pet parents to engage and train their dog at home. The Companion device uses computer vision combined with machine learning to precisely detect and analyze dogs' movements and behaviors. Using state-of-the-art positive reinforcement techniques, and its proprietary data and algorithms, the Companion rewards your dog for desired behaviors such as sit, down, stay and recall. Given Companion brings infinite patience and consistency to practicing positive behaviors, pet parents have the assurance that their dog will maintain these behaviors over time. Altogether, the dog's experience with Companion refines trained behaviors into consistent and repeatable actions and serves as a powerful foundation for our integrated coaching service.

"The Companion is the first step in creating more understanding with all of the animals around us. We know understanding inherently drives empathy," said John Honchariw, Companion CEO. "We help enable and foster extraordinarily deep bonds with the dogs we love...and this is just the start."

By training basic obedience skills, Companion teaches independence and confidence and offers highly engaging activities for the dog. As pet parents return to work following COVID, they need proven solutions to engage their dog when they're not at home and feel confident leaving their dogs.

"The Companion service is exactly what dog owners who are returning to the office need. The pandemic has brought about unprecedented dog adoption rates and as the world begins to move back to normal, those dogs are going to need a Companion to stay at home with them," said Cofounder and Managing Partner at Tuesday Capital, Patrick Gallagher.

"Our collaboration is the result of a longstanding interest in Companion's progress, and a deep conviction in the team and their vision," said Ethan Imboden, VP and Head of Venture Design at frog. "With their technology, the Companion team is enabling a new depth of connection between humans and animals. We've been thrilled to have the opportunity to both enrich this relationship and strengthen the company through design."

The technology has been privately offered in the SF Bay Area since 2018 and will begin shipping to select early adopters throughout 2021.

About CompanionCompanion develops technology and services to train dogs to high levels using machine learning, computer vision, top tier coaching and state of the art reward-based training. Our products and services create high-quality solutions to the most pressing dog training and welfare issues for consumers. The Companion team includes world-class technologists, animal welfare experts, researchers, and entrepreneurs. We've partnered with leading experts such as Mars Petcare and the San Francisco SPCA, cutting edge technologies from Google, and distinguished venture investors to bring a new category of products to the world.

Please reach out if you're interested in talking with us - we'd love to hear from you.

Media Contact:Mike West415-689-8574[emailprotected]

SOURCE Companion

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