Category Archives: Ai

Theres more AI news out there than anyone can possibly keep up with. But you can stay tolerably up to date on the most interesting developments with this column, which collects AI and machine learning advancements from around the world and explains why they might be important to tech, startups or civilization.

To begin on a lighthearted note: The ways researchers find to apply machine learning to the arts are always interesting though not always practical. A team from the University of Washington wanted to see if a computer vision system could learn to tell what is being played on a piano just from an overhead view of the keys and the players hands.

Audeo, the system trained by Eli Shlizerman, Kun Su and Xiulong Liu, watches video of piano playing and first extracts a piano-roll-like simple sequence of key presses. Then it adds expression in the form of length and strength of the presses, and lastly polishes it up for input into a MIDI synthesizer for output. The results are a little loose but definitely recognizable.

Image Credits: Shlizerman, et. al

To create music that sounds like it could be played in a musical performance was previously believed to be impossible, said Shlizerman. An algorithm needs to figure out the cues, or features, in the video frames that are related to generating music, and it needs to imagine the sound thats happening in between the video frames. It requires a system that is both precise and imaginative. The fact that we achieved music that sounded pretty good was a surprise.

Another from the field of arts and letters is this extremely fascinating research into computational unfolding of ancient letters too delicate to handle. The MIT team was looking at locked letters from the 17th century that are so intricately folded and sealed that to remove the letter and flatten it might permanently damage them. Their approach was to X-ray the letters and set a new, advanced algorithm to work deciphering the resulting imagery.

Diagram showing X-ray views of a letter and how it is analyzed to virtually unfold it. Image Credits: MIT

The algorithm ends up doing an impressive job at separating the layers of paper, despite their extreme thinness and tiny gaps between them, sometimes less than the resolution of the scan, MITs Erik Demaine said. We werent sure it would be possible. The work may be applicable to many kinds of documents that are difficult for simple X-ray techniques to unravel. Its a bit of a stretch to categorize this as machine learning, but it was too interesting not to include. Read the full paper at Nature Communications.

Image Credits: Asensio, et. al

You arrive at a charge point for your electric car and find it to be out of service. You might even leave a bad review online. In fact, thousands of such reviews exist and constitute a potentially very useful map for municipalities looking to expand electric vehicle infrastructure.

Georgia Techs Omar Asensio trained a natural language processing model on such reviews and it soon became an expert at parsing them by the thousands and squeezing out insights like where outages were common, comparative cost and other factors.

Continue reading here:

Deep Science: AI adventures in arts and letters - TechCrunch

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By 2025, more than 75% of venture capital and early-stage investor executive reviews will be informed by AI and data analytics. In other words, AI might determine whether a company makes it to a human evaluation at all, de-emphasizing the importance of pitch decks and financials. Thats according to a new whitepaper by Gartner, which predicts that in the next four years, the AI- and data-science-equipped investor will become commonplace.

Increased advanced analytics capabilities are shifting the early-stage venture investing strategy away from gut feel and qualitative decision-making to a platform-based quantitative process, according to Gartner senior research director Patrick Stakenas. Stakenas says data gathered from sources like LinkedIn, PitchBook, Crunchbase, and Owler, along with third-party data marketplaces, will be leveraged alongside diverse past and current investments.

This data is increasingly being used to build sophisticated models that can better determine the viability, strategy, and potential outcome of an investment in a short amount of time. Questions such as when to invest, where to invest, and how much to invest are becoming almost automated, Stakenas said. The personality traits and work patterns required for success will be quantified in the same manner that the product and its use in the market, market size, and financial details are currently measured. AI tools will be used to determine how likely a leadership team is to succeed based on employment history, field expertise, and previous business success.

As the Gartner report points out, current technology is capable of providing insights into customer desires and predicting future behavior. Unique profiles can be built with little to no human input and further developed via natural language processing AI that can determine qualities about a person from real-time or audio recordings. While this technology is currently used primarily for marketing and sales purposes, by 2025 investment organizations will be leveraging it to determine which leadership teams are most likely to succeed.

One venture capital firm San Francisco, California-based Signalfire is already using a proprietary platform called Beacon to track the performance of more than 6 million companies. At a cost of over $10 million per year, the platform draws on 10 million data sources, including academic publications, patent registries, open source contributions, regulatory filings, company webpages, sales data, social networks, and even raw credit card data. Companies that are outperforming are flagged up on a dashboard, allowing Signalfire to see deals ostensibly earlier than traditional venture firms.

This isnt to suggest that AI and machine learning are or will be a silver bullet when it comes to investment decisions. In an experiment last November, Harvard Business Review built an investment algorithm and compared its performance with the returns of 255 angel investors. Leveraging state-of-the-art techniques, a team trained the system to select the most promising investment opportunities among 623 deals from one of the largest European angel networks. The model, whose decisions were based on the same data available to investors, outperformed novice investors but fared worse than experienced investors.

Part of the problem with Harvard Business Reviews model was that it exhibited biases experienced investors did not. For example, the algorithm tended to pick white entrepreneurs rather than entrepreneurs of color and preferred investing in startups with male founders. Thats potentially because women and founders from other underrepresented groups tend to be disadvantaged in the funding process and ultimately raise less venture capital.

Because it might not be possible to completely eliminate these forms of bias, its crucial that investors take a hybrid approach to AI-informed decision-making with humans in the loop, according to Harvard Business Review. While its true that algorithms can have an easier time picking out better portfolios because they analyze data at scale, potentially avoiding bad investments, theres always a tradeoff between fairness and efficiency.

Managers and investors should consider that algorithms produce predictions about potential future outcomes rather than decisions. Depending on how predictions are intended to be used, they are based on human judgement that may (or may not) result in improved decision-making and action, Harvard Business Review wrote in its analysis. In complex and uncertain decision environments, the central question is, thus, not whether human decision-making should be replaced, but rather how it should be augmented by combining the strengths of human and artificial intelligence.

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Gartner: 75% of VCs will use AI to make investment decisions by 2025 - VentureBeat

VERONA, Italy--(BUSINESS WIRE)--Trueblue, a leading provider of AI solutions for Operational and Analytical CRM in the Life Science industry announces that Angelini Pharma, as part of a major global digital transformation process, has chosen AiDEA, the first AI-Digital Cloud Pharma CRM solution based on Microsoft Dynamics 365. Angelini Pharma is a leading pharmaceutical company committed to helping patients in the therapeutic areas of Central Nervous System (CNS) and Mental Health (including Pain), Rare Diseases and Consumer Healthcare.

Angelini's new digital vision is to reposition and elevate its Customer Engagement capabilities using Artificial Intelligence by implementing an innovative AI-Driven CRM system. This outlines not only a clear path towards Digital, but also the willingness to go international through best practices that enable interactions with key customers while supporting the company's global growth, thus evolving its commercial and operational capabilities.

Angelini Pharma will implement the AiDEA CRM suite in more than 24 subsidiaries worldwide, offering its employees a wide range of Artificial Intelligence applications. The leading Pharma Company will thus take advantage of actionable insights and omni-channel optimization algorithms designed to maximize the use of resources, optimize Customer Engagement Activities on all channels and better meet the needs of each stakeholder.

After a thorough evaluation process of the ideal partner and as a result of a former successful Data Warehouse and Corporate Business Intelligence project, Angelini Pharma chose Trueblue's, which demonstrated the skills, innovation, flexibility and business advantage of its platforms based on Microsoft technologies.

We decided to boost the deployment of our Multichannel strategy to better support our ongoing transformation, and while advancing significantly, we realized the need to further accelerate also expanding to solutions based on the use of Artificial Intelligence," says Pierluigi Antonelli, Angelini Pharmas CEO. "Trueblue's AiDEA Suite enables us to leverage our existing investments while accelerating our AI transformation to ensure our subsidiaries can better serve their customers".

On a global level, companies that are strategically scaling artificial intelligence report a return on investment nearly three times greater than those that are merely experimenting.

"We are excited about this relationship that has been lasting for several years and that makes Angelini Pharma the main player of a new paradigm, in this particularly important historical moment for the pharmaceutical context," says Marco Bonesini - CEO & Co-founder of Trueblue. "Our goal is to contribute to the growth of Angelini Pharma at international level with the strength of AiDEA and all the potential of an omni-channel AI-Driven solution based on Microsoft Dynamics 365, which is truly ready to change the CRM market in the pharmaceutical sector".

Elena Bonfiglioli, EMEA Healthcare Lead at Microsoft Corp. added, "AiDEA, enables pharma companies with the capabilities and tools to deliver superior experiences in every interaction with their customers. Integration with Dynamics 365 supports pharma companies in their digital transition."

Read more:

Angelini Pharma Chooses AiDEA's Artificial Intelligence to Boost Its Digital Transformation and International Growth - Business Wire

Artificial intelligence is poised to be widespread in hospital revenue cycle operations in just the next three years.

And that provides a big opportunity for Change Healthcare's end-to-end AI solutions, according to Neil de Crescenzo, president and CEO of Change Healthcare.

Two-thirds of doctors report using AI in some revenue cycle capacity today, and nearly all expect to be using it in three years, a Change Healthcare survey found.

"As providers begin to use AI more strategically, there is an opportunity for significant financial, operational and clinical gains, including improving the end-to-end revenue cycle, claims accuracy, denial reduction, clinical insights, and level of care prediction," de Crescenzo said during the company's third-quarter fiscal 2021 earnings call Thursday.

"These trends will continue to increase demand from payers and providers for Change Healthcare's platform of integrated solutions and services," he said.

Despite these tailwinds, Change Healthcare saw its software and analytics revenue drop 4% year over year from $387 million to $372 million during its third quarter ending Dec. 31.

Network solutions revenue was up 28% to $193 million from $151 million a year ago, and revenue for technology-enabled services totaled $222 million, down 8% year over year.

RELATED: UnitedHealth Group's Optum to buy Change Healthcare for $13B

Change Healthcare exited the third quarter on a mixed note. The company reported revenue of $785 million in the quarter, down 3% from $808 million during the same period last year. The company's top line missed Wall Street estimates by 0.1%.

Revenue was negatively impacted by the COVID-19 pandemic, partially offset by new sales volumes, company officials said

The Nashville, Tennessee-based healthcare technology company reported a profit of $110 million during the quarter and adjusted earnings per share of 34 cents. That beat Street estimates of 30 cents per share. The company posted adjusted net income of $106 million or 33 cents per share in the third quarter of fiscal 2020.

The company exited the quarter with cash and cash equivalents of $137 million compared with $167 million in the preceding quarter.

In early January, UnitedHealth Group's Optum unit announced plans to buy Change Healthcare for $13 billion, or $7.84 billion in cash plus about $5 billion in debt. That transaction will be completed in the second half of 2021, executives said.

"We are excited by the opportunity to unite two technology and service companies focused on serving health care. The combined capabilities will more effectively connect and simplify core clinical, administrative and payment processes, resulting in better health outcomes and experiences for everyone at lower costs," de Crescenzo said.

"We share a common mission and values, and importantly, a sense of urgency to provide our customers and those they serve with the more robust capabilities this union makes possible," he said.

RELATED: Change Healthcare see potential growth as IT budgets increase during COVID-19

Underlying market trends for the business remain positive on multiple fronts, he said, including federal rules being implemented surrounding interoperability and price transparency as well as continued advances in value-based care initiatives, including the Centers for Medicare & Medicaid Services' (CMS') new Medicare direct contracting model.

During the quarter, the company launched 13 new products spanning medical network, decision support, data solutions and interoperability solutions to help payers comply with the CMS patient access and interoperability rule, de Crescenzo said.

In November, Change Healthcare launched social determinants of health analytics, a national data resource that will enable health systems, insurers and life sciences organizations explore how geodemographic factors impact patient outcomes.

The company also entered into an agreement with Carnegie Mellon Universitys Delphi Research Group to roll out Delphi's enhanced COVIDcast real-time COVID-19 indicators. The addition of Change Healthcare's de-identified COVID-19 claims data helps researchers track and forecast pandemic patterns, the company said.

Change Healthcare also sold its Capacity Management business for $67 million.

"The sale aligned with our strategy to concentrate on the primary areas of our business that achieve the best outcomes for our customers through the power of the Change Healthcare platform," de Crescenzo said.

Read the rest here:

Change Healthcare betting on accelerated use of AI in hospitals with Optum acquisition - FierceHealthcare

Shell, C3.ai, Microsoft and Baker Hughes are collaborating on an Open AI Energy Initiative (OAI) that aims to grow AI use across the energy and process manufacturing industries.

The OAI is envisioned by the partners as an open ecosystem of AI technologies that will provide a framework for energy operators, service providers, equipment providers and the software vendors who serve them to create new AI and physics-based models, monitoring, diagnostics and more to help solve critical industry needs, according to the group.

Some 92 percent of all facility systems shutdowns and process upsets are unplanned, which often leads to service disruptions which cause problems for customers. That is one of the industry challenges the OAI will be targeting as it works to gather and disseminate AI technologies and information that can be freely used to prevent such disruptions, the group said.

As it begins its work, the OAI will first focus on using AI to improve systems reliability, uptime and performance of energy assets and processes.

Powering the OAIs launch is the BakerHughesC3 (BHC3) AI Suite and cloud services from Microsoft Azure, which are being used to provide a broad range of capabilities to OAI users. Included in the AI Suite is a BHC3 Reliability application, which will serve as a foundation for the first work. BHC3 Reliability is an AI-based application that provides reliability, process, and maintenance engineers with AI-enabled insights that can be used to predict process and equipment performance risks.

BHC3 AI Suites ability to integrate enterprise-scale data from disparate data sources and train AI reliability models that cover full plant operations while taking full advantage of Azure, Microsofts scalable, enterprise-class cloud infrastructure. The OAL will combine the BHC3 Reliability application and Azure resources with technologies from all four partners and other leading energy companies to offer interoperable AI models, monitoring, diagnostics, prescriptive actions and services, according to the group.

Ed Abbo, of C3.ai

What we're enabling is the transition and or digital transformation of the energy industry, Ed Abbo, president and Chief Technology Officer at C3.ai, told Enterprise.AI. What that means is that we're allowing for a marketplace of AI applications and AI algorithms that initially will start with reliability, to reduce what's referred to as non-productive time in the oil and gas industry and in process industries.

The OAI is being created as an open ecosystem so that other energy companies, power companies, oil and gas vendors, refining companies and software makers can participate, subscribe to the groups algorithms and then publish their algorithms through the marketplace, said Abbo.

Non-productive time in oil and gas is when a refinery is down because a piece of equipment isn't working or a pump or compressor is not working, he said. Where AI fits in is that these are algorithms that can anticipate or predict the need for maintenance in advance of failure, to basically make predictions based on the data from sensors and prior maintenance on things that are likely to fail in the not-so-distant future that would cause downtime.

Armed with those predictions and warnings, plant or systems operators are alerted so they can take actions to improve the operational efficiency and uptime of the facility, said Abbo.

The four partners of the OAI provide a very solid foundation for the nascent group because its representative of the ecosystem that we believe will form around this initiative, added Abbo. The fact that Shell is in it and is publishing their algorithms [as part of the project] is highly encouraging, because that means that other oil and gas companies will follow suit. The fact that Baker Hughes, as an equipment provider and all-field services provider, is included means that others will also participate. And software vendors like C3.ai and ... Microsoft [being involved] is an industry first using AI applications. I think we'll see this ecosystem grow.

Driving Targeted AI Changes for the Energy Sector

Dan Brennan, of Baker Hughes

Dan Brennan, a senior vice president and general manager for energy technology company Baker Hughes, said the OAI envisions driving AI to make changes that can help resolve the challenges being faced in these industries.

The opportunity here is leveraging AI technologies to take really a dramatically different approach, and it's what we refer to as a system of systems approach, said Brennan. By having scalable technology that allows operators to ingest data including maintenance records, telemetry and more, it can then be used to identify systems problems early. It's the word early that's the important thing here for the energy industry. If you're able to avoid or at least know within 30 or 60 days that there is a maintenance event that has to occur, there's a tremendous amount of logistics and scheduling that has to go in there. Is there a planned maintenance window coming up? Is there an unplanned maintenance window coming up? The opportunity here for AI is really to start to get awareness early in the process where there's potential degradation or failures coming in.

Those problems can include pipes that are about to crack, shaft bearings that will soon fail, vibrations that are starting to ominously grow within facilities and a myriad of other potential machine failures.

The short answer is it could be all the above, said Brennan. If you take the example of a refinery or petrochemical facility and really large complex facilities, they're generally very well-instrumented today. So, there could be data that's coming off of a condition monitoring system. Generally speaking, most of our customers have pretty mature implementations of these operational technology systems that are out there.

A Step Forward for AI in Industry

Kevin Prouty, an energy and manufacturing insights analyst with IDC, called the creation of the new OAI a solid move.

Kevin Prouty,, of IDC

Its the culmination of a series of trends in all industries, but especially in oil and gas, he said. Its taking an infrastructure platform (Microsoft Azure), an AI platform (C3.ai), an industry technologist (Baker Hughes), and an industry titan (Shell) and getting them all on the same AI page.

Through the OAI, the four partners potentially solve a lot of sticky issues that have plagued data management and AI, namely who owns the data, the models, and the IP, said Prouty. C3.ai and Baker Hughes have solved many of the technical issues with AI, but having a prebuilt platform that solves many of those issues will accelerate AI adoption and the push for Industry 4.0 in energy.

The use of AI in working to solve some of the biggest problems in the energy industry is probably the most compelling application of AI today, said Prouty.

Its that step towards autonomous operations that Industry 4.0 has been striving for, he said. Its still a way off, but getting the operating models out in the open and having non-technical issues addressed is a big first step.

The ideas seen in the OAI initiative could be used for other industries as well, he added. Chemical and petrochemical [initiatives] are only a short step away from oil and gas operations, he said. Also important is that Shell has made a significant commitment to support the initiative with its own models and analysis, added Prouty.

Related

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New Open AI Energy Initiative Launches to Expand AI Use in Energy Industry - EnterpriseAI

Artificial intelligence can drive a car, curate the films and documentaries that you watch, develop chess programmes capable of beating grandmasters and use your face to access your phone. And, one company claims, it can also predict when footballers are about to suffer an injury.

Off the field, football has gone through a huge transformation in the 21st century, with the emergence of GPS-driven player performance data in the early 2000s, followed in the 2010s by the advanced analytics that now form a major part of every top club's player recruitment strategy. Just last month, Manchester City announced the appointment of Laurie Shaw to a new post of lead AI scientist at the Etihad Stadium, taking him from his role as research scientist and lecturer at Harvard University.

Football has always searched out innovations to make small, but crucial, differences. Many have become staples of the game, including TechnoGym to improve biomechanics, IntelliGym to improve cognitive processing and cryogenic gym sessions to ease the strain on muscles. Others have fallen by the wayside. Anyone remember nasal strips or the ball-bending properties of Predator boots?

The use of AI to predict when players are on the brink of suffering an injury could prove to be the next game-changing innovation that becomes a key component at the elite end of the game.

In a game dominated by clubs wanting to discover the extra 1% in marginal gains, keeping a player fit is arguably the most important challenge facing any coach. A depleted squad can lead to negative results and, if a team suffers too many, the manager or coach is generally the one who pays the price. This season has been more challenging than most, with the COVID-19 pandemic leading to fixtures being crammed into a reduced time frame, and players being forced to play 2-3 games a week on a regular basis.

- Stream ESPN FC Daily on ESPN+ (U.S. only)- ESPN+ viewer's guide: Bundesliga, Serie A, MLS, FA Cup and more

The toll on players' fitness is borne out by the injury lists. Crystal Palace and Southampton fulfilled their midweek Premier League fixtures with 10 first-team squad members sidelined. Champions Liverpool lost to Brighton on Wednesday with eight absentees, including long-term injury victims Virgil van Dijk, Joe Gomez and Joel Matip. Research by premierinjuries.com shows that up to and including match-week 21 of the Premier League this season, there has been a five percent increase in time lost to injuries this season. At the same stage last season, there were 356 "time-loss absences" (a player missing at least one league game), but the number has jumped to 374 this time around. With COVID-related absences, the number is 435.

Liverpool had suffered 14 time-loss absences at this stage of last season, but they're now up to 29 in 2020-21. Their league position -- fourth place, seven points adrift of top spot -- suggests they are paying a price for their sharp increase in players lost to injury.

But finding reliable injury prevention technology is the holy grail of sports scientists and fitness coaches. By November, ESPN reported a 16% rise in muscle injuries in the Premier League compared to the same stage last season. So can AI successfully predict when players are about to be injured?

Since the start of the 2017-18 season, La Liga side Getafe have partnered with the California-based AI company Zone7 to break down performance data and predict when players are at risk of injury. In simple terms, clubs like Getafe in Spain, Scottish Premiership leaders Rangers and MLS sides Real Salt Lake and Toronto FC send their training and match data to Zone7, who analyze it using their algorithm and send back daily emails with information about players who may be straying close to the so-called "danger zone."

Between the start of the 2017-18 season and March 2020, when La Liga was suspended due to the COVID-19 pandemic, Getafe recorded a substantial reduction in injuries.

"Three seasons ago, during the first year with Zone7, we saw a reduction of 40% in injury volume," Javier Vidal, the Getafe's Head of Performance, said. "As the Zone7 engine became more reliable and we had access to more data in the second year, we saw a reduction of 66 percent in the volume of injuries.

"This means that of every three injuries we had two seasons ago, we now have only one."

Jordi Cruyff, the former Barcelona and Manchester United midfielder, told ESPN that he has become a "minor, minor investor" in Zone7 after trialling the AI tool during his time as sporting director at Maccabi Tel Aviv in 2017. But he admits that he was only convinced by the AI technology after monitoring the data, even though Maccabi's then-coach declined to use it.

"I presented the tool to our then-coach and he wasn't too interested." Cruyff told ESPN. "So for the four to five months the coach was in charge, he would follow his own plan, but we would still give our performance data to the company, which they would run through their algorithm. I would then receive an email before training each day with which players were at risk and it actually predicted five of seven injuries.

"I thought 'wow.' Once or twice could be a coincidence, but catching five out of seven muscular injuries is a different thing. I would wait until after training to be told if a player had been injured. I would then go back to look at my email and there was the name. We were lucky in some ways that the coach wasn't interested in it because it gave us the chance to test it.

2 Related

"It was the perfect test, although I wish the coach would have listened, because then we would have avoided some injuries."

Tal Brown, who founded Zone7 with Eyal Eliakim in 2017 having worked together in the Israeli Defense Forces Intelligence Corps, spoke to ESPN to explain how AI can be used to detect injury risk.

"Every single player is now using a GPS vest, they are being tested for strength and flexibility at their clubs, many teams distribute watches to their players to measure sleep, so the reality is that somebody working for a club needs to look at two dozen dashboards every day -- multiplied by 20 players, multiplied by six days a week," Brown said via Zoom. "It is becoming a puzzle that a human brain wasn't really meant to solve.

"We can use a chess metaphor. Chess programmes used to be pretty simplistic and the experts could beat them, but today, a Google chess programme is unbeatable. It's not because Google has taught that chess programme 10,000 equations manually, it is because the programme has automatically studied every recorded chess game played in the history of mankind and, using AI, has developed its own understanding and interpretation.

"We are not there yet as a company. We don't have access to every single football injury that ever occurred, but we are getting much better and there will be a point where a programme focused on injury risk will out-perform humans in interpreting data."

More than 50 clubs across the world now use Zone7's AI programme. Many wish to remain anonymous, in an effort to protect any competitive advantage that the tool may provide -- football clubs are notoriously protective of such proprietary data -- while others simply do not wish to discuss any pros or cons they have discovered while using it. Despite repeated attempts by ESPN to speak to Real Salt Lake and Toronto, neither MLS team responded to enquiries.

1:32

Julien Laurens puts Eden Hazard's latest injury into context for Real Madrid.

Rangers, 23 points clear at the top of the Scottish Premiership and on course for a first domestic title since 2011, adopted Zone7's AI tool last summer and, while keen to make a broader assessment after a full season of use, they believe it's been a valuable addition to their injury prevention strategy.

"I believe AI, coupled with the experience levels of those using it, will eventually become a bedrock within clubs' decision-making as data and technology advances," Jordan Milsom, Rangers' head of performance told ESPN. "Given our players had been exposed to one of the longest lockdowns of all [93 days] and the unknowns associated with such prolonged layoffs, we felt investing in such a system may well provide another layer of support for how we managed the players on what would clearly be a challenging season.

"We haven't used the system long enough compare season-to-season analysis, and it's important to understand we are a department that is data-informed and not data-driven. But it is my opinion that if such systems are used in this way, it can have many positive benefits."

Rangers manager Steven Gerrard has praised the club's fitness and sports science department, saying in December that the team were enabling his players to "hit top numbers," and Milson says that the AI data is helping to inform player rotation, even to the extent of highlighting which players should be substituted during games.

"All of our GPS and heart rate training load data from sessions and games is uploaded automatically into the Zone7 system," Milsom said. "The platform digests this, performs its modelling and provides us with risk alerts each day for players.

"Generally, there would be 1-2 players who may be flagged [for further monitoring]. Sometimes, these flags relate to overload -- other times it's under-load. This allows us to have a deeper dive into why specifically they are at risk. This information will feed into our general staff discussions to determine if any further areas support this information. As we typically compete every 3-4 days, if risk is associated with overload, I can often use that information to help support in-game substitutions as a means of maximising player availability, whilst potentially reducing risk through reduced minutes if and when possible."

The key to the success of the AI tool is the amount of data Zone7 are able to upload and analyse. While Brown stresses that "nobody ever sees your data. We don't own it and we're not allowed to retain a copy of it, post-relationship, so it's very strict," the volume of information provided by each client club is used to create a huge database that then enables the programme to predict injury risk.

"We can use 200 million hours of football data because we are working with 50-60 clients," Brown said. "As a result, we have 50-60 times more data than a typical team has, so the data set is very large. But what is important is that it's not just the injury in the sense of the date it occurred and what happened, it is every single day of training and games and medical data leading to the injury, going back as much as a year prior.

"That amount of information gives us the ability to look at the daily data leading to an incident and, using AI and deep learning, to find patterns that repeat themselves before hamstring injuries or groin injuries or knee injuries happen. That's how it works.

"If you are trying to forecast an event, which is an injury, you need to have a big database of incidents. A typical team would have something like 30-40 incidents a year for a squad, so multiply that by several years of historical data."

1:17

The Gab and Juls show analyse Liverpool's loss to Brighton and look forward to their next game against Man City.

ESPN has spoken to people in sports science who believe that AI is a positive innovation if used alongside existing methods. "Their results are impressive," said one sports scientist, who has worked with several Premier League clubs in the past and spoke on condition of not being named. "The issue is the level of individualisation with injury results is high, so lots of variant data only gives you a small answer. Therefore, it definitely has to be a blended approach."

Zone7's AI tool is not restricted to sports. In tandem with Garmin wearable devices and Zone7, medical staff in Israel are having their health and well-being monitored during the COVID-19 pandemic and there is a similar project with a major hospital in New York City. There are also projects ongoing with military and special forces. In football, however, Getafe are the best example of AI being used successfully to improve the fitness record of a team, as explained by head of performance Vidal.

"It would take 200 people all day to analyse the data, but with this, I get the recommendations within minutes." Vidal said. "We use our own high-quality ultrasound to clinically to evaluate players that show predefined risk indications. After starting to use Zone7, some players would report feeling fine despite the engine identifying immediate risk for them.

"In many cases, our ultrasound tests confirmed muscular damage, allowing us to address this before the injury occurred. These players could have sustained injury but for the AI detection."

Cruyff, now coaching in China with Shenzhen FC, believes AI can become a key component for teams, but he makes clear that AI alone cannot be regarded as the silver bullet to prevent all injuries.

"It's not a deciding tool," he said. "You can see a risk of injury and decide to take the risk or not. It's part of the modernisation of sport. You have so many things -- video analysts, GPS tracking devices -- and I think this is a part that maybe we missed, but it is coming, little by little."

Read more from the original source:

Soccer looks to AI for an edge: Could an algorithm really predict injuries? - ESPN

Serbia and international players leverage Data and AI to become vaccination leaders

2021 is a defining year in humanity, where government efforts towards vaccination will determine how we live our lives in the coming years. All over the world, countries have been working tirelessly on the distribution and administration of vaccines, attempting to halt the effects of ongoing mutations straight in their tracks. However, some nations have emerged as clear winners in this effort over others. By leveraging the power of advanced technologies and artificial intelligence, nations like Serbia, Greece, the UAE and Israel have been able to set up an optimized and effective vaccine distribution framework. We will take a deep dive into what each of these countries is doing correctly, and identify key takeaways as to what laggard countries can do to replicate their success.

Serbia provides a perfect case study. Citizens in Serbia have many options. They can choose if they want to get vaccinated via an approval mechanism, choose which vaccines they want to get and in which location they want to get vaccinated. I became very intrigued with Serbias solution and reached out to key stakeholders for their comments. According to Vukain Grozdi, Advisor to the Prime Minister, The vaccination campaign in Serbia was carefully planned and prepared in advance - even before we received the first shipment in December 2020. Since the speed of the immunization was critical, we decided to reengineer the entire process and to heavily rely on technology. The software we developed provides for speeding up each phase of the process by three times, in the same time taking in consideration our citizens preferences. In fact, Serbia is the only country in the world where citizens can choose the vaccine type.

Indeed, according to euronews, Serbia has the second fastest vaccine rollout in Europe, Furthermore, prime Minister of Serbia, Ana Brnabi stated, We opted for this unique approach in order to give our citizens the freedom to choose - as a means to increase the public trust and therefore boost the immunization rate... This solution is in fact a result of the extensive focus we put on digitalization for the past four years. Digital government and digital education, along with digitalization of the economy are the core of our mandate.

Immunization is now available across Serbia at 410 vaccination sites in almost all cities. In Belgrade, vaccination is performed at 18 sites, one of which is at the Belgrade Fair, where there are 50 vaccination booths with over 8,000 citizens can be vaccinated on a daily basis. Moreover, according to N1, The Financial Times ranked Serbia seventh globally and second in Europe on its list of countries with the most people inoculated against the coronavirus globally. Serbia has administered 4.5 doses of vaccines from different manufacturers per 100 people or about 300,000 doses overall as of January 26. That ranks it second on the list of European countries and seventh globally. Adding to this fact is Peter Janji, Deputy Secretary General of the Government. Upon consultation with these countries, it became clear to us that success could only be achieved using the right organization assisted by the right technology. Our team led by the Prime Minister secured a flawless organization and strong coordination across many different stakeholders. We then sketched a supporting technological solution to manage the immunization process. Developed only in weeks by our local teams, the information system provided for seamless automation of the process.

Moreover, The Government has also implemented a new modern vaccine delivery and scheduling platform, called the System for immunization management of the Republic of Serbia. According to Verica Jovanovi, Director of Serbias National Health Institute As an institution with decades of experience in immunizing the population, for the first time, we have a tool by which we can monitor both the epidemics and immunization of each citizen in real-time.

The internet based digital platform gives access to citizens and call center operators ( registration interest, notification systems and inquires management), medical staff and assisting volunteers (immunization registration, issuance of vaccination certificates), supply chain and warehouse workers (procurement and distribution of vaccines), as well as for management (orchestration, management of vaccination sites and medical staff, monitoring and reporting). Hundreds of primary healthcare centers, mass-vaccination sites at the Belgrade Fair, the National Institute of Public Health along with its regional branches, several government agencies, the National e-Government Portal, and several call centers facilitating information sharing, interoperability and governance are all integrated in the system. Citizens complete a simple electronic form available on the National e-Government Portal, or contact call centers for the operators to register their interest in the software. A complex AI algorithm automatically schedules the appointments a few days in advance for every particular citizen checking the desired vaccine type against the age eligibility, profession (priority groups), health conditions and available time slots at the vaccination sites.

This platform provides a competitive advantage in leveraging artificial intelligence algorithms. Citizens can swiftly register for immunizations through this management system, which provides notifications in real-time about progress, tracking and vaccination schedules. All citizens have access to the National eGovernment Portal, meaning that no section of the population is left in the dark. Aside from facilitating and expediting vaccination, the solution provides real-time monitoring and allows for informed analytics on critical aspects of the operation, such as data on general population interest in immunization and actual consumption. This helps decide if and where to boost public information campaigns, whether to procure additional doses etc. It also analyzes the number of vaccinated citizens according to age, profession and location, and helps impose new or relax the existing epidemiological measures. In addition, monitoring the distribution chain (including the cold-chain) end-to-end through the entirety of Serbias warehouses and vaccination sites, transportation equipment, and storage capacities is critically important for optimization of the distribution process.

Not only does it seem that the government is pleased, but also Serbian citizens across social media are expressing satisfaction with this digital government initiative and overall experience. By implementing an efficient digital national framework that makes use of AI, Serbia has fast-tracked its vaccine rollout at an impressive rate that continues to climb.

In addition to Serbia, the UAE has also implemented a variety of data and AI-driven solutions for vaccination. As per Business Standard, The UAE is one of the quickest nations in the world for vaccine rollout. Dubai is planning to immunize 70% of its population by the end of 2021, as per Reuters. Thanks to AI, such a plan is fully achievable. According to Trends Research, The UAEs utilization of AI technologies against the Covid-19 pandemic has been focused on the collection of accurate information to ensure that preventative and safety measures are efficient and successful. Some examples of the UAEs use of AI to fight Covid-19 include the launching of official Covid-19 testing and tracing app called Alhosn by the Ministry of Health and Prevention, which gives fast access to test results and contract tracing for accurate control of the virus, as an AI-based tool that has proved to be a secure medium for patients private information. In addition, Abu Dhabi has also used programmed robots to spray disinfected areas as part of its sterilization program. In Dubai, the ambulance service has rolled out a self-sanitization device that allows paramedics and their families to sterilize clothing through a sanitization corridor, which is an AI-driven tool used to disinfect clothes of paramedics and their families. Clearly, the data driven and AI fueled approach to vaccination has enabled the UAE to reach the top in terms of combatting Covid.

Greece is also an up-and-comer that is winning the vaccine rollout game. According to an article in Entrepreneur Magazine, Two data scientists built a machine learning system named Eva to help Greece safely reopen to nearly 80,000 tourists a day. The system, known as Eva, is nearly twice as efficient at detecting cases as random testing and it can predict spikes in other countries ten days before they show up in official case counts. Thanks to robust data stores, the Machine Learning algorithm employed by Eva is able to forecast the risk of spread and infection of Covid in advance, enabling the government to plan more efficiently, locate at-risk populations at a faster pace, and optimize the nations immunization protocols. According to the Greek Governments data-driven and forward-thinking approach, the country has been able to mitigate Covid and allow for more effective vaccine distribution amongst its citizens.

Lastly, Israel (the worlds startup nation) has also seen large success in its vaccine distribution. Israel has delivered five million doses of the vaccine to a population of about nine million - and about one million people have received two doses, according to BBC. Israel has already been recognized as the most organized and efficient health system in the world because of its experience in national emergencies coupled with first-class medical hospitals and R&D. This is due to a number of reasons, all related to the smart use of data and AI. One of the most important aspects of an efficient AI system is a robust set of data. Due to the way that the Israeli healthcare system operates, the nation has access to enormous quantities of medical records. Israels health system has been structured to streamline the central collection of real-time clinical information from all its citizens, creating one of the worlds largest medical data sets. This way, the nation became a world center for the development of digital and AI-personalized healthcare systems, and especially in the creation of efficient structures to fight Covid.

Best Practices on Vaccine Leadership

Based on the recent successes of vaccination in Serbia, the UAE, Greece and Israel, we have learned the following:

There are a limited number of best practices around the world. However, these learnings provide us a glimpse of what it takes for a nation to implement a successful vaccination campaign. This includes agility and organizational efficiency. The governments listed here stepped up to address humanity challenges and demonstrated the power of digital and AI technologies.

In Serbia, for example, many of these lessons will be embedded into the countrysNational Digital Health strategy. As per Mihailo Jovanovi, CEO of the Office for the IT and eGovernment of Serbia, Important lessons in agility were acquired since March 2020, but also the advanced govtech ecosystem built under the vision and leadership of the Prime Minister Ana Brnabi was crucial for the success of this operation. The solutions effectiveness and sophistication exceeded even our own expectations - the minute the vaccine touches the Serbian ground we know exactly which citizen is going to take it and we immediately notify him/her to show up at the chosen vaccination site. Everything is done in less than 48 hours!

Clearly, we can no longer afford to take the power of artificial intelligence for granted. In these times where we are dealing with assaults of multiple Covid variants on all fronts, the only solution that can fight covid faster than it could mutate is artificial intelligence.

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Serbia And Key International Sovereigns Lead With Data And AI To Become Vaccination Champions - Forbes

Wed all like to be able to look into a crystal ball and tell the future. With the use of AI and machine intelligence, we are now able to more accurately produce trend forecasts that accurately mimic reality. NWO.ai takes this one step further. The platformcombs through unstructured data to track 20M+ signals, adds a time dimension, eliminates noise, and prioritizes sources to identify microtrends and global cultural shifts. For example, the platform has recently considered and tracked possible geopolitical events and themes like will COVID-19 lead to war, will Bitcoin continue to rise, and the impact of the pandemic on Big Tobacco. The platform is already being used by several Fortune 500 companies across various industries to navigate these uncertain times.

AlleyWatch caught up with Cofounders Sourav Goswami and Pulkit Jaiswal to learn more about the companys recent launch out of stealth, future plans, and recent round of funding.

Who were your investors and how much did you raise?

We raised $3.5M in a seed round co-led by Hyperplane, Wavemaker, and Colle Capital with participation from Adit Ventures and SuperAngel.

Tell us about the product or service that NWO.ai offers.

Weve built the largest AI-enabled civilian database capturing the voice of the consumer. Using proprietary analysis of unstructured external data, were genetically sequencing the lifecycle of trends as theyre created and evolve.

What inspired the start of NWO.ai?

We started NWO originally to use disparate data sources to try to uncover geopolitical trends. As we curated and processed more data and began to incorporate proprietary methods of time-shifting data sources, we began to realize we were able to anticipate consumer and cultural shifts.

How is NWO.ai different?

Our platform goes beyond providing just descriptive statistics on the number of mentions of a keyword on a variety of data sources, but instead incorporates a time dimension and source prioritization component to determine not only the past performance of trends but also help predict future potential.

We have developed a novel cross-correlation, time-shifting algorithm that automatically weighs a number of data sources across various input streams and figures out the leading and lagging indicators, time-shifts the lagging ones and generates a composite signal.

What market does NWO.ai target and how big is it?

We are targeting the Total artificial intelligence market as a whole which is worth ~$40B according to Grand View Research, and expected to eclipse $700B by 2027. As a starting point, our first sights are set on the artificial intelligence application for supply chain and marketing use cases which is estimated at ~$8B, per Meticulous Research, and expected to cross $60B by 2027. Both these market estimates are growing fast as the focus on AI-powered decision making has become a key priority for businesses across almost all industries.

Whats your business model?

NWO.ai is a SaaS business that offers a paid monthly access to its web-interface platform. Clients pay a per-user monthly fee which is packaged into annual contracts.

How has COVID-19 impacted the business?

In todays fast-paced business environment, everything is evolving in a chain reaction of events. COVID-19 has radically accelerated a number of existing trends. There is no way to actively measure and anticipate cultural shifts.

Using the latest in machine learning and by tracking digital conversations on the internet, we enable monitoring key microtrends and issues that define our culture today.

In todays fast-paced business environment, everything is evolving in a chain reaction of events. COVID-19 has radically accelerated a number of existing trends. There is no way to actively measure and anticipate cultural shifts.

Using the latest in machine learning and by tracking digital conversations on the internet, we enable monitoring key microtrends and issues that define our culture today.

What was the funding process like?

Fundraising in the midst of a pandemic was definitely unique and nuanced. There were ironically more meetings, albeit via Zoom; and, since everyone was working from home effectively stretching available work hours, the product level diligence was very thorough with several follow-up Zooms. The reference checks and background checks took on additional importance, but it was also a great opportunity to assess how nimble and proactive our investors could be in a difficult environment. We are extremely proud and excited by the partners we made in this funding round.

What are the biggest challenges that you faced while raising capital?

Time. The pandemic stretched the process beyond what we had expected, and so we were in a race against the clock to ensure we concluded fundraising before we needed to make major capital commitments to business requirements.

What factors about your business led your investors to write the check?

I believe investors back entrepreneurs first, and the company thereafter. We were able to demonstrate our commitment and conviction to the investors over a period of 3 months. Moreover, we were able to engage in a partnership with SAP.io right before our funding, which I would imagine provided a great deal of comfort around our go-to-market strategy. Our biggest focus is to launch several POCs with the aim at converting them into annual contracts. We are targeting to showcase the value of our products within the larger consumer sector in businesses spanning from beauty brands to automotive manufacturers to insurance companies.

What advice can you offer companies in New York that do not have a fresh injection of capital in the bank?

Proof of concept with a beta customer who can provide feedback and credibility is of paramount importance. Until then, believe in your product and your team, and keep pushing ahead. Most importantly, when you do raise capital, make sure it is with great collaborators and partners.

Proof of concept with a beta customer who can provide feedback and credibility is of paramount importance. Until then, believe in your product and your team, and keep pushing ahead. Most importantly, when you do raise capital, make sure it is with great collaborators and partners.

Where do you see the company going now over the near term?

We are currently expanding our development team to increase the velocity of our product iteration process. We plan to continuously track and analyze our clients usage of the platform to define our short-term product roadmap, while also building toward our longer-term goals and company vision.

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NWO.ai Raises $3.5M to Predict Cultural Shifts Before They Happen Using AI - AlleyWatch

Amazon.com has revealed plans to install AI-powered video cameras in its branded delivery vans, in a move that the worlds largest e-commerce firm says would improve safety of both drivers and the communities in which they deliver.

The company recently started rolling out camera-based safety technology across its delivery fleet, it said in an emailed statement on Wednesday.

This technology will provide drivers real-time alerts to help them stay safe when they are on the road, the statement added.

The companys plans were earlier disclosed in an instructional video about the cameras, reported earlier in the day by technology publication the Information. (https://bit.ly/2MPF68U)

Amazon said in the video that the cameras, developed by transportation technology company Netradyne, use artificial intelligence (AI) to provide warnings about speeding and distracted driving among other things.

They have been shown to reduce collisions and improve driver behavior, Amazons Karolina Haraldsdottir, a senior manager for last-mile safety, said in the video.

Amazon has come under some scrutiny in the past for accidents involving delivery drivers.

Our intention with this technology is to set up drivers for success and provide them with support for being safer on road and handling incidents if and when they happen, Haraldsdottir said in the video.

The video explains that while the cameras will constantly record video, they only upload footage if triggered by actions like hard braking, driver drowsiness, following vehicles too closely.

(Reporting by Vishwadha Chander in Bengaluru; Editing by Rashmi Aich)

Topics Insurtech

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Amazon Plans Cameras with AI in Its Delivery Vans to Improve Driver Safety - Insurance Journal

The COVID-19 pandemic has affected people's behavior everywhere. Fear, apprehensiveness, sadness, anxiety, and other troublesome feelings have become part of the daily lives of many families since the first cases of the disease were officially recorded early last year.

These turbulent feelings are often expressed in dreams reflecting a heavier burden of mental suffering, fear of contamination, stress caused by social distancing, and lack of physical contact with others. In addition, dream narratives in the period include a larger proportion of terms relating to cleanliness and contamination, as well as anger and sadness.

All this is reported in astudy publishedinPLOS ONE. The principal investigator was Natlia Bezerra Mota, a neuroscientist and postdoctoral fellow at the Brain Institute of the Federal University of Rio Grande do Norte (UFRN), in Brazil.

The study was part of Mota's postdoctoral research and was supervised by Sidarta Ribeiro at UFRN and Mauro Copelli at the Federal University of Pernambuco (UFPE), both of whom are affiliated with the Neuromathematics Research, Innovation and Dissemination Center (NeuroMat).

Neuromat is hosted by the University of So Paulo (USP) and is one of many Research, Innovation and Dissemination Centers (RIDCs) supported by So Paulo Research Foundation - FAPESP.

The results are consistent with the hypothesis that dreams reflect the challenges of waking-life experience during the pandemic, and that the prevalence of negative emotions such as anger and sadness during the period reflects a higher emotional load to be processed, the authors write.

According to Mota, the findings are corroborated by those of other studies published later by researchers in the United States, Germany, and Finland.

The Brazilian study was initially reported in May in apreprint posted to medRxiv, and not yet peer-reviewed at that time (read more at:agencia.fapesp.br/33664). "It's the first study on the subject to look empirically at these signs of mental suffering and their association with the peculiarities of dreams during the pandemic," Mota told.

For Ribeiro, the authors of the study managed to document the continuity between what happens in the dream world and people's mental lives, especially psychological distress. "This is interesting from the standpoint of dream theory," he said. "Another point worth highlighting is that they did so quantitatively, using mathematics to extract semantics."

The group deployed natural language processing tools to analyze 239 dream reports by 67 subjects produced in March and April 2020, shortly after the World Health Organization (WHO) declared a pandemic.

According to Mota, researchers at USP, UFRN, and the Federal Universities of Minas Gerais (UFMG), Rio Grande do Sul (UFRGS) and Rio de Janeiro (UFRJ) are conducting a multicentric study involving the analysis of data collected during a longer period (from the start of the pandemic through July) to see how dreams are affected by the deaths of family members, loved ones, friends and co-workers. "The plan is to publish the findings as soon as they're ready so that mental health strategies can be based on this knowledge," she said.

Dream accounts recorded by the volunteers using a smartphone app were transcribed and analyzed using three software tools. The first focused on discourse structure, word count, and connectedness.

The other two focused on content. One ranged words in certain emotional categories against a list associated with positive and negative emotions. The other used a neural network to detect semantic similarity to specified keywords, such as contamination, cleanliness, sickness, health, death and life.

In theirPLOS ONEpublication, the researchers say "the significant similarity to 'cleanness' in dream reports points towards new social strategies (e.g. use of masks, avoidance of physical contact) and new hygiene practices (e.g. use of hand sanitizer and other cleaning products) that have become central to new social rules and behavior. Taken together, these findings seem to show that dream contents reflect the different sources of fear and frustration arising out of the current scenario".

Mota noted that more suffering was expressed in the dream reports submitted by female volunteers, although this was detected indirectly. "There are studies on gender difference in the literature. Women report more negative dreams and nightmares. I think this has to do with women's history and daily lives, with working a double or triple shift, and the heavier mental burden entailed by concerning themselves with a job plus the home and children. The pandemic has made this worse," she said.

Reference: Mota NB, Weissheimer J, Ribeiro M, et al. Dreaming during the Covid-19 pandemic: Computational assessment of dream reports reveals mental suffering related to fear of contagion. PLOS ONE. 2020;15(11):e0242903. doi:10.1371/journal.pone.0242903

This article has been republished from the following materials. Note: material may have been edited for length and content. For further information, please contact the cited source.

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AI Techniques Analyze Dream Content During the Pandemic - Technology Networks