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DrChrono, a digital health company that works with EHRs, is teaming up with Cognition IP to help it draft five patent applications. The new patents will primarily be focused on machine learning technology. The pair are also going to be working on pushing through two patents that have been stalled for years.

It was crucial to work with a partner that had a deep understanding of healthcare and machine learning,Daniel Kivatinos, cofounder and COO of DrChrono, said in a statement. We wanted to ensure that our intellectual property was patented and protected and Cognition IP helped us do just that by successfully expanding our portfolio.Investing in machine learning is critical to the future of ourhealthcare platform used by thousands of medical practices.

Virtual care company Pager announced that it is launching in South America. It inked a deal with insurer Seguros SURA in Colombia. As part of the deal, Pager will provide its members with a virtual care team.Patients will be able to chat, call and video chat their doctor or care team. The services include triage, telemedicine, appointment setups, transportationand follow up care. The service is already available in all 50 US states but this deal marks the first international launch for the company.

Convenient and cost-effective access to healthcare is a global issue Walter Jin, chairman and CEO of Pager, said in a statement."The partnership between Seguros SURA Colombias efficient health clinics and Pagers technology platform will showcase the next generation model for the future of healthcare. Pager and Seguros Sura Colombia collaboration shows that our consumer-first approach to healthcare transcends geographical borders.

Yale School of Medicine is teaming up with Foretell Reality in an effort to measure the effects of VR therapy on cancer patients. Specifically, the group is looking at the levels of anxiety and depression in cancer patients.

A major factor in this study is the convenience for patients to enter a VR-based chat room, something that is especially useful for people with rare diseases or those who live in rural areas Dr. Asher Marks, sssistant professor of pediatrics (Hematology / Oncology) and director of pediatric Neuro-Oncology, said in a statement. The VR technology offered by Foretell Reality allows users to jointly partake as avatars in a shared experience which cannot be replicated over a conference call or video chat. Additionally, patients in the study are presented the option to remain anonymous during the VR group session, giving them a unique opportunity to communicate with others in a way they may otherwise not be comfortable with.

UPMC Health Plan will be employing VirtualHealths HELIOS care management and coordination platform for one of its programs focused on long-term services and supports for patients eligible for both Medicare and Medicaid, the companies announced. The tool automates caseload assignments and task management for care managers, while also assisting with assessment, planning and authorization.

Forward-looking health plans understand that HELIOS will position them for success through a true whole-person, member-centered approach to care management, Adam Sabloff, CEO and founder of VirtualHealth, said in a statement. We are honored that UPMC Health Plan plans to utilize HELIOS to enhance its members care and experiences in its Community HealthChoices program.

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DrChrono teams up with Cognition IP on machine learning patents, insurer deal brings Pager to South America and more digital health deals -...

Graduate students Aryan Deshwal and Syrine Belakaria presenting at the NeurIPS conference.

Many researchers in artificial intelligence and machine learning aim to develop computer programs that can sift through huge amounts of data, learn from it, and guide future decisions.

But, what if the many data options are hugely expensive and difficult to acquire and one has to decide which data is best to spend your money on? What direction should scientists head with their next experiment?

A WSU research team is taking a different angle in machine learning research in what they say can be of great practical use, especially, to engineers and scientists.

Computer science graduate students Syrine Belakaria and Aryan Deshwal recently presented their research at major international artificial intelligence and machine learning conferences, including the 2019 Conference on Neural Information Processing Systems (NeurIPs) in Vancouver, Canada and the 2020 Association for the Advancement of Artificial Intelligence Conference in New York. The NeurIPS conference is the premier machine learning conference in the world with more than 14,000 attendees. Belakaria and Dewshwal are advised by Jana Doppa, the George and Joan Berry Chair Assistant Professor in WSUs School of Electrical Engineering and Computer Science.

The group will also present their collaborative work with computer engineering researchers at the Design, Automation and Test in Europe Conference (DATE-2020) in Grenoble, France.

The groups research is based on Doppas 2019 NSF Early Career Award and focuses on developing general-purpose learning and reasoning computer algorithms to support engineers and scientists in their efforts to optimize the way they conduct complex experiments. They are working to combine domain knowledge from engineers and scientists with data from past experiments to select future experiments, so that researchers can minimize the number of experiments needed to find near-optimal designs.

Doppas team has analyzed and experimentally evaluated the algorithms for diverse applications in electronic design automation, such as for analog circuit design, manycore chip design, or tuning compiler settings, and in materials science, such as for designing shape memory alloys and piezo-electric materials. They also proposed two algorithms to optimize multiple objectives with minimal experiments and have developed the first theoretical analysis for multi-objective setting. They also developed a novel learning to search framework to optimize combinatorial structures, which is very challenging when compared to continuous design spaces.

The common theme behind this work is better uncertainty management to select the sequence of experiments, Doppa said.

While Belakaria and Deshwal have contributed important research innovations in the field, they also have gained valuable learning opportunities during their studies. While at NeurIPS, the students had the chance to network with leaders in the field of machine learning as well as attend a special session for women and those who are underrepresented in the field. The session had more than one thousand attendees.

The conference gave the students a chance to see the real-world applications of AI, said Deshwal, and meeting professionals who are using machine learning to solve challenges in medical and science fields. A large number of prominent companies, such as Uber, Google, Facebook, and Amazon, sent representatives to the conference and hosted events.

Belakaria, who is originally from Tunisia, said it was amazing to attend a roundtable and sit down with female leaders in the explosive and competitive field. She appreciated getting advice on how many women in the field are finding success while balancing their work and personal lives.

Both she and Deshwal expressed appreciation for a supportive lab that has provided mentoring and has encouraged their growth and success.

Research is as emotional as it is academic, and our camaraderie helps us a lot, said Deshwal, originally from India. When you have a field that is moving so quickly such as machine learning, having people who are supportive is so important.

Being in a community where you feel safe, respected, and valued for your scientific contribution is very crucial for women in science. We feel welcome in the machine learning community, added Belakaria.

With WSU since 2014, Doppa is part of a major expansion on the part of the university to meet the growing demands in the fields of electrical engineering and computer science. Since 2015, research expenditures in WSUs School of Electrical Engineering and Computer Science have nearly doubled, as have the number of graduates from its computer science program.

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Using machine learning to solve real-world data problems for scientists and engineers - WSU News

Vertiv EMEA president Giordano Albertazzi looks back on data center expansion in the Nordics and the regions role as an efficient best execution venue for the future.

At the start of the new year its natural to look to thefuture. But its also worth taking some time to think back to the past.

Last year was not only another period of strong data center growthglobally, and in the Nordic region specifically, but also the end of a decadeof sustained digital transformation.

There have been dramatic shifts over the last ten years butthe growth in hyperscale facilities is one of the most defining and one withwhich the Nordic region is very well acquainted.

According to figures from industry analysts Synergy Researchthe total number of hyperscale sites has tripled since 2013 and there are nowmore than 500 such facilities worldwide

And it seems that growth shows no signs of abating. Accordingto Synergy, in addition to the 504 current hyperscale data centers, a further151 that are at various stages of planning or building.

A good numberof those sites will be sited in the Nordics if recent history is anything to goby. The region has already seen significant investment from cloud andhyperscale operators such as Facebook, AWS and Apple. Google was also one ofthe early entrants and invested $800 million inits Hamina, Finland facility in 2010. It recently announced plans to invest a further $600 million in an expansion ofthat site.

I was lucky enough to speak at the recent DataCloud Nordicsevent at the end of last year. My presentation preceded Googles country manager,Google Cloud, Denmark and Finland, Peter Harden, who described the companysgrowth plans for the region. Hamina, Finland is one of Googles mostsustainable facilities thanks in no small part to its Nordics location whichenables 100% renewable energy and innovative sea water cooling.

Continuing that theme of sustainability, if the last decadehas been about keeping pace with data demand, then the next ten years will beabout continued expansion but importantly efficient growth in the right locations,using the right technology and infrastructure. The scale of growth beingpredicted billions of new edge devices for example will necessitate asustainable approach.

That future we at Vertiv, and others, believe will be basedaround putting workloads where they make most sense from a cost, risk, latency,security and efficiency perspective. Or as industry analysts 451 Research putsit: TheBest Execution Venue (BEV). (a slightlyunwieldy term but an accurate one). BEV refers to the specific ITinfrastructure an app or workload should run on cloud, on-premise or at theedge for example but could also equally apply to geographic location of datacenters.

In that BEV future, the Nordics will become increasingly important for hosting a variety of workloads but the sweet-spot could be those that are less latency sensitive high performance compute (HPC) for example and can therefore benefit from the stable, renewable and cheap power as well as the abundance of free cooling. Several new sub-sea cables coming online over the near future will also address some of the connectivity issues the region has faced.

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A recent study by the Nordic Council of Ministers estimatesthat approximately EUR 2.2 bn. have been invested in the Nordics on initiateddata centre construction works over the last 12 to 18 months (2018). Mainlywithin hyperscale and cloud infrastructure. This number could exceed EUR 4 bn.annually within the next five to seven years because of increasing marketdemand and a pipeline of planned future projects.

Vertiv recently conducted some forward-looking research thatappears to reinforce the Nordics future potential. Vertiv first conducted itsData Center 2025 research back in 2014 to understand where the industry thoughtit was headed. In 2019, weupdated that study to find out how attitudes had shifted in the interveningfive years a half way point if you will be between 2014 and 2025.

The survey of more than 800 data center experts covers a range of technology areas but lets focus on a few that are important and relevant to the Nordics.

We mentioned the edge a little earlier when talking about BEV.Vertiv has identified fourkey edge archetypes that cover the edge use cases that our experts believewill drive edge deployments in the future. According to the 2025 research, ofthose participants who have edge sites today, or expect to have edge sites in2025, 53% expect the number of edge sites they support to grow by at least 100%with 20% expecting an increase of 400% or more.

So along with providing a great venue for future colo and cloud growth, the Nordics, like other regions, is also likely to see strong edge growth. That edge demand will require not only new data center form-factors such as prefabricated modular (PFM) data center designs but also monitoring and management software and specialist services.

Another challenge around edge compute, and the core for thatmatter, is energy availability and increasingly, access to clean, renewableenergy.

The results of the 2025 research revealed that respondentsare perhaps more realistic and pragmatic about the importance and access toclean power than back in 2014. Participants in the original survey projected22% of data center power would come from solar and an additional 12% from windby 2025. Thats a little more than one-third of data center power from thesetwo renewable sources, which seemed like an unrealistic projection at the time.

This years numbers for solar and wind (13% and 8% respectively) seem more realistic. However, importantly for Nordics countries with an abundance of hydropower, participants in this years survey expect hydro to be the largest energy source for data centers in 2025.

The data center 2025 research, also looked at one of theother big drivers for building capacity in the Nordics: access to efficientcooling.

According to the 2025 survey, around 42% of respondentsexpect future cooling requirements to be met by mechanical cooling systems. Liquidcooling and outside air also saw growth from 20% in 2014 to 22% in 2019, likelydriven by the more extreme rack densities being observed today. This growth inthe use of outside air obviously benefits temperate locations like the Nordics.

In summary, if the last ten years have been about simplykeeping up with data center demand, the next ten years will be about addingpurposeful capacity in the most efficient, sustainable and cost-effective way:the right data center type, thermal and power equipment, and location for theright workloads.

If the past is anything to go by, the Nordics will have an important role to play in that future.

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Artificial intelligence and machine learning for data centres and edge computing to feature at Datacloud Congress 2020 in Monaco - Data Economy

AI and machine learning (ML) applications have been at the centre of several high-profile controversies, witness the recent Apple Cardcredit limit differencesand Amazonsrecruitment toolbias.

Mind Foundry has been a pioneer in the development and use of humble and honest algorithms from the very beginning of its applications development. As Davide Zilli, Client Services Director atMind Foundryexplains, baked in transparency and explainability will be vital in winning the fight against biased algorithms and inspiring greater trust in AI and ML solutions.

Today in so many industries, from manufacturing and life sciences to financial services and retail, we rely on algorithms to conduct large-scale machine learning analysis. They are hugely effective for problem-solving and beneficial for augmenting human expertise within an organisation. But they are now under the spotlight for many reasons and regulation is on the horizon, withGartner projectingfour of the G7 countries will establish dedicated associations to oversee AI and ML design by 2023. It remains vital that we understand their reasoning and decision-making process at every step.

Algorithms need to be fully transparent in their decisions, easily validated and monitored by a human expert. Machine learning tools must introduce this full accountability to evolve beyond unexplainable black box solutions and eliminate the easy excuse of the algorithm made me do it!

Bias can be introduced into the machine learning process as early as the initial data upload and review stages. There are hundreds of parameters to take into consideration during data preparation, so it can often be difficult to strike a balance between removing bias and retaining useful data.

Gender for example might be a useful parameter when looking to identify specific disease risks or health threats, but using gender in many other scenarios is completely unacceptable if it risks introducing bias and, in turn, discrimination. Machine learning models will inevitably exploit any parameters such as gender in data sets they have access to, so it is vital for users to understand the steps taken for a model to reach a specific conclusion.

Removing the complexity of the data science procedure will help users discover and address bias faster and better understand the expected accuracy and outcomes of deploying a particular model.

Machine learning tools with built-in explainability allow users to demonstrate the reasoning behind applying ML to a tackle a specific problem, and ultimately justify the outcome. First steps towards this explainability would be features in the ML tool to enable the visual inspection of data with the platform alerting users to potential bias during preparation and metrics on model accuracy and health, including the ability to visualise what the model is doing.

Beyond this, ML platforms can take transparency further by introducing full user visibility, tracking each step through a consistent audit trail. This records how and when data sets have been imported, prepared and manipulated during the data science process. It also helps ensure compliance with national and industry regulations such as the European Unions GDPRright to explanation clause and helps effectively demonstrate transparency to consumers.

There is a further advantage here of allowing users to quickly replicate the same preparation and deployment steps, guaranteeing the same results from the same data particularly vital for achieving time efficiencies on repetitive tasks. We find for example in the Life Sciences sector, users are particularly keen on replicability and visibility for ML where it becomes an important facility in areas such as clinical trials and drug discovery.

There are so many different model types that it can be a challenge to select and deploy the best model for a task. Deep neural network models, for example, are inherently less transparent than probabilistic methods, which typically operate in a more honest and transparent manner.

Heres where many machine learning tools fall short. Theyre fully automated with no opportunity to review and select the most appropriate model. This may help users rapidly prepare data and deploy a machine learning model, but it provides little to no prospect of visual inspection to identify data and model issues.

An effective ML platform must be able to help identify and advise on resolving possible bias in a model during the preparation stage, and provide support through to creation where it will visualise what the chosen model is doing and provide accuracy metrics and then on to deployment, where it will evaluate model certainty and provide alerts when a model requires retraining.

Building greater visibility into data preparation and model deployment, we should look towards ML platforms that incorporate testing features, where users can test a new data set and receive best scores of the model performance. This helps identify bias and make changes to the model accordingly.

During model deployment, the most effective platforms will also extract extra features from data that are otherwise difficult to identify and help the user understand what is going on with the data at a granular level, beyond the most obvious insights.

The end goal is to put power directly into the hands of the users, enabling them to actively explore, visualise and manipulate data at each step, rather than simply delegating to an ML tool and risking the introduction of bias.

The introduction of explainability and enhanced governance into ML platforms is an important step towards ethical machine learning deployments, but we can and should go further.

Researchers and solution vendors hold a responsibility as ML educators to inform users of the use and abuses of bias in machine learning. We need to encourage businesses in this field to set up dedicated education programmes on machine learning including specific modules that cover ethics and bias, explaining how users can identify and in turn tackle or outright avoid the dangers.

Raising awareness in this manner will be a key step towards establishing trust for AI and ML in sensitive deployments such as medical diagnoses, financial decision-making and criminal sentencing.

AI and machine learning offer truly limitless potential to transform the way we work, learn and tackle problems across a range of industriesbut ensuring these operations are conducted in an open and unbiased manner is paramount to winning and retaining both consumer and corporate trust in these applications.

The end goal is truly humble, honest algorithms that work for us and enable us to make unbiased, categorical predictions and consistently provide context, explainability and accuracy insights.

Recent research shows that84% of CEOsagree that AI-based decisions must be explainable in order to be trusted. The time is ripe to embrace AI and ML solutions with baked in transparency.

Featured image: MaZi

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Keeping machine learning algorithms humble and honest in the 'ethics-first' era - TechNative