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Category Archives: Machine Learning
Amwell CMO: Google partnership will focus on AI, machine learning to expand into new markets – FierceHealthcare
Amwell is looking to evolve virtual care beyond just imitating in-person care.
To do that, the telehealth companyexpects to use its latestpartnership with Google Cloud toenable it to tap into artificial intelligence and machine learning technologies to create a better healthcare experience, according to Peter Antall, M.D., Amwell's chief medical officer.
"We have a shared vision to advance universal access to care thats cost-effective. We have a shared vision to expand beyond our borders to look at other markets. Ultimately, its a strategic technology collaboration that were most interested in," Antall said of the company's partnership with the tech giant during a STATvirtual event Tuesday.
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"What we bring to the table is that we can help provide applications for those technologiesthat will have meaningful effects on consumers and providers," he said.
The use of AI and machine learning can improve bot-based interactions or decision support for providers, he said. The two companies also want to explore the use of natural language processing and automated translation to provide more "value to clients and consumers," he said.
Joining a rush of healthcare technology IPOs in 2020, Amwell went public in August, raising$742 million. Google Cloud and Amwell also announced amultiyear strategic partnership aimed at expanding access to virtual care, accompanied by a$100 million investmentfrom Google.
During an HLTH virtual event earlier this month, Google Cloud director of healthcare solutions Aashima Gupta said cloud and artificial intelligence will "revolutionize telemedicine as we know it."
RELATED:Amwell files to go public with $100M boost from Google
"There's a collective realization in the industry that the future will not look like the past," said Gupta during the HTLH panel.
During the STAT event, Antall said Amwellis putting a big focus onvirtual primary care, which has become an area of interest for health plans and employers.
"It seems to be the next big frontier. Weve been working on it for three years, and were very excited. So much of healthcare is ongoing chronic conditions and so much of the healthcare spend is taking care ofchronic conditionsandtaking care of those conditions in the right care setting and not in the emergency department," he said.
The companyworks with 55 health plans, which support over 36,000 employers and collectively represent more than 80million covered lives, as well as 150 of the nations largest health systems. To date, Amwell says it has powered over 5.6million telehealth visits for its clients, including more than 2.9million in the six months ended June 30, 2020.
Amwell is interested in interacting with patients beyond telehealth visits through what Antall called "nudges" and synchronous communication to encouragecompliance with healthy behaviors, he said.
RELATED:Amwell CEOs on the telehealth boom and why it will 'democratize' healthcare
It's an area where Livongo, recently acquired by Amwell competitor Teladoc,has become the category leader by using digital health tools to help with chronic condition management.
"Were moving into similar areas, but doing it in a slightly different matter interms of how we address ongoing continuity of care and how we address certain disease states and overall wellness," Antallsaid, in reference to Livongo's capabilities.
The telehealth company also wants to expand into home healthcare through the integration of telehealth and remote care devices.
Virtual care companies have been actively pursuing deals to build out their service and product lines as the use of telehealth soars. To this end, Amwell recently deepened its relationship with remote device company Tyto Care. Through the partnership, the TytoHome handheld examination device that allows patients to exam their heart, lungs, skin, ears, abdomen, and throat at home, is nowpaired withAmwells telehealth platform.
Looking forward, there is the potential for patients to getlab testing, diagnostic testing, and virtual visits with physicians all at home, Antall said.
"I think were going to see a real revolution in terms ofhow much more we can do in the home going forward," he said.
RELATED:Amwell's stock jumps on speculation of potential UnitedHealth deal: media report
Amwell also is exploring the use of televisions in the home to interact with patients, he said.
"We've done work with some partners and we're working toward a future where, if it's easier for you to click your remote and initiate a telehealth visit that way, thats one option. In some populations, particularly the elderly, a TV could serve as a remote patient device where a doctor or nurse could proactively 'ring the doorbell' on the TV and askto check on the patient," Antall said.
"Its video technology that'salready there in most homes, you just need a camera to go with it and a little bit of software.Its one part of our strategy to be available for the whole spectrum of care and be able to interact in a variety of ways," he said.
Microsoft Introduces Lobe: A Free Machine Learning Application That Allows You To Create AI Models Without Coding – MarkTechPost
Microsoft has releasedLobe, a free desktop application that lets Windows and Mac users create customized AI models without writing any code. Several customers are already using the app for tracking tourist activity around coral reefs, the company said.
Lobeis available on Windows and Mac as a desktop app. Presently it only supports image classification by categorizing the image to a single label overall. Microsoft says that there will be new releases supporting other neural networks in the near future.
To create an AI in Lobe, a user first needs to import a collection of images. These images are used as a dataset to train the application. Lobe analyzes the input images and sifts through a built-in library of neural network architectures to find the most suitable model for processing the dataset. Then it trains the model on the provided data, creating an AI model optimized to scan images for the users specific object or action.
AutoML is a technology that can automate parts and most of the machine learning creation workflow, reducing the advancement costs. Microsoft has made AutoML features available to enterprises in its Azure public cloud. The existing AI tools in Azure target only advanced projects. Lobe being free, easy to access, and convenient to use can now support even simple use cases that were not adequately addressed by the existing AI tools.
The Nature Conservancy is a nonprofit environmental organization that used Lobe to create an AI. This model analyzes the pictures taken by tourists in the Caribbean to identify where and when visitors interact with coral reefs. A Seattle auto marketing firm,Sincro LLC,has developed an AI model that scans vehicle images in online ads to filter out pictures that are less appealing to the customers.
5 Emerging AI And Machine Learning Trends To Watch In 2021 – CRN: Technology news for channel partners and solution providers
Artificial Intelligence and machine learning have been hot topics in 2020 as AI and ML technologies increasingly find their way into everything from advanced quantum computing systems and leading-edge medical diagnostic systems to consumer electronics and smart personal assistants.
Revenue generated by AI hardware, software and services is expected to reach $156.5 billion worldwide this year, according to market researcher IDC, up 12.3 percent from 2019.
But it can be easy to lose sight of the forest for the trees when it comes to trends in the development and use of AI and ML technologies. As we approach the end of a turbulent 2020, heres a big-picture look at five key AI and machine learning trends not just in the types of applications they are finding their way into, but also in how they are being developed and the ways they are being used.
The Growing Role Of AI And Machine Learning In Hyperautomation
Hyperautomation, an IT mega-trend identified by market research firm Gartner, is the idea that most anything within an organization that can be automated such as legacy business processes should be automated. The pandemic has accelerated adoption of the concept, which is also known as digital process automation and intelligent process automation.
AI and machine learning are key components and major drivers of hyperautomation (along with other technologies like robot process automation tools). To be successful hyperautomation initiatives cannot rely on static packaged software. Automated business processes must be able to adapt to changing circumstances and respond to unexpected situations.
Thats where AI, machine learning models and deep learning technology come in, using learning algorithms and models, along with data generated by the automated system, to allow the system to automatically improve over time and respond to changing business processes and requirements. (Deep learning is a subset of machine learning that utilizes neural network algorithms to learn from large volumes of data.)
Bringing Discipline To AI Development Through AI Engineering
Only about 53 percent of AI projects successfully make it from prototype to full production, according to Gartner research. When trying to deploy newly developed AI systems and machine learning models, businesses and organizations often struggle with system maintainability, scalability and governance, and AI initiatives often fail to generate the hoped-for returns.
Businesses and organizations are coming to understand that a robust AI engineering strategy will improve the performance, scalability, interpretability and reliability of AI models and deliver the full value of AI investments, according to Gartners list of Top Strategic Technology Trends for 2021.
Developing a disciplined AI engineering process is key. AI engineering incorporates elements of DataOps, ModelOps and DevOps and makes AI a part of the mainstream DevOps process, rather than a set of specialized and isolated projects, according to Gartner.
Increased Use Of AI For Cybersecurity Applications
Artificial intelligence and machine learning technology is increasingly finding its way into cybersecurity systems for both corporate systems and home security.
Developers of cybersecurity systems are in a never-ending race to update their technology to keep pace with constantly evolving threats from malware, ransomware, DDS attacks and more. AI and machine learning technology can be employed to help identify threats, including variants of earlier threats.
AI-powered cybersecurity tools also can collect data from a companys own transactional systems, communications networks, digital activity and websites, as well as from external public sources, and utilize AI algorithms to recognize patterns and identify threatening activity such as detecting suspicious IP addresses and potential data breaches.
AI use in home security systems today is largely limited to systems integrated with consumer video cameras and intruder alarm systems integrated with a voice assistant, according to research firm IHS Markit. But IHS says AI use will expand to create smart homes where the system learns the ways, habits and preferences of its occupants improving its ability to identify intruders.
The Intersection Of AI/ML and IoT
The Internet of Things has been a fast-growing area in recent years with market researcher Transforma Insights forecasting that the global IoT market will grow to 24.1 billion devices in 2030, generating $1.5 trillion in revenue.
The use of AI/ML is increasingly intertwined with IoT. AI, machine learning and deep learning, for example, are already being employed to make IoT devices and services smarter and more secure. But the benefits flow both ways given that AI and ML require large volumes of data to operate successfully exactly what networks of IoT sensors and devices provide.
In an industrial setting, for example, IoT networks throughout a manufacturing plant can collect operational and performance data, which is then analyzed by AI systems to improve production system performance, boost efficiency and predict when machines will require maintenance.
What some are calling Artificial Intelligence of Things: (AIoT) could redefine industrial automation.
Persistent Ethical Questions Around AI Technology
Earlier this year as protests against racial injustice were at their peak, several leading IT vendors, including Microsoft, IBM and Amazon, announced that they would limit the use of their AI-based facial recognition technology by police departments until there are federal laws regulating the technologys use, according to a Washington Post story.
That has put the spotlight on a range of ethical questions around the increasing use of artificial intelligence technology. That includes the obvious misuse of AI for deepfake misinformation efforts and for cyberattacks. But it also includes grayer areas such as the use of AI by governments and law enforcement organizations for surveillance and related activities and the use of AI by businesses for marketing and customer relationship applications.
Thats all before delving into the even deeper questions about the potential use of AI in systems that could replace human workers altogether.
A December 2019 Forbes article said the first step here is asking the necessary questions and weve begun to do that. In some applications federal regulation and legislation may be needed, as with the use of AI technology for law enforcement.
In business, Gartner recommends the creation of external AI ethics boards to prevent AI dangers that could jeopardize a companys brand, draw regulatory actions or lead to boycotts or destroy business value. Such a board, including representatives of a companys customers, can provide guidance about the potential impact of AI development projects and improve transparency and accountability around AI projects.
Vanderbilt trans-institutional team shows how next-gen wearable sensor algorithms powered by machine learning could be key to preventing injuries that…
A trans-institutional team of Vanderbilt engineering, data science and clinical researchers has developed a novel approach for monitoring bone stress in recreational and professional athletes, with the goal of anticipating and preventing injury. Using machine learning and biomechanical modeling techniques, the researchers built multisensory algorithms that combine data from lightweight, low-profile wearable sensors in shoes to estimate forces on the tibia, or shin bonea common place for runners stress fractures.
The research builds off the researchers2019 study,which found that commercially available wearables do not accurately monitor stress fracture risks.Karl Zelik, assistant professor of mechanical engineering, biomedical engineering and physical medicine and rehabilitation, sought to develop a better technique to solve this problem.Todays wearablesmeasure ground reaction forceshow hard the foot impacts or pushes against the groundto assess injury risks like stress fractures to the leg, Zelik said. While it may seem intuitive to runners and clinicians that the force under your foot causes loading on your leg bones, most of your bone loading is actually from muscle contractions. Its this repetitive loading on the bone that causes wear and tear and increases injury risk to bones, including the tibia.
The article, Combining wearable sensor signals, machine learning and biomechanics to estimate tibial bone force and damage during running was publishedonlinein the journalHuman Movement Scienceon Oct. 22.
The algorithms have resulted in bone force data that is up to four times more accurate than available wearables, and the study found that traditional wearable metrics based on how hard the foot hits the ground may be no more accurate for monitoring tibial bone load than counting steps with a pedometer.
Bones naturally heal themselves, but if the rate of microdamage from repeated bone loading outpaces the rate of tissue healing, there is an increased risk of a stress fracture that can put a runner out of commission for two to three months. Small changes in bone load equate to exponential differences in bone microdamage, said Emily Matijevich, a graduate student and the director of theCenter for Rehabilitation Engineering and Assistive TechnologyMotion Analysis Lab. We have found that 10 percent errors in force estimates cause 100 percent errors in damage estimates. Largely over- or under-estimating the bone damage that results from running has severe consequences for athletes trying to understand their injury risk over time. This highlights why it is so important for us to develop more accurate techniques to monitor bone load and design next-generation wearables. The ultimate goal of this tech is to better understand overuse injury risk factors and then prompt runners to take rest days or modify training before an injury occurs.
The machine learning algorithm leverages the Least Absolute Shrinkage and Selection Operator regression, using a small group of sensors to generate highly accurate bone load estimates, with average errors of less than three percent, while simultaneously identifying the most valuable sensor inputs, saidPeter Volgyesi, a research scientist at the Vanderbilt Institute for Software Integrated Systems. I enjoyed being part of the team.This is a highly practical application of machine learning, markedly demonstrating the power of interdisciplinary collaboration with real-life broader impact.
This research represents a major leap forward in health monitoring capabilities. This innovation is one of the first examples of a wearable technology that is both practical to wear in daily life and can accuratelymonitor forces on and microdamage to musculoskeletal tissues.The team has begun applying similar techniques to monitor low back loading and injury risks, designed for people in occupations that require repetitive lifting and bending. These wearables could track the efficacy of post-injury rehab or inform return-to-play or return-to-work decisions.
We are excited about the potential for this kind of wearable technology to improve assessment, treatment and prevention of other injuries like Achilles tendonitis, heel stress fractures or low back strains, saidMatijevich, the papers corresponding author.The group has filed multiple patents on their invention and is in discussions with wearable tech companies to commercialize these innovations.
This research was funded by National Institutes of Health grant R01EB028105 and the Vanderbilt University Discovery Grant program.
Financial services firms have been increasingly incorporating Artificial Intelligence (AI) into their strategies to drive operational and cost efficiencies. Firms must ensure effective governance of any use of AI. The Financial Conduct Authority (FCA) is active in this area, currently collaborating with The Alan Turing Institute to examine a potential framework for transparency in the use of AI in financial markets.
In simple terms, AI involves algorithms that can make human-like decisions, often on the basis of large volumes of data, but typically at a much faster and more efficient rate. In 2019, the FCA and the Bank of England (BoE) issued a survey to almost 300 firms, including banks, credit brokers, e-money institutions, financial market infrastructure firms, investment managers, insurers, non-bank lenders and principal trading firms, to understand the extent to which they were using Machine Learning (ML), a sub-category of AI. While AI is a broad concept, ML involves a methodology whereby a computer programme learns to recognise patterns of data without being explicitly programmed.
The key findings included:
The use cases for ML identified by the FCA and BoE were largely focused around the following areas:
Anti-money laundering and countering the financing of terrorism
Financial institutions have to analyse customer data continuously from a wide-range of sources in order to comply with their AML obligations. The FCA and BoE found that ML was being used at several stages within the process to:
Firms were increasingly using Chatbots, which enable customers to contact firms without having to go through human agents via call centres or customer support. Chatbots can reduce the time and resources needed to resolve consumer queries.
ML can facilitate faster identification of user intent and recommend associated content which can help address consumers issues. For more complex matters which cannot be addressed by the Chatbot, the ML will transfer the consumer to a human agent who should be better placed to deal with the query.
Sales and trading
The FCA and BoE reported that ML use cases in sales and trading broadly fell under three categories ranging from client-facing to pricing and execution:
The majority of respondents in the insurance sector used ML to price general insurance products, including motor, marine, flight, building and contents insurance. In particular, ML applications were used for:
Insurance claims management
Of the respondents in the general insurance sector, 83% used ML for claims management in the following scenarios:
ML currently appears to provide only a supporting role in the asset management sector. Systems are often used to provide suggestions to fund management (which apply equally to portfolio decision-making or execution only trades):
All of these applications have back-up systems and human-in-the-loop safeguards. They are aimed at providing fund managers with suggestions, with a human in charge of the decision making and trade execution.
Although there is no overarching legal framework which governs the use of AI in financial services, Principle 3 of the FCAs Principles for Business makes clear that firms must take reasonable care to organise and control their affairs responsibly and effectively, with adequate risk management systems. If regulated activities being conducted by firms are increasingly dependent on ML or, more broadly, AI, firms will need to ensure that there is effective governance around the use of AI and that systems and controls adequately ensure that the use of ML and AI is not causing harm to consumers or the markets.
There are a number of risks in adopting AI, for example, algorithmic bias caused by insufficient or inaccurate data (note that the main barrier to widespread adoption of AI is the availability of data) and lack of training of systems and AI users, which could lead to poor decisions being made. It is therefore imperative that firms fully understand the design of the MI, have stress-tested the technology prior to its roll-out in business areas and have effective quality assurance and system feedback measures in place to detect and prevent poor outcomes.
Clear records should be kept of the data used by the ML, the decision making around the use of ML and how systems are trained and tested. Ultimately, firms should be able to explain how the ML reached a particular decision.
Where firms outsource to AI service providers, they retain the regulatory risk if things go wrong. As such, the regulated firm should ensure it carries out sufficient due diligence on the service provider, that it understands the underlying decision-making process of the service providers AI and ensure the contract includes adequate monitoring and oversight mechanisms where the AI services are important in the context of the firms regulated business, and appropriate termination provisions.
The FCA announced in July 2019 that it is working with The Alan Turing Institute on a year-long collaboration on AI transparency in which they will propose a high-level framework for thinking about transparency needs concerning uses of AI in financial markets. The Alan Turing Institute has already completed a project on explainable AI with the Information Commissioner in the content of data protection. A recent blog published by the FCA stated:
the need or desire to access information about a given AI system may be motivated by a variety of reasons there are a diverse range of concerns that may be addressed through transparency measures. one important function of transparency is to demonstrate trustworthiness which, in turn, is a key factor for the adoption and public acceptance of AI systems transparency may [also] enable customers to understand and where appropriate challenge the basis of particular outcomes.
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Using Machine Learning in Financial Services and the regulatory implications - Lexology
Zaloni Named to Now Tech: Machine Learning Data Catalogs Report, Announced as a Finalist for the NC Tech Awards, and Releases Arena 6.1 – PR Web
From controlling data sprawl to eliminating data bottlenecks, we believe Arenas birds-eye view across the entire data supply chain allows our clients to reduce IT costs, accelerate time to analytics, and achieve better AI and ML outcomes. - Susan Cook, CEO Zaloni
RESEARCH TRIANGLE PARK, N.C. (PRWEB) October 28, 2020
Zaloni, an award-winning leader in data management, today announced its inclusion in a recent Forrester report, titled Now Tech: Machine Learning Data Catalogs (MLDC), Q4 2020. Forrester, a global research and advisory firm for business and technology leaders, listed Zaloni as a midsize vendor in the MLDC Market in the report.
Defined by Forrester, A machine learning data catalog (MLDC) discovers, profiles, interprets and applies semantics and data policies to data and metadata using machine learning to enable data governance and DataOps, helping analysts, data scientists, and data consumers turn data into business outcomes. Having a secure MLDC foundation is vital for key technology trends -- internet of things (IoT), blockchain, AI, and intelligent security.
As a conclusive remark in the Forrester report: MLDCs will force organizations to address the unique processes and requirements of different data roles. Unlike other data management solutions that seek to process and automate the management of data within systems, MLDCs are workbenches for data consumption and delivery across engineers, stewards, and analyst roles.
For us, to be named a vendor in the MLDC Market by Forrester is a huge accomplishment, expressed CEO of Zaloni, Susan Cook. At Zaloni, we are passionate about making our clients lives easier with our end-to-end DataOps platform, Arena. From controlling data sprawl to eliminating data bottlenecks, we believe Arenas birds-eye view across the entire data supply chain allows our clients to reduce IT costs, accelerate time to analytics, and achieve better AI and ML outcomes.
To receive a complimentary copy of the report, visit: https://www.zaloni.com/resources/briefs-papers/forrester-ml-data-catalogs-2020/.
Zaloni Named NC Tech Award Finalist for Artificial Intelligence and Machine Learning
In addition to the inclusion in the Forrester Report, Zaloni has recently been named a finalist for the NC Tech Associations award for Best Use of Technology: Artificial Intelligence & Machine Learning for the 2020 year. The NC Tech Association recognizes North Carolina-based companies who are making an impact with technology among the state and beyond. Zaloni is looking forward to the NC Tech Award: Virtual Beacons Ceremony where the winners will be announced for all categories.
Zaloni's Arena 6.1 Release Extends Augmented Data Management
Zaloni released the latest version of the Arena platform, Arena 6.1. This release adds new features and enhancements that build upon the 6.0 releases focus on DataOps optimization and augmented data catalog. The latest release adds to the new streamlined user-interface to improve user experience and productivity. It also provides a new feature for importing and exporting metadata through Microsoft Excel.
Traditionally, Microsoft Excel has been a popular tool for managing and exchanging metadata outside of a governance and catalog tool. To jumpstart the process of building a catalog, Arena allows users to add and update catalog entities by uploading Microsoft Excel worksheets containing entity metadata helping to incorporate data catalog updates into existing business processes and workflows with the tools users already know and use.
Zaloni to Present Dataops for Improved AI & ML Outcomes at ODSC Virtual Conference
Zaloni is participating in the ODSC West Virtual Conference this week. Solutions Engineer, Cody Rich, will be presenting Wednesday, October 28th, at 3:30 PM PDT. Codys presentation will consist of a live Arena demo. This demo will walk viewers through our unified DataOps platform that bridges the gap between data engineers, stewards, analysts, and data scientists while optimizing the end-to-end data supply chain to process and deliver secure, trusted data rapidly. In addition to the presentation, Zaloni staff will be hosting a booth in the conferences Exhibitor Hall. If you are interested in learning more about Zaloni and our DataOps driven solutions with the Arena platform, make sure to visit us on Wednesday, October 28th, or Thursday, October 29th.
About ZaloniAt Zaloni, we believe in the unrealized power of data. Our DataOps software platform, Arena, streamlines data pipelines through an active catalog, automated control, and self-service consumption to reduce IT costs, accelerate analytics, and standardize security. We work with the world's leading companies, delivering exceptional data governance built on an extensible, machine-learning platform that both improves and safeguards enterprises data assets. To find out more visit http://www.zaloni.com.
Media Contact:Annie Bishopabishop@zaloni.com
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