Mediaboss Marketing

Westport, CT, Feb. 02, 2021 (GLOBE NEWSWIRE) --

BioSig Technologies, Inc. (NASDAQ: BSGM) (BioSig or the Company), a medical technology company commercializing an innovative signal processing platform designed to improve signal fidelity and uncover the full range of ECG and intra-cardiac signals, today announced a strategic collaboration with the Mayo Foundation for Medical Education and Research to develop a next-generation AI- and machine learning-powered software for its PURE EP system.

The new collaboration will include an R&D program that will expand the clinical value of the Companys proprietary hardware and software with advanced signal processing capabilities and aim to develop novel technological solutions by combining the electrophysiological signals delivered by the PURE EPand other data sources. The development program will be conducted under the leadership of Samuel J. Asirvatham, M.D., Mayo Clinics Vice-Chair of Innovation and Medical Director, Electrophysiology Laboratory, and Alexander D. Wissner-Gross, Ph.D., Managing Director of Reified LLC.

The global market for AI in healthcare is expected to grow from $4.9 billion in 2020 to $45.2 billion by 2026 at an estimated compound annual growth rate (CAGR) of 44.9%1. According to Accenture, key clinical health AI applications, when combined, can potentially create $150 billion in annual savings for the United States healthcare economy by 20262.

AI-powered algorithms that are developed on superior data from multiple biomarkers could drastically improve the way we deliver therapies, and therefore may help address the rising global demand for healthcare, commented Kenneth L Londoner, Chairman and CEO of BioSig Technologies, Inc. We believe that combining the clinical science of Mayo Clinic with the best-in-class domain expertise of Dr. Wissner-Gross and the technical leadership of our engineering team will enable us to develop powerful applications and help pave the way toward improved patient outcomes in cardiology and beyond.

Artificial intelligence presents a variety of novel opportunities for extracting clinically actionable information from existing electrophysiological signals that might otherwise be inaccessible. We are excited to contribute to the advancement of this field, said Dr. Wissner-Gross.

BioSig announced its partnership with Reified LLC, a provider of advanced artificial intelligence-focused technical advisory services to the private sector in late 2019. The new research program builds upon the progress achieved by this collaboration in 2020, which included an abstract for Computational Reconstruction of Electrocardiogram Lead Placement presented during the 2020 Computing in Cardiology Conference in Rimini, Italy, and the development of an initial suite of electrophysiological analytics for the PURE EPSystem.

BioSig signed a 10-year collaboration agreement with Mayo Clinic in March 2017. In November 2019, the Company announced that it signed three new patent and know-how license agreements with the Mayo Foundation for Medical Education and Research.

About BioSig TechnologiesBioSig Technologies is a medical technology company commercializing a proprietary biomedical signal processing platform designed toimprove signal fidelity and uncover the full range of ECG and intra-cardiac signals(www.biosig.com).

The Companys first product,PURE EP Systemis a computerized system intended for acquiring, digitizing, amplifying, filtering, measuring and calculating, displaying, recording and storing of electrocardiographic and intracardiac signals for patients undergoing electrophysiology (EP) procedures in an EP laboratory.

Forward-looking Statements

This press release contains forward-looking statements. Such statements may be preceded by the words intends, may, will, plans, expects, anticipates, projects, predicts, estimates, aims, believes, hopes, potential or similar words. Forward- looking statements are not guarantees of future performance, are based on certain assumptions and are subject to various known and unknown risks and uncertainties, many of which are beyond the Companys control, and cannot be predicted or quantified and consequently, actual results may differ materially from those expressed or implied by such forward-looking statements. Such risks and uncertainties include, without limitation, risks and uncertainties associated with (i) the geographic, social and economic impact of COVID-19 on our ability to conduct our business and raise capital in the future when needed, (ii) our inability to manufacture our products and product candidates on a commercial scale on our own, or in collaboration with third parties; (iii) difficulties in obtaining financing on commercially reasonable terms; (iv) changes in the size and nature of our competition; (v) loss of one or more key executives or scientists; and (vi) difficulties in securing regulatory approval to market our products and product candidates. More detailed information about the Company and the risk factors that may affect the realization of forward-looking statements is set forth in the Companys filings with the Securities and Exchange Commission (SEC), including the Companys Annual Report on Form 10-K and its Quarterly Reports on Form 10-Q. Investors and security holders are urged to read these documents free of charge on the SECs website at http://www.sec.gov. The Company assumes no obligation to publicly update or revise its forward-looking statements as a result of new information, future events or otherwise.

1 Artificial Intelligence in Healthcare Market with COVID-19 Impact Analysis by Offering, Technology, End-Use Application, End User and Region Global Forecast to 2026; Markets and Markets

2 Artificial Intelligence (AI): Healthcares New Nervous System https://www.accenture.com/us-en/insight-artificial-intelligence-healthcare%C2%A0

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BioSig and Mayo Clinic Collaborate on New R&D Program to Develop Transformative AI and Machine Learning Technologies for its PURE EP System - BioSpace

AI usage is growing rapidly. What does 2021 hold for the world of analytics, and how will AI drive it?

Progress of AI-powered operations looks set to grow this year.

As the world prepares to recover from the Covid-19 pandemic, businesses will need to increasingly rely on analytics to deal with new consumer behaviour.

According to Gartner analyst Rita Sallam, In the face of unprecedented market shifts, data and analytics leaders require an ever-increasing velocity and scale of analysis in terms of processing and access to accelerate innovation and forge new paths to a post-Covid-19 world.

Machine learning and artificial intelligence are finding increasingly significant use cases in data analytics for business. Here are five trends to watch out for in 2021.

Gartner predicts that by 2024, 75% of enterprises will shift towards putting AI and ML into operation. A big reason for this is the way the pandemic has changed consumer behaviour. Regression learning models that rely on historical data might not be valid anymore. In their place, reinforcement and distributed learning models will find more use, thanks to their adaptability.

A large share of businesses have already democratised their data through the use of embedded analytics dashboards. The use of AI to generate augmented analytics to drive business decisions will increase as businesses seek to react faster to shifting conditions. Powering data democratisation efforts with AI will help non-technical users make a greater number of business decisions, without having to rely on IT support to query data.

Companies such as Sisense already offer companies the ability to integrate powerful analytics into custom applications. As AI algorithms become smarter, its a given that theyll help companies use low-latency alerts to help managers react to quantifiable anomalies that indicate changes in their business. Also, AI is expected to play a major role in delivering dynamic data stories and might reduce a users role in data exploration.

A fact thats often forgotten in AI conversations is that these technologies are still nascent. Many of the major developments have been driven by open source efforts, but 2021 will see an increasing number of companies commercialise AI through product releases.

This event will truly be a marker of AI going mainstream. While open source has been highly beneficial to AI, scaling these projects for commercial purposes has been difficult. With companies investing more in AI research, expect a greater proliferation of AI technology in project management, data reusability, and transparency products.

Using AI for better data management is a particular focus of big companies right now. A Pathfinder report in 2018 found that a lack of skilled resources in data management was hampering AI development. However, with ML growing increasingly sophisticated, companies are beginning to use AI to manage data, which fuels even faster AI development.

As a result, metadata management becomes streamlined, and architectures become simpler. Moving forward, expect an increasing number of AI-driven solutions to be released commercially instead of on open source platforms.

Vendors such as Informatica are already using AI and ML algorithms to help develop better enterprise data management solutions for their clients. Everything from data extraction to enrichment is optimised by AI, according to the company.

This article explores the ways in which Kubernetes enhances the use of machine learning (ML) within the enterprise. Read here

Voice search and data is increasing by the day. With products such as Amazons Alexa and Googles Assistant finding their way into smartphones and growing adoption of smart speakers in our homes, natural language processing will increase.

Companies will wake up to the immense benefits of voice analytics and will provide their customers with voice tools. The benefits of enhanced NLP include better social listening, sentiment analysis, and increased personalisation.

Companies such as AX Semantics provide self-service natural language generation software that allows customers to self-automate text commands. Companies such as Porsche, Deloitte and Nivea are among their customers.

As augmented analytics make their way into embedded dashboards, low-level data analysis tasks will be automated. An area that is ripe for automation is data collection and synthesis. Currently, data scientists spend large amounts of time cleaning and collecting data. Automating these tasks by specifying standardised protocols will help companies employ their talent in tasks better suited to their abilities.

A side effect of data analysis automation will be the speeding up of analytics and reporting. As a result, we can expect businesses to make decisions faster along with installing infrastructure that allows them to respond and react to changing conditions quickly.

As the worlds of data and analytics come closer together, vendors who provide end-to-end stacks will provide better value to their customers. Combine this with increased data democratisation and its easy to see why legacy enterprise software vendors such as SAP offer everything from data management to analytics to storage solutions to their clients.

Tech experts provide their tips on how to effectively implement automation into your customer relationship management (CRM) process. Read here

IoT devices are making their way into not just B2C products but B2B, enterprise and public projects as well, from smart cities to industry 4.0.

Data is being generated at unprecedented rates, and to make sense of it, companies are increasingly turning to AI. With so much signal, this is a key help for arriving at insights.

While the rise of embedded and augmented analytics has already been discussed, its critical to point out that the sources of data are more varied than ever before. This makes the use of AI critical, since manual processes cannot process such large volumes efficiently.

As AI technology continues to make giant strides the business world is gearing up to take full advantage of it. Weve reached a stage where AI is powering further AI development, and the rate of progress will only increase.

See more here:
Five trends in machine learning-enhanced analytics to watch in 2021 - Information Age

Originally published in UX Collective, Jan 19, 2021.

For an industry that prides itself on moving fast, the tech community has been remarkably slow to adapt to the differences of designing with AI. Machine learning is an intrinsically fuzzy science, yet when it inevitably returns unpredictable results, we tend to react like its a puzzle to be solved; believing that with enough algorithmic brilliance, we can eventually fit all the pieces into place and render something approaching objective truth. But objectivity and truth are often far afield from the true promise of AI, as well soon discuss.

I think a lot of the confusion stems from language;in particular the way we talk about machine-like efficiency. Machines are expected to make precise measurements about whatever theyre pointed at; to produce data.

But machinelearningdoesnt produce data. Machine learning producespredictionsabout how observations in the present overlap with patterns from the past. In this way, its literally aninversionof the classicif-this-then-thatlogic thats driven conventional software development for so long. My colleague Rick Barraza has a great way of describing the distinction:

To continue reading this article, click here.

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When Are We Going to Start Designing AI With Purpose? Machine Learning Times - The Predictive Analytics Times

Credit: Clment Hlardot/Unsplash

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And you can rest assured that coding will be at the heart of almost every single one of those new positions.

Its no surprise that programming courses are being taught to our youngest students these days. From web development to gaming to data science, all the tech innovations well see over those next five years and beyond will come from innovators who understand how to make those static lines of code get together and dance.

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With courses taught by knowledgeable industry professionals, even newbies coming in with zero experience receive world-class training on in-demand programming skills on their way to becoming professionals themselves. Classes are based entirely around your own schedule with no deadlines or due dates so you can work at your own pace on bolstering your abilities.

Whether a student is interested in crafting mobile apps, mastering data science, or exploring machine learning and AI, these courses dont just tell you how to interact with these disciplines, they actually show you. Zenva coursework is based around creating real projects in tandem with the learning.

As you build a VR or AR app, or craft your first artificial neural networks using Python and TensorFlow, or create an awesome game, youll be building work for a professional portfolio that can help you land one of these prime coding positions. And with their ties to elite developer programs for outlets like Intel, Microsoft, and CompTIA, students can get on the fast track toward getting hired.

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Prices are subject to change.

Read next: Forget Hyperloop, check out Chinas new 620kmph maglev prototype

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Learn in-demand technical skills in Python, machine learning, and more with this academy - The Next Web

While COVID-19 circumstances have forced organizations to meet remotely on the Zoom application, it has enabled groups like the Rotary Club of New Canaan to invite speakers from far away.

The clubs Zoom Christmas party included a previous Rotary International Scholar, Yuri Nakashima, from her home in Japan. This past weeks luncheon speaker was New Canaan native John Gnuse, son of Rotarian Jeanne Gnuse, and her late husband, Tom. Gnuse spoke to the club from San Francisco, where he is managing director at Lazard, on the topic of The Machine Learning Revolution.

Happily, the Zoom format enabled his sister, Dr. Karen Gnuse Nead, in Rochester, N.Y., and uncle, William Pflaum, in Menlo Park, Calif., to attend as well.

Gnuses career has focused on mergers and acquisitions of major technology companies, e.g. Google, IBM, Microsoft, Amazon and Apple, etc., and as such, he is a great guide to the world of machine learning.

His talk highlighted the progress which advanced computing power, and capacity have made possible.

Machine learning refers to the ability for complex algorithms to improve accuracy, and performance based on continuous experience with additional training data.

With these capabilities, complex, iterative processes using with multiple parameters have yielded sophisticated neural networks that can learn.

This has yielded sophisticated tools, and solutions that were not previously possible, but which we rely on now for so much of daily life such as for web search, speech recognition, (Alexa, Siri), medical research and financial optimization models, etc., to name a few.

In answer to concerns about where advances in artificial intelligence will take us, John referred to the guardrails already in place, and those which continue to be applied as key elements of the machine learning revolution. The field raises significant legal, ethical and morality challenges, which will continue to be evaluated as do concerns regarding bias, and fairness as the results of these networks impact people everywhere.

For more on the club, contact Alex Grantcharov, president, at alex.grantcharov@edwardjones.com, follow the club at http://www.facebook.com/NewCanaanRotary, newcanaanrotary on Instagram or at the clubs website, newcanaanrotary.org

Read more:
New Canaan native speaks on Machine Learning Revolution - New Canaan Advertiser