Category Archives: Machine Learning

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

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

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.

Read more:
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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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

What is Machine Learning?

A useful way to introduce the machine learning methodology is by means of a comparison with the conventional engineering design flow.

This starts with an in-depth analysis of the problem domain, which culminates with the definition of a mathematical model. The mathematical model is meant to capture the key features of the problem under study and is typically the result of the work of a number of experts. The mathematical model is finally leveraged to derive hand-crafted solutions to the problem.

For instance, consider the problem of defining a chemical process to produce a given molecule. The conventional flow requires chemists to leverage their knowledge of models that predict the outcome of individual chemical reactions, in order to craft a sequence of suitable steps that synthesize the desired molecule. Another example is the design of speech translation or image/ video compression algorithms. Both of these tasks involve the definition of models and algorithms by teams of experts, such as linguists, psychologists, and signal processing practitioners, not infrequently during the course of long standardization meetings.

The engineering design flow outlined above may be too costly and inefficient for problems in which faster or less expensive solutions are desirable. The machine learning alternative is to collect large data sets, e.g., of labeled speech, images, or videos, and to use this information to train general-purpose learning machines to carry out the desired task. While the standard engineering flow relies on domain knowledge and on design optimized for the problem at hand, machine learning lets large amounts of data dictate algorithms and solutions. To this end, rather than requiring a precise model of the set-up understudy, machine learning requires the specification of an objective, of a model to be trained, and of an optimization technique.

Returning to the first example above, a machine learning approach would proceed by training a general-purpose machine to predict the outcome of known chemical reactions based on a large data set, and by then using the trained algorithm to explore ways to produce more complex molecules. In a similar manner, large data sets of images or videos would be used to train a general-purpose algorithm with the aim of obtaining compressed representations from which the original input can be recovered with some distortion.

When to Use Machine Learning?

Based on the discussion above, machine learning can offer an efficient alternative to the conventional engineering flow when development cost and time are the main concerns, or when the problem appears to be too complex to be studied in its full generality. On the flip side, the approach has the key disadvantages of providing generally suboptimal performance, or hindering interpretability of the solution, and applying only to a limited set of problems. In order to identify tasks for which machine learning methods may be useful, suggests the following criteria:1. the task involves a function that maps well-defined inputs to well-defined outputs;2. large data sets exist or can be created containing input-output pairs;3. the task provides clear feedback with clearly definable goals and metrics;4. the task does not involve long chains of logic or reasoning that depend on diverse background knowledge or common sense;5. the task does not require detailed explanations for how the decision was made;6. the task has a tolerance for error and no need for provably correct or optimal solutions;7. the phenomenon or function being learned should not change rapidly over time; and8. no specialized dexterity, physical skills, or mobility is required.

These criteria are useful guidelines for the decision of whether the machine learning methods are suitable for a given task of interest. They also offer a convenient demarcation line between machine learning as is intended today, with its focus on training and computational statistics tools, and more general notions of Artificial Intelligence (AI) based on knowledge and common sense.

In short, Machine learning is very useful and so progressive in the field of programming and topics related to computers.

See original here:
What is Machine Learning and its Uses? - Technotification

Amit Chaudhary, a machine learning (ML) researcher from Nepal, has recently introduced a browser extension that allows users to directly access videos related to research papers published on the platform arXiv.

ArXiv has become an essential resource for new machine learning (ML) papers. Initially, in 1991, it was launched as a storage site for physics preprints. In 2001 it was named ArXiv and had since been hosted by Cornell University. ArXiv has received close to 2 million submissions across various scientific research fields.

Amit obtained publicly released videos from 2020 ML conferences. He then indexed the videos and reverse-mapped them to the relevant arXiv links through pyarxiv, a dedicated wrapper for the arXiv API. The Google Chrome extension creates a video icon next to the paper title on the arXiv abstract page, enabling users to identify and access available videos related to the paper directly.

Many research teams are creating videos to accompany their papers. These videos can act as a guide by providing demo and other valuable information on the research document. In several situations, the videos are created as an alternative to traditional in-person presentations at AI conferences. This is useful in current circumstances as almost all panels have moved to virtual forms due to the Covid-19 pandemic.

The Papers-With-Video extension enables direct video links for around 3.7k arXiv ML papers. Amit aims to figure out how to pair documents and videos related effectively but has different titles, and with this, he hopes to expand coverage to 8k videos. He has proposed community feedback and has now tweaked the extensions functionality based on user remarks and suggestions.

The browser extension is not available on the Google Chrome Web Store yet. However, one can find the extension, installation guide, and further information on GitHub.

GitHub: https://github.com/amitness/papers-with-video

Paper List: https://gist.github.com/amitness/9e5ad24ab963785daca41e2c4cfa9a82

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A Nepalese Machine Learning (ML) Researcher Introduces Papers-With-Video Browser Extension Which Allows Users To Access Videos Related To Research...

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Chapter 2 Global Economic Impact on Industry

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