Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.

The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly.

Machine learning algorithms are often categorized as supervised or unsupervised.

Machine learning enables analysis of massive quantities of data. While it generally delivers faster, more accurate results in order to identify profitable opportunities or dangerous risks, it may also require additional time and resources to train it properly. Combining machine learning with AI and cognitive technologies can make it even more effective in processing large volumes of information.

View original post here:

What is Machine Learning? A definition - Expert System

TURIN, Italy--(BUSINESS WIRE)--The European Central Bank (ECB), in collaboration with Reply, leader in digital technology innovation, is organising the Supervisory Data Hackathon, a coding marathon focussing on the application of Machine Learning and Artificial Intelligence.

From 27 to 29 February 2020, at the ECB in Frankfurt, more than 80 participants from the ECB, Reply and further companies explore possibilities to gain deeper and faster insights into the large amount of supervisory data gathered by the ECB from financial institutions through regular financial reporting for risk analysis. The coding marathon provides a protected space to co-creatively develop new ideas and prototype solutions based on Artificial Intelligence within a short timeframe.

Ahead of the event, participants submit projects in the areas of data quality, interlinkages in supervisory reporting and risk indicators. The most promising submissions will be worked on for 48 hours during the event by the multidisciplinary teams composed of members from the ECB, Reply and other companies.

Reply has proven its Artificial Intelligence and Machine Learning capabilities with numerous projects in various industries and combines this technological expertise with in-depth knowledge of the financial services industry and its regulatory environment.

Coding marathons using the latest technologies are a substantial element in Replys toolset for sparking innovation through training and knowledge transfer internally and with clients and partners.

Reply Reply [MTA, STAR: REY] specialises in the design and implementation of solutions based on new communication channels and digital media. As a network of highly specialised companies, Reply defines and develops business models enabled by the new models of big data, cloud computing, digital media and the internet of things. Reply delivers consulting, system integration and digital services to organisations across the telecom and media; industry and services; banking and insurance; and public sectors. http://www.reply.com

Read this article:

REPLY: European Central Bank Explores the Possibilities of Machine Learning With a Coding Marathon Organised by Reply - Business Wire

Speechmatics and Soho2 have today announced their partnership to deliver consulting services to their customers, and a new product offering Speech2.

Soho2 has significant depth in delivering machine-learning driven solutions to market. The new product from Soho2 will give companies in legal, compliance and contact centers the invaluable ability to analyze voice data garnered from calls. Speech2 enables companies to bring new levels of flexibility to data analysis for high-volume, real time or recorded voice data through mission-critical, accurate speech recognition.

Using AI and machine learning, the solution will deliver an unparalleled ability to derive insight from voice data and also manage risk. The product can be deployed in any customer-managed environment to enable control over personal or sensitive data to be retained.

As part of the new product offering, Speechmatics - a UK leader in any context speech recognition technology - will transcribe voice data into accurate, contextual understanding for analysis. Speech2 will allow businesses to identify and address risks, as well as pinpoint missing sales opportunities. The product can also identify cases of fraud, while the legal industry can identify risks with the data, and even aid with event reconstruction.

George Tziahanas, Managing Partner of Soho2, said: Our experience demonstrates the potential for great innovation in machine learning, delivering huge commercial value to enterprises across industries. We teamed up with Speechmatics to ensure our latest services and product deliver the best speech recognition technology on the market. The partnership enables us to innovate with voice securely which is crucial to our customers and industries.

Jeff Palmer, VP of Sales at Speechmatics, added: Speech2 will deliver unparalleled insights and risk management abilities, using Speechmatics any-context speech recognition engine. Soho2 also brings depth in services that deliver high-value machine learning solutions, which will benefit their customer-base. Were excited to be working with Soho2 and seeing how their customers derive value from their voice data and view it with a renewed sense of curiosity.

Here is the original post:

Speechmatics and Soho2 apply machine learning to analyse voice data - Finextra

Social media can send up an early warning sign of illness, and data analysis can predict how it will spread.

The coronavirus illness (nCoV) is now an international public health emergency, bigger than the SARS outbreak of 2003. Unlike SARS, this time around scientists have better genome sequencing, machine learning, and predictive analysis tools to understand and monitor the outbreak.

During the SARS outbreak, it took five months for scientists to sequence the virus's genome. However, the first 2019-nCoV case was reported in December, and scientists had the genome sequenced by January 10, only a month later.

Researchers have been using mapping tools to track the spread of disease for several years. Ten European countries started Influenza Net in 2003 to track flu symptoms as reported by individuals, and the American version, Flu Near You, started a similar service in 2011.

Lauren Gardner, a civil engineering professor at Johns Hopkins and the co-director of the Center for Systems Science and Engineering, led the effort to launch a real-time map of the spread of the 2019-nCoV. The site displays statistics about deaths and confirmed cases of coronavirus on a worldwide map.

Este Geraghty, MD, MS, MPH, GISP, and chief medical officer and health solutions director at Esri, said that since the SARS outbreak in 2003 there has been a revolution in applied geography through web-based tools.

"Now as we deploy these tools to protect human lives, we can ingest real-time data and display results in interactive dashboards like the coronavirus dashboard built by Johns Hopkins University using ArcGIS," she said.

SEE:The top 10 languages for machine learning hosted on GitHub (free PDF)

With this outbreak, scientists have another source of data that did not exist in 2003: Twitter and Facebook. In 2014, Chicago's Department of Innovation and Technology built an algorithm that used social media mining and illness prediction technologies to target restaurants inspections. It worked: The algorithm found violations about 7.5 days before the normal inspection routine did.

Theresa Do, MPH, leader of the Federal Healthcare Advisory and Solutions team at SAS, said that social media can be used as an early indicator that something is going on.

"When you're thinking on a world stage, a lot of times they don't have a lot of these technological advances, but what they do have is cell phones, so they may be tweeting out 'My whole village is sick, something's going on here,' she said.

Do said an analysis of social media posts can be combined with other data sources to predict who is most likely to develop illnesses like the coronavirus illness.

"You can use social media as a source but then validate it against other data sources," she said. "It's not always generalizable (is generalizable a word?), but it can be a sentinel source."

Do said predictive analytics has made significant advances since 2003, including refining the ability to combine multiple data sources. For example, algorithms can look at names on plane tickets and compare that information with data from other sources to predict who has been traveling to certain areas.

"Algorithms can allow you to say 'with some likelihood' it's likely to be the same person," she said.

The current challenge is identifying gaps in the data. She said that researchers have to balance between the need for real-time data and privacy concerns.

"If you think about the different smartwatches that people wear, you can tell if people are active or not and use that as part of your model, but people aren't always willing to share that because then you can track where someone is at all times," she said.

Do said that the coronavirus outbreak resembles the SARS outbreak, but that governments are sharing data more openly this time.

"We may be getting a lot more positives than they're revealing and that plays a role in how we build the models," she said. "A country doesn't want to be looked at as having the most cases but that is how you save lives."

Get expert tips on mastering the fundamentals of big data analytics, and keep up with the latest developments in artificial intelligence. Delivered Mondays

This map from Johns Hopkins shows reported cases of 2019-nCoV as of January 30, 2020 at 9:30 pm. The yellow line in the graph is cases outside of China while the orange line shows reported cases inside the country.

Image: 2019-nCoV Global Cases by Johns Hopkins Center for Systems Science and Engineering

Go here to read the rest:

Combating the coronavirus with Twitter, data mining, and machine learning - TechRepublic

By: Casey Ross, National Technology Correspondent, StatNews.com

Surveillance data collected by healthmap.org show confirmed cases of the new coronavirus in China.

Artificial intelligence is not going to stop the new coronavirus or replace the role of expert epidemiologists. But for the first time in a global outbreak, it is becoming a useful tool in efforts to monitor and respond to the crisis, according to health data specialists.

In prior outbreaks, AI offered limited value, because of a shortage of data needed to provide updates quickly. But in recent days, millions of posts about coronavirus on social media and news sites are allowing algorithms to generate near-real-time information for public health officials tracking its spread.

The field has evolved dramatically, said John Brownstein, a computational epidemiologist at Boston Childrens Hospital who operates a public health surveillance site called healthmap.org that uses AI to analyze data from government reports, social media, news sites, and other sources.

During SARS, there was not a huge amount of information coming out of China, he said, referring to a 2003 outbreak of an earlier coronavirus that emerged from China, infecting more than 8,000 people and killing nearly 800. Now, were constantly mining news and social media.

Brownstein stressed that his AI is not meant to replace the information-gathering work of public health leaders, but to supplement their efforts by compiling and filtering information to help them make decisions in rapidly changing situations.

We use machine learning to scrape all the information, classify it, tag it, and filter it and then that information gets pushed to our colleagues at WHO that are looking at this information all day and making assessments, Brownstein said. There is still the challenge of parsing whether some of that information is meaningful or not.

These AI surveillance tools have been available in public health for more than a decade, but the recent advances in machine learning, combined with greater data availability, are making them much more powerful. They are also enabling uses that stretch beyond baseline surveillance, to help officials more accurately predict how far and how fast outbreaks will spread, and which types of people are most likely to be affected.

Machine learning is very good at identifying patterns in the data, such as risk factors that might identify zip codes or cohorts of people that are connected to the virus, said Don Woodlock, a vice president at InterSystems, a global vendor of electronic health records that is helping providers in China analyze data on coronavirus patients.

To continue reading this article click here.

See the rest here:

In Coronavirus Response, AI is Becoming a Useful Tool in a Global Outbreak - Machine Learning Times - machine learning & data science news - The...

Dublin, Feb. 04, 2020 (GLOBE NEWSWIRE) -- The "Reinforcement Learning: An Introduction to the Technology" report has been added to ResearchAndMarkets.com's offering.

These days, machine learning (ML), which is a subset of computer science, is one of the most rapidly growing fields in the technology world. It is considered to be a core field for implementing artificial intelligence (AI) and data science.

The adoption of data-intensive machine learning methods like reinforcement learning is playing a major role in decision-making across various industries such as healthcare, education, manufacturing, policing, financial modeling and marketing. The growing demand for more complex machine working is driving the demand for learning-based methods in the ML field. Reinforcement learning also presents a unique opportunity to address the dynamic behavior of systems.

This study was conducted in order to understand the current state of reinforcement learning and track its adoption along various verticals, and it seeks to put forth ways to fully exploit the benefits of this technology. This study will serve as a guide and benchmark for technology vendors, manufacturers of the hardware that supports AI, as well as the end-users who will finally use this technology. Decisionmakers will find the information useful in developing business strategies and in identifying areas for research and development.

The report includes:

Key Topics Covered

Chapter 1 Reinforcement Learning

Chapter 2 Bibliography

List of Tables Table 1: Reinforcement Learning vs. Supervised Learning vs. Unsupervised Learning Table 2: Global Machine Learning Market, by Region, Through 2024

List of Figures Figure 1: Reinforcement Learning Process Figure 2: Reinforcement Learning Workflow Figure 3: Artificial Intelligence vs. Machine Learning vs. Reinforcement Learning Figure 4: Machine Learning Applications Figure 5: Types of Machine Learning Figure 6: Reinforcement Learning Market Dynamics Figure 7: Global Machine Learning Market, by Region, 2018-2024

For more information about this report visit https://www.researchandmarkets.com/r/g0ad2f

Research and Markets also offers Custom Research services providing focused, comprehensive and tailored research.

CONTACT: ResearchAndMarkets.com Laura Wood, Senior Press Manager press@researchandmarkets.com For E.S.T Office Hours Call 1-917-300-0470 For U.S./CAN Toll Free Call 1-800-526-8630 For GMT Office Hours Call +353-1-416-8900

Go here to see the original:

Reinforcement Learning (RL) Market Report & Framework, 2020: An Introduction to the Technology - Yahoo Finance

Machine Learning services in the cloud are a critical area of the modern computing landscape, providing a way for organizations to better analyze data and derive new insights. Accessing these service via the cloud tends to be efficient in terms of cost and staff hours.

Machine Learning (often abbreviated as ML) is a subset of Artificial Intelligence (AI) and attempts to 'learn' from data sets in several different ways, including both supervised and unsupervised learning. There are many different technologies that can be used for machine learning, with a variety of commercial tools as well as open source framework.s

While organizations can choose to deploy machine learning frameworks on premises, it is typically a complex and resource intensive exercise. Machine Learning benefits from specialized hardware including inference chips and optimized GPUs. Machine Learning frameworks can also often be challenging to deploy and configure properly. Complexity has led to the rise of Machine Learning services in the cloud, that provide the right hardware and optimally configured software to that enable organizations to easily get started with Machine Learning.

There are several key features that are part of most machine learning cloud services.

AutoML - The automated Machine Learning feature automatically helps to build the right model. Machine Learning Studio - The studio concept is all about providing a developer environment where machine learning models and data modelling scenarios can be built. Open source framework support - The ability to support an existing framework such as TensorFlow, MXNet and Caffe is important as it helps to enable model portability.

When evaluating the different options for machine learning services in the cloud, consider the following criteria:

In this Datamation top companies list, we spotlight the vendors that offer the top machine learning services in the cloud.

Value proposition for potential buyers: Alibaba is a great option for users that have machine learning needs where data sets reside around the world and especially in Asia, where Alibaba is a leading cloud service.

Value proposition for potential buyers: Amazon Web Services has the broadest array of machine learning services in the cloud today, leading with its SageMaker portfolio that includes capabilities for building, training and deploying models in the cloud.

Value proposition for potential buyers: Google's set of Machine Learning services are also expansive and growing, with both generic as well as purpose built services for specific use-cases.

Value proposition for potential buyers: IBM Watson Machine learning enables users to run models on any cloud, or just on the the IBM Cloud

Value proposition for potential buyers: For organizations that have already bought into Microsoft Azure cloud, Azure Machine Learning is good fit, providing a cloud environment to train, deploy and manage machine learning models.

Value proposition for potential buyers: Oracle Machine learning is a useful tools for organizations already using Oracle Cloud applications, to help build data mining notebooks.

Value proposition for potential buyers: Salesforce Einstein is a purpose built machine learning platform that is tightly integrated with the Salesforce platform.

More:

Top Machine Learning Services in the Cloud - Datamation

NEW YORK, Feb. 3, 2020 /PRNewswire/ --

Report Includes: - A general framework for deep Reinforcement Learning (RL) also known as a semi-supervised learning model in machine learning paradigm

Read the full report: https://www.reportlinker.com/p05843529/?utm_source=PRN

- Assessing the breadth and depth of RL applications in real-world domains, including increased data efficiency and stability as well as multi-tasking - Understanding of the RL algorithm from different aspects; and persuade the decision makers and researchers to put more efforts on RL research

Reasons for Doing This Report: These days, machine learning (ML), which is a subset of computer science, is one of the most rapidly growing fields in the technology world.It is considered to be a core field for implementing artificial intelligence (AI) and data science.

The adoption of data-intensive machine learning methods like reinforcement learning is playing a major role in decision-making across various industries such as healthcare, education, manufacturing, policing, financial modelling and marketing.The growing demand for more complex machine working is driving the demand of learning-based methods in the ML field.

Reinforcement learning also presents a unique opportunity to address the dynamic behavior of systems. This study was conducted in order to understand the current state of reinforcement learning and track its adoption along various verticals, and it seeks to put forth ways to fully exploit the benefits of this technology.This study will serve as a guide and benchmark for technology vendors, manufacturers of the hardware that supports AI, as well as the end users who will finally use this technology.

Decisionmakers will find the information useful in developing business strategies and in identifying areas for research and development.

Read the full report: https://www.reportlinker.com/p05843529/?utm_source=PRN

About Reportlinker ReportLinker is an award-winning market research solution. Reportlinker finds and organizes the latest industry data so you get all the market research you need - instantly, in one place.

__________________________ Contact Clare: clare@reportlinker.com US: (339)-368-6001 Intl: +1 339-368-6001

View original content:http://www.prnewswire.com/news-releases/reinforcement-learning-an-introduction-to-the-technology-300997487.html

SOURCE Reportlinker

Continue reading here:

Reinforcement Learning: An Introduction to the Technology - Yahoo Finance

A couple of weeks after the Coronavirus outbreak and the disease has become a full-blown pandemic. According to official Chinese statistics, more than 130 people have died from the mysterious virus.

Contagious diseases may be diagnosed by men and women in face masks and lab coats, but warning signs of an epidemic can be detected by computer programmers sitting thousands of miles away. Around the tenth of January, news of a flu outbreak in Chinas Hubei province started making its way to mainstream media. It then spread to other parts of the country, and subsequently, overseas.

But the first to report of an impending biohazard was BlueDot, a Canadian firm that specializes in infectious disease surveillance. They predicted an impending outbreak of coronavirus on December 31 using an artificial intelligence-powered system that combs through animal and plant disease networks, news reports in vernacular websites, government documents, and other online sources to warn its clients against traveling to danger zones like Wuhan, much before foreign governments started issuing travel advisories.

They further used global airline ticketing data to correctly predict that the virus would spread to Seoul, Bangkok, Taipei, and Tokyo. Machine learning and natural language processing techniques were also employed to create models that process large amounts of data in real time. This includes airline ticketing data, news reports in 65 languages, animal and plant disease networks.

iStock

We know that governments may not be relied upon to provide information in a timely fashion. We can pick up news of possible outbreaks, little murmurs or forums or blogs of indications of some kind of unusual events going on, Kamran Khan, founder and CEO of BlueDot told a news magazine.

The death toll from the Coronavirus rose to 81 in China, with thousands of new cases registered each day. The government has extended the Lunar New Year holiday by three days to restrict the movement of people across the country, and thereby lower the chances of more people contracting the respiratory disease.

However, a lockdown of the affected area could be detrimental to public health, putting at risk the domestic population, even as medical supplies dwindle, causing much anger and resentment.

24 May, 2018

24 May, 2018

24 May, 2018

24 May, 2018

24 May, 2018

Excerpt from:

This tech firm used AI & machine learning to predict Coronavirus outbreak; warned people about danger zones - Economic Times

NEWPORT NEWS, Va., Jan. 30, 2020 More than 1,600 nuclear physicists worldwide depend on the Continuous Electron Beam Accelerator Facility for their research. Located at the Department of Energys Thomas Jefferson National Accelerator Facility in Newport News, Va., CEBAF is a DOE User Facility that is scheduled to conduct research for limited periods each year, so it must perform at its best during each scheduled run.

But glitches in any one of CEBAFs tens of thousands of components can cause the particle accelerator to temporarily fault and interrupt beam delivery, sometimes by mere seconds but other times by many hours. Now, accelerator scientists are turning to machine learning in hopes that they can more quickly recover CEBAF from faults and one day even prevent them.

Anna Shabalina is a Jefferson Lab staff member and principal investigator on the project, which has been funded by theLaboratory Directed Research & Development programfor the fiscal year 2020. The program provides the resources for Jefferson Lab personnel to make rapid and significant contributions to critical science and technology problems of mission relevance to the lab and the DOE.

Shabalina says her team is specifically concerned with the types of faults that most often bring CEBAF grinding to a halt: those that concern the superconducting radiofrequency acceleration cavities.

Machine learning is quickly gaining popularity, particularly for optimizing, automating and speeding up data analysis, Shabalina says. This is exactly what is needed to reduce the workload for SRF cavity fault classification.

SRF cavities are the backbone of CEBAF. They configure electromagnetic fields to add energy to the electrons as they travel through the CEBAF accelerator. If an SRF cavity faults, the cavity is turned off, disrupting the electron beam and potentially requiring a reconfiguration that limits the energy of the electrons that are being accelerated for experiments.

Shabalina and her team plan to use a recently deployed data acquisition system that records data from individual cavities. The system records 17 parameters from a cavity that faults; it also records the 17 parameters from a cavity if one of its near neighbors faults.

At present, system experts visually inspect each data set by hand to identify the type of fault and which component caused it. The information is a valuable tool that helps CEBAF operators for how to mitigate the fault.

Each cavity fault leaves a unique signature in the data, Shabalina says. Machine learning is particularly well suited for finding patterns, even in noisy data.

The team plans to work off of this strength of machine learning to build a model that recognizes the various types of faults. When shown enough input signals and corresponding fault types, the model is expected to be able to identify the fault patterns in CEBAFs complex signals. The next step would then be to run the model during CEBAF operations so that it can classify in real time the different kinds of faults that cause the machine to automatically trip off.

We plan to develop machine learning models to identify the type of the fault and the cavity causing instability. This will give operators the ability to apply pointed measures to quickly bring the cavities back online for researchers, Shabalina explains.

If successful, the project would also open the possibility of extending the model to identify precursors to cavity trips, so that operators would have an early warning system of possible faults and can take action to prevent them from ever occurring.

About Jefferson Science Associates, LLC

Jefferson Science Associates, LLC, a joint venture of the Southeastern Universities Research Association, Inc. and PAE, manages and operates the Thomas Jefferson National Accelerator Facility, or Jefferson Lab, for the U.S. Department of Energys Office of Science. DOEs Office of Science is the single largest supporter of basic research in the physical sciences in the United Statesand is working to address some of the most pressing challenges of our time. For more information, visithttps://energy.gov/science.

Source: Thomas Jefferson National Accelerator Facility (Jefferson Lab)

See the original post:

New Project at Jefferson Lab Aims to Use Machine Learning to Improve Up-Time of Particle Accelerators - HPCwire