Cloud Hosting

What Is Machine Learning?

Machine learning is theconcept that a computer program can learn and adapt to new data without human interference. Machine learning is a field of artificial intelligence (AI) that keeps a computers built-in algorithms current regardless of changes in the worldwide economy.

Various sectors of the economy are dealing with huge amounts of data available in different formats from disparate sources. The enormous amount of data, known as big data, is becoming easily available and accessible due to the progressive use of technology. Companies and governments realize the huge insights that can be gained from tapping into big data but lack the resources and time required to comb through its wealth of information. As such, artificial intelligence measures are being employed by different industries to gather, process, communicate, and share useful information from data sets. One method of AI that is increasingly utilized for big data processing is machine learning.

The various data applications of machine learning are formed through a complex algorithm or source code built into the machine or computer. This programming code creates a model that identifies the data and builds predictions around the data it identifies. The model uses parameters built in the algorithm to form patterns for its decision-making process. When new or additional data becomes available, the algorithm automatically adjusts the parameters to check for a pattern change, if any. However, the model shouldnt change.

Machine learning is used in different sectors for various reasons. Trading systems can be calibrated to identify new investment opportunities. Marketing and e-commerce platforms can be tuned to provide accurate and personalized recommendations to their users based on the users internet search history or previous transactions. Lending institutions can incorporate machine learning to predict bad loans and build a credit risk model. Information hubs can use machine learning to cover huge amounts of news stories from all corners of the world. Banks can create fraud detection tools from machine learning techniques. The incorporation of machine learning in the digital-savvy era is endless as businesses and governments become more aware of the opportunities that big data presents.

How machine learning works can be better explained by an illustration in the financial world. Traditionally, investment players in the securities market like financial researchers, analysts, asset managers, individual investors scour through a lot of information from different companies around the world to make profitable investment decisions. However, some pertinent information may not be widely publicized by the media and may be privy to only a select few who have the advantage of being employees of the company or residents of the country where the information stems from. In addition, theres only so much information humans can collect and process within a given time frame. This is where machine learning comes in.

An asset management firm may employ machine learning in its investment analysis and research area. Say the asset manager only invests in mining stocks. The model built into the system scans the web and collects all types of news events from businesses, industries, cities, and countries, and this information gathered makes up the data set. The asset managers and researchers of the firm would not have been able to get the information in the data set using their human powers and intellects. The parameters built alongside the model extracts only data about mining companies, regulatory policies on the exploration sector, and political events in select countries from the data set. Saya mining company XYZ just discovered a diamond mine in a small town in South Africa, the machine learning app would highlight this as relevant data. The model could then use an analytics tool called predictive analytics to make predictions on whether the mining industry will be profitable for a time period, or which mining stocks are likely to increase in value at a certain time. This information is relayed to the asset manager to analyze and make a decision for his portfolio. The asset manager may make a decision to invest millions of dollars into XYZ stock.

In the wake of an unfavorable event, such as South African miners going on strike, the computer algorithm adjusts its parameters automatically to create a new pattern. This way, the computational model built into the machine stays current even with changes in world events and without needing a human to tweak its code to reflect the changes. Because the asset manager received this new data on time, they are able to limit his losses by exiting the stock.

Read more:
Machine Learning Definition - Investopedia

In a briefing on Monday, research leaders across tech, academia and the government joined the White House to announce an open data set full of scientific literature on the novel coronavirus. The COVID-19 Open Research Dataset, known as CORD-19, will also add relevant new research moving forward, compiling it into one centralized hub. The new data set is machine readable, making it easily parsed for machine learning purposes a key advantage according to researchers involved in the ambitious project.

In a press conference, U.S. CTO Michael Kratsios called the new data set the most extensive collection of machine readable coronavirus literature to date. Kratsios characterized the project as a call to action for the AI community, which can employ machine learning techniques to surface unique insights in the body of data. To come up with guidance for researchers combing through the data, the National Academies of Sciences, Engineering, and Medicine collaborated with the World Health Organization to come up with high priority questions about the coronavirus related to genetics, incubation, treatment, symptoms and prevention.

The partnership, announced today by the White House Office of Science and Technology Policy, brings together the Chan Zuckerberg Initiative, Microsoft Research, the Allen Institute for Artificial Intelligence, the National Institutes of Healths National Library of Medicine, Georgetown Universitys Center for Security and Emerging Technology, Cold Spring Harbor Laboratory and the Kaggle AI platform, owned by Google.

The database brings together nearly 30,000 scientific articles about the virus known as SARS-CoV-2. as well as related viruses in the broader coronavirus group. Around half of those articles make the full text available. Critically, the database will include pre-publication research from resources like medRxiv and bioRxiv, open access archives for pre-print health sciences and biology research.

Sharing vital information across scientific and medical communities is key to accelerating our ability to respond to the coronavirus pandemic, Chan Zuckerberg Initiative Head of Science Cori Bargmann said of the project.

The Chan Zuckerberg Initiative hopes that the global machine learning community will be able to help the science community connect the dots on some of the enduring mysteries about the novel coronavirus as scientists pursue knowledge around prevention, treatment and a vaccine.

For updates to the CORD-19 data set, the Chan Zuckerberg Initiative will track new research on a dedicated page on Meta, the research search engine the organization acquired in 2017.

The CORD-19 data set announcement is certain to roll out more smoothly than the White Houses last attempt at a coronavirus-related partnership with the tech industry. The White House came under criticism last week for President Trumps announcement that Google would build a dedicated website for COVID-19 screening. In fact, the site was in development by Verily, Alphabets life science research group, and intended to serve California residents, beginning with San Mateo and Santa Clara County. (Alphabet is the parent company of Google.)

The site, now live, offers risk screening through an online questionnaire to direct high-risk individuals toward local mobile testing sites. At this time, the project has no plans for a nationwide rollout.

Google later clarified that the company is undertaking its own efforts to bring crucial COVID-19 information to users across its products, but that may have become conflated with Verilys much more limited screening site rollout. On Twitter, Googles comms team noted that Google is indeed working with the government on a website, but not one intended to screen potential COVID-19 patients or refer them to local testing sites.

In a partial clarification over the weekend, Vice President Pence, one of the Trump administrations designated point people on the pandemic, indicated that the White House is working with Google but also working with many other tech companies. Its not clear if that means a central site will indeed launch soon out of a White House collaboration with Silicon Valley, but Pence hinted that might be the case. If that centralized site will handle screening and testing location referral is not clear.

Our best estimate is that some point early in the week we will have a website that goes up, Pence said.

Read the original:
With launch of COVID-19 data hub, the White House issues a call to action for AI researchers - TechCrunch

TTP plc (TTP), an independent technology and product development company, has announced that it has established a partnership with NHS Highland and The University of the Highlands and Islands, to use clinical data interpretation and machine learning algorithms to predict which patients with atrial fibrillation (AF) are able to be successfully treated using electrical cardioversion (ECV).

AF is a common heart condition, causing an abnormally fast heart rate and irregular rhythm, which can lead to significant morbidity (e.g. stroke, heart failure), and mortality. Treatment options include drugs and/or ECV, however although morbidity and mortality can be significantly reduced with ECV, it is successful at one year in only 30% of patients. The ECV procedure also carries risk and is expensive to carry out. Consequently, there is an urgent need to predict which patients with AF are most suitable for treatment using ECV. Successfully predicting treatment suitability using remotely collectable data becomes especially valuable in geographies where population density is sparse, and where patients may often travel long distances for treatment.

NHS Highland and The University of the Highlands and Islands will work together to gather clinical data including electrocardiograms (ECGs), age, gender, comorbidities, medications and outcomes, all of which will be anonymized at source. TTP will analyze and interpret the data, using this information to determine any clinical notable ECG-derived factors that may influence short and long-term AF outcomes post ECV. TTP will also use the data to rapidly prototype and train machine learning algorithms for clinical prediction and risk scoring. The output of the project has the potential to be taken forward to be deployed in clinical settings such as the NHS as a risk stratification/clinical decision support tool.

Dr. Michelle Griffin, Clinical Innovator at TTP plc, said: This project will harness TTPs medical understanding as well as our technical capability, enabling us to gain new physiological understanding of the mechanics of ECV as an AF treatment, and why it works or does not work in certain patients. We are delighted to have been chosen as a partner and look forward to working with the NHS and University groups.

Professor Steve Leslie, Consultant Cardiologist, NHS Highland, commented: Currently, decisions on whether to proceed with ECV are based on varying factors and can be fairly subjective. There is a clear need for an evidence-based test to help guide physicians when treating AF, improve patient outcome and reduce unnecessary burden on the NHS.

The NHS Highland and University of the Highlands and Islands research group has received 15k funding for the project via a grant from the Collaborative Campus Challenge Fund, provided by Highlands and Islands Enterprise.

Here is the original post:
Using Machine Learning to Improve Management of Atrial Fibrillation - Technology Networks

What Can Machine Learning Tell Us About WR Prospects?

One of my favorite parts of draft season is trying to model the incoming prospects. This year, I wanted to try something new, so I dove into the world of machine learning models. Using machine learning to detail the value of a WR prospect is very useful for dynasty fantasy football.

Machine learning leverages artificial intelligence to identify patterns (learn) from the data, and build an appropriate model. I took over 60 different variables and 366 receiving prospects between the 2004 and 2016 NFL Drafts, and let the machine do its thing. As with any machine, some human intervention is necessary, and I fine-tuned everything down to a 24-model ensemble built upon different logistic regressions.

Just like before, the model presents the likelihood of a WR hitting 200 or more PPR points in at least one of his first three seasons. Here are the nine different components featured, in order of significance:

This obviously represents a massive change from the original model, proving once again that machines are smarter than humans. I decided to move over to ESPN grades and ranks instead of NFL Draft Scout for a few reasons:

Those changes alone made strong improvements to the model, and it should be noted that the ESPN overall ranks have been very closely tied to actual NFL Draft position.

Having an idea of draft position will always help a model since draft position usually begets a bunch of opportunity at the NFL level.

Since the model is built on drafts up until 2016, I figured perhaps youd want to see the results from the last three drafts before seeing the 2020 outputs.

It is encouraging to see some hits towards the top of the model, but there are obviously some misses as well. Your biggest takeaway here should be just how difficult it is to hit that 200 point threshold. Only two prospects the last three years have even a 40% chance of success. The model is telling us not to be over-confident, and that is a good thing.

Now that youve already seen some results, here are the 2020 model outputs.

Tee Higgins as the top WR is likely surprising for a lot of people, but it shouldnt be. Higgins had a fantastic career at Clemson, arguably the best school in the country over the course of his career. He is a proven touchdown scorer, and is just over 21 years old with a prototypical body-type.

Nobody is surprised that the second WR on this list is from Alabama, but they are likely shocked to see that a data-based model has Henry Ruggs over Jerry Jeudy. The pair is honestly a lot closer that many people think in a lot of the peripheral statistics. The major edge for Ruggs comes on the ground. He had a 75 yard rushing touchdown, which really underlines his special athleticism and play-making ability.

The name that likely stands out the most is Geraud Sanders, who comes in ahead of Jerry Jeudy despite being a relative unknown out of Air Force. You can mentally bump him down a good bit. The academy schools are a bit of a glitch in the system, as their offensive approach usually yields some outrageous efficiency. Since 2015, 12 of the top 15 seasons in adjusted receiving yards per pass attempt came from either an academy school or Georgia Techs triple-option attack. Sanders isnt a total zero, his profile looks very impressive, but I would have him closer to a 10% chance of success given his likely Day 3 or undrafted outcome in the NFL Draft.

Go here to see the original:
The Top Machine Learning WR Prospect Will Surprise You - RotoExperts