Cardiac pain originates from the heart muscle, most typically when blood flow to the heart (through vessels called coronary arteries) become blocked. In the heart muscle, there are nerve endings which transmit signals to the brain which get interpreted as chest pain. Unfortunately, just like other pain arising in other organs in the body, cardiac pain is poorly localized. Think of when you had stomach cramps after eating some food you probably shouldn't have--the pain is often vague and generalized, as opposed to being isolated to one specific location. Furthermore sensations arising from other organs in the chest, such as the esophagus, can produce pain indistinguishable from cardiac pain.

Editor's note: This month marks the centennial of the birth of Claude Shannon, the American mathematician and electrical engineer whose groundbreaking work laid out the theoretical foundation for modern digital communications. To celebrate the occasion, we're republishing online a memorable profile of Shannon that IEEE Spectrum ran in its April 1992 issue. Written by former Spectrum editor John Horgan, who interviewed Shannon at his home in Winchester, Mass., the profile reveals the many facets of Shannon's character: While best known as the father of information theory, Shannon was also an inventor, tinkerer, puzzle solver, and prankster. The 1992 profile included a portrait of Shannon taken by Boston-area photographer Stanley Rowin. On this page we're reproducing that portrait along with other Shannon photos by Rowin that Spectrum has never published. Shannon died in 2001 at age 84 after a long battle with Alzheimer's disease. He is regarded as one of the greatest electrical engineering heroes of all time.

First it was software, then the website, then the app. While it is still too early to say for certain whether or not this will indeed be the case, the hands of intelligent automation are becoming more and more common across multiple industries and fintech is certainly among them. Here are three reasons why AI and smart bots will play a greater role in payments. The first quarter of 2016 saw payments firms raise USD274.3m in disclosed investment deals. This was down 45% on figures from Q1 2015, where payments startups raised USD500m.

Your.MD is truly revolutionary and can make a seismic impact on the global healthcare system by providing accessible, trustworthy and instant healthcare to everyone with a mobile phone, said Matteo Berlucchi, Your.MD's chief executive in the announcement. Our OneStop Health is an integral part of our vision; if you can't get to your doctor or need to get a blood test, a prescription or make an appointment to see the best local specialist, Your.MD can facilitate it – safely and responsibly by connecting you to the best service providers and products who can help you get what you need.

In this article we conclude our four part series on basic model testing. When fitting and selecting models in a data science project, how do you know that your final model is good? And how sure are you that it's better than the models that you rejected? In this concluding Part 4 of our four part mini-series "How do you know if your model is going to work?" we demonstrate cross-validation techniques. Cross validation techniques attempt to improve statistical efficiency by repeatedly splitting data into train and test and re-performing model fit and model evaluation.

Guest blog post by Christopher Dole and other contributors, originally posted here. Deep Learning is one of the most revolutionary and disruptive technologies ever developed in Data Science. Essentially, this is a class of algorithms inspired by how the human brain works, and it has the ability to automate and replace most of the world's jobs. This is what enables self-driving cars to function and what allows Spotify to create very customized playlists and recommendations. This is how YouTube is able to identify faces and animals in videos and how Siri can understand and process free speech in milliseconds.

If you think of each day as a conference crammed into a day, you get a good flavor of things. Here are some of the interesting things I saw.

The last few years have seen considerable advances in artificial intelligence (AI) using technology inspired by animal brains. Neural networks and deep reinforcement learning allowed Google Deep Mind's AlphaGo AI to beat champion Go player Lee Se-dol. The same methods are being used to teach AI to explore in Minecraft and win deathmatches in Doom. Neural networks can even influence the art world. Google's Deep Mind famously produces surreal art that took the internet by storm.

Deep Learning is one of the most revolutionary and disruptive technologies ever developed in Data Science. Essentially, this is a class of algorithms inspired by how the human brain works, and it has the ability to automate and replace most of the world's jobs. This is what enables self-driving cars to function and what allows Spotify to create very customized playlists and recommendations. This is how YouTube is able to identify faces and animals in videos and how Siri can understand and process free speech in milliseconds. Deep Learning has also led to several recent advancements in healthcare.

Last week an artificial intelligence won the first Go game against the human. Yesterday, the same software lost because it rated as highly improbable a move by the human player. While machine learning progresses exponentially, it keeps on having trouble coping with rare events. As long as artificial intelligences are used to play games, this issue does not really matter. Ignoring the event is arguably a good strategy in such a context, as it ensures that the majority of games will be won over a large number of games.