Historically most buzz words of the month have lost their meaning before the month ends. This graphic is a very good top level overview of the parts of each of these main categories and I would expect no less from Gartner. It's not the definitions as much as it is the interpretation. Benefits from using high-level computing such as AI and data mining will outweigh the costs and the ROI return will be much quicker. This is shown in manufacturing using robotics (another AI derivative).

A considerable number of articles cover machine learning and its ability to protect us from cyberattacks. Still, it's important to separate the hype from the reality and see what exactly machine learning (ML), deep learning (DL) and artificial intelligence (AI) algorithms can do right now in cybersecurity. First of all, I have to disappoint you. Unfortunately, machine learning will never be a silver bullet for cybersecurity compared to image recognition or natural language processing, two areas where machine learning is thriving. There will always be a person who tries to find issues in our systems and bypass them.

"Coditany of Timeness" is a convincing lo-fi black metal album, complete with atmospheric interludes, tremolo guitar, frantic blast beats and screeching vocals. But the record, which you can listen to on Bandcamp, wasn't created by musicians. Instead, it was generated by two musical technologists using a deep learning software that ingests a musical album, processes it, and spits out an imitation of its style. To create Coditany, the software broke "Diotima," a 2011 album by a New York black metal band called Krallice, into small segments of audio. Then they fed each segment through a neural network -- a type of artificial intelligence modeled loosely on a biological brain -- and asked it to guess what the waveform of the next individual sample of audio would be.

Free-text radiology reports can be automatically classified by convolutional neural networks (CNNs) powered by deep-learning algorithms with accuracy that's equal to or better than that achieved by traditional--and decidedly labor-intensive--natural language processing (NLP) methods. That's the conclusion of researchers led by Matthew Lungren, MD, MPH, of Stanford University. The team tested a CNN model they developed for mining pulmonary-embolism findings from thoracic CT reports generated at two institutions. Radiology published their study, lead-authored by Matthew Chen, MS, also of Stanford, online Nov. 13. The researchers analyzed annotations made by two radiologists for the presence, chronicity and location of pulmonary embolisms, then compared their CNN's performance with that of an NLP model considered quite proficient in this task, called PeFinder. They note that PeFinder and similar existing NLP techniques demand a "relatively high burden of development, including domain-specific feature engineering, complex annotations and laborious coding for specific tasks."

A considerable number of articles cover machine learning and its ability to protect us from cyberattacks. Still, it's important to separate the hype from the reality and see what exactly machine learning (ML), deep learning (DL) and artificial intelligence (AI) algorithms can do right now in cybersecurity. First of all, I have to disappoint you. Unfortunately, machine learning will never be a silver bullet for cybersecurity compared to image recognition or natural language processing, two areas where machine learning is thriving. There will always be a person who tries to find issues in our systems and bypass them.

AI can now solve some of the hardest Sudoku puzzles to a high degree of accuracy, according to new research that teaches machines to logically reason. Sudoku was, if you can recall, all the rage in the West at least a decade ago. There are several techniques and algorithms to crack the puzzles within a second, however, it's an interesting problem for deep-learning software to tackle, as it's an exercise for neural networks to practice complex reasoning. A paper describing the method that uses recurrent relational networks was published through ArXiv earlier this month. It builds upon DeepMind's previous work with relational networks (RNs), a type of neural network that focuses on the relationship between pairs of objects.

Machine learning is taking the tech world by storm. Recently, an announcement that Google was open-sourcing Tensor Flow, their machine learning (ML) software, and Microsoft quickly followed suit. Baidu and Amazon unveiled their own deep learning platforms a few months later, while Facebook began supporting the development of two ML frameworks. But the revolution has spread far beyond the tech realm. As machine learning (ML) continues to take over the tech world, companies and researchers outside the tech bubble have started using ML in strange and surprising ways.

At the beginning of 2016, most serious Go players would have told you that a machine would never beat a Go world champion. Then, Google's AlphaGo AI beat the world's strongest player, Ke Jie 3-0. Six months later, Alpha Go Zero destroyed AlphaGo, defeating it 89 games to 11. AlphaGo was an incredible accomplishment for deep learning systems, and it's a fascinating story. Deep Learning and the Game of Go opens up the world of deep learning and AI by teaching you to build your own Go-playing machine. You'll explore key deep learning ideas like neural networks and reinforcement learning and maybe even step up your Go game a notch or two.

One challenge of neural or deep architectures is that it is difficult to determine what exactly is going on in the machine learning algorithm that makes a classifier decide how to classify inputs. This is a huge problem in deep learning: we can get fantastic classification accuracies, but we don't really know what criteria a classifier uses to make its classification decision. However, decision trees can present us with a graphical representation of how the classifier reaches its decision. We'll be discussing the CART (Classification and Regression Trees) framework, which creates decision trees. First, we'll introduce the concept of decision trees, then we'll discuss each component of the CART framework to better understand how decision trees are generated. Before discussing decision trees, we should first get comfortable with trees, specifically binary trees.

John Lynn is the Founder of the HealthcareScene.com These EMR and Healthcare IT related articles have been viewed over 16 million times. John also manages Healthcare IT Central and Healthcare IT Today, the leading career Health IT job board and blog. John is highly involved in social media, and in addition to his blogs can also be found on Twitter: @techguy and @ehrandhit and LinkedIn. It seems like these days every healthcare IT company out there is saying they're doing machine learning, AI, deep learning, etc.