Machine Learning is a form of Artificial Intelligence (AI) which allows computers to learn by way of observation and experience, rather than rigid pre-programming. Machine Learning uses computer programs that are capable of growth and change as they process new data. Using algorithms, Machine Learning allows computers to develop habitual responses based on the repeated behaviors and actions of the person using the computer. The concept of learning repeated behaviors is important. As models are presented with new data, they adapt, learning from earlier experiences to provide reliable, consistent results and responses. While the science of Machine Learning is not new, it has been gaining a renewed popularity as it becomes a fundamental building block in AI technology, Big Data, and the evolution of virtual assistants.

Enterprise technology vendors are rushing to get on the chatbot bandwagon. Last week saw IBM Corp. and Cisco Systems Inc. add their names to the list by announcing a partnership to deliver artifical intelligence services for the latter's collaboration software. According to the companies, the development effort will focus primarily on providing "real-time advice and handling tasks". They didn't go into more detail, but offered a few examples of how the upcoming chatbot functionality might come handy. Financial advisors, for instance, could have certain investment recommendations generated automatically when talking with clients via Cisco WebEx.

Google and the U.K.'s government health service have partnered to study whether computers can be trained to spot degenerative eye problems early enough to prevent blindness. Google DeepMind, the London-based artificial intelligence unit owned by Alphabet Inc., announced a research partnership today with the National Health Service to gain access to a million anonymous eye scans. DeepMind will use the data to train its computers to identify eye defects. The aim is to give doctors a digital tool that can read an eye-scan test and recognize problems faster. Earlier detection of eye disorders related to diabetes and age-related macular degeneration could allow doctors to prevent loss of vision in many people, according to a statement by DeepMind Tuesday announcing the project with the Moorfields Eye Hospital NHS Foundation Trust.

NVIDIA invented the graphics processing unit (GPU) in 1999. To some, it seems counterintuitive that a chip originally designed to play 3D games has become the engine of today's AI revolution. But in fact the problem of computer graphics has features in common with many other applications, from computational fluid dynamics and medical imaging to computer vision and natural language processing. At a high level, the unifying factor is that these problems can be parallelised. Our chip might be called a'graphics' processor, but in fact it's an incredibly versatile parallel processing engine which is playing a pivotal role in democratising AI.

As new technologies and trends emerge in the marketplace, small and medium-sized companies must look out for ways they too can benefit from improvements and advancements. Startups have demonstrated time and time again in recent years that the company willing to put new technology to use to solve an old problem (Netflix, Uber, Airbnb, etc.) are the companies that will succeed -- no matter how big or small they are when they begin. These technologies mean that work that once might have been too time consuming or expensive for a small company to do becomes quick and relatively inexpensive. Companies that can't afford a dedicated customer service representative can outsource much of that work to a chatbot that can answer simple customer service questions. While a very small business might not be able to employ the latest in robotics in-house, the advent of more automated manufacturing will make manufacturing more affordable and small runs of products more achievable. This will open up production possibilities for many small businesses.

Google's DeepMind AI business unit is hoping to teach computers to diagnose eye disease, using patient data from a U.K. hospital. Using deep learning techniques, DeepMind hopes to improve diagnosis of two eye conditions: age-related macular degeneration and diabetic retinopathy, both of which can lead to sight loss. If these conditions are detected early enough, patients' sight can be saved. One way doctors look for signs of these diseases is by examining the interior of the eye, opposite the lens, an area called the fundus. They can do this either directly, with an ophthalmoscope, or by taking a digital fundus scan.

It aims to implement a wide array of machine learning methods and functions as a "swiss army knife" for machine learning researchers. This README serves as a guide for what mlpack is, how to install it, how to run it, and where to find more documentation. Citations are beneficial for the growth and improvement of mlpack. All of those should be available in your distribution's package manager. If not, you will have to compile each of them by hand.

Do you get what overfitting means in machine learning? If you don't, then you better learn about it if you want to use or leverage machine learning. Because overfitting can ruin the effectiveness of machine learning. I wrote this blog because I found existing explanations of overfitting to be too technical. I hope this one is more consumable by non specialists. Machine learning involves a fairly complex workflow, see Machine Learning Algorithm!

Ed Rex believes we're heading towards a world where artificial intelligence will master creativity. Professor Lynda Gratton explains how people should prepare for it. "The secret to creativity is knowing how to hide your sources." This quote is often attributed to Albert Einstein but also to philosopher C.E.M. Joad, among others: a well-hidden source indeed. But the idea behind the phrase raises questions: if creativity is copied, is it original?