Think of the evolution of artificial intelligence as an umbrella. Without AI there would be no machine learning. And machine learning has given birth to deep learning. However, it might be most logical to turn that umbrella upside down, because with deep learning, the sky is the limit. One easy way to understand the difference between these three types of intelligence and learning is to draw a parallel to the age-old and very familiar analog training and education process.

DeepMind wants to solve the problem of patient deterioration in hospitals. The Google sister-company fed its AI the historical medical records of about 700,000 US veterans in hopes it will learn to predict changes in patient condition that, unchecked, lead to death. The partnership between DeepMind and the Veterans Administration (VA) brings some of the top minds in artificial intelligence research together with "world-renowned clinicians and researchers" working for the government. Basically, the US government is turning to, arguably, the smartest computer on the planet in order to find a cure for human-error. According to the laws of 1980s movies the robots will be attacking by the time you finish reading this sentence.

Before I wrote the JavaScripts, I got a master's in AI (almost a decade ago), and wrote a thesis on a weird and new area in Reinforcement Learning. Or at least it was new then. With all the hype around Machine Learning and Deep Learning, I thought it would be neat if I wrote a little primer on what Reinforcement Learning really means, and why it's different than just another neural net. Richard Sutton and Andrew Barto wrote an amazing book called "Reinforcement Learning: an introduction"; it's my favourite non-fiction book I have ever read in my life, and it's why I fell in love with RL. The complete draft is available for free here, and if you're into math, and want to explore this topic further, I can't recommend it enough.

In part one of the machine learning crash course, we introduced the field of supervised machine learning (ML) by walking through popular algorithms like linear regression and logistic regression. But supervised learning is just one of the many types of algorithms in the vast machine learning / artificial intelligence space. In this article, we take a look at two other subdisciplines: Unsupervised learning and deep learning. When performing supervised learning, our datasets consisted of labeled examples. In the linear regression example, we had TV advertising data labeled with the amount of sales generated.

Breakthroughs in data science and machine learning are happening at a break-neck pace. If you are working in this field, it's extremely important to keep yourself updated with what's new. Following GitHub repositories is one such way to do so. You can see the latest developments, interesting projects and their applications. I can not tell how much learning can happen through this.

In the last years there has been quite some fuzz in the news about two seemingly unrelated topics: the decrease of bees and other pollinators and, simultaneously, the increase of artificial intelligence and data science. Reading up a bit on pollination and how research on pollination works I realized that although a lot is known, there is, as in all sciences, way more unknown. This triggered my curiosity, wouldn't it be possible to use AI for the study of pollinators? In this post I explain a simple way to do so. I wrote some code, which can be found om my github. But as a teaser: I wanted to quantify the number of visitations to the flowers in this movie.

IBM is focused on delivering new AI capabilities in the cloud and on premises to help enterprises gain insights from their data and create new value with that data, the company reports. IBM has been working with NVIDIA to bring its latest GPU (graphics processing unit) technology, NVIDIA Tesla V100, to the cloud and offers a suite of GPUs including the P100, K80 and M60 on IBM Cloud bare metal and virtual servers. To power on-premises workloads, IBM also offers CPU-to-GPU NVIDIA NVLink connection on its POWER9 servers. Now IBM is introducing the NVIDIA Tesla V100 GPU to support AI, deep learning and HPC workloads on the cloud. Users can equip individual IBM Cloud bare metal servers with up to two NVIDIA Tesla V100 PCIe GPU accelerators, NVIDIA's latest, most advanced GPU architecture.

The team went on to create what would become another master gamer in the AlphaGo family, this one called simply AlphaZero. In a paper posted to the scientific preprint site ArXiv.org in December, DeepMind researchers revealed that after starting again from scratch, the trained-up AlphaZero outperformed AlphaGo Zero--in other words, it beat the bot that beat the bot that beat the best Go players in the world. And when it was given the rules for chess or the Japanese chess variant shogi, AlphaZero quickly learned to defeat bespoke top-level algorithms for those games, too. Experts marveled at the program's aggressive, unfamiliar style. "I always wondered how it would be if a superior species landed on Earth and showed us how they played chess," the Danish grandmaster Peter Heine Nielsen told a BBC interviewer.

We are currently collecting all feedback and already started working on a more complete report -- stay tuned! For most of us, deep learning still happens on Nvidia GPUs. There is currently no alternative with practical relevance. Google's Tensor Processing Unit (TPU), a custom-developed chip for deep learning, promises to change that. Nine months after the initial announcement, Google last week finally released TPUv2 to early beta users on the Google Cloud Platform.

In the broad sweep of AI's current worldly ambitions, machine learning healthcare applications seem to top the list for funding and press in the last three years. Since early 2013, IBM's Watson has been used in the medical field, and after winning an astounding series of games against with world's best living Go player, Google DeepMind's team decided to throw their weight behind the medical opportunities of their technologies as well. Many of the machine learning (ML) industry's hottest young startups are knuckling down significant portions of their efforts to healthcare, including Nervanasys (recently acquired by Intel), Ayasdi (raised $94MM as of 02/16), Sentient.ai With all the excitement in the investor and research communities, we at TechEmergence have found most machine learning executives have a hard time putting a finger on where machine learning is making its mark on healthcare today. We've written this article, not to be a complete catalogue of possible applications, but to highlight a number of current and future uses of machine learning in the medical field, with relevant links to external sources and related TechEmergence interviews. The list below is by no means complete, but provides a useful lay-of-the-land of some of ML's impact in the healthcare industry.