Recently, the Google RankBrain system has started to garner quite the buzz within the SEO community -- but to a large degree, it's not entirely being understood. We've even had some somewhat link-baity post titles that didn't help things either. And of course, Google didn't do itself any favors including the word "rank" in the name. So, let's start with a statement by Google's Gary Illyes: " Lemme try one last time: Rankbrain lets us understand queries better. No affect on crawling nor indexing or replace anything in ranking" – via Twitter The core, from what we understand, is more about better assessment of queries and the classifications therein.

Apple's Siri voice assistant is one of the most widely available bots in the world. Most people with an iPhone can ask it questions with the push of a button, and over the years, Apple has used that data to improve Siri. Still, there are definitely still some very common questions that Siri, on its own, wouldn't have the answer to. Although Siri uses advanced machine learning to parse your questions, its artificial intelligence is not advanced enough to come up with clever responses to abstract questions. So Apple has clearly enlisted a few writers to come up with canned responses to common Siri queries.

Researcher David Silver and colleagues designed a computer program capable of beating a top-level Go player – a marvelous technological feat and important threshold in the development of artificial intelligence, or AI. It stresses once more that humans aren't at the center of the universe, and that human cognition isn't the pinnacle of intelligence. I remember well when IBM's computer Deep Blue beat chess master Garry Kasparov. Where I'd played – and lost to – chess-playing computers myself, the Kasparov defeat solidified my personal belief that artificial intelligence will become reality, probably even in my lifetime. I might one day be able to talk to things similar to my childhood heroes C-3PO and R2-D2.

The Internet of Things (IoT), with its ubiquitous sensors and streams of big data for big insights, has an estimated market valuation of 1.7 trillion. Apparently, the "sensoring" of the world is a seriously big deal, generating insights into people, processes, and products on a scale that is almost incomprehensible. Certainly, 1.7 trillion is almost an incomprehensible figure. The corresponding forecasts for data-driven insights that lead to such a valuation are expected to be on a similarly large scale to justify those astronomical projections. But insights are not hardcoded within Raspberry Pi or Arduino kits, though IFTTT (If-This-Then-That) kits might be a satisfactory solution (more about that later).

This blog post is authored by Brandon Rohrer, Senior Data Scientist at Microsoft. The raw stuff of data science is a collection of numbers and names. Measurements, prices, dates, times, products, titles, actions--everything is fair game. You can use images, text, audio, video and other complex data too, as long as you have a way to reduce it to numbers and names. The mechanics of getting data can be quite complex. But this guide is focused on the data science, so I'll leave that topic for another time. Data science is the process of using names and numbers to answer a question.

Summary: News of the coming great extinction has the chattering classes agog with fear. The rapid evolution of algorithms, software, and robots will make many kinds of jobs as extinct as the Great Auk. This will reshape the world into a wonderland -- or unleash disastrous social turmoil. Yet another of these coordinated-looking propaganda barrages warn us of the danger. These headlines are correct, but about the wrong subject.

In case you haven't heard yet, Data Science is all the craze. Courses, posts, and schools are springing up everywhere. However, every time I take a look at one of those offerings, I see that a lot of emphasis is put on specific learning algorithms. Of course, understanding how logistic regression or deep learning works is cool, but once you start working with data, you find out that there are other things equally important, or maybe even more. I can't really blame these courses.

Matthew Finnegan and Christina Mercer recently wrote in ComputerWorld UK, "With businesses increasingly keen on incorporating artificial intelligence into their operations, machine learning – the ability for a system to learn from large data sets rather than following preset rules – offers a number of benefits. This might mean building predictive models for fraud prevention, for example, or personalising content on a website. The opportunity is not lost on many of the major tech firms Google, Microsoft, IBM and AWS all offer machine learning capabilities. Here are some of the top machine learning tools to get started with artificial intelligence in the enterprise."

So you know the Bayes rule. How does it relate to machine learning? It can be quite difficult to grasp how the puzzle pieces fit together - we know it took us a while. This article is an introduction we wish we had back then. While we have some grasp on the matter, we're not experts, so the following might contain inaccuracies or even outright errors.

Huba Gaspar, Global Marketing Manager at Gravity R&D outlines one of the hottest tech buzzwords Deep Learning (DL) and possible ways in which DL based approaches can revolutionize personalization technologies. Deep learning is a sub-field of machine learning and it comprises several approaches to tackling the single most important goal of AI research: allowing computers to model our world well enough to exhibit something like what we humans call intelligence. On a basic conceptual level, deep learning approaches share a very basic trait. DL algorithms interpret the raw data through multiple processing layers. Each of these layers takes the output of the previous one as its input and creates a more abstract representation of it.