This week we completed four in-depth training webinars focused on WhizzML, BigML's new domain-specific language for automating Machine Learning workflows, implementing high-level Machine Learning algorithms, and easily sharing them with others. We already have our first batch of WhizzML graduates merely a week after launch. However, many of you were either not able to secure a live webinar spot or not able to join us at the scheduled date and time. Don't fret if you missed any of these training sessions. You can now watch the whole series at your own pace on BigML's YouTube channel.

CrowdFlower, the essential human-in-the-loop platform for data science teams, today announced its recent 10M venture funding. The investment round was led by Canvas Ventures, Trinity Ventures, and Microsoft. The capital will be used to fuel the adoption of CrowdFlower AI which combines training data, machine learning and human-in-the-loop in a single platform. "We've seen companies like Tesla and Uber build large data science teams and adopt AI and machine learning to solve billion dollar problems like driverless cars," said Lukas Biewald, founder and chief executive officer at CrowdFlower. "But we wanted to bring AI and machine learning within the reach of every business to attack million dollar problems such as classifying customer support tickets or generating customer insights from social data. With CrowdFlower AI, companies can now easily deploy machine learning models that are commercially viable at a much lower price point."

Facebook is known for a variety of mantras embedded in its culture, often spelled out on signs at its offices or recited by CEO Mark Zuckerberg and other executives: "Code wins arguments," "Move fast and break things," or "Done is better than perfect." A sign on the wall at the company's New York office perfectly sums up the approach Yann LeCun brings to his leadership of Facebook's nascent efforts in the field of artificial intelligence and machine learning: "Always be Open." Artificial intelligence has become a vital part of scaling Facebook. It's already being used to recognize the faces of your friends in photographs, and curate your newsfeed. DeepText, an engine for reading text that was unveiled last week, can understand "with near-human accuracy" the content in thousands of posts per second, in more than 20 different languages. Soon, the text will be translated into a dozen different languages, automatically. Facebook is working on recognizing your voice and identifying people inside of videos so that you can fast forward to the moment when your friend walks into view. Facebook wants to dominate in AI and machine learning, just as it already does in social networking and instant messaging.

Nasa has announced that it has found evidence of flowing water on Mars. Scientists have long speculated that Recurring Slope Lineae -- or dark patches -- on Mars were made up of briny water but the new findings prove that those patches are caused by liquid water, which it has established by finding hydrated salts. Several hundred camped outside the London store in Covent Garden. The 6s will have new features like a vastly improved camera and a pressure-sensitive "3D Touch" display

Stuart Armstrong is a philosopher at the University of Oxford and one of the paper's authors. Machines are becoming more intelligent every year thanks to advances being made by companies like Google, Facebook, Microsoft, and many others. AI agents, as they're sometimes known, can already beat us at complex board games like Go and they're becoming more competent in a range of other areas. Now a London AI research lab owned by Google has carried out a study to make sure we can pull the plug on self-learning machines when we want to. DeepMind, acquired by Google for a reported 400 million in 2014, teamed up with scientists at the University of Oxford to find a way to make sure AI agents don't learn to prevent, or seek to prevent humans from taking control.

When you go to the grocery store, you see that items of a similar nature are displayed nearby to each other. When you organize the clothes in your closet, you put similar items together (e.g. Every personal organizing tip on the web to save you from your clutter suggests some sort of grouping of similar items together. Even we don't notice it, we are involved in grouping similar objects together in every aspect of our life. This is called clustering in machine learning, so in this post I will provide an overview of data mining clustering methods. In machine learning or data mining, clustering assigns similar objects together in order to discover structures in data that doesn't have any labels.

Software engineering is about developing programs or tools to automate tasks. Instead of "doing things manually," we write programs; a program is basically just a machine-readable set of instructions that can be executed by a computer. Let's consider a classic example: e-mail spam filtering. Assuming that we have access to the source code of our e-mail client and know how to handle it, we could come up with an instinctive set of rules that may help us with our spam problem. For example: if not "sender in contacts": if "subject line contains BUY!: e-mail spam folder:" else if ... It is intuitive to say that coming up with these rules is a pretty tedious task.

As spatial query systems such as EQS (Unreal Engine 4), TPS (CryEngine), and PQS (Luminous Studio) have matured, auto-generated spatial queries are increasingly relied upon for robust dynamic position selection. We present a series of techniques and extensions to these systems used by Square Enix to produce novel behaviors and improve position selection in our current generation of AAA RPG titles. In addition, we have expanded UE4's Environment Query System to serve as a general-purpose utility system; we show how minor modifications allowed the team to use EQS to coordinate combat, reduce behavior tree complexity with a hybrid BT/US approach, and increase character AI quality for a range of tasks such as action and target selection. Attendees will learn how to get the most out of modern spatial query systems with a combination of new techniques and best practices to maximize quality and extend their application to new areas.

Why do you think technologies such as AI and predictive coding are apparently gaining momentum in term of their uptake in the UK legal market? First and foremost, the legal market isn't an isolated bubble. There is so much happening right now in the world at large about AI and cognitive computing that the legal market simply can't be immune. Our behaviours in the workplace are driven (and increasingly so) by our experiences in the rest of our lives, so when there's a constant stream of mainstream news about AI, it's inevitable that it must impact the legal market. Add into the mix a number of other legal market factors – the increasing price sensitivity of clients, the explosion in data, the long-standing feeling that there must be a better way to do repetitive knowledge tasks than simply adding more junior lawyers – and you've got the perfect conditions for these technologies to take hold.

When it comes to robots replacing humans, we might think we have the upper hand since we're the ones who build and program them but that's not neccesarily the case anymore. Google is taking a different approach to training its robots – it's letting them teach each other. Researchers at Google have released a report showing how they connected 14 robotic arms together and used convolutional neural networks to let them teach themselves how to pick things up. The approach mimics how young children learn between the ages of one and four years old, and is essentially helping the robots to develop reliable hand-eye coordination. Typically, a robot would be programmed to carry out specific tasks, but this method shows how they can learn through trial-and-error in combination with a neural network – the same way a child learns how to do something by watching other people.