Google has always used its annual I/O conference to connect to developers in its sprawling empire. It announces new tools and initiatives, sprinkles in a little hype, and then tells those watching: choose us, and together we'll go far. But while in previous years this message has been directed at coders working with Android and Chrome -- the world's biggest mobile OS and web browser respectively -- yesterday, CEO Sundar Pichai made it clear that the next platform the company wants to dominate could be even bigger: artificial intelligence. For Google, this doesn't just mean using AI to improve its own products. The company wants individuals and small companies around the world to also get on board.

By now, many of us have heard about or might even own one of the popular, sleek multi-functional voice-first devices, such as the Amazon Echo, also known as "Alexa", the name used when waking the device to give a verbal command. Quick joke: How do you make Alexa laugh? This joke is terrible for many reasons, not the least of which is that I ended up anthropomorphized a digital device, which may be one of the biggest issues with this devices. Related: There's No Doubt That Amazon Alexa Is the Next Big Thing First, according to Voice Labs Voice Report for 2017, 6.5 million voice-first devices -- defined as an always-on piece of hardware utilizing artificial intelligence (AI) with primarily a voice interface, both for input and output -- were shipped in 2016. In 2017, that number is estimated to grow to 24.5 million devices shipped, thanks in large part to its appearance during the Super Bowl commercials.

Machine-learning experts are in short supply as companies in many industries rush to take advantage of recent strides in the power of artificial intelligence. Google's CEO says one solution to the skills shortage is to have machine-learning software take over some of the work of creating machine-learning software. At Google's annual developer conference today, Pichai introduced a project called AutoML coming out of the company's Google Brain artificial intelligence research group. Researchers there have shown that their learning algorithms can automate one of the trickiest parts of the job of designing machine-learning software to take on a particular task. In some cases, their automated system came up with designs that rivals or beats the best work of human machine-learning experts.

Helping the elderly stay in their homes with an assistive robot, giving those with disabilities greater independence by increasing their mobility and improving safety for workers across industries with a single invention sounds ambitious, but that's just Genesis Robotics wants to do with its newly unveiled LiveDrive, a direct-drive robotic actuator. "I think we can even help people walk," Genesis Robotics and LiveDrive President Michael Gibney told International Business Times. "If we can get an exoskeleton, get people out of a wheel chair, we'll be able to really change people's lives." Genesis Robotics hopes to change the way robots are made with its LiveDrive actuator. LiveDrive aims to replace bulky motors, drive belts and gearboxes that limit existing robots in terms of load-bearing, precision, speed and flexibility of use.

Twitter has updated its privacy policy and announced plans to collect more data about its users, and keep hold of that data for longer. However, it's also introduced new tools to let you see what the site and its partners think they know about you and your interests. The micro-blogging site has never been profitable, and is working on changing that this year. The I.F.O. is fuelled by eight electric engines, which is able to push the flying object to an estimated top speed of about 120mph. The giant human-like robot bears a striking resemblance to the military robots starring in the movie'Avatar' and is claimed as a world first by its creators from a South Korean robotic company Waseda University's saxophonist robot WAS-5, developed by professor Atsuo Takanishi and Kaptain Rock playing one string light saber guitar perform jam session A man looks at an exhibit entitled'Mimus' a giant industrial robot which has been reprogrammed to interact with humans during a photocall at the new Design Museum in South Kensington, London Electrification Guru Dr. Wolfgang Ziebart talks about the electric Jaguar I-PACE concept SUV before it was unveiled before the Los Angeles Auto Show in Los Angeles, California, U.S The Jaguar I-PACE Concept car is the start of a new era for Jaguar.

Designs for the much-requested ginger emoji have been revealed. Jeremy Burge, the founder of Emojipedia, posted a series of sample images on Twitter, showing off what the final designs could look like. He included pictures of famous redheads Jessica Chastain and Ed Sheeran alongside the emoji. The I.F.O. is fuelled by eight electric engines, which is able to push the flying object to an estimated top speed of about 120mph. The giant human-like robot bears a striking resemblance to the military robots starring in the movie'Avatar' and is claimed as a world first by its creators from a South Korean robotic company Waseda University's saxophonist robot WAS-5, developed by professor Atsuo Takanishi and Kaptain Rock playing one string light saber guitar perform jam session A man looks at an exhibit entitled'Mimus' a giant industrial robot which has been reprogrammed to interact with humans during a photocall at the new Design Museum in South Kensington, London Electrification Guru Dr. Wolfgang Ziebart talks about the electric Jaguar I-PACE concept SUV before it was unveiled before the Los Angeles Auto Show in Los Angeles, California, U.S The Jaguar I-PACE Concept car is the start of a new era for Jaguar.

Engineer at @Parietal_INRIA, contributes to scikit-learn.

Statistics, though a central set of tools for data science, are often overlooked in favor of more solidly technical skills like programming. Even machine learning learning algorithms, with their reliance on mathematical concepts such as algebra and calculus -- not to mention statistics! However, if you are unable to fully understand the basic descriptive statistics terminology included herein, you are definitely lacking foundational knowledge that is needed to build a whole series of much more robust and useful professional concepts on top of. So here is a collection of 15 basic descriptive statistics key terms, explained in easy to understand language. A comprehensive example follows, which includes a bit of Python code.

A tree has many analogies in real life, and turns out that it has influenced a wide area of machine learning, covering both classification and regression. In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making. As the name goes, it uses a tree-like model of decisions. Though a commonly used tool in data mining for deriving a strategy to reach a particular goal, its also widely used in machine learning, which will be the main focus of this article. For this let's consider a very basic example that uses titanic data set for predicting whether a passenger will survive or not.

In this guide, we'll take a practical, concise tour through modern machine learning algorithms. While other such lists exist, they don't really explain the practical tradeoffs of each algorithm, which we hope to do here. We'll discuss the advantages and disadvantages of each algorithm based on our experience. Categorizing machine learning algorithms is tricky, and there are several reasonable approaches; they can be grouped into generative/discriminative, parametric/non-parametric, supervised/unsupervised, and so on. However, from our experience, this isn't always the most practical way to group algorithms.