Game lovers are likely to be familiar with Nvidia's graphics processors. The US tech firm continues to derive the bulk of its revenue from gaming-related semiconductors, but there is now a new growth segment -- chips for artificial intelligence (AI). Business related to deep learning, a core part of an artificially intelligent machine, is one key factor behind Nvidia's impressive first-quarter results. The advanced machine learning function is being applied to numerous areas, including speech recognition, video search, face detection and self-driving vehicles. Nvidia is already working closely with automaker Tesla in development of driverless car technology, which deploys AI to detect pedestrians and track lanes and to recognize traffic signs.

I've written a number of blog posts about regression analysis and I've collected them here to create a regression tutorial. This tutorial covers many aspects of regression analysis including: choosing the type of regression analysis to use, specifying the model, interpreting the results, determining how well the model fits, making predictions, and checking the assumptions. At the end, I include examples of different types of regression analyses. If you're learning regression analysis right now, you might want to bookmark this tutorial! Before we begin the regression analysis tutorial, there are several important questions to answer.

They continue, "(2) Speed and Scale Matter. In order to analyze, swiftly and accurately, billions of events in real-time, machine learning models require a level of computational power and scalability that cannot be accomplished using old-school on-premise architecture and conventional database methods. Cloud-based architectures can significantly augment the efficacy of machine learning. Algorithms can be infused with the collective knowledge of a crowdsourced community where threat intelligence is aggregated and updated instantly. Identified attacks can then be turned into a new detection and learned by the algorithm, and shared with others within the cloud network to prevent the attack – sending the bad actors back to the drawing board."

I wanted to put some of my ideas together, so as usual I'm writing a blog post. These are all pretty basic ideas but maybe they are helpful to people who are new to thinking about machine learning! When explaining what machine learning is, I'm giving the example of predicting the country someone lives in from their first name. So John might be American and Johannes might be German. In this case, it's really easy to imagine what data you might want to do a good job at this -- just get the first names and current countries of every person in the world!

Google today said it was unveiling a virtual assistant tool at its annual Google I/O developer conference in Mountain View called Google Assistant. This appears to be similar to what Google Now can do already, but it appears to be an upgraded version of it. You can ask a question for an answer, and follow up with multiple questions, with Google picking the conversation out and returning the right answer. The service is integrated in its new conversational user interface products: Google Assistant is also built into Allo, its new independent chat bot app, as well as Google Home, its Amazon Echo competitor. "Think of the assistant, we think of it as a conversational assistant, we want users to have an ongoing two-way dialog," CEO Sundar Pichai said.

For today's post, I've drawn material not just from one paper, but from five! The subject matter is'word2vec' – the work of Mikolov et al. at Google on efficient vector representations of words (and what you can do with them). From the first of these papers ('Efficient estimation…') we get a description of theContinuous Bag-of-Words and Continuous Skip-gram models for learning word vectors (we'll talk about what a word vector is in a moment…). From the second paper we get more illustrations of the power of word vectors, some additional information on optimisations for the skip-gram model (hierarchical softmax and negative sampling), and a discussion of applying word vectors to phrases. The third paper ('Linguistic Regularities…') describes vector-oriented reasoning based on word vectors and introduces the famous "King – Man Woman Queen" example.

Can you explain me what machine learning is? I often get this question from colleagues and customers, and answering it is tricky. What is tricky is to give the intuition behind what machine learning is really useful for. I'll review common answers and give you my preferred one. The first category of answer to the question is what IBM calls cognitive computing.

Thousands of developers will trek to Google's developer conference, I/O, on Wednesday in Mountain View to hear the latest updates on Android, virtual reality and machine learning. The search giant will hold a two-hour keynote presentation that analysts expect will delve into new features like split screens on its Android smartphone operating system, a rumored all-in-one virtual reality headset and, perhaps, a device similar to the Amazon Echo that recognizes voice commands. "Google is at the point now where they are really looking at expanding way beyond search and mobile platforms," said Tim Bajarin, president of advisory services firm Creative Strategies. Last year's Google I/O -- the initials stand for "input, output" -- drew 5,600 people. The event, being held this year at the Shoreline Amphitheatre, has turned into a nearly weeklong affair.

The SPS IPC Drives event--held every November in Nuremberg, Germany--disappointed some attendees this past fall by not showing many steps forward for Industrial Internet of Things (IIoT) technologies. Yes, the marketing noise was loud, but little was actually being delivered. I am proud that Hilscher stood out from the crowd with its new portfolio of IIoT products. With connectivity being our core business, it was clear to us as we initially approached the IIoT concept that some sort of hardware (e.g., gateways) would be needed. After all, IIoT is just another "gateway" challenge, right?

Google wants to play an even bigger role in managing people's daily lives, while also nudging them into an alternate reality, as the Internet company responds to competitive threats posed by Facebook, Amazon and Apple. As part of an onslaught of upcoming products, Google will implant a more personable form of artificial intelligence into an Internet-connected device called Home, which echoes the Echo, Amazon.com's Meanwhile, Google will also delve deeper into the still-nascent realm of virtual reality with a system called Daydream that's meant to challenge Facebook-owned Oculus's early lead in fabricating artificial worlds. In an attempt to outshine Apple, Google is also adding features to its Android operating system, including the ability to run apps without actually installing them on a device. That feature, called Instant Apps, might have been the biggest breakthrough that Google announced Wednesday at its annual developers conference held in an amphitheater located a few blocks from its Mountain View, California, headquarters.