This Saturday, save big on robot vacuums, travel mugs, TVs, and more. If you make a purchase by clicking one of our links, we may earn a small share of the revenue. However, our picks and opinions are independent from USA Today's newsroom and any business incentives. The weekend is my favorite time to shop for myself. I'm really able to focus on the things I need (OK, things I want) and put more effort into researching my options.

I've spent a lot of time over the last year or so with Google's AIY Projects Voice Kit, including some time investigating how well TensorFlow ran locally on the Raspberry Pi attempting to use models based around the initial data release of Google's Open Speech Recording to customise the offline "wake word" for my voice-controlled Magic Mirror. Back at the start of last year this was a hard thing to do, it was really pushing the Raspberry Pi to its limits. However as machine learning software, such as TensorFlow Lite and other tools, have matured we've seen models being run successfully on much more minimal hardware. With the privacy concerns raised by cloud connected voice devices, as well as the sometime inconvenient need for a network connection, it's inevitable that we'll start to see more offline devices. While we've seen a number of "wake word" engines--a piece of code and a trained network that monitors for the special word like "Alexa" or "OK Google" that activates your voice assistant --these, like pretty much all modern voice recognition engines, need training data and the availability of that sort of data has really held smaller players.

Abstract: Neural Processes (NPs) (Garnelo et al 2018a;b) approach regression by learning to map a context set of observed input-output pairs to a distribution over regression functions. Each function models the distribution of the output given an input, conditioned on the context. NPs have the benefit of fitting observed data efficiently with linear complexity in the number of context input-output pairs, and can learn a wide family of conditional distributions; they learn predictive distributions conditioned on context sets of arbitrary size. Nonetheless, we show that NPs suffer a fundamental drawback of underfitting, giving inaccurate predictions at the inputs of the observed data they condition on. We address this issue by incorporating attention into NPs, allowing each input location to attend to the relevant context points for the prediction.

Riddled with heavy jargon and tiresome paperwork, insurance has long been perceived to be a drab subject by the common man. Not to mention, the process -- from the selection of scheme to claim settlement -- has traditionally been too tedious and time-consuming for the consumer, who often needs to rely on an agent or a financial planner. The future of insurance, though, promises to be less complicated and more user friendly, thanks to technology. This intersection of insurance and technology, called InsureTech, is set to disrupt all aspects of insurance. With the integration of technologies such as data analytics, AI, machine learning, automation etc. InsureTech is predicted to transform the landscape of insurance much like what FinTech did for the finance industry.

In this video, we'll show you an example of how you can use AutoGraph to write complex, high-performance TensorFlow code using normal Python. AutoGraph helps you write complicated graph code using normal Python, & automatically transforms your code into the equivalent TensorFlow graph code. Be sure to check out some of our other TensorFlow tips, in this playlist http://bit.ly/2mptadn

To help fill this void, enterprises are increasingly turning to a tried and true source: higher education. The talent pool for emerging technologies, such as artificial intelligence (AI), machine learning (ML) and internet of things (IoT), will fall short in filling at least 30 percent of global demand, according to IDC. Organizations struggle to hire data scientists and analytics experts who can munge and extract insights from data. "CIOs will also realize that talent shortage will be a moving target driven by supply and demand and they will have to find adaptive, flexible approaches to meet changing needs," IDC analysts wrote in a recent research report. The idea of scouring campuses for tech talent isn't new, but anecdotal evidence suggests that companies are redoubling their efforts to lure -- and help train -- future technologists by partnering with colleges and universities on innovation labs and internship programs aimed at developing real-world digital skills.

Some time ago when we talked about artificial intelligence we immediately thought of those futuristic films with technologies that seem impossible today. But just look around to realize that this area has evolved in such a way that it is already possible to integrate artificial intelligence into many of our day-to-day activities. Of course, because of the incredible possibilities that this type of technology brings, it has received attention from several industries, including marketing. By the way, did you know that artificial intelligence and marketing are already walking side by side? How does this partnership work?

What is the difference between AI and Machine learning? AI is the concept of machines performing tasks that are characteristic of human intelligence -- it is the all-encompassing phase that is highlighted in multiple Sci-Fi movies like Terminator, Matrix, etc. The concept of AI is to address things like recognizing objects and sounds, learning, planning and problem solving. Today most of the AI used in a business context is specific to one area, it displays characteristics of the human intelligence in one specific area like sound, image recognition or problem solving. The evolution of AI to replicate multiple aspects of human intelligence is the next stage in its evolution and that is the focus of new emerging AI initiatives across industries.

WHEN SOPHIA THE ROBOT first switched on, the world couldn't get enough. It had a cheery personality, it joked with late-night hosts, it had facial expressions that echoed our own. Here it was, finally -- a robot plucked straight out of science fiction, the closest thing to true artificial intelligence that we had ever seen. There's no doubt that Sophia is an impressive piece of engineering. It didn't take much to convince people of Sophia's apparent humanity -- many of Futurism's own articles refer to the robot as "her."

As you ratchet through the gears of Priya Sarukkai Chabria's novel Clone, prepare to strap yourself in for an engaging albeit slow-moving ride. I'd recommend reading this book after having already finished the author's second full-length work of fiction, Generation 14, which inaugurated several themes that she treats in this book as well. Sarukkai Chabria amplifies the trajectory of the character of Clone 14/54/G in this new book, further adding to the world she created by drawing starker borders, providing more concrete definitions and clarifying the existing distinctions to a greater degree. To list some things that Clone did not prove to be: a tear-jerker; a sentimental novel; a book of speculative fiction written according to the conventional lines of plot-character-action. What it turned out to be instead, was a fresh, genre-bending variety of Indian speculative fiction – a compound comprising elements of magic realism, stream-of-consciousness narration, fabulist storytelling and certain characteristics of historical fiction.