Machine learning is headed for a major growth spurt. After ticking past the $1 billion mark in 2016, the machine learning market is expected to hit $39.98 billion by 2025, according to a new report by Research and Markets. Where will all that growth come from? Machine learning was born in 1959, coined by computer scientist Arthur Samuel -- but only recently has the larger business community come to understand its value. In the next few years, it will be adopted by everyone from Fortune 500 firms to mom-and-pop shops.

While the folks at Facebook are probably ready for a break, we're highlighting all of the gaming news from GDC 2018 and preparing for next week's Apple event. 'Once you commit to a television format, you're a TV movie.'Spielberg In an interview, Steven Spielberg stated that content on Netflix should be considered television...and ineligible for Oscars. Count the apologies.Bad Password: Let's stop pretending Facebook cares Columnist Violet Blue reminds us of a critical factor in this Cambridge Analytica mess: That Facebook only pretends to care -- in the ways least affecting its business model -- isn't new. Most companies put two test drivers in each vehicle.Uber's self-driving policies, tech face questions after fatal crash While we still haven't seen a final determination of fault in the incident where a self-driving Uber SUV struck and killed a pedestrian, the New York Times points out statistics that suggest its technology isn't yet up to par with the competition.

A team of Microsoft researchers said Wednesday that they believe they have created the first machine translation system that can translate sentences of news articles from Chinese to English with the same quality and accuracy as a person. Researchers in the company's Asia and U.S. labs said that their system achieved human parity on a commonly used test set of news stories, called newstest2017, which was developed by a group of industry and academic partners and released at a research conference called WMT17 last fall. To ensure the results were both accurate and on par with what people would have done, the team hired external bilingual human evaluators, who compared Microsoft's results to two independently produced human reference translations. Xuedong Huang, a technical fellow in charge of Microsoft's speech, natural language and machine translation efforts, called it a major milestone in one of the most challenging natural language processing tasks. "Hitting human parity in a machine translation task is a dream that all of us have had," Huang said.

In honor of AI week at Taoti Creative, I thought jot down a few thoughts about where AI is taking marketing because if you're not using some form of it yet, you're going to be left in the dust. They all use deep learning, machine learning and AI technologies to personalize customer experience and make shopping easier and more immersive. Here's why: AI helps people find content they want when they want it. At the same time, it boosts PPC results so organizations can optimize where they're spending media dollars. Best of all, by personalizing what comes up on the mobile phone or computer screen, engagement goes up.

I don't use TF for its speed, I use it because it's practical. I can use the excellent Keras API to painlessly build my applications, and I know I can run them on my CPU, my GPU or the cloud with very minor changes. For a competitor to be better, being faster isn't enough, it has to be equally good in all the other important ways as well.

Social networking sites such as Twitter have provided a great opportunity for organizations such as public libraries to disseminate information for public relations purposes. However, there is a need to analyze vast amounts of social media data. This study presents a computational approach to explore the content of tweets posted by nine public libraries in the northeastern United States of America. In December 2017, this study extracted more than 19,000 tweets from the Twitter accounts of seven state libraries and two urban public libraries. Computational methods were applied to collect the tweets and discover meaningful themes. This paper shows how the libraries have used Twitter to represent their services and provides a starting point for different organizations to evaluate the themes of their public tweets.

CDs and vinyls are outselling digital downloads for the first time since 2011 thanks to streaming services, industry figures reveal. Apps like Spotify and Apple Music have seen an astronomical rise in popularity in recent years and this has all but destroyed the digital download market. As more people choose to stream music rather than own it, sales of physical media are now falling at a slower rate than their digital counterparts. This has been driven, in part, by a resurgence in vinyl sales among audiophiles, who prize the format's unique sound. CD's and vinyl copies of music are outselling their digital counterparts for the first time since 2011.

These tiny hard drives have fallen out of favor, but we all have them. Columnist Kim Komando has ways to re-use and repurpose. Many households have moved on from thumb drives. But there are ways to repurpose them for safer, faster computing. Thumb drives used to be so cool.

One of the common questions we get asked is: "Why do we need machine learning to improve streaming quality?" This is a really important question, especially given the recent hype around machine learning and AI which can lead to instances where we have a "solution in search of a problem." In this blog post, we describe some of the technical challenges we face for video streaming at Netflix and how statistical models and machine learning techniques can help overcome these challenges. Well over half of those members live outside the United States, where there is a great opportunity to grow and bring Netflix to more consumers. Providing a quality streaming experience for this global audience is an immense technical challenge.

One of the common questions we get asked is: "Why do we need machine learning to improve streaming quality?" This is a really important question, especially given the recent hype around machine learning and AI which can lead to instances where we have a "solution in search of a problem." In this blog post, we describe some of the technical challenges we face for video streaming at Netflix and how statistical models and machine learning techniques can help overcome these challenges. Well over half of those members live outside the United States, where there is a great opportunity to grow and bring Netflix to more consumers. Providing a quality streaming experience for this global audience is an immense technical challenge.