For investors in Boo.com, WebVan and eToys, the bursting of the dotcom bubble came as a bit of a shock. Companies like this raised vast sums on the promise that the worldwide web would change everything. Then, in the spring of 2000, stock markets collapsed. Some economists had long been sceptical about the promise of computers. In 1987, we didn't have the web, but spreadsheets and databases were appearing in every workplace - and having, it seemed, no impact whatsoever.

This led ProPublica -- with the support of the Google News Lab -- to form Documenting Hate earlier this year, a collaborative reporting project that aims to create a national database for hate crimes by collecting and categorizing news stories related to hate crime attacks and abuses from across the country. Now, with ProPublica, we are launching a new machine learning tool to help journalists covering hate news leverage this data in their reporting. The Documenting Hate News Index -- built by the Google News Lab, data visualization studio Pitch Interactive and ProPublica -- takes a raw feed of Google News articles from the past six months and uses the Google Cloud Natural Language API to create a visual tool to help reporters find news happening across the country. The feed is generated from news articles that cover events suggestive of hate crime, bias or abuse -- such as anti-semitic graffiti or local court reports about incidents.

Hate crimes in America have historically been difficult to track since there is very little official data collected. What data does exist is incomplete and not very useful for reporters keen to learn more. This led ProPublica -- with the support of the Google News Lab -- to form Documenting Hate earlier this year, a collaborative reporting project that aims to create a national database for hate crimes by collecting and categorizing news stories related to hate crime attacks and abuses from across the country. Now, with ProPublica, we are launching a new machine learning tool to help journalists covering hate news leverage this data in their reporting. The Documenting Hate News Index -- built by the Google News Lab, data visualization studio Pitch Interactive and ProPublica -- takes a raw feed of Google News articles from the past six months and uses the Google Cloud Natural Language API to create a visual tool to help reporters find news happening across the country.

"The feed is generated from news articles that cover events suggestive of hate crime, bias or abuse -- such as anti-semitic graffiti or local court reports about incidents," Google writes. "We are monitoring it to look our for errant stories that slip in, i.e. searches for phrases that just include the word'hate' -- it hasn't happened yet, but we will be paying close attention." The web app is available as of today and Google says that it'll keep tweaking it over the next few months as use-case data starts rolling in.

Humanoid robots have come eerily close to overcoming the uncanny valley. With the right features in place, they are almost indistinguishable from their organic counterparts. The latest iterations are able to talk like us, walk like us, and express a wide range of emotions. Some of them are able to hold a conversation, others are able to remember the last interaction you had with them. As a result of their highly advanced status, these life-like robots could prove useful in helping out the elderly, children, or any person who needs assistance with day-to-day tasks or interactions.

Hamon and his team created an algorithm that analyzes the tiniest of human movements, using a camera, and determines what that person is feeling. SLL is trying to solve one of the oldest problems in the world: people lie. Being able to determine the viability of a TV show, or how people feel about a specific scene in a movie is a pretty neat trick. SLL does more than provide analytics for TV shows and movies, in fact its ambitions might be some of the highest we've ever seen for an AI company.

If you have and you want to learn the science behind them, you have come to the right place. In this course, I will show you how these companies use Recommender systems or Machine Learning to influence your purchasing decisions. This course is timely and extremely relevant now as almost all major service-oriented companies function on recommender systems. You will understand how these systems work and learn how to build and use your own recommender systems, just like these big companies do. Learn how to build the recommender systems that are being used by almost every big service-oriented company in today's world with this introductory course for beginners.

The reference books that line the shelves of developer Ultra Ultra's modest Copenhagen office offers insight into the aesthetic of its first game, Echo. Prometheus: The Art of the Film and Star Wars provide sci-fi reference points, while Metal Gear Solid, Blame! and Neon Genesis Evangelion--all three of which are represented in some form on the shelf--provide the inspiration for character design. Echo is made up of many familiar parts, but parts that are remixed in a way that makes them feel new. This is fundamental to Echo not just aesthetically, but also mechanically. Unsurprisingly, given Ultra Ultra's staff of ex-IO Interactive Hitman developers, Echo is a stealth game.

I was 8-years-old and remember being both terrified and intrigued about something that was being talked about everywhere. This wasn't a storyline out of a science fiction movie or novel, this was real, and happening here on Earth. Millions of people were going to witness something that maybe happens a couple of times in our lifetime: A total solar eclipse. Our teachers were planning lessons about this incredible celestial event. Chalkboard diagrams, planetary mobiles and handmade viewing devices were being created out of shoe boxes.

Technologies are described herein for active machine learning. An active machine learning method can include initiating active machine learning through an active machine learning system configured to train an auxiliary machine learning model to produce at least one new labeled observation, refining a capacity of a target machine learning model based on the active machine learning, and retraining the auxiliary machine learning model with the at least one new labeled observation subsequent to refining the capacity of the target machine learning model. Additionally, the target machine learning model is a limited-capacity machine learning model according to the description provided herein.