If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
On Leap Day this year, Dr Sue Black was sitting on the sofa when her partner presented her with a puzzle. It was his laptop screen, which showed an Enigma machine simulator containing an encrypted message asking her to marry him. On Leap Day this year, Dr Sue Black was sitting on the sofa when her partner presented her with a puzzle. Dr Black, author and computer scientist, is well known for founding the high profile campaign to save Bletchley Park, the central site for Britain's codebreakers during World War Two. She is a senior research associate at University College London.
LOS ANGELES (Reuters) - Volvo's North American CEO, Lex Kerssemakers, lost his cool as the automaker's semi-autonomous prototype sporadically refused to drive itself during a press event at the Los Angeles Auto Show. "It can't find the lane markings!" Kerssemakers griped to Mayor Eric Garcetti, who was at the wheel. "You need to paint the bloody roads here!" Shoddy infrastructure has become a roadblock to the development of self-driving cars, vexing engineers and adding time and cost.
Microsoft has made the code behind its infamous'Tay' chatbot open source, meaning any developer can now use it to create their very own racist, sexist artificial intelligence. The release is part of the company's new Bot Framework, a toolkit which can be used to create conversational chat programs for a variety of tasks. The framework was announced at the opening event of the Build 2016 developer conference, where Microsoft CEO Satya Nadella heralded bots as the next big platform to be tackled by tech companies. Tay, a Twitter chatbot designed to mimic the online behaviour of a teenage girl, was unveiled last week as a demonstration of what the company's new technology could do. By monitoring what other users said to it, Tay developed an understanding of speech and was able to write new tweets of its own.
Tired of crossing your fingers while trying to locate a WiFi signal? What if, instead, the WiFi could locate us? The STEM robot wars are heating up. Cubetto is the latest reason why you wish you were still a kid. In a new paper, a research team led by professor Dina Katabi of MIT's Computer Science and Artificial Intelligence Lab (CSAIL) presents a system that enables a single WiFi access point to locate users to within tens of centimeters, without any external sensors.
The Codeybot dances, plays music, changes colors and even shoots laser beams -- all to entice little tykes to start programming. The coding toy from Shenzhen-based startup Makeblock is just the latest on the edutainment bandwagon taking things up a techie notch. It joins a talking, projectile-firing robot, a remote-controlled origami robot, code-teaching drones, and even a crawly "Code-a-Pillar" in a trend of code-teaching toys that seems to have become all the rage in the past few months alone. Within 24 hours of launching on Kickstarter on Tuesday, the Codeybot has already reached nearly 90% of its 100,000 funding goal. The toy looks like a self-balancing white cheese triangle on wheels that moves with a remote control app.
In this post will explain about User based Collaborative Filtering. This algorithm usually works by searching a large group of people and finding a smaller set with tastes similar to yours. It looks at other things they like and combines them to create a ranked list of suggestions. Creating Similarity score for people helps us to identify similar people. We use Cosine based Similarity function to calculate the similarity between the users.
"If network effects are one of the most important concepts for software-based businesses, then that may be especially true of data network effects -- a network effect that results from data. Particularly given the prevalence of machine learning and deep learning in startups today. But simply having a huge corpus of data does not a network effect make! So how can startups ensure they don't get a lot of data exhaust but get insight out of and add value to that data and the network? How can they make sure that the (arguably inevitable) data aspect of their business isn't just a sideshow or accident?
You gather tons of keywords over the Internet with a web crawler (crawling Wikipedia or DMOZ directories), and compute the frequencies for each keyword, and for each "keyword pair". A "keyword pair" is two keywords found on a same web page, or close to each other on a same web page. Also by keyword, I mean stuff like "California insurance", so a keyword usually contains more than one token, but rarely more than three. With all the frequencies, you can create a table (typically containing many million keywords, even after keyword cleaning), where each entry is a pair of keywords and 3 numbers, e.g.
With the meteoric rise of Machine learning, developers are now keen on finding out how can they make their .Net applications smarter. Also, .NET developers are interested into moving into the world of devices and how to apply machine learning techniques to, well, machines. This book is packed with real-world examples to easily use machine learning techniques in your business applications. You will begin with introduction to F# and prepare yourselves for machine learning using .NET framework. You will be writing a simple linear regression model using an example which predicts sales of a product.
Google has made its Cloud Machine Learning platform available to developers today, giving them access to the platform that powers Google Photos, Translate and the Google Inbox app. The move means that developers can build Google's translation, photo, and speech recognition APIs into their own apps utilising the Cloud Machine Learning platform moving forward. The platform was launched at NEXT 2016, Google's Cloud Platform conference and is now available in limited preview. "Cloud Machine Learning will take machine learning mainstream, giving data scientists and developers a way to build a new class of intelligent applications," Fausto Ibarra, Google's director of product management wrote in a blog post. "It provides access to the same technologies that power Google Now, Google Photos, and voice recognition in Google Search as easy to use REST APIs."