KITT.AI wants to help developers add voice activation features to almost any device for free. The Seattle startup today unveiled its first software toolkit called Snowboy, which lets developers add verbal "hotword detection" to devices. It's the same technology that tech giants like Amazon and Apple use for products like Alexa and Siri, but now KITT.AI is enabling anyone to easily add the functionality to their own hardware. The idea is to let users talk to devices in order to "wake" or "command" them to do something. Snowboy uses deep neural network technology that learns and recognizes unique patterns in voices.

A federal judge in California has ruled that three Illinois men can proceed with their class-action lawsuit challenging Facebook's facial recognition software used to identify people in uploaded photos. Adam Pezen, Carlo Licata and Nimesh Patel, all of the Chicago area, separately sued the social media giant last year, the Chicago Tribune reported (http://trib.in/1USAw5s The cases were combined and transferred last summer to the Northern District of California court. The men allege that Facebook was illegally collecting biometric data from people "tagged" in photos posted by other users and that its use of facial recognition software violates Illinois' Biometric Information Privacy Act. In 2008, Illinois enacted the Biometric Information Privacy Act to regulate how individuals, companies and organizations can collect and use biometric data, which determine a person's identity based on unique biological or physical characteristics.

The site launched about a week ago and the app hasn't even been released yet, but the concept has already proven popular, NBC News reported. Thousands of people have already signed up to nab a spot on the waitlist and this past Friday, the site had 200 sign-up requests an hour. "This is about finding the right partner and not caring if they're on the other side of the border," CEO Joe Goldman explained to The Guardian. "You should go to a place where you'll be happy. For a number of Americans, in the event of a Trump presidency, that place would be Canada."

Data science and cognitive computing are 2 leading contemporary analytics computing paradigms. Though distinct areas of specialization, both models entail considerable overlap in technologies and tools, and often share similar goals, outcomes, and aspirations. Both archetypes are data-driven, are often large scale, and increasingly rely on high performance computing. Data science encompasses machine learning and other analytic processes, statistics and related branches of mathematics, in order to extract insight from data and use it to tell stories. Data science employs all sorts of different tools from a variety of related areas, including data mining, artificial intelligence, and Big Data.

Visit us in Chicago on Thursday, June 2nd at 6:30pm to see a Machine Learning presentation by Jeremy Watt, instructor of the upcoming Metis course titled Machine Learning: Algorithms & Applications and author of Machine Learning Refined. This is an Open House for Jeremy's upcoming 6-week evening course at Metis, which starts on July 11th and will be held on Monday and Wednesday evenings from 6:30 - 9:30pm through August 17th . Please RSVP if you'd like to see a demonstration from Jeremy, and to learn more about the course structure and outcomes. Pizza and drinks will be served. Jeremy holds a PhD in Computer Science and Electrical Engineering from Northwestern University where he conducted research in machine learning and computer vision while actively consulting with partners in finance and insurance, as well as startups in the e-commerce and healthcare space.

Steven Hawking and Elon Musk are pretty smart guys. So when they say the Terminator movie franchise might be a preview of coming attractions, people take notice. But while these mega-brains spar against futurists such as Raymond Kurzweil, who welcome the coming of the age of the machines, it's important to understand the technology elements that are coming together to make Artificial Intelligence not only a reality, but ubiquitous in our daily lives. Cloud Computing is transforming the notion that individuals and companies have to own the physical equipment and space their data is housed in. A recent article in the Financial Times titled, Artificial Intelligence In the Cloud, The Next Great Disruptor, featured the idea that A.I. and Cloud are a match made in heaven.

With the creators of Siri offering up a new personal assistant that won't just tell you what pizza is but can order one for you, artificial intelligence is showing a huge leap forward. Viv is an artificial intelligence (AI) platform built by Dag Kittlaus and Adam Cheyer, the creators of the AI behind Apple's Siri, the most well-known digital assistant in the world. Siri is known for answering questions, like how old Harrison Ford is, and reminding you to buy milk on the way home. Earlier this week during an onstage demo at Disrupt NYC, Kittlaus showed off Viv. This created an online buzz about the assistant's ability to not just answer questions or fire up a timer on a smartphone, but to answer complex questions and to interact with third-party services as well as online businesses. "Assuming we can take the founders at their word, Viv is more than just an evolutionary step in the development of personal assistants.

Researchers from Stanford University have created a humanoid robot or robot mermaid to explore sunken treasures and relics. Tagged as OceanOne, the robo-mermaid uses artificial intelligence and virtual reality technology to allow human beings to operate it remotely, as per Stanford News. The robot mermaid looks like a human with hands that are installed with sensors to enable OceanOne to discern if an item is fragile or not. It also has two cameras as its eyes and an artificial human brain for navigating the deep sea and analyzing data. According to CNN, OceanOne's first journey to the deep water was to retrieve a vase from the ruins of Louis XIV's ship La Lune.

DESCRIPTION Have you ever thought about what it takes to detect and prevent fraudulent activity among hundreds of millions of eCommerce transactions across the globe? What would you do to increase trust in an online marketplace where millions of buyers and sellers transact? How would you build systems that evolve over time to proactively identify and neutralize new and emerging fraud threats? Our mission in Transaction Risk Management Services (TRMS) is to make Amazon.com the safest place to transact online. TRMS safeguards every financial transaction across all Amazon sites, while striving to ensure that these efforts are transparent to our legitimate customers.

This is a very basic technique that can be applied to most machine learning algorithms you will come across when you're doing NLP. Suppose for example, you are creating a "bag of words" model, and you have just collected data from a set of documents with a very small vocabulary. You would naturally assume that the probability of seeing the word "cat" is 1/3, and similarly P(dog) 1/3 and P(parrot) 1/3. Now, suppose I want to determine the probability of P(mouse). Since "mouse" does not appear in my dictionary, its count is 0, therefore P(mouse) 0. If you wanted to do something like calculate a likelihood, you'd have P(document) P(words that are not mouse) \times P(mouse) 0 We simply add 1 to the numerator and the vocabulary size (V total number of distinct words) to the denominator of our probability estimate.