Graphic designers, rejoice – the hours upon hours of struggling to figure out what a type face is will finally be over as Adobe is adding an artificial intelligence tool to help detect and identify fonts from any type of picture, sketch or screenshot. The DeepFont system features advanced machine-learning algorithms that send pictures of typefaces from Photoshop software on a user's computer to be compared to a huge database of over 20,000 fonts in the cloud, and within seconds, results are sent back to the user, akin to the way the music discovery app Shazam works. "You highlight the text area that you are interested in being recognised, and it will give you a list of the top five fonts that match what you highlighted," Anil Kamath, Adobe's VP of Technology and head of the data science team told the BBC. "That applies to an image that you can take with your phone. So, you might write something on a white board, take a picture of it and ask the software to suggest fonts that it corresponds to."

As much as we love to fawn over artificial intelligence (AI), it's still not great at recognizing and parsing natural language. That's why Google is open sourcing its new language parsing model for English, which it calls'Parsey McParseface.' Before you even ask, the name has no meaning. When Google was trying to figure out what to call its language parsing technology, someone suggested Parsey McParseface; it's a bit like Apple's Liam, which has no clever backstory either. The overall AI model model is called SyntaxNet (please make your SkyNet jokes now); 'ol Parsey is just for English. Our biggest ever edition of TNW Conference is fast approaching!

Every year, the Association for Computing Machinery--the world's largest scientific and educational computing society--gathers to explore the future of computer interaction in a legendary conference called CHI. It's an amazing event, in which thousands of researchers, scientists, and futurists get together to push the boundaries of what it means to interact with machines. It's a dizzying collision involving enough ideas about what the future of man and machine will look like to put the world's science-fiction authors out of their jobs for good. This year's CHI 2016 conference in San Jose was no exception--but among the hundreds of projects, here are eight that stood out. The problem: In VR, objects might look real, but they don't feel real.

Robots and artificial intelligence have come a long way since a Roomba entered your home to vacuum your floor and Siri gave you advice on the best Italian restaurants in your parents' neighborhood. According to a 2013 University of Oxford study, half of American jobs could be automated within the next two decades. The study identified transportation, logistics and administrative jobs as the most vulnerable to automation. Others assert it is only a matter of time before robots replace teachers, travel agents, interpreters and a host of other professions. With the prospect of such jobs disappearing, many futurists and economists are considering the possibility of a jobless future.

IBM's technology has won Jeopardy, managed companies and is now practicing law. ROSS, 'the world's first artificially intelligent attorney' powered by Watson, recently landed a position at New York law firm Baker & Hostetler handling the firm's bankruptcy practice. The machine is designed to understand language, provide answers to questions, formulate hypotheses and monitor developments in the legal system. IBM's technology has won Jeopardy, managed companies and is now practicing law. ROSS, 'the world's first artificially intelligent attorney' powered by Watson, has just landed a position at New York law firm Baker & Hostetler handling the firm's bankruptcy practice Lawyers ask ROSS research questions in natural language, just like they were talking to a colleague, and the AI'reads' through the law, gathers evidence, draws inferences and returns with a'highly relevant', evidence-based answer.

Should Marriott International acquire Starwood Hotels & Resorts Worldwide as is widely expected, it will gain a company whose smartphone check-in, key-less entry and robot bellhops are progressive in the increasingly technology-focused hospitality sector. But Starwood's digital crown jewel is invisible to guests: Its analytics software platform enables the hotelier to automatically recalibrate pricing of its properties using hundreds of variables that influence supply and demand. Starwood spent more than 50 million over three years on this revenue optimization system, or ROS as the company calls it, which has helped the chain improve demand forecasting by 20 percent since 2015, says David Flueck, Starwood's vice president of global revenue. As ROS "learns" it will price rooms more efficiently, ideally boosting revenues and profitability. "What we're trying to do within our industry space is to say: How do we exploit access to data to create a more robust capability?" says Starwood CIO Martha Poulter.

The k-Nearest Neighbors algorithm (or kNN for short) is an easy algorithm to understand and to implement, and a powerful tool to have at your disposal. In this tutorial you will implement the k-Nearest Neighbors algorithm from scratch in Python (2.7). The implementation will be specific for classification problems and will be demonstrated using the Iris flowers classification problem. This tutorial is for you if you are a Python programmer, or a programmer who can pick-up python quickly, and you are interested in how to implement the k-Nearest Neighbors algorithm from scratch. The model for kNN is the entire training dataset.

Rescale's Design-of-Experiments (DOE) framework is an easy way to optimize the performance of machine learning models. This article will discuss a workflow for doing hyper-parameter optimization on deep neural networks. For an introduction to DOEs on Rescale, see this webinar. Deep neural networks (DNNs) are a popular machine learning model used today in many many applications including robotics, self-driving cars, image search, facial recognition, and speech recognition. In this article we will train some neural networks to do image classification and show how to use Rescale to maximize the performance of your DNN models.

FLINT, MI - MAY 4: President Barack Obama speaks at Northwest High School about the Flint water contamination crisis May 4, 2016 in Flint, Michigan. While in Flint, the President heard first-hand from residents about the water crisis, and received an in-person briefing on the federal efforts that are in place to help respond to the needs of the city's residents. Artificial Intelligence or AI is a hot topic today because of its potential to carry out tasks in a more impressive way. AI is fast and more accurate compared to humans. Barack Obama is planning to use AI in the government.

For good reason, deep learning is increasingly capturing mainstream attention. Just recently, on March 15th, Google DeepMind's AlphaGo AI -- technology based on deep neural networks -- beat Lee Sedol, one of the world's best Go players, in a professional Go match. Behind the scenes, deep learning is an active, fast-paced research area that's proliferating quickly among some of the world's most innovative companies. We are asked frequently about our favorite resources to get up to speed on deep learning and follow its rapid developments. As such, we've outlined below some of our favorite resources. While certainly not comprehensive, there's a lot here, and we'll continue to update this list -- if there's something we should add, let us know.