You can now hold neural nets in the palm of your hand. Last week, Facebook unveiled a tool called "style transfer" that applies visual effects to live phone video in real time. Making your clips look like an episode of The Simpsons or a Van Gogh painting may seem gimmicky, but the artificial intelligence required to do this would usually need to run on massive servers. Google squeezed a neural network into its Google Translate app last year. Now, Facebook has developed a deep learning system called Caffe2Go that is condensed enough to run directly in mobile apps on iOS and Android.

Seoul: Samsung Electronics Co. plans to equip its next Galaxy S smartphones with a Siri-like digital assistant, seeking to make a comeback after the global debacle that precipitated the death of its flawed Note 7 lineup. Samsung, which last month acquired US-based artificial intelligence software company Viv Labs Inc., said the Galaxy S8 slated for next year will come with AI-enabled features "significantly differentiated" from those of Apple Inc.'s Siri or Google, executive vice president Rhee In-jong told reporters. Those services now offer up potentially useful information from the weather to flight times based on user activity. The flagship Galaxy S line will prove crucial to salvaging Samsung's reputation in the wake of the fiasco surrounding a Note 7 device prone to bursting into flame. The recall and eventual cessation of the line is estimated to cost upwards of $6 billion and pushed profits at its mobile division to a record low in the September quarter.

Machine learning is a very hot topic for many key reasons, and because it provides the ability to automatically obtain deep insights, recognize unknown patterns, and create high performing predictive models from data, all without requiring explicit programming instructions. This is a summary (with links) to an article series that's intended to be a comprehensive, in-depth guide to machine learning, and should be useful to everyone from business executives to machine learning practitioners. It covers virtually all aspects of machine learning (and many related fields) at a high level, and should serve as a sufficient introduction or reference to the terminology, concepts, tools, considerations, and techniques in the field. The first chapter of the series starts with both a formal and informal definition of machine learning. This is followed by a discussion of the machine learning process end-to-end, the different types of machine learning, potential goals and outputs, and a categorized overview of the most widely used machine learning algorithms.

To get your morning started right, take a look at the latest stories published since yestereday. Scholars may be in a run for their money with new developments in the field of artificial intelligence (AI) search engines. Tagged In Microsoft Germany Artificial Intelligence University Of California, San Diego Neuroscience Natural Language Processing Oren Etzioni Computer Science Scientific American Outline Of Health Sciences Scopus Max Planck Society EBSCO Information Services Pub Med Microsoft Academic Search The partnership between Continental and the Department of Engineering Science at the University of Oxford will focus on the possible uses and development of artificial intelligence algorithms. Tagged In Artificial Intelligence Continental AG University Of Oxford If you don't mind Google knowing everything about your life, then there are some benefits to handing over your data to the big G. Tagged In Android (operating System) IOS Artificial Intelligence Machine Learning Rapid Transit Spotify Apple Music Google Play Music Just over five years ago, IBM's Watson supercomputer crushed opponents in the televised quiz show Jeopardy.

Artificial Intelligence (AI) is not one big, specific technology. Rather, it is comprised of one or more building block technologies. So, to understand AI, you have to understand each of these nine building block technologies. Now, you could argue that there are more technologies than the ones listed here, but any additional technology can fit under one of these building blocks. Knowledge engineering is a process to understand and then represent human knowledge in data structures, semantic models, and heuristics (rules).

If you have a question about this talk, please contact Mohammad Taher Pilehvar. The efficacy of applying machine learning to real-world problems has benefitted from the affordable availability of computational resources (CPU cycles, RAM) in abundance. In this presentation, I aim to present how linguistic and machine learning can be usefully combined to solve customer problems. To this end, I will describe some example applications of machine learning to problems from the financial, legal & regulatory and pharmacology domains, all using different use cases and expectations yet all based on natural language processing aided by supervised learning. I conclude by contrasting academic research from scientific research conducted in an industry environment.

Google Play Music, Google's music streaming service, is getting smarter. On Monday, an update to the Google Play Music app will kick in a new feature that can predict what you want to hear next. In fact, Google is so confident in the app's accuracy, that it completely revamped the welcome screen on Play Music so you can just tap to start listening to a playlist suited for you based on the time, your location, and a variety of other factors. Google says it's able to accomplish this through machine learning, the technology that's starting to power a lot of intelligent services Google makes like Google Photos and the new Google Assistant. It also plays into Google's broader mission to use machine learning and artificial intelligence in all of its products.

I'd pick a universally accessible binary classification problem: learning which foods are yummy and which are yucky. We want to teach a computer to recognize which foods are yummy and which foods are yucky. But the computer doesn't have a mouth or any way of tasting the food. Instead, we need to teach it by showing it examples of foods ("labeled training data"), some of which are yummy foods ("positive examples") and some of which are yucky foods ("negative examples"). For each labeled example, we also provide the computer with ways to describe the food ("features").

The next frontier for artificial intelligence may involve teaching computers to distinguish albacore tuna from its yellowfin cousin. The Nature Conservancy, an environmental non-profit, is working with several Pacific Island nations and a big tuna fishing company to more easily count and identify fish caught at sea using cutting edge technology. The goal is to use trendy artificial intelligence techniques like deep learning to help fishermen reduce the number of protected animals like sharks and turtles that are accidentally caught along with the tuna. The Nature Conservancy hopes that the program could prevent overfishing and help threatened and endangered sea life recover without putting fishermen out of work. "We have real optimism that data science community can help us differentiate a turtle from a tuna, and flag when a shark comes on board," said Mark Zimring, a project director for The Nature Conservancy.

How do you feel about conversations with bots so far? Are they engaging, disappointing or interesting? For bot makers, there are lots of challenges in crafting more engaging bots. Challenge 1: Getting inspiration for how to build your bot based on how others built theirs. You can't just search for bots based on the meta details in conversations, because there isn't such a database.