A Recommender System predicts the likelihood that a user would prefer an item. Based on previous user interaction with the data source that the system takes the information from (besides the data from other users, or historical trends), the system is capable of recommending an item to a user. Think about the fact that Amazon recommends you books that they think you could like; Amazon might be making effective use of a Recommender System behind the curtains. This simple definition, allows us to think in a diverse set of applications where Recommender Systems might be useful. Applications such as documents, movies, music, romantic partners, or who to follow on Twitter, are pervasive and widely known in the world of Information Retrieval.

TJBot is open source and we'd love to see what you can make with it. It comes with an initial set of recipes (step-by-step instructions) to help you connect your TJBot to Watson cognitive services and bring him to life. The recipes are based on Raspberry Pi. Use one of the starter recipes or create your own. If you have created your own recipe, we would love to include it as a featured recipe!

You are parent and wish to teach your 8 year old boy how to play violin? But does this have anything to do with artificial intelligence (AI)? Recent scientific experiments have shown that very young babies -- as young as 9-month old -- that learn music can significantly improve many of their cognitive functions, such as their future language acquisition. Children who learn how to play music young get both better verbal and language learning skills than the ones who don't, because they gain enhanced sound representation abilities and modify their brain connectivity. For adults, learning language is a great example.

Artificial intelligence is the future of computer science and technology. Its impact will be almost immeasurable. The field is wide open today, with so much to learn, and so many ways to contribute. We have collaborated with industry leaders to bring you cutting-edge curriculum covering topics such as search and optimization; logic, reasoning, and planning; building models of probability; natural language processing; computer vision, and much more. You'll master skills and tools used by the most innovative AI teams across the globe as you delve into specializations, and gain experience solving real-world challenges.

Exari, the leading provider of enterprise contract management software, announced today that together with IACCM it will host a live and complimentary webinar titled "An Artificial Intelligence Survival Guide for Contracts Professionals," on Tuesday February 28, from 11:00AM to 12:00PM Eastern Standard Time. The webinar will offer expert advice from Jamie Wodetzki, Co-Founder and Chief Product Officer at Exari and Tim Cummins, CEO of IACCM. Interested professionals can register for the webinar here. "We are in a golden age of machine learning," said Jamie Wodetzki. "Artificial Intelligence (AI) is transforming our daily lives and our professional careers. AI has entered into the contract management space and is already giving us new and different ways to explore key facets of contract documents."

Much has been made of the so-called'robot revolution' with Artificial Intelligence (AI) technologies forecast to transform many industries beyond all recognition. Once the stuff of science fiction movies, these emerging trends are fast becoming serious discussion points for organisations seeking to save time and money through automated processes and data crunching. For the UK public sector, tasked with protecting critical services on limited budgets whilst delivering value for money to taxpayers, such initiatives appear to be an obvious benefit. This week for the first time we have heard a prediction around what a truly digital public sector might look like. A new research paper from the Reform think tank has predicted that around 250,000 public sector jobs could be taken by robots in the next 15 years.

Successful Student has compiled the 25 Best Artificial Intelligence Colleges in the United States. Artificial Intelligence (AI), also known as machine learning, is a discipline within computer science. Artificial Intelligence is usually conceived of as doing more than just computing numbers (such as a calculator), but is more conceptual in nature (such as describing subjective qualities, or giving meanings to different contexts). An example of AI would be speech recognition and communicating, such as Apple's Siri, or Amazon's Alexa. Amazon has announced three new AI tools for anyone wanting to build apps on Amazon Web Services: Amazon Lex, Amazon Polly, and Amazon Rekognition. According to Amazon "This frees developers to focus on defining and building an entirely new generation of apps that can see, hear, speak, understand, and interact with the world around them." For those interested in developing apps, see our 20 Best App Development Colleges article. Google, Facebook, Amazon, Apple and Microsoft are all working on AI. Facebook's FAIR (Facebook Artificial Intelligence Research) program engages with academia to assist in solving long term problems in AI. Facebook is hiring AI experts around the world to assist in their project.

I present a robotics localization challenge based on the inexpensive Neato XV robotic vacuum cleaner platform. The challenge teaches skills such as computational modeling, probabilistic inference, efficiency vs. accuracy tradeoffs, debugging, parameter tuning, and benchmarking of algorithmic performance. Rather than allowing students to pursue any localization algorithm of their choosing, here, I propose a challenge structured around the particle filter family of algorithms. This additional scaffolding allows students at all levels to successfully implement one approach to the challenge, while providing enough flexibility and richness to enable students to pursue their own creative ideas. Additionally, I provide infrastructure for automatic evaluation of systems through the collection of ground truth robot location data via ceiling-mounted location tags that are automatically scanned using an upward facing camera attached to the robot. The robot and supporting hardware can be purchased for under $400 dollars, and the challenge can even be run without any robots at all using a set of recorded sensor traces.

The Model AI Assignments session seeks to gather and disseminate the best assignment designs of the Artificial Intelligence (AI) Education community. Recognizing that assignments form the core of student learning experience, we here present abstracts of six AI assignments from the 2017 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs.

The problem of automatically fixing programming errors is a very active research topic in software engineering. This is a challenging problem as fixing even a single error may require analysis of the entire program. In practice, a number of errors arise due to programmer's inexperience with the programming language or lack of attention to detail. We call these common programming errors. These are analogous to grammatical errors in natural languages. Compilers detect such errors, but their error messages are usually inaccurate. In this work, we present an end-to-end solution, called DeepFix, that can fix multiple such errors in a program without relying on any external tool to locate or fix them. At the heart of DeepFix is a multi-layered sequence-to-sequence neural network with attention which is trained to predict erroneous program locations along with the required correct statements. On a set of 6971 erroneous C programs written by students for 93 programming tasks, DeepFix could fix 1881 (27%) programs completely and 1338 (19%) programs partially.