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) …
That's the warning of a whole range of experts who warn that the connected home – the idea that appliances and gadgets throughout the home – might be turned on their users. The technology is intended to make life easier for the people who use it, but like many new developments comes with terrifying warnings for their users. That's because the same things required to use the smart home – internet connections, microphones and cameras – also make them perfect targets for hackers. And because they occupy such an intimate place in people's homes, once they're spying on you they can learn some of the most intimate data there is. Those problems primarily hit cheap devices, many of which are made with little interest in how private they are.
Oryx 2 is a realization of the lambda architecture built on Apache Spark and Apache Kafka, but with specialization for real-time large scale machine learning. It is a framework for building applications, but also includes packaged, end-to-end applications for collaborative filtering, classification, regression and clustering. Developers can consume Oryx 2 as a framework for building custom applications as well. Following the architecture overview below, proceed to Making an Oryx App to learn how to create a new application.
Artificial intelligence and music sound like two things that shouldn't have anything in common. To put it simply, music is very much not an artificial thing. On the other hand, there are a ton of benefits to AI that the music industry can benefit from--for one, the issue of music licensing. Perhaps that's why a music industry group is working closely with a university noted for creating an AI-produced Christmas carol. The Society of Composers, Authors and Music Publishers of Canada (SOCAN) recently announced a partnership with the University of Toronto's Department of Computer Science Innovation Lab to help deduce new strategies for tackling the increasingly complex problem of music licensing.
When Antoine Blondeau, the co-founder and chairman of Sentient Technologies, first began working with artificial intelligence, the concept was still known as "algorithmic science." In the decades since, AI has evolved into a much sexier topic because, according to Blondeau, when operating on mission speed and plugged into the right workflow, AI will learn quicker and faster than humans--resulting in business models that can evolve at machine speed. "That is the revolution," notes Blondeau....
When I was beginning my way in data science, I often faced the problem of choosing the most appropriate algorithm for my specific problem. If you're like me, when you open some article about machine learning algorithms, you see dozens of detailed descriptions. The paradox is that they don't ease the choice. In this article for Statsbot, I will try to explain basic concepts and give some intuition of using different kinds of machine learning algorithms in different tasks. At the end of the article, you'll find the structured overview of the main features of described algorithms.
Summary: Reinforcement Learning (RL) is likely to be the next big push in artificial intelligence. It's the core technique for robotics, smart IoT, game play, and many other emerging areas. But the concept of modeling in RL is very different from our statistical techniques and deep learning. In this two part series we'll take a look at the basics of RL models, how they're built and used. In the next part, we'll address some of the complexities that make development a challenge.
Industrial NTL detection systems are still largely based on expert knowledge when deciding whether to carry out costly on-site inspections of customers. Electricity providers are reluctant to move to large-scale deployments of machine learning systems as the latter may suggest a large number of unnecessary inspections. Therefore, electricity providers want to understand why a specific customer was predicted to cause electricity theft or not. As a consequence, the models used should be interpretable, for example by using decision tree models rather than black box-like models such as deep learning. We have also recently proposed a method for visualizing prediction results at various granularity levels in a spatial hologram.
According to a Cisco whitepaper examining the rapid expansion of the internet of things, there will be more than 50 billion internet-connected devices in the world by 2020, representing a hundred-fold increase since 2003. This proliferation of connected devices is making our everyday lives and work easier, but the convenience comes with a number of risks. Research from PwC indicates that the number of worldwide cybersecurity incidents rose by 38% in 2015, the largest increase in any of the 12 years the firm has conducted its Global State of Information Security Survey. Due in large part to the ever-increasing number of connected devices in the workplace, the average North American enterprise is inundated by alerts from its cybersecurity systems; 2014 research estimated the number at 10,000. The most active enterprise networks often receive an astounding 150,000 alerts per day.