The concept of a robot assistant in the home is moving from sci-fi to reality thanks to a new product that claims to be the first personalised household robot. BIG-i is able to interpret voice commands and perform simple household tasks, such as controlling other smart devices and providing reminders. NXROBO, the company behind the robot, believes that in the future every family will have a robot which will act as the'hub for all smart home appliances.' BIG-i is a natural-interaction robot with mobility, 3D vision, voice programming, and active perception. The robot, which stands 2ft 6ins (80 centimetres) tall, is able to move freely around the home as necessary, avoiding obstacles and can be controlled via a smartphone app as well as voice commands. It also has facial recognition technology that allows it to recognise family and friends in order to carry out specific tasks, such as reminding the children to remember their school lunchbox before they leave the house.

Can artificial intelligence (AI) help address global challenges such as poverty, hunger, health, education, equality and the protection of our environment? The International Telecommunications Union (ITU) is hosting the AI for Good Global Summit in Geneva this June. The summit, to be held in partnership with UN agencies, including OHCHR, UNESCO, UNICEF, UNICRI, UNIDO, UNITAR and UN Global Pulse, will evaluate opportunities presented by AI and how it can benefit humanity. It seeks to convene representatives of government, industry, UN agencies, civil society, and the AI research community to explore the latest developments in AI and their implications for regulation, ethics and security and privacy. Breakout sessions will invite participants to collaborate and propose strategies for the development of AI applications and systems to promote sustainable living, reduce poverty and deliver citizen-centric public services.

Today, the Rochester Institute of Technology (RIT) is kicking off a new seminar series focused on connecting the campus's growing artificial intelligence (AI) community. The series has evolved out of a long tradition of AI research at the institution. There are 43 AI-focused courses and approximately 40 faculty-researchers in 27 lab groups across RIT involved in using AI and related areas, RIT News reported. This past February, RIT hosted the Move78 retreat, which brought together individuals from the Kate Gleason College of Engineering, B. Thomas Golisano College of Computing and Information Sciences, Saunders College of Business, the College of Science and the College of Liberal Art. More than 200 faculty, students and staff members teaching or researching AI had a chance to learn more about the field, as well as the direction that RIT might take to expand its capabilities. "The retreat is to determine three things: What will RIT's role be in this arena and what role will we have in new AI discoveries," Provost Jeremy Haefner told RIT News.

For centuries, humans have been fretting over "technological unemployment" or the loss of jobs caused by technological change. Never has this sentiment been accentuated more than it is today, at the cusp of the next industrial revolution. With developments in artificial intelligence continuing at a chaotic pace, fears of robots ultimately replacing humans are increasing. TNW Conference won best European Event 2016 for our festival vibe. See what's in store for 2017.

The other day, I went to meet someone in downtown Sydney, Australia. On my way, back on the local train, I looked at my mobile to check my emails and found a message asking me whether I would like to meet the person I had just connected with on my LinkedIn network. So, was this some form of artificial intelligence (AI) at play? We now live in a brave new world where AI is the next frontier. We keep hearing about bots, chatbots, teacherbots, digital assistants, machine learning, deep learning and many more such words and often wonder what do they mean.

Even more amazing, the researchers never explicitly programmed this AI communication. Instead, it "evolved" as a response to a reinforcement learning problem. While the jargon can get a bit technical, the OpenAI blog does a decent job of parsing it. The important thing to grok is the language was never defined, but rather hit upon as a solution to a general problem of learning to communicate. This type of AI method is called reinforcement learning, and involves the use of a reward signal to continually guide the agent towards an optimum outcome.

Feature hashing is a powerful technique for handling sparse, high-dimensional features in machine learning. It is fast, simple, memory-efficient, and well suited to online learning scenarios. While an approximation, it has surprisingly low accuracy tradeoffs in many machine learning problems. In this post, I will cover the basics of feature hashing and how to use it for flexible, scalable feature encoding and engineering. I'll also mention feature hashing in the context of Apache Spark's MLlib machine learning library.

Stochastic gradient descent based algorithms are typically used as the general optimization tools for most deep learning models. A Restricted Boltzmann Machine (RBM) is a probabilistic generative model that can be stacked to construct deep architectures. For RBM with Bernoulli inputs, non-Euclidean algorithm such as stochastic spectral descent (SSD) has been specifically designed to speed up the convergence with improved use of the gradient estimation by sampling methods. However, the existing algorithm and corresponding theoretical justification depend on the assumption that the possible configurations of inputs are finite, like binary variables. The purpose of this paper is to generalize SSD for Gaussian RBM being capable of mod- eling continuous data, regardless of the previous assumption. We propose the gradient descent methods in non-Euclidean space of parameters, via de- riving the upper bounds of logarithmic partition function for RBMs based on Schatten-infinity norm. We empirically show that the advantage and improvement of SSD over stochastic gradient descent (SGD).

In this paper, we address the inverse problem, or the statistical machine learning problem, in Markov random fields with a non-parametric pair-wise energy function with continuous variables. The inverse problem is formulated by maximum likelihood estimation. The exact treatment of maximum likelihood estimation is intractable because of two problems: (1) it includes the evaluation of the partition function and (2) it is formulated in the form of functional optimization. We avoid Problem (1) by using Bethe approximation. Bethe approximation is an approximation technique equivalent to the loopy belief propagation. Problem (2) can be solved by using orthonormal function expansion. Orthonormal function expansion can reduce a functional optimization problem to a function optimization problem. Our method can provide an analytic form of the solution of the inverse problem within the framework of Bethe approximation.

The race is on to attract as much expertise in artificial intelligence as possible at tech companies large and small, and more than a few Silicon Valley giants are looking inward to convert tech talent they already possess into the AI resources they increasingly need. Facebook has its own AI course, which is oversubscribed, according to a new report by Wired, and which is led by one of the leading AI researchers in the world. Facebook's Larry Zitnick, who is a key leader at the social networking company's Artificial Intelligence Research Lab, as well as a Microsoft Research and CMU Robotics alum, teaches a class on deep learning for Facebook employees that draws over-capacity crowds. Zitnick's course sparks strong competition among engineers who already rank among the best in the world, each vying to come to grips with and excel at a field outside of their original purview, but one that few fail to recognize is the hottest in tech. On the other hand, AI and deep learning increasingly touch all aspects of the technology business, so experts with understanding of where the overlap might prove most useful in their own original discipline are also going to be very much in demand. There are external efforts underway to help create more of these polyglot deep learning pros, including at online educational firms like Udacity, but new talent isn't rolling in fast enough from outside sources, traditional and non-traditional alike.