Though scientists have long known that mice can pick out scents -- the smell of food, say, or the odor of a predator -- they have been at a loss to explain how they are able to perform that seemingly complex task so easily. But a new study, led by Venkatesh Murthy, professor of molecular and cellular biology, suggests that the means of processing smells may be far simpler than researchers realized. Using a machine-learning algorithm, Murthy and colleagues were able to "train" a computer to recognize the neural patterns associated with various scents, and to identify whether specific odors were present in a mix of smells. The study is described in a Sept. 1 paper in the journal Neuron. Along with Murthy, the paper was co-authored by Alexander Mathis, Dan Rokni, and Vikrant Kapoor, postdoctoral fellows working in Murthy's lab, and Professor Matthias Bethge from the Werner Reichardt Centre for Integrative Neuroscience & Institute of Theoretical Physics in Germany.

Waiopehu College pupils, from left, Kate Nicholson, Niko Tofa and Sammy Heyward. More than 100 teens have explored the ever-evolving world of artificial intelligence. The 130 budding young scientists, from Freyberg High School, Manawatu College and Waiopehu College, learned about the benefits and difficulties faced in a technology-rich future at a conference in Levin on Friday. Mechanical masseuses and construction robots that could work in all weather conditions and give workers a sleep-in were among ideas the pupils – aged 11 to 13 – came up with for the future. Centre for Science and Citizenship founders Dr Deborah Stevens, left, and Dr Lynne Bowyer.

I describe here the projects that I worked on, as well as career progress, starting 25 years ago as a PhD student in statistics, until today, and the transformation from statistician to data scientist that occurred slowly and started more than 20 years ago. This also illustrates many applications of data science, most are still active. My interest in mathematics started when I was 7 or 8, I remember being fascinated by the powers of 2 in primary school, and later purchasing cheap russian math books (Mir publisher) translated in French, for my entertainement. In high school, I participated in the mathematical olympiads, and did my own math research during math classes, rather than listening to the very boring lessons. When I attended college, I stopped showing up in the classroom altogether - afterall, you could just read the syllabus, memorize the material before the exam and regurgitate it at the exam.

The overall goal is to target treatment specifically to each individual so that clinical outcomes for that individual are optimized. One direction of attack is to use patient data to discover decision rules which specify the treatment to use as a function of a vector of features from the patient. Regression and classification are important statistical tools for estimating such rules based on either observational data or data from a randomized trial, and machine learning can help with this because of its ability to artfully handle high dimensional feature spaces with potentially complex interactions.

The use of robotic tutors in primary school classrooms is one step closer according to research recently published in the open access journal Frontiers in Computational Neuroscience. Dr Imbernòn Cuadrado and his co-workers at the Department of Artificial Intelligence in Madrid have developed an integrated computational architecture (ARTIE) for use with software applications in schools. "The main goal of our work was to design a system that can detect the emotional state of primary school children interacting with educational software and make pedagogic interventions with a robot tutor that can ultimately improve the learning experience," says Luis Imbernòn Cuadrado. Online educational resources are becoming increasingly common in the classroom, although they have not taken into sufficient account that the learning ability of primary school children is particularly sensitive to their emotional state. This is perhaps where robot tutors can step in to assist teachers.

At Udacity, we believe applications of artificial intelligence will bring transformative change to all industries, and not in some distant science-fiction future--we are seeing rapidly growing demand for AI-related skills right now, and new artificial intelligence jobs are emerging every day. This is exactly why we created our recently announced Artificial Intelligence Nanodegree program. Many of these jobs are still very new however, and we've learned from our program applicants--who already number in the thousands!--that So we took it upon ourselves to answer this question. To begin, we needed concrete data.

Product Managers have enthusiastically adopted the data-driven approach to building products and have learnt not to rely solely on experience. For some features it is a continuous process that helps the Build-Measure-Learn iteration. Intuition backed by data is a product manager's most powerful weapon. If we have already made the shift towards data then why do we need Machine Learning, you ask? In this post, I am going to share why I believe every Product Manager should understand Machine Learning and where to start.

As artificial intelligence (AI) begins to power more technology across industries, it's been truly exciting to see what our community of developers can create with Watson. Developers are inspiring us to advance the technology that is transforming society, and they are the reason why such a wide variety of businesses are bringing cognitive solutions to market. With AI becoming more ubiquitous in the technology we use every day, developers need to continue to sharpen their cognitive computing skills. They are seeking ways to gain a competitive edge in a workforce that increasingly needs professionals who understand how to build AI solutions. It is for this reason that today at World of Watson in Las Vegas we announced with Udacity the introduction of a Nanodegree program that incorporates expertise from IBM Watson and covers the basics of artificial intelligence. The "AI Nanodegree" program will be helpful for those looking to establish a foundational understanding of artificial intelligence.

Why are we training kids to compete with machines? I was struck by a statement in this promotional video for IBM's Watson AI technology that said, In the 30 or so years of working with digital platforms across the education and creative sectors I've noticed that these sort of claims appear every time a new bit of tech arrives. Watson, of course, is very smart technology. It hasn't passed the Turing test but it did beat the human champions on the TV trivia game show Jeopardy! It achieves this with some impressive computing power. Designed to answer questions within 3 seconds Watson's main innovation is its ability to quickly execute more than 100 different language analysis techniques to analyse the question, find and generate candidate answers, and ultimately score and rank them.