Cambridge Science Festival 2016 was the biggest and best ever, organisers have revealed. More than 350 events were held during the fortnight-long festival, attracting 60,000 visitors. Cambridge University, which runs the events, said the festival's main theme, artificial intelligence, sparked "considerable interest". This year was the 22nd in the festival's history, and it finished last Sunday with dozens of events on the Cambridge Biomedical Campus. A spokeswoman said: "We had two momentous weeks of over 350 events and 60,000 visits, making it the largest festival to date in terms of both events and visits. "This year was marked by the considerable interest from both the public and the media in many of the artificial intelligence and machine learning events, testament to both the fascination and the concern we all feel with our growing interaction and reliance on machines.

The United Nations Children's Fund, perhaps better known as UNICEF, has recently launched a new initiative through which they will begin to invest more money into technology start-ups that have the potential to better the lives of disadvantaged and vulnerable children all over the world. The new initiative, called Innovation Fund, has put a special focus on certain technologies that have the potential to help children, which include 3D printing, blockchain, wearables and sensors, artificial intelligence, and renewable energy. UNICEF, the United Nations program that has as its mandate the promotion of children's rights all over the world, has through its Innovation initiative put its focus on progessive projects and ideas that have the potential to help their cause. As stated on their website, UNICEF Innovation is "tasked with identifying, prototyping, and scaling technologies and practices that strengthen UNICEF's work." UNICEF is currently accepting submissions from various start-ups until February 26, 2016 through their website, though there are some requirements for being considered for funding.

UPDATE: Mar 20, 2016 - Added my new follow-up course on Deep Learning, which covers ways to speed up and improve vanilla backpropagation: momentum and Nesterov momentum, adaptive learning rate algorithms like AdaGrad and RMSProp, utilizing the GPU on AWS EC2, and stochastic batch gradient descent. We look at TensorFlow and Theano starting from the basics - variables, functions, expressions, and simple optimizations - from there, building a neural network seems simple! Deep learning is all the rage these days. What exactly is deep learning? Well, it all boils down to neural networks.

Manuela Veloso is the new head of Carnegie Mellon University's Machine Learning Department. Manuela Veloso, a computer scientist renowned for her work in artificial intelligence and robotics, is the new head of Carnegie Mellon University's Machine Learning Department, Andrew Moore, dean of the School of Computer Science, announced today. She succeeds Tom Mitchell, E. Fredkin University Professor and the founding head of the Machine Learning Department (MLD), who remains a member of the faculty. Veloso, the Herbert A. Simon Professor of Computer Science, has been a faculty member since earning her Ph.D. in computer science at Carnegie Mellon in 1992. "Carnegie Mellon's AI community has long nurtured the field of machine learning -- software that acquires knowledge and improves its performance with experience -- culminating in the creation of the world's first machine learning department 10 years ago," Moore said.

In a sparse lecture room at Stanford University, six students are rehearsing a presentation they'll later give to a roomful of VIPs from the university's artificial intelligence lab. Papers are strewn across the table. Hoodies hang over the cloth-covered cushion chairs. One student wears a pair of Pi earrings. Another wears a t-shirt that reads: "i: Be rational! A sheet of white poster board sits in the corner, with a few words scrawled in black marker. "Monitoring Hand Sanitation in Hospitals Using Computer Vision," it says. After a while, they give it a dry run. Scripts in hand, the students describe the images they captured from cameras mounted above hand dispensers at Stanford's Lucile Packard Children's Hospital, and they explain the machine learning techniques they've used--including something called "climbing the hill"--to analyze the footage and automatically determine whether doctors and visitors are practicing proper hand hygiene.

Editor's note: Kevin Gray is president of Cannon Gray LLC, a marketing science and analytics consultancy. He would like to thank Marco Vriens of Ipsos for his helpful comments on a draft of this article. Machine learning gets a lot of buzz these days, usually in connection with big data and artificial intelligence (AI). But what exactly is it? Broadly speaking, machine learners are computer algorithms designed for pattern recognition, curve fitting, classification and clustering.

Semidefinite programming has become a key optimization tool in many areas of applied mathematics, signal processing and machine learning. SDPs often arise naturally from the problem structure, or are derived as surrogate optimizations that are relaxations of difficult combinatorial problems [7, 1, 8]. In spite of the importance of SDPs in principle--promising efficient algorithms with polynomial runtime guarantees--it is widely recognized that current optimization algorithms based on interior point methods can handle only relatively small problems. Thus, a considerable gap exists between the theory and applicability of SDP formulations. Scalable algorithms for semidefinite programming, and closely related families of nonconvex programs more generally, are greatly needed. A parallel development is the surprising effectiveness of simple classical procedures such as gradient descent for large scale problems, as explored in the recent machine learning literature. In many areas of machine learning and signal processing such as classification, deep learning, and phase retrieval, gradient descent methods, in particular first order stochastic optimization, have led to remarkably efficient algorithms that can attack very large scale problems [3, 2, 10, 6]. In this paper we build on this work to develop first-order algorithms for solving the rank minimization problem under random measurements and a closely related family of semidefinite programs. Our algorithms are efficient and scalable, and we prove that they attain linear convergence to the global optimum under natural assumptions.

We all have memories of high school. Most of them, like the time spent with our friends, are great memories that we will always cherish. However, other memories, like the stress of taking the SAT's, are ones we try not to think about. In fact, just thinking about it now makes me sweat. Being a teenager is stressful enough, but the idea that so much of your future depends on this one test is beyond nerve wracking. That is why so many parents try to help by paying for their kids to take test prep courses, so they will walk in on test day feeling prepared.

Hi all - I'm seeking advice on how to best learn the math required for doing machine learning research, particularly with regard to neural nets (and other graphical models - sorry if I'm using these terms incorrectly). My background is in cognitive science, but of a particularly computational flavor, so I've been exposed to the high level ideas behind "connectionist" models, and have used them as a sort of black box in the context of comparing their performance to human behavioral data. But my undergrad coursework is conspicuously lacking in math. I recently got a job as a software engineer in a lab that works on deep learning (in NLP applications), and I want to be able to understand the math well enough to contribute to research. The lab PI and I have discussed my interest in eventually converting to a grad student, so I want to make sure my math abilities are solid as soon as I can.

Associate Professor Julian Togelius works at the intersection of artificial intelligence (AI) and games--a largely unexplored juncture that he has shown can be the site of visionary and mind-expanding research. Could games provide a better AI test bed than robots, which--despite the way they excite public imagination--can be slow, unwieldy and expensive? According to him, the answer is resoundingly yes. "I'm teaching computers to be more creative than humans," he says. Togelius, a member of the NYU Tandon School of Engineering's Department of Computer Science and Engineering, is at the forefront of the study of procedural content generation (PCG)--the process of creating game content (such as levels, maps, rules, and environments) by employing algorithms, rather than direct user input.