In an era of ubiquitous large-scale streaming data, the availability of data far exceeds the capacity of expert human analysts. In many settings, such data is either discarded or stored unprocessed in datacenters. This paper proposes a method of online data thinning, in which large-scale streaming datasets are winnowed to preserve unique, anomalous, or salient elements for timely expert analysis. At the heart of this proposed approach is an online anomaly detection method based on dynamic, low-rank Gaussian mixture models. Specifically, the high-dimensional covariances matrices associated with the Gaussian components are associated with low-rank models. According to this model, most observations lie near a union of subspaces. The low-rank modeling mitigates the curse of dimensionality associated with anomaly detection for high-dimensional data, and recent advances in subspace clustering and subspace tracking allow the proposed method to adapt to dynamic environments. Furthermore, the proposed method allows subsampling, is robust to missing data, and uses a mini-batch online optimization approach. The resulting algorithms are scalable, efficient, and are capable of operating in real time. Experiments on wide-area motion imagery and e-mail databases illustrate the efficacy of the proposed approach.

As Torrance observes: '[c]reativity defies precise definition... even if we had a precise conception of creativity, I am certain we would have difficulty putting it into words' [15, p. 43]. Many other authors have expressed similar difficulties [7, 10, 16]. In their review of research into human creativity, Hennessey and Amabile ask a significant follow-on question: 'Even if this mysterious phenomenon can be isolated, quantified, and dissected, why bother? Wouldn't it make more sense to revel in the mystery and wonder of it all?' [11, p. 570] Two answers to this question are offered by Hennessey and Amabile, both of which are identified as desirable: to gain a deeper understanding of creativity and to learn how to boost people's creativity. Creativity can and should be studied and measured scientifically, but the lack of a commonly-agreed understanding causes problems for measurement [10]. Plucker et al. make recommendations about best practice based on their own survey of the creativity literature: 'we argue that creativity researchers must (a) explicitly define what they mean by creativity, (b) avoid using scores of creativity measures as the sole definition of creativity (e.g., creativity is what creativity tests measure and creativity tests measure creativity, therefore we will use a score on a creativity test as our outcome variable), (c) discuss how the definition they are using is similar to or different from other definitions, and (d) address the question of creativity for whom and in what context.' [9, p.92] In short, we need to specify and justify the standards that we use to judge creativity. A more objective and well-articulated account of how creativity is manifested enables researchers to make a worthwhile contribution [8-10]. Particularly, in research we would like to focus on what processes and concepts relevant to creativity are'sufficiently important to warrant study' [17, p. 15], based on an accumulation of the body of work on creativity to date [17].

Nearly 50 million students are now returning to classrooms--from kindergarten through 12th grade. They will spend approximately eight hours a day at school and additional hours doing homework. They will be educated, in public schools alone, by the equivalent of over 3 million full-time teachers. And they will, with rare exception, learn a dismal fraction of what they ought to be learning to be creative, confident and critical thinkers about themselves and the world around them. As a parent myself, I literally apologized to each of my children--and not just once--for the fact that so much of their time as grade school and junior high school and high school students (even at private school) was being spent on memorization, regurgitation and rote learning that amounted to busy work and the warehousing of them, physically and mentally.

Four UC Berkeley researchers developed a program to help grade papers during their time working as teaching assistants – and now, they've added artificial intelligence to their app to help instructors speed up the grading process. The team launched the online grading app Gradescope two years ago and have accumulated 10 million answers to around 100,000 questions from a wide range of college courses – the app has already shortened the grading process by 50 percent due to its friendly interface and the ability for multiple teaching assistants to grade papers in parallel. Their new AI features addresses three challenges: identify question types, distinguishing between different written marks, and recognizing handwriting. AI helps turn grading into an automated, highly repeatable exercise by learning to identify and group answers, and thus treat them as batches. The addition of AI promises to slash grading times by as much as 90 percent, said Sergey Karayev, a Gradescope co-founder who finished his PhD in computer science in 2014.

During the christmas vacation of 2015, I got a motivational boost again and decided try out Kaggle. So I spent quite some time experimenting with various algorithms for their Homesite Quote Conversion, Otto Group Product Classification and Bike Sharing Demand contests. The main takeaway from this was the experience of iteratively improving the results by experimenting with the algorithms and the data. I learned to trust my logic when doing machine learning. If tweaking a parameter or engineering a new feature seems like a good idea logically, it's quite likely that it actually will help.

Last week in San Francisco, Singularity University hosted its first-ever Global Summit. In three days, we heard over 100 science and technology experts give talks in more categories than one human mind can fully process. Whether you attended the conference and need help making sense of the information or missed it and want a taste of the action, I've collected Singularity Hub articles on some of the major themes to give you takeaways from the event. If you're curious for a look inside the conference, you can watch: Singularity University Global Summit is the culmination of the Exponential Conference Series and the definitive place to witness converging exponential technologies and understand how they'll impact the world. As technology permeates almost every aspect of life, industries and institutions need to adapt how they think and operate.

Information Theory Here is the deal, a probability density function (pdf) is as much as we can know about a radom variable. Machine Learning is about estimating "momements" (you should learn that) of a pdf. If your random variable is not Gaussian, you will need more than mean and variance to correctly describe it (mean and var are the 1st and 2nd order moments).

We are assisting a top international company currently building a Machine Learning and Data Analytics team in Dublin source a number of Software Engineers with proven experience implementing and applying Machine Learning techniques and methodologies in a commercial environment. This is a fantastic opportunity for a Senior Software Engineers with expertise in Machine Learning and Cognitive Computing technologies join a new operation with huge expansion plans for 2016/17 and beyond. This is a fantastic opportunity to work inside a top international company utilising cutting edge tools and techniques.

My learning preference is to watch lecture videos and thankfully there are several excellent courses online.

This article was originally published by Scientific American. The idea that we have brains hardwired with a mental template for learning grammar -- famously espoused by Noam Chomsky of the Massachusetts Institute of Technology -- has dominated linguistics for almost half a century. Recently, though, cognitive scientists and linguists have abandoned Chomsky's "universal grammar" theory in droves because of new research examining many different languages -- and the way young children learn to understand and speak the tongues of their communities. That work fails to support Chomsky's assertions. The research suggests a radically different view, in which learning of a child's first language does not rely on an innate grammar module. Instead the new research shows that young children use various types of thinking that may not be specific to language at all -- such as the ability to classify the world into categories (people or objects, for instance) and to understand the relations among things. These capabilities, coupled with a unique hu man ability to grasp what others intend to communicate, allow language to happen. The new findings indicate that if researchers truly want to understand how children, and others, learn languages, they need to look outside of Chomsky's theory for guidance.