K-12 educators are deep in the midst of rethinking the design of the classroom and the responsibilities of the teacher within it. The internet brought a rush of new educational resources and technologies, like high-quality, free instructional videos from Khan Academy (which has 2.9 million YouTube subscribers) and crowdsourced lesson plans from nonprofit websites like ReadWriteThink and Teacher.org. Deciding on the best way to integrate these resources into schools has become another factor in the perpetual discussion about how to improve American public school education. As these new technologies made their way into schools, the phrase "blended learning" was coined to describe education environments where the traditional teacher-led classroom is augmented by digital media and online resources. As schools reconfigure their classrooms around blended learning, the role of the teacher is transitioning from "the sage on the stage" to the "guide on the side."

The research, published Thursday in the journal, Scientific Reports, explains how they did it. First, the team spent 75 days recording two groups of 11 bats held in separate cages. The team then went through the video footage to suss out which individuals were squeaking at each other, what they were squeaking about -- food, sleep, perch or sex (or lack thereof) -- and the ultimate outcome of the argument. Finally, they trained the machine learning algorithm with 15,000 calls from seven adult females using those variables. In the end, the algorithm managed to correctly identify the bat making the call (compared to the video footage) 71 percent of the time, the subject of that argument 61 percent of the time and the eventual outcome 41 percent of the time.

In the race to build the best AI, there's already one clear winner As Google, Facebook, Microsoft, and Baidu take turns leapfrogging each other in artificial intelligence innovation, one company stands to profit from any outcome: Nvidia. Graphics processor units, the company's biggest moneymaker, have become the industry standard for deep learning, a flavor of artificial intelligence widely used by tech companies to build personal virtual assistants, image recognition for tagging photos, and even the software behind self-driving cars. Despite talks from Microsoft and Google about developing their own proprietary chips, almost every major tech company is partnered up with Nvidia and uses its hardware. Last month, Microsoft announced a partnership to work with Nvidia's AI-tailored DGX-1 supercomputer, and Google's recently revamped cloud services will offer the option to run on Nvidia GPUs in 2017. Facebook's open-source Big Sur design for their server racks also rely on Nvidia hardware.

Visual signals exhibit strong correlation across scales that is often modeled and exploited to enhance image processing algorithms [2], [28]. An important example of this idea is the multi-scale coding of images using the wavelet-tree model which provides a sparse as well as a predictive model for the occurrence of nonzero wavelet coefficients across scales [33]. Specifically, the wavelet tree model arranges the wavelet coefficients of an image onto a tree such that nodes on the tree correspond to the coefficients and each level corresponds to coefficients associated with a particular scale. Under such an organization, the dominant nonzero coefficients form a connected rooted sub-tree [5], i.e., children of a node with small wavelet coefficients are expected to take small values as well. The wavelet tree model is central to many compression [29], sensing [7], [9], and processing algorithms [5].

What are Convolutional Neural Networks and why are they important? Convolutional Neural Networks (ConvNets or CNNs) are a category of Neural Networks that have proven very effective in areas such as image recognition and classification. ConvNets have been successful in identifying faces, objects and traffic signs apart from powering vision in robots and self driving cars. In Figure 1 above, a ConvNet is able to recognize scenes and the system is able to suggest relevant tags such as'bridge', 'railway' and'tennis' while Figure 2 shows an example of ConvNets being used for recognizing everyday objects, humans and animals. Lately, ConvNets have been effective in several Natural Language Processing tasks (such as sentence classification) as well. ConvNets, therefore, are an important tool for most machine learning practitioners today. However, understanding ConvNets and learning to use them for the first time can sometimes be an intimidating experience. The primary purpose of this blog post is to develop an understanding of how Convolutional Neural Networks work on images. If you are new to neural networks in general, I would recommend reading this short tutorial on Multi Layer Perceptrons to get an idea about how they work, before proceeding. Multi Layer Perceptrons are referred to as "Fully Connected Layers" in this post.

For four years in the mid-1970s an unusual experiment took place in the small Canadian town of Dauphin. Statistically significant benefits for those who took part included fewer physician contacts related to mental health and fewer hospital admissions for "accident and injury." Mental health diagnoses in Dauphin also fell. Once the experiment ended, these public health benefits evaporated.1 What was the treatment being tested? It was what has become known as a basic income--a regular, unconditional payment made to each and every citizen.

I'm the Chief Data Officer at VideoAmp, a startup focused on cross-screen advertising. I led the team to build a data platform on Apache Spark that handles over 300,000 requests per second, and machine learning pipelines that process close to a petabyte of data. Previously, I was the Chief Data Scientist for Pasadena Labs, a machine learning startup for online marketing. I got my PhD in Machine Learning from California Institute of Technology (Caltech), where I focused on Deep Learning and Behavioral economics.

Every year, financial services magazine Euromoney gives out numerous awards for excellence to firms in many categories, at country, regional, and global levels. The story of its digital transformation is all the more remarkable because one of its goals for its technology – in fact, for the entire bank – is to disappear from view. DBS is a midsized Asian bank with about 22,000 employees, created by the Government of Singapore in 1968 to help modernize the island nation. However, when DBS brought in Paul Cobban, who is now Chief Operating Officer, Technology and Operations for DBS, to spearhead the bank's transformation in 2009, they were far from best – in fact, they were among the worst. Cobban recalls an eye-opening story from his first day at the bank. "I was in a taxi and I mentioned I worked at DBS," Cobban recalls.

As Google, Facebook, Microsoft, and Baidu take turns leapfrogging each other in artificial intelligence innovation, one company stands to profit from any outcome: Nvidia. Graphics processor units, the company's biggest moneymaker, have become the industry standard for deep learning, a flavor of artificial intelligence widely used by tech companies to build personal virtual assistants, image recognition for tagging photos, and even the software behind self-driving cars. Despite talks from Microsoft and Google about developing their own proprietary chips, almost every major tech company is partnered up with Nvidia and uses its hardware. Last month, Microsoft announced a partnership to work with Nvidia's AI-tailored DGX-1 supercomputer, and Google's recently revamped cloud services will offer the option to run on Nvidia GPUs in 2017. Facebook's open-source Big Sur design for their server racks also rely on Nvidia hardware.

Imagine the holy grail: getting the right message to the right customer at exactly the right time -- every time. And what if you could deliver hyper-relevant cross-channel customer experiences that amplify loyalty and result in increased Average Order Value, reduced churn, and increased conversions faster? As hyper-scalable programmatic technology shifts from the adtech space and into the martech world, organizations are, for the first time, able to leverage predictive scoring on an incredible scale built upon a real-time view of every customer. Leading companies have already implemented user-centric strategies that place an emphasis on marrying systems of record, systems of intelligence, and systems of action to create what is now being called Programmatic CRM. Join master marketers in our latest VB Live executive event, where you'll learn how to turn martech innovation into user-centric, budget-stretching, personalized marketing that works.