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The Virus That Learns

The Virus That Learns | Social Foraging | Scoop.it

If you don’t have an immune system, you don’t last long in this parasite-riddled world. Your body receives a steady stream of invaders–viruses, bacteria, and other pathogens–which it has to recognize and fight. In many cases, it’s a brutal battle with an ultimate goal of eradication. In other cases, the immune system simply keeps strangers in check, preventing them from spreading. As many as a third of all humans have cysts in their brains containing a single-celled parasite called Toxoplasma. As long as the parasite stays in its cyst, the immune system lets it be. If Toxoplasma breaks out and starts to multiply, however, the immune system picks off the new cells. And if people lose their immune system–due to HIV infection, for example–Toxoplasma runs rampant and causes devastating brain damage.

 

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Social Foraging
Dynamics of Social Interaction
Curated by Ashish Umre
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Why neurons mix: high dimensionality for higher cognition

Why neurons mix: high dimensionality for higher cognition | Social Foraging | Scoop.it
Neurons often respond to diverse combinations of task-relevant variables. This form of mixed selectivity plays an important computational role which is related to the dimensionality of the neural representations: high-dimensional representations with mixed selectivity allow a simple linear readout to generate a huge number of different potential responses. In contrast, neural representations based on highly specialized neurons are low dimensional and they preclude a linear readout from generating several responses that depend on multiple task-relevant variables. Here we review the conceptual and theoretical framework that explains the importance of mixed selectivity and the experimental evidence that recorded neural representations are high-dimensional. We end by discussing the implications for the design of future experiments.
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IBM opens Watson Internet of Things global HQ in Munich to drive cognitive computing research

IBM opens Watson Internet of Things global HQ in Munich to drive cognitive computing research | Social Foraging | Scoop.it
IBM has opened a brand new global headquarters for Watson Internet of Things (IoT) in Munich, Germany in an effort to drive the innovation and development of connected devices and cognitive computing.
Cognitive computing software uses natural language processing, machine learning and artificial intelligence to collect and analyse unstructured data and help users make better informed decisions.
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A Google DeepMind Algorithm Uses Deep Learning and More to Master the Game of Go

A Google DeepMind Algorithm Uses Deep Learning and More to Master the Game of Go | Social Foraging | Scoop.it
Google has achieved one of the long-standing “grand challenges” of AI, building a computer capable of beating expert players of the board game Go—more intuitive AI assistants could follow.
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One-Eyed Bug Vision Helps Drones Land

One-Eyed Bug Vision Helps Drones Land | Social Foraging | Scoop.it
In an effort to build—and control—ever smaller drones, researchers have been looking at how insects navigate. Insects use a technique called optical flow, based on the apparent speed of objects passing by in their field of vision. In fact, humans use optical flow to give us a sense of how fast we’re going when we’re driving. 

But unlike humans in cars, drones have a third dimension to worry about. They also have to keep track of their height in order to land successfully.  Stereo vision would allow them to estimate distances, but if the baseline between sensors is too small, those measurements are imprecise.
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Microsoft posts AI toolkit on GitHub

Microsoft posts AI toolkit on GitHub | Social Foraging | Scoop.it
The move to fully open-source the CNTK tools is part of Microsoft's effort to gain mind share and boost Azure pickup.
The kit, built with an emphasis on performance, provides a unified computational network framework describing deep neural networks as a series of computational steps via a directed graph, according to Microsoft Research.
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Science of Winning Soccer: Emergent pattern-forming dynamics in association football

Science of Winning Soccer: Emergent pattern-forming dynamics in association football | Social Foraging | Scoop.it
Quantitative analysis is increasingly being used in team sports to better understand performance in these stylized, delineated, complex social systems. Here we provide a first step toward understanding the pattern-forming dynamics that emerge from collective offensive and defensive behavior in team sports. We propose a novel method of analysis that captures how teams occupy sub-areas of the field as the ball changes location. We used the method to analyze a game of association football (soccer) based upon a hypothesis that local player numerical dominance is key to defensive stability and offensive opportunity. We found that the teams consistently allocated more players than their opponents in sub-areas of play closer to their own goal. This is consistent with a predominantly defensive strategy intended to prevent yielding even a single goal. We also find differences between the two teams' strategies: while both adopted the same distribution of defensive, midfield, and attacking players (a 4:3:3 system of play), one team was significantly more effective both in maintaining defensive and offensive numerical dominance for defensive stability and offensive opportunity. That team indeed won the match with an advantage of one goal (2 to 1) but the analysis shows the advantage in play was more pervasive than the single goal victory would indicate. Our focus on the local dynamics of team collective behavior is distinct from the traditional focus on individual player capability. It supports a broader view in which specific player abilities contribute within the context of the dynamics of multiplayer team coordination and coaching strategy. By applying this complex system analysis to association football, we can understand how players' and teams' strategies result in successful and unsuccessful relationships between teammates and opponents in the area of play.
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Build a mobile gaming analytics platform

Build a mobile gaming analytics platform | Social Foraging | Scoop.it
Popular mobile games can attract millions of players and generate terabytes of game-related data in a short burst of time. This places extraordinary pressure on the infrastructure powering these games and requires scalable data analytics services to provide timely, actionable insights in a cost-effective way.

To address these needs, a growing number of successful gaming companies use Google’s web-scale analytics services to create personalized experiences for their players. They use telemetry and smart instrumentation to gain insight into how players engage with the game and to answer questions like: At what game level are players stuck? What virtual goods did they buy? And what's the best way to tailor the game to appeal to both casual and hardcore players?
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Looking back at the 2015 Analytics Software Market

Looking back at the 2015 Analytics Software Market | Social Foraging | Scoop.it
There have been several trends (open source, cloud hosting, SQL on Hadoop) that have continued to play out, as well as the emergence of AWS Redshift as a major force in data warehouses.

Additionally, a number of startups have converged around the ecological niches the emergence of Redshift has created in an otherwise stagnant market.
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Streaming analytics enter the fast lane

Streaming analytics enter the fast lane | Social Foraging | Scoop.it

A living application doesn’t need to store data any more than a living organism needs to “store” its blood. Streaming data analytics is in fact the bloodstream of modern applications.

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Microservices: the Polyglot of Technical Architecture

Microservices: the Polyglot of Technical Architecture | Social Foraging | Scoop.it
According to Gartner, there are 4.9 billion connected devices in the world today, and that number is expected to reach 25 billion by 2025. Customers expect service at their fingertips. Even when it comes to traditional industries like banking, retail, finance, government and telecommunication, consumers want to engage in the personal and seamless ways they engage with social media and e-commerce. They rely on social ratings, peer views and online research, and expect to engage via different channels.  They are concerned about the “experience” in the decision journey, and this changing nature is forcing enterprises to transform from the conventional IT organization to a real time, data-driven, customer-centric, performance-intensive agile business.
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Deep Learning in a Nutshell: Core Concepts

Deep Learning in a Nutshell: Core Concepts | Social Foraging | Scoop.it
This post is the first in a series I’ll be writing for Parallel Forall that aims to provide an intuitive and gentle introduction to deep learning. It covers the most important deep learning concepts and aims to provide an understanding of each concept rather than its mathematical and theoretical details. While the mathematical terminology is sometimes necessary and can further understanding, these posts use analogies and images whenever possible to provide easily digestible bits comprising an intuitive overview of the field of deep learning.
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An Introduction to React Native

An Introduction to React Native | Social Foraging | Scoop.it
Earlier in 2015, at their annual React.js conference, Facebook made an exciting announcement with the release of React Native: a platform bringing the brilliance of React JS to the process of developing native apps. For years web developers and designers have looked for a platform allowing them to use their web-based skills to create mobile apps, the question remains, is React Native the platform to bridge this gap? Furthermore, is it a game-changer in the landscape of platform architecture and development?
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Cooperation and Shared Beliefs about Trust in the Assurance Game

Cooperation and Shared Beliefs about Trust in the Assurance Game | Social Foraging | Scoop.it
Determinants of cooperation include ingroup vs. outgroup membership, and individual traits, such as prosociality and trust. We investigated whether these factors can be overridden by beliefs about people’s trust. We manipulated the information players received about each other’s level of general trust, “high” or “low”. These levels were either measured (Experiment 1) or just arbitrarily assigned labels (Experiment 2). Players’ choices whether to cooperate or defect in a stag hunt (or an assurance game)—where it is mutually beneficial to cooperate, but costly if the partner should fail to do so—were strongly predicted by what they were told about the other player’s trust label, as well as by what they were told that the other player was told about their own label. Our findings demonstrate the importance for cooperation in a risky coordination game of both first- and second-order beliefs about how much people trust each other. This supports the idea that institutions can influence cooperation simply by influencing beliefs.
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Coupling News Sentiment with Web Browsing Data Improves Prediction of Intra-Day Price Dynamics

Coupling News Sentiment with Web Browsing Data Improves Prediction of Intra-Day Price Dynamics | Social Foraging | Scoop.it
The new digital revolution of big data is deeply changing our capability of understanding society and forecasting the outcome of many social and economic systems. Unfortunately, information can be very heterogeneous in the importance, relevance, and surprise it conveys, affecting severely the predictive power of semantic and statistical methods. Here we show that the aggregation of web users’ behavior can be elicited to overcome this problem in a hard to predict complex system, namely the financial market. Specifically, our in-sample analysis shows that the combined use of sentiment analysis of news and browsing activity of users of Yahoo! Finance greatly helps forecasting intra-day and daily price changes of a set of 100 highly capitalized US stocks traded in the period 2012–2013. Sentiment analysis or browsing activity when taken alone have very small or no predictive power. Conversely, when considering a news signal where in a given time interval we compute the average sentiment of the clicked news, weighted by the number of clicks, we show that for nearly 50% of the companies such signal Granger-causes hourly price returns. Our result indicates a “wisdom-of-the-crowd” effect that allows to exploit users’ activity to identify and weigh properly the relevant and surprising news, enhancing considerably the forecasting power of the news sentiment.
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Machine learning: Trends, perspectives, and prospects

Machine learning: Trends, perspectives, and prospects | Social Foraging | Scoop.it
Machine learning addresses the question of how to build computers that improve automatically through experience. It is one of today’s most rapidly growing technical fields, lying at the intersection of computer science and statistics, and at the core of artificial intelligence and data science. Recent progress in machine learning has been driven both by the development of new learning algorithms and theory and by the ongoing explosion in the availability of online data and low-cost computation. The adoption of data-intensive machine-learning methods can be found throughout science, technology and commerce, leading to more evidence-based decision-making across many walks of life, including health care, manufacturing, education, financial modeling, policing, and marketing.
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Mastering the game of Go with deep neural networks and tree search

Mastering the game of Go with deep neural networks and tree search | Social Foraging | Scoop.it
The game of Go has long been viewed as the most challenging of classic games for artificial intelligence owing to its enormous search space and the difficulty of evaluating board positions and moves. Here we introduce a new approach to computer Go that uses ‘value networks’ to evaluate board positions and ‘policy networks’ to select moves. These deep neural networks are trained by a novel combination of supervised learning from human expert games, and reinforcement learning from games of self-play. Without any lookahead search, the neural networks play Go at the level of state-of-the-art Monte Carlo tree search programs that simulate thousands of random games of self-play. We also introduce a new search algorithm that combines Monte Carlo simulation with value and policy networks. Using this search algorithm, our program AlphaGo achieved a 99.8% winning rate against other Go programs, and defeated the human European Go champion by 5 games to 0. This is the first time that a computer program has defeated a human professional player in the full-sized game of Go, a feat previously thought to be at least a decade away.
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Deep Learning with Spark and TensorFlow

Deep Learning with Spark and TensorFlow | Social Foraging | Scoop.it
Neural networks have seen spectacular progress during the last few years and they are now the state of the art in image recognition and automated translation.  TensorFlow is a new framework released by Google for numerical computations and neural networks. In this blog post, we are going to demonstrate how to use TensorFlow and Spark together to train and apply deep learning models.
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SanDisk Maximizes Production Quality with Machine Learning and Analytics Powered by Cloudera Enterprise

SanDisk Maximizes Production Quality with Machine Learning and Analytics Powered by Cloudera Enterprise | Social Foraging | Scoop.it
Cloudera, the global provider of the fastest, easiest, and most secure data management and analytics platform built on Apache Hadoop and the latest open source technologies, announced today that SanDisk, a global leader in flash storage, has deployed Cloudera Enterprise as an enterprise data hub to store, process, analyze, and test all of its product quality data. With Cloudera, SanDisk is for the first time incorporating end-to-end analytics and machine learning into its manufacturing operations, reducing drive errors, predicting failures, and ultimately ensuring superior reliability, quality, and performance of its products.
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Reality Check: Combining Choice Experiments with Market Data to Estimate the Importance of Product Attributes

Reality Check: Combining Choice Experiments with Market Data to Estimate the Importance of Product Attributes | Social Foraging | Scoop.it
Discrete choice models estimated using hypothetical choices made in a survey setting (i.e., choice experiments) are widely used to estimate the importance of product attributes in order to make product design and marketing mix decisions. Choice experiments allow the researcher to estimate preferences for product features that do not yet exist in the market. However, parameters estimated from experimental data often show marked inconsistencies with those inferred from the market, reducing their usefulness in forecasting and decision making. We propose an approach for combining choice-based conjoint data with individual-level purchase data to produce estimates that are more consistent with the market. Unlike prior approaches for calibrating conjoint models so that they correctly predict aggregate market shares for a “baseline” market, the proposed approach is designed to produce parameters that are more consistent with those that can be inferred from individual-level market data.
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Automation Is the New Reality for Big Data Initiatives

Automation Is the New Reality for Big Data Initiatives | Social Foraging | Scoop.it
The preeminence of data science was inextricably linked to the emergence of big data. Combining business savvy, analytics, and data curation, this discipline was hailed as an enterprise-wide savior for the rapidity of the disparate forms of big data that threatened to overwhelm it.

Numerous developments within the past several months, however, have created a different reality for big data and its future. Its technologies were refined. The self-service movement within the data sphere thrived. The result? Big data came to occupy the central place in the data landscape as critical elements of data science – preparation, analytics, and integration – became automated.

Thanks to the self-service movement’s proliferation, even the smallest of organizations can now access big data’s advantages. “There’s been a lot of discussion about self-service…and having data analysts get at the data directly,” MapR Chief Marketing Officer Jack Norris said. “But you also have to recognize, what do you mean by ‘the data,’ and what has to happen to ‘the data’ before that self-service takes place?”
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Bian Wu's curator insight, January 29, 8:18 PM

so it's actually about self-service ...

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React Native Ready for Enterprise Late This Year

React Native, a new approach to creating mobile apps unveiled by Facebook early last year, is an important development that's still maturing and unlikely to be suitable for enterprise adoption until late this year, IDC analyst Al Hilwa said in recent research report.
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Real Time Analytics with KAFKA and Spark Streaming

Real Time Analytics with KAFKA and Spark Streaming | Social Foraging | Scoop.it
Lately Real-Time processing has been gaining a lot of popularity. However, one thing to note is that the concept of Real-Time, RT, has been around for some time. Traditional enterprise software vendors have been providing tools to do real time processing, formerly known as “Complex Event Processing” or CEP systems, for quite some time. This raises an obvious question, if real time processing is not a new concept, then why is it becoming popular only now.
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Deep Learning in a Nutshell: History and Training

Deep Learning in a Nutshell: History and Training | Social Foraging | Scoop.it
Part 2 of an intuitive and gentle introduction to deep learning. Covers the most important deep learning concepts, giving an understanding rather than

 

In this second part, we look briefly into the history of deep learning and then proceed to methods of training deep learning architectures quickly and efficiently. The third part focuses on learning algorithms, unsupervised and sequence learning.

 


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A Full Hardware Guide to Deep Learning

A Full Hardware Guide to Deep Learning | Social Foraging | Scoop.it
Deep Learning is very computationally intensive, so you will need a fast CPU with many cores, right? Or is it maybe wasteful to buy a fast CPU? One of the worst things you can do when building a deep learning system is to waste money on hardware that is unnecessary. Here I will guide you step by step through the hardware you will need for a cheap high performance system.
In my work on parallelizing deep learning I built a GPU cluster for which I needed to make careful hardware selections. Despite careful research and reasoning I made my fair share of mistakes when I selected the hardware parts which often became clear to me when I used the cluster in practice. Here I want to share what I have learned so you will not step into the same traps as I did.
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Feature-based face representations and image reconstruction from behavioral and neural data

Feature-based face representations and image reconstruction from behavioral and neural data | Social Foraging | Scoop.it
The reconstruction of images from neural data can provide a unique window into the content of human perceptual representations. Although recent efforts have established the viability of this enterprise using functional magnetic resonance imaging (MRI) patterns, these efforts have relied on a variety of prespecified image features. Here, we take on the twofold task of deriving features directly from empirical data and of using these features for facial image reconstruction. First, we use a method akin to reverse correlation to derive visual features from functional MRI patterns elicited by a large set of homogeneous face exemplars. Then, we combine these features to reconstruct novel face images from the corresponding neural patterns. This approach allows us to estimate collections of features associated with different cortical areas as well as to successfully match image reconstructions to corresponding face exemplars. Furthermore, we establish the robustness and the utility of this approach by reconstructing images from patterns of behavioral data. From a theoretical perspective, the current results provide key insights into the nature of high-level visual representations, and from a practical perspective, these findings make possible a broad range of image-reconstruction applications via a straightforward methodological approach.
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