Social Foraging
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Social Foraging
Dynamics of Social Interaction
Curated by Ashish Umre
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IBM invests in IoT analytics with new 'Quarks' tool

IBM invests in IoT analytics with new 'Quarks' tool | Social Foraging | Scoop.it
IBM has released a new technology that brings continuous streaming analytics to the open source community.

Quarks embeds streaming analytics onto Internet of Things (IoT) devices - and the company has submitted a proposal to Apache Software Foundation to request incubation of the technology.

Analysing data at the edge continuously can help companies generate insights more quickly and reduce network communication costs, according to IBM.
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IBM Acquiring Truven Health Analytics For $2.6 Billion And Adding It To Watson Health

IBM Acquiring Truven Health Analytics For $2.6 Billion And Adding It To Watson Health | Social Foraging | Scoop.it
IBM announced its intent to buy Truven Health Analytics today for a whopping $2.6 billion. It is the fourth major purchase for Watson Health since the unit was established in 2014.

Watson Health was formed when IBM purchased Phytel and Explorys in April, 2014. Both companies had the common denominator of being data-driven health companies. The unit added Merge Healthcare for another billion dollars last August to give the company access to a huge store of imaging data.
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Cyber War Game in Temporal Networks

Cyber War Game in Temporal Networks | Social Foraging | Scoop.it
In a cyber war game where a network is fully distributed and characterized by resource constraints and high dynamics, attackers or defenders often face a situation that may require optimal strategies to win the game with minimum effort. Given the system goal states of attackers and defenders, we study what strategies attackers or defenders can take to reach their respective system goal state (i.e., winning system state) with minimum resource consumption. However, due to the dynamics of a network caused by a node’s mobility, failure or its resource depletion over time or action(s), this optimization problem becomes NP-complete. We propose two heuristic strategies in a greedy manner based on a node’s two characteristics: resource level and influence based on k -hop reachability. We analyze complexity and optimality of each algorithm compared to optimal solutions for a small-scale static network. Further, we conduct a comprehensive experimental study for a large-scale temporal network to investigate best strategies, given a different environmental setting of network temporality and density. We demonstrate the performance of each strategy under various scenarios of attacker/defender strategies in terms of win probability, resource consumption, and system vulnerability.
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Layering on Machine Learning to Speed Data Transformation

Layering on Machine Learning to Speed Data Transformation | Social Foraging | Scoop.it
There are few more widely recognized names in modern database research than Dr. Joseph Hellerstein. The Berkeley professor and Trifacta co-founder has spawned new approaches to relatively old problems on the programmatic and database design and implementation fronts.

Well before the tech world was awash in tales of “big data” woes, Hellerstein and teams were looking ahead at the future problems of data manipulation, transformation, and visualization, which culminated in the Wrangler project, which matched data manipulation and visualization tools with several new layers of automation and flexibility. At the time, around 2011, these allowed additional capabilities in terms of what databases could do—and just as important, the focus on performance made sure it could handle it all faster and more efficiently.

For anyone that has followed news about the open source Wrangler project Hellerstein and collaborators from Stanford development and how that fed into startup, Trifacta, it will be clear that the work had value outside of research contexts. And chances are, if you’ve been following Trifacta beyond research, it’s likely because the company has scored an incredible amount of funding since its launch ($76 million, including last week’s most recent influx of $35 million) and has notable use cases across a large swath of the Fortune 500 with companies like Time Warner, Intel, Thomson Reuters, Dow, Capital One, and many others climbing on board with their approach to data transformation, preparation, and exploration. What is notable here is that in an ecosystem that is so crowded with analytics, visualization, and data staging vendors, Trifacta has managed not only to stand out—but to stand alone. And in a relatively short amount of time to boot.
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The Search Party taps machine learning to spot variations in 15 million resumes

The Search Party taps machine learning to spot variations in 15 million resumes | Social Foraging | Scoop.it
Head of data science, Dylan Hogg, goes through the use of a custom clustering algorithm and deep neural network
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Asynchronous Methods for Deep Reinforcement Learning

We propose a conceptually simple and lightweight framework for deep reinforcement learning that uses asynchronous gradient descent for optimization of deep neural network controllers. We present asynchronous variants of four standard reinforcement learning algorithms and show that parallel actor-learners have a stabilizing effect on training allowing all four methods to successfully train neural network controllers. The best performing method, an asynchronous variant of actor-critic, surpasses the current state-of-the-art on the Atari domain while training for half the time on a single multi-core CPU instead of a GPU. Furthermore, we show that asynchronous actor-critic succeeds on a wide variety of continuous motor control problems as well as on a new task involving finding rewards in random 3D mazes using a visual input.
 
 
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Deciding with Data: How Society Can Use Algorithms to Make Better Choices

Deciding with Data: How Society Can Use Algorithms to Make Better Choices | Social Foraging | Scoop.it
Using software to solve complex problems by analyzing data—known as algorithmic decision-making—offers incredible potential for the public and private sectors to operate more effectively, efficiently, and equitably. For example, the technology has helped streamline wait lists for life-saving organ transplants, improve policing by predicting crime hotspots, and better target charitable giving to the poorest households in rural Kenya.

Despite these benefits, skeptics argue algorithmic decision-making will be inherently exploitative, discriminatory, or simply unreliable, and thus in need of greater government oversight. But countless real-world examples of algorithms unlocking tremendous social and economic benefits indicate otherwise: algorithms can be more effective and less biased than humans when it comes to making important decisions.

Join the Center for Data Innovation for a panel discussion about how public and private sector leaders are using algorithms to make better decisions and what an increasingly data-driven world means for the future of algorithmic decision-making.
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Apache Spark rises to become most active open source project in big data

Apache Spark rises to become most active open source project in big data | Social Foraging | Scoop.it
Apache Spark continues to attract attention in the big data world, where it's expected to help drive the next wave of innovation.
A survey on Hadoop from big data company Syncsort showed that 70% of survey participants are most interested in Spark, higher even than MapReduce, the current adoption leader, at 55%.

Syncsort surveyed 250 IT professionals. From that group, 66% were from firms with more than $100 million in annual revenue.

A healthy interest is not a surprise. In Apache Spark's relatively short life, there's been much discussion of its ascendancy. In September, Databricks, the company behind Spark, released results from a survey showing that Spark is the most active open source project in big data with more than 600 contributors within the past year, which is up from 315 in 2014. Plus, Spark is in use not just in the IT industry, but areas like finance, retail, advertising, education, health care, and more. That survey also showed that 51% of Spark users are using three or more Spark components.
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Exploring gambles reveals foundational difficulty behind economic theory (and a solution)

Exploring gambles reveals foundational difficulty behind economic theory (and a solution) | Social Foraging | Scoop.it
This included Ole Peters, a Fellow at the London Mathematical Laboratory in the U.K., as well as an external professor at the Santa Fe Institute in New Mexico, and Murray Gell-Mann, a physicist who was awarded the 1969 Nobel Prize in physics for his contributions to the theory of elementary particles by introducing quarks, and is now a Distinguished Fellow at the Santa Fe Institute. They found it particularly curious that a field so central to how we live together as a society seems so unsure about so many of its key questions.
So they asked: Might there be a foundational difficulty underlying our current economic theory? Is there some hidden assumption, possibly hundreds of years old, behind not one but many of the current scientific problems in economic theory? Such a foundational problem could have far-reaching practical consequences because economic theory informs economic policy.
As they report in the journal Chaos, from AIP Publishing, the story that emerged is a fascinating example of scientific history, of how human understanding evolves, gets stuck, gets unstuck, branches, and so on.
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MIT Builds Low-Power Artificial Intelligence Chip for Smartphones

MIT Builds Low-Power Artificial Intelligence Chip for Smartphones | Social Foraging | Scoop.it
A team of US researchers has built an energy-friendly chip that can perform powerful artificial intelligence (AI) tasks, enabling future mobile devices to implement "neural networks" modelled on the human brain.

The team from Massachusetts Institute of Technology (MIT) developed a new chip designed specifically to implement neural networks.

It is 10 times as efficient as a mobile GPU (Graphics Processing Unit) so it could enable mobile devices to run powerful AI algorithms locally rather than uploading data to the Internet for processing.

The GPU is a specialised circuit designed to accelerate the image output in a frame buffer intended for output to a display.

Modern smartphones are equipped with advanced embedded chipsets that can do many different tasks depending on their programming.
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The 3 A’s of Enterprise Integration

The 3 A’s of Enterprise Integration | Social Foraging | Scoop.it
As organizations look to increase their agility, IT and lines of business need to connect faster. Companies need to adopt cloud applications more quickly and they need to be able to access and analyze all their data, whether from a legacy data warehouse, a new SaaS application, or an unstructured data source such as social media. In short, a unified integration platform has become a critical requirement for most enterprises.

According to Gartner, “unnecessarily segregated application and data integration efforts lead to counterproductive practices and escalating deployment costs.”

Don’t let your organization get caught in that trap. Whether you are evaluating what you already have or shopping for something completely new, you should measure any platform by how well it address the “three A’s” of integration: Anything, Anytime, Anywhere.
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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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Optimal Information Representation and Criticality in an Adaptive Sensory Recurrent Neuronal Network

Optimal Information Representation and Criticality in an Adaptive Sensory Recurrent Neuronal Network | Social Foraging | Scoop.it
Recurrent connections play an important role in cortical function, yet their exact contribution to the network computation remains unknown. The principles guiding the long-term evolution of these connections are poorly understood as well. Therefore, gaining insight into their computational role and into the mechanism shaping their pattern would be of great importance. To that end, we studied the learning dynamics and emergent recurrent connectivity in a sensory network model based on a first-principle information theoretic approach. As a test case, we applied this framework to a model of a hypercolumn in the visual cortex and found that the evolved connections between orientation columns have a "Mexican hat" profile, consistent with empirical data and previous modeling work. Furthermore, we found that optimal information representation is achieved when the network operates near a critical point in its dynamics. Neuronal networks working near such a phase transition are most sensitive to their inputs and are thus optimal in terms of information representation. Nevertheless, a mild change in the pattern of interactions may cause such networks to undergo a transition into a different regime of behavior in which the network activity is dominated by its internal recurrent dynamics and does not reflect the objective input. We discuss several mechanisms by which the pattern of interactions can be driven into this supercritical regime and relate them to various neurological and neuropsychiatric phenomena.
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The Neural Network That Remembers

The Neural Network That Remembers | Social Foraging | Scoop.it
“On tap at the brewpub. A nice dark red color with a nice head that left a lot of lace on the glass. Aroma is of raspberries and chocolate. Not much depth to speak of despite consisting of raspberries. The bourbon is pretty subtle as well. I really don’t know that find a flavor this beer tastes like. I would prefer a little more carbonization to come through. It’s pretty drinkable, but I wouldn’t mind if this beer was available.”

Besides the overpowering bouquet of raspberries in this guy’s beer, this review is remarkable for another reason. It was produced by a computer program instructed to hallucinate a review for a “fruit/vegetable beer.” Using a powerful artificial-intelligence tool called a recurrent neural network, the software that produced this passage isn’t even programmed to know what words are, much less to obey the rules of English syntax. Yet, by mining the patterns in reviews from the barflies at BeerAdvocate.com, the program learns how to generate similarly coherent (or incoherent) reviews.
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Making the Impossible Possible with Tachyon: Accelerate Spark Jobs from Hours to Seconds

Making the Impossible Possible with Tachyon: Accelerate Spark Jobs from Hours to Seconds | Social Foraging | Scoop.it
Cluster computing and Big Data technologies have enabled analysis on and insights into data. For example, a big data application might process data in HDFS, a disk-based, distributed file system. However, there are many reasons to avoid storing your on data disk, such as for data regulations, or for reducing latency. Therefore, if you need to avoid disk read/writes, you can use Spark to process the data, and temporarily cache the results in memory.

There are a number of use cases where you might want to avoid storing your data on disk in a cluster, in which case our configuration of Tachyon makes this data available in-memory in the long-term and shared among multiple applications.

However, in our environment at Barclays, our data is not in HDFS, but rather, in a conventional relational database management system (RDBMS). Therefore, we have developed an efficient workflow in Spark for directly reading from an RDBMS (through a JDBC driver) and holding this data in memory as a type-safe RDD (type safety is a critical requirement of production-quality Big Data applications). Since the database schema is not well documented, we read the raw data into a dynamically-typed Spark DataFrame, then analyze the data structure and content, and finally cast it into an RDD. But there is a problem with this approach.
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Eternal data archiving with 5D nanostructured glass - Holds 360 TB and could last for billions of years

Eternal data archiving with 5D nanostructured glass - Holds 360 TB and could last for billions of years | Social Foraging | Scoop.it
Scientists at the University of Southampton have made a major step forward in the development of digital data storage that is capable of surviving for billions of years.

Using nanostructured glass, scientists from the University’s Optoelectronics Research Centre (ORC) have developed the recording and retrieval processes of five dimensional (5D) digital data by femtosecond laser writing.

The storage allows unprecedented properties including 360 TB/disc data capacity, thermal stability up to 1,000°C and virtually unlimited lifetime at room temperature (13.8 billion years at 190°C ) opening a new era of eternal data archiving.
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Google Takes On Amazon's Lambda

Google Takes On Amazon's Lambda | Social Foraging | Scoop.it
Google has released Cloud Functions, a rival for the Amazon's AWS Lambda service.
The Cloud Functions service is currently in alpha version, and is a managed Node.js environment that you can use to create small, single-purpose functions that respond to cloud events without the need to manage a server or a runtime environment.
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Cloud Computing, Data Analytics, and Open Science: A New Paradigm for Discovery

Cloud Computing, Data Analytics, and Open Science: A New Paradigm for Discovery | Social Foraging | Scoop.it
In fields ranging from genomics to quantum physics, researchers are increasingly using data-intensive computing to generate new insights and discoveries. Because of the volume of data involved in this research, scientists often store, analyze, and share it in the cloud. By leveraging the nearly infinite scale and tremendous computer power available in the cloud, they also are developing novel analytics tools and conducting more open and collaborative research that is accelerating the growth of scientific knowledge.
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IBM Watson IoT Platform Analytics for Real-Time Insights

IBM Watson IoT Platform Analytics for Real-Time Insights | Social Foraging | Scoop.it
IBM® Watson IoT Real-Time Insights enables you to perform analytics on real-time data from your IoT devices to gain insights about their health and the overall state of your operations. IBM® Watson IoT Real-Time Insights connects to the IBM Watson IoT Platform for real-time device data feeds. The incoming data is interpreted through a virtual data model that can be augmented with asset master data from an asset management system.

In addition, user-defined rules are applied to the real-time streaming data to identify conditions that need attention. The action engine lets you define automated responses to the detected conditions, such as sending an email, triggering an IFTTT recipe, executing a Node-RED workflow, or using webhooks to connect to a variety of web services. And finally, real-time data is also displayed in a configurable dashboard for an at-a-glance view of the location, data, metrics, and alerts for your IoT devices.
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The Future of News: Why discovery matters more than personalization

The Future of News: Why discovery matters more than personalization | Social Foraging | Scoop.it
We know, based on previous Future of News conversations, that the future of journalism is very much digital, that the industry is changing, practically on a daily basis. That it’s a demanding and challenging calling. But we also know that the journalists, editors, and publishers who have chosen this profession wouldn’t have it any other way. And Rich Jaroslovsky, Chief Journalist of SmartNews, is no exception.

With more than three decades of professional journalism experience and a resume he himself calls eccentric, Jaroslovsky epitomizes the curiosity, determination, and idealism of an industry that has taken some serious hits in recent years but refuses to back down.
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Shaikh Mohammed inaugurates 'Museum of the Future' in Dubai

Shaikh Mohammed inaugurates 'Museum of the Future' in Dubai | Social Foraging | Scoop.it
the Museum focuses on shaping the future of robotics and artificial intelligence and their impact on human life.

His Highness Shaikh Mohammed bin Rashid Al Maktoum, Vice President and Prime Minister of the UAE and Ruler of Dubai, today inaugurated the Museum of the Future at the Madinat Jumeirah in Dubai.

Organised by the Dubai Foundation for the Museum of the Future as part of the World Government Summit 2016, the Museum focuses on shaping the future of robotics and artificial intelligence and their impact on human life. It also offers a unique interactive experience to visitors by attempting to explore the future of this sector.

Shaikh Mohammed will also officiate the opening of the fourth World Government Summit when it gets underway on February8, 2016 under the theme 'Shaping Future Governments'.
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Microsoft AI Technology Xiaolce Test On Chinese Social Networks Shows Positive Results

Microsoft AI Technology Xiaolce Test On Chinese Social Networks Shows Positive Results | Social Foraging | Scoop.it
Microsoft's AI program XiaoIce constantly analyzes the user's emotional state to simulate personal conversations.
XiaoIce combines facts and data in Microsoft’s Bing search engine with recent advances in natural language processing.
XiaoIce has had more than 10 billion conversations with people, most of them about private matters.
XiaoIce is a huge hit in China, and could be a big boost to Microsoft's efforts in the AI space.
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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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