Tommorrow's Science & Technology Issues
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Advancements in R&D that will be reality in tomorrow's life
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Adaptive Computation: The Multidisciplinary Legacy of John H. Holland

Adaptive Computation: The Multidisciplinary Legacy of John H. Holland | Tommorrow's Science & Technology Issues | Scoop.it

John Holland was unusual in his ability to absorb the essence of other disciplines, articulate grand overarching principles, and then back them up with computational mechanisms and mathematics. Unlike most researchers, Holland moved seamlessly among these three modes of thinking, developing models that were years ahead of their time. A close reading of his work reveals the antecedents of many ideas prevalent in machine learning today (such as reinforcement learning in non-Markovian environments and active learning). His seminal genetic algorithm spawned the field of evolutionary computation, and his insights and wisdom helped define what are today referred to as the "sciences of complexity."

 

Adaptive Computation: The Multidisciplinary Legacy of John H. Holland
By Stephanie Forrest, Melanie Mitchell
Communications of the ACM, Vol. 59 No. 8, Pages 58-63
http://dx.doi.org/10.1145/2964342 ;


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Amazon’s Giving Away the AI Behind Its Product Recommendations

Amazon’s Giving Away the AI Behind Its Product Recommendations | Tommorrow's Science & Technology Issues | Scoop.it
Amazon has become the latest tech giant that's giving away some of its most sophisticated technology.

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The Formation of Social Conventions in Real-Time Environments

Why are some behaviors governed by strong social conventions while others are not? We experimentally investigate two factors contributing to the formation of conventions in a game of impure coordination: the continuity of interaction within each round of play (simultaneous vs. real-time) and the stakes of the interaction (high vs. low differences between payoffs). To maximize efficiency and fairness in this game, players must coordinate on one of two equally advantageous equilibria. In agreement with other studies manipulating continuity of interaction, we find that players who were allowed to interact continuously within rounds achieved outcomes with greater efficiency and fairness than players who were forced to make simultaneous decisions. However, the stability of equilibria in the real-time condition varied systematically and dramatically with stakes: players converged on more stable patterns of behavior when stakes are high. To account for this result, we present a novel analysis of the dynamics of continuous interaction and signaling within rounds. We discuss this previously unconsidered interaction between within-trial and across-trial dynamics as a form of social canalization. When stakes are low in a real-time environment, players can satisfactorily coordinate ‘on the fly’, but when stakes are high there is increased pressure to establish and adhere to shared expectations that persist across rounds.

 

Hawkins RXD, Goldstone RL (2016) The Formation of Social Conventions in Real-Time Environments. PLoS ONE 11(3): e0151670. http://dx.doi.org/10.1371/journal.pone.0151670


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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 | Tommorrow's Science & Technology Issues | 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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Collective action problem in heterogeneous groups

Collective action problem in heterogeneous groups | Tommorrow's Science & Technology Issues | Scoop.it

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António F Fonseca's curator insight, November 4, 2015 4:54 AM

A very good review paper about collective action. Applying Mancur Olson's insights to Biology and groups.

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Mapping Systemic Risk: Critical Degree and Failures Distribution in Financial Networks

Mapping Systemic Risk: Critical Degree and Failures Distribution in Financial Networks | Tommorrow's Science & Technology Issues | Scoop.it
The financial crisis illustrated the need for a functional understanding of systemic risk in strongly interconnected financial structures. Dynamic processes on complex networks being intrinsically difficult to model analytically, most recent studies of this problem have relied on numerical simulations. Here we report analytical results in a network model of interbank lending based on directly relevant financial parameters, such as interest rates and leverage ratios. We obtain a closed-form formula for the “critical degree” (the number of creditors per bank below which an individual shock can propagate throughout the network), and relate failures distributions to network topologies, in particular scalefree ones. Our criterion for the onset of contagion turns out to be isomorphic to the condition for cooperation to evolve on graphs and social networks, as recently formulated in evolutionary game theory. This remarkable connection supports recent calls for a methodological rapprochement between finance and ecology.

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malek's comment, August 13, 2015 10:23 AM
though provoking, wonder why we coin financial risk with moral hazards?
pdeppisch's comment, August 13, 2015 12:40 PM
Because cheating / pulling a fast one is what financial services is all about: http://www.motherjones.com/politics/2010/01/mortgage-sharks-foreclosing Loan Sharks!
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Can Apps Transform Learning Into Games?

Can Apps Transform Learning Into Games? | Tommorrow's Science & Technology Issues | Scoop.it
There have been a number of recent education app releases, including DragonBox and PenyoPal Food Frenzy. Learning games are becoming increasingly successful.  It’s not about tricking kids int...

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Tesla's super-secret battery roadmap, Master Plan, and other goodies

Tesla's super-secret battery roadmap, Master Plan, and other goodies | Tommorrow's Science & Technology Issues | Scoop.it
Ok, maybe it's not so secret... but still cool!
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Moshers, Heavy Metal and Emergent Behaviour | MIT Technology Review

Moshers, Heavy Metal and Emergent Behaviour | MIT Technology Review | Tommorrow's Science & Technology Issues | Scoop.it
The collective behaviour of moshers at heavy metal concerts is mathematically similar to a disordered 2D gas, say physicists.
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Predicting the Future By Mining Online News and Other Web Data | MIT Technology Review

Predicting the Future By Mining Online News and Other Web Data | MIT Technology Review | Tommorrow's Science & Technology Issues | Scoop.it
Prototype software can give early warnings of disease or violence outbreaks by spotting clues in news reports.
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Predicting the Future By Mining Online News and Other Web Data | MIT Technology Review

Predicting the Future By Mining Online News and Other Web Data | MIT Technology Review | Tommorrow's Science & Technology Issues | Scoop.it
Prototype software can give early warnings of disease or violence outbreaks by spotting clues in news reports.
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Gamification: Is it game over?

Gamification: Is it game over? | Tommorrow's Science & Technology Issues | Scoop.it
Taking the rules of video games and applying them to dull office work or how we exercise is billed as the next big thing. But can it really work?

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Smartphones might soon develop emotional intelligence

Smartphones might soon develop emotional intelligence
Christos Tatsiopoulos's insight:
Smartphones might soon develop emotional intelligence
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A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data

A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data | Tommorrow's Science & Technology Issues | Scoop.it
Anomaly detection is the process of identifying unexpected items or events in datasets, which differ from the norm. In contrast to standard classification tasks, anomaly detection is often applied on unlabeled data, taking only the internal structure of the dataset into account. This challenge is known as unsupervised anomaly detection and is addressed in many practical applications, for example in network intrusion detection, fraud detection as well as in the life science and medical domain. Dozens of algorithms have been proposed in this area, but unfortunately the research community still lacks a comparative universal evaluation as well as common publicly available datasets. These shortcomings are addressed in this study, where 19 different unsupervised anomaly detection algorithms are evaluated on 10 different datasets from multiple application domains. By publishing the source code and the datasets, this paper aims to be a new well-funded basis for unsupervised anomaly detection research. Additionally, this evaluation reveals the strengths and weaknesses of the different approaches for the first time. Besides the anomaly detection performance, computational effort, the impact of parameter settings as well as the global/local anomaly detection behavior is outlined. As a conclusion, we give an advise on algorithm selection for typical real-world tasks.

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JavaScript Conquered the Web. Now It’s Taking Over the Desktop

JavaScript Conquered the Web. Now It’s Taking Over the Desktop | Tommorrow's Science & Technology Issues | Scoop.it
Electron is a software development platform created by Github that lets developers use JavaScript along with other web technologies like HTML and CSS to create desktop applications that can run on Windows, Macintosh OS X, and Linux. The company released the first full version of Electron yesterday. But some of tech’s biggest names have already put the tool to work to push JavaScript beyond the browser.

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IBM Watson IoT Platform Analytics for Real-Time Insights

IBM Watson IoT Platform Analytics for Real-Time Insights | Tommorrow's Science & Technology Issues | 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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Understanding Human-Machine Networks: A Cross-Disciplinary Survey

In the current hyper-connected era, modern Information and Communication Technology systems form sophisticated networks where not only do people interact with other people, but also machines take an increasingly visible and participatory role. Such human-machine networks (HMNs) are embedded in the daily lives of people, both or personal and professional use. They can have a significant impact by producing synergy and innovations.
The challenge in designing successful HMNs is that they cannot be developed and implemented in the same manner as networks of machines nodes alone, nor following a wholly human-centric view of the network. The problem requires an interdisciplinary approach. Here, we review current research of relevance to HMNs across many disciplines. Extending the previous theoretical concepts of socio-technical systems, actor-network theory, and social machines, we concentrate on the interactions among humans and between humans and machines. We identify eight types of HMNs: public-resource computing, crowdsourcing, web search engines, crowdsensing, online markets, social media, multiplayer online games and virtual worlds, and mass collaboration. We systematically select literature on each of these types and review it with a focus on implications for designing HMNs. Moreover, we discuss risks associated with HMNs and identify emerging design and development trends.

 

Understanding Human-Machine Networks: A Cross-Disciplinary Survey
Milena Tsvetkova, Taha Yasseri, Eric T. Meyer, J. Brian Pickering, Vegard Engen, Paul Walland, Marika Lüders, Asbjørn Følstad, George Bravos

http://arxiv.org/abs/1511.05324


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Inside Facebook’s Quest for Software That Understands You | MIT Technology Review

Inside Facebook’s Quest for Software That Understands You | MIT Technology Review | Tommorrow's Science & Technology Issues | Scoop.it
A reincarnation of one of the oldest ideas in artificial intelligence could finally make it possible to truly converse with our computers. And Facebook has a chance to make it happen first.
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Semi-Supervised Fuzzy Clustering with Feature Discrimination

Semi-Supervised Fuzzy Clustering with Feature Discrimination | Tommorrow's Science & Technology Issues | Scoop.it
Semi-supervised clustering algorithms are increasingly employed for discovering hidden structure in data with partially labelled patterns. In order to make the clustering approach useful and acceptable to users, the information provided must be simple, natural and limited in number. To improve recognition capability, we apply an effective feature enhancement procedure to the entire data-set to obtain a single set of features or weights by weighting and discriminating the information provided by the user. By taking pairwise constraints into account, we propose a semi-supervised fuzzy clustering algorithm with feature discrimination (SFFD) incorporating a fully adaptive distance function. Experiments on several standard benchmark data sets demonstrate the effectiveness of the proposed method.

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Raspberry Pi Car Computer: Raspboard - Technabob (blog)

Raspberry Pi Car Computer: Raspboard - Technabob (blog) | Tommorrow's Science & Technology Issues | Scoop.it
Harrison Kinsley aka sentdex had his Honda S2000 get with the times by installing a dash camera, a diagnostic tracker and more for a little over $100 (USD).

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Whatever next? Predictive brains, situated agents, and the future of cognitive science

Brains, it has recently been argued, are essentially prediction machines. They are bundles of cells that support perception and action by constantly attempting to match incoming sensory inputs with top-down expectations or predictions. This is achieved using a hierarchical generative model that aims to minimize prediction error within a bidirectional cascade of cortical processing. Such accounts offer a unifying model of perception and action, illuminate the functional role of attention, and may neatly capture the special contribution of cortical processing to adaptive success. This target article critically examines this “hierarchical prediction machine” approach, concluding that it offers the best clue yet to the shape of a unified science of mind and action.

 

Whatever next? Predictive brains, situated agents, and the future of cognitive science
Andy Clark

Behavioral and Brain Sciences / Volume 36 / Issue 03 / June 2013, pp 181-204

http://dx.doi.org/10.1017/S0140525X12000477


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Harshal Hayatnagarkar's comment, May 22, 2013 12:37 AM
Singularity is near !
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James B. Glattfelder: Who controls the world? | Video on TED.com

James Glattfelder studies complexity: how an interconnected system -- say, a swarm of birds -- is more than the sum of its parts. And complexity theory, it turns out, can reveal a lot about how the economy works.
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First Programmable T-shirt

First Programmable T-shirt | Tommorrow's Science & Technology Issues | Scoop.it
This is a project by two fundamentally different companies Cute Circuit, which produces wearing technologies, and Ballantine’s, 1st European and 2nd world producer of whisky.
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The emerging science of 'collective intelligence' — and the rise of the global brain

The emerging science of 'collective intelligence' — and the rise of the global brain | Tommorrow's Science & Technology Issues | Scoop.it
Over at the Edge there's a fascinating article by Thomas W. Malone about the work he and others are doing to understand the rise of collective human intelligence —
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