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Which Twitter tribe are you? Researchers discover new wave of online communities which even have their own languages

Which Twitter tribe are you? Researchers discover new wave of online communities which even have their own languages | Social Foraging | Scoop.it

Twitter users are spontaneously forming tribe-like communities of like-minded people who even share their own distinct languages, new research has found.


Scientists from Royal Holloway, University of London, and Princeton University in New Jersey found they could use the language in Tweets to group users into communities with a common character, occupation or interest.


They suggest that the use of a common language could allow members of such 'Twitter tribes' to quickly identify like-minded users, and that further investigation could yield insights into how sub-cultures evolve online.

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Social Foraging
Dynamics of Social Interaction
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Neural Networks with Google CoLaboratory | Artificial Intelligence Getting started

Google Recently Launched its internal tool for collaborating on writing Data Science Code. The Project called Google CoLaboratory (g.co/colab) is based on the Jupyter Open Source Project and is integrated with Google Drive. Colaboratory allows users to work on Jupyter Notebooks as easily as working on Google Docs or spreadsheets.

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How the Internet of Things (IoT) Will Change the Pricing of Things

How the Internet of Things (IoT) Will Change the Pricing of Things | Social Foraging | Scoop.it
As traditional product manufacturers become providers of data-driven services, they must rethink the most basic questions about customers, value, and pricing.
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Blockchain technology is transforming the food industry - Food for thought?

We have entered the new year and for many of us, it’s a fresh start. A chance to reflect on a few things we could have done better in 2017 and enter this year with a positive attitude to make a change. Have you established a new years resolution this year? A recent poll was taken to determine what the most popular new year's resolution was. In first place, with almost twice as many votes as anything else was to become a healthier person in the year ahead. Working to become a healthier you, is without a doubt the best project you can ever undertake because you are the most important thing in your life. Make yourself the best person you can be. The main catalyst which deters most people from a healthy lifestyle is the assumption that we don’t have time to exercise and for many of us, we simply don’t. The funny thing is though, when you force yourself to exercise regularly, your energy levels begin to rise. You become extremely productive at work and have an abundance of spare time all of a sudden. You then realise that your body and mind were functioning at 60% when you were neglecting exercise.
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Deep Learning for NLP, advancements and trends in 2017 - Tryolabs Blog

Deep Learning for NLP, advancements and trends in 2017 - Tryolabs Blog | Social Foraging | Scoop.it
In this article I will go through some advancements for NLP in 2017 that rely on DL techniques. I do not pretend to be exhaustive: it would simply be impossible given the vast amount of scientific papers, frameworks and tools available. I just want to share with you some of the works that I liked the most this year. I think 2017 has been a great year for our field. The use of DL in NLP keeps widening, yielding amazing results in some cases, and all signs point to the fact that this trend will not stop.
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A Deep Reinforced Model for Abstractive Summarization

Attentional, RNN-based encoder-decoder models for abstractive summarization have achieved good performance on short input and output sequences. For longer documents and summaries however these models often include repetitive and incoherent phrases. We introduce a neural network model with a novel intra-attention that attends over the input and continuously generated output separately, and a new training method that combines standard supervised word prediction and reinforcement learning (RL). Models trained only with supervised learning often exhibit "exposure bias" - they assume ground truth is provided at each step during training. However, when standard word prediction is combined with the global sequence prediction training of RL the resulting summaries become more readable. We evaluate this model on the CNN/Daily Mail and New York Times datasets. Our model obtains a 41.16 ROUGE-1 score on the CNN/Daily Mail dataset, an improvement over previous state-of-the-art models. Human evaluation also shows that our model produces higher quality summaries.
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Rolls-Royce And Google Partner To Create Smarter, Autonomous Ships Based On AI And Machine Learning

Rolls-Royce And Google Partner To Create Smarter, Autonomous Ships Based On AI And Machine Learning | Social Foraging | Scoop.it
Rolls-Royce marine has partnered with Google Cloud to apply neural net and machine learning technology to shipping. Their grand plan is to have a fully autonomous ship at sea by 2020, but the technology will also drive safety and efficiency improvements across the shipping industry.
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The Problem with Saying “Don’t Bring Me Problems, Bring Me Solutions”

The Problem with Saying “Don’t Bring Me Problems, Bring Me Solutions” | Social Foraging | Scoop.it
Three ways to encourage a culture of communication.
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Global Internet of Things (IoT) Software Market by Solution (Real-Time Streaming Analytics, Security Solution, Data Management), Service, Platform, Application Area and Region – Forecast to 2022 – ...

Global Internet of Things (IoT) Software Market by Solution (Real-Time Streaming Analytics, Security Solution, Data Management), Service, Platform, Application Area and Region – Forecast to 2022 – ... | Social Foraging | Scoop.it
Internet of Things (IoT) is an ecosystem of connected physical objects that are accessible through the internet. M2M / IoT Software and Services. Machine to Machine (M2M) and Internet of Things (IoT) projects follow a common technological paradigm: intelligent devices, seamlessly connected to the Internet, enable remote services and provide actionable data.

The report offers a multi-step view of the Global Internet Of Things (IoT) Software Market. The first approach focuses through an impression of the market. This passage includes several arrangements, definitions, the chain assembly of the industry in one piece, and the various uses for the global market. This section also integrates an all-inclusive analysis of the different enlargement plans and government strategies that influence the market, its cost assemblies and industrialized processes. The current growth and development patterns of this market have been encapsulated in this study.
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How Governments Are Preparing for Artificial Intelligence

How Governments Are Preparing for Artificial Intelligence | Social Foraging | Scoop.it
Artificial intelligence (AI) has the potential to dramatically transform huge swathes of the economy and society for the better, and as the technology continues to make headlines many countries are developing plans to ensure they can take full advantage of these benefits. Below is a high-level overv
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Cruise is running an autonomous ride hailing service for employees in SF

Cruise is running an autonomous ride hailing service for employees in SF | Social Foraging | Scoop.it
The service is called "Cruise Anywhere," and it allows employees to use a smartphone app to get anywhere they need to go in SF, seven days a week. Cruise..
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Yandex open sources CatBoost, a gradient boosting machine learning library

Yandex open sources CatBoost, a gradient boosting machine learning library | Social Foraging | Scoop.it
Artificial intelligence is now powering a growing number of computing functions, and today the developer community today is getting another AI boost, courtesy of Yandex. Today, the Russian search giant — which, like its US counterpart Google, has extended into a myriad of other business lines, from mobile to maps and more — announced the launch of CatBoost, an open source machine learning library based on gradient boosting — the branch of ML that is specifically designed to help “teach” systems when you have a very sparse amount of data, and especially when the data may not all be sensorial (such as audio, text or imagery), but includes transactional or historical data, too.
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Meet Albert, the AI marketing platform that's better at your marketing job than you

Meet Albert, the AI marketing platform that's better at your marketing job than you | Social Foraging | Scoop.it
THE LAST couple of years have seen much moral panic about the tide of automation – scare stories about robots making human jobs redundant.
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AI Could Revolutionize War as Much as Nukes

AI Could Revolutionize War as Much as Nukes | Social Foraging | Scoop.it
IN 1899, THE world’s most powerful nations signed a treaty at The Hague that banned military use of aircraft, fearing the emerging technology’s destructive power. Five years later the moratorium was allowed to expire, and before long aircraft were helping to enable the slaughter of World War I. “Some technologies are so powerful as to be irresistible,” says Greg Allen, a fellow at the Center for New American Security, a non-partisan Washington DC think tank. “Militaries around the world have essentially come to the same conclusion with respect to artificial intelligence.”
Allen is coauthor of a 132-page new report on the effect of artificial intelligence on national security. One of its conclusions is that the impact of technologies such as autonomous robots on war and international relations could rival that of nuclear weapons. The report was produced by Harvard’s Belfer Center for Science and International Affairs, at the request of IARPA, the research agency of the Office of the Director of National Intelligence. It lays out why technologies like drones with bird-like agility, robot hackers, and software that generates photo-real fake video are on track to make the American military and its rivals much more powerful.
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Your city is watching you: Machine learning and computer vision will transform our cities

Your city is watching you: Machine learning and computer vision will transform our cities | Social Foraging | Scoop.it
Machine learning and computer vision will transform our cities
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Five Surprises About How Indians Shop Online (and Offline)

Five Surprises About How Indians Shop Online (and Offline) | Social Foraging | Scoop.it
New BCG research overturns old ideas about how Indians shop online—and offline. Retailers must understand changing consumer behavior in India or risk losing out to startups.
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Facebook wants your face data — in the name of privacy, it says

Facebook wants your face data — in the name of privacy, it says | Social Foraging | Scoop.it
Facebook will let you know when someone posts a photo of you — even if you aren't tagged in it — making it the latest tech giant to add more facial-recognition technology into users' everyday lives.
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Gartner Shares Findings from North Pole Inc. Data and Analytics Assessment - Doug Laney

Gartner Shares Findings from North Pole Inc. Data and Analytics Assessment - Doug Laney | Social Foraging | Scoop.it
“We do a jolly good job year after year,” claims NPI’s Founder and CEO, Santa Claus, “but I have really put the pressure on my data and analytics leadership team to help us innovate and transform to become a fully digital enterprise.” He also added with a wink that there is an imperative to do so because, “the limited physical space of our workshop is starting to krampus.”

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Rescuing Collective Wisdom when the Average Group Opinion Is Wrong

The total knowledge contained within a collective supersedes the knowledge of even its most intelligent member. Yet the collective knowledge will remain inaccessible to us unless we are able to find efficient knowledge aggregation methods that produce reliable decisions based on the behavior or opinions of the collective’s members. It is often stated that simple averaging of a pool of opinions is a good and in many cases the optimal way to extract knowledge from a crowd. The method of averaging has been applied to analysis of decision-making in very different fields, such as forecasting, collective animal behavior, individual psychology, and machine learning. Two mathematical theorems, Condorcet’s theorem and Jensen’s inequality, provide a general theoretical justification for the averaging procedure. Yet the necessary conditions which guarantee the applicability of these theorems are often not met in practice. Under such circumstances, averaging can lead to suboptimal and sometimes very poor performance. Practitioners in many different fields have independently developed procedures to counteract the failures of averaging. We review such knowledge aggregation procedures and interpret the methods in the light of a statistical decision theory framework to explain when their application is justified. Our analysis indicates that in the ideal case, there should be a matching between the aggregation procedure and the nature of the knowledge distribution, correlations, and associated error costs. This leads us to explore how machine learning techniques can be used to extract near-optimal decision rules in a data-driven manner. We end with a discussion of open frontiers in the domain of knowledge aggregation and collective intelligence in general.

 

Rescuing Collective Wisdom when the Average Group Opinion Is Wrong

Andres Laan, Gabriel Madirolas, and Gonzalo G. de Polavieja

Front. Robot. AI, 06 November 2017 | https://doi.org/10.3389/frobt.2017.00056


Via Complexity Digest
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Applying deep learning to distinguish drivers from passengers using sensor data

Applying deep learning to distinguish drivers from passengers using sensor data | Social Foraging | Scoop.it
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Noise correlations in the human brain and their impact on pattern classification

Noise correlations in the human brain and their impact on pattern classification | Social Foraging | Scoop.it
Author summary A central challenge in cognitive neuroscience is decoding mental representations from patterns of brain activity. With functional magnetic resonance imaging (fMRI), multivariate decoding methods like multivoxel pattern analysis (MVPA) have produced numerous discoveries about the brain. However, what information these methods draw upon remains the subject of debate. Typically, each voxel is thought to contribute information through its selectivity (i.e., how differently it responds to the classes being decoded), with improved sensitivity reflecting the aggregation of selectivity across voxels. We show that this interpretation downplays an important factor: MVPA is also highly attuned to noise correlations between voxels with opposite selectivity. Across several analyses of an fMRI dataset, we demonstrate a positive relationship between the magnitude of noise correlations and multivariate decoding performance. Indeed, voxels more selective for one class, or heavily weighted in MVPA, tend to be more strongly correlated with voxels selective for the opposite class. Furthermore, using a model to simulate different levels of selectivity and noise correlations, we find that the benefit of noise correlations for decoding is a general property of fMRI data. These findings help elucidate the computational underpinnings of multivariate decoding in cognitive neuroscience and provide insight into the nature of neural representations.
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Peculiar Genes Selection: A new features selection method to improve classification performances in imbalanced data sets

Peculiar Genes Selection: A new features selection method to improve classification performances in imbalanced data sets | Social Foraging | Scoop.it
High-Throughput technologies provide genomic and trascriptomic data that are suitable for biomarker detection for classification purposes. However, the high dimension of the output of such technologies and the characteristics of the data sets analysed represent an issue for the classification task. Here we present a new feature selection method based on three steps to detect class-specific biomarkers in case of high-dimensional data sets. The first step detects the differentially expressed genes according to the experimental conditions tested in the experimental design, the second step filters out the features with low discriminative power and the third step detects the class-specific features and defines the final biomarker as the union of the class-specific features. The proposed procedure is tested on two microarray datasets, one characterized by a strong imbalance between the size of classes and the other one where the size of classes is perfectly balanced. We show that, using the proposed feature selection procedure, the classification performances of a Support Vector Machine on the imbalanced data set reach a 82% whereas other methods do not exceed 73%. Furthermore, in case of perfectly balanced dataset, the classification performances are comparable with other methods. Finally, the Gene Ontology enrichments performed on the signatures selected with the proposed pipeline, confirm the biological relevance of our methodology. The download of the package with the implementation of Peculiar Genes Selection, ‘PGS’, is available for R users at: http://github.com/mbeccuti/PGS.
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Oryx Vision raises $50M to build better LiDAR for self-driving vehicles

Oryx Vision raises $50M to build better LiDAR for self-driving vehicles | Social Foraging | Scoop.it
Israeli startup Oryx Vision has raised a $50 million Series B round led by Third Point Ventures and WRV to help continue to develop and commercialize its innovative LiDAR tech, which is designed to be as simple as a digital camera with greater reliability and sensitivity than existing LiDAR, while also achieving a low cost.

Oryx’s LiDAR has no moving parts, and uses antennas in place of photodetectors to retrieve both range and velocity information for the points of light in its high-resolution scans of its surroundings. Oryx says its unique method means that the system is “a million times more sensitive” than existing LiDAR systems, and is also able to deal better with interference from sunlight, and from other LiDARs in operation on the road.
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Crewless electric cargo ships may be on the horizon in Norway

Crewless electric cargo ships may be on the horizon in Norway | Social Foraging | Scoop.it
SpaceX's drone landing ships have already proven that uncrewed vessels can handle some of the most dangerous jobs at sea. Now, two Norwegian companies are poised to put robo-boats into one of the most dull: hauling cargo down the fjord.

Two Norwegian companies are teaming together to construct a short-range, all-electric coastal container ship that will eventually operate autonomously—eliminating up to 40,000 diesel truck trips per year. The ship, the Yara Birkeland, will begin operations in 2018 with a crew, but it's expected to operate largely autonomously (and crewless) by 2020 (regulatory clearance permitting, of course).
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Beijing Wants A.I. to Be Made in China by 2030

Beijing Wants A.I. to Be Made in China by 2030 | Social Foraging | Scoop.it
A new plan from the top of the Chinese government calls for the country to become a powerhouse in artificial intelligence in just over a decade.
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UK to bring in drone registration

UK to bring in drone registration | Social Foraging | Scoop.it
The UK government has announced plans to introduce drone registration and safety awareness courses for owners of the small unmanned aircraft.
It will affect anyone who owns a drone which weighs more than 250 grams (8oz).
Drone maker DJI said it was in favour of the measures.
There is no time frame or firm plans as to how the new rules will be enforced and the Department of Transport admitted that "the nuts and bolts still have to be ironed out".
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