Science, Technology, and Current Futurism
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Science, Technology, and Current Futurism
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A.I. Downs Expert Human Fighter Pilot In Dogfight Simulation

A.I. Downs Expert Human Fighter Pilot In Dogfight Simulation | Science, Technology, and Current Futurism | Scoop.it
The secret to ALPHA's superhuman flying skills is a decision-making system called a genetic fuzzy tree, a subtype of fuzzy logic algorithms. The system approaches complex problems much like a human would, says Ernest, breaking the larger task into smaller subtasks, which include high-level tactics, firing, evasion, and defensiveness. By considering only the most relevant variables, it can make complex decisions with extreme speed. As a result, the A.I. can calculate the best maneuvers in a complex, dynamic environment, over 250 times faster than its human opponent can blink.
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Phys.Org Mobile: New algorithm can separate unstructured text into topics with high accuracy and reproducibility

Phys.Org Mobile: New algorithm can separate unstructured text into topics with high accuracy and reproducibility | Science, Technology, and Current Futurism | Scoop.it
"To create a better algorithm, Amaral took a network approach. The result, called TopicMapping, begins by preprocessing data to replace words with their stem (so "star" and "stars" would be considered the same word). It then builds a network of connecting words and identifies a "community" of related words (just as one could look for communities of people in Facebook). The words within a given community define a topic. "The algorithm was able to perfectly separate the documents according to language and was able to reproduce its results. It also had high accuracy and reproducibility when separating 23,000 scientific papers and 1.2 million Wikipedia articles by topic."
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AIntelligent Algorithm Made Discovery That Slipped Past Art Historians For Years | The Creators Project

AIntelligent Algorithm Made Discovery That Slipped Past Art Historians For Years | The Creators Project | Science, Technology, and Current Futurism | Scoop.it

A recent project used nuanced imaging technology and classification systems to robotize the process of understanding how famous artists have influenced one another.

 

Could a computer program influence how we understand art history and the canon? Or, could an artificially intelligent algorithm do the work of art experts for them? A recent researcher project doesn't quite suggest such a reality, but it does demonstrate that machines can highlight subtleties within arts and culture that humans have previously never noticed.In a paper titled "Toward Automated Discovery Of Artistic Influence" by Babak Saleh and a team of computer science researchers at Rutgers, the academics explained how they used nuanced imaging technology and classification systems to robotize the process of understanding how famous artists have influenced and inspired one another.

 

For their research, the team chose 1,700 paintings by 66 artists, covering the 15th to the late 20th century. Using a technique that analyzes visual concepts called "classemes"—wherein objects, color shades, subjects' movement, and more are marked—the researchers created a list of 3,000 classemes for each painting, data which The Physics arXiv Blog compares to a vector. Then, they used an artificially intelligent algorithm to evaluate the vectors and look for similarities or overlapping qualities among the 1,700 paintings. ArXiv adds, "To create a ground truth against which to measure their results, they also collate expert opinions on which these artists have influenced the others."...


Via Jeff Domansky
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Jeff Domansky's curator insight, August 28, 2014 10:45 AM

Fascinating application of technology to art and creativity. Good read. 9/10

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EVOLUTION [V6] - Learn to Walk (genetic algorithm & Neural Network) - YouTube

Sticky Creatures learn to walk Evolution perforemed by Genetic Algorithm Brain with Neural Network [from V6 there are muscles (angle constrains)] executable:...

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How Your Location Data Is Being Used To Predict The Events You Will Want To Attend | MIT Technology Review

How Your Location Data Is Being Used To Predict The Events You Will Want To Attend | MIT Technology Review | Science, Technology, and Current Futurism | Scoop.it
The next generation of recommendation engines will use your location data to suggest music festivals, sporting events and conferences you will want to attend.
Sharrock's insight:

I wonder if this event recommendation technology would be useful in education. Could a curriculum be developed in place of task analysis? 

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How Deep Learning Analytics Mimic the Mind

How Deep Learning Analytics Mimic the Mind | Science, Technology, and Current Futurism | Scoop.it
How Deep Learning Analytics Mimic the Mind / how FICO uses neural networks for fraud detection http://t.co/qh7r6dTCQb

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Company Builds a Face-Scanning Emotion Detector, Seeks Important Uses for It | MIT Technology Review

Company Builds a Face-Scanning Emotion Detector, Seeks Important Uses for It | MIT Technology Review | Science, Technology, and Current Futurism | Scoop.it
A technology for reading emotions on faces can help companies sell candy. Now its creators hope it also can take on bigger problems.
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Machine Vision Algorithm Chooses the Most Creative Paintings in History

Machine Vision Algorithm Chooses the Most Creative Paintings in History | Science, Technology, and Current Futurism | Scoop.it
Creativity is one of humanity’s uniquely defining qualities. Numerous thinkers have explored the qualities that creativity must have, and most pick out two important factors: whatever the process of creativity produces, it must be novel and it must be influential.

The history of art is filled with good examples in the form of paintings that are unlike any that have appeared before and that have hugely influenced those that follow. Leonardo’s 1469 Madonna and child with a pomegranate, Goya’s 1780 Christ crucified or Monet’s 1865 Haystacks at Chailly at sunrise and so on. Others paintings are more derivative, showing many similarities with those that have gone before and so are thought of as less creative.

The job of distinguishing the most creative from the others falls to art historians. And it is no easy task. It requires, at the very least, an encyclopedic knowledge of the history of art. The historian must then spot novel features and be able to recognize similar features in future paintings to determine their influence.

Those are tricky tasks for a human and until recently, it would have been unimaginable that a computer could take them on. But today that changes thanks to the work of Ahmed Elgammal and Babak Saleh at the University of Washington, who say they have a machine that can do just this.

Via Ashish Umre
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Machine learning algorithm makes impossible screening of advanced materials possible

Machine learning algorithm makes impossible screening of advanced materials possible | Science, Technology, and Current Futurism | Scoop.it

Researchers at the University of Ottawa in Ottawa, Ontario, have used machine learning tools to develop models that can rapidly identify the best performing MOFs for CO2 capture in a fraction of the time it would take to screen the entire database. By pre-screening for the top MOFs, the model greatly reduces the number of MOFs that require more intensive screening, and could decrease the overall computing time by an order of magnitude. The study is published in a recent issue of The Journal of Physical Chemistry Letters.

Read more at: http://phys.org/news/2014-09-machine-algorithm-impossible-screening-advanced.html#jCp

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Collaborative learning for robots | KurzweilAI

Collaborative learning for robots | KurzweilAI | Science, Technology, and Current Futurism | Scoop.it

Researchers from MIT’s Laboratory for Information and Decision Systems have developed an algorithm in which distributed agents — such as robots exploring a building — collect data and analyze it independently. Pairs of agents, such as robots passing each other in the hall, then exchange analyses.

In experiments involving several different data sets, the researchers’ distributed algorithm actually outperformed a standard algorithm that works on data aggregated at a single location, as described in an arXiv paper.

 


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Artificial Intelligence to make lawyers redundant | Machines Like Us

Artificial Intelligence to make lawyers redundant | Machines Like Us | Science, Technology, and Current Futurism | Scoop.it
Ipselex, a secretive Hong Kong artificial intelligence company, today announced the launch of its web platform.
Sharrock's insight:

excerpt: "Ipselex, a secretive Hong Kong artificial intelligence company, today announced the launch of its web platform. The platform offers API-like access to a brain in the cloud that has taught itself to understand and make predictions about patents and patent applications."

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Using IBM Watson Foundations to read emotions | The Big Data Hub

Using IBM Watson Foundations to read emotions | The Big Data Hub | Science, Technology, and Current Futurism | Scoop.it

 I (the author) learned that nViso makes facial analytics tools that can help consumer-focused businesses better understand their customers by analyzing human non-verbal signals. Further, the company uses Watson Foundations to provide the analytics and data management infrastructure that support facial image analysis.

A closer look at nViso shows that its core expertise is facial imaging analysis. To conduct this analysis, the company:

Digitally captures data streams on cloud and mobile platformsUses facial imaging software to analyze that captured digital data to look for human non-verbal signals (emotional responses)Enables customers to use the data that it collects to start asking new and different questions about customer preferences, and can become more attuned to consumer reactions to marketing campaignsAutomates the process of analyzing customer emotional reactions, making it easier and less costly to gather consumer information. 

Sharrock's insight:

It's amazing that a machine can analyze non-verbal facial communication. If it proves accurate and valuable, the combination of sickened outcry and the innovative applications will (could possibly) lead to incredible social change. People who are disenfranchised may begin to see an increase in justice statistics. Detecting the emotions and other nonverbal cues of jurors, plaintiffs, witnesses, even--one day--judges, may lead to unprecedented impacts. It would not simply support judicial officials and statisticians. The AI machines themselves may learn a great deal about making judgments and develop algorithms to replace judges at various levels of court cases or to provide alternative judgments (in the beginnings of the replacements). Airlines, citizen/customs agents, and other agents providing security might also receive support. 

 

However, there is considerable evidence that people implementing the machine-enhanced judgments may still reject those judgments to instead go with "the gut" or their so-called "intuition", but as the machine algorithms improve their accuracy, humans may also trust them more and will employ them as a greater part in their decision making.

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Mass Personalization Is Coming. Are We Ready For It?

Mass Personalization Is Coming. Are We Ready For It? | Science, Technology, and Current Futurism | Scoop.it
As big data opens up a new world of possibilities, we’re going to have to come to terms with what we really want. Excerpt: "technology can decipher signals that we aren’t even aware of. Mattersight is a company that has developed software that can analyze your personality during a routine customer service call. In his book, Honest Signals, MIT’s Sandy Pentland describes a machine that can predict behavior from subtle physical cues."
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How Quantum Computers and Machine Learning Will Revolutionize Big Data - Wired Science

How Quantum Computers and Machine Learning Will Revolutionize Big Data - Wired Science | Science, Technology, and Current Futurism | Scoop.it
Sharrock's insight:

There are two major ideas here. They herald a new period in the knowledge era...if these two or three hurdles are overcome: 1) breakthroughs in techniques to analyze the semantics of natural language

2) data integration--"Google’s Alon Halevy believes that the real breakthroughs in big data analysis are likely to come from integration — specifically, integrating across very different data sets. “No matter how much you speed up the computers or the way you put computers together, the real issues are at the data level,” he said. For example, a raw data set could include thousands of different tables scattered around the Web, each one listing crime rates in New York, but each may use different terminology and column headers, known as “schema.” A header of “New York” can describe the state, the five boroughs of New York City, or just Manhattan. You must understand the relationship between the schemas before the data in all those tables can be integrated."

3) the invention of the quanturm computer (jury is still out on current D-Wave Computer)

 

What will the new age be called? I think it whill have something to do with multidisciplinary combinations and integration and collaboration. The ability to draw similar/analogous data from every kind of database is beyond cryptography, but the "semantic web" doesn't seem to capture the world changing power of this ability.