Archive for the ‘information science’ category

Jul 18, 2017

The regulatory backing of bitcoin is a time-bound computer algorithm

Posted by in categories: bitcoin, computing, information science

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Jul 4, 2017

This is the Closest Thing We Have to a Master Equation of the Universe

Posted by in categories: information science, mathematics, physics, space

The grand theory of almost everything actually represents a collection of several mathematical models that proved to be timeless interpretations of the laws of physics.

Here is a brief tour of the topics covered in this gargantuan equation.

This version of the Standard Model is written in the Lagrangian form. The Lagrangian is a fancy way of writing an equation to determine the state of a changing system and explain the maximum possible energy the system can maintain.

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Jul 1, 2017

New Report Predicts Over 100,000 Legal Jobs Will Be Lost To Automation

Posted by in categories: employment, information science, law, robotics/AI

An extensive new analysis by Deloitte estimates that over 100,000 jobs will be lost to technological automation within the next two decades. Increasing technological advances have helped replace menial roles in the office and do repetitive tasks.

To paraphrase the Bard’s famous quote: “The first thing we do, let’s replace all the lawyers with automated algorithms.”

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Jul 1, 2017

Misunderstanding Terrorism — With Marc Sageman | Radio Cafe

Posted by in categories: big data, counterterrorism, governance, government, information science, policy, terrorism

There is a radio edit (about one half hour) and an unabridged version (about one hour long).

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Jun 26, 2017

9 Artificial Intelligence Startups in Medical Imaging

Posted by in categories: biotech/medical, information science, robotics/AI

You don’t have to be a gambler to appreciate the complexities of the card game Texas Hold ‘Em. It involves a strategy that needs to evolve based on the players around the table, it takes a certain amount of intuition, and it doesn’t require the player to win every hand. Just a few days ago, an artificial intelligence (AI) algorithm named Libratus beat four professional poker players at a no-limit Texas Hold ‘Em tournament played out over 20 days.

If you have even the slightest understanding of how to write code, you would realize that it is impossible to actually code a software program to do that with such “imperfect information”. The AI algorithm did exceptionally well and was utilizing strategies that humans had never used before. Professional poker players are in no danger of losing their jobs, but the incredible capabilities of what AI is mastering these days should make everyone wonder just how safe their jobs actually are.

Let’s take the $3 billion medical imaging market. It’s no secret that AI is now performing certain medical imaging tasks better than human doctors. Pundits say “well, people will always trust a human doctor over an AI” and the answer we’d have to that is “not if the AI is going to give a more accurate answer “. It’s only a matter of time before every X-ray machine is connected to the cloud and one human doctor per hospital puts his hand on your shoulder when he reads you the output from the AI algorithm. Kind of like this:

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Jun 24, 2017

MIT and Google researchers have made AI that can link sound, sight, and text to understand the world

Posted by in categories: information science, robotics/AI

If we ever want future robots to do our bidding, they’ll have to understand the world around them in a complete way—if a robot hears a barking noise, what’s making it? What does a dog look like, and what do dogs need?

AI research has typically treated the ability to recognize images, identify noises, and understand text as three different problems, and built algorithms suited to each individual task. Imagine if you could only use one sense at a time, and couldn’t match anything you heard to anything you saw. That’s AI today, and part of the reason why we’re so far from creating an algorithm that can learn like a human. But two new papers from MIT and Google explain first steps for making AI see, hear, and read in a holistic way—an approach that could upend how we teach our machines about the world.

“It doesn’t matter if you see a car or hear an engine, you instantly recognize the same concept. The information in our brain is aligned naturally,” says Yusuf Aytar, a post-doctoral AI research at MIT who co-authored the paper.

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Jun 18, 2017

This Artificially Intelligent Robot Composes and Performs Its Own Music

Posted by in categories: information science, media & arts, robotics/AI

Shimon—a four-armed marimba playing robot—has been around for years, but its developers at Georgia Tech have recently taken this futuristic musical machine to the next level. Using deep learning, the robot can now study large datasets from well-known musicians, and then produce and perform its own original compositions.

Shimon was originally developed by Gil Weinberg, director of Georgia Tech’s Center for Music Technology. Under its original programming, the robot was capable of improvising music as it played alongside human performers, using an “interestingness” algorithm to make sure it wasn’t just copying its bandmates. But now, thanks to the efforts of Ph.D. student Mason Bretan, Shimon has become an accomplished composer, capable of autonomously generating the melodic and harmonic structure of a song. And you know what? Shimon’s songs are actually quite good!

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Jun 18, 2017

Building the Public Goods of the Twenty-First Century

Posted by in categories: information science, internet

Meanwhile there was a Big New Development. The Internet and digital technology came of age. And here’s the thing. Digital artefacts – whether they’re an algorithm, a website, an app or a coding language – are always and everywhere potential public goods. Once produced digital artefacts are essentially costless to replicate which raises the question of whether they can or should be made freely available to all.

Digital public goods in the age of the data revolution.

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Jun 16, 2017

When artificial intelligence is bad news for the boss

Posted by in categories: business, economics, information science, robotics/AI

The two academic authors from Massachusetts Institute of Technology, who became the pin-up boys of the Davos crowd for their previous book on The Second Machine Age (2014), do a neat job of scanning the technological horizon and highlighting significant landmarks. This is a clear and crisply written account of machine intelligence, big data and the sharing economy. But McAfee and Brynjolfsson also wisely acknowledge the limitations of their futurology and avoid over-simplification. No one can really have much idea how the business world is going to evolve or predict the precise interplay between all these fast-changing forces.

A new book by the authors of ‘The Second Machine Age’ suggests that digital disruption is coming to the corner office.

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Jun 10, 2017

Ml4SETI Hackathon and Code Challenge

Posted by in categories: alien life, information science

The SETI Institute is hosting a global, public hackathon and code challenge to find a robust signal classification algorithm for use in our mission to find E.T. radio communication.

The Data Set

Each night, the SETI Institute observes signals across the radio frequency spectrum using the Allen Telescope Array (ATA). The signal detection system at the ATA searches for narrow-band radio signals coming directly from particular targets in the sky.

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