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Category: information science – Page 319

Scientists discover nature’s algorithm for intelligence

But if there is some kind of unifying computational principle governing our grey matter, what is it? Dr. Tsien has studied this for over a decade, and he believes he’s found the answer in something called the Theory of Connectivity.

“Many people have long speculated that there has to be a basic design principle from which intelligence originates and the brain evolves, like how the double helix of DNA and genetic codes are universal for every organism,” Tsien said. “We present evidence that the brain may operate on an amazingly simple mathematical logic.”

The Theory of Connectivity holds that a simple algorithm, called a power-of-two-based permutation taking the form of n=2i-1 can be used to explain the circuitry of the brain. To unpack the formula, let’s define a few key concepts from the theory of connectivity, specifically the idea of a neuronal clique. A neuronal clique is a group of neurons which “fire together” and cluster into functional connectivity motifs, or FCMs, which the brain uses to recognize specific patterns or ideas. One can liken it to branches on a tree, with the neuronal clique being the smallest unit of connectivity, a mere twig, which when combined with other cliques, link up to form an FCM. The more complex the idea being represented in the brain, the more convoluted the FCM. The n in n=2i-1 specifies the number of neuronal cliques that will fire in response to a given input, i.

Google’s machine learning algorithm gets human help in quest for fusion power

Hot on the heels of last month’s nuclear fusion breakthrough comes the first results from a multi-year partnership between Google and Tri Alpha Energy, the world’s largest private fusion company. The two organizations joined forces in 2014 in the hopes that Google’s machine learning algorithms could advance plasma research and bring us closer to the dream of fusion power.

China’s “Minority Report” Style Plans Will Use AI to Predict Who Will Commit Crimes

Authorities in China are exploring predictive analytics, facial recognition, and other artificial intelligence (AI) technologies to help prevent crime in advance. Based on behavior patterns, authorities will notify local police about potential offenders.

Cloud Walk, a company headquartered in Guangzhou, has been training its facial recognition and big data rating systems to track movements based on risk levels. Those who are frequent visitors to weapons shops or transportation hubs are likely to be flagged in the system, and even places like hardware stores have been deemed “high risk” by authorities.

A Cloud Walk spokesman told The Financial Times, “Of course, if someone buys a kitchen knife that’s OK, but if the person also buys a sack and a hammer later, that person is becoming suspicious.” Cloud Walk’s software is connected to the police database across more than 50 cities and provinces, and can flag suspicious characters in real time.

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

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.

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

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.”

9 Artificial Intelligence Startups in Medical Imaging

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:

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

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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