More broadly, OpenWorm raises fascinating questions about what we mean when we say something is alive. If and when this project succeeds in modeling the worm successfully, we’ll be faced with a new and fascinating concept to think with: a virtual organism. Imagine downloading the worm and running it in a virtual petri dish on your computer. What, exactly, will you be looking at? Will you consider it to be alive? What would convince you?
Is This Virtual Worm the First Sign of the Singularity? - Alexis C. Madrigal - The Atlantic (via wildcat2030)

(via wildcat2030)

The future of Google Glass?

Digital creative agency Playground knows that Glass is in its early days, but they imagine a future in which Glass helps with navigation, shopping, hobbies and much more. And according to Playground, “All of our examples are actually possible right now. Smartphones (batteries not included) have enough raw processing power to run this software today. If only current batteries were ten times more efficient and there was a robust native hardware API for Glass. Well, it’s coming. Sooner than we think.”

via the content brief [more @playgroundincfuturescope:futuretechreport

Smart machines probably won’t kill us all—but they’ll definitely take our jobs, and sooner than you think. Read Kevin Drum’s latest, from the magazine.
via motherjones Illustration by Roberto Parada

Smart machines probably won’t kill us all—but they’ll definitely take our jobs, and sooner than you think. Read Kevin Drum’s latest, from the magazine.

motherjones 

The Man Behind the Google Brain: Andrew Ng and the Quest for the New AI 
There’s a theory that human intelligence stems from a single algorithm.
The idea arises from experiments suggesting that the portion of your brain dedicated to processing sound from your ears could also handle sight for your eyes. This is possible only while your brain is in the earliest stages of development, but it implies that the brain is — at its core — a general-purpose machine that can be tuned to specific tasks.
About seven years ago, Stanford computer science professor Andrew Ng stumbled across this theory, and it changed the course of his career, reigniting a passion for artificial intelligence, or AI. “For the first time in my life,” Ng says, “it made me feel like it might be possible to make some progress on a small part of the AI dream within our lifetime.”
In the early days of artificial intelligence, Ng says, the prevailing opinion was that human intelligence derived from thousands of simple agents working in concert, what MIT’s Marvin Minsky called “The Society of Mind.” To achieve AI, engineers believed, they would have to build and combine thousands of individual computing modules. One agent, or algorithm, would mimic language. Another would handle speech. And so on. It seemed an insurmountable feat.
When he was a kid, Andrew Ng dreamed of building machines that could think like people, but when he got to college and came face-to-face with the AI research of the day, he gave up. Later, as a professor, he would actively discourage his students from pursuing the same dream. But then he ran into the “one algorithm” hypothesis, popularized by Jeff Hawkins, an AI entrepreneur who’d dabbled in neuroscience research. And the dream returned.
It was a shift that would change much more than Ng’s career. Ng now leads a new field of computer science research known as Deep Learning, which seeks to build machines that can process data in much the same way the brain does, and this movement has extended well beyond academia, into big-name corporations like Google and Apple. In tandem with other researchers at Google, Ng is building one of the most ambitious artificial-intelligence systems to date, the so-called Google Brain.
This movement seeks to meld computer science with neuroscience — something that never quite happened in the world of artificial intelligence. “I’ve seen a surprisingly large gulf between the engineers and the scientists,” Ng says. Engineers wanted to build AI systems that just worked, he says, but scientists were still struggling to understand the intricacies of the brain. For a long time, neuroscience just didn’t have the information needed to help improve the intelligent machines engineers wanted to build.
What’s more, scientists often felt they “owned” the brain, so there was little collaboration with researchers in other fields, says Bruno Olshausen, a computational neuroscientist and the director of the Redwood Center for Theoretical Neuroscience at the University of California, Berkeley.
The end result is that engineers started building AI systems that didn’t necessarily mimic the way the brain operated. They focused on building pseudo-smart systems that turned out to be more like a Roomba vacuum cleaner than Rosie the robot maid from the Jetsons.
But, now, thanks to Ng and others, this is starting to change. “There is a sense from many places that whoever figures out how the brain computes will come up with the next generation of computers,” says Dr. Thomas Insel, the director of the National Institute of Mental Health.
Read more via neurosciencestuff

The Man Behind the Google Brain: Andrew Ng and the Quest for the New AI 

There’s a theory that human intelligence stems from a single algorithm.

The idea arises from experiments suggesting that the portion of your brain dedicated to processing sound from your ears could also handle sight for your eyes. This is possible only while your brain is in the earliest stages of development, but it implies that the brain is — at its core — a general-purpose machine that can be tuned to specific tasks.

About seven years ago, Stanford computer science professor Andrew Ng stumbled across this theory, and it changed the course of his career, reigniting a passion for artificial intelligence, or AI. “For the first time in my life,” Ng says, “it made me feel like it might be possible to make some progress on a small part of the AI dream within our lifetime.”

In the early days of artificial intelligence, Ng says, the prevailing opinion was that human intelligence derived from thousands of simple agents working in concert, what MIT’s Marvin Minsky called “The Society of Mind.” To achieve AI, engineers believed, they would have to build and combine thousands of individual computing modules. One agent, or algorithm, would mimic language. Another would handle speech. And so on. It seemed an insurmountable feat.

When he was a kid, Andrew Ng dreamed of building machines that could think like people, but when he got to college and came face-to-face with the AI research of the day, he gave up. Later, as a professor, he would actively discourage his students from pursuing the same dream. But then he ran into the “one algorithm” hypothesis, popularized by Jeff Hawkins, an AI entrepreneur who’d dabbled in neuroscience research. And the dream returned.

It was a shift that would change much more than Ng’s career. Ng now leads a new field of computer science research known as Deep Learning, which seeks to build machines that can process data in much the same way the brain does, and this movement has extended well beyond academia, into big-name corporations like Google and Apple. In tandem with other researchers at Google, Ng is building one of the most ambitious artificial-intelligence systems to date, the so-called Google Brain.

This movement seeks to meld computer science with neuroscience — something that never quite happened in the world of artificial intelligence. “I’ve seen a surprisingly large gulf between the engineers and the scientists,” Ng says. Engineers wanted to build AI systems that just worked, he says, but scientists were still struggling to understand the intricacies of the brain. For a long time, neuroscience just didn’t have the information needed to help improve the intelligent machines engineers wanted to build.

What’s more, scientists often felt they “owned” the brain, so there was little collaboration with researchers in other fields, says Bruno Olshausen, a computational neuroscientist and the director of the Redwood Center for Theoretical Neuroscience at the University of California, Berkeley.

The end result is that engineers started building AI systems that didn’t necessarily mimic the way the brain operated. They focused on building pseudo-smart systems that turned out to be more like a Roomba vacuum cleaner than Rosie the robot maid from the Jetsons.

But, now, thanks to Ng and others, this is starting to change. “There is a sense from many places that whoever figures out how the brain computes will come up with the next generation of computers,” says Dr. Thomas Insel, the director of the National Institute of Mental Health.

Read more via neurosciencestuff

muaj:bulbJ

muaj:bulbJ

(via taitran)

Thermal invisibility cloak in first demonstration
Researchers have built and tested a form of invisibility cloak that can hide objects from heat.Similar cloaking efforts are underway to make objects invisible to light and even sound waves, but this is the first device to work with heat.The prototype, to be outlined in Physical Review Letters, contained a 5cm-wide flat region impervious to heat flowing around it.The technology could be put to use in thermal management in electronics.
via futurescope [read more @bbc] [via @livingarchitect] [paper]

Thermal invisibility cloak in first demonstration

Researchers have built and tested a form of invisibility cloak that can hide objects from heat.Similar cloaking efforts are underway to make objects invisible to light and even sound waves, but this is the first device to work with heat.The prototype, to be outlined in Physical Review Letters, contained a 5cm-wide flat region impervious to heat flowing around it.The technology could be put to use in thermal management in electronics.

via futurescope [read more @bbc] [via @livingarchitect] [paper]

(via alexanderpf)

I feel the same way.  The de-industrialization of America is killing me. 

Will my sons end up with a bunch lathes, English wheels, mills and welders when I shuffle off this mortal coil?  Yep.  Will they have any idea how to use that ephemera?  If I do my job correctly they will.  It beats the hell out of leaving them my dated electronic devices.

via mymentalrepository

(via buffleheadcabin)

I can’t wait until these features are integrated into Google Glas..

GIGS2GO is a small set of ‘Tear and Share’ USB drives, about the same size as a credit card, that can be torn off and used or handed out to others… the four-pack of thumb drives is made from 100% post-consumer molded paper pulp with no plastic. You can tear off an individual 1GB drive like a phone number on a flyer for a cat-sitter.

alexanderpf: Cool concept — but is the PCB and USB connector made out of paper pulp? I don’t think so. The storage definitely isn’t. Why not make this out of something that can be just as easily recycled — HDPE plastic or aluminum?

If this is a cheap 10k read/write cycle chip why not offer a service where you can mail in used drives to get a pack of new ones at a reduced rate.

If the silicon chip can be easily attached and detached to a sturdy carrier it may be possible to isolate the chip and perform thermal annealing — restoring the memory for even more read/write cycles…

The concept shown above is still very linear and just reinforces the thinking of: take. make. waste.

(via emergentfutures)

Machines making machines meets networked intelligence.

A tumblr dedicated to the technique, products, and goals of modern making/hacking/engineering.

alexanderpf.com

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