Sometimes you get the feeling that the Terminator movies are not as far fetched as they once seemed.
First up, scientists have successfully created an artificial organism. Via cnet:
Scientists at the J. Craig Venter Institute have created a synthetic cell that can survive and reproduce itself according to an artificial DNA sequence, promising designer genomes with which researchers can produce sophisticated artificial organisms.
The new bacterial cell, “Mycoplasma mycoides JCVI-syn1.0,” is the result of a 15-year, $30 million effort by genetics pioneer Craig Venter. The study, led by the institute’s Dan Gibson, is reported in the May 21 edition of the journal Science.
The team of 25 researchers took Mycoplasma capricolum bacteria and completely rewrote its genetic code of more than 1 million base pairs of DNA. The data was sequenced as chemical DNA fragments and sewn together using yeast and E. coli bacteria.
The synthetic genome was transplanted into empty Mycoplasma mycoides bacteria, which were transformed into a new species. The creature’s software-like name, JCVI-syn1.0, reflects its status as the first of its kind.
To prove the genome is synthetic and to assert their ownership, the scientists even “watermarked” it by forming encoded words with the alphabet of genes and proteins. They included three quotations, among them a line from “A Portrait of the Artist as a Young Man” by James Joyce: “To live, to err, to fall, to triumph, to recreate life out of life.” They also added a URL and e-mail address to allow researchers who decode the words to notify the institute. (emphasis added)
Next up, DARPA (Defense Advanced Research Projects Agency) is creating self learning software. Via Danger Room:
What LeCun and Fergus are trying to do is make code that can get it right on a first, unsupervised example — using layer after layer of code to abstract the essential attributes of an object. This first step is to turn an image into numbers: For a 100 x 100 pixel image, the software produces a grid of 10,000 numbers; 9 x 9 “masks” are then applied to that grid, to uncover attributes of the image. The first feature spotted is an object’s edge. (The human brain makes a similar initial pass.) Several more “masks” follow. The final output? A series of 256 numbers that identifies the input.
The two are only six weeks into the project, but they’ve already got demos up and running.
The Deep Learning algorithm and I had never met, but with a quick shot by a small webcam on LeCun’s laptop, the layers of code captured my features and could immediately distinguish me from other objects and people in LeCun’s office. The same thing happens when LeCun introduces the system to two different coffee mugs — it takes mere seconds for the computer to acquaint itself with each, then distinguish one from the other.
And this is only the beginning. Darpa also wants a system that can spot activities, like running, jumping or getting out of a car. The final version will operate unsupervised, by being programmed to hold itself accountable for errors — and then auto-correct them at each algorithmic layer.
It should also be able to apply the layered algorithmic technique to text. Right now, computer systems can parse sentences to categorize them as positive or negative, based on how often different words appear in the text. By applying layers of analysis, the Deep Learning machine will — LeCun and Fergus hope — spot sarcasm and irony too.
“Ideally, what we’ll come away with is a ‘generic learning box’ that can identify every data cue,” Fergus tells Danger Room. (emphasis added)
And lastly,the Air Force is collecting vast amounts of information from the areas they patrol using UAV’s. and are trying to create one system to analyze the data. Via Defense Industry Daily:
In addition to the proliferation of UAVs and the exponential expansion of sensor capability, the Pentagon is engaged in an effort to break down proprietary barriers between UAV systems. This effort is intended to allow commanders on the battlefield as well as analysts back in CONUS to access important information no matter which system collects it.
For example, the popular MQ-1 Predator UAV system comes in a package with 4 vehicles, 1 ground control solution (GCS), and a data link suite that consists of UHF and VHF radio relay links, a C-band line-of-sight data link, and Ku-band satellite data links.
Unfortunately, the Predator GCS can only control and process information from Predator vehicles. The RQ-4 Global Hawk GCS controls and processes information from Global Hawks. And other UAVs use their own proprietary GCS systems.
In 2008, the Pentagon launched an effort to develop and demonstrate a common, open GCS architecture supporting everything from MQ-8 Fire Scout unmanned helicopter to long-range Global Hawk. The intent is to end the packaging of UAVs and GCS by manufacturers as 1 proprietary system. The Pentagon wants GCSs to be able to control multiple types of UAVs and share information across platforms. See “It’s Better to Share: Breaking Down UAV GCS Barriers” for more information.
Scary robots:
They knew where we live:
