Reblog and click the picture
below.
my childhood is ruined. D:
WHAT
nonononononononononononono!
OMFG HAHAHAHAHA WHAT
WTF!!!!!!!!!!!!!!!!!!!!!!!!!!!
woww lol was not expecting that
well things definitely change over the years
well i wasn’t expecting that at all
![thescienceofreality:
This Week in Science:
Tractor beam [miniature] here.Temperature of the universe here.DNA storage here.Dung beetles here.Proto-bird here.Quadruple helix DNA here.](http://24.media.tumblr.com/80def9fd80a3fa16fa5df4072c8f8884/tumblr_mhb1gfhnvn1r39hw6o1_500.jpg)
Tractor beam [miniature] here.
Temperature of the universe here.
DNA storage here.
Dung beetles here.
Proto-bird here.
Quadruple helix DNA here.
A painter who lost his passion for art after going into treatment for a mental health issue, Patrick Tresset, sought to recapture his creativity by creating a robot who could draw in his style.
“When we draw, the difficulty is not in making the lines. The difficulty is in the perception of the subject and the perception of the drawing in progress.” But sometimes, it may help to make it seem that the robot has difficulty in making the lines—Tresset has found that people feel more empathy for the machines when they make human-esque mistakes like crooked or tilted lines. (He calls this “clumsy robotics.”) Humans are inclined to want to identify with robots, especially those with faces: Give a person a bot, and he or she will probably name it. But why is that connection important in robots that draw? Tresset believes that if the person being sketched feels something for the machine wielding the pen, he or she will find the 30-minute sketching process “more touching.” Plus, if the sitter assigns a personality to the robot, it might alter the human’s emotional response to the final product.
It’s an interesting feedback loop the robot creates: mechanically induced faults and artificial humanity create empathy in the subject which translates to that genuine emotion being captured by the robot in the sketch.
Beyond their pretty remarkable ability to “think” and problem-solve, slime molds are just plain beautiful.
John Bonner, a professor emeritus at Princeton, has been studying them for seventy years. He’s been fascinated by the ability of this “bag of amoebae encased in a thin slime sheath” to operate like a simple brain, despite its biological simplicity. He’s used the gooey little guys to further the study of evolution and development for over half a century, and some of the images he’s collected are stunning.
The GIFs above are from this collection of half-century-old film clips captured by a young Bonner, showing the life cycle of a slime mold. Lastly, you absolutely do not want to miss this gorgeous new collection of close-up slime mold photos SciAm’s Alex Wild.
Old and new, these little creatures are as beautiful in form as they are amazing in biology.
Check my archive for today’s other slime mold posts!!
Scientists image brain structures that deteriorate in Parkinson’s
A new imaging technique developed at MIT offers the first glimpse of the degeneration of two brain structures affected by Parkinson’s disease.
The technique, which combines several types of magnetic resonance imaging (MRI), could allow doctors to better monitor patients’ progression and track the effectiveness of potential new treatments, says Suzanne Corkin, MIT professor emerita of neuroscience and leader of the research team. The first author of the paper is David Ziegler, who received his PhD in brain and cognitive sciences from MIT in 2011.
The study, appearing in the Nov. 26 online edition of the Archives of Neurology, is also the first to provide clinical evidence for the theory that Parkinson’s neurodegeneration begins deep in the brain and advances upward.
“This progression has never been shown in living people, and that’s what was special about this study. With our new imaging methods, we can see these structures more clearly than anyone had seen them before,” Corkin says.
Recent study by connectome researchers, published in the journal Science, revealed that the brain’s neurons are not the haphazard tangle that some had thought, but are arranged in a tidy grid that resembles a city street map.
And if you have ever wondered what makes you, you, thensome of the world’s top neuroscientists might say: “You are your connectome.”
The connectome refers to the exquisitely interconnected network of neurons (nerve cells) in your brain. Like the genome, the microbiome, and other exciting “ome” fields, the effort to map the connectome and decipher the electrical signals that zap through it to generate your thoughts, feelings, and behaviors has become possible through development of powerful new tools and technologies.
![neurosciencestuff:
Is “Deep Learning” a Revolution in Artificial Intelligence?
Can a new technique known as deep learning revolutionize artificial intelligence as the New York Times suggests?
The technology on which the Times focusses, deep learning, has its roots in a tradition of “neural networks” that goes back to the late nineteen-fifties. At that time, Frank Rosenblatt attempted to build a kind of mechanical brain called the Perceptron, which was billed as “a machine which senses, recognizes, remembers, and responds like the human mind.” The system was capable of categorizing (within certain limits) some basic shapes like triangles and squares. Crowds were amazed by its potential, and even The New Yorker was taken in, suggesting that this “remarkable machine…[was] capable of what amounts to thought.”
But the buzz eventually fizzled; a critical book written in 1969 by Marvin Minsky and his collaborator Seymour Papert showed that Rosenblatt’s original system was painfully limited, literally blind to some simple logical functions like “exclusive-or” (As in, you can have the cake or the pie, but not both). What had become known as the field of “neural networks” all but disappeared.
Read more](http://25.media.tumblr.com/tumblr_medk6dptYH1rog5d1o1_400.jpg)
Is “Deep Learning” a Revolution in Artificial Intelligence?
Can a new technique known as deep learning revolutionize artificial intelligence as the New York Times suggests?
The technology on which the Times focusses, deep learning, has its roots in a tradition of “neural networks” that goes back to the late nineteen-fifties. At that time, Frank Rosenblatt attempted to build a kind of mechanical brain called the Perceptron, which was billed as “a machine which senses, recognizes, remembers, and responds like the human mind.” The system was capable of categorizing (within certain limits) some basic shapes like triangles and squares. Crowds were amazed by its potential, and even The New Yorker was taken in, suggesting that this “remarkable machine…[was] capable of what amounts to thought.”
But the buzz eventually fizzled; a critical book written in 1969 by Marvin Minsky and his collaborator Seymour Papert showed that Rosenblatt’s original system was painfully limited, literally blind to some simple logical functions like “exclusive-or” (As in, you can have the cake or the pie, but not both). What had become known as the field of “neural networks” all but disappeared.
