Japanese researchers announced Monday that they used stem cells to create viable mouse egg cells entirely in the lab.
Scientists from the University of Kyoto coaxed skin cells into egg cells, which they then fertilized and implanted into female mice to successfully breed a new generation of mice. The technique had a low success rate, but it’s the first time lab-grown egg cells have been used to produce healthy offspring. Read More
The Planet Earth series, originally produced 10 years ago by the BBC, is over 8 hours of the world’s badass natural wonders captured using state-of-art cinematography.
You can still Netflix-binge the original show, tears rolling down your cheek while beholding the all-consuming beauty of terra firma, its exotic inhabitants and the voice of legend Sir David Attenborough. It was the most expensive nature documentary ever commissioned by the BBC, and it took five years to make. Read More
Clive Wearing is a noted British musician, but he’s perhaps best known as the man with a 30-second memory.
In the 1980s, Wearing contracted a strain of herpes virus that attacked his brain and destroyed his ability to form new memories. He might forget what he’s eating before food reaches his mouth. He struggles to frame experiences of the present with conceptions of time and place. Life for him is often akin to waking up from a coma — every 20 seconds.
In a certain sense, artificial neural networks are Clive; they operate without working memory, erasing everything they learned when assigned to a new task. This limits the complexity of operations they can accomplish, because in the real world, countless variables are in constant flux.
Now, the team from Google DeepMind has built a hybrid computing system, what they’re calling a “differentiable neural computer” (DNC), which pairs a neural network with an external memory system. The hybrid system learned how to form memories and use them to answer questions about maps of the London Underground transit system and family trees.
“Like a conventional computer, it can use its memory to represent and manipulate complex data structures but, like a neural network, it can learn to do so from data,” the authors wrote in their paper, which was published Wednesday in the journal Nature.
Neural networks don’t execute functions with sets of preprogrammed commands; they create their own rules of operation through pattern recognition.
Researchers feed an artificial neural network a training set of solved solutions to a specific task and all the data passes through hierarchical layers of interconnected nodes, or neurons. As more training data is fed through the layers, a simple computation that occurs at each node is automatically adjusted until the output matches the training set solutions. It’s sort of like tuning a guitar through trial and error.
In this way, neural nets can parse data in images to recognize faces in photos or translate languages from text all on their own, based on patterns we would never recognize. But this skill can only go so far, and if you want that neural net to perform a new task, it needs to reset and consume another training set to tune itself. With memory, a neural network can keep its knowledge on file and use what it learned for another task.
“Neural networks excel at pattern recognition and quick, reactive decision-making, but we are only just beginning to build neural networks that can think slowly – that is, deliberate or reason using knowledge,” DeepMind researchers wrote in a blog post Wednesday.
DeepMind researchers couldn’t be reached Wednesday, because the team was “heads down preparing for launch,” according to an email from a DeepMind spokesperson.
Researchers fed the DNC maps of the London Underground system, and the neural net found patterns between station locations and the routes connecting them. Then, it saved these basic parameters in its memory — it offloaded its foundational “knowledge” into memory matrices. It built a simple, symbolic representation of the Underground in its memory. And again, it did this all without programmed commands.
An unaided neural network had trouble charting a course from station to station, and only arrived at the correct location 37 percent of the time after 2 million training examples. But a neural network enhanced with memory reached the correct destination, and found the optimized route, 98.8 percent of the time after only 1 million training examples, researchers say.
It could do similar work with a family tree. Researchers trained the neural net with information about parent, child and sibling relationships. It then stored these basic parameters in its memory, which allowed it to answer far more nuanced questions like ““Who is Freya’s maternal great uncle?” by drawing upon its memory when needed.
Algorithms crafted by AI researchers were already solving these same rational, symbolic reasoning problems back in the 1970s. And other deep learning methods are far better than a DNC at logical data mining tasks. Again, the big difference is the DNC taught itself how to parse the data and how to use its memory, but it’s practical uses will be limited for now.
“Other machine learning techniques already exist that are much better suited to tasks like this,” says Pedro Domingos, a professor of computer science at the University of Washington and author of The Master Algorithm. He wasn’t involved with the study. “Symbolic learning algorithms already exist, and perform much better than what (DeepMind is) doing.”
It’s worth emphasizing here that neural networks are simply crunching numbers, so anthropomorphizing what they do only breeds misconceptions about the field in general. What we might consider “knowledge” is incredibly fluid, and disputed. Still, DeepMind researchers drew human-computer parallels in describing their work.
“There are interesting parallels between the memory mechanisms of a DNC and the functional capabilities of the mammalian hippocampus,” researchers wrote.
Without prior programming, the DNC compiles information into a set of remembered facts that it can draw upon to solve complex problems — it doesn’t have to reinvent the wheel with each new task. It’s sort of what babies do once they’re about 10 to 12 months old.
Infants younger than 10 months commit the classic “a not b error”: A researcher puts a toy under box A ten times consecutively and the baby crawls to box A for a reward every time. But when the researcher puts the toy under box B, in full sight of the infant, it still goes to box A because it’s a executing a learned pattern.
Try that with a 1-year-old, and they won’t be tricked. That’s because they are making connections between their memory and what’s unfolding in front of their eyes. They’re using symbolic reasoning. The toy doesn’t disappear when it’s under box B, you just can’t see it.
How, exactly, the human brain stores symbolic representations of the world through electrical impulses alone is still hotly debated. But a DNC, researchers say, may serve as a rudimentary analog for this process. As DeepMind researchers wrote in their blog:
“The question of how human memory works is ancient and our understanding still developing. We hope that DNCs provide both a new tool for computer science and a new metaphor for cognitive science and neuroscience: here is a learning machine that, without prior programming, can organise information into connected facts and use those facts to solve problems.”
But let’s not get ahead of ourselves.
“The problem with a lot this is, at the end of the day, we know almost nothing about how the brain works,” says Domingos. “No matter what I do I can always make some sort of parallel between what a system is doing and the brain, but it isn’t long before these analogies depart.”
For perspective, building symbolic “knowledge” of London Underground maps and family trees required 512 memory matrix locations. To deal with a flood of dynamic information about the world like even an infant can, researchers say, it would likely require thousands if not millions more memory locations — we still don’t know how the brain does it, so, frankly, this is just speculation.
“We have a long way to go before we understand fully the algorithms the human brain uses to support these processes,” Jay McClelland, director of the Center for Mind, Brain and Computation at Stanford University told IEEE Spectrum.
DeepMind has constructed a very, very preliminary foundation, and hybrid neural networks could eventually be scaled up to, for example, generate commentaries about the content of videos. These are things humans can do with ease, in any situation. A DNC still needs millions of training examples to accomplish a quite narrow task. Right now, it isn’t clear what practical function a DNC could perform that existing deep learning algorithms can’t already do better. A DNC, in other words, is another clever way to accomplish a task in a field that’s awash in clever solutions.
“Adding memory only seems like a big deal in the context of neural networks; for other learning methods, it’s trivial,” says Domingos.
Still, this demonstration serves as proof that memory, or knowledge, can be a powerful thing.
The universe seems a little less lonely today.
Astronomers from the University of Nottingham conducted a new survey of the universe’s galaxy population and concluded that previous estimates lowballed the census by a factor of about 10. Using data from Hubble and telescopes around the world, as well as a new mathematical model, they estimate that there are ten times more galaxies in the observable universe than we thought; previous estimates put the number of galaxies in the universe at around 200 billion. Read More
If you were walking around Antarctica toward the end of the Cretaceous 66 million years ago, you may have heard a very familiar sound: the riotous honking of ducks.
That’s the conclusion of an analysis of the oldest bird vocal organ ever discovered. Although it may predate modern birds by over 60 million years, it nevertheless bears striking similarities to the fleshy folds and cartilaginous rings that ducks use to communicate today. The find pushes back the evolution of modern-looking avian vocal organs to the time of the dinosaurs and promises to shed light on how they first evolved and changed through the ages. Read More
The slightest whiff of aliens is enough to send the public into a frenzy. There have been quiet rumblings after a pre-print paper was released on ArXiv from two French-Canadian researchers who interpreted certain sky signal data to be possibly of intelligent extraterrestrial origin.
According to their research, it’s not just one star candidate. There are several, all coming from data in the Sloan Digital Sky Survey. These stars experienced rapid bursts of light that, to some researchers, would be the calling card of an intelligent civilization turning on an optical (rather than radio) beacon. There’s something quite tantalizing about the conclusion, “We find that the detected signals have exactly the shape of an ETI signal predicted in the previous publication and are therefore in agreement with this hypothesis,” and the paper has been accepted into Publications of the Astronomical Society of the Pacific. Read More
Pale Red Dot fulfilled its goal of finding a planet around Proxima Centauri. But a new group, going under the name Project Blue, is ready to turn its attention toward the largest stars in the system: Alpha Centauri A & B.
The goal? To do what others have failed to do: find a planet there. But not just any planet. The two year initiative will concentrate on finding a planet in the habitable zone of one of the stars. And they’ll try to snap a picture of it, a more distant “Pale Blue Dot” picture. Read More
On one particularly hideous day 66 million years ago, Earth burned. A city-sized asteroid struck off the coast of Mexico’s Yucatan Peninsula and pushed the planetary reset button, wiping out species from plankton to plesiosaurs.
Some 75 percent of all life — including every land animal larger than 55 pounds — went extinct. The calamity changed the course of evolution. Without this Chicxulub impact, named for a small nearby town, humanity would not exist. Read More
The more the mice ran, the longer they lived.
Researchers from the University of Ottawa and Ottawa Hospital have discovered that compounds produced during exercise helped brain-damaged mice significantly extend their lifespans. They identified a key protein that they say likely makes all the difference, and their findings could help pinpoint therapies for degenerative neurological conditions like multiple sclerosis (MS). Read More
It’s easy to forget if you live far from a fault line, but Earth has a heartbeat you can feel.
At the junctures between continental plates, slippages create earthquakes and magma boils up from below in a never-ending process of creation and destruction. Now, for the first time, a global visualization of seismic and volcanic data lets us spy on the planet as it moves and shakes.
The interactive map, from the Global Volcanism Program at the Smithsonian’s National Museum of Natural History tracks every recorded volcanic eruption, earthquake and major sulfur dioxide emission since 1960. Sulfur dioxide emissions, caused by volcanic activity, appear beginning in 1978, when satellites started recording global data in the ultraviolet spectrum of light. The project combines data from the program with information from the U.S. Geological Survey and NASA satellites. Read More