Tag Archives: Should

#431839 The Hidden Human Workforce Powering ...

Posted on December 20, 2017 by Android

The tech industry touts its ability to automate tasks and remove slow and expensive humans from the equation. But in the background, a lot of the legwork training machine learning systems, solving problems software can’t, and cleaning up its mistakes is still done by people.
This was highlighted recently when Expensify, which promises to automatically scan photos of receipts to extract data for expense reports, was criticized for sending customers’ personally identifiable receipts to workers on Amazon’s Mechanical Turk (MTurk) crowdsourcing platform.
The company uses text analysis software to read the receipts, but if the automated system falls down then the images are passed to a human for review. While entrusting this job to random workers on MTurk was maybe not so wise—and the company quickly stopped after the furor—the incident brought to light that this kind of human safety net behind AI-powered services is actually very common.
As Wired notes, similar services like Ibotta and Receipt Hog that collect receipt information for marketing purposes also use crowdsourced workers. In a similar vein, while most users might assume their Facebook newsfeed is governed by faceless algorithms, the company has been ramping up the number of human moderators it employs to catch objectionable content that slips through the net, as has YouTube. Twitter also has thousands of human overseers.
Humans aren’t always witting contributors either. The old text-based reCAPTCHA problems Google used to use to distinguish humans from machines was actually simultaneously helping the company digitize books by getting humans to interpret hard-to-read text.
“Every product that uses AI also uses people,” Jeffrey Bigham, a crowdsourcing expert at Carnegie Mellon University, told Wired. “I wouldn’t even say it’s a backstop so much as a core part of the process.”
Some companies are not shy about their use of crowdsourced workers. Startup Eloquent Labs wants to insert them between customer service chatbots and human agents who step in when the machines fail. Many times the AI is pretty certain what particular work means, and an MTurk worker can step in and quickly classify them faster and cheaper than a service agent.
Fashion retailer Gilt provides “pre-emptive shipping,” which uses data analytics to predict what people will buy to get products to them faster. The company uses MTurk workers to provide subjective critiques of clothing that feed into their models.
MTurk isn’t the only player. Companies like Cloudfactory and Crowdflower provide crowdsourced human manpower tailored to particular niches, and some companies prefer to maintain their own communities of workers. Unlabel uses an army of 50,000 humans to check and edit the translations its artificial intelligence system produces for customers.
Most of the time these human workers aren’t just filling in the gaps, they’re also helping to train the machine learning component of these companies’ services by providing new examples of how to solve problems. Other times humans aren’t used “in-the-loop” with AI systems, but to prepare data sets they can learn from by labeling images, text, or audio.
It’s even possible to use crowdsourced workers to carry out tasks typically tackled by machine learning, such as large-scale image analysis and forecasting.
Zooniverse gets citizen scientists to classify images of distant galaxies or videos of animals to help academics analyze large data sets too complex for computers. Almanis creates forecasts on everything from economics to politics with impressive accuracy by giving those who sign up to the website incentives for backing the correct answer to a question. Researchers have used MTurkers to power a chatbot, and there’s even a toolkit for building algorithms to control this human intelligence called TurKit.
So what does this prominent role for humans in AI services mean? Firstly, it suggests that many tools people assume are powered by AI may in fact be relying on humans. This has obvious privacy implications, as the Expensify story highlighted, but should also raise concerns about whether customers are really getting what they pay for.
One example of this is IBM’s Watson for oncology, which is marketed as a data-driven AI system for providing cancer treatment recommendations. But an investigation by STAT highlighted that it’s actually largely driven by recommendations from a handful of (admittedly highly skilled) doctors at Memorial Sloan Kettering Cancer Center in New York.
Secondly, humans intervening in AI-run processes also suggests AI is still largely helpless without us, which is somewhat comforting to know among all the doomsday predictions of AI destroying jobs. At the same time, though, much of this crowdsourced work is monotonous, poorly paid, and isolating.
As machines trained by human workers get better at all kinds of tasks, this kind of piecemeal work filling in the increasingly small gaps in their capabilities may get more common. While tech companies often talk about AI augmenting human intelligence, for many it may actually end up being the other way around.
Image Credit: kentoh / Shutterstock.com Continue reading →

Posted in Human Robots

#431836 Do Our Brains Use Deep Learning to Make ...

Posted on December 20, 2017 by Android

The first time Dr. Blake Richards heard about deep learning, he was convinced that he wasn’t just looking at a technique that would revolutionize artificial intelligence. He also knew he was looking at something fundamental about the human brain.
That was the early 2000s, and Richards was taking a course with Dr. Geoff Hinton at the University of Toronto. Hinton, a pioneer architect of the algorithm that would later take the world by storm, was offering an introductory course on his learning method inspired by the human brain.
The key words here are “inspired by.” Despite Richards’ conviction, the odds were stacked against him. The human brain, as it happens, seems to lack a critical function that’s programmed into deep learning algorithms. On the surface, the algorithms were violating basic biological facts already proven by neuroscientists.
But what if, superficial differences aside, deep learning and the brain are actually compatible?
Now, in a new study published in eLife, Richards, working with DeepMind, proposed a new algorithm based on the biological structure of neurons in the neocortex. Also known as the cortex, this outermost region of the brain is home to higher cognitive functions such as reasoning, prediction, and flexible thought.
The team networked their artificial neurons together into a multi-layered network and challenged it with a classic computer vision task—identifying hand-written numbers.
The new algorithm performed well. But the kicker is that it analyzed the learning examples in a way that’s characteristic of deep learning algorithms, even though it was completely based on the brain’s fundamental biology.
“Deep learning is possible in a biological framework,” concludes the team.
Because the model is only a computer simulation at this point, Richards hopes to pass the baton to experimental neuroscientists, who could actively test whether the algorithm operates in an actual brain.
If so, the data could then be passed back to computer scientists to work out the next generation of massively parallel and low-energy algorithms to power our machines.
It’s a first step towards merging the two fields back into a “virtuous circle” of discovery and innovation.
The blame game
While you’ve probably heard of deep learning’s recent wins against humans in the game of Go, you might not know the nitty-gritty behind the algorithm’s operations.
In a nutshell, deep learning relies on an artificial neural network with virtual “neurons.” Like a towering skyscraper, the network is structured into hierarchies: lower-level neurons process aspects of an input—for example, a horizontal or vertical stroke that eventually forms the number four—whereas higher-level neurons extract more abstract aspects of the number four.
To teach the network, you give it examples of what you’re looking for. The signal propagates forward in the network (like climbing up a building), where each neuron works to fish out something fundamental about the number four.
Like children trying to learn a skill the first time, initially the network doesn’t do so well. It spits out what it thinks a universal number four should look like—think a Picasso-esque rendition.
But here’s where the learning occurs: the algorithm compares the output with the ideal output, and computes the difference between the two (dubbed “error”). This error is then “backpropagated” throughout the entire network, telling each neuron: hey, this is how far off you were, so try adjusting your computation closer to the ideal.
Millions of examples and tweakings later, the network inches closer to the desired output and becomes highly proficient at the trained task.
This error signal is crucial for learning. Without efficient “backprop,” the network doesn’t know which of its neurons are off kilter. By assigning blame, the AI can better itself.
The brain does this too. How? We have no clue.
Biological No-Go
What’s clear, though, is that the deep learning solution doesn’t work.
Backprop is a pretty needy function. It requires a very specific infrastructure for it to work as expected.
For one, each neuron in the network has to receive the error feedback. But in the brain, neurons are only connected to a few downstream partners (if that). For backprop to work in the brain, early-level neurons need to be able to receive information from billions of connections in their downstream circuits—a biological impossibility.
And while certain deep learning algorithms adapt a more local form of backprop— essentially between neurons—it requires their connection forwards and backwards to be symmetric. This hardly ever occurs in the brain’s synapses.
More recent algorithms adapt a slightly different strategy, in that they implement a separate feedback pathway that helps the neurons to figure out errors locally. While it’s more biologically plausible, the brain doesn’t have a separate computational network dedicated to the blame game.
What it does have are neurons with intricate structures, unlike the uniform “balls” that are currently applied in deep learning.
Branching Networks
The team took inspiration from pyramidal cells that populate the human cortex.
“Most of these neurons are shaped like trees, with ‘roots’ deep in the brain and ‘branches’ close to the surface,” says Richards. “What’s interesting is that these roots receive a different set of inputs than the branches that are way up at the top of the tree.”
This is an illustration of a multi-compartment neural network model for deep learning. Left: Reconstruction of pyramidal neurons from mouse primary visual cortex. Right: Illustration of simplified pyramidal neuron models. Image Credit: CIFAR
Curiously, the structure of neurons often turn out be “just right” for efficiently cracking a computational problem. Take the processing of sensations: the bottoms of pyramidal neurons are right smack where they need to be to receive sensory input, whereas the tops are conveniently placed to transmit feedback errors.
Could this intricate structure be evolution’s solution to channeling the error signal?
The team set up a multi-layered neural network based on previous algorithms. But rather than having uniform neurons, they gave those in middle layers—sandwiched between the input and output—compartments, just like real neurons.
When trained with hand-written digits, the algorithm performed much better than a single-layered network, despite lacking a way to perform classical backprop. The cell-like structure itself was sufficient to assign error: the error signals at one end of the neuron are naturally kept separate from input at the other end.
Then, at the right moment, the neuron brings both sources of information together to find the best solution.
There’s some biological evidence for this: neuroscientists have long known that the neuron’s input branches perform local computations, which can be integrated with signals that propagate backwards from the so-called output branch.
However, we don’t yet know if this is the brain’s way of dealing blame—a question that Richards urges neuroscientists to test out.
What’s more, the network parsed the problem in a way eerily similar to traditional deep learning algorithms: it took advantage of its multi-layered structure to extract progressively more abstract “ideas” about each number.
“[This is] the hallmark of deep learning,” the authors explain.
The Deep Learning Brain
Without doubt, there will be more twists and turns to the story as computer scientists incorporate more biological details into AI algorithms.
One aspect that Richards and team are already eyeing is a top-down predictive function, in which signals from higher levels directly influence how lower levels respond to input.
Feedback from upper levels doesn’t just provide error signals; it could also be nudging lower processing neurons towards a “better” activity pattern in real-time, says Richards.
The network doesn’t yet outperform other non-biologically derived (but “brain-inspired”) deep networks. But that’s not the point.
“Deep learning has had a huge impact on AI, but, to date, its impact on neuroscience has been limited,” the authors say.
Now neuroscientists have a lead they could experimentally test: that the structure of neurons underlie nature’s own deep learning algorithm.
“What we might see in the next decade or so is a real virtuous cycle of research between neuroscience and AI, where neuroscience discoveries help us to develop new AI and AI can help us interpret and understand our experimental data in neuroscience,” says Richards.
Image Credit: christitzeimaging.com / Shutterstock.com Continue reading →

Posted in Human Robots

#431808 Should you get your child an AI doll ...

Posted on December 15, 2017 by Android

The technological revolution has hit the doll aisle this holiday season in the form of artificial intelligence dolls. The dolls blend a physical toy with either a mobile device and app, or technological sensors, to simulate signs of intelligence. Continue reading →

Posted in Human Robots

#431690 Oxford Study Says Alien Life Would ...

Posted on December 5, 2017 by Android

The alternative universe known as science fiction has given our culture a menagerie of alien species. From overstuffed teddy bears like Ewoks and Wookies to terrifying nightmares such as Alien and Predator, our collective imagination of what form alien life from another world may take has been irrevocably imprinted by Hollywood.
It might all be possible, or all these bug-eyed critters might turn out to be just B-movie versions of how real extraterrestrials will appear if and when they finally make the evening news.
One thing for certain is that aliens from another world will be shaped by the same evolutionary forces as here on Earth—natural selection. That’s the conclusion of a team of scientists from the University of Oxford in a study published this month in the International Journal of Astrobiology.
A complex alien that comprises a hierarchy of entities, where each lower level collection of entities has aligned evolutionary interests.Image Credit: Helen S. Cooper/University of Oxford.
The researchers suggest that evolutionary theory—famously put forth by Charles Darwin in his seminal book On the Origin of Species 158 years ago this month—can be used to make some predictions about alien species. In particular, the team argues that extraterrestrials will undergo natural selection, because that is the only process by which organisms can adapt to their environment.
“Adaptation is what defines life,” lead author Samuel Levin tells Singularity Hub.
While it’s likely that NASA or some SpaceX-like private venture will eventually kick over a few space rocks and discover microbial life in the not-too-distant future, the sorts of aliens Levin and his colleagues are interested in describing are more complex. That’s because natural selection is at work.
A quick evolutionary theory 101 refresher: Natural selection is the process by which certain traits are favored over others in a given population. For example, take a group of brown and green beetles. It just so happens that birds prefer foraging on green beetles, allowing more brown beetles to survive and reproduce than the more delectable green ones. Eventually, if these population pressures persist, brown beetles will become the dominant type. Brown wins, green loses.
And just as human beings are the result of millions of years of adaptations—eyes and thumbs, for example—aliens will similarly be constructed from parts that were once free living but through time came together to work as one organism.
“Life has so many intricate parts, so much complexity, for that to happen (randomly),” Levin explains. “It’s too complex and too many things working together in a purposeful way for that to happen by chance, as how certain molecules come about. Instead you need a process for making it, and natural selection is that process.”
Just don’t expect ET to show up as a bipedal humanoid with a large head and almond-shaped eyes, Levin says.
“They can be built from entirely different chemicals and so visually, superficially, unfamiliar,” he explains. “They will have passed through the same evolutionary history as us. To me, that’s way cooler and more exciting than them having two legs.”
Need for Data
Seth Shostak, a lead astronomer at the SETI Institute and host of the organization’s Big Picture Science radio show, wrote that while the argument is interesting, it doesn’t answer the question of ET’s appearance.
Shostak argues that a more productive approach would invoke convergent evolution, where similar environments lead to similar adaptations, at least assuming a range of Earth-like conditions such as liquid oceans and thick atmospheres. For example, an alien species that evolved in a liquid environment would evolve a streamlined body to move through water.
“Happenstance and the specifics of the environment will produce variations on an alien species’ planet as it has on ours, and there’s really no way to predict these,” Shostak concludes. “Alas, an accurate cosmic bestiary cannot be written by the invocation of biological mechanisms alone. We need data. That requires more than simply thinking about alien life. We need to actually discover it.”
Search Is On
The search is on. On one hand, the task seems easy enough: There are at least 100 billion planets in the Milky Way alone, and at least 20 percent of those are likely to be capable of producing a biosphere. Even if the evolution of life is exceedingly rare—take a conservative estimate of .001 percent or 200,000 planets, as proposed by the Oxford paper—you have to like the odds.
Of course, it’s not that easy by a billion light years.
Planet hunters can’t even agree on what signatures of life they should focus on. The idea is that where there’s smoke there’s fire. In the case of an alien world home to biological life, astrobiologists are searching for the presence of “biosignature gases,” vapors that could only be produced by alien life.
As Quanta Magazine reported, scientists do this by measuring a planet’s atmosphere against starlight. Gases in the atmosphere absorb certain frequencies of starlight, offering a clue as to what is brewing around a particular planet.
The presence of oxygen would seem to be a biological no-brainer, but there are instances where a planet can produce a false positive, meaning non-biological processes are responsible for the exoplanet’s oxygen. Scientists like Sara Seager, an astrophysicist at MIT, have argued there are plenty of examples of other types of gases produced by organisms right here on Earth that could also produce the smoking gun, er, planet.

Life as We Know It
Indeed, the existence of Earth-bound extremophiles—organisms that defy conventional wisdom about where life can exist, such as in the vacuum of space—offer another clue as to what kind of aliens we might eventually meet.
Lynn Rothschild, an astrobiologist and synthetic biologist in the Earth Science Division at NASA’s Ames Research Center in Silicon Valley, takes extremophiles as a baseline and then supersizes them through synthetic biology.
For example, say a bacteria is capable of surviving at 120 degrees Celsius. Rothschild’s lab might tweak an organism’s DNA to see if it could metabolize at 150 degrees. The idea, as she explains, is to expand the envelope for life without ever getting into a rocket ship.

While researchers may not always agree on the “where” and “how” and “what” of the search for extraterrestrial life, most do share one belief: Alien life must be out there.
“It would shock me if there weren’t [extraterrestrials],” Levin says. “There are few things that would shock me more than to find out there aren’t any aliens…If I had to bet on it, I would bet on the side of there being lots and lots of aliens out there.”
Image Credit: NASA Continue reading →

Posted in Human Robots

#431682 Oxford Study Says Alien Life Would ...

Posted on December 4, 2017 by Android

The alternative universe known as science fiction has given our culture a menagerie of alien species. From overstuffed teddy bears like Ewoks and Wookies to terrifying nightmares such as Alien and Predator, our collective imagination of what form alien life from another world may take has been irrevocably imprinted by Hollywood.
It might all be possible, or all these bug-eyed critters might turn out to be just B-movie versions of how real extraterrestrials will appear if and when they finally make the evening news.
One thing for certain is that aliens from another world will be shaped by the same evolutionary forces as here on Earth—natural selection. That’s the conclusion of a team of scientists from the University of Oxford in a study published this month in the International Journal of Astrobiology.
A complex alien that comprises a hierarchy of entities, where each lower level collection of entities has aligned evolutionary interests.Image Credit: Helen S. Cooper/University of Oxford.
The researchers suggest that evolutionary theory—famously put forth by Charles Darwin in his seminal book On the Origin of Species 158 years ago this month—can be used to make some predictions about alien species. In particular, the team argues that extraterrestrials will undergo natural selection, because that is the only process by which organisms can adapt to their environment.
“Adaptation is what defines life,” lead author Samuel Levin tells Singularity Hub.
While it’s likely that NASA or some SpaceX-like private venture will eventually kick over a few space rocks and discover microbial life in the not-too-distant future, the sorts of aliens Levin and his colleagues are interested in describing are more complex. That’s because natural selection is at work.
A quick evolutionary theory 101 refresher: Natural selection is the process by which certain traits are favored over others in a given population. For example, take a group of brown and green beetles. It just so happens that birds prefer foraging on green beetles, allowing more brown beetles to survive and reproduce than the more delectable green ones. Eventually, if these population pressures persist, brown beetles will become the dominant type. Brown wins, green loses.
And just as human beings are the result of millions of years of adaptations—eyes and thumbs, for example—aliens will similarly be constructed from parts that were once free living but through time came together to work as one organism.
“Life has so many intricate parts, so much complexity, for that to happen (randomly),” Levin explains. “It’s too complex and too many things working together in a purposeful way for that to happen by chance, as how certain molecules come about. Instead you need a process for making it, and natural selection is that process.”
Just don’t expect ET to show up as a bipedal humanoid with a large head and almond-shaped eyes, Levin says.
“They can be built from entirely different chemicals and so visually, superficially, unfamiliar,” he explains. “They will have passed through the same evolutionary history as us. To me, that’s way cooler and more exciting than them having two legs.”
Need for Data
Seth Shostak, a lead astronomer at the SETI Institute and host of the organization’s Big Picture Science radio show, wrote that while the argument is interesting, it doesn’t answer the question of ET’s appearance.
Shostak argues that a more productive approach would invoke convergent evolution, where similar environments lead to similar adaptations, at least assuming a range of Earth-like conditions such as liquid oceans and thick atmospheres. For example, an alien species that evolved in a liquid environment would evolve a streamlined body to move through water.
“Happenstance and the specifics of the environment will produce variations on an alien species’ planet as it has on ours, and there’s really no way to predict these,” Shostak concludes. “Alas, an accurate cosmic bestiary cannot be written by the invocation of biological mechanisms alone. We need data. That requires more than simply thinking about alien life. We need to actually discover it.”
Search is On
The search is on. On one hand, the task seems easy enough: There are at least 100 billion planets in the Milky Way alone, and at least 20 percent of those are likely to be capable of producing a biosphere. Even if the evolution of life is exceedingly rare—take a conservative estimate of .001 percent or 200,000 planets, as proposed by the Oxford paper—you have to like the odds.
Of course, it’s not that easy by a billion light years.
Planet hunters can’t even agree on what signatures of life they should focus on. The idea is that where there’s smoke there’s fire. In the case of an alien world home to biological life, astrobiologists are searching for the presence of “biosignature gases,” vapors that could only be produced by alien life.
As Quanta Magazine reported, scientists do this by measuring a planet’s atmosphere against starlight. Gases in the atmosphere absorb certain frequencies of starlight, offering a clue as to what is brewing around a particular planet.
The presence of oxygen would seem to be a biological no-brainer, but there are instances where a planet can produce a false positive, meaning non-biological processes are responsible for the exoplanet’s oxygen. Scientists like Sara Seager, an astrophysicist at MIT, have argued there are plenty of examples of other types of gases produced by organisms right here on Earth that could also produce the smoking gun, er, planet.

Life as We Know It
Indeed, the existence of Earth-bound extremophiles—organisms that defy conventional wisdom about where life can exist, such as in the vacuum of space—offer another clue as to what kind of aliens we might eventually meet.
Lynn Rothschild, an astrobiologist and synthetic biologist in the Earth Science Division at NASA’s Ames Research Center in Silicon Valley, takes extremophiles as a baseline and then supersizes them through synthetic biology.
For example, say a bacteria is capable of surviving at 120 degrees Celsius. Rothschild’s lab might tweak an organism’s DNA to see if it could metabolize at 150 degrees. The idea, as she explains, is to expand the envelope for life without ever getting into a rocket ship.

While researchers may not always agree on the “where” and “how” and “what” of the search for extraterrestrial life, most do share one belief: Alien life must be out there.
“It would shock me if there weren’t [extraterrestrials],” Levin says. “There are few things that would shock me more than to find out there aren’t any aliens…If I had to bet on it, I would bet on the side of there being lots and lots of aliens out there.”
Image Credit: NASA Continue reading →

Posted in Human Robots