Tag Archives: Processing
#431424 A ‘Google Maps’ for the Mouse Brain ...
Ask any neuroscientist to draw you a neuron, and it’ll probably look something like a star with two tails: one stubby with extensive tree-like branches, the other willowy, lengthy and dotted with spindly spikes.
While a decent abstraction, this cartoonish image hides the uncomfortable truth that scientists still don’t know much about what many neurons actually look like, not to mention the extent of their connections.
But without untangling the jumbled mess of neural wires that zigzag across the brain, scientists are stumped in trying to answer one of the most fundamental mysteries of the brain: how individual neuronal threads carry and assemble information, which forms the basis of our thoughts, memories, consciousness, and self.
What if there was a way to virtually trace and explore the brain’s serpentine fibers, much like the way Google Maps allows us to navigate the concrete tangles of our cities’ highways?
Thanks to an interdisciplinary team at Janelia Research Campus, we’re on our way. Meet MouseLight, the most extensive map of the mouse brain ever attempted. The ongoing project has an ambitious goal: reconstructing thousands—if not more—of the mouse’s 70 million neurons into a 3D map. (You can play with it here!)
With map in hand, neuroscientists around the world can begin to answer how neural circuits are organized in the brain, and how information flows from one neuron to another across brain regions and hemispheres.
The first release, presented Monday at the Society for Neuroscience Annual Conference in Washington, DC, contains information about the shape and sizes of 300 neurons.
And that’s just the beginning.
“MouseLight’s new dataset is the largest of its kind,” says Dr. Wyatt Korff, director of project teams. “It’s going to change the textbook view of neurons.”
http://mouselight.janelia.org/assets/carousel/ML-Movie.mp4
Brain Atlas
MouseLight is hardly the first rodent brain atlasing project.
The Mouse Brain Connectivity Atlas at the Allen Institute for Brain Science in Seattle tracks neuron activity across small circuits in an effort to trace a mouse’s connectome—a complete atlas of how the firing of one neuron links to the next.
MICrONS (Machine Intelligence from Cortical Networks), the $100 million government-funded “moonshot” hopes to distill brain computation into algorithms for more powerful artificial intelligence. Its first step? Brain mapping.
What makes MouseLight stand out is its scope and level of detail.
MICrONS, for example, is focused on dissecting a cubic millimeter of the mouse visual processing center. In contrast, MouseLight involves tracing individual neurons across the entire brain.
And while connectomics outlines the major connections between brain regions, the birds-eye view entirely misses the intricacies of each individual neuron. This is where MouseLight steps in.
Slice and Dice
With a width only a fraction of a human hair, neuron projections are hard to capture in their native state. Tug or squeeze the brain too hard, and the long, delicate branches distort or even shred into bits.
In fact, previous attempts at trying to reconstruct neurons at this level of detail topped out at just a dozen, stymied by technological hiccups and sky-high costs.
A few years ago, the MouseLight team set out to automate the entire process, with a few time-saving tweaks. Here’s how it works.
After injecting a mouse with a virus that causes a handful of neurons to produce a green-glowing protein, the team treated the brain with a sugar alcohol solution. This step “clears” the brain, transforming the beige-colored organ to translucent, making it easier for light to penetrate and boosting the signal-to-background noise ratio. The brain is then glued onto a small pedestal and ready for imaging.
Building upon an established method called “two-photon microscopy,” the team then tweaked several parameters to reduce imaging time from days (or weeks) down to a fraction of that. Endearingly known as “2P” by the experts, this type of laser microscope zaps the tissue with just enough photos to light up a single plane without damaging the tissue—sharper plane, better focus, crisper image.
After taking an image, the setup activates its vibrating razor and shaves off the imaged section of the brain—a waspy slice about 200 micrometers thick. The process is repeated until the whole brain is imaged.
This setup increased imaging speed by 16 to 48 times faster than conventional microscopy, writes team leader Dr. Jayaram Chandrashekar, who published a version of the method early last year in eLife.
The resulting images strikingly highlight every crook and cranny of a neuronal branch, popping out against a pitch-black background. But pretty pictures come at a hefty data cost: each image takes up a whopping 20 terabytes of data—roughly the storage space of 4,000 DVDs, or 10,000 hours of movies.
Stitching individual images back into 3D is an image-processing nightmare. The MouseLight team used a combination of computational power and human prowess to complete this final step.
The reconstructed images are handed off to a mighty team of seven trained neuron trackers. With the help of tracing algorithms developed in-house and a keen eye, each member can track roughly a neuron a day—significantly less time than the week or so previously needed.
A Numbers Game
Even with just 300 fully reconstructed neurons, MouseLight has already revealed new secrets of the brain.
While it’s widely accepted that axons, the neurons’ outgoing projection, can span the entire length of the brain, these extra-long connections were considered relatively rare. (In fact, one previously discovered “giant neuron” was thought to link to consciousness because of its expansive connections).
Images captured from two-photon microscopy show an axon and dendrites protruding from a neuron’s cell body (sphere in center). Image Credit: Janelia Research Center, MouseLight project team
MouseLight blows that theory out of the water.
The data clearly shows that “giant neurons” are far more common than previously thought. For example, four neurons normally associated with taste had wiry branches that stretched all the way into brain areas that control movement and process touch.
“We knew that different regions of the brain talked to each other, but seeing it in 3D is different,” says Dr. Eve Marder at Brandeis University.
“The results are so stunning because they give you a really clear view of how the whole brain is connected.”
With a tested and true system in place, the team is now aiming to add 700 neurons to their collection within a year.
But appearance is only part of the story.
We can’t tell everything about a person simply by how they look. Neurons are the same: scientists can only infer so much about a neuron’s function by looking at their shape and positions. The team also hopes to profile the gene expression patterns of each neuron, which could provide more hints to their roles in the brain.
MouseLight essentially dissects the neural infrastructure that allows information traffic to flow through the brain. These anatomical highways are just the foundation. Just like Google Maps, roads form only the critical first layer of the map. Street view, traffic information and other add-ons come later for a complete look at cities in flux.
The same will happen for understanding our ever-changing brain.
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Posted in Human Robots
#431385 Here’s How to Get to Conscious ...
“We cannot be conscious of what we are not conscious of.” – Julian Jaynes, The Origin of Consciousness in the Breakdown of the Bicameral Mind
Unlike the director leads you to believe, the protagonist of Ex Machina, Andrew Garland’s 2015 masterpiece, isn’t Caleb, a young programmer tasked with evaluating machine consciousness. Rather, it’s his target Ava, a breathtaking humanoid AI with a seemingly child-like naïveté and an enigmatic mind.
Like most cerebral movies, Ex Machina leaves the conclusion up to the viewer: was Ava actually conscious? In doing so, it also cleverly avoids a thorny question that has challenged most AI-centric movies to date: what is consciousness, and can machines have it?
Hollywood producers aren’t the only people stumped. As machine intelligence barrels forward at breakneck speed—not only exceeding human performance on games such as DOTA and Go, but doing so without the need for human expertise—the question has once more entered the scientific mainstream.
Are machines on the verge of consciousness?
This week, in a review published in the prestigious journal Science, cognitive scientists Drs. Stanislas Dehaene, Hakwan Lau and Sid Kouider of the Collège de France, University of California, Los Angeles and PSL Research University, respectively, argue: not yet, but there is a clear path forward.
The reason? Consciousness is “resolutely computational,” the authors say, in that it results from specific types of information processing, made possible by the hardware of the brain.
There is no magic juice, no extra spark—in fact, an experiential component (“what is it like to be conscious?”) isn’t even necessary to implement consciousness.
If consciousness results purely from the computations within our three-pound organ, then endowing machines with a similar quality is just a matter of translating biology to code.
Much like the way current powerful machine learning techniques heavily borrow from neurobiology, the authors write, we may be able to achieve artificial consciousness by studying the structures in our own brains that generate consciousness and implementing those insights as computer algorithms.
From Brain to Bot
Without doubt, the field of AI has greatly benefited from insights into our own minds, both in form and function.
For example, deep neural networks, the architecture of algorithms that underlie AlphaGo’s breathtaking sweep against its human competitors, are loosely based on the multi-layered biological neural networks that our brain cells self-organize into.
Reinforcement learning, a type of “training” that teaches AIs to learn from millions of examples, has roots in a centuries-old technique familiar to anyone with a dog: if it moves toward the right response (or result), give a reward; otherwise ask it to try again.
In this sense, translating the architecture of human consciousness to machines seems like a no-brainer towards artificial consciousness. There’s just one big problem.
“Nobody in AI is working on building conscious machines because we just have nothing to go on. We just don’t have a clue about what to do,” said Dr. Stuart Russell, the author of Artificial Intelligence: A Modern Approach in a 2015 interview with Science.
Multilayered consciousness
The hard part, long before we can consider coding machine consciousness, is figuring out what consciousness actually is.
To Dehaene and colleagues, consciousness is a multilayered construct with two “dimensions:” C1, the information readily in mind, and C2, the ability to obtain and monitor information about oneself. Both are essential to consciousness, but one can exist without the other.
Say you’re driving a car and the low fuel light comes on. Here, the perception of the fuel-tank light is C1—a mental representation that we can play with: we notice it, act upon it (refill the gas tank) and recall and speak about it at a later date (“I ran out of gas in the boonies!”).
“The first meaning we want to separate (from consciousness) is the notion of global availability,” explains Dehaene in an interview with Science. When you’re conscious of a word, your whole brain is aware of it, in a sense that you can use the information across modalities, he adds.
But C1 is not just a “mental sketchpad.” It represents an entire architecture that allows the brain to draw multiple modalities of information from our senses or from memories of related events, for example.
Unlike subconscious processing, which often relies on specific “modules” competent at a defined set of tasks, C1 is a global workspace that allows the brain to integrate information, decide on an action, and follow through until the end.
Like The Hunger Games, what we call “conscious” is whatever representation, at one point in time, wins the competition to access this mental workspace. The winners are shared among different brain computation circuits and are kept in the spotlight for the duration of decision-making to guide behavior.
Because of these features, C1 consciousness is highly stable and global—all related brain circuits are triggered, the authors explain.
For a complex machine such as an intelligent car, C1 is a first step towards addressing an impending problem, such as a low fuel light. In this example, the light itself is a type of subconscious signal: when it flashes, all of the other processes in the machine remain uninformed, and the car—even if equipped with state-of-the-art visual processing networks—passes by gas stations without hesitation.
With C1 in place, the fuel tank would alert the car computer (allowing the light to enter the car’s “conscious mind”), which in turn checks the built-in GPS to search for the next gas station.
“We think in a machine this would translate into a system that takes information out of whatever processing module it’s encapsulated in, and make it available to any of the other processing modules so they can use the information,” says Dehaene. “It’s a first sense of consciousness.”
Meta-cognition
In a way, C1 reflects the mind’s capacity to access outside information. C2 goes introspective.
The authors define the second facet of consciousness, C2, as “meta-cognition:” reflecting on whether you know or perceive something, or whether you just made an error (“I think I may have filled my tank at the last gas station, but I forgot to keep a receipt to make sure”). This dimension reflects the link between consciousness and sense of self.
C2 is the level of consciousness that allows you to feel more or less confident about a decision when making a choice. In computational terms, it’s an algorithm that spews out the probability that a decision (or computation) is correct, even if it’s often experienced as a “gut feeling.”
C2 also has its claws in memory and curiosity. These self-monitoring algorithms allow us to know what we know or don’t know—so-called “meta-memory,” responsible for that feeling of having something at the tip of your tongue. Monitoring what we know (or don’t know) is particularly important for children, says Dehaene.
“Young children absolutely need to monitor what they know in order to…inquire and become curious and learn more,” he explains.
The two aspects of consciousness synergize to our benefit: C1 pulls relevant information into our mental workspace (while discarding other “probable” ideas or solutions), while C2 helps with long-term reflection on whether the conscious thought led to a helpful response.
Going back to the low fuel light example, C1 allows the car to solve the problem in the moment—these algorithms globalize the information, so that the car becomes aware of the problem.
But to solve the problem, the car would need a “catalog of its cognitive abilities”—a self-awareness of what resources it has readily available, for example, a GPS map of gas stations.
“A car with this sort of self-knowledge is what we call having C2,” says Dehaene. Because the signal is globally available and because it’s being monitored in a way that the machine is looking at itself, the car would care about the low gas light and behave like humans do—lower fuel consumption and find a gas station.
“Most present-day machine learning systems are devoid of any self-monitoring,” the authors note.
But their theory seems to be on the right track. The few examples whereby a self-monitoring system was implemented—either within the structure of the algorithm or as a separate network—the AI has generated “internal models that are meta-cognitive in nature, making it possible for an agent to develop a (limited, implicit, practical) understanding of itself.”
Towards conscious machines
Would a machine endowed with C1 and C2 behave as if it were conscious? Very likely: a smartcar would “know” that it’s seeing something, express confidence in it, report it to others, and find the best solutions for problems. If its self-monitoring mechanisms break down, it may also suffer “hallucinations” or even experience visual illusions similar to humans.
Thanks to C1 it would be able to use the information it has and use it flexibly, and because of C2 it would know the limit of what it knows, says Dehaene. “I think (the machine) would be conscious,” and not just merely appearing so to humans.
If you’re left with a feeling that consciousness is far more than global information sharing and self-monitoring, you’re not alone.
“Such a purely functional definition of consciousness may leave some readers unsatisfied,” the authors acknowledge.
“But we’re trying to take a radical stance, maybe simplifying the problem. Consciousness is a functional property, and when we keep adding functions to machines, at some point these properties will characterize what we mean by consciousness,” Dehaene concludes.
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#431362 Does Regulating Artificial Intelligence ...
Some people are afraid that heavily armed artificially intelligent robots might take over the world, enslaving humanity—or perhaps exterminating us. These people, including tech-industry billionaire Elon Musk and eminent physicist Stephen Hawking, say artificial intelligence technology needs to be regulated to manage the risks. But Microsoft founder Bill Gates and Facebook’s Mark Zuckerberg disagree, saying the technology is not nearly advanced enough for those worries to be realistic.
As someone who researches how AI works in robotic decision-making, drones and self-driving vehicles, I’ve seen how beneficial it can be. I’ve developed AI software that lets robots working in teams make individual decisions as part of collective efforts to explore and solve problems. Researchers are already subject to existing rules, regulations and laws designed to protect public safety. Imposing further limitations risks reducing the potential for innovation with AI systems.
How is AI regulated now?
While the term “artificial intelligence” may conjure fantastical images of human-like robots, most people have encountered AI before. It helps us find similar products while shopping, offers movie and TV recommendations, and helps us search for websites. It grades student writing, provides personalized tutoring, and even recognizes objects carried through airport scanners.
In each case, the AI makes things easier for humans. For example, the AI software I developed could be used to plan and execute a search of a field for a plant or animal as part of a science experiment. But even as the AI frees people from doing this work, it is still basing its actions on human decisions and goals about where to search and what to look for.
In areas like these and many others, AI has the potential to do far more good than harm—if used properly. But I don’t believe additional regulations are currently needed. There are already laws on the books of nations, states, and towns governing civil and criminal liabilities for harmful actions. Our drones, for example, must obey FAA regulations, while the self-driving car AI must obey regular traffic laws to operate on public roadways.
Existing laws also cover what happens if a robot injures or kills a person, even if the injury is accidental and the robot’s programmer or operator isn’t criminally responsible. While lawmakers and regulators may need to refine responsibility for AI systems’ actions as technology advances, creating regulations beyond those that already exist could prohibit or slow the development of capabilities that would be overwhelmingly beneficial.
Potential risks from artificial intelligence
It may seem reasonable to worry about researchers developing very advanced artificial intelligence systems that can operate entirely outside human control. A common thought experiment deals with a self-driving car forced to make a decision about whether to run over a child who just stepped into the road or veer off into a guardrail, injuring the car’s occupants and perhaps even those in another vehicle.
Musk and Hawking, among others, worry that a hyper-capable AI system, no longer limited to a single set of tasks like controlling a self-driving car, might decide it doesn’t need humans anymore. It might even look at human stewardship of the planet, the interpersonal conflicts, theft, fraud, and frequent wars, and decide that the world would be better without people.
Science fiction author Isaac Asimov tried to address this potential by proposing three laws limiting robot decision-making: Robots cannot injure humans or allow them “to come to harm.” They must also obey humans—unless this would harm humans—and protect themselves, as long as this doesn’t harm humans or ignore an order.
But Asimov himself knew the three laws were not enough. And they don’t reflect the complexity of human values. What constitutes “harm” is an example: Should a robot protect humanity from suffering related to overpopulation, or should it protect individuals’ freedoms to make personal reproductive decisions?
We humans have already wrestled with these questions in our own, non-artificial intelligences. Researchers have proposed restrictions on human freedoms, including reducing reproduction, to control people’s behavior, population growth, and environmental damage. In general, society has decided against using those methods, even if their goals seem reasonable. Similarly, rather than regulating what AI systems can and can’t do, in my view it would be better to teach them human ethics and values—like parents do with human children.
Artificial intelligence benefits
People already benefit from AI every day—but this is just the beginning. AI-controlled robots could assist law enforcement in responding to human gunmen. Current police efforts must focus on preventing officers from being injured, but robots could step into harm’s way, potentially changing the outcomes of cases like the recent shooting of an armed college student at Georgia Tech and an unarmed high school student in Austin.
Intelligent robots can help humans in other ways, too. They can perform repetitive tasks, like processing sensor data, where human boredom may cause mistakes. They can limit human exposure to dangerous materials and dangerous situations, such as when decontaminating a nuclear reactor, working in areas humans can’t go. In general, AI robots can provide humans with more time to pursue whatever they define as happiness by freeing them from having to do other work.
Achieving most of these benefits will require a lot more research and development. Regulations that make it more expensive to develop AIs or prevent certain uses may delay or forestall those efforts. This is particularly true for small businesses and individuals—key drivers of new technologies—who are not as well equipped to deal with regulation compliance as larger companies. In fact, the biggest beneficiary of AI regulation may be large companies that are used to dealing with it, because startups will have a harder time competing in a regulated environment.
The need for innovation
Humanity faced a similar set of issues in the early days of the internet. But the United States actively avoided regulating the internet to avoid stunting its early growth. Musk’s PayPal and numerous other businesses helped build the modern online world while subject only to regular human-scale rules, like those preventing theft and fraud.
Artificial intelligence systems have the potential to change how humans do just about everything. Scientists, engineers, programmers, and entrepreneurs need time to develop the technologies—and deliver their benefits. Their work should be free from concern that some AIs might be banned, and from the delays and costs associated with new AI-specific regulations.
This article was originally published on The Conversation. Read the original article.
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Posted in Human Robots
#431159 How Close Is Turing’s Dream of ...
The quest for conversational artificial intelligence has been a long one.
When Alan Turing, the father of modern computing, racked his considerable brains for a test that would truly indicate that a computer program was intelligent, he landed on this area. If a computer could convince a panel of human judges that they were talking to a human—if it could hold a convincing conversation—then it would indicate that artificial intelligence had advanced to the point where it was indistinguishable from human intelligence.
This gauntlet was thrown down in 1950 and, so far, no computer program has managed to pass the Turing test.
There have been some very notable failures, however: Joseph Weizenbaum, as early as 1966—when computers were still programmed with large punch-cards—developed a piece of natural language processing software called ELIZA. ELIZA was a machine intended to respond to human conversation by pretending to be a psychotherapist; you can still talk to her today.
Talking to ELIZA is a little strange. She’ll often rephrase things you’ve said back at you: so, for example, if you say “I’m feeling depressed,” she might say “Did you come to me because you are feeling depressed?” When she’s unsure about what you’ve said, ELIZA will usually respond with “I see,” or perhaps “Tell me more.”
For the first few lines of dialogue, especially if you treat her as your therapist, ELIZA can be convincingly human. This was something Weizenbaum noticed and was slightly alarmed by: people were willing to treat the algorithm as more human than it really was. Before long, even though some of the test subjects knew ELIZA was just a machine, they were opening up with some of their deepest feelings and secrets. They were pouring out their hearts to a machine. When Weizenbaum’s secretary spoke to ELIZA, even though she knew it was a fairly simple computer program, she still insisted Weizenbaum leave the room.
Part of the unexpected reaction ELIZA generated may be because people are more willing to open up to a machine, feeling they won’t be judged, even if the machine is ultimately powerless to do or say anything to really help. The ELIZA effect was named for this computer program: the tendency of humans to anthropomorphize machines, or think of them as human.
Weizenbaum himself, who later became deeply suspicious of the influence of computers and artificial intelligence in human life, was astonished that people were so willing to believe his script was human. He wrote, “I had not realized…that extremely short exposures to a relatively simple computer program could induce powerful delusional thinking in quite normal people.”
“Consciously, you know you’re talking to a big block of code stored somewhere out there in the ether. But subconsciously, you might feel like you’re interacting with a human.”
The ELIZA effect may have disturbed Weizenbaum, but it has intrigued and fascinated others for decades. Perhaps you’ve noticed it in yourself, when talking to an AI like Siri, Alexa, or Google Assistant—the occasional response can seem almost too real. Consciously, you know you’re talking to a big block of code stored somewhere out there in the ether. But subconsciously, you might feel like you’re interacting with a human.
Yet the ELIZA effect, as enticing as it is, has proved a source of frustration for people who are trying to create conversational machines. Natural language processing has proceeded in leaps and bounds since the 1960s. Now you can find friendly chatbots like Mitsuku—which has frequently won the Loebner Prize, awarded to the machines that come closest to passing the Turing test—that aim to have a response to everything you might say.
In the commercial sphere, Facebook has opened up its Messenger program and provided software for people and companies to design their own chatbots. The idea is simple: why have an app for, say, ordering pizza when you can just chatter to a robot through your favorite messenger app and make the order in natural language, as if you were telling your friend to get it for you?
Startups like Semantic Machines hope their AI assistant will be able to interact with you just like a secretary or PA would, but with an unparalleled ability to retrieve information from the internet. They may soon be there.
But people who engineer chatbots—both in the social and commercial realm—encounter a common problem: the users, perhaps subconsciously, assume the chatbots are human and become disappointed when they’re not able to have a normal conversation. Frustration with miscommunication can often stem from raised initial expectations.
So far, no machine has really been able to crack the problem of context retention—understanding what’s been said before, referring back to it, and crafting responses based on the point the conversation has reached. Even Mitsuku will often struggle to remember the topic of conversation beyond a few lines of dialogue.
“For everything you say, there could be hundreds of responses that would make sense. When you travel a layer deeper into the conversation, those factors multiply until you end up with vast numbers of potential conversations.”
This is, of course, understandable. Conversation can be almost unimaginably complex. For everything you say, there could be hundreds of responses that would make sense. When you travel a layer deeper into the conversation, those factors multiply until—like possible games of Go or chess—you end up with vast numbers of potential conversations.
But that hasn’t deterred people from trying, most recently, tech giant Amazon, in an effort to make their AI voice assistant, Alexa, friendlier. They have been running the Alexa Prize competition, which offers a cool $500,000 to the winning AI—and a bonus of a million dollars to any team that can create a ‘socialbot’ capable of sustaining a conversation with human users for 20 minutes on a variety of themes.
Topics Alexa likes to chat about include science and technology, politics, sports, and celebrity gossip. The finalists were recently announced: chatbots from universities in Prague, Edinburgh, and Seattle. Finalists were chosen according to the ratings from Alexa users, who could trigger the socialbots into conversation by saying “Hey Alexa, let’s chat,” although the reviews for the socialbots weren’t always complimentary.
By narrowing down the fields of conversation to a specific range of topics, the Alexa Prize has cleverly started to get around the problem of context—just as commercially available chatbots hope to do. It’s much easier to model an interaction that goes a few layers into the conversational topic if you’re limiting those topics to a specific field.
Developing a machine that can hold almost any conversation with a human interlocutor convincingly might be difficult. It might even be a problem that requires artificial general intelligence to truly solve, rather than the previously-employed approaches of scripted answers or neural networks that associate inputs with responses.
But a machine that can have meaningful interactions that people might value and enjoy could be just around the corner. The Alexa Prize winner is announced in November. The ELIZA effect might mean we will relate to machines sooner than we’d thought.
So, go well, little socialbots. If you ever want to discuss the weather or what the world will be like once you guys take over, I’ll be around. Just don’t start a therapy session.
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Posted in Human Robots