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#437982 Superintelligent AI May Be Impossible to ...
It may be theoretically impossible for humans to control a superintelligent AI, a new study finds. Worse still, the research also quashes any hope for detecting such an unstoppable AI when it’s on the verge of being created.
Slightly less grim is the timetable. By at least one estimate, many decades lie ahead before any such existential computational reckoning could be in the cards for humanity.
Alongside news of AI besting humans at games such as chess, Go and Jeopardy have come fears that superintelligent machines smarter than the best human minds might one day run amok. “The question about whether superintelligence could be controlled if created is quite old,” says study lead author Manuel Alfonseca, a computer scientist at the Autonomous University of Madrid. “It goes back at least to Asimov’s First Law of Robotics, in the 1940s.”
The Three Laws of Robotics, first introduced in Isaac Asimov's 1942 short story “Runaround,” are as follows:
A robot may not injure a human being or, through inaction, allow a human being to come to harm.
A robot must obey the orders given it by human beings except where such orders would conflict with the First Law.
A robot must protect its own existence as long as such protection does not conflict with the First or Second Laws.
In 2014, philosopher Nick Bostrom, director of the Future of Humanity Institute at the University of Oxford, not only explored ways in which a superintelligent AI could destroy us but also investigated potential control strategies for such a machine—and the reasons they might not work.
Bostrom outlined two possible types of solutions of this “control problem.” One is to control what the AI can do, such as keeping it from connecting to the Internet, and the other is to control what it wants to do, such as teaching it rules and values so it would act in the best interests of humanity. The problem with the former is that Bostrom thought a supersmart machine could probably break free from any bonds we could make. With the latter, he essentially feared that humans might not be smart enough to train a superintelligent AI.
Now Alfonseca and his colleagues suggest it may be impossible to control a superintelligent AI, due to fundamental limits inherent to computing itself. They detailed their findings this month in the Journal of Artificial Intelligence Research.
The researchers suggested that any algorithm that sought to ensure a superintelligent AI cannot harm people had to first simulate the machine’s behavior to predict the potential consequences of its actions. This containment algorithm then would need to halt the supersmart machine if it might indeed do harm.
However, the scientists said it was impossible for any containment algorithm to simulate the AI’s behavior and predict with absolute certainty whether its actions might lead to harm. The algorithm could fail to correctly simulate the AI’s behavior or accurately predict the consequences of the AI’s actions and not recognize such failures.
“Asimov’s first law of robotics has been proved to be incomputable,” Alfonseca says, “and therefore unfeasible.”
We may not even know if we have created a superintelligent machine, the researchers say. This is a consequence of Rice’s theorem, which essentially states that one cannot in general figure anything out about what a computer program might output just by looking at the program, Alfonseca explains.
On the other hand, there’s no need to spruce up the guest room for our future robot overlords quite yet. Three important caveats to the research still leave plenty of uncertainty to the group’s predictions.
First, Alfonseca estimates AI’s moment of truth remains, he says, “At least two centuries in the future.”
Second, he says researchers do not know if so-called artificial general intelligence, also known as strong AI, is theoretically even feasible. “That is, a machine as intelligent as we are in an ample variety of fields,” Alfonseca explains.
Last, Alfonseca says, “We have not proved that superintelligences can never be controlled—only that they can’t always be controlled.”
Although it may not be possible to control a superintelligent artificial general intelligence, it should be possible to control a superintelligent narrow AI—one specialized for certain functions instead of being capable of a broad range of tasks like humans. “We already have superintelligences of this type,” Alfonseca says. “For instance, we have machines that can compute mathematics much faster than we can. This is [narrow] superintelligence, isn’t it?” Continue reading
Posted in Human Robots
#437964 How Explainable Artificial Intelligence ...
The field of artificial intelligence has created computers that can drive cars, synthesize chemical compounds, fold proteins, and detect high-energy particles at a superhuman level.
However, these AI algorithms cannot explain the thought processes behind their decisions. A computer that masters protein folding and also tells researchers more about the rules of biology is much more useful than a computer that folds proteins without explanation.
Therefore, AI researchers like me are now turning our efforts toward developing AI algorithms that can explain themselves in a manner that humans can understand. If we can do this, I believe that AI will be able to uncover and teach people new facts about the world that have not yet been discovered, leading to new innovations.
Learning From Experience
One field of AI, called reinforcement learning, studies how computers can learn from their own experiences. In reinforcement learning, an AI explores the world, receiving positive or negative feedback based on its actions.
This approach has led to algorithms that have independently learned to play chess at a superhuman level and prove mathematical theorems without any human guidance. In my work as an AI researcher, I use reinforcement learning to create AI algorithms that learn how to solve puzzles such as the Rubik’s Cube.
Through reinforcement learning, AIs are independently learning to solve problems that even humans struggle to figure out. This has got me and many other researchers thinking less about what AI can learn and more about what humans can learn from AI. A computer that can solve the Rubik’s Cube should be able to teach people how to solve it, too.
Peering Into the Black Box
Unfortunately, the minds of superhuman AIs are currently out of reach to us humans. AIs make terrible teachers and are what we in the computer science world call “black boxes.”
AI simply spits out solutions without giving reasons for its solutions. Computer scientists have been trying for decades to open this black box, and recent research has shown that many AI algorithms actually do think in ways that are similar to humans. For example, a computer trained to recognize animals will learn about different types of eyes and ears and will put this information together to correctly identify the animal.
The effort to open up the black box is called explainable AI. My research group at the AI Institute at the University of South Carolina is interested in developing explainable AI. To accomplish this, we work heavily with the Rubik’s Cube.
The Rubik’s Cube is basically a pathfinding problem: Find a path from point A—a scrambled Rubik’s Cube—to point B—a solved Rubik’s Cube. Other pathfinding problems include navigation, theorem proving and chemical synthesis.
My lab has set up a website where anyone can see how our AI algorithm solves the Rubik’s Cube; however, a person would be hard-pressed to learn how to solve the cube from this website. This is because the computer cannot tell you the logic behind its solutions.
Solutions to the Rubik’s Cube can be broken down into a few generalized steps—the first step, for example, could be to form a cross while the second step could be to put the corner pieces in place. While the Rubik’s Cube itself has over 10 to the 19th power possible combinations, a generalized step-by-step guide is very easy to remember and is applicable in many different scenarios.
Approaching a problem by breaking it down into steps is often the default manner in which people explain things to one another. The Rubik’s Cube naturally fits into this step-by-step framework, which gives us the opportunity to open the black box of our algorithm more easily. Creating AI algorithms that have this ability could allow people to collaborate with AI and break down a wide variety of complex problems into easy-to-understand steps.
A step-by-step refinement approach can make it easier for humans to understand why AIs do the things they do. Forest Agostinelli, CC BY-ND
Collaboration Leads to Innovation
Our process starts with using one’s own intuition to define a step-by-step plan thought to potentially solve a complex problem. The algorithm then looks at each individual step and gives feedback about which steps are possible, which are impossible and ways the plan could be improved. The human then refines the initial plan using the advice from the AI, and the process repeats until the problem is solved. The hope is that the person and the AI will eventually converge to a kind of mutual understanding.
Currently, our algorithm is able to consider a human plan for solving the Rubik’s Cube, suggest improvements to the plan, recognize plans that do not work and find alternatives that do. In doing so, it gives feedback that leads to a step-by-step plan for solving the Rubik’s Cube that a person can understand. Our team’s next step is to build an intuitive interface that will allow our algorithm to teach people how to solve the Rubik’s Cube. Our hope is to generalize this approach to a wide range of pathfinding problems.
People are intuitive in a way unmatched by any AI, but machines are far better in their computational power and algorithmic rigor. This back and forth between man and machine utilizes the strengths from both. I believe this type of collaboration will shed light on previously unsolved problems in everything from chemistry to mathematics, leading to new solutions, intuitions and innovations that may have, otherwise, been out of reach.
This article is republished from The Conversation under a Creative Commons license. Read the original article.
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Posted in Human Robots
#437957 Meet Assembloids, Mini Human Brains With ...
It’s not often that a twitching, snowman-shaped blob of 3D human tissue makes someone’s day.
But when Dr. Sergiu Pasca at Stanford University witnessed the tiny movement, he knew his lab had achieved something special. You see, the blob was evolved from three lab-grown chunks of human tissue: a mini-brain, mini-spinal cord, and mini-muscle. Each individual component, churned to eerie humanoid perfection inside bubbling incubators, is already a work of scientific genius. But Pasca took the extra step, marinating the three components together inside a soup of nutrients.
The result was a bizarre, Lego-like human tissue that replicates the basic circuits behind how we decide to move. Without external prompting, when churned together like ice cream, the three ingredients physically linked up into a fully functional circuit. The 3D mini-brain, through the information highway formed by the artificial spinal cord, was able to make the lab-grown muscle twitch on demand.
In other words, if you think isolated mini-brains—known formally as brain organoids—floating in a jar is creepy, upgrade your nightmares. The next big thing in probing the brain is assembloids—free-floating brain circuits—that now combine brain tissue with an external output.
The end goal isn’t to freak people out. Rather, it’s to recapitulate our nervous system, from input to output, inside the controlled environment of a Petri dish. An autonomous, living brain-spinal cord-muscle entity is an invaluable model for figuring out how our own brains direct the intricate muscle movements that allow us stay upright, walk, or type on a keyboard.
It’s the nexus toward more dexterous brain-machine interfaces, and a model to understand when brain-muscle connections fail—as in devastating conditions like Lou Gehrig’s disease or Parkinson’s, where people slowly lose muscle control due to the gradual death of neurons that control muscle function. Assembloids are a sort of “mini-me,” a workaround for testing potential treatments on a simple “replica” of a person rather than directly on a human.
From Organoids to Assembloids
The miniature snippet of the human nervous system has been a long time in the making.
It all started in 2014, when Dr. Madeleine Lancaster, then a post-doc at Stanford, grew a shockingly intricate 3D replica of human brain tissue inside a whirling incubator. Revolutionarily different than standard cell cultures, which grind up brain tissue to reconstruct as a flat network of cells, Lancaster’s 3D brain organoids were incredibly sophisticated in their recapitulation of the human brain during development. Subsequent studies further solidified their similarity to the developing brain of a fetus—not just in terms of neuron types, but also their connections and structure.
With the finding that these mini-brains sparked with electrical activity, bioethicists increasingly raised red flags that the blobs of human brain tissue—no larger than the size of a pea at most—could harbor the potential to develop a sense of awareness if further matured and with external input and output.
Despite these concerns, brain organoids became an instant hit. Because they’re made of human tissue—often taken from actual human patients and converted into stem-cell-like states—organoids harbor the same genetic makeup as their donors. This makes it possible to study perplexing conditions such as autism, schizophrenia, or other brain disorders in a dish. What’s more, because they’re grown in the lab, it’s possible to genetically edit the mini-brains to test potential genetic culprits in the search for a cure.
Yet mini-brains had an Achilles’ heel: not all were made the same. Rather, depending on the region of the brain that was reverse engineered, the cells had to be persuaded by different cocktails of chemical soups and maintained in isolation. It was a stark contrast to our own developing brains, where regions are connected through highways of neural networks and work in tandem.
Pasca faced the problem head-on. Betting on the brain’s self-assembling capacity, his team hypothesized that it might be possible to grow different mini-brains, each reflecting a different brain region, and have them fuse together into a synchronized band of neuron circuits to process information. Last year, his idea paid off.
In one mind-blowing study, his team grew two separate portions of the brain into blobs, one representing the cortex, the other a deeper part of the brain known to control reward and movement, called the striatum. Shockingly, when put together, the two blobs of human brain tissue fused into a functional couple, automatically establishing neural highways that resulted in one of the most sophisticated recapitulations of a human brain. Pasca crowned this tissue engineering crème-de-la-crème “assembloids,” a portmanteau between “assemble” and “organoids.”
“We have demonstrated that regionalized brain spheroids can be put together to form fused structures called brain assembloids,” said Pasca at the time.” [They] can then be used to investigate developmental processes that were previously inaccessible.”
And if that’s possible for wiring up a lab-grown brain, why wouldn’t it work for larger neural circuits?
Assembloids, Assemble
The new study is the fruition of that idea.
The team started with human skin cells, scraped off of eight healthy people, and transformed them into a stem-cell-like state, called iPSCs. These cells have long been touted as the breakthrough for personalized medical treatment, before each reflects the genetic makeup of its original host.
Using two separate cocktails, the team then generated mini-brains and mini-spinal cords using these iPSCs. The two components were placed together “in close proximity” for three days inside a lab incubator, gently floating around each other in an intricate dance. To the team’s surprise, under the microscope using tracers that glow in the dark, they saw highways of branches extending from one organoid to the other like arms in a tight embrace. When stimulated with electricity, the links fired up, suggesting that the connections weren’t just for show—they’re capable of transmitting information.
“We made the parts,” said Pasca, “but they knew how to put themselves together.”
Then came the ménage à trois. Once the mini-brain and spinal cord formed their double-decker ice cream scoop, the team overlaid them onto a layer of muscle cells—cultured separately into a human-like muscular structure. The end result was a somewhat bizarre and silly-looking snowman, made of three oddly-shaped spherical balls.
Yet against all odds, the brain-spinal cord assembly reached out to the lab-grown muscle. Using a variety of tools, including measuring muscle contraction, the team found that this utterly Frankenstein-like snowman was able to make the muscle component contract—in a way similar to how our muscles twitch when needed.
“Skeletal muscle doesn’t usually contract on its own,” said Pasca. “Seeing that first twitch in a lab dish immediately after cortical stimulation is something that’s not soon forgotten.”
When tested for longevity, the contraption lasted for up to 10 weeks without any sort of breakdown. Far from a one-shot wonder, the isolated circuit worked even better the longer each component was connected.
Pasca isn’t the first to give mini-brains an output channel. Last year, the queen of brain organoids, Lancaster, chopped up mature mini-brains into slices, which were then linked to muscle tissue through a cultured spinal cord. Assembloids are a step up, showing that it’s possible to automatically sew multiple nerve-linked structures together, such as brain and muscle, sans slicing.
The question is what happens when these assembloids become more sophisticated, edging ever closer to the inherent wiring that powers our movements. Pasca’s study targets outputs, but what about inputs? Can we wire input channels, such as retinal cells, to mini-brains that have a rudimentary visual cortex to process those examples? Learning, after all, depends on examples of our world, which are processed inside computational circuits and delivered as outputs—potentially, muscle contractions.
To be clear, few would argue that today’s mini-brains are capable of any sort of consciousness or awareness. But as mini-brains get increasingly more sophisticated, at what point can we consider them a sort of AI, capable of computation or even something that mimics thought? We don’t yet have an answer—but the debates are on.
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Posted in Human Robots
#437924 How a Software Map of the Entire Planet ...
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“3D map data is the scaffolding of the 21st century.”
–Edward Miller, Founder, Scape Technologies, UK
Covered in cameras, sensors, and a distinctly spaceship looking laser system, Google’s autonomous vehicles were easy to spot when they first hit public roads in 2015. The key hardware ingredient is a spinning laser fixed to the roof, called lidar, which provides the car with a pair of eyes to see the world. Lidar works by sending out beams of light and measuring the time it takes to bounce off objects back to the source. By timing the light’s journey, these depth-sensing systems construct fully 3D maps of their surroundings.
3D maps like these are essentially software copies of the real world. They will be crucial to the development of a wide range of emerging technologies including autonomous driving, drone delivery, robotics, and a fast-approaching future filled with augmented reality.
Like other rapidly improving technologies, lidar is moving quickly through its development cycle. What was an expensive technology on the roof of a well-funded research project is now becoming cheaper, more capable, and readily available to consumers. At some point, lidar will come standard on most mobile devices and is now available to early-adopting owners of the iPhone 12 Pro.
Consumer lidar represents the inevitable shift from wealthy tech companies generating our world’s map data, to a more scalable crowd-sourced approach. To develop the repository for their Street View Maps product, Google reportedly spent $1-2 billion sending cars across continents photographing every street. Compare that to a live-mapping service like Waze, which uses crowd-sourced user data from its millions of users to generate accurate and real-time traffic conditions. Though these maps serve different functions, one is a static, expensive, unchanging map of the world while the other is dynamic, real-time, and constructed by users themselves.
Soon millions of people may be scanning everything from bedrooms to neighborhoods, resulting in 3D maps of significant quality. An online search for lidar room scans demonstrates just how richly textured these three-dimensional maps are compared to anything we’ve had before. With lidar and other depth-sensing systems, we now have the tools to create exact software copies of everywhere and everything on earth.
At some point, likely aided by crowdsourcing initiatives, these maps will become living breathing, real-time representations of the world. Some refer to this idea as a “digital twin” of the planet. In a feature cover story, Kevin Kelly, the cofounder of Wired magazine, calls this concept the “mirrorworld,” a one-to-one software map of everything.
So why is that such a big deal? Take augmented reality as an example.
Of all the emerging industries dependent on such a map, none are more invested in seeing this concept emerge than those within the AR landscape. Apple, for example, is not-so-secretly developing a pair of AR glasses, which they hope will deliver a mainstream turning point for the technology.
For Apple’s AR devices to work as anticipated, they will require virtual maps of the world, a concept AR insiders call the “AR cloud,” which is synonymous with the “mirrorworld” concept. These maps will be two things. First, they will be a tool that creators use to place AR content in very specific locations; like a world canvas to paint on. Second, they will help AR devices both locate and understand the world around them so they can render content in a believable way.
Imagine walking down a street wanting to check the trading hours of a local business. Instead of pulling out your phone to do a tedious search online, you conduct the equivalent of a visual google search simply by gazing at the store. Albeit a trivial example, the AR cloud represents an entirely non-trivial new way of managing how we organize the world’s information. Access to knowledge can be shifted away from the faraway monitors in our pocket, to its relevant real-world location.
Ultimately this describes a blurring of physical and digital infrastructure. Our public and private spaces will thus be comprised equally of both.
No example demonstrates this idea better than Pokémon Go. The game is straightforward enough; users capture virtual characters scattered around the real world. Today, the game relies on traditional GPS technology to place its characters, but GPS is accurate only to within a few meters of a location. For a car navigating on a highway or locating Pikachus in the world, that level of precision is sufficient. For drone deliveries, driverless cars, or placing a Pikachu in a specific location, say on a tree branch in a park, GPS isn’t accurate enough. As astonishing as it may seem, many experimental AR cloud concepts, even entirely mapped cities, are location specific down to the centimeter.
Niantic, the $4 billion publisher behind Pokémon Go, is aggressively working on developing a crowd-sourced approach to building better AR Cloud maps by encouraging their users to scan the world for them. Their recent acquisition of 6D.ai, a mapping software company developed by the University of Oxford’s Victor Prisacariu through his work at Oxford’s Active Vision Lab, indicates Niantic’s ambition to compete with the tech giants in this space.
With 6D.ai’s technology, Niantic is developing the in-house ability to generate their own 3D maps while gaining better semantic understanding of the world. By going beyond just knowing there’s a temporary collection of orange cones in a certain location, for example, the game may one day understand the meaning behind this; that a temporary construction zone means no Pokémon should spawn here to avoid drawing players to this location.
Niantic is not the only company working on this. Many of the big tech firms you would expect have entire teams focused on map data. Facebook, for example, recently acquired the UK-based Scape technologies, a computer vision startup mapping entire cities with centimeter precision.
As our digital maps of the world improve, expect a relentless and justified discussion of privacy concerns as well. How will society react to the idea of a real-time 3D map of their bedroom living on a Facebook or Amazon server? Those horrified by the use of facial recognition AI being used in public spaces are unlikely to find comfort in the idea of a machine-readable world subject to infinite monitoring.
The ability to build high-precision maps of the world could reshape the way we engage with our planet and promises to be one of the biggest technology developments of the next decade. While these maps may stay hidden as behind-the-scenes infrastructure powering much flashier technologies that capture the world’s attention, they will soon prop up large portions of our technological future.
Keep that in mind when a car with no driver is sharing your road.
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Posted in Human Robots