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#432467 Dungeons and Dragons, Not Chess and Go: ...
Everyone had died—not that you’d know it, from how they were laughing about their poor choices and bad rolls of the dice. As a social anthropologist, I study how people understand artificial intelligence (AI) and our efforts towards attaining it; I’m also a life-long fan of Dungeons and Dragons (D&D), the inventive fantasy roleplaying game. During a recent quest, when I was playing an elf ranger, the trainee paladin (or holy knight) acted according to his noble character, and announced our presence at the mouth of a dragon’s lair. The results were disastrous. But while success in D&D means “beating the bad guy,” the game is also a creative sandbox, where failure can count as collective triumph so long as you tell a great tale.
What does this have to do with AI? In computer science, games are frequently used as a benchmark for an algorithm’s “intelligence.” The late Robert Wilensky, a professor at the University of California, Berkeley and a leading figure in AI, offered one reason why this might be. Computer scientists “looked around at who the smartest people were, and they were themselves, of course,” he told the authors of Compulsive Technology: Computers as Culture (1985). “They were all essentially mathematicians by training, and mathematicians do two things—they prove theorems and play chess. And they said, hey, if it proves a theorem or plays chess, it must be smart.” No surprise that demonstrations of AI’s “smarts” have focused on the artificial player’s prowess.
Yet the games that get chosen—like Go, the main battlefield for Google DeepMind’s algorithms in recent years—tend to be tightly bounded, with set objectives and clear paths to victory or defeat. These experiences have none of the open-ended collaboration of D&D. Which got me thinking: do we need a new test for intelligence, where the goal is not simply about success, but storytelling? What would it mean for an AI to “pass” as human in a game of D&D? Instead of the Turing test, perhaps we need an elf ranger test?
Of course, this is just a playful thought experiment, but it does highlight the flaws in certain models of intelligence. First, it reveals how intelligence has to work across a variety of environments. D&D participants can inhabit many characters in many games, and the individual player can “switch” between roles (the fighter, the thief, the healer). Meanwhile, AI researchers know that it’s super difficult to get a well-trained algorithm to apply its insights in even slightly different domains—something that we humans manage surprisingly well.
Second, D&D reminds us that intelligence is embodied. In computer games, the bodily aspect of the experience might range from pressing buttons on a controller in order to move an icon or avatar (a ping-pong paddle; a spaceship; an anthropomorphic, eternally hungry, yellow sphere), to more recent and immersive experiences involving virtual-reality goggles and haptic gloves. Even without these add-ons, games can still produce biological responses associated with stress and fear (if you’ve ever played Alien: Isolation you’ll understand). In the original D&D, the players encounter the game while sitting around a table together, feeling the story and its impact. Recent research in cognitive science suggests that bodily interactions are crucial to how we grasp more abstract mental concepts. But we give minimal attention to the embodiment of artificial agents, and how that might affect the way they learn and process information.
Finally, intelligence is social. AI algorithms typically learn through multiple rounds of competition, in which successful strategies get reinforced with rewards. True, it appears that humans also evolved to learn through repetition, reward and reinforcement. But there’s an important collaborative dimension to human intelligence. In the 1930s, the psychologist Lev Vygotsky identified the interaction of an expert and a novice as an example of what became called “scaffolded” learning, where the teacher demonstrates and then supports the learner in acquiring a new skill. In unbounded games, this cooperation is channelled through narrative. Games of It among small children can evolve from win/lose into attacks by terrible monsters, before shifting again to more complex narratives that explain why the monsters are attacking, who is the hero, and what they can do and why—narratives that aren’t always logical or even internally compatible. An AI that could engage in social storytelling is doubtless on a surer, more multifunctional footing than one that plays chess; and there’s no guarantee that chess is even a step on the road to attaining intelligence of this sort.
In some ways, this failure to look at roleplaying as a technical hurdle for intelligence is strange. D&D was a key cultural touchstone for technologists in the 1980s and the inspiration for many early text-based computer games, as Katie Hafner and Matthew Lyon point out in Where Wizards Stay up Late: The Origins of the Internet (1996). Even today, AI researchers who play games in their free time often mention D&D specifically. So instead of beating adversaries in games, we might learn more about intelligence if we tried to teach artificial agents to play together as we do: as paladins and elf rangers.
This article was originally published at Aeon and has been republished under Creative Commons.
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#432331 $10 million XPRIZE Aims for Robot ...
Ever wished you could be in two places at the same time? The XPRIZE Foundation wants to make that a reality with a $10 million competition to build robot avatars that can be controlled from at least 100 kilometers away.
The competition was announced by XPRIZE founder Peter Diamandis at the SXSW conference in Austin last week, with an ambitious timeline of awarding the grand prize by October 2021. Teams have until October 31st to sign up, and they need to submit detailed plans to a panel of judges by the end of next January.
The prize, sponsored by Japanese airline ANA, has given contestants little guidance on how they expect them to solve the challenge other than saying their solutions need to let users see, hear, feel, and interact with the robot’s environment as well as the people in it.
XPRIZE has also not revealed details of what kind of tasks the robots will be expected to complete, though they’ve said tasks will range from “simple” to “complex,” and it should be possible for an untrained operator to use them.
That’s a hugely ambitious goal that’s likely to require teams to combine multiple emerging technologies, from humanoid robotics to virtual reality high-bandwidth communications and high-resolution haptics.
If any of the teams succeed, the technology could have myriad applications, from letting emergency responders enter areas too hazardous for humans to helping people care for relatives who live far away or even just allowing tourists to visit other parts of the world without the jet lag.
“Our ability to physically experience another geographic location, or to provide on-the-ground assistance where needed, is limited by cost and the simple availability of time,” Diamandis said in a statement.
“The ANA Avatar XPRIZE can enable creation of an audacious alternative that could bypass these limitations, allowing us to more rapidly and efficiently distribute skill and hands-on expertise to distant geographic locations where they are needed, bridging the gap between distance, time, and cultures,” he added.
Interestingly, the technology may help bypass an enduring hand break on the widespread use of robotics: autonomy. By having a human in the loop, you don’t need nearly as much artificial intelligence analyzing sensory input and making decisions.
Robotics software is doing a lot more than just high-level planning and strategizing, though. While a human moves their limbs instinctively without consciously thinking about which muscles to activate, controlling and coordinating a robot’s components requires sophisticated algorithms.
The DARPA Robotics Challenge demonstrated just how hard it was to get human-shaped robots to do tasks humans would find simple, such as opening doors, climbing steps, and even just walking. These robots were supposedly semi-autonomous, but on many tasks they were essentially tele-operated, and the results suggested autonomy isn’t the only problem.
There’s also the issue of powering these devices. You may have noticed that in a lot of the slick web videos of humanoid robots doing cool things, the machine is attached to the roof by a large cable. That’s because they suck up huge amounts of power.
Possibly the most advanced humanoid robot—Boston Dynamics’ Atlas—has a battery, but it can only run for about an hour. That might be fine for some applications, but you don’t want it running out of juice halfway through rescuing someone from a mine shaft.
When it comes to the link between the robot and its human user, some of the technology is probably not that much of a stretch. Virtual reality headsets can create immersive audio-visual environments, and a number of companies are working on advanced haptic suits that will let people “feel” virtual environments.
Motion tracking technology may be more complicated. While even consumer-grade devices can track peoples’ movements with high accuracy, you will probably need to don something more like an exoskeleton that can both pick up motion and provide mechanical resistance, so that when the robot bumps into an immovable object, the user stops dead too.
How hard all of this will be is also dependent on how the competition ultimately defines subjective terms like “feel” and “interact.” Will the user need to be able to feel a gentle breeze on the robot’s cheek or be able to paint a watercolor? Or will simply having the ability to distinguish a hard object from a soft one or shake someone’s hand be enough?
Whatever the fidelity they decide on, the approach will require huge amounts of sensory and control data to be transmitted over large distances, most likely wirelessly, in a way that’s fast and reliable enough that there’s no lag or interruptions. Fortunately 5G is launching this year, with a speed of 10 gigabits per second and very low latency, so this problem should be solved by 2021.
And it’s worth remembering there have already been some tentative attempts at building robotic avatars. Telepresence robots have solved the seeing, hearing, and some of the interacting problems, and MIT has already used virtual reality to control robots to carry out complex manipulation tasks.
South Korean company Hankook Mirae Technology has also unveiled a 13-foot-tall robotic suit straight out of a sci-fi movie that appears to have made some headway with the motion tracking problem, albeit with a human inside the robot. Toyota’s T-HR3 does the same, but with the human controlling the robot from a “Master Maneuvering System” that marries motion tracking with VR.
Combining all of these capabilities into a single machine will certainly prove challenging. But if one of the teams pulls it off, you may be able to tick off trips to the Seven Wonders of the World without ever leaving your house.
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#432262 How We Can ‘Robot-Proof’ Education ...
Like millions of other individuals in the workforce, you’re probably wondering if you will one day be replaced by a machine. If you’re a student, you’re probably wondering if your chosen profession will even exist by the time you’ve graduated. From driving to legal research, there isn’t much that technology hasn’t already automated (or begun to automate). Many of us will need to adapt to this disruption in the workforce.
But it’s not enough for students and workers to adapt, become lifelong learners, and re-skill themselves. We also need to see innovation and initiative at an institutional and governmental level. According to research by The Economist, almost half of all jobs could be automated by computers within the next two decades, and no government in the world is prepared for it.
While many see the current trend in automation as a terrifying threat, others see it as an opportunity. In Robot-Proof: Higher Education in the Age of Artificial Intelligence, Northeastern University president Joseph Aoun proposes educating students in a way that will allow them to do the things that machines can’t. He calls for a new paradigm that teaches young minds “to invent, to create, and to discover”—filling the relevant needs of our world that robots simply can’t fill. Aoun proposes a much-needed novel framework that will allow us to “robot-proof” education.
Literacies and Core Cognitive Capacities of the Future
Aoun lays a framework for a new discipline, humanics, which discusses the important capacities and literacies for emerging education systems. At its core, the framework emphasizes our uniquely human abilities and strengths.
The three key literacies include data literacy (being able to manage and analyze big data), technological literacy (being able to understand exponential technologies and conduct computational thinking), and human literacy (being able to communicate and evaluate social, ethical, and existential impact).
Beyond the literacies, at the heart of Aoun’s framework are four cognitive capacities that are crucial to develop in our students if they are to be resistant to automation: critical thinking, systems thinking, entrepreneurship, and cultural agility.
“These capacities are mindsets rather than bodies of knowledge—mental architecture rather than mental furniture,” he writes. “Going forward, people will still need to know specific bodies of knowledge to be effective in the workplace, but that alone will not be enough when intelligent machines are doing much of the heavy lifting of information. To succeed, tomorrow’s employees will have to demonstrate a higher order of thought.”
Like many other experts in education, Joseph Aoun emphasizes the importance of critical thinking. This is important not just when it comes to taking a skeptical approach to information, but also being able to logically break down a claim or problem into multiple layers of analysis. We spend so much time teaching students how to answer questions that we often neglect to teach them how to ask questions. Asking questions—and asking good ones—is a foundation of critical thinking. Before you can solve a problem, you must be able to critically analyze and question what is causing it. This is why critical thinking and problem solving are coupled together.
The second capacity, systems thinking, involves being able to think holistically about a problem. The most creative problem-solvers and thinkers are able to take a multidisciplinary perspective and connect the dots between many different fields. According to Aoun, it “involves seeing across areas that machines might be able to comprehend individually but that they cannot analyze in an integrated way, as a whole.” It represents the absolute opposite of how most traditional curricula is structured with emphasis on isolated subjects and content knowledge.
Among the most difficult-to-automate tasks or professions is entrepreneurship.
In fact, some have gone so far as to claim that in the future, everyone will be an entrepreneur. Yet traditionally, initiative has been something students show in spite of or in addition to their schoolwork. For most students, developing a sense of initiative and entrepreneurial skills has often been part of their extracurricular activities. It needs to be at the core of our curricula, not a supplement to it. At its core, teaching entrepreneurship is about teaching our youth to solve complex problems with resilience, to become global leaders, and to solve grand challenges facing our species.
Finally, with an increasingly globalized world, there is a need for more workers with cultural agility, the ability to build amongst different cultural contexts and norms.
One of the major trends today is the rise of the contingent workforce. We are seeing an increasing percentage of full-time employees working on the cloud. Multinational corporations have teams of employees collaborating at different offices across the planet. Collaboration across online networks requires a skillset of its own. As education expert Tony Wagner points out, within these digital contexts, leadership is no longer about commanding with top-down authority, but rather about leading by influence.
An Emphasis on Creativity
The framework also puts an emphasis on experiential or project-based learning, wherein the heart of the student experience is not lectures or exams but solving real-life problems and learning by doing, creating, and executing. Unsurprisingly, humans continue to outdo machines when it comes to innovating and pushing intellectual, imaginative, and creative boundaries, making jobs involving these skills the hardest to automate.
In fact, technological trends are giving rise to what many thought leaders refer to as the imagination economy. This is defined as “an economy where intuitive and creative thinking create economic value, after logical and rational thinking have been outsourced to other economies.” Consequently, we need to develop our students’ creative abilities to ensure their success against machines.
In its simplest form, creativity represents the ability to imagine radical ideas and then go about executing them in reality.
In many ways, we are already living in our creative imaginations. Consider this: every invention or human construct—whether it be the spaceship, an architectural wonder, or a device like an iPhone—once existed as a mere idea, imagined in someone’s mind. The world we have designed and built around us is an extension of our imaginations and is only possible because of our creativity. Creativity has played a powerful role in human progress—now imagine what the outcomes would be if we tapped into every young mind’s creative potential.
The Need for a Radical Overhaul
What is clear from the recommendations of Aoun and many other leading thinkers in this space is that an effective 21st-century education system is radically different from the traditional systems we currently have in place. There is a dramatic contrast between these future-oriented frameworks and the way we’ve structured our traditional, industrial-era and cookie-cutter-style education systems.
It’s time for a change, and incremental changes or subtle improvements are no longer enough. What we need to see are more moonshots and disruption in the education sector. In a world of exponential growth and accelerating change, it is never too soon for a much-needed dramatic overhaul.
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#431836 Do Our Brains Use Deep Learning to Make ...
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.
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