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In the fictional worlds of film and TV, artificial intelligence has been depicted as so advanced that it is indistinguishable from humans. But what if we’re actually getting closer to a world where AI is capable of thinking and feeling?
Tech company UneeQ is embarking on that journey with its “digital humans.” These avatars act as visual interfaces for customer service chatbots, virtual assistants, and other applications. UneeQ’s digital humans appear lifelike not only in terms of language and tone of voice, but also because of facial movements: raised eyebrows, a tilt of the head, a smile, even a wink. They transform a transaction into an interaction: creepy yet astonishing, human, but not quite.
What lies beneath UneeQ’s digital humans? Their 3D faces are modeled on actual human features. Speech recognition enables the avatar to understand what a person is saying, and natural language processing is used to craft a response. Before the avatar utters a word, specific emotions and facial expressions are encoded within the response.
UneeQ may be part of a larger trend towards humanizing computing. ObEN’s digital avatars serve as virtual identities for celebrities, influencers, gaming characters, and other entities in the media and entertainment industry. Meanwhile, Soul Machines is taking a more biological approach, with a “digital brain” that simulates aspects of the human brain to modulate the emotions “felt” and “expressed” by its “digital people.” Amelia is employing a similar methodology in building its “digital employees.” It emulates parts of the brain involved with memory to respond to queries and, with each interaction, learns to deliver more engaging and personalized experiences.
Shiwali Mohan, an AI systems scientist at the Palo Alto Research Center, is skeptical of these digital beings. “They’re humanlike in their looks and the way they sound, but that in itself is not being human,” she says. “Being human is also how you think, how you approach problems, and how you break them down; and that takes a lot of algorithmic design. Designing for human-level intelligence is a different endeavor than designing graphics that behave like humans. If you think about the problems we’re trying to design these avatars for, we might not need something that looks like a human—it may not even be the right solution path.”
And even if these avatars appear near-human, they still evoke an uncanny valley feeling. “If something looks like a human, we have high expectations of them, but they might behave differently in ways that humans just instinctively know how other humans react. These differences give rise to the uncanny valley feeling,” says Mohan.
Yet the demand is there, with Amelia seeing high adoption of its digital employees across the financial, health care, and retail sectors. “We find that banks and insurance companies, which are so risk-averse, are leading the adoption of such disruptive technologies because they understand that the risk of non-adoption is much greater than the risk of early adoption,” says Chetan Dube, Amelia’s CEO. “Unless they innovate their business models and make them much more efficient digitally, they might be left behind.” Dube adds that the COVID-19 pandemic has accelerated adoption of digital employees in health care and retail as well.
Amelia, Soul Machines, and UneeQ are taking their digital beings a step further, enabling organizations to create avatars themselves using low-code or no-code platforms: Digital Employee Builder for Amelia, Creator for UneeQ, and Digital DNA Studio for Soul Machines. Unreal Engine, a game engine developed by Epic Games, is doing the same with MetaHuman Creator, a tool that allows anyone to create photorealistic digital humans. “The biggest motivation for Digital Employee Builder is to democratize AI,” Dube says.
Mohan is cautious about this approach. “AI has problems with bias creeping in from data sets and into the way it speaks. The AI community is still trying to figure out how to measure and counter that bias,” she says. “[Companies] have to have an AI expert on board that can recommend the right things to build for.”
Despite being wary of the technology, Mohan supports the purpose behind these virtual beings and is optimistic about where they’re headed. “We do need these tools that support humans in different kinds of things. I think the vision is the pro, and I’m behind that vision,” she says. “As we develop more sophisticated AI technology, we would then have to implement novel ways of interacting with that technology. Hopefully, all of that is designed to support humans in their goals.” Continue reading
Deep reinforcement learning is having a superstar moment.
Powering smarter robots. Simulating human neural networks. Trouncing physicians at medical diagnoses and crushing humanity’s best gamers at Go and Atari. While far from achieving the flexible, quick thinking that comes naturally to humans, this powerful machine learning idea seems unstoppable as a harbinger of better thinking machines.
Except there’s a massive roadblock: they take forever to run. Because the concept behind these algorithms is based on trial and error, a reinforcement learning AI “agent” only learns after being rewarded for its correct decisions. For complex problems, the time it takes an AI agent to try and fail to learn a solution can quickly become untenable.
But what if you could try multiple solutions at once?
This week, an international collaboration led by Dr. Philip Walther at the University of Vienna took the “classic” concept of reinforcement learning and gave it a quantum spin. They designed a hybrid AI that relies on both quantum and run-of-the-mill classic computing, and showed that—thanks to quantum quirkiness—it could simultaneously screen a handful of different ways to solve a problem.
The result is a reinforcement learning AI that learned over 60 percent faster than its non-quantum-enabled peers. This is one of the first tests that shows adding quantum computing can speed up the actual learning process of an AI agent, the authors explained.
Although only challenged with a “toy problem” in the study, the hybrid AI, once scaled, could impact real-world problems such as building an efficient quantum internet. The setup “could readily be integrated within future large-scale quantum communication networks,” the authors wrote.
Learning from trial and error comes intuitively to our brains.
Say you’re trying to navigate a new convoluted campground without a map. The goal is to get from the communal bathroom back to your campsite. Dead ends and confusing loops abound. We tackle the problem by deciding to turn either left or right at every branch in the road. One will get us closer to the goal; the other leads to a half hour of walking in circles. Eventually, our brain chemistry rewards correct decisions, so we gradually learn the correct route. (If you’re wondering…yeah, true story.)
Reinforcement learning AI agents operate in a similar trial-and-error way. As a problem becomes more complex, the number—and time—of each trial also skyrockets.
“Even in a moderately realistic environment, it may simply take too long to rationally respond to a given situation,” explained study author Dr. Hans Briegel at the Universität Innsbruck in Austria, who previously led efforts to speed up AI decision-making using quantum mechanics. If there’s pressure that allows “only a certain time for a response, an agent may then be unable to cope with the situation and to learn at all,” he wrote.
Many attempts have tried speeding up reinforcement learning. Giving the AI agent a short-term “memory.” Tapping into neuromorphic computing, which better resembles the brain. In 2014, Briegel and colleagues showed that a “quantum brain” of sorts can help propel an AI agent’s decision-making process after learning. But speeding up the learning process itself has eluded our best attempts.
The Hybrid AI
The new study went straight for that previously untenable jugular.
The team’s key insight was to tap into the best of both worlds—quantum and classical computing. Rather than building an entire reinforcement learning system using quantum mechanics, they turned to a hybrid approach that could prove to be more practical. Here, the AI agent uses quantum weirdness as it’s trying out new approaches—the “trial” in trial and error. The system then passes the baton to a classical computer to give the AI its reward—or not—based on its performance.
At the heart of the quantum “trial” process is a quirk called superposition. Stay with me. Our computers are powered by electrons, which can represent only two states—0 or 1. Quantum mechanics is far weirder, in that photons (particles of light) can simultaneously be both 0 and 1, with a slightly different probability of “leaning towards” one or the other.
This noncommittal oddity is part of what makes quantum computing so powerful. Take our reinforcement learning example of navigating a new campsite. In our classic world, we—and our AI—need to decide between turning left or right at an intersection. In a quantum setup, however, the AI can (in a sense) turn left and right at the same time. So when searching for the correct path back to home base, the quantum system has a leg up in that it can simultaneously explore multiple routes, making it far faster than conventional, consecutive trail and error.
“As a consequence, an agent that can explore its environment in superposition will learn significantly faster than its classical counterpart,” said Briegel.
It’s not all theory. To test out their idea, the team turned to a programmable chip called a nanophotonic processor. Think of it as a CPU-like computer chip, but it processes particles of light—photons—rather than electricity. These light-powered chips have been a long time in the making. Back in 2017, for example, a team from MIT built a fully optical neural network into an optical chip to bolster deep learning.
The chips aren’t all that exotic. Nanophotonic processors act kind of like our eyeglasses, which can carry out complex calculations that transform light that passes through them. In the glasses case, they let people see better. For a light-based computer chip, it allows computation. Rather than using electrical cables, the chips use “wave guides” to shuttle photons and perform calculations based on their interactions.
The “error” or “reward” part of the new hardware comes from a classical computer. The nanophotonic processor is coupled to a traditional computer, where the latter provides the quantum circuit with feedback—that is, whether to reward a solution or not. This setup, the team explains, allows them to more objectively judge any speed-ups in learning in real time.
In this way, a hybrid reinforcement learning agent alternates between quantum and classical computing, trying out ideas in wibbly-wobbly “multiverse” land while obtaining feedback in grounded, classic physics “normality.”
A Quantum Boost
In simulations using 10,000 AI agents and actual experimental data from 165 trials, the hybrid approach, when challenged with a more complex problem, showed a clear leg up.
The key word is “complex.” The team found that if an AI agent has a high chance of figuring out the solution anyway—as for a simple problem—then classical computing works pretty well. The quantum advantage blossoms when the task becomes more complex or difficult, allowing quantum mechanics to fully flex its superposition muscles. For these problems, the hybrid AI was 63 percent faster at learning a solution compared to traditional reinforcement learning, decreasing its learning effort from 270 guesses to 100.
Now that scientists have shown a quantum boost for reinforcement learning speeds, the race for next-generation computing is even more lit. Photonics hardware required for long-range light-based communications is rapidly shrinking, while improving signal quality. The partial-quantum setup could “aid specifically in problems where frequent search is needed, for example, network routing problems” that’s prevalent for a smooth-running internet, the authors wrote. With a quantum boost, reinforcement learning may be able to tackle far more complex problems—those in the real world—than currently possible.
“We are just at the beginning of understanding the possibilities of quantum artificial intelligence,” said lead author Walther.
Image Credit: Oleg Gamulinskiy from Pixabay Continue reading