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#431603 What We Can Learn From the Second Life ...
For every new piece of technology that gets developed, you can usually find people saying it will never be useful. The president of the Michigan Savings Bank in 1903, for example, said, “The horse is here to stay but the automobile is only a novelty—a fad.” It’s equally easy to find people raving about whichever new technology is at the peak of the Gartner Hype Cycle, which tracks the buzz around these newest developments and attempts to temper predictions. When technologies emerge, there are all kinds of uncertainties, from the actual capacity of the technology to its use cases in real life to the price tag.
Eventually the dust settles, and some technologies get widely adopted, to the extent that they can become “invisible”; people take them for granted. Others fall by the wayside as gimmicky fads or impractical ideas. Picking which horses to back is the difference between Silicon Valley millions and Betamax pub-quiz-question obscurity. For a while, it seemed that Google had—for once—backed the wrong horse.
Google Glass emerged from Google X, the ubiquitous tech giant’s much-hyped moonshot factory, where highly secretive researchers work on the sci-fi technologies of the future. Self-driving cars and artificial intelligence are the more mundane end for an organization that apparently once looked into jetpacks and teleportation.
The original smart glasses, Google began selling Google Glass in 2013 for $1,500 as prototypes for their acolytes, around 8,000 early adopters. Users could control the glasses with a touchpad, or, activated by tilting the head back, with voice commands. Audio relay—as with several wearable products—is via bone conduction, which transmits sound by vibrating the skull bones of the user. This was going to usher in the age of augmented reality, the next best thing to having a chip implanted directly into your brain.
On the surface, it seemed to be a reasonable proposition. People had dreamed about augmented reality for a long time—an onboard, JARVIS-style computer giving you extra information and instant access to communications without even having to touch a button. After smartphone ubiquity, it looked like a natural step forward.
Instead, there was a backlash. People may be willing to give their data up to corporations, but they’re less pleased with the idea that someone might be filming them in public. The worst aspect of smartphones is trying to talk to people who are distractedly scrolling through their phones. There’s a famous analogy in Revolutionary Road about an old couple’s loveless marriage: the husband tunes out his wife’s conversation by turning his hearing aid down to zero. To many, Google Glass seemed to provide us with a whole new way to ignore each other in favor of our Twitter feeds.
Then there’s the fact that, regardless of whether it’s because we’re not used to them, or if it’s a more permanent feature, people wearing AR tech often look very silly. Put all this together with a lack of early functionality, the high price (do you really feel comfortable wearing a $1,500 computer?), and a killer pun for the users—Glassholes—and the final recipe wasn’t great for Google.
Google Glass was quietly dropped from sale in 2015 with the ominous slogan posted on Google’s website “Thanks for exploring with us.” Reminding the Glass users that they had always been referred to as “explorers”—beta-testing a product, in many ways—it perhaps signaled less enthusiasm for wearables than the original, Google Glass skydive might have suggested.
In reality, Google went back to the drawing board. Not with the technology per se, although it has improved in the intervening years, but with the uses behind the technology.
Under what circumstances would you actually need a Google Glass? When would it genuinely be preferable to a smartphone that can do many of the same things and more? Beyond simply being a fashion item, which Google Glass decidedly was not, even the most tech-evangelical of us need a convincing reason to splash $1,500 on a wearable computer that’s less socially acceptable and less easy to use than the machine you’re probably reading this on right now.
Enter the Google Glass Enterprise Edition.
Piloted in factories during the years that Google Glass was dormant, and now roaring back to life and commercially available, the Google Glass relaunch got under way in earnest in July of 2017. The difference here was the specific audience: workers in factories who need hands-free computing because they need to use their hands at the same time.
In this niche application, wearable computers can become invaluable. A new employee can be trained with pre-programmed material that explains how to perform actions in real time, while instructions can be relayed straight into a worker’s eyeline without them needing to check a phone or switch to email.
Medical devices have long been a dream application for Google Glass. You can imagine a situation where people receive real-time information during surgery, or are augmented by artificial intelligence that provides additional diagnostic information or questions in response to a patient’s symptoms. The quest to develop a healthcare AI, which can provide recommendations in response to natural language queries, is on. The famously untidy doctor’s handwriting—and the associated death toll—could be avoided if the glasses could take dictation straight into a patient’s medical records. All of this is far more useful than allowing people to check Facebook hands-free while they’re riding the subway.
Google’s “Lens” application indicates another use for Google Glass that hadn’t quite matured when the original was launched: the Lens processes images and provides information about them. You can look at text and have it translated in real time, or look at a building or sign and receive additional information. Image processing, either through neural networks hooked up to a cloud database or some other means, is the frontier that enables driverless cars and similar technology to exist. Hook this up to a voice-activated assistant relaying information to the user, and you have your killer application: real-time annotation of the world around you. It’s this functionality that just wasn’t ready yet when Google launched Glass.
Amazon’s recent announcement that they want to integrate Alexa into a range of smart glasses indicates that the tech giants aren’t ready to give up on wearables yet. Perhaps, in time, people will become used to voice activation and interaction with their machines, at which point smart glasses with bone conduction will genuinely be more convenient than a smartphone.
But in many ways, the real lesson from the initial failure—and promising second life—of Google Glass is a simple question that developers of any smart technology, from the Internet of Things through to wearable computers, must answer. “What can this do that my smartphone can’t?” Find your answer, as the Enterprise Edition did, as Lens might, and you find your product.
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Posted in Human Robots
#431371 Amazon Is Quietly Building the Robots of ...
Science fiction is the siren song of hard science. How many innocent young students have been lured into complex, abstract science, technology, engineering, or mathematics because of a reckless and irresponsible exposure to Arthur C. Clarke at a tender age? Yet Arthur C. Clarke has a very famous quote: “Any sufficiently advanced technology is indistinguishable from magic.”
It’s the prospect of making that… ahem… magic leap that entices so many people into STEM in the first place. A magic leap that would change the world. How about, for example, having humanoid robots? They could match us in dexterity and speed, perceive the world around them as we do, and be programmed to do, well, more or less anything we can do.
Such a technology would change the world forever.
But how will it arrive? While true sci-fi robots won’t get here right away—the pieces are coming together, and the company best developing them at the moment is Amazon. Where others have struggled to succeed, Amazon has been quietly progressing. Notably, Amazon has more than just a dream, it has the most practical of reasons driving it into robotics.
This practicality matters. Technological development rarely proceeds by magic; it’s a process filled with twists, turns, dead-ends, and financial constraints. New technologies often have to answer questions like “What is this good for, are you being realistic?” A good strategy, then, can be to build something more limited than your initial ambition, but useful for a niche market. That way, you can produce a prototype, have a reasonable business plan, and turn a profit within a decade. You might call these “stepping stone” applications that allow for new technologies to be developed in an economically viable way.
You need something you can sell to someone, soon: that’s how you get investment in your idea. It’s this model that iRobot, developers of the Roomba, used: migrating from military prototypes to robotic vacuum cleaners to become the “boring, successful robot company.” Compare this to Willow Garage, a genius factory if ever there was one: they clearly had ambitions towards a general-purpose, multi-functional robot. They built an impressive device—PR2—and programmed the operating system, ROS, that is still the industry and academic standard to this day.
But since they were unable to sell their robot for much less than $250,000, it was never likely to be a profitable business. This is why Willow Garage is no more, and many workers at the company went into telepresence robotics. Telepresence is essentially videoconferencing with a fancy robot attached to move the camera around. It uses some of the same software (for example, navigation and mapping) without requiring you to solve difficult problems of full autonomy for the robot, or manipulating its environment. It’s certainly one of the stepping-stone areas that various companies are investigating.
Another approach is to go to the people with very high research budgets: the military.
This was the Boston Dynamics approach, and their incredible achievements in bipedal locomotion saw them getting snapped up by Google. There was a great deal of excitement and speculation about Google’s “nightmare factory” whenever a new slick video of a futuristic militarized robot surfaced. But Google broadly backed away from Replicant, their robotics program, and Boston Dynamics was sold. This was partly due to PR concerns over the Terminator-esque designs, but partly because they didn’t see the robotics division turning a profit. They hadn’t found their stepping stones.
This is where Amazon comes in. Why Amazon? First off, they just announced that their profits are up by 30 percent, and yet the company is well-known for their constantly-moving Day One philosophy where a great deal of the profits are reinvested back into the business. But lots of companies have ambition.
One thing Amazon has that few other corporations have, as well as big financial resources, is viable stepping stones for developing the technologies needed for this sort of robotics to become a reality. They already employ 100,000 robots: these are of the “pragmatic, boring, useful” kind that we’ve profiled, which move around the shelves in warehouses. These robots are allowing Amazon to develop localization and mapping software for robots that can autonomously navigate in the simple warehouse environment.
But their ambitions don’t end there. The Amazon Robotics Challenge is a multi-million dollar competition, open to university teams, to produce a robot that can pick and package items in warehouses. The problem of grasping and manipulating a range of objects is not a solved one in robotics, so this work is still done by humans—yet it’s absolutely fundamental for any sci-fi dream robot.
Google, for example, attempted to solve this problem by hooking up 14 robot hands to machine learning algorithms and having them grasp thousands of objects. Although results were promising, the 10 to 20 percent failure rate for grasps is too high for warehouse use. This is a perfect stepping stone for Amazon; should they crack the problem, they will likely save millions in logistics.
Another area where humanoid robotics—especially bipedal locomotion, or walking, has been seriously suggested—is in the last mile delivery problem. Amazon has shown willingness to be creative in this department with their notorious drone delivery service. In other words, it’s all very well to have your self-driving car or van deliver packages to people’s doors, but who puts the package on the doorstep? It’s difficult for wheeled robots to navigate the full range of built environments that exist. That’s why bipedal robots like CASSIE, developed by Oregon State, may one day be used to deliver parcels.
Again: no one more than Amazon stands to profit from cracking this technology. The line from robotics research to profit is very clear.
So, perhaps one day Amazon will have robots that can move around and manipulate their environments. But they’re also working on intelligence that will guide those robots and make them truly useful for a variety of tasks. Amazon has an AI, or at least the framework for an AI: it’s called Alexa, and it’s in tens of millions of homes. The Alexa Prize, another multi-million-dollar competition, is attempting to make Alexa more social.
To develop a conversational AI, at least using the current methods of machine learning, you need data on tens of millions of conversations. You need to understand how people will try to interact with the AI. Amazon has access to this in Alexa, and they’re using it. As owners of the leading voice-activated personal assistant, they have an ecosystem of developers creating apps for Alexa. It will be integrated with the smart home and the Internet of Things. It is a very marketable product, a stepping stone for robot intelligence.
What’s more, the company can benefit from its huge sales infrastructure. For Amazon, having an AI in your home is ideal, because it can persuade you to buy more products through its website. Unlike companies like Google, Amazon has an easy way to make a direct profit from IoT devices, which could fuel funding.
For a humanoid robot to be truly useful, though, it will need vision and intelligence. It will have to understand and interpret its environment, and react accordingly. The way humans learn about our environment is by getting out and seeing it. This is something that, for example, an Alexa coupled to smart glasses would be very capable of doing. There are rumors that Alexa’s AI will soon be used in security cameras, which is an ideal stepping stone task to train an AI to process images from its environment, truly perceiving the world and any threats it might contain.
It’s a slight exaggeration to say that Amazon is in the process of building a secret robot army. The gulf between our sci-fi vision of robots that can intelligently serve us, rather than mindlessly assemble cars, is still vast. But in quietly assembling many of the technologies needed for intelligent, multi-purpose robotics—and with the unique stepping stones they have along the way—Amazon might just be poised to leap that gulf. As if by magic.
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Posted in Human Robots
#431186 The Coming Creativity Explosion Belongs ...
Does creativity make human intelligence special?
It may appear so at first glance. Though machines can calculate, analyze, and even perceive, creativity may seem far out of reach. Perhaps this is because we find it mysterious, even in ourselves. How can the output of a machine be anything more than that which is determined by its programmers?
Increasingly, however, artificial intelligence is moving into creativity’s hallowed domain, from art to industry. And though much is already possible, the future is sure to bring ever more creative machines.
What Is Machine Creativity?
Robotic art is just one example of machine creativity, a rapidly growing sub-field that sits somewhere between the study of artificial intelligence and human psychology.
The winning paintings from the 2017 Robot Art Competition are strikingly reminiscent of those showcased each spring at university exhibitions for graduating art students. Like the works produced by skilled artists, the compositions dreamed up by the competition’s robotic painters are aesthetically ambitious. One robot-made painting features a man’s bearded face gazing intently out from the canvas, his eyes locking with the viewer’s. Another abstract painting, “inspired” by data from EEG signals, visually depicts the human emotion of misery with jagged, gloomy stripes of black and purple.
More broadly, a creative machine is software (sometimes encased in a robotic body) that synthesizes inputs to generate new and valuable ideas, solutions to complex scientific problems, or original works of art. In a process similar to that followed by a human artist or scientist, a creative machine begins its work by framing a problem. Next, its software specifies the requirements the solution should have before generating “answers” in the form of original designs, patterns, or some other form of output.
Although the notion of machine creativity sounds a bit like science fiction, the basic concept is one that has been slowly developing for decades.
Nearly 50 years ago while a high school student, inventor and futurist Ray Kurzweil created software that could analyze the patterns in musical compositions and then compose new melodies in a similar style. Aaron, one of the world’s most famous painting robots, has been hard at work since the 1970s.
Industrial designers have used an automated, algorithm-driven process for decades to design computer chips (or machine parts) whose layout (or form) is optimized for a particular function or environment. Emily Howell, a computer program created by David Cope, writes original works in the style of classical composers, some of which have been performed by human orchestras to live audiences.
What’s different about today’s new and emerging generation of robotic artists, scientists, composers, authors, and product designers is their ubiquity and power.
“The recent explosion of artificial creativity has been enabled by the rapid maturation of the same exponential technologies that have already re-drawn our daily lives.”
I’ve already mentioned the rapidly advancing fields of robotic art and music. In the realm of scientific research, so-called “robotic scientists” such as Eureqa and Adam and Eve develop new scientific hypotheses; their “insights” have contributed to breakthroughs that are cited by hundreds of academic research papers. In the medical industry, creative machines are hard at work creating chemical compounds for new pharmaceuticals. After it read over seven million words of 20th century English poetry, a neural network developed by researcher Jack Hopkins learned to write passable poetry in a number of different styles and meters.
The recent explosion of artificial creativity has been enabled by the rapid maturation of the same exponential technologies that have already re-drawn our daily lives, including faster processors, ubiquitous sensors and wireless networks, and better algorithms.
As they continue to improve, creative machines—like humans—will perform a broad range of creative activities, ranging from everyday problem solving (sometimes known as “Little C” creativity) to producing once-in-a-century masterpieces (“Big C” creativity). A creative machine’s outputs could range from a design for a cast for a marble sculpture to a schematic blueprint for a clever new gadget for opening bottles of wine.
In the coming decades, by automating the process of solving complex problems, creative machines will again transform our world. Creative machines will serve as a versatile source of on-demand talent.
In the battle to recruit a workforce that can solve complex problems, creative machines will put small businesses on equal footing with large corporations. Art and music lovers will enjoy fresh creative works that re-interpret the style of ancient disciplines. People with a health condition will benefit from individualized medical treatments, and low-income people will receive top-notch legal advice, to name but a few potentially beneficial applications.
How Can We Make Creative Machines, Unless We Understand Our Own Creativity?
One of the most intriguing—yet unsettling—aspects of watching robotic arms skillfully oil paint is that we humans still do not understand our own creative process. Over the centuries, several different civilizations have devised a variety of models to explain creativity.
The ancient Greeks believed that poets drew inspiration from a transcendent realm parallel to the material world where ideas could take root and flourish. In the Middle Ages, philosophers and poets attributed our peculiarly human ability to “make something of nothing” to an external source, namely divine inspiration. Modern academic study of human creativity has generated vast reams of scholarship, but despite the value of these insights, the human imagination remains a great mystery, second only to that of consciousness.
Today, the rise of machine creativity demonstrates (once again), that we do not have to fully understand a biological process in order to emulate it with advanced technology.
Past experience has shown that jet planes can fly higher and faster than birds by using the forward thrust of an engine rather than wings. Submarines propel themselves forward underwater without fins or a tail. Deep learning neural networks identify objects in randomly-selected photographs with super-human accuracy. Similarly, using a fairly straightforward software architecture, creative software (sometimes paired with a robotic body) can paint, write, hypothesize, or design with impressive originality, skill, and boldness.
At the heart of machine creativity is simple iteration. No matter what sort of output they produce, creative machines fall into one of three categories depending on their internal architecture.
Briefly, the first group consists of software programs that use traditional rule-based, or symbolic AI, the second group uses evolutionary algorithms, and the third group uses a variation of a form of machine learning called deep learning that has already revolutionized voice and facial recognition software.
1) Symbolic creative machines are the oldest artificial artists and musicians. In this approach—also known as “good old-fashioned AI (GOFAI) or symbolic AI—the human programmer plays a key role by writing a set of step-by-step instructions to guide the computer through a task. Despite the fact that symbolic AI is limited in its ability to adapt to environmental changes, it’s still possible for a robotic artist programmed this way to create an impressively wide variety of different outputs.
2) Evolutionary algorithms (EA) have been in use for several decades and remain powerful tools for design. In this approach, potential solutions “compete” in a software simulator in a Darwinian process reminiscent of biological evolution. The human programmer specifies a “fitness criterion” that will be used to score and rank the solutions generated by the software. The software then generates a “first generation” population of random solutions (which typically are pretty poor in quality), scores this first generation of solutions, and selects the top 50% (those random solutions deemed to be the best “fit”). The software then takes another pass and recombines the “winning” solutions to create the next generation and repeats this process for thousands (and sometimes millions) of generations.
3) Generative deep learning (DL) neural networks represent the newest software architecture of the three, since DL is data-dependent and resource-intensive. First, a human programmer “trains” a DL neural network to recognize a particular feature in a dataset, for example, an image of a dog in a stream of digital images. Next, the standard “feed forward” process is reversed and the DL neural network begins to generate the feature, for example, eventually producing new and sometimes original images of (or poetry about) dogs. Generative DL networks have tremendous and unexplored creative potential and are able to produce a broad range of original outputs, from paintings to music to poetry.
The Coming Explosion of Machine Creativity
In the near future as Moore’s Law continues its work, we will see sophisticated combinations of these three basic architectures. Since the 1950s, artificial intelligence has steadily mastered one human ability after another, and in the process of doing so, has reduced the cost of calculation, analysis, and most recently, perception. When creative software becomes as inexpensive and ubiquitous as analytical software is today, humans will no longer be the only intelligent beings capable of creative work.
This is why I have to bite my tongue when I hear the well-intended (but shortsighted) advice frequently dispensed to young people that they should pursue work that demands creativity to help them “AI-proof” their futures.
Instead, students should gain skills to harness the power of creative machines.
There are two skills in which humans excel that will enable us to remain useful in a world of ever-advancing artificial intelligence. One, the ability to frame and define a complex problem so that it can be handed off to a creative machine to solve. And two, the ability to communicate the value of both the framework and the proposed solution to the other humans involved.
What will happen to people when creative machines begin to capably tread on intellectual ground that was once considered the sole domain of the human mind, and before that, the product of divine inspiration? While machines engaging in Big C creativity—e.g., oil painting and composing new symphonies—tend to garner controversy and make the headlines, I suspect the real world-changing application of machine creativity will be in the realm of everyday problem solving, or Little C. The mainstream emergence of powerful problem-solving tools will help people create abundance where there was once scarcity.
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#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