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By now, you’ve probably seen Google’s new Duplex software, which promises to call people on your behalf to book appointments for haircuts and the like. As yet, it only exists in demo form, but already it seems like Google has made a big stride towards capturing a market that plenty of companies have had their eye on for quite some time. This software is impressive, but it raises questions.
Many of you will be familiar with the stilted, robotic conversations you can have with early chatbots that are, essentially, glorified menus. Instead of pressing 1 to confirm or 2 to re-enter, some of these bots would allow for simple commands like “Yes” or “No,” replacing the buttons with limited ability to recognize a few words. Using them was often a far more frustrating experience than attempting to use a menu—there are few things more irritating than a robot saying, “Sorry, your response was not recognized.”
Google Duplex scheduling a hair salon appointment:
Google Duplex calling a restaurant:
Even getting the response recognized is hard enough. After all, there are countless different nuances and accents to baffle voice recognition software, and endless turns of phrase that amount to saying the same thing that can confound natural language processing (NLP), especially if you like your phrasing quirky.
You may think that standard customer-service type conversations all travel the same route, using similar words and phrasing. But when there are over 80,000 ways to order coffee, and making a mistake is frowned upon, even simple tasks require high accuracy over a huge dataset.
Advances in audio processing, neural networks, and NLP, as well as raw computing power, have meant that basic recognition of what someone is trying to say is less of an issue. Soundhound’s virtual assistant prides itself on being able to process complicated requests (perhaps needlessly complicated).
The deeper issue, as with all attempts to develop conversational machines, is one of understanding context. There are so many ways a conversation can go that attempting to construct a conversation two or three layers deep quickly runs into problems. Multiply the thousands of things people might say by the thousands they might say next, and the combinatorics of the challenge runs away from most chatbots, leaving them as either glorified menus, gimmicks, or rather bizarre to talk to.
Yet Google, who surely remembers from Glass the risk of premature debuts for technology, especially the kind that ask you to rethink how you interact with or trust in software, must have faith in Duplex to show it on the world stage. We know that startups like Semantic Machines and x.ai have received serious funding to perform very similar functions, using natural-language conversations to perform computing tasks, schedule meetings, book hotels, or purchase items.
It’s no great leap to imagine Google will soon do the same, bringing us closer to a world of onboard computing, where Lens labels the world around us and their assistant arranges it for us (all the while gathering more and more data it can convert into personalized ads). The early demos showed some clever tricks for keeping the conversation within a fairly narrow realm where the AI should be comfortable and competent, and the blog post that accompanied the release shows just how much effort has gone into the technology.
Yet given the privacy and ethics funk the tech industry finds itself in, and people’s general unease about AI, the main reaction to Duplex’s impressive demo was concern. The voice sounded too natural, bringing to mind Lyrebird and their warnings of deepfakes. You might trust “Do the Right Thing” Google with this technology, but it could usher in an era when automated robo-callers are far more convincing.
A more human-like voice may sound like a perfectly innocuous improvement, but the fact that the assistant interjects naturalistic “umm” and “mm-hm” responses to more perfectly mimic a human rubbed a lot of people the wrong way. This wasn’t just a voice assistant trying to sound less grinding and robotic; it was actively trying to deceive people into thinking they were talking to a human.
Google is running the risk of trying to get to conversational AI by going straight through the uncanny valley.
“Google’s experiments do appear to have been designed to deceive,” said Dr. Thomas King of the Oxford Internet Institute’s Digital Ethics Lab, according to Techcrunch. “Their main hypothesis was ‘can you distinguish this from a real person?’ In this case it’s unclear why their hypothesis was about deception and not the user experience… there should be some kind of mechanism there to let people know what it is they are speaking to.”
From Google’s perspective, being able to say “90 percent of callers can’t tell the difference between this and a human personal assistant” is an excellent marketing ploy, even though statistics about how many interactions are successful might be more relevant.
In fact, Duplex runs contrary to pretty much every major recommendation about ethics for the use of robotics or artificial intelligence, not to mention certain eavesdropping laws. Transparency is key to holding machines (and the people who design them) accountable, especially when it comes to decision-making.
Then there are the more subtle social issues. One prominent effect social media has had is to allow people to silo themselves; in echo chambers of like-minded individuals, it’s hard to see how other opinions exist. Technology exacerbates this by removing the evolutionary cues that go along with face-to-face interaction. Confronted with a pair of human eyes, people are more generous. Confronted with a Twitter avatar or a Facebook interface, people hurl abuse and criticism they’d never dream of using in a public setting.
Now that we can use technology to interact with ever fewer people, will it change us? Is it fair to offload the burden of dealing with a robot onto the poor human at the other end of the line, who might have to deal with dozens of such calls a day? Google has said that if the AI is in trouble, it will put you through to a human, which might help save receptionists from the hell of trying to explain a concept to dozens of dumbfounded AI assistants all day. But there’s always the risk that failures will be blamed on the person and not the machine.
As AI advances, could we end up treating the dwindling number of people in these “customer-facing” roles as the buggiest part of a fully automatic service? Will people start accusing each other of being robots on the phone, as well as on Twitter?
Google has provided plenty of reassurances about how the system will be used. They have said they will ensure that the system is identified, and it’s hardly difficult to resolve this problem; a slight change in the script from their demo would do it. For now, consumers will likely appreciate moves that make it clear whether the “intelligent agents” that make major decisions for us, that we interact with daily, and that hide behind social media avatars or phone numbers are real or artificial.
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Technology evangelists dream about a future where we’re all liberated from the more mundane aspects of our jobs by artificial intelligence. Other futurists go further, imagining AI will enable us to become superhuman, enhancing our intelligence, abandoning our mortal bodies, and uploading ourselves to the cloud.
Paradise is all very well, although your mileage may vary on whether these scenarios are realistic or desirable. The real question is, how do we get there?
Economist John Maynard Keynes notably argued in favor of active intervention when an economic crisis hits, rather than waiting for the markets to settle down to a more healthy equilibrium in the long run. His rebuttal to critics was, “In the long run, we are all dead.” After all, if it takes 50 years of upheaval and economic chaos for things to return to normality, there has been an immense amount of human suffering first.
Similar problems arise with the transition to a world where AI is intimately involved in our lives. In the long term, automation of labor might benefit the human species immensely. But in the short term, it has all kinds of potential pitfalls, especially in exacerbating inequality within societies where AI takes on a larger role. A new report from the Institute for Public Policy Research has deep concerns about the future of work.
While the report doesn’t foresee the same gloom and doom of mass unemployment that other commentators have considered, the concern is that the gains in productivity and economic benefits from AI will be unevenly distributed. In the UK, jobs that account for £290 billion worth of wages in today’s economy could potentially be automated with current technology. But these are disproportionately jobs held by people who are already suffering from social inequality.
Low-wage jobs are five times more likely to be automated than high-wage jobs. A greater proportion of jobs held by women are likely to be automated. The solution that’s often suggested is that people should simply “retrain”; but if no funding or assistance is provided, this burden is too much to bear. You can’t expect people to seamlessly transition from driving taxis to writing self-driving car software without help. As we have already seen, inequality is exacerbated when jobs that don’t require advanced education (even if they require a great deal of technical skill) are the first to go.
No Room for Beginners
Optimists say algorithms won’t replace humans, but will instead liberate us from the dull parts of our jobs. Lawyers used to have to spend hours trawling through case law to find legal precedents; now AI can identify the most relevant documents for them. Doctors no longer need to look through endless scans and perform diagnostic tests; machines can do this, leaving the decision-making to humans. This boosts productivity and provides invaluable tools for workers.
But there are issues with this rosy picture. If humans need to do less work, the economic incentive is for the boss to reduce their hours. Some of these “dull, routine” parts of the job were traditionally how people getting into the field learned the ropes: paralegals used to look through case law, but AI may render them obsolete. Even in the field of journalism, there’s now software that will rewrite press releases for publication, traditionally something close to an entry-level task. If there are no entry-level jobs, or if entry-level now requires years of training, the result is to exacerbate inequality and reduce social mobility.
Automating Our Biases
The adoption of algorithms into employment has already had negative impacts on equality. Cathy O’Neil, mathematics PhD from Harvard, raises these concerns in her excellent book Weapons of Math Destruction. She notes that algorithms designed by humans often encode the biases of that society, whether they’re racial or based on gender and sexuality.
Google’s search engine advertises more executive-level jobs to users it thinks are male. AI programs predict that black offenders are more likely to re-offend than white offenders; they receive correspondingly longer sentences. It needn’t necessarily be that bias has been actively programmed; perhaps the algorithms just learn from historical data, but this means they will perpetuate historical inequalities.
Take candidate-screening software HireVue, used by many major corporations to assess new employees. It analyzes “verbal and non-verbal cues” of candidates, comparing them to employees that historically did well. Either way, according to Cathy O’Neil, they are “using people’s fear and trust of mathematics to prevent them from asking questions.” With no transparency or understanding of how the algorithm generates its results, and no consensus over who’s responsible for the results, discrimination can occur automatically, on a massive scale.
Combine this with other demographic trends. In rich countries, people are living longer. An increasing burden will be placed on a shrinking tax base to support that elderly population. A recent study said that due to the accumulation of wealth in older generations, millennials stand to inherit more than any previous generation, but it won’t happen until they’re in their 60s. Meanwhile, those with savings and capital will benefit as the economy shifts: the stock market and GDP will grow, but wages and equality will fall, a situation that favors people who are already wealthy.
Even in the most dramatic AI scenarios, inequality is exacerbated. If someone develops a general intelligence that’s near-human or super-human, and they manage to control and monopolize it, they instantly become immensely wealthy and powerful. If the glorious technological future that Silicon Valley enthusiasts dream about is only going to serve to make the growing gaps wider and strengthen existing unfair power structures, is it something worth striving for?
What Makes a Utopia?
We urgently need to redefine our notion of progress. Philosophers worry about an AI that is misaligned—the things it seeks to maximize are not the things we want maximized. At the same time, we measure the development of our countries by GDP, not the quality of life of workers or the equality of opportunity in the society. Growing wealth with increased inequality is not progress.
Some people will take the position that there are always winners and losers in society, and that any attempt to redress the inequalities of our society will stifle economic growth and leave everyone worse off. Some will see this as an argument for a new economic model, based around universal basic income. Any moves towards this will need to take care that it’s affordable, sustainable, and doesn’t lead towards an entrenched two-tier society.
Walter Schiedel’s book The Great Leveller is a huge survey of inequality across all of human history, from the 21st century to prehistoric cave-dwellers. He argues that only revolutions, wars, and other catastrophes have historically reduced inequality: a perfect example is the Black Death in Europe, which (by reducing the population and therefore the labor supply that was available) increased wages and reduced inequality. Meanwhile, our solution to the financial crisis of 2007-8 may have only made the problem worse.
But in a world of nuclear weapons, of biowarfare, of cyberwarfare—a world of unprecedented, complex, distributed threats—the consequences of these “safety valves” could be worse than ever before. Inequality increases the risk of global catastrophe, and global catastrophes could scupper any progress towards the techno-utopia that the utopians dream of. And a society with entrenched inequality is no utopia at all.
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Before I started working on real-world robots, I wrote about their fictional and historical ancestors. This isn’t so far removed from what I do now. In factories, labs, and of course science fiction, imaginary robots keep fueling our imagination about artificial humans and autonomous machines.
Real-world robots remain surprisingly dysfunctional, although they are steadily infiltrating urban areas across the globe. This fourth industrial revolution driven by robots is shaping urban spaces and urban life in response to opportunities and challenges in economic, social, political, and healthcare domains. Our cities are becoming too big for humans to manage.
Good city governance enables and maintains smooth flow of things, data, and people. These include public services, traffic, and delivery services. Long queues in hospitals and banks imply poor management. Traffic congestion demonstrates that roads and traffic systems are inadequate. Goods that we increasingly order online don’t arrive fast enough. And the WiFi often fails our 24/7 digital needs. In sum, urban life, characterized by environmental pollution, speedy life, traffic congestion, connectivity and increased consumption, needs robotic solutions—or so we are led to believe.
Is this what the future holds? Image Credit: Photobank gallery / Shutterstock.com
In the past five years, national governments have started to see automation as the key to (better) urban futures. Many cities are becoming test beds for national and local governments for experimenting with robots in social spaces, where robots have both practical purpose (to facilitate everyday life) and a very symbolic role (to demonstrate good city governance). Whether through autonomous cars, automated pharmacists, service robots in local stores, or autonomous drones delivering Amazon parcels, cities are being automated at a steady pace.
Many large cities (Seoul, Tokyo, Shenzhen, Singapore, Dubai, London, San Francisco) serve as test beds for autonomous vehicle trials in a competitive race to develop “self-driving” cars. Automated ports and warehouses are also increasingly automated and robotized. Testing of delivery robots and drones is gathering pace beyond the warehouse gates. Automated control systems are monitoring, regulating and optimizing traffic flows. Automated vertical farms are innovating production of food in “non-agricultural” urban areas around the world. New mobile health technologies carry promise of healthcare “beyond the hospital.” Social robots in many guises—from police officers to restaurant waiters—are appearing in urban public and commercial spaces.
Vertical indoor farm. Image Credit: Aisyaqilumaranas / Shutterstock.com
As these examples show, urban automation is taking place in fits and starts, ignoring some areas and racing ahead in others. But as yet, no one seems to be taking account of all of these various and interconnected developments. So, how are we to forecast our cities of the future? Only a broad view allows us to do this. To give a sense, here are three examples: Tokyo, Dubai, and Singapore.
Currently preparing to host the Olympics 2020, Japan’s government also plans to use the event to showcase many new robotic technologies. Tokyo is therefore becoming an urban living lab. The institution in charge is the Robot Revolution Realization Council, established in 2014 by the government of Japan.
Tokyo: city of the future. Image Credit: ESB Professional / Shutterstock.com
The main objectives of Japan’s robotization are economic reinvigoration, cultural branding, and international demonstration. In line with this, the Olympics will be used to introduce and influence global technology trajectories. In the government’s vision for the Olympics, robot taxis transport tourists across the city, smart wheelchairs greet Paralympians at the airport, ubiquitous service robots greet customers in 20-plus languages, and interactively augmented foreigners speak with the local population in Japanese.
Tokyo shows us what the process of state-controlled creation of a robotic city looks like.
Singapore, on the other hand, is a “smart city.” Its government is experimenting with robots with a different objective: as physical extensions of existing systems to improve management and control of the city.
In Singapore, the techno-futuristic national narrative sees robots and automated systems as a “natural” extension of the existing smart urban ecosystem. This vision is unfolding through autonomous delivery robots (the Singapore Post’s delivery drone trials in partnership with AirBus helicopters) and driverless bus shuttles from Easymile, EZ10.
Meanwhile, Singapore hotels are employing state-subsidized service robots to clean rooms and deliver linen and supplies, and robots for early childhood education have been piloted to understand how robots can be used in pre-schools in the future. Health and social care is one of the fastest growing industries for robots and automation in Singapore and globally.
Dubai is another emerging prototype of a state-controlled smart city. But rather than seeing robotization simply as a way to improve the running of systems, Dubai is intensively robotizing public services with the aim of creating the “happiest city on Earth.” Urban robot experimentation in Dubai reveals that authoritarian state regimes are finding innovative ways to use robots in public services, transportation, policing, and surveillance.
National governments are in competition to position themselves on the global politico-economic landscape through robotics, and they are also striving to position themselves as regional leaders. This was the thinking behind the city’s September 2017 test flight of a flying taxi developed by the German drone firm Volocopter—staged to “lead the Arab world in innovation.” Dubai’s objective is to automate 25% of its transport system by 2030.
It is currently also experimenting with Barcelona-based PAL Robotics’ humanoid police officer and Singapore-based vehicle OUTSAW. If the experiments are successful, the government has announced it will robotize 25% of the police force by 2030.
While imaginary robots are fueling our imagination more than ever—from Ghost in the Shell to Blade Runner 2049—real-world robots make us rethink our urban lives.
These three urban robotic living labs—Tokyo, Singapore, Dubai—help us gauge what kind of future is being created, and by whom. From hyper-robotized Tokyo to smartest Singapore and happy, crime-free Dubai, these three comparisons show that, no matter what the context, robots are perceived as a means to achieve global futures based on a specific national imagination. Just like the films, they demonstrate the role of the state in envisioning and creating that future.
This article was originally published on The Conversation. Read the original article.
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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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Leah Weiss believes that when we pay attention to how we do our work—our thoughts and feelings about what we do and why we do it—we can tap into a much deeper reservoir of courage, creativity, meaning, and resilience.
As a researcher, educator, and author, Weiss teaches a course called “Leading with Compassion and Mindfulness” at the Stanford Graduate School of Business, one of the most competitive MBA programs in the world, and runs programs at HopeLab.
Weiss is the author of the new book How We Work: Live Your Purpose, Reclaim your Sanity and Embrace the Daily Grind, endorsed by the Dalai Lama, among others. I caught up with Leah to learn more about how the practice of mindfulness can deepen our individual and collective purpose and passion.
Lisa Kay Solomon: We’re hearing a lot about mindfulness these days. What is mindfulness and why is it so important to bring into our work? Can you share some of the basic tenets of the practice?
Leah Weiss, PhD: Mindfulness is, in its most literal sense, “the attention to inattention.” It’s as simple as noticing when you’re not paying attention and then re-focusing. It is prioritizing what is happening right now over internal and external noise.
The ability to work well with difficult coworkers, handle constructive feedback and criticism, regulate emotions at work—all of these things can come from regular mindfulness practice.
Some additional benefits of mindfulness are a greater sense of compassion (both self-compassion and compassion for others) and a way to seek and find purpose in even mundane things (and especially at work). From the business standpoint, mindfulness at work leads to increased productivity and creativity, mostly because when we are focused on one task at a time (as opposed to multitasking), we produce better results.
We spend more time with our co-workers than we do with our families; if our work relationships are negative, we suffer both mentally and physically. Even worse, we take all of those negative feelings home with us at the end of the work day. The antidote to this prescription for unhappiness is to have clear, strong purpose (one third of people do not have purpose at work and this is a major problem in the modern workplace!). We can use mental training to grow as people and as employees.
LKS: What are some recommendations you would make to busy leaders who are working around the clock to change the world?
LW: I think the most important thing is to remember to tend to our relationship with ourselves while trying to change the world. If we’re beating up on ourselves all the time we’ll be depleted.
People passionate about improving the world can get into habits of believing self-care isn’t important. We demand a lot of ourselves. It’s okay to fail, to mess up, to make mistakes—what’s important is how we learn from those mistakes and what we tell ourselves about those instances. What is the “internal script” playing in your own head? Is it positive, supporting, and understanding? It should be. If it isn’t, you can work on it. And the changes you make won’t just improve your quality of life, they’ll make you more resilient to weather life’s inevitable setbacks.
A close second recommendation is to always consider where everyone in an organization fits and help everyone (including yourself) find purpose. When you know what your own purpose is and show others their purpose, you can motivate a team and help everyone on a team gain pride in and at work. To get at this, make sure to ask people on your team what really lights them up. What sucks their energy and depletes them? If we know our own answers to these questions and relate them to the people we work with, we can create more engaged organizations.
LKS: Can you envision a future where technology and mindfulness can work together?
LW: Technology and mindfulness are already starting to work together. Some artificial intelligence companies are considering things like mindfulness and compassion when building robots, and there are numerous apps that target spreading mindfulness meditations in a widely-accessible way.
LKS: Looking ahead at our future generations who seem more attached to their devices than ever, what advice do you have for them?
LW: It’s unrealistic to say “stop using your device so much,” so instead, my suggestion is to make time for doing things like scrolling social media and make the same amount of time for putting your phone down and watching a movie or talking to a friend. No matter what it is that you are doing, make sure you have meta-awareness or clarity about what you’re paying attention to. Be clear about where your attention is and recognize that you can be a steward of attention. Technology can support us in this or pull us away from this; it depends on how we use it.
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