Tag Archives: 2016
#436184 Why People Demanded Privacy to Confide ...
This is part four of a six-part series on the history of natural language processing.
Between 1964 and 1966, Joseph Weizenbaum, a German American computer scientist at MIT’s artificial intelligence lab, developed the first-ever chatbot [PDF].
While there were already some rudimentary digital language generators in existence—programs that could spit out somewhat coherent lines of text—Weizenbaum’s program was the first designed explicitly for interactions with humans. The user could type in some statement or set of statements in their normal language, press enter, and receive a response from the machine. As Weizenbaum explained, his program made “certain kinds of natural-language conversation between man and computer possible.”
He named the program Eliza after Eliza Doolittle, the working-class hero of George Bernard Shaw’s Pygmalion who learns how to talk with an upper-class accent. The new Eliza was written for the 36-bit IBM 7094, an early transistorized mainframe computer, in a programming language that Weizenbaum developed called MAD-SLIP.
Because computer time was a valuable resource, Eliza could only be run via a time-sharing system; the user interacted with the program remotely via an electric typewriter and printer. When the user typed in a sentence and pressed enter, a message was sent to the mainframe computer. Eliza scanned the message for the presence of a keyword and used it in a new sentence to form a response that was sent back, printed out, and read by the user.
To encourage ongoing dialogue, Weizenbaum designed Eliza to simulate the type of conversational style used by a Rogerian psychoanalyst. The program would take something the user said and reflect it back with a question, as can be seen in the example below. (Notice how the program takes keywords like “boyfriend” and “depressed” and re-uses them.)
Human: Men are all alike.
Computer: IN WHAT WAY?
Human: They're always bugging us about something or other.
Computer: CAN YOU THINK OF A SPECIFIC EXAMPLE?
Human: Well, my boyfriend made me come here.
Computer: YOUR BOYFRIEND MADE YOU COME HERE
Human: He says I'm depressed much of the time.
Computer: I AM SORRY TO HEAR YOU ARE DEPRESSED
Human: It's true. I am unhappy.
Weizenbaum chose this mode of dialogue for Eliza because it gave the impression that the computer understood what was being said without having to offer anything new to the conversation. It created the illusion of comprehension and engagement in a mere 200 lines of code.
To test Eliza’s capacity to engage an interlocutor, Weizenbaum invited students and colleagues into his office and let them chat with the machine while he looked on. He noticed, with some concern, that during their brief interactions with Eliza, many users began forming emotional attachments to the algorithm. They would open up to the machine and confess problems they were facing in their lives and relationships.
During their brief interactions with Eliza, many users began forming emotional attachments to the algorithm.
Even more surprising was that this sense of intimacy persisted even after Weizenbaum described how the machine worked and explained that it didn’t really understand anything that was being said. Weizenbaum was most troubled when his secretary, who had watched him build the program from scratch over many months, insisted that he leave the room so she could talk to Eliza in private.
For Weizenbaum, this experiment with Eliza made him question an idea that Alan Turing had proposed in 1950 about machine intelligence. In his paper, entitled “Computing Machinery and Intelligence,” Turing suggested that if a computer could conduct a convincingly human conversation in text, one could assume it was intelligent—an idea that became the basis of the famous Turing Test.
But Eliza demonstrated that convincing communication between a human and a machine could take place even if comprehension only flowed from one side: The simulation of intelligence, rather than intelligence itself, was enough to fool people. Weizenbaum called this the Eliza effect, and believed it was a type of “delusional thinking” that humanity would collectively suffer from in the digital age. This insight was a profound shock for Weizenbaum, and one that came to define his intellectual trajectory over the next decade.
The simulation of intelligence, rather than intelligence itself, was enough to fool people.
In 1976, he published Computing Power and Human Reason: From Judgment to Calculation [PDF], which offered a long meditation on why people are willing to believe that a simple machine might be able to understand their complex human emotions.
In this book, he argues that the Eliza effect signifies a broader pathology afflicting “modern man.” In a world conquered by science, technology, and capitalism, people had grown accustomed to viewing themselves as isolated cogs in a large and uncaring machine. In such a diminished social world, Weizenbaum reasoned, people had grown so desperate for connection that they put aside their reason and judgment in order to believe that a program could care about their problems.
Weizenbaum spent the rest of his life developing this humanistic critique of artificial intelligence and digital technology. His mission was to remind people that their machines were not as smart as they were often said to be. And that even though it sometimes appeared as though they could talk, they were never really listening.
This is the fourth installment of a six-part series on the history of natural language processing. Last week’s post described Andrey Markov and Claude Shannon’s painstaking efforts to create statistical models of language for text generation. Come back next Monday for part five, “In 2016, Microsoft’s Racist Chatbot Revealed the Dangers of Conversation.”
You can also check out our prior series on the untold history of AI. Continue reading
Posted in Human Robots
#436126 Quantum Computing Gets a Boost From AI ...
Illustration: Greg Mably
Anyone of a certain age who has even a passing interest in computers will remember the remarkable breakthrough that IBM made in 1997 when its Deep Blue chess-playing computer defeated Garry Kasparov, then the world chess champion. Computer scientists passed another such milestone in March 2016, when DeepMind (a subsidiary of Alphabet, Google’s parent company) announced that its AlphaGo program had defeated world-champion player Lee Sedol in the game of Go, a board game that had vexed AI researchers for decades. Recently, DeepMind’s algorithms have also bested human players in the computer games StarCraft IIand Quake Arena III.
Some believe that the cognitive capacities of machines will overtake those of human beings in many spheres within a few decades. Others are more cautious and point out that our inability to understand the source of our own cognitive powers presents a daunting hurdle. How can we make thinking machines if we don’t fully understand our own thought processes?
Citizen science, which enlists masses of people to tackle research problems, holds promise here, in no small part because it can be used effectively to explore the boundary between human and artificial intelligence.
Some citizen-science projects ask the public to collect data from their surroundings (as eButterfly does for butterflies) or to monitor delicate ecosystems (as Eye on the Reef does for Australia’s Great Barrier Reef). Other projects rely on online platforms on which people help to categorize obscure phenomena in the night sky (Zooniverse) or add to the understanding of the structure of proteins (Foldit). Typically, people can contribute to such projects without any prior knowledge of the subject. Their fundamental cognitive skills, like the ability to quickly recognize patterns, are sufficient.
In order to design and develop video games that can allow citizen scientists to tackle scientific problems in a variety of fields, professor and group leader Jacob Sherson founded ScienceAtHome (SAH), at Aarhus University, in Denmark. The group began by considering topics in quantum physics, but today SAH hosts games covering other areas of physics, math, psychology, cognitive science, and behavioral economics. We at SAH search for innovative solutions to real research challenges while providing insight into how people think, both alone and when working in groups.
It is computationally intractable to completely map out a higher-dimensional landscape: It is called the curse of high dimensionality, and it plagues many optimization problems.
We believe that the design of new AI algorithms would benefit greatly from a better understanding of how people solve problems. This surmise has led us to establish the Center for Hybrid Intelligence within SAH, which tries to combine human and artificial intelligence, taking advantage of the particular strengths of each. The center’s focus is on the gamification of scientific research problems and the development of interfaces that allow people to understand and work together with AI.
Our first game, Quantum Moves, was inspired by our group’s research into quantum computers. Such computers can in principle solve certain problems that would take a classical computer billions of years. Quantum computers could challenge current cryptographic protocols, aid in the design of new materials, and give insight into natural processes that require an exact solution of the equations of quantum mechanics—something normal computers are inherently bad at doing.
One candidate system for building such a computer would capture individual atoms by “freezing” them, as it were, in the interference pattern produced when a laser beam is reflected back on itself. The captured atoms can thus be organized like eggs in a carton, forming a periodic crystal of atoms and light. Using these atoms to perform quantum calculations requires that we use tightly focused laser beams, called optical tweezers, to transport the atoms from site to site in the light crystal. This is a tricky business because individual atoms do not behave like particles; instead, they resemble a wavelike liquid governed by the laws of quantum mechanics.
In Quantum Moves, a player manipulates a touch screen or mouse to move a simulated laser tweezer and pick up a trapped atom, represented by a liquidlike substance in a bowl. Then the player must bring the atom back to the tweezer’s initial position while trying to minimize the sloshing of the liquid. Such sloshing would increase the energy of the atom and ultimately introduce errors into the operations of the quantum computer. Therefore, at the end of a move, the liquid should be at a complete standstill.
To understand how people and computers might approach such a task differently, you need to know something about how computerized optimization algorithms work. The countless ways of moving a glass of water without spilling may be regarded as constituting a “solution landscape.” One solution is represented by a single point in that landscape, and the height of that point represents the quality of the solution—how smoothly and quickly the glass of water was moved. This landscape might resemble a mountain range, where the top of each mountain represents a local optimum and where the challenge is to find the highest peak in the range—the global optimum.
Illustration: Greg Mably
Researchers must compromise between searching the landscape for taller mountains (“exploration”) and climbing to the top of the nearest mountain (“exploitation”). Making such a trade-off may seem easy when exploring an actual physical landscape: Merely hike around a bit to get at least the general lay of the land before surveying in greater detail what seems to be the tallest peak. But because each possible way of changing the solution defines a new dimension, a realistic problem can have thousands of dimensions. It is computationally intractable to completely map out such a higher-dimensional landscape. We call this the curse of high dimensionality, and it plagues many optimization problems.
Although algorithms are wonderfully efficient at crawling to the top of a given mountain, finding good ways of searching through the broader landscape poses quite a challenge, one that is at the forefront of AI research into such control problems. The conventional approach is to come up with clever ways of reducing the search space, either through insights generated by researchers or with machine-learning algorithms trained on large data sets.
At SAH, we attacked certain quantum-optimization problems by turning them into a game. Our goal was not to show that people can beat computers in this arena but rather to understand the process of generating insights into such problems. We addressed two core questions: whether allowing players to explore the infinite space of possibilities will help them find good solutions and whether we can learn something by studying their behavior.
Today, more than 250,000 people have played Quantum Moves, and to our surprise, they did in fact search the space of possible moves differently from the algorithm we had put to the task. Specifically, we found that although players could not solve the optimization problem on their own, they were good at searching the broad landscape. The computer algorithms could then take those rough ideas and refine them.
Herbert A. Simon said that “solving a problem simply means representing it so as to make the solution transparent.” Apparently, that’s what our games can do with their novel user interfaces.
Perhaps even more interesting was our discovery that players had two distinct ways of solving the problem, each with a clear physical interpretation. One set of players started by placing the tweezer close to the atom while keeping a barrier between the atom trap and the tweezer. In classical physics, a barrier is an impenetrable obstacle, but because the atom liquid is a quantum-mechanical object, it can tunnel through the barrier into the tweezer, after which the player simply moved the tweezer to the target area. Another set of players moved the tweezer directly into the atom trap, picked up the atom liquid, and brought it back. We called these two strategies the “tunneling” and “shoveling” strategies, respectively.
Such clear strategies are extremely valuable because they are very difficult to obtain directly from an optimization algorithm. Involving humans in the optimization loop can thus help us gain insight into the underlying physical phenomena that are at play, knowledge that may then be transferred to other types of problems.
Quantum Moves raised several obvious issues. First, because generating an exceptional solution required further computer-based optimization, players were unable to get immediate feedback to help them improve their scores, and this often left them feeling frustrated. Second, we had tested this approach on only one scientific challenge with a clear classical analogue, that of the sloshing liquid. We wanted to know whether such gamification could be applied more generally, to a variety of scientific challenges that do not offer such immediately applicable visual analogies.
We address these two concerns in Quantum Moves 2. Here, the player first generates a number of candidate solutions by playing the original game. Then the player chooses which solutions to optimize using a built-in algorithm. As the algorithm improves a player’s solution, it modifies the solution path—the movement of the tweezer—to represent the optimized solution. Guided by this feedback, players can then improve their strategy, come up with a new solution, and iteratively feed it back into this process. This gameplay provides high-level heuristics and adds human intuition to the algorithm. The person and the machine work in tandem—a step toward true hybrid intelligence.
In parallel with the development of Quantum Moves 2, we also studied how people collaboratively solve complex problems. To that end, we opened our atomic physics laboratory to the general public—virtually. We let people from around the world dictate the experiments we would run to see if they would find ways to improve the results we were getting. What results? That’s a little tricky to explain, so we need to pause for a moment and provide a little background on the relevant physics.
One of the essential steps in building the quantum computer along the lines described above is to create the coldest state of matter in the universe, known as a Bose-Einstein condensate. Here millions of atoms oscillate in synchrony to form a wavelike substance, one of the largest purely quantum phenomena known. To create this ultracool state of matter, researchers typically use a combination of laser light and magnetic fields. There is no familiar physical analogy between such a strange state of matter and the phenomena of everyday life.
The result we were seeking in our lab was to create as much of this enigmatic substance as was possible given the equipment available. The sequence of steps to accomplish that was unknown. We hoped that gamification could help to solve this problem, even though it had no classical analogy to present to game players.
Images: ScienceAtHome
Fun and Games: The
Quantum Moves game evolved over time, from a relatively crude early version [top] to its current form [second from top] and then a major revision,
Quantum Moves 2 [third from top].
Skill Lab: Science Detective games [bottom] test players’ cognitive skills.
In October 2016, we released a game that, for two weeks, guided how we created Bose-Einstein condensates in our laboratory. By manipulating simple curves in the game interface, players generated experimental sequences for us to use in producing these condensates—and they did so without needing to know anything about the underlying physics. A player would generate such a solution, and a few minutes later we would run the sequence in our laboratory. The number of ultracold atoms in the resulting Bose-Einstein condensate was measured and fed back to the player as a score. Players could then decide either to try to improve their previous solution or to copy and modify other players’ solutions. About 600 people from all over the world participated, submitting 7,577 solutions in total. Many of them yielded bigger condensates than we had previously produced in the lab.
So this exercise succeeded in achieving our primary goal, but it also allowed us to learn something about human behavior. We learned, for example, that players behave differently based on where they sit on the leaderboard. High-performing players make small changes to their successful solutions (exploitation), while poorly performing players are willing to make more dramatic changes (exploration). As a collective, the players nicely balance exploration and exploitation. How they do so provides valuable inspiration to researchers trying to understand human problem solving in social science as well as to those designing new AI algorithms.
How could mere amateurs outperform experienced experimental physicists? The players certainly weren’t better at physics than the experts—but they could do better because of the way in which the problem was posed. By turning the research challenge into a game, we gave players the chance to explore solutions that had previously required complex programming to study. Indeed, even expert experimentalists improved their solutions dramatically by using this interface.
Insight into why that’s possible can probably be found in the words of the late economics Nobel laureate Herbert A. Simon: “Solving a problem simply means representing it so as to make the solution transparent [PDF].” Apparently, that’s what our games can do with their novel user interfaces. We believe that such interfaces might be a key to using human creativity to solve other complex research problems.
Eventually, we’d like to get a better understanding of why this kind of gamification works as well as it does. A first step would be to collect more data on what the players do while they are playing. But even with massive amounts of data, detecting the subtle patterns underlying human intuition is an overwhelming challenge. To advance, we need a deeper insight into the cognition of the individual players.
As a step forward toward this goal, ScienceAtHome created Skill Lab: Science Detective, a suite of minigames exploring visuospatial reasoning, response inhibition, reaction times, and other basic cognitive skills. Then we compare players’ performance in the games with how well these same people did on established psychological tests of those abilities. The point is to allow players to assess their own cognitive strengths and weaknesses while donating their data for further public research.
In the fall of 2018 we launched a prototype of this large-scale profiling in collaboration with the Danish Broadcasting Corp. Since then more than 20,000 people have participated, and in part because of the publicity granted by the public-service channel, participation has been very evenly distributed across ages and by gender. Such broad appeal is rare in social science, where the test population is typically drawn from a very narrow demographic, such as college students.
Never before has such a large academic experiment in human cognition been conducted. We expect to gain new insights into many things, among them how combinations of cognitive abilities sharpen or decline with age, what characteristics may be used to prescreen for mental illnesses, and how to optimize the building of teams in our work lives.
And so what started as a fun exercise in the weird world of quantum mechanics has now become an exercise in understanding the nuances of what makes us human. While we still seek to understand atoms, we can now aspire to understand people’s minds as well.
This article appears in the November 2019 print issue as “A Man-Machine Mind Meld for Quantum Computing.”
About the Authors
Ottó Elíasson, Carrie Weidner, Janet Rafner, and Shaeema Zaman Ahmed work with the ScienceAtHome project at Aarhus University in Denmark. Continue reading
Posted in Human Robots
#435824 A Q&A with Cruise’s head of AI, ...
In 2016, Cruise, an autonomous vehicle startup acquired by General Motors, had about 50 employees. At the beginning of 2019, the headcount at its San Francisco headquarters—mostly software engineers, mostly working on projects connected to machine learning and artificial intelligence—hit around 1000. Now that number is up to 1500, and by the end of this year it’s expected to reach about 2000, sprawling into a recently purchased building that had housed Dropbox. And that’s not counting the 200 or so tech workers that Cruise is aiming to install in a Seattle, Wash., satellite development center and a handful of others in Phoenix, Ariz., and Pasadena, Calif.
Cruise’s recent hires aren’t all engineers—it takes more than engineering talent to manage operations. And there are hundreds of so-called safety drivers that are required to sit in the 180 or so autonomous test vehicles whenever they roam the San Francisco streets. But that’s still a lot of AI experts to be hiring in a time of AI engineer shortages.
Hussein Mehanna, head of AI/ML at Cruise, says the company’s hiring efforts are on track, due to the appeal of the challenge of autonomous vehicles in drawing in AI experts from other fields. Mehanna himself joined Cruise in May from Google, where he was director of engineering at Google Cloud AI. Mehanna had been there about a year and a half, a relatively quick career stop after a short stint at Snap following four years working in machine learning at Facebook.
Mehanna has been immersed in AI and machine learning research since his graduate studies in speech recognition and natural language processing at the University of Cambridge. I sat down with Mehanna to talk about his career, the challenges of recruiting AI experts and autonomous vehicle development in general—and some of the challenges specific to San Francisco. We were joined by Michael Thomas, Cruise’s manager of AI/ML recruiting, who had also spent time recruiting AI engineers at Google and then Facebook.
IEEE Spectrum: When you were at Cambridge, did you think AI was going to take off like a rocket?
Mehanna: Did I imagine that AI was going to be as dominant and prevailing and sometimes hyped as it is now? No. I do recall in 2003 that my supervisor and I were wondering if neural networks could help at all in speech recognition. I remember my supervisor saying if anyone could figure out how use a neural net for speech he would give them a grant immediately. So he was on the right path. Now neural networks have dominated vision, speech, and language [processing]. But that boom started in 2012.
“In the early days, Facebook wasn’t that open to PhDs, it actually had a negative sentiment about researchers, and then Facebook shifted”
I didn’t [expect it], but I certainly aimed for it when [I was at] Microsoft, where I deliberately pushed my career towards machine learning instead of big data, which was more popular at the time. And [I aimed for it] when I joined Facebook.
In the early days, Facebook wasn’t that open to PhDs, or researchers. It actually had a negative sentiment about researchers. And then Facebook shifted to becoming one of the key places where PhD students wanted to do internships or join after they graduated. It was a mindset shift, they were [once] at a point in time where they thought what was needed for success wasn’t research, but now it’s different.
There was definitely an element of risk [in taking a machine learning career path], but I was very lucky, things developed very fast.
IEEE Spectrum: Is it getting harder or easier to find AI engineers to hire, given the reported shortages?
Mehanna: There is a mismatch [between job openings and qualified engineers], though it is hard to quantify it with numbers. There is good news as well: I see a lot more students diving deep into machine learning and data in their [undergraduate] computer science studies, so it’s not as bleak as it seems. But there is massive demand in the market.
Here at Cruise, demand for AI talent is just growing and growing. It might be is saturating or slowing down at other kinds of companies, though, [which] are leveraging more traditional applications—ad prediction, recommendations—that have been out there in the market for a while. These are more mature, better understood problems.
I believe autonomous vehicle technologies is the most difficult AI problem out there. The magnitude of the challenge of these problems is 1000 times more than other problems. They aren’t as well understood yet, and they require far deeper technology. And also the quality at which they are expected to operate is off the roof.
The autonomous vehicle problem is the engineering challenge of our generation. There’s a lot of code to write, and if we think we are going to hire armies of people to write it line by line, it’s not going to work. Machine learning can accelerate the process of generating the code, but that doesn’t mean we aren’t going to have engineers; we actually need a lot more engineers.
Sometimes people worry that AI is taking jobs. It is taking some developer jobs, but it is actually generating other developer jobs as well, protecting developers from the mundane and helping them build software faster and faster.
IEEE Spectrum: Are you concerned that the demand for AI in industry is drawing out the people in academia who are needed to educate future engineers, that is, the “eating the seed corn” problem?
Mehanna: There are some negative examples in the industry, but that’s not our style. We are looking for collaborations with professors, we want to cultivate a very deep and respectful relationship with universities.
And there’s another angle to this: Universities require a thriving industry for them to thrive. It is going to be extremely beneficial for academia to have this flourishing industry in AI, because it attracts more students to academia. I think we are doing them a fantastic favor by building these career opportunities. This is not the same as in my early days, [when] people told me “don’t go to AI; go to networking, work in the mobile industry; mobile is flourishing.”
IEEE Spectrum: Where are you looking as you try to find a thousand or so engineers to hire this year?
Thomas: We look for people who want to use machine learning to solve problems. They can be in many different industries—in the financial markets, in social media, in advertising. The autonomous vehicle industry is in its infancy. You can compare it to mobile in the early days: When the iPhone first came out, everyone was looking for developers with mobile experience, but you weren’t going to find them unless you went to straight to Apple, [so you had to hire other kinds of engineers]. This is the same type of thing: it is so new that you aren’t going to find experts in this area, because we are all still learning.
“You don’t have to be an autonomous vehicle expert to flourish in this world. It’s not too late to move…now would be a great time for AI experts working on other problems to shift their attention to autonomous vehicles.”
Mehanna: Because autonomous vehicle technology is the new frontier for AI experts, [the number of] people with both AI and autonomous vehicle experience is quite limited. So we are acquiring AI experts wherever they are, and helping them grow into the autonomous vehicle area. You don’t have to be an autonomous vehicle expert to flourish in this world. It’s not too late to move; even though there is a lot of great tech developed, there’s even more innovation ahead, so now would be a great time for AI experts working on other problems or applications to shift their attention to autonomous vehicles.
It feels like the Internet in 1980. It’s about to happen, but there are endless applications [to be developed over] the next few decades. Even if we can get a car to drive safely, there is the question of how can we tune the ride comfort, and then applying it all to different cities, different vehicles, different driving situations, and who knows to what other applications.
I can see how I can spend a lifetime career trying to solve this problem.
IEEE Spectrum: Why are you doing most of your development in San Francisco?
Mehanna: I think the best talent of the world is in Silicon Valley, and solving the autonomous vehicle problem is going to require the best of the best. It’s not just the engineering talent that is here, but [also] the entrepreneurial spirit. Solving the problem just as a technology is not going to be successful, you need to solve the product and the technology together. And the entrepreneurial spirit is one of the key reasons Cruise secured 7.5 billion in funding [besides GM, the company has a number of outside investors, including Honda, Softbank, and T. Rowe Price]. That [funding] is another reason Cruise is ahead of many others, because this problem requires deep resources.
“If you can do an autonomous vehicle in San Francisco you can do it almost anywhere.”
[And then there is the driving environment.] When I speak to my peers in the industry, they have a lot of respect for us, because the problems to solve in San Francisco technically are an order of magnitude harder. It is a tight environment, with a lot of pedestrians, and driving patterns that, let’s put it this way, are not necessarily the best in the nation. Which means we are seeing more problems ahead of our competitors, which gets us to better [software]. I think if you can do an autonomous vehicle in San Francisco you can do it almost anywhere.
A version of this post appears in the September 2019 print magazine as “AI Engineers: The Autonomous-Vehicle Industry Wants You.” Continue reading
Posted in Human Robots