Tag Archives: position
#436202 Trump CTO Addresses AI, Facial ...
Michael Kratsios, the Chief Technology Officer of the United States, took the stage at Stanford University last week to field questions from Stanford’s Eileen Donahoe and attendees at the 2019 Fall Conference of the Institute for Human-Centered Artificial Intelligence (HAI).
Kratsios, the fourth to hold the U.S. CTO position since its creation by President Barack Obama in 2009, was confirmed in August as President Donald Trump’s first CTO. Before joining the Trump administration, he was chief of staff at investment firm Thiel Capital and chief financial officer of hedge fund Clarium Capital. Donahoe is Executive Director of Stanford’s Global Digital Policy Incubator and served as the first U.S. Ambassador to the United Nations Human Rights Council during the Obama Administration.
The conversation jumped around, hitting on both accomplishments and controversies. Kratsios touted the administration’s success in fixing policy around the use of drones, its memorandum on STEM education, and an increase in funding for basic research in AI—though the magnitude of that increase wasn’t specified. He pointed out that the Trump administration’s AI policy has been a continuation of the policies of the Obama administration, and will continue to build on that foundation. As proof of this, he pointed to Trump’s signing of the American AI Initiative earlier this year. That executive order, Kratsios said, was intended to bring various government agencies together to coordinate their AI efforts and to push the idea that AI is a tool for the American worker. The AI Initiative, he noted, also took into consideration that AI will cause job displacement, and asked private companies to pledge to retrain workers.
The administration, he said, is also looking to remove barriers to AI innovation. In service of that goal, the government will, in the next month or so, release a regulatory guidance memo instructing government agencies about “how they should think about AI technologies,” said Kratsios.
U.S. vs China in AI
A few of the exchanges between Kratsios and Donahoe hit on current hot topics, starting with the tension between the U.S. and China.
Donahoe:
“You talk a lot about unique U.S. ecosystem. In which aspect of AI is the U.S. dominant, and where is China challenging us in dominance?
Kratsios:
“They are challenging us on machine vision. They have more data to work with, given that they have surveillance data.”
Donahoe:
“To what extent would you say the quantity of data collected and available will be a determining factor in AI dominance?”
Kratsios:
“It makes a big difference in the short term. But we do research on how we get over these data humps. There is a future where you don’t need as much data, a lot of federal grants are going to [research in] how you can train models using less data.”
Donahoe turned the conversation to a different tension—that between innovation and values.
Donahoe:
“A lot of conversation yesterday was about the tension between innovation and values, and how do you hold those things together and lead in both realms.”
Kratsios:
“We recognized that the U.S. hadn’t signed on to principles around developing AI. In May, we signed [the Organization for Economic Cooperation and Development Principles on Artificial Intelligence], coming together with other Western democracies to say that these are values that we hold dear.
[Meanwhile,] we have adversaries around the world using AI to surveil people, to suppress human rights. That is why American leadership is so critical: We want to come out with the next great product. And we want our values to underpin the use cases.”
A member of the audience pushed further:
“Maintaining U.S. leadership in AI might have costs in terms of individuals and society. What costs should individuals and society bear to maintain leadership?”
Kratsios:
“I don’t view the world that way. Our companies big and small do not hesitate to talk about the values that underpin their technology. [That is] markedly different from the way our adversaries think. The alternatives are so dire [that we] need to push efforts to bake the values that we hold dear into this technology.”
Facial recognition
And then the conversation turned to the use of AI for facial recognition, an application which (at least for police and other government agencies) was recently banned in San Francisco.
Donahoe:
“Some private sector companies have called for government regulation of facial recognition, and there already are some instances of local governments regulating it. Do you expect federal regulation of facial recognition anytime soon? If not, what ought the parameters be?”
Kratsios:
“A patchwork of regulation of technology is not beneficial for the country. We want to avoid that. Facial recognition has important roles—for example, finding lost or displaced children. There are use cases, but they need to be underpinned by values.”
A member of the audience followed up on that topic, referring to some data presented earlier at the HAI conference on bias in AI:
“Frequently the example of finding missing children is given as the example of why we should not restrict use of facial recognition. But we saw Joy Buolamwini’s presentation on bias in data. I would like to hear your thoughts about how government thinks we should use facial recognition, knowing about this bias.”
Kratsios:
“Fairness, accountability, and robustness are things we want to bake into any technology—not just facial recognition—as we build rules governing use cases.”
Immigration and innovation
A member of the audience brought up the issue of immigration:
“One major pillar of innovation is immigration, does your office advocate for it?”
Kratsios:
“Our office pushes for best and brightest people from around the world to come to work here and study here. There are a few efforts we have made to move towards a more merit-based immigration system, without congressional action. [For example, in] the H1-B visa system, you go through two lotteries. We switched the order of them in order to get more people with advanced degrees through.”
The government’s tech infrastructure
Donahoe brought the conversation around to the tech infrastructure of the government itself:
“We talk about the shiny object, AI, but the 80 percent is the unsexy stuff, at federal and state levels. We don’t have a modern digital infrastructure to enable all the services—like a research cloud. How do we create this digital infrastructure?”
Kratsios:
“I couldn’t agree more; the least partisan issue in Washington is about modernizing IT infrastructure. We spend like $85 billion a year on IT at the federal level, we can certainly do a better job of using those dollars.” Continue reading
Posted in Human Robots
#436180 Bipedal Robot Cassie Cal Learns to ...
There’s no particular reason why knowing how to juggle would be a useful skill for a robot. Despite this, robots are frequently taught how to juggle things. Blind robots can juggle, humanoid robots can juggle, and even drones can juggle. Why? Because juggling is hard, man! You have to think about a bunch of different things at once, and also do a bunch of different things at once, which this particular human at least finds to be overly stressful. While juggling may not stress robots out, it does require carefully coordinated sensing and computing and actuation, which means that it’s as good a task as any (and a more entertaining task than most) for testing the capabilities of your system.
UC Berkeley’s Cassie Cal robot, which consists of two legs and what could be called a torso if you were feeling charitable, has just learned to juggle by bouncing a ball on what would be her head if she had one of those. The idea is that if Cassie can juggle while balancing at the same time, she’ll be better able to do other things that require dynamic multitasking, too. And if that doesn’t work out, she’ll still be able to join the circus.
Cassie’s juggling is assisted by an external motion capture system that tracks the location of the ball, but otherwise everything is autonomous. Cassie is able to juggle the ball by leaning forwards and backwards, left and right, and moving up and down. She does this while maintaining her own balance, which is the whole point of this research—successfully executing two dynamic behaviors that may sometimes be at odds with one another. The end goal here is not to make a better juggling robot, but rather to explore dynamic multitasking, a skill that robots will need in order to be successful in human environments.
This work is from the Hybrid Robotics Lab at UC Berkeley, led by Koushil Sreenath, and is being done by Katherine Poggensee, Albert Li, Daniel Sotsaikich, Bike Zhang, and Prasanth Kotaru.
For a bit more detail, we spoke with Albert Li via email.
Image: UC Berkeley
UC Berkeley’s Cassie Cal getting ready to juggle.
IEEE Spectrum: What would be involved in getting Cassie to juggle without relying on motion capture?
Albert Li: Our motivation for starting off with motion capture was to first address the control challenge of juggling on a biped without worrying about implementing the perception. We actually do have a ball detector working on a camera, which would mean we wouldn’t have to rely on the motion capture system. However, we need to mount the camera in a way that it would provide the best upwards field of view, and we also have develop a reliable estimator. The estimator is particularly important because when the ball gets close enough to the camera, we actually can’t track the ball and have to assume our dynamic models describe its motion accurately enough until it bounces back up.
What keeps Cassie from juggling indefinitely?
There are a few factors that affect how long Cassie can sustain a juggle. While in simulation the paddle exhibits homogeneous properties like its stiffness and damping, in reality every surface has anisotropic contact properties. So, there are parts of the paddle which may be better for juggling than others (and importantly, react differently than modeled). These differences in contact are also exacerbated due to how the paddle is cantilevered when mounted on Cassie. When the ball hits these areas, it leads to a larger than expected error in a juggle. Due to the small size of the paddle, the ball may then just hit the paddle’s edge and end the juggling run. Over a very long run, this is a likely occurrence. Additionally, some large juggling errors could cause Cassie’s feet to slip slightly, which ends up changing the stable standing position over time. Since this version of the controller assumes Cassie is stationary, this change in position eventually leads to poor juggles and failure.
Would Cassie be able to juggle while walking (or hovershoe-ing)?
Walking (and hovershoe-ing) while juggling is a far more challenging problem and is certainly a goal for future research. Some of these challenges include getting the paddle to precise poses to juggle the ball while also moving to avoid any destabilizing effects of stepping incorrectly. The number of juggles per step of walking could also vary and make the mathematics of the problem more challenging. The controller goal is also more involved. While the current goal of the juggling controller is to juggle the ball to a static apex position, with a walking juggling controller, we may instead want to hit the ball forwards and also walk forwards to bounce it, juggle the ball along a particular path, etc. Solving such challenges would be the main thrusts of the follow-up research.
Can you give an example of a practical task that would be made possible by using a controller like this?
Studying juggling means studying contact behavior and leveraging our models of it to achieve a known objective. Juggling could also be used to study predictable post-contact flight behavior. Consider the scenario where a robot is attempting to make a catch, but fails, letting the ball to bounce off of its hand, and then recovering the catch. This behavior could also be intentional: It is often easier to first execute a bounce to direct the target and then perform a subsequent action. For example, volleyball players could in principle directly hit a spiked ball back, but almost always bump the ball back up and then return it.
Even beyond this motivating example, the kinds of models we employ to get juggling working are more generally applicable to any task that involves contact, which could include tasks besides bouncing like sliding and rolling. For example, clearing space on a desk by pushing objects to the side may be preferable than individually manipulating each and every object on it.
You mention collaborative juggling or juggling multiple balls—is that something you’ve tried yet? Can you talk a bit more about what you’re working on next?
We haven’t yet started working on collaborative or multi-ball juggling, but that’s also a goal for future work. Juggling multiple balls statically is probably the most reasonable next goal, but presents additional challenges. For instance, you have to encode a notion of juggling urgency (if the second ball isn’t hit hard enough, you have less time to get the first ball up before you get back to the second one).
On the other hand, collaborative human-robot juggling requires a more advanced decision-making framework. To get robust multi-agent juggling, the robot will need to employ some sort of probabilistic model of the expected human behavior (are they likely to move somewhere? Are they trying to catch the ball high or low? Is it safe to hit the ball back?). In general, developing such human models is difficult since humans are fairly unpredictable and often don’t exhibit rational behavior. This will be a focus of future work.
[ Hybrid Robotics Lab ] Continue reading
Posted in Human Robots
#436167 Is it Time for Tech to Stop Moving Fast ...
On Monday, I attended the 2019 Fall Conference of Stanford’s Institute for Human Centered Artificial Intelligence (HAI). That same night I watched the Season 6 opener for the HBO TV show Silicon Valley. And the debates featured in both surrounded the responsibility of tech companies for the societal effects of the technologies they produce. The two events have jumbled together in my mind, perhaps because I was in a bit of a brain fog, thanks to the nasty combination of a head cold and the smoke that descended on Silicon Valley from the northern California wildfires. But perhaps that mixture turned out to be a good thing.
What is clear, in spite of the smoke, is that this issue is something a lot of people are talking about, inside and outside of Silicon Valley (witness the viral video of Rep. Alexandria Ocasio-Cortez (D-NY) grilling Facebook CEO Mark Zuckerberg).
So, to add to that conversation, here’s my HBO Silicon Valley/Stanford HAI conference mashup.
Silicon Valley’s fictional CEO Richard Hendriks, in the opening scene of the episode, tells Congress that Facebook, Google, and Amazon only care about exploiting personal data for profit. He states:
“These companies are kings, and they rule over kingdoms far larger than any nation in history.”
Meanwhile Marietje Schaake, former member of the European Parliament and a fellow at HAI, told the conference audience of 900:
“There is a lot of power in the hands of few actors—Facebook decides who is a news source, Microsoft will run the defense department’s cloud…. I believe we need a deeper debate about which tasks need to stay in the hands of the public.”
Eric Schmidt, former CEO and executive chairman of Google, agreed. He says:
“It is important that we debate now the ethics of what we are doing, and the impact of the technology that we are building.”
Stanford Associate Professor Ge Wang, also speaking at the HAI conference, pointed out:
“‘Doing no harm’ is a vital goal, and it is not easy. But it is different from a proactive goal, to ‘do good.’”
Had Silicon Valley’s Hendricks been there, he would have agreed. He said in the episode:
“Just because it’s successful, doesn’t mean it’s good. Hiroshima was a successful implementation.”
The speakers at the HAI conference discussed the implications of moving fast and breaking things, of putting untested and unregulated technology into the world now that we know that things like public trust and even democracy can be broken.
Google’s Schmidt told the HAI audience:
“I don’t think that everything that is possible should be put into the wild in society, we should answer the question, collectively, how much risk are we willing to take.
And Silicon Valley denizens real and fictional no longer think it’s OK to just say sorry afterwards. Says Schmidt:
“When you ask Facebook about various scandals, how can they still say ‘We are very sorry; we have a lot of learning to do.’ This kind of naiveté stands out of proportion to the power tech companies have. With great power should come great responsibility, or at least modesty.”
Schaake argued:
“We need more guarantees, institutions, and policies than stated good intentions. It’s about more than promises.”
Fictional CEO Hendricks thinks saying sorry is a cop-out as well. In the episode, a developer admits that his app collected user data in spite of Hendricks assuring Congress that his company doesn’t do that:
“You didn’t know at the time,” the developer says. “Don’t beat yourself up about it. But in the future, stop saying it. Or don’t; I don’t care. Maybe it will be like Google saying ‘Don’t be evil,’ or Facebook saying ‘I’m sorry, we’ll do better.’”
Hendricks doesn’t buy it:
“This stops now. I’m the boss, and this is over.”
(Well, he is fictional.)
How can government, the tech world, and the general public address this in a more comprehensive way? Out in the real world, the “what to do” discussion at Stanford HAI surrounded regulation—how much, what kind, and when.
Says the European Parliament’s Schaake:
“An often-heard argument is that government should refrain from regulating tech because [regulation] will stifle innovation. [That argument] implies that innovation is more important than democracy or the rule of law. Our problems don’t stem from over regulation, but under regulation of technologies.”
But when should that regulation happen. Stanford provost emeritus John Etchemendy, speaking from the audience at the HAI conference, said:
“I’ve been an advocate of not trying to regulate before you understand it. Like San Francisco banning of use of facial recognition is not a good example of regulation; there are uses of facial recognition that we should allow. We want regulations that are just right, that prevent the bad things and allow the good things. So we are going to get it wrong either way, if we regulate to soon or hold off, we will get some things wrong.”
Schaake would opt for regulating sooner rather than later. She says that she often hears the argument that it is too early to regulate artificial intelligence—as well as the argument that it is too late to regulate ad-based political advertising, or online privacy. Neither, to her, makes sense. She told the HAI attendees:
“We need more than guarantees than stated good intentions.”
U.S. Chief Technology Officer Michael Kratsios would go with later rather than sooner. (And, yes, the country has a CTO. President Barack Obama created the position in 2009; Kratsios is the fourth to hold the office and the first under President Donald Trump. He was confirmed in August.) Also speaking at the HAI conference, Kratsios argued:
“I don’t think we should be running to regulate anything. We are a leader [in technology] not because we had great regulations, but we have taken a free market approach. We have done great in driving innovation in technologies that are born free, like the Internet. Technologies born in captivity, like autonomous vehicles, lag behind.”
In the fictional world of HBO’s Silicon Valley, startup founder Hendricks has a solution—a technical one of course: the decentralized Internet. He tells Congress:
“The way we win is by creating a new, decentralized Internet, one where the behavior of companies like this will be impossible, forever. Where it is the users, not the kings, who have sovereign control over their data. I will help you build an Internet that is of the people, by the people, and for the people.”
(This is not a fictional concept, though it is a long way from wide use. Also called the decentralized Web, the concept takes the content on today’s Web and fragments it, and then replicates and scatters those fragments to hosts around the world, increasing privacy and reducing the ability of governments to restrict access.)
If neither regulation nor technology comes to make the world safe from the unforeseen effects of new technologies, there is one more hope, according to Schaake: the millennials and subsequent generations.
Tech companies can no longer pursue growth at all costs, not if they want to keep attracting the talent they need, says Schaake. She noted that, “the young generation looks at the environment, at homeless on the streets,” and they expect their companies to tackle those and other issues and make the world a better place. Continue reading
Posted in Human Robots
#436155 This MIT Robot Wants to Use Your ...
MIT researchers have demonstrated a new kind of teleoperation system that allows a two-legged robot to “borrow” a human operator’s physical skills to move with greater agility. The system works a bit like those haptic suits from the Spielberg movie “Ready Player One.” But while the suits in the film were used to connect humans to their VR avatars, the MIT suit connects the operator to a real robot.
The robot is called Little HERMES, and it’s currently just a pair of little legs, about a third the size of an average adult. It can step and jump in place or walk a short distance while supported by a gantry. While that in itself is not very impressive, the researchers say their approach could help bring capable disaster robots closer to reality. They explain that, despite recent advances, building fully autonomous robots with motor and decision-making skills comparable to those of humans remains a challenge. That’s where a more advanced teleoperation system could help.
The researchers, João Ramos, now an assistant professor at the University of Illinois at Urbana-Champaign, and Sangbae Kim, director of MIT’s Biomimetic Robotics Lab, describe the project in this week’s issue of Science Robotics. In the paper, they argue that existing teleoperation systems often can’t effectively match the operator’s motions to that of a robot. In addition, conventional systems provide no physical feedback to the human teleoperator about what the robot is doing. Their new approach addresses these two limitations, and to see how it would work in practice, they built Little HERMES.
Image: Science Robotics
The main components of MIT’s bipedal robot Little HERMES: (A) Custom actuators designed to withstand impact and capable of producing high torque. (B) Lightweight limbs with low inertia and fast leg swing. (C) Impact-robust and lightweight foot sensors with three-axis contact force sensor. (D) Ruggedized IMU to estimates the robot’s torso posture, angular rate, and linear acceleration. (E) Real-time computer sbRIO 9606 from National Instruments for robot control. (F) Two three-cell lithium-polymer batteries in series. (G) Rigid and lightweight frame to minimize the robot mass.
Early this year, the MIT researchers wrote an in-depth article for IEEE Spectrum about the project, which includes Little HERMES and also its big brother, HERMES (for Highly Efficient Robotic Mechanisms and Electromechanical System). In that article, they describe the two main components of the system:
[…] We are building a telerobotic system that has two parts: a humanoid capable of nimble, dynamic behaviors, and a new kind of two-way human-machine interface that sends your motions to the robot and the robot’s motions to you. So if the robot steps on debris and starts to lose its balance, the operator feels the same instability and instinctively reacts to avoid falling. We then capture that physical response and send it back to the robot, which helps it avoid falling, too. Through this human-robot link, the robot can harness the operator’s innate motor skills and split-second reflexes to keep its footing.
You could say we’re putting a human brain inside the machine.
Image: Science Robotics
The human-machine interface built by the MIT researchers for controlling Little HERMES is different from conventional ones in that it relies on the operator’s reflexes to improve the robot’s stability. The researchers call it the balance-feedback interface, or BFI. The main modules of the BFI include: (A) Custom interface attachments for torso and feet designed to capture human motion data at high speed (1 kHz). (B) Two underactuated modules to track the position and orientation of the torso and apply forces to the operator. (C) Each actuation module has three DoFs, one of which is a push/pull rod actuated by a DC brushless motor. (D) A series of linkages with passive joints connected to the operator’s feet and track their spatial translation. (E) Real-time controller cRIO 9082 from National Instruments to close the BFI control loop. (F) Force plate to estimated the operator’s center of pressure position and measure the shear and normal components of the operator’s net contact force.
Here’s more footage of the experiments, showing Little HERMES stepping and jumping in place, walking a few steps forward and backward, and balancing. Watch until the end to see a compilation of unsuccessful stepping experiments. Poor Little HERMES!
In the new Science Robotics paper, the MIT researchers explain how they solved one of the key challenges in making their teleoperation system effective:
The challenge of this strategy lies in properly mapping human body motion to the machine while simultaneously informing the operator how closely the robot is reproducing the movement. Therefore, we propose a solution for this bilateral feedback policy to control a bipedal robot to take steps, jump, and walk in synchrony with a human operator. Such dynamic synchronization was achieved by (i) scaling the core components of human locomotion data to robot proportions in real time and (ii) applying feedback forces to the operator that are proportional to the relative velocity between human and robot.
Little HERMES is now taking its first steps, quite literally, but the researchers say they hope to use robotic legs with similar design as part of a more advanced humanoid. One possibility they’ve envisioned is a fast-moving quadruped robot that could run through various kinds of terrain and then transform into a bipedal robot that would use its hands to perform dexterous manipulations. This could involve merging some of the robots the MIT researchers have built in their lab, possibly creating hybrids between Cheetah and HERMES, or Mini Cheetah and Little HERMES. We can’t wait to see what the resulting robots will look like.
[ Science Robotics ] Continue reading
Posted in Human Robots