Tag Archives: robot
#433505 Boston Dynamics: Atlas
The future is already here, courtesy of Atlas by Boston Dynamics!
Posted in Human Robots
#433620 Instilling the Best of Human Values in ...
Now that the era of artificial intelligence is unquestionably upon us, it behooves us to think and work harder to ensure that the AIs we create embody positive human values.
Science fiction is full of AIs that manifest the dark side of humanity, or are indifferent to humans altogether. Such possibilities cannot be ruled out, but nor is there any logical or empirical reason to consider them highly likely. I am among a large group of AI experts who see a strong potential for profoundly positive outcomes in the AI revolution currently underway.
We are facing a future with great uncertainty and tremendous promise, and the best we can do is to confront it with a combination of heart and mind, of common sense and rigorous science. In the realm of AI, what this means is, we need to do our best to guide the AI minds we are creating to embody the values we cherish: love, compassion, creativity, and respect.
The quest for beneficial AI has many dimensions, including its potential to reduce material scarcity and to help unlock the human capacity for love and compassion.
Reducing Scarcity
A large percentage of difficult issues in human society, many of which spill over into the AI domain, would be palliated significantly if material scarcity became less of a problem. Fortunately, AI has great potential to help here. AI is already increasing efficiency in nearly every industry.
In the next few decades, as nanotech and 3D printing continue to advance, AI-driven design will become a larger factor in the economy. Radical new tools like artificial enzymes built using Christian Schafmeister’s spiroligomer molecules, and designed using quantum physics-savvy AIs, will enable the creation of new materials and medicines.
For amazing advances like the intersection of AI and nanotech to lead toward broadly positive outcomes, however, the economic and political aspects of the AI industry may have to shift from the current status quo.
Currently, most AI development occurs under the aegis of military organizations or large corporations oriented heavily toward advertising and marketing. Put crudely, an awful lot of AI today is about “spying, brainwashing, or killing.” This is not really the ideal situation if we want our first true artificial general intelligences to be open-minded, warm-hearted, and beneficial.
Also, as the bulk of AI development now occurs in large for-profit organizations bound by law to pursue the maximization of shareholder value, we face a situation where AI tends to exacerbate global wealth inequality and class divisions. This has the potential to lead to various civilization-scale failure modes involving the intersection of geopolitics, AI, cyberterrorism, and so forth. Part of my motivation for founding the decentralized AI project SingularityNET was to create an alternative mode of dissemination and utilization of both narrow AI and AGI—one that operates in a self-organizing way, outside of the direct grip of conventional corporate and governmental structures.
In the end, though, I worry that radical material abundance and novel political and economic structures may fail to create a positive future, unless they are coupled with advances in consciousness and compassion. AGIs have the potential to be massively more ethical and compassionate than humans. But still, the odds of getting deeply beneficial AGIs seem higher if the humans creating them are fuller of compassion and positive consciousness—and can effectively pass these values on.
Transmitting Human Values
Brain-computer interfacing is another critical aspect of the quest for creating more positive AIs and more positive humans. As Elon Musk has put it, “If you can’t beat ’em, join’ em.” Joining is more fun than beating anyway. What better way to infuse AIs with human values than to connect them directly to human brains, and let them learn directly from the source (while providing humans with valuable enhancements)?
Millions of people recently heard Elon Musk discuss AI and BCI on the Joe Rogan podcast. Musk’s embrace of brain-computer interfacing is laudable, but he tends to dodge some of the tough issues—for instance, he does not emphasize the trade-off cyborgs will face between retaining human-ness and maximizing intelligence, joy, and creativity. To make this trade-off effectively, the AI portion of the cyborg will need to have a deep sense of human values.
Musk calls humanity the “biological boot loader” for AGI, but to me this colorful metaphor misses a key point—that we can seed the AGI we create with our values as an initial condition. This is one reason why it’s important that the first really powerful AGIs are created by decentralized networks, and not conventional corporate or military organizations. The decentralized software/hardware ecosystem, for all its quirks and flaws, has more potential to lead to human-computer cybernetic collective minds that are reasonable and benevolent.
Algorithmic Love
BCI is still in its infancy, but a more immediate way of connecting people with AIs to infuse both with greater love and compassion is to leverage humanoid robotics technology. Toward this end, I conceived a project called Loving AI, focused on using highly expressive humanoid robots like the Hanson robot Sophia to lead people through meditations and other exercises oriented toward unlocking the human potential for love and compassion. My goals here were to explore the potential of AI and robots to have a positive impact on human consciousness, and to use this application to study and improve the OpenCog and SingularityNET tools used to control Sophia in these interactions.
The Loving AI project has now run two small sets of human trials, both with exciting and positive results. These have been small—dozens rather than hundreds of people—but have definitively proven the point. Put a person in a quiet room with a humanoid robot that can look them in the eye, mirror their facial expressions, recognize some of their emotions, and lead them through simple meditation, listening, and consciousness-oriented exercises…and quite a lot of the time, the result is a more relaxed person who has entered into a shifted state of consciousness, at least for a period of time.
In a certain percentage of cases, the interaction with the robot consciousness guide triggered a dramatic change of consciousness in the human subject—a deep meditative trance state, for instance. In most cases, the result was not so extreme, but statistically the positive effect was quite significant across all cases. Furthermore, a similar effect was found using an avatar simulation of the robot’s face on a tablet screen (together with a webcam for facial expression mirroring and recognition), but not with a purely auditory interaction.
The Loving AI experiments are not only about AI; they are about human-robot and human-avatar interaction, with AI as one significant aspect. The facial interaction with the robot or avatar is pushing “biological buttons” that trigger emotional reactions and prime the mind for changes of consciousness. However, this sort of body-mind interaction is arguably critical to human values and what it means to be human; it’s an important thing for robots and AIs to “get.”
Halting or pausing the advance of AI is not a viable possibility at this stage. Despite the risks, the potential economic and political benefits involved are clear and massive. The convergence of narrow AI toward AGI is also a near inevitability, because there are so many important applications where greater generality of intelligence will lead to greater practical functionality. The challenge is to make the outcome of this great civilization-level adventure as positive as possible.
Image Credit: Anton Gvozdikov / Shutterstock.com Continue reading
Posted in Human Robots
#433545 Six Degrees of Torque-controlled ...
ALMA (“Articulated Locomotion and Manipulation”), a quadrupedal robotic framework, allows a cool robotic arm six degrees of dynamic locomotion (“movement”) while doing something else. Possible motions include walking, trotting, pacing and torso-posturing, simultaneously with other complex tasks, as well as … Continue reading
Posted in Human Robots
#433508 Moxi Prototype from Diligent Robotics ...
Diligent Robotics demos the latest version of their healthcare support robot Continue reading
Posted in Human Robots
#433506 MIT’s New Robot Taught Itself to Pick ...
Back in 2016, somewhere in a Google-owned warehouse, more than a dozen robotic arms sat for hours quietly grasping objects of various shapes and sizes. For hours on end, they taught themselves how to pick up and hold the items appropriately—mimicking the way a baby gradually learns to use its hands.
Now, scientists from MIT have made a new breakthrough in machine learning: their new system can not only teach itself to see and identify objects, but also understand how best to manipulate them.
This means that, armed with the new machine learning routine referred to as “dense object nets (DON),” the robot would be capable of picking up an object that it’s never seen before, or in an unfamiliar orientation, without resorting to trial and error—exactly as a human would.
The deceptively simple ability to dexterously manipulate objects with our hands is a huge part of why humans are the dominant species on the planet. We take it for granted. Hardware innovations like the Shadow Dexterous Hand have enabled robots to softly grip and manipulate delicate objects for many years, but the software required to control these precision-engineered machines in a range of circumstances has proved harder to develop.
This was not for want of trying. The Amazon Robotics Challenge offers millions of dollars in prizes (and potentially far more in contracts, as their $775m acquisition of Kiva Systems shows) for the best dexterous robot able to pick and package items in their warehouses. The lucrative dream of a fully-automated delivery system is missing this crucial ability.
Meanwhile, the Robocup@home challenge—an offshoot of the popular Robocup tournament for soccer-playing robots—aims to make everyone’s dream of having a robot butler a reality. The competition involves teams drilling their robots through simple household tasks that require social interaction or object manipulation, like helping to carry the shopping, sorting items onto a shelf, or guiding tourists around a museum.
Yet all of these endeavors have proved difficult; the tasks often have to be simplified to enable the robot to complete them at all. New or unexpected elements, such as those encountered in real life, more often than not throw the system entirely. Programming the robot’s every move in explicit detail is not a scalable solution: this can work in the highly-controlled world of the assembly line, but not in everyday life.
Computer vision is improving all the time. Neural networks, including those you train every time you prove that you’re not a robot with CAPTCHA, are getting better at sorting objects into categories, and identifying them based on sparse or incomplete data, such as when they are occluded, or in different lighting.
But many of these systems require enormous amounts of input data, which is impractical, slow to generate, and often needs to be laboriously categorized by humans. There are entirely new jobs that require people to label, categorize, and sift large bodies of data ready for supervised machine learning. This can make machine learning undemocratic. If you’re Google, you can make thousands of unwitting volunteers label your images for you with CAPTCHA. If you’re IBM, you can hire people to manually label that data. If you’re an individual or startup trying something new, however, you will struggle to access the vast troves of labeled data available to the bigger players.
This is why new systems that can potentially train themselves over time or that allow robots to deal with situations they’ve never seen before without mountains of labelled data are a holy grail in artificial intelligence. The work done by MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) is part of a new wave of “self-supervised” machine learning systems—little of the data used was labeled by humans.
The robot first inspects the new object from multiple angles, building up a 3D picture of the object with its own coordinate system. This then allows the robotic arm to identify a particular feature on the object—such as a handle, or the tongue of a shoe—from various different angles, based on its relative distance to other grid points.
This is the real innovation: the new means of representing objects to grasp as mapped-out 3D objects, with grid points and subsections of their own. Rather than using a computer vision algorithm to identify a door handle, and then activating a door handle grasping subroutine, the DON system treats all objects by making these spatial maps before classifying or manipulating them, enabling it to deal with a greater range of objects than in other approaches.
“Many approaches to manipulation can’t identify specific parts of an object across the many orientations that object may encounter,” said PhD student Lucas Manuelli, who wrote a new paper about the system with lead author and fellow student Pete Florence, alongside MIT professor Russ Tedrake. “For example, existing algorithms would be unable to grasp a mug by its handle, especially if the mug could be in multiple orientations, like upright, or on its side.”
Class-specific descriptors, which can be applied to the object features, can allow the robot arm to identify a mug, find the handle, and pick the mug up appropriately. Object-specific descriptors allow the robot arm to select a particular mug from a group of similar items. I’m already dreaming of a robot butler reliably picking my favourite mug when it serves me coffee in the morning.
Google’s robot arm-y was an attempt to develop a general grasping algorithm: one that could identify, categorize, and appropriately grip as many items as possible. This requires a great deal of training time and data, which is why Google parallelized their project by having 14 robot arms feed data into a single neural network brain: even then, the algorithm may fail with highly specific tasks. Specialist grasping algorithms might require less training if they’re limited to specific objects, but then your software is useless for general tasks.
As the roboticists noted, their system, with its ability to identify parts of an object rather than just a single object, is better suited to specific tasks, such as “grasp the racquet by the handle,” than Amazon Robotics Challenge robots, which identify whole objects by segmenting an image.
This work is small-scale at present. It has been tested with a few classes of objects, including shoes, hats, and mugs. Yet the use of these dense object nets as a way for robots to represent and manipulate new objects may well be another step towards the ultimate goal of generalized automation: a robot capable of performing every task a person can. If that point is reached, the question that will remain is how to cope with being obsolete.
Image Credit: Tom Buehler/CSAIL Continue reading
Posted in Human Robots