Category Archives: Human Robots

Everything about Humanoid Robots and Androids

#436140 Let’s Build Robots That Are as Smart ...

Illustration: Nicholas Little

Let’s face it: Robots are dumb. At best they are idiot savants, capable of doing one thing really well. In general, even those robots require specialized environments in which to do their one thing really well. This is why autonomous cars or robots for home health care are so difficult to build. They’ll need to react to an uncountable number of situations, and they’ll need a generalized understanding of the world in order to navigate them all.

Babies as young as two months already understand that an unsupported object will fall, while five-month-old babies know materials like sand and water will pour from a container rather than plop out as a single chunk. Robots lack these understandings, which hinders them as they try to navigate the world without a prescribed task and movement.

But we could see robots with a generalized understanding of the world (and the processing power required to wield it) thanks to the video-game industry. Researchers are bringing physics engines—the software that provides real-time physical interactions in complex video-game worlds—to robotics. The goal is to develop robots’ understanding in order to learn about the world in the same way babies do.

Giving robots a baby’s sense of physics helps them navigate the real world and can even save on computing power, according to Lochlainn Wilson, the CEO of SE4, a Japanese company building robots that could operate on Mars. SE4 plans to avoid the problems of latency caused by distance from Earth to Mars by building robots that can operate independently for a few hours before receiving more instructions from Earth.

Wilson says that his company uses simple physics engines such as PhysX to help build more-independent robots. He adds that if you can tie a physics engine to a coprocessor on the robot, the real-time basic physics intuitions won’t take compute cycles away from the robot’s primary processor, which will often be focused on a more complicated task.

Wilson’s firm occasionally still turns to a traditional graphics engine, such as Unity or the Unreal Engine, to handle the demands of a robot’s movement. In certain cases, however, such as a robot accounting for friction or understanding force, you really need a robust physics engine, Wilson says, not a graphics engine that simply simulates a virtual environment. For his projects, he often turns to the open-source Bullet Physics engine built by Erwin Coumans, who is now an employee at Google.

Bullet is a popular physics-engine option, but it isn’t the only one out there. Nvidia Corp., for example, has realized that its gaming and physics engines are well-placed to handle the computing demands required by robots. In a lab in Seattle, Nvidia is working with teams from the University of Washington to build kitchen robots, fully articulated robot hands and more, all equipped with Nvidia’s tech.

When I visited the lab, I watched a robot arm move boxes of food from counters to cabinets. That’s fairly straightforward, but that same robot arm could avoid my body if I got in its way, and it could adapt if I moved a box of food or dropped it onto the floor.

The robot could also understand that less pressure is needed to grasp something like a cardboard box of Cheez-It crackers versus something more durable like an aluminum can of tomato soup.

Nvidia’s silicon has already helped advance the fields of artificial intelligence and computer vision by making it possible to process multiple decisions in parallel. It’s possible that the company’s new focus on virtual worlds will help advance the field of robotics and teach robots to think like babies.

This article appears in the November 2019 print issue as “Robots as Smart as Babies.” 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

#436123 A Path Towards Reasonable Autonomous ...

Editor’s Note: The debate on autonomous weapons systems has been escalating over the past several years as the underlying technologies evolve to the point where their deployment in a military context seems inevitable. IEEE Spectrum has published a variety of perspectives on this issue. In summary, while there is a compelling argument to be made that autonomous weapons are inherently unethical and should be banned, there is also a compelling argument to be made that autonomous weapons could potentially make conflicts less harmful, especially to non-combatants. Despite an increasing amount of international attention (including from the United Nations), progress towards consensus, much less regulatory action, has been slow. The following workshop paper on autonomous weapons systems policy is remarkable because it was authored by a group of experts with very different (and in some cases divergent) views on the issue. Even so, they were able to reach consensus on a roadmap that all agreed was worth considering. It’s collaborations like this that could be the best way to establish a reasonable path forward on such a contentious issue, and with the permission of the authors, we’re excited to be able to share this paper (originally posted on Georgia Tech’s Mobile Robot Lab website) with you in its entirety.

Autonomous Weapon Systems: A Roadmapping Exercise
Over the past several years, there has been growing awareness and discussion surrounding the possibility of future lethal autonomous weapon systems that could fundamentally alter humanity’s relationship with violence in war. Lethal autonomous weapons present a host of legal, ethical, moral, and strategic challenges. At the same time, artificial intelligence (AI) technology could be used in ways that improve compliance with the laws of war and reduce non-combatant harm. Since 2014, states have come together annually at the United Nations to discuss lethal autonomous weapons systems1. Additionally, a growing number of individuals and non-governmental organizations have become active in discussions surrounding autonomous weapons, contributing to a rapidly expanding intellectual field working to better understand these issues. While a wide range of regulatory options have been proposed for dealing with the challenge of lethal autonomous weapons, ranging from a preemptive, legally binding international treaty to reinforcing compliance with existing laws of war, there is as yet no international consensus on a way forward.

The lack of an international policy consensus, whether codified in a formal document or otherwise, poses real risks. States could fall victim to a security dilemma in which they deploy untested or unsafe weapons that pose risks to civilians or international stability. Widespread proliferation could enable illicit uses by terrorists, criminals, or rogue states. Alternatively, a lack of guidance on which uses of autonomy are acceptable could stifle valuable research that could reduce the risk of non-combatant harm.

International debate thus far has predominantly centered around whether or not states should adopt a preemptive, legally-binding treaty that would ban lethal autonomous weapons before they can be built. Some of the authors of this document have called for such a treaty and would heartily support it, if states were to adopt it. Other authors of this document have argued an overly expansive treaty would foreclose the possibility of using AI to mitigate civilian harm. Options for international action are not binary, however, and there are a range of policy options that states should consider between adopting a comprehensive treaty or doing nothing.

The purpose of this paper is to explore the possibility of a middle road. If a roadmap could garner sufficient stakeholder support to have significant beneficial impact, then what elements could it contain? The exercise whose results are presented below was not to identify recommendations that the authors each prefer individually (the authors hold a broad spectrum of views), but instead to identify those components of a roadmap that the authors are all willing to entertain2. We, the authors, invite policymakers to consider these components as they weigh possible actions to address concerns surrounding autonomous weapons3.

Summary of Issues Surrounding Autonomous Weapons

There are a variety of issues that autonomous weapons raise, which might lend themselves to different approaches. A non-exhaustive list of issues includes:

The potential for beneficial uses of AI and autonomy that could improve precision and reliability in the use of force and reduce non-combatant harm.
Uncertainty about the path of future technology and the likelihood of autonomous weapons being used in compliance with the laws of war, or international humanitarian law (IHL), in different settings and on various timelines.
A desire for some degree of human involvement in the use of force. This has been expressed repeatedly in UN discussions on lethal autonomous weapon systems in different ways.
Particular risks surrounding lethal autonomous weapons specifically targeting personnel as opposed to vehicles or materiel.
Risks regarding international stability.
Risk of proliferation to terrorists, criminals, or rogue states.
Risk that autonomous systems that have been verified to be acceptable can be made unacceptable through software changes.
The potential for autonomous weapons to be used as scalable weapons enabling a small number of individuals to inflict very large-scale casualties at low cost, either intentionally or accidentally.

Summary of Components

A time-limited moratorium on the development, deployment, transfer, and use of anti-personnel lethal autonomous weapon systems4. Such a moratorium could include exceptions for certain classes of weapons.
Define guiding principles for human involvement in the use of force.
Develop protocols and/or technological means to mitigate the risk of unintentional escalation due to autonomous systems.
Develop strategies for preventing proliferation to illicit uses, such as by criminals, terrorists, or rogue states.
Conduct research to improve technologies and human-machine systems to reduce non-combatant harm and ensure IHL compliance in the use of future weapons.

Component 1:

States should consider adopting a five-year, renewable moratorium on the development, deployment, transfer, and use of anti-personnel lethal autonomous weapon systems. Anti-personnel lethal autonomous weapon systems are defined as weapons systems that, once activated, can select and engage dismounted human targets without further intervention by a human operator, possibly excluding systems such as:

Fixed-point defensive systems with human supervisory control to defend human-occupied bases or installations
Limited, proportional, automated counter-fire systems that return fire in order to provide immediate, local defense of humans
Time-limited pursuit deterrent munitions or systems
Autonomous weapon systems with size above a specified explosive weight limit that select as targets hand-held weapons, such as rifles, machine guns, anti-tank weapons, or man-portable air defense systems, provided there is adequate protection for non-combatants and ensuring IHL compliance5

The moratorium would not apply to:

Anti-vehicle or anti-materiel weapons
Non-lethal anti-personnel weapons
Research on ways of improving autonomous weapon technology to reduce non-combatant harm in future anti-personnel lethal autonomous weapon systems
Weapons that find, track, and engage specific individuals whom a human has decided should be engaged within a limited predetermined period of time and geographic region

Motivation:

This moratorium would pause development and deployment of anti-personnel lethal autonomous weapons systems to allow states to better understand the systemic risks of their use and to perform research that improves their safety, understandability, and effectiveness. Particular objectives could be to:

ensure that, prior to deployment, anti-personnel lethal autonomous weapons can be used in ways that are equal to or outperform humans in their compliance with IHL (other conditions may also apply prior to deployment being acceptable);
lay the groundwork for a potentially legally binding diplomatic instrument; and
decrease the geopolitical pressure on countries to deploy anti-personnel lethal autonomous weapons before they are reliable and well-understood.

Compliance Verification:

As part of a moratorium, states could consider various approaches to compliance verification. Potential approaches include:

Developing an industry cooperation regime analogous to that mandated under the Chemical Weapons Convention, whereby manufacturers must know their customers and report suspicious purchases of significant quantities of items such as fixed-wing drones, quadcopters, and other weaponizable robots.
Encouraging states to declare inventories of autonomous weapons for the purposes of transparency and confidence-building.
Facilitating scientific exchanges and military-to-military contacts to increase trust, transparency, and mutual understanding on topics such as compliance verification and safe operation of autonomous systems.
Designing control systems to require operator identity authentication and unalterable records of operation; enabling post-hoc compliance checks in case of plausible evidence of non-compliant autonomous weapon attacks.
Relating the quantity of weapons to corresponding capacities for human-in-the-loop operation of those weapons.
Designing weapons with air-gapped firing authorization circuits that are connected to the remote human operator but not to the on-board automated control system.
More generally, avoiding weapon designs that enable conversion from compliant to non-compliant categories or missions solely by software updates.
Designing weapons with formal proofs of relevant properties—e.g., the property that the weapon is unable to initiate an attack without human authorization. Proofs can, in principle, be provided using cryptographic techniques that allow the proofs to be checked by a third party without revealing any details of the underlying software.
Facilitate access to (non-classified) AI resources (software, data, methods for ensuring safe operation) to all states that remain in compliance and participate in transparency activities.

Component 2:

Define and universalize guiding principles for human involvement in the use of force.

Humans, not machines, are legal and moral agents in military operations.
It is a human responsibility to ensure that any attack, including one involving autonomous weapons, complies with the laws of war.
Humans responsible for initiating an attack must have sufficient understanding of the weapons, the targets, the environment and the context for use to determine whether that particular attack is lawful.
The attack must be bounded in space, time, target class, and means of attack in order for the determination about the lawfulness of that attack to be meaningful.
Militaries must invest in training, education, doctrine, policies, system design, and human-machine interfaces to ensure that humans remain responsible for attacks.

Component 3:

Develop protocols and/or technological means to mitigate the risk of unintentional escalation due to autonomous systems.

Specific potential measures include:

Developing safe rules for autonomous system behavior when in proximity to adversarial forces to avoid unintentional escalation or signaling. Examples include:

No-first-fire policy, so that autonomous weapons do not initiate hostilities without explicit human authorization.
A human must always be responsible for providing the mission for an autonomous system.
Taking steps to clearly distinguish exercises, patrols, reconnaissance, or other peacetime military operations from attacks in order to limit the possibility of reactions from adversary autonomous systems, such as autonomous air or coastal defenses.

Developing resilient communications links to ensure recallability of autonomous systems. Additionally, militaries should refrain from jamming others’ ability to recall their autonomous systems in order to afford the possibility of human correction in the event of unauthorized behavior.

Component 4:

Develop strategies for preventing proliferation to illicit uses, such as by criminals, terrorists, or rogue states:

Targeted multilateral controls to prevent large-scale sale and transfer of weaponizable robots and related military-specific components for illicit use.
Employ measures to render weaponizable robots less harmful (e.g., geofencing; hard-wired kill switch; onboard control systems largely implemented in unalterable, non-reprogrammable hardware such as application-specific integrated circuits).

Component 5:

Conduct research to improve technologies and human-machine systems to reduce non-combatant harm and ensure IHL-compliance in the use of future weapons, including:

Strategies to promote human moral engagement in decisions about the use of force
Risk assessment for autonomous weapon systems, including the potential for large-scale effects, geopolitical destabilization, accidental escalation, increased instability due to uncertainty about the relative military balance of power, and lowering thresholds to initiating conflict and for violence within conflict
Methodologies for ensuring the reliability and security of autonomous weapon systems
New techniques for verification, validation, explainability, characterization of failure conditions, and behavioral specifications.

About the Authors (in alphabetical order)

Ronald Arkin directs the Mobile Robot Laboratory at Georgia Tech.

Leslie Kaelbling is co-director of the Learning and Intelligent Systems Group at MIT.

Stuart Russell is a professor of computer science and engineering at UC Berkeley.

Dorsa Sadigh is an assistant professor of computer science and of electrical engineering at Stanford.

Paul Scharre directs the Technology and National Security Program at the Center for a New American Security (CNAS).

Bart Selman is a professor of computer science at Cornell.

Toby Walsh is a professor of artificial intelligence at the University of New South Wales (UNSW) Sydney.

The authors would like to thank Max Tegmark for organizing the three-day meeting from which this document was produced.

1 Autonomous Weapons System (AWS): A weapon system that, once activated, can select and engage targets without further intervention by a human operator. BACK TO TEXT↑

2 There is no implication that some authors would not personally support stronger recommendations. BACK TO TEXT↑

3 For ease of use, this working paper will frequently shorten “autonomous weapon system” to “autonomous weapon.” The terms should be treated as synonymous, with the understanding that “weapon” refers to the entire system: sensor, decision-making element, and munition. BACK TO TEXT↑

4 Anti-personnel lethal autonomous weapon system: A weapon system that, once activated, can select and engage dismounted human targets with lethal force and without further intervention by a human operator. BACK TO TEXT↑

5 The authors are not unanimous about this item because of concerns about ease of repurposing for mass-casualty missions targeting unarmed humans. The purpose of the lower limit on explosive payload weight would be to minimize the risk of such repurposing. There is precedent for using explosive weight limit as a mechanism of delineating between anti-personnel and anti-materiel weapons, such as the 1868 St. Petersburg Declaration Renouncing the Use, in Time of War, of Explosive Projectiles Under 400 Grammes Weight. BACK TO TEXT↑ Continue reading

Posted in Human Robots

#436119 How 3D Printing, Vertical Farming, and ...

Food. What we eat, and how we grow it, will be fundamentally transformed in the next decade.

Already, indoor farming is projected to be a US$40.25 billion industry by 2022, with a compound annual growth rate of 9.65 percent. Meanwhile, the food 3D printing industry is expected to grow at an even higher rate, averaging 50 percent annual growth.

And converging exponential technologies—from materials science to AI-driven digital agriculture—are not slowing down. Today’s breakthroughs will soon allow our planet to boost its food production by nearly 70 percent, using a fraction of the real estate and resources, to feed 9 billion by mid-century.

What you consume, how it was grown, and how it will end up in your stomach will all ride the wave of converging exponentials, revolutionizing the most basic of human needs.

Printing Food
3D printing has already had a profound impact on the manufacturing sector. We are now able to print in hundreds of different materials, making anything from toys to houses to organs. However, we are finally seeing the emergence of 3D printers that can print food itself.

Redefine Meat, an Israeli startup, wants to tackle industrial meat production using 3D printers that can generate meat, no animals required. The printer takes in fat, water, and three different plant protein sources, using these ingredients to print a meat fiber matrix with trapped fat and water, thus mimicking the texture and flavor of real meat.

Slated for release in 2020 at a cost of $100,000, their machines are rapidly demonetizing and will begin by targeting clients in industrial-scale meat production.

Anrich3D aims to take this process a step further, 3D printing meals that are customized to your medical records, heath data from your smart wearables, and patterns detected by your sleep trackers. The company plans to use multiple extruders for multi-material printing, allowing them to dispense each ingredient precisely for nutritionally optimized meals. Currently in an R&D phase at the Nanyang Technological University in Singapore, the company hopes to have its first taste tests in 2020.

These are only a few of the many 3D food printing startups springing into existence. The benefits from such innovations are boundless.

Not only will food 3D printing grant consumers control over the ingredients and mixtures they consume, but it is already beginning to enable new innovations in flavor itself, democratizing far healthier meal options in newly customizable cuisine categories.

Vertical Farming
Vertical farming, whereby food is grown in vertical stacks (in skyscrapers and buildings rather than outside in fields), marks a classic case of converging exponential technologies. Over just the past decade, the technology has surged from a handful of early-stage pilots to a full-grown industry.

Today, the average American meal travels 1,500-2,500 miles to get to your plate. As summed up by Worldwatch Institute researcher Brian Halweil, “We are spending far more energy to get food to the table than the energy we get from eating the food.” Additionally, the longer foods are out of the soil, the less nutritious they become, losing on average 45 percent of their nutrition before being consumed.

Yet beyond cutting down on time and transportation losses, vertical farming eliminates a whole host of issues in food production. Relying on hydroponics and aeroponics, vertical farms allows us to grow crops with 90 percent less water than traditional agriculture—which is critical for our increasingly thirsty planet.

Currently, the largest player around is Bay Area-based Plenty Inc. With over $200 million in funding from Softbank, Plenty is taking a smart tech approach to indoor agriculture. Plants grow on 20-foot-high towers, monitored by tens of thousands of cameras and sensors, optimized by big data and machine learning.

This allows the company to pack 40 plants in the space previously occupied by 1. The process also produces yields 350 times greater than outdoor farmland, using less than 1 percent as much water.

And rather than bespoke veggies for the wealthy few, Plenty’s processes allow them to knock 20-35 percent off the costs of traditional grocery stores. To date, Plenty has their home base in South San Francisco, a 100,000 square-foot farm in Kent, Washington, an indoor farm in the United Arab Emirates, and recently started construction on over 300 farms in China.

Another major player is New Jersey-based Aerofarms, which can now grow two million pounds of leafy greens without sunlight or soil.

To do this, Aerofarms leverages AI-controlled LEDs to provide optimized wavelengths of light for each plant. Using aeroponics, the company delivers nutrients by misting them directly onto the plants’ roots—no soil required. Rather, plants are suspended in a growth mesh fabric made from recycled water bottles. And here too, sensors, cameras, and machine learning govern the entire process.

While 50-80 percent of the cost of vertical farming is human labor, autonomous robotics promises to solve that problem. Enter contenders like Iron Ox, a firm that has developed the Angus robot, capable of moving around plant-growing containers.

The writing is on the wall, and traditional agriculture is fast being turned on its head.

Materials Science
In an era where materials science, nanotechnology, and biotechnology are rapidly becoming the same field of study, key advances are enabling us to create healthier, more nutritious, more efficient, and longer-lasting food.

For starters, we are now able to boost the photosynthetic abilities of plants. Using novel techniques to improve a micro-step in the photosynthesis process chain, researchers at UCLA were able to boost tobacco crop yield by 14-20 percent. Meanwhile, the RIPE Project, backed by Bill Gates and run out of the University of Illinois, has matched and improved those numbers.

And to top things off, The University of Essex was even able to improve tobacco yield by 27-47 percent by increasing the levels of protein involved in photo-respiration.

In yet another win for food-related materials science, Santa Barbara-based Apeel Sciences is further tackling the vexing challenge of food waste. Now approaching commercialization, Apeel uses lipids and glycerolipids found in the peels, seeds, and pulps of all fruits and vegetables to create “cutin”—the fatty substance that composes the skin of fruits and prevents them from rapidly spoiling by trapping moisture.

By then spraying fruits with this generated substance, Apeel can preserve foods 60 percent longer using an odorless, tasteless, colorless organic substance.

And stores across the US are already using this method. By leveraging our advancing knowledge of plants and chemistry, materials science is allowing us to produce more food with far longer-lasting freshness and more nutritious value than ever before.

Convergence
With advances in 3D printing, vertical farming, and materials sciences, we can now make food smarter, more productive, and far more resilient.

By the end of the next decade, you should be able to 3D print a fusion cuisine dish from the comfort of your home, using ingredients harvested from vertical farms, with nutritional value optimized by AI and materials science. However, even this picture doesn’t account for all the rapid changes underway in the food industry.

Join me next week for Part 2 of the Future of Food for a discussion on how food production will be transformed, quite literally, from the bottom up.

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Image Credit: Vanessa Bates Ramirez Continue reading

Posted in Human Robots

#436114 Video Friday: Transferring Human Motion ...

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We’ll also be posting a weekly calendar of upcoming robotics events for the next few months; here’s what we have so far (send us your events!):

ARSO 2019 – October 31-1, 2019 – Beijing, China
ROSCon 2019 – October 31-1, 2019 – Macau
IROS 2019 – November 4-8, 2019 – Macau
Let us know if you have suggestions for next week, and enjoy today’s videos.

We are very sad to say that MIT professor emeritus Woodie Flowers has passed away. Flowers will be remembered for (among many other things, like co-founding FIRST) the MIT 2.007 course that he began teaching in the mid-1970s, famous for its student competitions.

These competitions got a bunch of well-deserved publicity over the years; here’s one from 1985:

And the 2.007 competitions are still going strong—this year’s theme was Moonshot, and you can watch a replay of the event here.

[ MIT ]

Looks like Aibo is getting wireless integration with Hitachi appliances, which turns out to be pretty cute:

What is this magical box where you push a button and 60 seconds later fluffy pancakes come out?!

[ Aibo ]

LiftTiles are a “modular and reconfigurable room-scale shape display” that can turn your floor and walls into on-demand structures.

[ LiftTiles ]

Ben Katz, a grad student in MIT’s Biomimetics Robotics Lab, has been working on these beautiful desktop-sized Furuta pendulums:

That’s a crowdfunding project I’d pay way too much for.

[ Ben Katz ]

A clever bit of cable manipulation from MIT, using GelSight tactile sensors.

[ Paper ]

A useful display of industrial autonomy on ANYmal from the Oxford Robotics Group.

This video is of a demonstration for the ORCA Robotics Hub showing the ANYbotics ANYmal robot carrying out industrial inspection using autonomy software from Oxford Robotics Institute.

[ ORCA Hub ] via [ DRS ]

Thanks Maurice!

Meet Katie Hamilton, a software engineer at NASA’s Ames Research Center, who got into robotics because she wanted to help people with daily life. Katie writes code for robots, like Astrobee, who are assisting astronauts with routine tasks on the International Space Station.

[ NASA Astrobee ]

Transferring human motion to a mobile robotic manipulator and ensuring safe physical human-robot interaction are crucial steps towards automating complex manipulation tasks in human-shared environments. In this work we present a robot whole-body teleoperation framework for human motion transfer. We validate our approach through several experiments using the TIAGo robot, showing this could be an easy way for a non-expert to teach a rough manipulation skill to an assistive robot.

[ Paper ]

This is pretty cool looking for an autonomous boat, but we’ll see if they can build a real one by 2020 since at the moment it’s just an average rendering.

[ ProMare ]

I had no idea that asparagus grows like this. But, sure does make it easy for a robot to harvest.

[ Inaho ]

Skip to 2:30 in this Pepper unboxing video to hear the noise it makes when tickled.

[ HIT Lab NZ ]

In this interview, Jean Paul Laumond discusses his movement from mathematics to robotics and his career contributions to the field, especially in regards to motion planning and anthropomorphic motion. Describing his involvement at CNRS and in other robotics projects, such as HILARE, he comments on the distinction in perception between the robotics approach and a mathematics one.

[ IEEE RAS History ]

Here’s a couple of videos from the CMU Robotics Institute archives, showing some of the work that took place over the last few decades.

[ CMU RI ]

In this episode of the Artificial Intelligence Podcast, Lex Fridman speaks with David Ferrucci from IBM about Watson and (you guessed it) artificial intelligence.

David Ferrucci led the team that built Watson, the IBM question-answering system that beat the top humans in the world at the game of Jeopardy. He is also the Founder, CEO, and Chief Scientist of Elemental Cognition, a company working engineer AI systems that understand the world the way people do. This conversation is part of the Artificial Intelligence podcast.

[ AI Podcast ]

This week’s CMU RI Seminar is by Pieter Abbeel from UC Berkeley, on “Deep Learning for Robotics.”

Programming robots remains notoriously difficult. Equipping robots with the ability to learn would by-pass the need for what otherwise often ends up being time-consuming task specific programming. This talk will describe recent progress in deep reinforcement learning (robots learning through their own trial and error), in apprenticeship learning (robots learning from observing people), and in meta-learning for action (robots learning to learn). This work has led to new robotic capabilities in manipulation, locomotion, and flight, with the same approach underlying advances in each of these domains.

[ CMU RI ] Continue reading

Posted in Human Robots