Tag Archives: 2014

#436437 Why AI Will Be the Best Tool for ...

Dmitry Kaminskiy speaks as though he were trying to unload everything he knows about the science and economics of longevity—from senolytics research that seeks to stop aging cells from spewing inflammatory proteins and other molecules to the trillion-dollar life extension industry that he and his colleagues are trying to foster—in one sitting.

At the heart of the discussion with Singularity Hub is the idea that artificial intelligence will be the engine that drives breakthroughs in how we approach healthcare and healthy aging—a concept with little traction even just five years ago.

“At that time, it was considered too futuristic that artificial intelligence and data science … might be more accurate compared to any hypothesis of human doctors,” said Kaminskiy, co-founder and managing partner at Deep Knowledge Ventures, an investment firm that is betting big on AI and longevity.

How times have changed. Artificial intelligence in healthcare is attracting more investments and deals than just about any sector of the economy, according to data research firm CB Insights. In the most recent third quarter, AI healthcare startups raised nearly $1.6 billion, buoyed by a $550 million mega-round from London-based Babylon Health, which uses AI to collect data from patients, analyze the information, find comparable matches, then make recommendations.

Even without the big bump from Babylon Health, AI healthcare startups raised more than $1 billion last quarter, including two companies focused on longevity therapeutics: Juvenescence and Insilico Medicine.

The latter has risen to prominence for its novel use of reinforcement learning and general adversarial networks (GANs) to accelerate the drug discovery process. Insilico Medicine recently published a seminal paper that demonstrated how such an AI system could generate a drug candidate in just 46 days. Co-founder and CEO Alex Zhavoronkov said he believes there is no greater goal in healthcare today—or, really, any venture—than extending the healthy years of the human lifespan.

“I don’t think that there is anything more important than that,” he told Singularity Hub, explaining that an unhealthy society is detrimental to a healthy economy. “I think that it’s very, very important to extend healthy, productive lifespan just to fix the economy.”

An Aging Crisis
The surge of interest in longevity is coming at a time when life expectancy in the US is actually dropping, despite the fact that we spend more money on healthcare than any other nation.

A new paper in the Journal of the American Medical Association found that after six decades of gains, life expectancy for Americans has decreased since 2014, particularly among young and middle-aged adults. While some of the causes are societal, such as drug overdoses and suicide, others are health-related.

While average life expectancy in the US is 78, Kaminskiy noted that healthy life expectancy is about ten years less.

To Zhavoronkov’s point about the economy (a topic of great interest to Kaminskiy as well), the US spent $1.1 trillion on chronic diseases in 2016, according to a report from the Milken Institute, with diabetes, cardiovascular conditions, and Alzheimer’s among the most costly expenses to the healthcare system. When the indirect costs of lost economic productivity are included, the total price tag of chronic diseases in the US is $3.7 trillion, nearly 20 percent of GDP.

“So this is the major negative feedback on the national economy and creating a lot of negative social [and] financial issues,” Kaminskiy said.

Investing in Longevity
That has convinced Kaminskiy that an economy focused on extending healthy human lifespans—including the financial instruments and institutions required to support a long-lived population—is the best way forward.

He has co-authored a book on the topic with Margaretta Colangelo, another managing partner at Deep Knowledge Ventures, which has launched a specialized investment fund, Longevity.Capital, focused on the longevity industry. Kaminskiy estimates that there are now about 20 such investment funds dedicated to funding life extension companies.

In November at the inaugural AI for Longevity Summit in London, he and his collaborators also introduced the Longevity AI Consortium, an academic-industry initiative at King’s College London. Eventually, the research center will include an AI Longevity Accelerator program to serve as a bridge between startups and UK investors.

Deep Knowledge Ventures has committed about £7 million ($9 million) over the next three years to the accelerator program, as well as establishing similar consortiums in other regions of the world, according to Franco Cortese, a partner at Longevity.Capital and director of the Aging Analytics Agency, which has produced a series of reports on longevity.

A Cure for What Ages You
One of the most recent is an overview of Biomarkers for Longevity. A biomarker, in the case of longevity, is a measurable component of health that can indicate a disease state or a more general decline in health associated with aging. Examples range from something as simple as BMI as an indicator of obesity, which is associated with a number of chronic diseases, to sophisticated measurements of telomeres, the protective ends of chromosomes that shorten as we age.

While some researchers are working on moonshot therapies to reverse or slow aging—with a few even arguing we could expand human life on the order of centuries—Kaminskiy said he believes understanding biomarkers of aging could make more radical interventions unnecessary.

In this vision of healthcare, people would be able to monitor their health 24-7, with sensors attuned to various biomarkers that could indicate the onset of everything from the flu to diabetes. AI would be instrumental in not just ingesting the billions of data points required to develop such a system, but also what therapies, treatments, or micro-doses of a drug or supplement would be required to maintain homeostasis.

“Consider it like Tesla with many, many detectors, analyzing the behavior of the car in real time, and a cloud computing system monitoring those signals in real time with high frequency,” Kaminskiy explained. “So the same shall be applied for humans.”

And only sophisticated algorithms, Kaminskiy argued, can make longevity healthcare work on a mass scale but at the individual level. Precision medicine becomes preventive medicine. Healthcare truly becomes a system to support health rather than a way to fight disease.

Image Credit: Photo by h heyerlein on Unsplash Continue reading

Posted in Human Robots

#436234 Robot Gift Guide 2019

Welcome to the eighth edition of IEEE Spectrum’s Robot Gift Guide!

This year we’re featuring 15 robotic products that we think will make fantastic holiday gifts. As always, we tried to include a broad range of robot types and prices, focusing mostly on items released this year. (A reminder: While we provide links to places where you can buy these items, we’re not endorsing any in particular, and a little bit of research may result in better deals.)

If you need even more robot gift ideas, take a look at our past guides: 2018, 2017, 2016, 2015, 2014, 2013, and 2012. Some of those robots are still great choices and might be way cheaper now than when we first posted about them. And if you have suggestions that you’d like to share, post a comment below to help the rest of us find the perfect robot gift.

Skydio 2

Image: Skydio

What makes robots so compelling is their autonomy, and the Skydio 2 is one of the most autonomous robots we’ve ever seen. It uses an array of cameras to map its environment and avoid obstacles in real-time, making flight safe and effortless and enabling the kinds of shots that would be impossible otherwise. Seriously, this thing is magical, and it’s amazing that you can actually buy one.
$1,000
Skydio
UBTECH Jimu MeeBot 2

Image: UBTECH

The Jimu MeeBot 2.0 from UBTECH is a STEM education robot designed to be easy to build and program. It includes six servo motors, a color sensor, and LED lights. An app for iPhone or iPad provides step-by-step 3D instructions, and helps you code different behaviors for the robot. It’s available exclusively from Apple.
$130
Apple
iRobot Roomba s9+

Image: iRobot

We know that $1,400 is a crazy amount of money to spend on a robot vacuum, but the Roomba s9+ is a crazy robot vacuum. As if all of its sensors and mapping intelligence wasn’t enough, it empties itself, which means that you can have your floors vacuumed every single day for a month and you don’t have to even think about it. This is what home robots are supposed to be.
$1,400
iRobot
PFF Gita

Photo: Piaggio Fast Forward

Nobody likes carrying things, which is why Gita is perfect for everyone with an extra $3,000 lying around. Developed by Piaggio Fast Forward, this autonomous robot will follow you around with a cargo hold full of your most important stuff, and do it in a way guaranteed to attract as much attention as possible.
$3,250
Gita
DJI Mavic Mini

Photo: DJI

It’s tiny, it’s cheap, and it takes good pictures—what more could you ask for from a drone? And for $400, this is an excellent drone to get if you’re on a budget and comfortable with manual flight. Keep in mind that while the Mavic Mini is small enough that you don’t need to register it with the FAA, you do still need to follow all the same rules and regulations.
$400
DJI
LEGO Star Wars Droid Commander

Image: LEGO

Designed for kids ages 8+, this LEGO set includes more than 1,000 pieces, enough to build three different droids: R2-D2, Gonk Droid, and Mouse Droid. Using a Bluetooth-controlled robotic brick called Move Hub, which connects to the LEGO BOOST Star Wars app, kids can change how the robots behave and solve challenges, learning basic robotics and coding skills.
$200
LEGO
Sony Aibo

Photo: Sony

Robot pets don’t get much more sophisticated (or expensive) than Sony’s Aibo. Strictly speaking, it’s one of the most complex consumer robots you can buy, and Sony continues to add to Aibo’s software. Recent new features include user programmability, and the ability to “feed” it.
$2,900 (free aibone and paw pads until 12/29/2019)
Sony
Neato Botvac D4 Connected

Photo: Neato

The Neato Botvac D4 may not have all of the features of its fancier and more expensive siblings, but it does have the features that you probably care the most about: The ability to make maps of its environment for intelligent cleaning (using lasers!), along with user-defined no-go lines that keep it where you want it. And it cleans quite well, too.
$530 $350 (sale)
Neato Robotics
Cubelets Curiosity Set

Photo: Modular Robotics

Cubelets are magnetic blocks that you can snap together to make an endless variety of robots with no programming and no wires. The newest set, called Curiosity, is designed for kids ages 4+ and comes with 10 robotic cubes. These include light and distance sensors, motors, and a Bluetooth module, which connects the robot constructions to the Cubelets app.
$250
Modular Robotics
Tertill

Photo: Franklin Robotics

Tertill does one simple job: It weeds your garden. It’s waterproof, dirt proof, solar powered, and fully autonomous, meaning that you can leave it out in your garden all summer and just enjoy eating your plants rather than taking care of them.
$350
Tertill
iRobot Root

Photo: iRobot

Root was originally developed by Harvard University as a tool to help kids progressively learn to code. iRobot has taken over Root and is now supporting the curriculum, which starts for kids before they even know how to read and should keep them busy for years afterwards.
$200
iRobot
LOVOT

Image: Lovot

Let’s be honest: Nobody is really quite sure what LOVOT is. We can all agree that it’s kinda cute, though. And kinda weird. But cute. Created by Japanese robotics startup Groove X, LOVOT does have a whole bunch of tech packed into its bizarre little body and it will do its best to get you to love it.
$2,750 (¥300,000)
LOVOT
Sphero RVR

Photo: Sphero

RVR is a rugged, versatile, easy to program mobile robot. It’s a development platform designed to be a bridge between educational robots like Sphero and more sophisticated and expensive systems like Misty. It’s mostly affordable, very expandable, and comes from a company with a lot of experience making robots.
$250
Sphero
“How to Train Your Robot”

Image: Lawrence Hall of Science

Aimed at 4th and 5th graders, “How to Train Your Robot,” written by Blooma Goldberg, Ken Goldberg, and Ashley Chase, and illustrated by Dave Clegg, is a perfect introduction to robotics for kids who want to get started with designing and building robots. But the book isn’t just for beginners: It’s also a fun, inspiring read for kids who are already into robotics and want to go further—it even introduces concepts like computer simulations and deep learning. You can download a free digital copy or request hardcopies here.
Free
UC Berkeley
MIT Mini Cheetah

Photo: MIT

Yes, Boston Dynamics’ Spot, now available for lease, is probably the world’s most famous quadruped, but MIT is starting to pump out Mini Cheetahs en masse for researchers, and while we’re not exactly sure how you’d manage to get one of these things short of stealing one directly for MIT, a Mini Cheetah is our fantasy robotics gift this year. Mini Cheetah looks like a ton of fun—it’s portable, highly dynamic, super rugged, and easy to control. We want one!
Price N/A
MIT Biomimetic Robotics Lab

For more tech gift ideas, see also IEEE Spectrum’s annual Gift Guide. 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

#435757 Robotic Animal Agility

An off-shore wind power platform, somewhere in the North Sea, on a freezing cold night, with howling winds and waves crashing against the impressive structure. An imperturbable ANYmal is quietly conducting its inspection.

ANYmal, a medium sized dog-like quadruped robot, walks down the stairs, lifts a “paw” to open doors or to call the elevator and trots along corridors. Darkness is no problem: it knows the place perfectly, having 3D-mapped it. Its laser sensors keep it informed about its precise path, location and potential obstacles. It conducts its inspection across several rooms. Its cameras zoom in on counters, recording the measurements displayed. Its thermal sensors record the temperature of machines and equipment and its ultrasound microphone checks for potential gas leaks. The robot also inspects lever positions as well as the correct positioning of regulatory fire extinguishers. As the electronic buzz of its engines resumes, it carries on working tirelessly.

After a little over two hours of inspection, the robot returns to its docking station for recharging. It will soon head back out to conduct its next solitary patrol. ANYmal played alongside Mulder and Scully in the “X-Files” TV series*, but it is in no way a Hollywood robot. It genuinely exists and surveillance missions are part of its very near future.

Off-shore oil platforms, the first test fields and probably the first actual application of ANYmal. ©ANYbotics

This quadruped robot was designed by ANYbotics, a spinoff of the Swiss Federal Institute of Technology in Zurich (ETH Zurich). Made of carbon fibre and aluminium, it weighs about thirty kilos. It is fully ruggedised, water- and dust-proof (IP-67). A kevlar belly protects its main body, carrying its powerful brain, batteries, network device, power management system and navigational systems.

ANYmal was designed for all types of terrain, including rubble, sand or snow. It has been field tested on industrial sites and is at ease with new obstacles to overcome (and it can even get up after a fall). Depending on its mission, its batteries last 2 to 4 hours.

On its jointed legs, protected by rubber pads, it can walk (at the speed of human steps), trot, climb, curl upon itself to crawl, carry a load or even jump and dance. It is the need to move on all surfaces that has driven its designers to choose a quadruped. “Biped robots are not easy to stabilise, especially on irregular terrain” explains Dr Péter Fankhauser, co-founder and chief business development officer of ANYbotics. “Wheeled or tracked robots can carry heavy loads, but they are bulky and less agile. Flying drones are highly mobile, but cannot carry load, handle objects or operate in bad weather conditions. We believe that quadrupeds combine the optimal characteristics, both in terms of mobility and versatility.”

What served as a source of inspiration for the team behind the project, the Robotic Systems Lab of the ETH Zurich, is a champion of agility on rugged terrain: the mountain goat. “We are of course still a long way” says Fankhauser. “However, it remains our objective on the longer term.

The first prototype, ALoF, was designed already back in 2009. It was still rather slow, very rigid and clumsy – more of a proof of concept than a robot ready for application. In 2012, StarlETH, fitted with spring joints, could hop, jump and climb. It was with this robot that the team started participating in 2014 in ARGOS, a full-scale challenge, launched by the Total oil group. The idea was to present a robot capable of inspecting an off-shore drilling station autonomously.

Up against dozens of competitors, the ETH Zurich team was the only team to enter the competition with such a quadrupedal robot. They didn’t win, but the multiple field tests were growing evermore convincing. Especially because, during the challenge, the team designed new joints with elastic actuators made in-house. These joints, inspired by tendons and muscles, are compact, sealed and include their own custom control electronics. They can regulate joint torque, position and impedance directly. Thanks to this innovation, the team could enter the same competition with a new version of its robot, ANYmal, fitted with three joints on each leg.

The ARGOS experience confirms the relevance of the selected means of locomotion. “Our robot is lighter, takes up less space on site and it is less noisy” says Fankhauser. “It also overcomes bigger obstacles than larger wheeled or tracked robots!” As ANYmal generated public interest and its transformation into a genuine product seemed more than possible, the startup ANYbotics was launched in 2016. It sold not only its robot, but also its revolutionary joints, called ANYdrive.

Today, ANYmal is not yet ready for sale to companies. However, ANYbotics has a growing number of partnerships with several industries, testing the robot for a few days or several weeks, for all types of tasks. Last October, for example, ANYmal navigated its way through the dark sewage system of the city of Zurich in order to test its capacity to help workers in similar difficult, repetitive and even dangerous tasks.

Why such an early interest among companies? “Because many companies want to integrate robots into their maintenance tasks” answers Fankhauser. “With ANYmal, they can actually evaluate its feasibility and plan their strategy. Eventually, both the architecture and the equipment of buildings could be rethought to be adapted to these maintenance robots”.

ANYmal requires ruggedised, sealed and extremely reliable interconnection solutions, such as LEMO. ©ANYbotics

Through field demonstrations and testing, ANYbotics can gather masses of information (up to 50,000 measurements are recorded every second during each test!) “It helps us to shape the product.” In due time, the startup will be ready to deliver a commercial product which really caters for companies’ needs.

Inspection and surveillance tasks on industrial sites are not the only applications considered. The startup is also thinking of agricultural inspections – with its onboard sensors, ANYmal is capable of mapping its environment, measuring bio mass and even taking soil samples. In the longer term, it could also be used for search and rescue operations. By the way, the robot can already be switched to “remote control” mode at any time and can be easily tele-operated. It is also capable of live audio and video transmission.

The transition from the prototype to the marketed product stage will involve a number of further developments. These include increasing ANYmal’s agility and speed, extending its capacity to map large-scale environments, improving safety, security, user handling and integrating the system with the customer’s data management software. It will also be necessary to enhance the robot’s reliability “so that it can work for days, weeks, or even months without human supervision.” All required certifications will have to be obtained. The locomotion system, which had triggered the whole business, is only one of a number of considerations of ANYbotics.

Designed for extreme environments, for ANYmal smoke is not a problem and it can walk in the snow, through rubble or in water. ©ANYbotics

The startup is not all alone. In fact, it has sold ANYmal robots to a dozen major universities who use them to develop their know-how in robotics. The startup has also founded ANYmal Research, a community including members such as Toyota Research Institute, the German Aerospace Center and the computer company Nvidia. Members have full access to ANYmal’s control software, simulations and documentation. Sharing has boosted both software and hardware ideas and developments (built on ROS, the open-source Robot Operating System). In particular, payload variations, providing for expandability and scalability. For instance, one of the universities uses a robotic arm which enables ANYmal to grasp or handle objects and open doors.

Among possible applications, ANYbotics mentions entertainment. It is not only about playing in more films or TV series, but rather about participating in various attractions (trade shows, museums, etc.). “ANYmal is so novel that it attracts a great amount of interest” confirms Fankhauser with a smile. “Whenever we present it somewhere, people gather around.”

Videos of these events show a fascinated and sometimes slightly fearful audience, when ANYmal gets too close to them. Is it fear of the “bad robot”? “This fear exists indeed and we are happy to be able to use ANYmal also to promote public awareness towards robotics and robots.” Reminiscent of a young dog, ANYmal is truly adapted for the purpose.

However, Péter Fankhauser softens the image of humans and sophisticated robots living together. “These coming years, robots will continue to work in the background, like they have for a long time in factories. Then, they will be used in public places in a selective and targeted way, for instance for dangerous missions. We will need to wait another ten years before animal-like robots, such as ANYmal will share our everyday lives!”

At the Consumer Electronics Show (CES) in Las Vegas in January, Continental, the German automotive manufacturing company, used robots to demonstrate a last-mile delivery. It showed ANYmal getting out of an autonomous vehicle with a parcel, climbing onto the front porch, lifting a paw to ring the doorbell, depositing the parcel before getting back into the vehicle. This futuristic image seems very close indeed.

*X-Files, season 11, episode 7, aired in February 2018 Continue reading

Posted in Human Robots

#435707 AI Agents Startle Researchers With ...

After 25 million games, the AI agents playing hide-and-seek with each other had mastered four basic game strategies. The researchers expected that part.

After a total of 380 million games, the AI players developed strategies that the researchers didn’t know were possible in the game environment—which the researchers had themselves created. That was the part that surprised the team at OpenAI, a research company based in San Francisco.

The AI players learned everything via a machine learning technique known as reinforcement learning. In this learning method, AI agents start out by taking random actions. Sometimes those random actions produce desired results, which earn them rewards. Via trial-and-error on a massive scale, they can learn sophisticated strategies.

In the context of games, this process can be abetted by having the AI play against another version of itself, ensuring that the opponents will be evenly matched. It also locks the AI into a process of one-upmanship, where any new strategy that emerges forces the opponent to search for a countermeasure. Over time, this “self-play” amounted to what the researchers call an “auto-curriculum.”

According to OpenAI researcher Igor Mordatch, this experiment shows that self-play “is enough for the agents to learn surprising behaviors on their own—it’s like children playing with each other.”

Reinforcement is a hot field of AI research right now. OpenAI’s researchers used the technique when they trained a team of bots to play the video game Dota 2, which squashed a world-champion human team last April. The Alphabet subsidiary DeepMind has used it to triumph in the ancient board game Go and the video game StarCraft.

Aniruddha Kembhavi, a researcher at the Allen Institute for Artificial Intelligence (AI2) in Seattle, says games such as hide-and-seek offer a good way for AI agents to learn “foundational skills.” He worked on a team that taught their AllenAI to play Pictionary with humans, viewing the gameplay as a way for the AI to work on common sense reasoning and communication. “We are, however, quite far away from being able to translate these preliminary findings in highly simplified environments into the real world,” says Kembhavi.

Illustration: OpenAI

AI agents construct a fort during a hide-and-seek game developed by OpenAI.

In OpenAI’s game of hide-and-seek, both the hiders and the seekers received a reward only if they won the game, leaving the AI players to develop their own strategies. Within a simple 3D environment containing walls, blocks, and ramps, the players first learned to run around and chase each other (strategy 1). The hiders next learned to move the blocks around to build forts (2), and then the seekers learned to move the ramps (3), enabling them to jump inside the forts. Then the hiders learned to move all the ramps into their forts before the seekers could use them (4).

The two strategies that surprised the researchers came next. First the seekers learned that they could jump onto a box and “surf” it over to a fort (5), allowing them to jump in—a maneuver that the researchers hadn’t realized was physically possible in the game environment. So as a final countermeasure, the hiders learned to lock all the boxes into place (6) so they weren’t available for use as surfboards.

Illustration: OpenAI

An AI agent uses a nearby box to surf its way into a competitor’s fort.

In this circumstance, having AI agents behave in an unexpected way wasn’t a problem: They found different paths to their rewards, but didn’t cause any trouble. However, you can imagine situations in which the outcome would be rather serious. Robots acting in the real world could do real damage. And then there’s Nick Bostrom’s famous example of a paper clip factory run by an AI, whose goal is to make as many paper clips as possible. As Bostrom told IEEE Spectrum back in 2014, the AI might realize that “human bodies consist of atoms, and those atoms could be used to make some very nice paper clips.”

Bowen Baker, another member of the OpenAI research team, notes that it’s hard to predict all the ways an AI agent will act inside an environment—even a simple one. “Building these environments is hard,” he says. “The agents will come up with these unexpected behaviors, which will be a safety problem down the road when you put them in more complex environments.”

AI researcher Katja Hofmann at Microsoft Research Cambridge, in England, has seen a lot of gameplay by AI agents: She started a competition that uses Minecraft as the playing field. She says the emergent behavior seen in this game, and in prior experiments by other researchers, shows that games can be a useful for studies of safe and responsible AI.

“I find demonstrations like this, in games and game-like settings, a great way to explore the capabilities and limitations of existing approaches in a safe environment,” says Hofmann. “Results like these will help us develop a better understanding on how to validate and debug reinforcement learning systems–a crucial step on the path towards real-world applications.”

Baker says there’s also a hopeful takeaway from the surprises in the hide-and-seek experiment. “If you put these agents into a rich enough environment they will find strategies that we never knew were possible,” he says. “Maybe they can solve problems that we can’t imagine solutions to.” Continue reading

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