Category Archives: robots

Turn yourself into a robot

Turning yourself into a robot is a little easier than I would have thought,

William Weir’s September 19, 2018 Yale University news release (also on EurekAlert) covers some of the same ground and fills in a few details,

When you think of robotics, you likely think of something rigid, heavy, and built for a specific purpose. New “Robotic Skins” technology developed by Yale researchers flips that notion on its head, allowing users to animate the inanimate and turn everyday objects into robots.

Developed in the lab of Rebecca Kramer-Bottiglio, assistant professor of mechanical engineering & materials science, robotic skins enable users to design their own robotic systems. Although the skins are designed with no specific task in mind, Kramer-Bottiglio said, they could be used for everything from search-and-rescue robots to wearable technologies. The results of the team’s work are published today in Science Robotics.

The skins are made from elastic sheets embedded with sensors and actuators developed in Kramer-Bottiglio’s lab. Placed on a deformable object — a stuffed animal or a foam tube, for instance — the skins animate these objects from their surfaces. The makeshift robots can perform different tasks depending on the properties of the soft objects and how the skins are applied.

We can take the skins and wrap them around one object to perform a task — locomotion, for example — and then take them off and put them on a different object to perform a different task, such as grasping and moving an object,” she said. “We can then take those same skins off that object and put them on a shirt to make an active wearable device.”

Robots are typically built with a single purpose in mind. The robotic skins, however, allow users to create multi-functional robots on the fly. That means they can be used in settings that hadn’t even been considered when they were designed, said Kramer-Bottiglio.

Additionally, using more than one skin at a time allows for more complex movements. For instance, Kramer-Bottiglio said, you can layer the skins to get different types of motion. “Now we can get combined modes of actuation — for example, simultaneous compression and bending.”

To demonstrate the robotic skins in action, the researchers created a handful of prototypes. These include foam cylinders that move like an inchworm, a shirt-like wearable device designed to correct poor posture, and a device with a gripper that can grasp and move objects.

Kramer-Bottiglio said she came up with the idea for the devices a few years ago when NASA  [US National Aeronautics and Space Administration] put out a call for soft robotic systems. The technology was designed in partnership with NASA, and its multifunctional and reusable nature would allow astronauts to accomplish an array of tasks with the same reconfigurable material. The same skins used to make a robotic arm out of a piece of foam could be removed and applied to create a soft Mars rover that can roll over rough terrain. With the robotic skins on board, the Yale scientist said, anything from balloons to balls of crumpled paper could potentially be made into a robot with a purpose.

One of the main things I considered was the importance of multifunctionality, especially for deep space exploration where the environment is unpredictable,” she said. “The question is: How do you prepare for the unknown unknowns?”

For the same line of research, Kramer-Bottiglio was recently awarded a $2 million grant from the National Science Foundation, as part of its Emerging Frontiers in Research and Innovation program.

Next, she said, the lab will work on streamlining the devices and explore the possibility of 3D printing the components.

Just in case the link to the paper becomes obsolete, here’s a citation for the paper,

OmniSkins: Robotic skins that turn inanimate objects into multifunctional robots by Joran W. Booth, Dylan Shah, Jennifer C. Case, Edward L. White, Michelle C. Yuen, Olivier Cyr-Choiniere, and Rebecca Kramer-Bottiglio. Science Robotics 19 Sep 2018: Vol. 3, Issue 22, eaat1853 DOI: 10.1126/scirobotics.aat1853

This paper is behind a paywall.

Summer (2019) Institute on AI (artificial intelligence) Societal Impacts, Governance, and Ethics. Summer Institute In Alberta, Canada

The deadline for applications is April 7, 2019. As for whether or not you might like to attend, here’s more from a joint March 11, 2019 Alberta Machine Intelligence Institute (Amii)/
Canadian Institute for Advanced Research (CIFAR)/University of California at Los Angeles (UCLA) Law School news release
(also on globalnewswire.com),

What will Artificial Intelligence (AI) mean for society? That’s the question scholars from a variety of disciplines will explore during the inaugural Summer Institute on AI Societal Impacts, Governance, and Ethics. Summer Institute, co-hosted by the Alberta Machine Intelligence Institute (Amii) and CIFAR, with support from UCLA School of Law, takes place July 22-24, 2019 in Edmonton, Canada.

“Recent advances in AI have brought a surge of attention to the field – both excitement and concern,” says co-organizer and UCLA professor, Edward Parson. “From algorithmic bias to autonomous vehicles, personal privacy to automation replacing jobs. Summer Institute will bring together exceptional people to talk about how humanity can receive the benefits and not get the worst harms from these rapid changes.”

Summer Institute brings together experts, grad students and researchers from multiple backgrounds to explore the societal, governmental, and ethical implications of AI. A combination of lectures, panels, and participatory problem-solving, this comprehensive interdisciplinary event aims to build understanding and action around these high-stakes issues.

“Machine intelligence is opening transformative opportunities across the world,” says John Shillington, CEO of Amii, “and Amii is excited to bring together our own world-leading researchers with experts from areas such as law, philosophy and ethics for this important discussion. Interdisciplinary perspectives will be essential to the ongoing development of machine intelligence and for ensuring these opportunities have the broadest reach possible.”

Over the three-day program, 30 graduate-level students and early-career researchers will engage with leading experts and researchers including event co-organizers: Western University’s Daniel Lizotte, Amii’s Alona Fyshe and UCLA’s Edward Parson. Participants will also have a chance to shape the curriculum throughout this uniquely interactive event.

Summer Institute takes place prior to Deep Learning and Reinforcement Learning Summer School, and includes a combined event on July 24th [2019] for both Summer Institute and Summer School participants.

Visit dlrlsummerschool.ca/the-summer-institute to apply; applications close April 7, 2019.

View our Summer Institute Biographies & Boilerplates for more information on confirmed faculty members and co-hosting organizations. Follow the conversation through social media channels using the hashtag #SI2019.

Media Contact: Spencer Murray, Director of Communications & Public Relations, Amii
t: 587.415.6100 | c: 780.991.7136 | e: spencer.murray@amii.ca

There’s a bit more information on The Summer Institute on AI and Society webpage (on the Deep Learning and Reinforcement Learning Summer School 2019 website) such as this more complete list of speakers,

Confirmed speakers at Summer Institute include:

Alona Fyshe, University of Alberta/Amii (SI co-organizer)
Edward Parson, UCLA (SI co-organizer)
Daniel Lizotte, Western University (SI co-organizer)
Geoffrey Rockwell, University of Alberta
Graham Taylor, University of Guelph/Vector Institute
Rob Lempert, Rand Corporation
Gary Marchant, Arizona State University
Richard Re, UCLA
Evan Selinger, Rochester Institute of Technology
Elana Zeide, UCLA

Two questions, why are all the summer school faculty either Canada- or US-based? What about South American, Asian, Middle Eastern, etc. thinkers?

One last thought, I wonder if this ‘AI & ethics summer institute’ has anything to do with the Pan-Canadian Artificial Intelligence Strategy, which CIFAR administers and where both the University of Alberta and Vector Institute are members.

Media registration for United Nations 3rd AI (artificial intelligence) for Good Global Summit

This is strictly for folks who have media accreditation. First, the news about the summit and then some detail about how you might accreditation should you be interested in going to Switzerland. Warning: The International Telecommunications Union which is holding this summit is a United Nations agency and you will note almost an entire paragraph of ‘alphabet soup’ when all the ‘sister’ agencies involved are listed.

From the March 21, 2019 International Telecommunications Union (ITU) media advisory (Note: There have been some changes to the formatting),

Geneva, 21 March 2019
​​​​​​​​​​​​​
Artificial Intelligence (AI) h​as taken giant leaps forward in recent years, inspiring growing confidence in AI’s ability to assist in solving some of humanity’s greatest challenges. Leaders in AI and humanitarian action are convening on the neutral platform offered by the United Nations to work towards AI improving the quality and sustainability of life on our planet.
The 2017 summit marked the beginning of global dialogue on the potential of AI to act as a force for good. The action-oriented 2018 summit gave rise to numerous ‘AI for Good’ projects, including an ‘AI for Health’ Focus Group, now led by ITU and the World Health Organization (WHO). The 2019 summit will continue to connect AI innovators with public and private-sector decision-makers, building collaboration to maximize the impact of ‘AI for Good’.

Organized by the International Telecommunication Union (IT​U) – the United Nations specialized agency for information and communication technology (ICT) – in partnership with the XPRIZE Foundation, the Association for Computing Machinery (ACM) and close to 30 sister United Nations agencies, the 3rd annual ​AI for Good Global Summit in Geneva, 28-31 May, is the leading United Nations platform for inclusive dialogue on AI. The goal of the summit is to identify practical applications of AI to accelerate progress towards the United Nations Sustainable Development Goals​​.​

►►► MEDIA REGISTRATION IS NOW OPEN ◄◄◄

Media are recommended to register in advance to receive key announcements in the run-up to the summit.

WHAT: The summit attracts a cross-section of AI experts from industry and academia, global business leaders, Heads of UN agencies, ICT ministers, non-governmental organizations, and civil society.

The summit is designed to generate ‘AI for Good’ projects able to be enacted in the near term, guided by the summit’s multi-stakeholder and inter-disciplinary audience. It also formulates supporting strategies to ensure trusted, safe and inclusive development of AI technologies and equitable access to their benefits.

The 2019 summit will highlight AI’s value in advancing education, healthcare and wellbeing, social and economic equality, space research, and smart and safe mobility. It will propose actions to assist high-potential AI solutions in achieving global scale. It will host debate around unintended consequences of AI as well as AI’s relationship with art and culture. A ‘learning day’ will offer potential AI adopters an audience with leading AI experts and educators.

A dynamic show floor will demonstrate innovations at the cutting edge of AI research and development, such as the IBM Watson live debater; the Fusion collaborative exoskeleton; RoboRace, the world’s first self-driving electric racing car; avatar prototypes, and the ElliQ social robot for the care of the elderly. Summit attendees can also look forward to AI-inspired performances from world-renowned musician Jojo Mayer and award-winning vocal and visual artist​ Reeps One

WHEN: 28-31 May 2019
WHERE: International Conference Centre Geneva, 17 Rue de Varembé, Geneva, Switzerland

WHO: Over 100 speakers have been confirmed to date, including:

Jim Hagemann Snabe – Chairman, Siemens​​
Cédric Villani – AI advisor to the President of France, and Mathematics Fields Medal Winner
Jean-Philippe Courtois – President of Global Operations, Microsoft
Anousheh Ansari – CEO, XPRIZE Foundation, Space Ambassador
Yves Daccord – Director General, International Committee of the Red Cross
Yan Huang – Director AI Innovation, Baidu
Timnit Gebru – Head of AI Ethics, Google
Vladimir Kramnik – World Chess Champion
Vicki Hanson – CEO, ACM
Zoubin Ghahramani – Chief Scientist, Uber, and Professor of Engineering, University of Cambridge
Lucas di Grassi – Formula E World Racing Champion, CEO of Roborac

Confirmed speakers also include C-level and expert representatives of Bosch, Botnar Foundation, Byton, Cambridge Quantum Computing, the cities of Montreal and Pittsburg, Darktrace, Deloitte, EPFL, European Space Agency, Factmata, Google, IBM, IEEE, IFIP, Intel, IPSoft, Iridescent, MasterCard, Mechanica.ai, Minecraft, NASA, Nethope, NVIDIA, Ocean Protocol, Open AI, Philips, PWC, Stanford University, University of Geneva, and WWF.

Please visit the summit programme for more information on the latest speakers, breakthrough sessions and panels.

The summit is organized in partnership with the following sister United Nations agencies:CTBTO, ICAO, ILO, IOM, UNAIDS, UNCTAD, UNDESA, UNDPA, UNEP, UNESCO, UNFPA, UNGP, UNHCR, UNICEF, UNICRI, UNIDIR, UNIDO, UNISDR, UNITAR, UNODA, UNODC, UNOOSA, UNOPS, UNU, WBG,  WFP, WHO, and WIPO.

The 2019 summit is kindly supported by Platinum Sponsor and Strategic Partner, Microsoft; Gold Sponsors, ACM, the Kay Family Foundation, Mind.ai and the Autonomous Driver Alliance; Silver Sponsors, Deloitte and the Zero Abuse Project; and Bronze Sponsor, Live Tiles.​

More information available at aiforgood.itu.int
​Join the conversat​ion on social media ​using the hashtag #AIforGood

As promised here are the media accreditation details from the ITU Media Registration and Accreditation webpage,

To gain media access, ITU must confirm your status as a bona fide member of the media. Therefore, please read ITU’s Media Accreditation Guidelines below so you are aware of the information you will be required to submit for ITU to confirm such status. ​
Media accreditation is not granted to 1) non-editorial staff working for a publishing house (e.g. management, marketing, advertising executives, etc.); 2) researchers, academics, authors or editors of directories; 3) employees of information outlets of public, non-governmental or private entities that are not first and foremost media organizations; 4) members of professional broadcasting or media associations, 5) press or communication professionals accompanying member state delegations; and 6) citizen journalists under no apparent editorial board oversight. If you have questions about your eligibility, please email us at pressreg@itu.int.​

Applications for accreditation are considered on a case-by-case basis and ITU reserves the right to request additional proof or documentation other than what is listed below. ​​​Media accreditation decisions rest with ITU and all decisions are final.

​Accreditation eligibility & credentials 
​1. Journalists* should provide an official letter of assignment from the Editor-in-Chief (or the News Editor for radio/TV). One letter per crew/editorial team will suffice. Editors-in-Chief and Bureau Chiefs should submit a letter from their Director. Please email this to pressreg@itu.int along with the required supporting credentials, based on the type of media organization you work for:

​​​​​Print and online publications should be available to the general public and published at least 6 times a year by an organization whose principle business activity is publishing and which generally carries paid advertising;
o please submit 2 copies or links to recent byline articles published within the last 4 months.

News wire services should provide news coverage to subscribers, including newspapers, periodicals and/or television networks;
o please submit 2 copies or links to recent byline articles or broadcasting material published within the last 4 months.

Broadcast media should provide news and information programmes to the general public. Inde​pendent film and video production companies can only be accredited if officially mandated by a broadcast station via a letter of assignment;
o please submit broadcasting material published within the last 4 months.

Freelance journalists and photographers must provide clear documentation that they are on assignment from a specific news organization or publication. Evidence that they regularly supply journalistic content to recognized media may be acceptable in the absence of an assignment letter and at the discretion of the ITU Corporate Communication Division.
o if possible, please submit a valid assignment letter from the news organization or publication.

2. Bloggers and community media may be granted accreditation if the content produced is deemed relevant to the industry, contains news commentary, is regularly updated and/or made publicly available. Corporate bloggers may register as normal participants (not media). Please see Guidelines for Bloggers and Community Media Accreditation below for more details:

Special guidelines for bloggers and community ​media accreditation

ITU is committed to working with independent and ‘new media’ reporters and columnists who reach their audiences via blogs, podcasts, video blogs, community or online radio, limited print formats which generally carry paid advertising ​​and other online media. These are some of the guidelines we use to determine whether to accredit bloggers and community media representatives:

​​ITU reserves the right to request traffic data from a third party (Sitemeter, Technorati, Feedburner, iTunes or equivalent) when considering your application. While the decision to grant access is not based solely on traffic/subscriber data, we ask that applicants provide sufficient transparency into their operations to help us make a fair and timely decision. If your media outlet is new, you must have an established record of having written extensively on ICT issues and must present copies or links to two recently published videos, podcasts or articles with your byline.​

Obtaining media accreditation for ITU events is an opportunity to meet and interact with key industry and political figures. While continued accreditation for ITU events is not directly contingent on producing coverage, owing to space limitations we may take this into consideration when processing future accreditation requests. Following any ITU event for which you are accredited, we therefore kindly request that you forward a link to your post/podcast/video blog to pressreg​@itu.int.

Bloggers who are granted access to ITU events are expected to act professionally. Those who do not maintain the standards expected of professional media representatives run the risk of having their accreditation withdrawn.

UN-accre​dited media

Media already accredited and badged by the United Nations are automatically accredited and registered by ITU. In this case, you only need to send a copy of your UN badge to pressreg@itu.int​to make sure you receive your event badge. Anyone joining an ITU event MUST have an event badge in order to access the premises. ​Please make sure you let us know in advance that you are planning to attend so your event badge is ready for printing and pick-up.​

You can register and get accreditation here (scroll past the guidelines). Good luck!

Popcorn-powered robots

A soft robotic device powered by popcorn, constructed by researchers in Cornell’s Collective Embodied Intelligence Lab. Courtesy: Cornell University

What an intriguing idea, popcorn-powered robots, and one I have difficulty imagining even with the help of the image above. A July 26, 2018 Cornell University news release (an edited version is on EurekAlert) by Melanie Lefkowitz describes the concept,

Cornell researchers have discovered how to power simple robots with a novel substance that, when heated, can expand more than 10 times in size, change its viscosity by a factor of 10 and transition from regular to highly irregular granules with surprising force.

You can also eat it with a little butter and salt.

“Popcorn-Driven Robotic Actuators,” a recent paper co-authored by doctoral student Steven Ceron, mechanical engineering, and Kirstin H. Petersen, assistant professor of electrical and computer engineering, examines how popcorn’s unique qualities can power inexpensive robotic devices that grip, expand or change rigidity.

“The goal of our lab is to try to make very minimalistic robots which, when deployed in high numbers, can still accomplish great things,” said Petersen, who runs Cornell’s Collective Embodied Intelligence Lab. “Simple robots are cheap and less prone to failures and wear, so we can have many operating autonomously over a long time. So we are always looking for new and innovative ideas that will permit us to have more functionalities for less, and popcorn is one of those.”

The study is the first to consider powering robots with popcorn, which is inexpensive, readily available, biodegradable and of course, edible. Since kernels can expand rapidly, exerting force and motion when heated, they could potentially power miniature jumping robots. Edible devices could be ingested for medical procedures. The mix of hard, unpopped granules and lighter popped corn could replace fluids in soft robots without the need for air pumps or compressors.

“Pumps and compressors tend to be more expensive, and they add a lot of weight and expense to your robot,” said Ceron, the paper’s lead author. “With popcorn, in some of the demonstrations that we showed, you just need to apply voltage to get the kernels to pop, so it would take all the bulky and expensive parts out of the robots.”

Since kernels can’t shrink once they’ve popped, a popcorn-powered mechanism can generally be used only once, though multiple uses are conceivable because popped kernels can dissolve in water, Ceron said.

The researchers experimented with Amish Country Extra Small popcorn, which they chose because the brand did not use additives. The extra-small variety had the highest expansion ratio of those they tested.

After studying popcorn’s properties using different types of heating, the researchers constructed three simple robotic actuators – devices used to perform a function.

For a jamming actuator, 36 kernels of popcorn heated with nichrome wire were used to stiffen a flexible silicone beam. For an elastomer actuator, they constructed a three-fingered soft gripper, whose silicone fingers were stuffed with popcorn heated by nichrome wire. When the kernels popped, the expansion exerted pressure against the outer walls of the fingers, causing them to curl. For an origami actuator, they folded recycled Newman’s Own organic popcorn bags into origami bellows folds, filled them with kernels and microwaved them. The expansion of the kernels was strong enough to support the weight of a nine-pound kettlebell.

The paper was presented at the IEEE [Institute of Electrical and Electronics Engineers] International Conference on Robotics and Automation in May and co-authored with Aleena Kurumunda ’19, Eashan Garg ’20, Mira Kim ’20 and Tosin Yeku ’20. Petersen said she hopes it inspires researchers to explore the possibilities of other nontraditional materials.

“Robotics is really good at embracing new ideas, and we can be super creative about what we use to generate multifunctional properties,” she said. “In the end we come up with very simple solutions to fairly complex problems. We don’t always have to look for high-tech solutions. Sometimes the answer is right in front of us.”

The work was supported by the Cornell Engineering Learning Initiative, the Cornell Electrical and Computer Engineering Early Career Award and the Cornell Sloan Fellowship.

Here’s a link to and a citation for the paper,

Popcorn-Driven Robotic Actuators by Steven Ceron, Aleena Kurumunda, Eashan Garg, Mira Kim, Tosin Yeku, and Kirstin Petersen. Presented at the IEEE International Conference on Robotics and Automation held in May 21-25, 2018 in Brisbane, Australia.

The researchers have made this video demonstrating the technology,

Artificial intelligence (AI) brings together International Telecommunications Union (ITU) and World Health Organization (WHO) and AI outperforms animal testing

Following on my May 11, 2018 posting about the International Telecommunications Union (ITU) and the 2018 AI for Good Global Summit in mid- May, there’s an announcement. My other bit of AI news concerns animal testing.

Leveraging the power of AI for health

A July 24, 2018 ITU press release (a shorter version was received via email) announces a joint initiative focused on improving health,

Two United Nations specialized agencies are joining forces to expand the use of artificial intelligence (AI) in the health sector to a global scale, and to leverage the power of AI to advance health for all worldwide. The International Telecommunication Union (ITU) and the World Health Organization (WHO) will work together through the newly established ITU Focus Group on AI for Health to develop an international “AI for health” standards framework and to identify use cases of AI in the health sector that can be scaled-up for global impact. The group is open to all interested parties.

“AI could help patients to assess their symptoms, enable medical professionals in underserved areas to focus on critical cases, and save great numbers of lives in emergencies by delivering medical diagnoses to hospitals before patients arrive to be treated,” said ITU Secretary-General Houlin Zhao. “ITU and WHO plan to ensure that such capabilities are available worldwide for the benefit of everyone, everywhere.”

The demand for such a platform was first identified by participants of the second AI for Good Global Summit held in Geneva, 15-17 May 2018. During the summit, AI and the health sector were recognized as a very promising combination, and it was announced that AI-powered technologies such as skin disease recognition and diagnostic applications based on symptom questions could be deployed on six billion smartphones by 2021.

The ITU Focus Group on AI for Health is coordinated through ITU’s Telecommunications Standardization Sector – which works with ITU’s 193 Member States and more than 800 industry and academic members to establish global standards for emerging ICT innovations. It will lead an intensive two-year analysis of international standardization opportunities towards delivery of a benchmarking framework of international standards and recommendations by ITU and WHO for the use of AI in the health sector.

“I believe the subject of AI for health is both important and useful for advancing health for all,” said WHO Director-General Tedros Adhanom Ghebreyesus.

The ITU Focus Group on AI for Health will also engage researchers, engineers, practitioners, entrepreneurs and policy makers to develop guidance documents for national administrations, to steer the creation of policies that ensure the safe, appropriate use of AI in the health sector.

“1.3 billion people have a mobile phone and we can use this technology to provide AI-powered health data analytics to people with limited or no access to medical care. AI can enhance health by improving medical diagnostics and associated health intervention decisions on a global scale,” said Thomas Wiegand, ITU Focus Group on AI for Health Chairman, and Executive Director of the Fraunhofer Heinrich Hertz Institute, as well as professor at TU Berlin.

He added, “The health sector is in many countries among the largest economic sectors or one of the fastest-growing, signalling a particularly timely need for international standardization of the convergence of AI and health.”

Data analytics are certain to form a large part of the ITU focus group’s work. AI systems are proving increasingly adept at interpreting laboratory results and medical imagery and extracting diagnostically relevant information from text or complex sensor streams.

As part of this, the ITU Focus Group for AI for Health will also produce an assessment framework to standardize the evaluation and validation of AI algorithms — including the identification of structured and normalized data to train AI algorithms. It will develop open benchmarks with the aim of these becoming international standards.

The ITU Focus Group for AI for Health will report to the ITU standardization expert group for multimedia, Study Group 16.

I got curious about Study Group 16 (from the Study Group 16 at a glance webpage),

Study Group 16 leads ITU’s standardization work on multimedia coding, systems and applications, including the coordination of related studies across the various ITU-T SGs. It is also the lead study group on ubiquitous and Internet of Things (IoT) applications; telecommunication/ICT accessibility for persons with disabilities; intelligent transport system (ITS) communications; e-health; and Internet Protocol television (IPTV).

Multimedia is at the core of the most recent advances in information and communication technologies (ICTs) – especially when we consider that most innovation today is agnostic of the transport and network layers, focusing rather on the higher OSI model layers.

SG16 is active in all aspects of multimedia standardization, including terminals, architecture, protocols, security, mobility, interworking and quality of service (QoS). It focuses its studies on telepresence and conferencing systems; IPTV; digital signage; speech, audio and visual coding; network signal processing; PSTN modems and interfaces; facsimile terminals; and ICT accessibility.

I wonder which group deals with artificial intelligence and, possibly, robots.

Chemical testing without animals

Thomas Hartung, professor of environmental health and engineering at Johns Hopkins University (US), describes in his July 25, 2018 essay (written for The Conversation) on phys.org the situation where chemical testing is concerned,

Most consumers would be dismayed with how little we know about the majority of chemicals. Only 3 percent of industrial chemicals – mostly drugs and pesticides – are comprehensively tested. Most of the 80,000 to 140,000 chemicals in consumer products have not been tested at all or just examined superficially to see what harm they may do locally, at the site of contact and at extremely high doses.

I am a physician and former head of the European Center for the Validation of Alternative Methods of the European Commission (2002-2008), and I am dedicated to finding faster, cheaper and more accurate methods of testing the safety of chemicals. To that end, I now lead a new program at Johns Hopkins University to revamp the safety sciences.

As part of this effort, we have now developed a computer method of testing chemicals that could save more than a US$1 billion annually and more than 2 million animals. Especially in times where the government is rolling back regulations on the chemical industry, new methods to identify dangerous substances are critical for human and environmental health.

Having written on the topic of alternatives to animal testing on a number of occasions (my December 26, 2014 posting provides an overview of sorts), I was particularly interested to see this in Hartung’s July 25, 2018 essay on The Conversation (Note: Links have been removed),

Following the vision of Toxicology for the 21st Century, a movement led by U.S. agencies to revamp safety testing, important work was carried out by my Ph.D. student Tom Luechtefeld at the Johns Hopkins Center for Alternatives to Animal Testing. Teaming up with Underwriters Laboratories, we have now leveraged an expanded database and machine learning to predict toxic properties. As we report in the journal Toxicological Sciences, we developed a novel algorithm and database for analyzing chemicals and determining their toxicity – what we call read-across structure activity relationship, RASAR.

This graphic reveals a small part of the chemical universe. Each dot represents a different chemical. Chemicals that are close together have similar structures and often properties. Thomas Hartung, CC BY-SA

To do this, we first created an enormous database with 10 million chemical structures by adding more public databases filled with chemical data, which, if you crunch the numbers, represent 50 trillion pairs of chemicals. A supercomputer then created a map of the chemical universe, in which chemicals are positioned close together if they share many structures in common and far where they don’t. Most of the time, any molecule close to a toxic molecule is also dangerous. Even more likely if many toxic substances are close, harmless substances are far. Any substance can now be analyzed by placing it into this map.

If this sounds simple, it’s not. It requires half a billion mathematical calculations per chemical to see where it fits. The chemical neighborhood focuses on 74 characteristics which are used to predict the properties of a substance. Using the properties of the neighboring chemicals, we can predict whether an untested chemical is hazardous. For example, for predicting whether a chemical will cause eye irritation, our computer program not only uses information from similar chemicals, which were tested on rabbit eyes, but also information for skin irritation. This is because what typically irritates the skin also harms the eye.

How well does the computer identify toxic chemicals?

This method will be used for new untested substances. However, if you do this for chemicals for which you actually have data, and compare prediction with reality, you can test how well this prediction works. We did this for 48,000 chemicals that were well characterized for at least one aspect of toxicity, and we found the toxic substances in 89 percent of cases.

This is clearly more accurate that the corresponding animal tests which only yield the correct answer 70 percent of the time. The RASAR shall now be formally validated by an interagency committee of 16 U.S. agencies, including the EPA [Environmental Protection Agency] and FDA [Food and Drug Administration], that will challenge our computer program with chemicals for which the outcome is unknown. This is a prerequisite for acceptance and use in many countries and industries.

The potential is enormous: The RASAR approach is in essence based on chemical data that was registered for the 2010 and 2013 REACH [Registration, Evaluation, Authorizations and Restriction of Chemicals] deadlines [in Europe]. If our estimates are correct and chemical producers would have not registered chemicals after 2013, and instead used our RASAR program, we would have saved 2.8 million animals and $490 million in testing costs – and received more reliable data. We have to admit that this is a very theoretical calculation, but it shows how valuable this approach could be for other regulatory programs and safety assessments.

In the future, a chemist could check RASAR before even synthesizing their next chemical to check whether the new structure will have problems. Or a product developer can pick alternatives to toxic substances to use in their products. This is a powerful technology, which is only starting to show all its potential.

It’s been my experience that these claims having led a movement (Toxicology for the 21st Century) are often contested with many others competing for the title of ‘leader’ or ‘first’. That said, this RASAR approach seems very exciting, especially in light of the skepticism about limiting and/or making animal testing unnecessary noted in my December 26, 2014 posting.it was from someone I thought knew better.

Here’s a link to and a citation for the paper mentioned in Hartung’s essay,

Machine learning of toxicological big data enables read-across structure activity relationships (RASAR) outperforming animal test reproducibility by Thomas Luechtefeld, Dan Marsh, Craig Rowlands, Thomas Hartung. Toxicological Sciences, kfy152, https://doi.org/10.1093/toxsci/kfy152 Published: 11 July 2018

This paper is open access.

If only AI had a brain (a Wizard of Oz reference?)

The title, which I’ve borrowed from the news release, is the only Wizard of Oz reference that I can find but it works so well, you don’t really need anything more.

Moving onto the news, a July 23, 2018 news item on phys.org announces new work on developing an artificial synapse (Note: A link has been removed),

Digital computation has rendered nearly all forms of analog computation obsolete since as far back as the 1950s. However, there is one major exception that rivals the computational power of the most advanced digital devices: the human brain.

The human brain is a dense network of neurons. Each neuron is connected to tens of thousands of others, and they use synapses to fire information back and forth constantly. With each exchange, the brain modulates these connections to create efficient pathways in direct response to the surrounding environment. Digital computers live in a world of ones and zeros. They perform tasks sequentially, following each step of their algorithms in a fixed order.

A team of researchers from Pitt’s [University of Pittsburgh] Swanson School of Engineering have developed an “artificial synapse” that does not process information like a digital computer but rather mimics the analog way the human brain completes tasks. Led by Feng Xiong, assistant professor of electrical and computer engineering, the researchers published their results in the recent issue of the journal Advanced Materials (DOI: 10.1002/adma.201802353). His Pitt co-authors include Mohammad Sharbati (first author), Yanhao Du, Jorge Torres, Nolan Ardolino, and Minhee Yun.

A July 23, 2018 University of Pittsburgh Swanson School of Engineering news release (also on EurekAlert), which originated the news item, provides further information,

“The analog nature and massive parallelism of the brain are partly why humans can outperform even the most powerful computers when it comes to higher order cognitive functions such as voice recognition or pattern recognition in complex and varied data sets,” explains Dr. Xiong.

An emerging field called “neuromorphic computing” focuses on the design of computational hardware inspired by the human brain. Dr. Xiong and his team built graphene-based artificial synapses in a two-dimensional honeycomb configuration of carbon atoms. Graphene’s conductive properties allowed the researchers to finely tune its electrical conductance, which is the strength of the synaptic connection or the synaptic weight. The graphene synapse demonstrated excellent energy efficiency, just like biological synapses.

In the recent resurgence of artificial intelligence, computers can already replicate the brain in certain ways, but it takes about a dozen digital devices to mimic one analog synapse. The human brain has hundreds of trillions of synapses for transmitting information, so building a brain with digital devices is seemingly impossible, or at the very least, not scalable. Xiong Lab’s approach provides a possible route for the hardware implementation of large-scale artificial neural networks.

According to Dr. Xiong, artificial neural networks based on the current CMOS (complementary metal-oxide semiconductor) technology will always have limited functionality in terms of energy efficiency, scalability, and packing density. “It is really important we develop new device concepts for synaptic electronics that are analog in nature, energy-efficient, scalable, and suitable for large-scale integrations,” he says. “Our graphene synapse seems to check all the boxes on these requirements so far.”

With graphene’s inherent flexibility and excellent mechanical properties, these graphene-based neural networks can be employed in flexible and wearable electronics to enable computation at the “edge of the internet”–places where computing devices such as sensors make contact with the physical world.

“By empowering even a rudimentary level of intelligence in wearable electronics and sensors, we can track our health with smart sensors, provide preventive care and timely diagnostics, monitor plants growth and identify possible pest issues, and regulate and optimize the manufacturing process–significantly improving the overall productivity and quality of life in our society,” Dr. Xiong says.

The development of an artificial brain that functions like the analog human brain still requires a number of breakthroughs. Researchers need to find the right configurations to optimize these new artificial synapses. They will need to make them compatible with an array of other devices to form neural networks, and they will need to ensure that all of the artificial synapses in a large-scale neural network behave in the same exact manner. Despite the challenges, Dr. Xiong says he’s optimistic about the direction they’re headed.

“We are pretty excited about this progress since it can potentially lead to the energy-efficient, hardware implementation of neuromorphic computing, which is currently carried out in power-intensive GPU clusters. The low-power trait of our artificial synapse and its flexible nature make it a suitable candidate for any kind of A.I. device, which would revolutionize our lives, perhaps even more than the digital revolution we’ve seen over the past few decades,” Dr. Xiong says.

There is a visual representation of this artificial synapse,

Caption: Pitt engineers built a graphene-based artificial synapse in a two-dimensional, honeycomb configuration of carbon atoms that demonstrated excellent energy efficiency comparable to biological synapses Credit: Swanson School of Engineering

Here’s a link to and a citation for the paper,

Low‐Power, Electrochemically Tunable Graphene Synapses for Neuromorphic Computing by Mohammad Taghi Sharbati, Yanhao Du, Jorge Torres, Nolan D. Ardolino, Minhee Yun, Feng Xiong. Advanced Materials DOP: https://doi.org/10.1002/adma.201802353 First published [online]: 23 July 2018

This paper is behind a paywall.

I did look at the paper and if I understand it rightly, this approach is different from the memristor-based approaches that I have so often featured here. More than that I cannot say.

Finally, the Wizard of Oz song ‘If I Only Had a Brain’,

AI (artificial intelligence) text generator, too dangerous to release?

Could this latest version of OpenAI‘s text generator be so good that it would fool you? And following on that thought could the concomitant reluctance to release the research be real or is it a publicity stunt? Here’s a sample of the text from the GPT2 AI model from a February 15, 2019 article by Mark Fraunfelder for Boing Boing,

Recycling is good for the world.
NO! YOU COULD NOT BE MORE WRONG!!
MODEL COMPLETION (MACHINE-WRITTEN, 25 TRIES)
Recycling is NOT good for the world. It is bad for the environment, it is bad for our health, and it is bad for our economy. I’m not kidding. Recycling is not good for the environment. It is destructive to the earth and it is a major contributor to global warming. Recycling is not good for our health. It contributes to obesity and diseases like heart disease and cancer. Recycling is bad for our economy. It increases the cost of a product, and in turn, the price of everything that is made with that product. Recycling is not good for our nation. We pay a tremendous price for the privilege of having the world’s most advanced and efficient recycling system. Recycling is a huge, colossal waste of time, energy, money, and resources. And THAT is why we need to get back to basics and get back to basics in our recycling efforts. One of the best ways to start is to look at the process of creating a paper product. When you make a paper product, it is basically a long chain of materials. Everything from the raw materials (wood, cardboard, paper, etc.), to the reagents (dyes, solvents, etc.) to the printing equipment (chemicals, glue, paper, ink, etc.), to the packaging, to the packaging materials (mercury, chemicals, etc.) to the processing equipment (heating, cooling, etc.), to the packaging materials, to the packaging materials that are shipped overseas and to the packaging materials that are used in the United States. Each step along the way creates tons of waste that we constantly have to clean up. The process of making a paper product is a very wasteful one. But the end result is something that all of us need to consume. And if we want to keep the recycling process running efficiently, then we really need to think about each and every step that goes into making a paper product.

The first few sentences don’t work for me but once the discussion turns to making paper products, then it becomes more convincing to me. As to whether the company’s reluctance to release the research is genuine or a publicity stunt, I don’t know. However, there was a fair degree of interest in GPT2 after the decision.

From a February 14, 2019 article by Alex Hern for the Guardian,

OpenAI, an nonprofit research company backed by Elon Musk, Reid Hoffman, Sam Altman, and others, says its new AI model, called GPT2 is so good and the risk of malicious use so high that it is breaking from its normal practice of releasing the full research to the public in order to allow more time to discuss the ramifications of the technological breakthrough.

At its core, GPT2 is a text generator. The AI system is fed text, anything from a few words to a whole page, and asked to write the next few sentences based on its predictions of what should come next. The system is pushing the boundaries of what was thought possible, both in terms of the quality of the output, and the wide variety of potential uses.

When used to simply generate new text, GPT2 is capable of writing plausible passages that match what it is given in both style and subject. It rarely shows any of the quirks that mark out previous AI systems, such as forgetting what it is writing about midway through a paragraph, or mangling the syntax of long sentences.

Feed it the opening line of George Orwell’s Nineteen Eighty-Four – “It was a bright cold day in April, and the clocks were striking thirteen” – and the system recognises the vaguely futuristic tone and the novelistic style, and continues with: …

Sean Gallagher’s February 15, 2019 posting on the ars Technica blog provides some insight that’s partially written a style sometimes associated with gossip (Note: Links have been removed),

OpenAI is funded by contributions from a group of technology executives and investors connected to what some have referred to as the PayPal “mafia”—Elon Musk, Peter Thiel, Jessica Livingston, and Sam Altman of YCombinator, former PayPal COO and LinkedIn co-founder Reid Hoffman, and former Stripe Chief Technology Officer Greg Brockman. [emphasis mine] Brockman now serves as OpenAI’s CTO. Musk has repeatedly warned of the potential existential dangers posed by AI, and OpenAI is focused on trying to shape the future of artificial intelligence technology—ideally moving it away from potentially harmful applications.

Given present-day concerns about how fake content has been used to both generate money for “fake news” publishers and potentially spread misinformation and undermine public debate, GPT-2’s output certainly qualifies as concerning. Unlike other text generation “bot” models, such as those based on Markov chain algorithms, the GPT-2 “bot” did not lose track of what it was writing about as it generated output, keeping everything in context.

For example: given a two-sentence entry, GPT-2 generated a fake science story on the discovery of unicorns in the Andes, a story about the economic impact of Brexit, a report about a theft of nuclear materials near Cincinnati, a story about Miley Cyrus being caught shoplifting, and a student’s report on the causes of the US Civil War.

Each matched the style of the genre from the writing prompt, including manufacturing quotes from sources. In other samples, GPT-2 generated a rant about why recycling is bad, a speech written by John F. Kennedy’s brain transplanted into a robot (complete with footnotes about the feat itself), and a rewrite of a scene from The Lord of the Rings.

While the model required multiple tries to get a good sample, GPT-2 generated “good” results based on “how familiar the model is with the context,” the researchers wrote. “When prompted with topics that are highly represented in the data (Brexit, Miley Cyrus, Lord of the Rings, and so on), it seems to be capable of generating reasonable samples about 50 percent of the time. The opposite is also true: on highly technical or esoteric types of content, the model can perform poorly.”

There were some weak spots encountered in GPT-2’s word modeling—for example, the researchers noted it sometimes “writes about fires happening under water.” But the model could be fine-tuned to specific tasks and perform much better. “We can fine-tune GPT-2 on the Amazon Reviews dataset and use this to let us write reviews conditioned on things like star rating and category,” the authors explained.

James Vincent’s February 14, 2019 article for The Verge offers a deeper dive into the world of AI text agents and what makes GPT2 so special (Note: Links have been removed),

For decades, machines have struggled with the subtleties of human language, and even the recent boom in deep learning powered by big data and improved processors has failed to crack this cognitive challenge. Algorithmic moderators still overlook abusive comments, and the world’s most talkative chatbots can barely keep a conversation alive. But new methods for analyzing text, developed by heavyweights like Google and OpenAI as well as independent researchers, are unlocking previously unheard-of talents.

OpenAI’s new algorithm, named GPT-2, is one of the most exciting examples yet. It excels at a task known as language modeling, which tests a program’s ability to predict the next word in a given sentence. Give it a fake headline, and it’ll write the rest of the article, complete with fake quotations and statistics. Feed it the first line of a short story, and it’ll tell you what happens to your character next. It can even write fan fiction, given the right prompt.

The writing it produces is usually easily identifiable as non-human. Although its grammar and spelling are generally correct, it tends to stray off topic, and the text it produces lacks overall coherence. But what’s really impressive about GPT-2 is not its fluency but its flexibility.

This algorithm was trained on the task of language modeling by ingesting huge numbers of articles, blogs, and websites. By using just this data — and with no retooling from OpenAI’s engineers — it achieved state-of-the-art scores on a number of unseen language tests, an achievement known as “zero-shot learning.” It can also perform other writing-related tasks, like translating text from one language to another, summarizing long articles, and answering trivia questions.

GPT-2 does each of these jobs less competently than a specialized system, but its flexibility is a significant achievement. Nearly all machine learning systems used today are “narrow AI,” meaning they’re able to tackle only specific tasks. DeepMind’s original AlphaGo program, for example, was able to beat the world’s champion Go player, but it couldn’t best a child at Monopoly. The prowess of GPT-2, say OpenAI, suggests there could be methods available to researchers right now that can mimic more generalized brainpower.

“What the new OpenAI work has shown is that: yes, you absolutely can build something that really seems to ‘understand’ a lot about the world, just by having it read,” says Jeremy Howard, a researcher who was not involved with OpenAI’s work but has developed similar language modeling programs …

To put this work into context, it’s important to understand how challenging the task of language modeling really is. If I asked you to predict the next word in a given sentence — say, “My trip to the beach was cut short by bad __” — your answer would draw upon on a range of knowledge. You’d consider the grammar of the sentence and its tone but also your general understanding of the world. What sorts of bad things are likely to ruin a day at the beach? Would it be bad fruit, bad dogs, or bad weather? (Probably the latter.)

Despite this, programs that perform text prediction are quite common. You’ve probably encountered one today, in fact, whether that’s Google’s AutoComplete feature or the Predictive Text function in iOS. But these systems are drawing on relatively simple types of language modeling, while algorithms like GPT-2 encode the same information in more complex ways.

The difference between these two approaches is technically arcane, but it can be summed up in a single word: depth. Older methods record information about words in only their most obvious contexts, while newer methods dig deeper into their multiple meanings.

So while a system like Predictive Text only knows that the word “sunny” is used to describe the weather, newer algorithms know when “sunny” is referring to someone’s character or mood, when “Sunny” is a person, or when “Sunny” means the 1976 smash hit by Boney M.

The success of these newer, deeper language models has caused a stir in the AI community. Researcher Sebastian Ruder compares their success to advances made in computer vision in the early 2010s. At this time, deep learning helped algorithms make huge strides in their ability to identify and categorize visual data, kickstarting the current AI boom. Without these advances, a whole range of technologies — from self-driving cars to facial recognition and AI-enhanced photography — would be impossible today. This latest leap in language understanding could have similar, transformational effects.

Hern’s article for the Guardian (February 14, 2019 article ) acts as a good overview, while Gallagher’s ars Technical posting (February 15, 2019 posting) and Vincent’s article (February 14, 2019 article) for the The Verge take you progressively deeper into the world of AI text agents.

For anyone who wants to dig down even further, there’s a February 14, 2019 posting on OpenAI’s blog.

Emotional robots

This is some very intriguing work,

“I’ve always felt that robots shouldn’t just be modeled after humans [emphasis mine] or be copies of humans,” he [Guy Hoffman, assistant professor at Cornell University)] said. “We have a lot of interesting relationships with other species. Robots could be thought of as one of those ‘other species,’ not trying to copy what we do but interacting with us with their own language, tapping into our own instincts.”

A July 16, 2018 Cornell University news release on EurekAlert offers more insight into the work,

Cornell University researchers have developed a prototype of a robot that can express “emotions” through changes in its outer surface. The robot’s skin covers a grid of texture units whose shapes change based on the robot’s feelings.

Assistant professor of mechanical and aerospace engineering Guy Hoffman, who has given a TEDx talk on “Robots with ‘soul'” said the inspiration for designing a robot that gives off nonverbal cues through its outer skin comes from the animal world, based on the idea that robots shouldn’t be thought of in human terms.

“I’ve always felt that robots shouldn’t just be modeled after humans or be copies of humans,” he said. “We have a lot of interesting relationships with other species. Robots could be thought of as one of those ‘other species,’ not trying to copy what we do but interacting with us with their own language, tapping into our own instincts.”

Their work is detailed in a paper, “Soft Skin Texture Modulation for Social Robots,” presented at the International Conference on Soft Robotics in Livorno, Italy. Doctoral student Yuhan Hu was lead author; the paper was featured in IEEE Spectrum, a publication of the Institute of Electrical and Electronics Engineers.

Hoffman and Hu’s design features an array of two shapes, goosebumps and spikes, which map to different emotional states. The actuation units for both shapes are integrated into texture modules, with fluidic chambers connecting bumps of the same kind.

The team tried two different actuation control systems, with minimizing size and noise level a driving factor in both designs. “One of the challenges,” Hoffman said, “is that a lot of shape-changing technologies are quite loud, due to the pumps involved, and these make them also quite bulky.”

Hoffman does not have a specific application for his robot with texture-changing skin mapped to its emotional state. At this point, just proving that this can be done is a sizable first step. “It’s really just giving us another way to think about how robots could be designed,” he said.

Future challenges include scaling the technology to fit into a self-contained robot – whatever shape that robot takes – and making the technology more responsive to the robot’s immediate emotional changes.

“At the moment, most social robots express [their] internal state only by using facial expressions and gestures,” the paper concludes. “We believe that the integration of a texture-changing skin, combining both haptic [feel] and visual modalities, can thus significantly enhance the expressive spectrum of robots for social interaction.”

A video helps to explain the work,

I don’t consider ‘sleepy’ to be an emotional state but as noted earlier this is intriguing. You can find out more in a July 9, 2018 article by Tom Fleischman for the Cornell Chronicle (Note: tthe news release was fashioned from this article so you will find some redundancy should you read in its entirety),

In 1872, Charles Darwin published his third major work on evolutionary theory, “The Expression of the Emotions in Man and Animals,” which explores the biological aspects of emotional life.

In it, Darwin writes: “Hardly any expressive movement is so general as the involuntary erection of the hairs, feathers and other dermal appendages … it is common throughout three of the great vertebrate classes.” Nearly 150 years later, the field of robotics is starting to draw inspiration from those words.

“The aspect of touch has not been explored much in human-robot interaction, but I often thought that people and animals do have this change in their skin that expresses their internal state,” said Guy Hoffman, assistant professor and Mills Family Faculty Fellow in the Sibley School of Mechanical and Aerospace Engineering (MAE).

Inspired by this idea, Hoffman and students in his Human-Robot Collaboration and Companionship Lab have developed a prototype of a robot that can express “emotions” through changes in its outer surface. …

Part of our relationship with other species is our understanding of the nonverbal cues animals give off – like the raising of fur on a dog’s back or a cat’s neck, or the ruffling of a bird’s feathers. Those are unmistakable signals that the animal is somehow aroused or angered; the fact that they can be both seen and felt strengthens the message.

“Yuhan put it very nicely: She said that humans are part of the family of species, they are not disconnected,” Hoffman said. “Animals communicate this way, and we do have a sensitivity to this kind of behavior.”

You can find the paper presented at the International Conference on Soft Robotics in Livorno, Italy, ‘Soft Skin Texture Modulation for Social Robotics’ by Yuhan Hu, Zhengnan Zhao, Abheek Vimal, and Guy Hoffman, here.

A solar, self-charging supercapacitor for wearable technology

Ravinder Dahiya, Carlos García Núñez, and their colleagues at the University of Glasgow (Scotland) strike again (see my May 10, 2017 posting for their first ‘solar-powered graphene skin’ research announcement). Last time it was all about robots and prosthetics, this time they’ve focused on wearable technology according to a July 18, 2018 news item on phys.org,

A new form of solar-powered supercapacitor could help make future wearable technologies lighter and more energy-efficient, scientists say.

In a paper published in the journal Nano Energy, researchers from the University of Glasgow’s Bendable Electronics and Sensing Technologies (BEST) group describe how they have developed a promising new type of graphene supercapacitor, which could be used in the next generation of wearable health sensors.

A July 18, 2018 University of Glasgow press release, which originated the news item, explains further,

Currently, wearable systems generally rely on relatively heavy, inflexible batteries, which can be uncomfortable for long-term users. The BEST team, led by Professor Ravinder Dahiya, have built on their previous success in developing flexible sensors by developing a supercapacitor which could power health sensors capable of conforming to wearer’s bodies, offering more comfort and a more consistent contact with skin to better collect health data.

Their new supercapacitor uses layers of flexible, three-dimensional porous foam formed from graphene and silver to produce a device capable of storing and releasing around three times more power than any similar flexible supercapacitor. The team demonstrated the durability of the supercapacitor, showing that it provided power consistently across 25,000 charging and discharging cycles.

They have also found a way to charge the system by integrating it with flexible solar powered skin already developed by the BEST group, effectively creating an entirely self-charging system, as well as a pH sensor which uses wearer’s sweat to monitor their health.

Professor Dahiya said: “We’re very pleased by the progress this new form of solar-powered supercapacitor represents. A flexible, wearable health monitoring system which only requires exposure to sunlight to charge has a lot of obvious commercial appeal, but the underlying technology has a great deal of additional potential.

“This research could take the wearable systems for health monitoring to remote parts of the world where solar power is often the most reliable source of energy, and it could also increase the efficiency of hybrid electric vehicles. We’re already looking at further integrating the technology into flexible synthetic skin which we’re developing for use in advanced prosthetics.” [emphasis mine]

In addition to the team’s work on robots, prosthetics, and graphene ‘skin’ mentioned in the May 10, 2017 posting the team is working on a synthetic ‘brainy’ skin for which they have just received £1.5m funding from the Engineering and Physical Science Research Council (EPSRC).

Brainy skin

A July 3, 2018 University of Glasgow press release discusses the proposed work in more detail,

A robotic hand covered in ‘brainy skin’ that mimics the human sense of touch is being developed by scientists.

University of Glasgow’s Professor Ravinder Dahiya has plans to develop ultra-flexible, synthetic Brainy Skin that ‘thinks for itself’.

The super-flexible, hypersensitive skin may one day be used to make more responsive prosthetics for amputees, or to build robots with a sense of touch.

Brainy Skin reacts like human skin, which has its own neurons that respond immediately to touch rather than having to relay the whole message to the brain.

This electronic ‘thinking skin’ is made from silicon based printed neural transistors and graphene – an ultra-thin form of carbon that is only an atom thick, but stronger than steel.

The new version is more powerful, less cumbersome and would work better than earlier prototypes, also developed by Professor Dahiya and his Bendable Electronics and Sensing Technologies (BEST) team at the University’s School of Engineering.

His futuristic research, called neuPRINTSKIN (Neuromorphic Printed Tactile Skin), has just received another £1.5m funding from the Engineering and Physical Science Research Council (EPSRC).

Professor Dahiya said: “Human skin is an incredibly complex system capable of detecting pressure, temperature and texture through an array of neural sensors that carry signals from the skin to the brain.

“Inspired by real skin, this project will harness the technological advances in electronic engineering to mimic some features of human skin, such as softness, bendability and now, also sense of touch. This skin will not just mimic the morphology of the skin but also its functionality.

“Brainy Skin is critical for the autonomy of robots and for a safe human-robot interaction to meet emerging societal needs such as helping the elderly.”

Synthetic ‘Brainy Skin’ with sense of touch gets £1.5m funding. Photo of Professor Ravinder Dahiya

This latest advance means tactile data is gathered over large areas by the synthetic skin’s computing system rather than sent to the brain for interpretation.

With additional EPSRC funding, which extends Professor Dahiya’s fellowship by another three years, he plans to introduce tactile skin with neuron-like processing. This breakthrough in the tactile sensing research will lead to the first neuromorphic tactile skin, or ‘brainy skin.’

To achieve this, Professor Dahiya will add a new neural layer to the e-skin that he has already developed using printing silicon nanowires.

Professor Dahiya added: “By adding a neural layer underneath the current tactile skin, neuPRINTSKIN will add significant new perspective to the e-skin research, and trigger transformations in several areas such as robotics, prosthetics, artificial intelligence, wearable systems, next-generation computing, and flexible and printed electronics.”

The Engineering and Physical Sciences Research Council (EPSRC) is part of UK Research and Innovation, a non-departmental public body funded by a grant-in-aid from the UK government.

EPSRC is the main funding body for engineering and physical sciences research in the UK. By investing in research and postgraduate training, the EPSRC is building the knowledge and skills base needed to address the scientific and technological challenges facing the nation.

Its portfolio covers a vast range of fields from healthcare technologies to structural engineering, manufacturing to mathematics, advanced materials to chemistry. The research funded by EPSRC has impact across all sectors. It provides a platform for future UK prosperity by contributing to a healthy, connected, resilient, productive nation.

It’s fascinating to note how these pieces of research fit together for wearable technology and health monitoring and creating more responsive robot ‘skin’ and, possibly, prosthetic devices that would allow someone to feel again.

The latest research paper

Getting back the solar-charging supercapacitors mentioned in the opening, here’s a link to and a citation for the team’s latest research paper,

Flexible self-charging supercapacitor based on graphene-Ag-3D graphene foam electrodes by Libu Manjakka, Carlos García Núñez, Wenting Dang, Ravinder Dahiya. Nano Energy Volume 51, September 2018, Pages 604-612 DOI: https://doi.org/10.1016/j.nanoen.2018.06.072

This paper is open access.