Tag Archives: self-driving cars

Brainy and brainy: a novel synaptic architecture and a neuromorphic computing platform called SpiNNaker

I have two items about brainlike computing. The first item hearkens back to memristors, a topic I have been following since 2008. (If you’re curious about the various twists and turns just enter  the term ‘memristor’ in this blog’s search engine.) The latest on memristors is from a team than includes IBM (US), École Politechnique Fédérale de Lausanne (EPFL; Swizterland), and the New Jersey Institute of Technology (NJIT; US). The second bit comes from a Jülich Research Centre team in Germany and concerns an approach to brain-like computing that does not include memristors.

Multi-memristive synapses

In the inexorable march to make computers function more like human brains (neuromorphic engineering/computing), an international team has announced its latest results in a July 10, 2018 news item on Nanowerk,

Two New Jersey Institute of Technology (NJIT) researchers, working with collaborators from the IBM Research Zurich Laboratory and the École Polytechnique Fédérale de Lausanne, have demonstrated a novel synaptic architecture that could lead to a new class of information processing systems inspired by the brain.

The findings are an important step toward building more energy-efficient computing systems that also are capable of learning and adaptation in the real world. …

A July 10, 2018 NJIT news release (also on EurekAlert) by Tracey Regan, which originated by the news item, adds more details,

The researchers, Bipin Rajendran, an associate professor of electrical and computer engineering, and S. R. Nandakumar, a graduate student in electrical engineering, have been developing brain-inspired computing systems that could be used for a wide range of big data applications.

Over the past few years, deep learning algorithms have proven to be highly successful in solving complex cognitive tasks such as controlling self-driving cars and language understanding. At the heart of these algorithms are artificial neural networks – mathematical models of the neurons and synapses of the brain – that are fed huge amounts of data so that the synaptic strengths are autonomously adjusted to learn the intrinsic features and hidden correlations in these data streams.

However, the implementation of these brain-inspired algorithms on conventional computers is highly inefficient, consuming huge amounts of power and time. This has prompted engineers to search for new materials and devices to build special-purpose computers that can incorporate the algorithms. Nanoscale memristive devices, electrical components whose conductivity depends approximately on prior signaling activity, can be used to represent the synaptic strength between the neurons in artificial neural networks.

While memristive devices could potentially lead to faster and more power-efficient computing systems, they are also plagued by several reliability issues that are common to nanoscale devices. Their efficiency stems from their ability to be programmed in an analog manner to store multiple bits of information; however, their electrical conductivities vary in a non-deterministic and non-linear fashion.

In the experiment, the team showed how multiple nanoscale memristive devices exhibiting these characteristics could nonetheless be configured to efficiently implement artificial intelligence algorithms such as deep learning. Prototype chips from IBM containing more than one million nanoscale phase-change memristive devices were used to implement a neural network for the detection of hidden patterns and correlations in time-varying signals.

“In this work, we proposed and experimentally demonstrated a scheme to obtain high learning efficiencies with nanoscale memristive devices for implementing learning algorithms,” Nandakumar says. “The central idea in our demonstration was to use several memristive devices in parallel to represent the strength of a synapse of a neural network, but only chose one of them to be updated at each step based on the neuronal activity.”

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

Neuromorphic computing with multi-memristive synapses by Irem Boybat, Manuel Le Gallo, S. R. Nandakumar, Timoleon Moraitis, Thomas Parnell, Tomas Tuma, Bipin Rajendran, Yusuf Leblebici, Abu Sebastian, & Evangelos Eleftheriou. Nature Communications volume 9, Article number: 2514 (2018) DOI: https://doi.org/10.1038/s41467-018-04933-y Published 28 June 2018

This is an open access paper.

Also they’ve got a couple of very nice introductory paragraphs which I’m including here, (from the June 28, 2018 paper in Nature Communications; Note: Links have been removed),

The human brain with less than 20 W of power consumption offers a processing capability that exceeds the petaflops mark, and thus outperforms state-of-the-art supercomputers by several orders of magnitude in terms of energy efficiency and volume. Building ultra-low-power cognitive computing systems inspired by the operating principles of the brain is a promising avenue towards achieving such efficiency. Recently, deep learning has revolutionized the field of machine learning by providing human-like performance in areas, such as computer vision, speech recognition, and complex strategic games1. However, current hardware implementations of deep neural networks are still far from competing with biological neural systems in terms of real-time information-processing capabilities with comparable energy consumption.

One of the reasons for this inefficiency is that most neural networks are implemented on computing systems based on the conventional von Neumann architecture with separate memory and processing units. There are a few attempts to build custom neuromorphic hardware that is optimized to implement neural algorithms2,3,4,5. However, as these custom systems are typically based on conventional silicon complementary metal oxide semiconductor (CMOS) circuitry, the area efficiency of such hardware implementations will remain relatively low, especially if in situ learning and non-volatile synaptic behavior have to be incorporated. Recently, a new class of nanoscale devices has shown promise for realizing the synaptic dynamics in a compact and power-efficient manner. These memristive devices store information in their resistance/conductance states and exhibit conductivity modulation based on the programming history6,7,8,9. The central idea in building cognitive hardware based on memristive devices is to store the synaptic weights as their conductance states and to perform the associated computational tasks in place.

The two essential synaptic attributes that need to be emulated by memristive devices are the synaptic efficacy and plasticity. …

It gets more complicated from there.

Now onto the next bit.

SpiNNaker

At a guess, those capitalized N’s are meant to indicate ‘neural networks’. As best I can determine, SpiNNaker is not based on the memristor. Moving on, a July 11, 2018 news item on phys.org announces work from a team examining how neuromorphic hardware and neuromorphic software work together,

A computer built to mimic the brain’s neural networks produces similar results to that of the best brain-simulation supercomputer software currently used for neural-signaling research, finds a new study published in the open-access journal Frontiers in Neuroscience. Tested for accuracy, speed and energy efficiency, this custom-built computer named SpiNNaker, has the potential to overcome the speed and power consumption problems of conventional supercomputers. The aim is to advance our knowledge of neural processing in the brain, to include learning and disorders such as epilepsy and Alzheimer’s disease.

A July 11, 2018 Frontiers Publishing news release on EurekAlert, which originated the news item, expands on the latest work,

“SpiNNaker can support detailed biological models of the cortex–the outer layer of the brain that receives and processes information from the senses–delivering results very similar to those from an equivalent supercomputer software simulation,” says Dr. Sacha van Albada, lead author of this study and leader of the Theoretical Neuroanatomy group at the Jülich Research Centre, Germany. “The ability to run large-scale detailed neural networks quickly and at low power consumption will advance robotics research and facilitate studies on learning and brain disorders.”

The human brain is extremely complex, comprising 100 billion interconnected brain cells. We understand how individual neurons and their components behave and communicate with each other and on the larger scale, which areas of the brain are used for sensory perception, action and cognition. However, we know less about the translation of neural activity into behavior, such as turning thought into muscle movement.

Supercomputer software has helped by simulating the exchange of signals between neurons, but even the best software run on the fastest supercomputers to date can only simulate 1% of the human brain.

“It is presently unclear which computer architecture is best suited to study whole-brain networks efficiently. The European Human Brain Project and Jülich Research Centre have performed extensive research to identify the best strategy for this highly complex problem. Today’s supercomputers require several minutes to simulate one second of real time, so studies on processes like learning, which take hours and days in real time are currently out of reach.” explains Professor Markus Diesmann, co-author, head of the Computational and Systems Neuroscience department at the Jülich Research Centre.

He continues, “There is a huge gap between the energy consumption of the brain and today’s supercomputers. Neuromorphic (brain-inspired) computing allows us to investigate how close we can get to the energy efficiency of the brain using electronics.”

Developed over the past 15 years and based on the structure and function of the human brain, SpiNNaker — part of the Neuromorphic Computing Platform of the Human Brain Project — is a custom-built computer composed of half a million of simple computing elements controlled by its own software. The researchers compared the accuracy, speed and energy efficiency of SpiNNaker with that of NEST–a specialist supercomputer software currently in use for brain neuron-signaling research.

“The simulations run on NEST and SpiNNaker showed very similar results,” reports Steve Furber, co-author and Professor of Computer Engineering at the University of Manchester, UK. “This is the first time such a detailed simulation of the cortex has been run on SpiNNaker, or on any neuromorphic platform. SpiNNaker comprises 600 circuit boards incorporating over 500,000 small processors in total. The simulation described in this study used just six boards–1% of the total capability of the machine. The findings from our research will improve the software to reduce this to a single board.”

Van Albada shares her future aspirations for SpiNNaker, “We hope for increasingly large real-time simulations with these neuromorphic computing systems. In the Human Brain Project, we already work with neuroroboticists who hope to use them for robotic control.”

Before getting to the link and citation for the paper, here’s a description of SpiNNaker’s hardware from the ‘Spiking neural netowrk’ Wikipedia entry, Note: Links have been removed,

Neurogrid, built at Stanford University, is a board that can simulate spiking neural networks directly in hardware. SpiNNaker (Spiking Neural Network Architecture) [emphasis mine], designed at the University of Manchester, uses ARM processors as the building blocks of a massively parallel computing platform based on a six-layer thalamocortical model.[5]

Now for the link and citation,

Performance Comparison of the Digital Neuromorphic Hardware SpiNNaker and the Neural Network Simulation Software NEST for a Full-Scale Cortical Microcircuit Model by
Sacha J. van Albada, Andrew G. Rowley, Johanna Senk, Michael Hopkins, Maximilian Schmidt, Alan B. Stokes, David R. Lester, Markus Diesmann, and Steve B. Furber. Neurosci. 12:291. doi: 10.3389/fnins.2018.00291 Published: 23 May 2018

As noted earlier, this is an open access paper.

Robots in Vancouver and in Canada (two of two)

This is the second of a two-part posting about robots in Vancouver and Canada. The first part included a definition, a brief mention a robot ethics quandary, and sexbots. This part is all about the future. (Part one is here.)

Canadian Robotics Strategy

Meetings were held Sept. 28 – 29, 2017 in, surprisingly, Vancouver. (For those who don’t know, this is surprising because most of the robotics and AI research seems to be concentrated in eastern Canada. if you don’t believe me take a look at the speaker list for Day 2 or the ‘Canadian Stakeholder’ meeting day.) From the NSERC (Natural Sciences and Engineering Research Council) events page of the Canadian Robotics Network,

Join us as we gather robotics stakeholders from across the country to initiate the development of a national robotics strategy for Canada. Sponsored by the Natural Sciences and Engineering Research Council of Canada (NSERC), this two-day event coincides with the 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2017) in order to leverage the experience of international experts as we explore Canada’s need for a national robotics strategy.

Where
Vancouver, BC, Canada

When
Thursday September 28 & Friday September 29, 2017 — Save the date!

Download the full agenda and speakers’ list here.

Objectives

The purpose of this two-day event is to gather members of the robotics ecosystem from across Canada to initiate the development of a national robotics strategy that builds on our strengths and capacities in robotics, and is uniquely tailored to address Canada’s economic needs and social values.

This event has been sponsored by the Natural Sciences and Engineering Research Council of Canada (NSERC) and is supported in kind by the 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2017) as an official Workshop of the conference.  The first of two days coincides with IROS 2017 – one of the premiere robotics conferences globally – in order to leverage the experience of international robotics experts as we explore Canada’s need for a national robotics strategy here at home.

Who should attend

Representatives from industry, research, government, startups, investment, education, policy, law, and ethics who are passionate about building a robust and world-class ecosystem for robotics in Canada.

Program Overview

Download the full agenda and speakers’ list here.

DAY ONE: IROS Workshop 

“Best practices in designing effective roadmaps for robotics innovation”

Thursday September 28, 2017 | 8:30am – 5:00pm | Vancouver Convention Centre

Morning Program:“Developing robotics innovation policy and establishing key performance indicators that are relevant to your region” Leading international experts share their experience designing robotics strategies and policy frameworks in their regions and explore international best practices. Opening Remarks by Prof. Hong Zhang, IROS 2017 Conference Chair.

Afternoon Program: “Understanding the Canadian robotics ecosystem” Canadian stakeholders from research, industry, investment, ethics and law provide a collective overview of the Canadian robotics ecosystem. Opening Remarks by Ryan Gariepy, CTO of Clearpath Robotics.

Thursday Evening Program: Sponsored by Clearpath Robotics  Workshop participants gather at a nearby restaurant to network and socialize.

Learn more about the IROS Workshop.

DAY TWO: NSERC-Sponsored Canadian Robotics Stakeholder Meeting
“Towards a national robotics strategy for Canada”

Friday September 29, 2017 | 8:30am – 5:00pm | University of British Columbia (UBC)

On the second day of the program, robotics stakeholders from across the country gather at UBC for a full day brainstorming session to identify Canada’s unique strengths and opportunities relative to the global competition, and to align on a strategic vision for robotics in Canada.

Friday Evening Program: Sponsored by NSERC Meeting participants gather at a nearby restaurant for the event’s closing dinner reception.

Learn more about the Canadian Robotics Stakeholder Meeting.

I was glad to see in the agenda that some of the international speakers represented research efforts from outside the usual Europe/US axis.

I have been in touch with one of the organizers (also mentioned in part one with regard to robot ethics), Ajung Moon (her website is here), who says that there will be a white paper available on the Canadian Robotics Network website at some point in the future. I’ll keep looking for it and, in the meantime, I wonder what the 2018 Canadian federal budget will offer robotics.

Robots and popular culture

For anyone living in Canada or the US, Westworld (television series) is probably the most recent and well known ‘robot’ drama to premiere in the last year.As for movies, I think Ex Machina from 2014 probably qualifies in that category. Interestingly, both Westworld and Ex Machina seem quite concerned with sex with Westworld adding significant doses of violence as another  concern.

I am going to focus on another robot story, the 2012 movie, Robot & Frank, which features a care robot and an older man,

Frank (played by Frank Langella), a former jewel thief, teaches a robot the skills necessary to rob some neighbours of their valuables. The ethical issue broached in the film isn’t whether or not the robot should learn the skills and assist Frank in his thieving ways although that’s touched on when Frank keeps pointing out that planning his heist requires he live more healthily. No, the problem arises afterward when the neighbour accuses Frank of the robbery and Frank removes what he believes is all the evidence. He believes he’s going successfully evade arrest until the robot notes that Frank will have to erase its memory in order to remove all of the evidence. The film ends without the robot’s fate being made explicit.

In a way, I find the ethics query (was the robot Frank’s friend or just a machine?) posed in the film more interesting than the one in Vikander’s story, an issue which does have a history. For example, care aides, nurses, and/or servants would have dealt with requests to give an alcoholic patient a drink. Wouldn’t there  already be established guidelines and practices which could be adapted for robots? Or, is this question made anew by something intrinsically different about robots?

To be clear, Vikander’s story is a good introduction and starting point for these kinds of discussions as is Moon’s ethical question. But they are starting points and I hope one day there’ll be a more extended discussion of the questions raised by Moon and noted in Vikander’s article (a two- or three-part series of articles? public discussions?).

How will humans react to robots?

Earlier there was the contention that intimate interactions with robots and sexbots would decrease empathy and the ability of human beings to interact with each other in caring ways. This sounds a bit like the argument about smartphones/cell phones and teenagers who don’t relate well to others in real life because most of their interactions are mediated through a screen, which many seem to prefer. It may be partially true but, arguably,, books too are an antisocial technology as noted in Walter J. Ong’s  influential 1982 book, ‘Orality and Literacy’,  (from the Walter J. Ong Wikipedia entry),

A major concern of Ong’s works is the impact that the shift from orality to literacy has had on culture and education. Writing is a technology like other technologies (fire, the steam engine, etc.) that, when introduced to a “primary oral culture” (which has never known writing) has extremely wide-ranging impacts in all areas of life. These include culture, economics, politics, art, and more. Furthermore, even a small amount of education in writing transforms people’s mentality from the holistic immersion of orality to interiorization and individuation. [emphases mine]

So, robotics and artificial intelligence would not be the first technologies to affect our brains and our social interactions.

There’s another area where human-robot interaction may have unintended personal consequences according to April Glaser’s Sept. 14, 2017 article on Slate.com (Note: Links have been removed),

The customer service industry is teeming with robots. From automated phone trees to touchscreens, software and machines answer customer questions, complete orders, send friendly reminders, and even handle money. For an industry that is, at its core, about human interaction, it’s increasingly being driven to a large extent by nonhuman automation.

But despite the dreams of science-fiction writers, few people enter a customer-service encounter hoping to talk to a robot. And when the robot malfunctions, as they so often do, it’s a human who is left to calm angry customers. It’s understandable that after navigating a string of automated phone menus and being put on hold for 20 minutes, a customer might take her frustration out on a customer service representative. Even if you know it’s not the customer service agent’s fault, there’s really no one else to get mad at. It’s not like a robot cares if you’re angry.

When human beings need help with something, says Madeleine Elish, an anthropologist and researcher at the Data and Society Institute who studies how humans interact with machines, they’re not only looking for the most efficient solution to a problem. They’re often looking for a kind of validation that a robot can’t give. “Usually you don’t just want the answer,” Elish explained. “You want sympathy, understanding, and to be heard”—none of which are things robots are particularly good at delivering. In a 2015 survey of over 1,300 people conducted by researchers at Boston University, over 90 percent of respondents said they start their customer service interaction hoping to speak to a real person, and 83 percent admitted that in their last customer service call they trotted through phone menus only to make their way to a human on the line at the end.

“People can get so angry that they have to go through all those automated messages,” said Brian Gnerer, a call center representative with AT&T in Bloomington, Minnesota. “They’ve been misrouted or been on hold forever or they pressed one, then two, then zero to speak to somebody, and they are not getting where they want.” And when people do finally get a human on the phone, “they just sigh and are like, ‘Thank God, finally there’s somebody I can speak to.’ ”

Even if robots don’t always make customers happy, more and more companies are making the leap to bring in machines to take over jobs that used to specifically necessitate human interaction. McDonald’s and Wendy’s both reportedly plan to add touchscreen self-ordering machines to restaurants this year. Facebook is saturated with thousands of customer service chatbots that can do anything from hail an Uber, retrieve movie times, to order flowers for loved ones. And of course, corporations prefer automated labor. As Andy Puzder, CEO of the fast-food chains Carl’s Jr. and Hardee’s and former Trump pick for labor secretary, bluntly put it in an interview with Business Insider last year, robots are “always polite, they always upsell, they never take a vacation, they never show up late, there’s never a slip-and-fall, or an age, sex, or race discrimination case.”

But those robots are backstopped by human beings. How does interacting with more automated technology affect the way we treat each other? …

“We know that people treat artificial entities like they’re alive, even when they’re aware of their inanimacy,” writes Kate Darling, a researcher at MIT who studies ethical relationships between humans and robots, in a recent paper on anthropomorphism in human-robot interaction. Sure, robots don’t have feelings and don’t feel pain (not yet, anyway). But as more robots rely on interaction that resembles human interaction, like voice assistants, the way we treat those machines will increasingly bleed into the way we treat each other.

It took me a while to realize that what Glaser is talking about are AI systems and not robots as such. (sigh) It’s so easy to conflate the concepts.

AI ethics (Toby Walsh and Suzanne Gildert)

Jack Stilgoe of the Guardian published a brief Oct. 9, 2017 introduction to his more substantive (30 mins.?) podcast interview with Dr. Toby Walsh where they discuss stupid AI amongst other topics (Note: A link has been removed),

Professor Toby Walsh has recently published a book – Android Dreams – giving a researcher’s perspective on the uncertainties and opportunities of artificial intelligence. Here, he explains to Jack Stilgoe that we should worry more about the short-term risks of stupid AI in self-driving cars and smartphones than the speculative risks of super-intelligence.

Professor Walsh discusses the effects that AI could have on our jobs, the shapes of our cities and our understandings of ourselves. As someone developing AI, he questions the hype surrounding the technology. He is scared by some drivers’ real-world experimentation with their not-quite-self-driving Teslas. And he thinks that Siri needs to start owning up to being a computer.

I found this discussion to cast a decidedly different light on the future of robotics and AI. Walsh is much more interested in discussing immediate issues like the problems posed by ‘self-driving’ cars. (Aside: Should we be calling them robot cars?)

One ethical issue Walsh raises is with data regarding accidents. He compares what’s happening with accident data from self-driving (robot) cars to how the aviation industry handles accidents. Hint: accident data involving air planes is shared. Would you like to guess who does not share their data?

Sharing and analyzing data and developing new safety techniques based on that data has made flying a remarkably safe transportation technology.. Walsh argues the same could be done for self-driving cars if companies like Tesla took the attitude that safety is in everyone’s best interests and shared their accident data in a scheme similar to the aviation industry’s.

In an Oct. 12, 2017 article by Matthew Braga for Canadian Broadcasting Corporation (CBC) news online another ethical issue is raised by Suzanne Gildert (a participant in the Canadian Robotics Roadmap/Strategy meetings mentioned earlier here), Note: Links have been removed,

… Suzanne Gildert, the co-founder and chief science officer of Vancouver-based robotics company Kindred. Since 2014, her company has been developing intelligent robots [emphasis mine] that can be taught by humans to perform automated tasks — for example, handling and sorting products in a warehouse.

The idea is that when one of Kindred’s robots encounters a scenario it can’t handle, a human pilot can take control. The human can see, feel and hear the same things the robot does, and the robot can learn from how the human pilot handles the problematic task.

This process, called teleoperation, is one way to fast-track learning by manually showing the robot examples of what its trainers want it to do. But it also poses a potential moral and ethical quandary that will only grow more serious as robots become more intelligent.

“That AI is also learning my values,” Gildert explained during a talk on robot ethics at the Singularity University Canada Summit in Toronto on Wednesday [Oct. 11, 2017]. “Everything — my mannerisms, my behaviours — is all going into the AI.”

At its worst, everything from algorithms used in the U.S. to sentence criminals to image-recognition software has been found to inherit the racist and sexist biases of the data on which it was trained.

But just as bad habits can be learned, good habits can be learned too. The question is, if you’re building a warehouse robot like Kindred is, is it more effective to train those robots’ algorithms to reflect the personalities and behaviours of the humans who will be working alongside it? Or do you try to blend all the data from all the humans who might eventually train Kindred robots around the world into something that reflects the best strengths of all?

I notice Gildert distinguishes her robots as “intelligent robots” and then focuses on AI and issues with bias which have already arisen with regard to algorithms (see my May 24, 2017 posting about bias in machine learning, AI, and .Note: if you’re in Vancouver on Oct. 26, 2017 and interested in algorithms and bias), there’s a talk being given by Dr. Cathy O’Neil, author the Weapons of Math Destruction, on the topic of Gender and Bias in Algorithms. It’s not free but  tickets are here.)

Final comments

There is one more aspect I want to mention. Even as someone who usually deals with nanobots, it’s easy to start discussing robots as if the humanoid ones are the only ones that exist. To recapitulate, there are humanoid robots, utilitarian robots, intelligent robots, AI, nanobots, ‘microscopic bots, and more all of which raise questions about ethics and social impacts.

However, there is one more category I want to add to this list: cyborgs. They live amongst us now. Anyone who’s had a hip or knee replacement or a pacemaker or a deep brain stimulator or other such implanted device qualifies as a cyborg. Increasingly too, prosthetics are being introduced and made part of the body. My April 24, 2017 posting features this story,

This Case Western Reserve University (CRWU) video accompanies a March 28, 2017 CRWU news release, (h/t ScienceDaily March 28, 2017 news item)

Bill Kochevar grabbed a mug of water, drew it to his lips and drank through the straw.

His motions were slow and deliberate, but then Kochevar hadn’t moved his right arm or hand for eight years.

And it took some practice to reach and grasp just by thinking about it.

Kochevar, who was paralyzed below his shoulders in a bicycling accident, is believed to be the first person with quadriplegia in the world to have arm and hand movements restored with the help of two temporarily implanted technologies. [emphasis mine]

A brain-computer interface with recording electrodes under his skull, and a functional electrical stimulation (FES) system* activating his arm and hand, reconnect his brain to paralyzed muscles.

Does a brain-computer interface have an effect on human brain and, if so, what might that be?

In any discussion (assuming there is funding for it) about ethics and social impact, we might want to invite the broadest range of people possible at an ‘earlyish’ stage (although we’re already pretty far down the ‘automation road’) stage or as Jack Stilgoe and Toby Walsh note, technological determinism holds sway.

Once again here are links for the articles and information mentioned in this double posting,

That’s it!

ETA Oct. 16, 2017: Well, I guess that wasn’t quite ‘it’. BBC’s (British Broadcasting Corporation) Magazine published a thoughtful Oct. 15, 2017 piece titled: Can we teach robots ethics?

Robots, Dallas (US), ethics, and killing

I’ve waited a while before posting this piece in the hope that the situation would calm. Sadly, it took longer than hoped as there was an additional shooting incident of police officers in Baton Rouge on July 17, 2016. There’s more about that shooting in a July 18, 2016 news posting by Steve Visser for CNN.)

Finally: Robots, Dallas, ethics, and killing: In the wake of the Thursday, July 7, 2016 shooting in Dallas (Texas, US) and subsequent use of a robot armed with a bomb to kill  the suspect, a discussion about ethics has been raised.

This discussion comes at a difficult period. In the same week as the targeted shooting of white police officers in Dallas, two African-American males were shot and killed in two apparently unprovoked shootings by police. The victims were Alton Sterling in Baton Rouge, Louisiana on Tuesday, July 5, 2016 and, Philando Castile in Minnesota on Wednesday, July 6, 2016. (There’s more detail about the shootings prior to Dallas in a July 7, 2016 news item on CNN.) The suspect in Dallas, Micah Xavier Johnson, a 25-year-old African-American male had served in the US Army Reserve and been deployed in Afghanistan (there’s more in a July 9, 2016 news item by Emily Shapiro, Julia Jacobo, and Stephanie Wash for abcnews.go.com). All of this has taken place within the context of a movement started in 2013 in the US, Black Lives Matter.

Getting back to robots, most of the material I’ve seen about ‘killing or killer’ robots has so far involved industrial accidents (very few to date) and ethical issues for self-driven cars (see a May 31, 2016 posting by Noah J. Goodall on the IEEE [Institute of Electrical and Electronics Engineers] Spectrum website).

The incident in Dallas is apparently the first time a US police organization has used a robot as a bomb, although it has been an occasional practice by US Armed Forces in combat situations. Rob Lever in a July 8, 2016 Agence France-Presse piece on phys.org focuses on the technology aspect,

The “bomb robot” killing of a suspected Dallas shooter may be the first lethal use of an automated device by American police, and underscores growing role of technology in law enforcement.

Regardless of the methods in Dallas, the use of robots is expected to grow, to handle potentially dangerous missions in law enforcement and the military.


Researchers at Florida International University meanwhile have been working on a TeleBot that would allow disabled police officers to control a humanoid robot.

The robot, described in some reports as similar to the “RoboCop” in films from 1987 and 2014, was designed “to look intimidating and authoritative enough for citizens to obey the commands,” but with a “friendly appearance” that makes it “approachable to citizens of all ages,” according to a research paper.

Robot developers downplay the potential for the use of automated lethal force by the devices, but some analysts say debate on this is needed, both for policing and the military.

A July 9, 2016 Associated Press piece by Michael Liedtke and Bree Fowler on phys.org focuses more closely on ethical issues raised by the Dallas incident,

When Dallas police used a bomb-carrying robot to kill a sniper, they also kicked off an ethical debate about technology’s use as a crime-fighting weapon.

The strategy opens a new chapter in the escalating use of remote and semi-autonomous devices to fight crime and protect lives. It also raises new questions over when it’s appropriate to dispatch a robot to kill dangerous suspects instead of continuing to negotiate their surrender.

“If lethally equipped robots can be used in this situation, when else can they be used?” says Elizabeth Joh, a University of California at Davis law professor who has followed U.S. law enforcement’s use of technology. “Extreme emergencies shouldn’t define the scope of more ordinary situations where police may want to use robots that are capable of harm.”

In approaching the question about the ethics, Mike Masnick’s July 8, 2016 posting on Techdirt provides a surprisingly sympathetic reading for the Dallas Police Department’s actions, as well as, asking some provocative questions about how robots might be better employed by police organizations (Note: Links have been removed),

The Dallas Police have a long history of engaging in community policing designed to de-escalate situations, rather than encourage antagonism between police and the community, have been handling all of this with astounding restraint, frankly. Many other police departments would be lashing out, and yet the Dallas Police Dept, while obviously grieving for a horrible situation, appear to be handling this tragic situation professionally. And it appears that they did everything they could in a reasonable manner. They first tried to negotiate with Johnson, but after that failed and they feared more lives would be lost, they went with the robot + bomb option. And, obviously, considering he had already shot many police officers, I don’t think anyone would question the police justification if they had shot Johnson.

But, still, at the very least, the whole situation raises a lot of questions about the legality of police using a bomb offensively to blow someone up. And, it raises some serious questions about how other police departments might use this kind of technology in the future. The situation here appears to be one where people reasonably concluded that this was the most effective way to stop further bloodshed. And this is a police department with a strong track record of reasonable behavior. But what about other police departments where they don’t have that kind of history? What are the protocols for sending in a robot or drone to kill someone? Are there any rules at all?

Furthermore, it actually makes you wonder, why isn’t there a focus on using robots to de-escalate these situations? What if, instead of buying military surplus bomb robots, there were robots being designed to disarm a shooter, or detain him in a manner that would make it easier for the police to capture him alive? Why should the focus of remote robotic devices be to kill him? This isn’t faulting the Dallas Police Department for its actions last night. But, rather, if we’re going to enter the age of robocop, shouldn’t we be looking for ways to use such robotic devices in a manner that would help capture suspects alive, rather than dead?

Gordon Corera’s July 12, 2016 article on the BBC’s (British Broadcasting Corporation) news website provides an overview of the use of automation and of ‘killing/killer robots’,

Remote killing is not new in warfare. Technology has always been driven by military application, including allowing killing to be carried out at distance – prior examples might be the introduction of the longbow by the English at Crecy in 1346, then later the Nazi V1 and V2 rockets.

More recently, unmanned aerial vehicles (UAVs) or drones such as the Predator and the Reaper have been used by the US outside of traditional military battlefields.

Since 2009, the official US estimate is that about 2,500 “combatants” have been killed in 473 strikes, along with perhaps more than 100 non-combatants. Critics dispute those figures as being too low.

Back in 2008, I visited the Creech Air Force Base in the Nevada desert, where drones are flown from.

During our visit, the British pilots from the RAF deployed their weapons for the first time.

One of the pilots visibly bristled when I asked him if it ever felt like playing a video game – a question that many ask.

The military uses encrypted channels to control its ordnance disposal robots, but – as any hacker will tell you – there is almost always a flaw somewhere that a determined opponent can find and exploit.

We have already seen cars being taken control of remotely while people are driving them, and the nightmare of the future might be someone taking control of a robot and sending a weapon in the wrong direction.

The military is at the cutting edge of developing robotics, but domestic policing is also a different context in which greater separation from the community being policed risks compounding problems.

The balance between risks and benefits of robots, remote control and automation remain unclear.

But Dallas suggests that the future may be creeping up on us faster than we can debate it.

The excerpts here do not do justice to the articles, if you’re interested in this topic and have the time, I encourage you to read all the articles cited here in their entirety.

*(ETA: July 25, 2016 at 1405 hours PDT: There is a July 25, 2016 essay by Carrie Sheffield for Salon.com which may provide some insight into the Black Lives matter movement and some of the generational issues within the US African-American community as revealed by the movement.)*