Category Archives: robots

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.

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.

Electrode-filled elastic fiber for wearable electronics and robots

This work comes out of Switzerland. A May 25, 2018 École Polytechnique Fédérale de Lausanne (EPFL) press release (also on EurekAlert) announces their fibers,

EPFL scientists have found a fast and simple way to make super-elastic, multi-material, high-performance fibers. Their fibers have already been used as sensors on robotic fingers and in clothing. This breakthrough method opens the door to new kinds of smart textiles and medical implants.

It’s a whole new way of thinking about sensors. The tiny fibers developed at EPFL are made of elastomer and can incorporate materials like electrodes and nanocomposite polymers. The fibers can detect even the slightest pressure and strain and can withstand deformation of close to 500% before recovering their initial shape. All that makes them perfect for applications in smart clothing and prostheses, and for creating artificial nerves for robots.

The fibers were developed at EPFL’s Laboratory of Photonic Materials and Fiber Devices (FIMAP), headed by Fabien Sorin at the School of Engineering. The scientists came up with a fast and easy method for embedding different kinds of microstructures in super-elastic fibers. For instance, by adding electrodes at strategic locations, they turned the fibers into ultra-sensitive sensors. What’s more, their method can be used to produce hundreds of meters of fiber in a short amount of time. Their research has just been published in Advanced Materials.

Heat, then stretch
To make their fibers, the scientists used a thermal drawing process, which is the standard process for optical-fiber manufacturing. They started by creating a macroscopic preform with the various fiber components arranged in a carefully designed 3D pattern. They then heated the preform and stretched it out, like melted plastic, to make fibers of a few hundreds microns in diameter. And while this process stretched out the pattern of components lengthwise, it also contracted it crosswise, meaning the components’ relative positions stayed the same. The end result was a set of fibers with an extremely complicated microarchitecture and advanced properties.

Until now, thermal drawing could be used to make only rigid fibers. But Sorin and his team used it to make elastic fibers. With the help of a new criterion for selecting materials, they were able to identify some thermoplastic elastomers that have a high viscosity when heated. After the fibers are drawn, they can be stretched and deformed but they always return to their original shape.

Rigid materials like nanocomposite polymers, metals and thermoplastics can be introduced into the fibers, as well as liquid metals that can be easily deformed. “For instance, we can add three strings of electrodes at the top of the fibers and one at the bottom. Different electrodes will come into contact depending on how the pressure is applied to the fibers. This will cause the electrodes to transmit a signal, which can then be read to determine exactly what type of stress the fiber is exposed to – such as compression or shear stress, for example,” says Sorin.

Artificial nerves for robots

Working in association with Professor Dr. Oliver Brock (Robotics and Biology Laboratory, Technical University of Berlin), the scientists integrated their fibers into robotic fingers as artificial nerves. Whenever the fingers touch something, electrodes in the fibers transmit information about the robot’s tactile interaction with its environment. The research team also tested adding their fibers to large-mesh clothing to detect compression and stretching. “Our technology could be used to develop a touch keyboard that’s integrated directly into clothing, for instance” says Sorin.

The researchers see many other potential applications. Especially since the thermal drawing process can be easily tweaked for large-scale production. This is a real plus for the manufacturing sector. The textile sector has already expressed interest in the new technology, and patents have been filed.

There’s a video of the lead researcher discussing the work as he offers some visual aids,

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

Superelastic Multimaterial Electronic and Photonic Fibers and Devices via Thermal Drawing by Yunpeng Qu, Tung Nguyen‐Dang, Alexis Gérald Page, Wei Yan, Tapajyoti Das Gupta, Gelu Marius Rotaru, René M. Rossi, Valentine Dominique Favrod, Nicola Bartolomei, Fabien Sorin. Advanced Materials First published: 25 May 2018 https://doi.org/10.1002/adma.201707251

This paper is behind a paywall.

A potpourri of robot/AI stories: killers , kindergarten teachers, a Balenciaga-inspired AI fashion designer, a conversational android, and more

Following on my August 29, 2018 post (Sexbots, sexbot ethics, families, and marriage), I’m following up with a more general piece.

Robots, AI (artificial intelligence), and androids (humanoid robots), the terms can be confusing since there’s a tendency to use them interchangeably. Confession: I do it too, but, not this time. That said, I have multiple news bits.

Killer ‘bots and ethics

The U.S. military is already testing a Modular Advanced Armed Robotic System. Credit: Lance Cpl. Julien Rodarte, U.S. Marine Corps

That is a robot.

For the purposes of this posting, a robot is a piece of hardware which may or may not include an AI system and does not mimic a human or other biological organism such that you might, under circumstances, mistake the robot for a biological organism.

As for what precipitated this feature (in part), it seems there’s been a United Nations meeting in Geneva, Switzerland held from August 27 – 31, 2018 about war and the use of autonomous robots, i.e., robots equipped with AI systems and designed for independent action. BTW, it’s the not first meeting the UN has held on this topic.

Bonnie Docherty, lecturer on law and associate director of armed conflict and civilian protection, international human rights clinic, Harvard Law School, has written an August 21, 2018 essay on The Conversation (also on phys.org) describing the history and the current rules around the conduct of war, as well as, outlining the issues with the military use of autonomous robots (Note: Links have been removed),

When drafting a treaty on the laws of war at the end of the 19th century, diplomats could not foresee the future of weapons development. But they did adopt a legal and moral standard for judging new technology not covered by existing treaty language.

This standard, known as the Martens Clause, has survived generations of international humanitarian law and gained renewed relevance in a world where autonomous weapons are on the brink of making their own determinations about whom to shoot and when. The Martens Clause calls on countries not to use weapons that depart “from the principles of humanity and from the dictates of public conscience.”

I was the lead author of a new report by Human Rights Watch and the Harvard Law School International Human Rights Clinic that explains why fully autonomous weapons would run counter to the principles of humanity and the dictates of public conscience. We found that to comply with the Martens Clause, countries should adopt a treaty banning the development, production and use of these weapons.

Representatives of more than 70 nations will gather from August 27 to 31 [2018] at the United Nations in Geneva to debate how to address the problems with what they call lethal autonomous weapon systems. These countries, which are parties to the Convention on Conventional Weapons, have discussed the issue for five years. My co-authors and I believe it is time they took action and agreed to start negotiating a ban next year.

Docherty elaborates on her points (Note: A link has been removed),

The Martens Clause provides a baseline of protection for civilians and soldiers in the absence of specific treaty law. The clause also sets out a standard for evaluating new situations and technologies that were not previously envisioned.

Fully autonomous weapons, sometimes called “killer robots,” would select and engage targets without meaningful human control. They would be a dangerous step beyond current armed drones because there would be no human in the loop to determine when to fire and at what target. Although fully autonomous weapons do not yet exist, China, Israel, Russia, South Korea, the United Kingdom and the United States are all working to develop them. They argue that the technology would process information faster and keep soldiers off the battlefield.

The possibility that fully autonomous weapons could soon become a reality makes it imperative for those and other countries to apply the Martens Clause and assess whether the technology would offend basic humanity and the public conscience. Our analysis finds that fully autonomous weapons would fail the test on both counts.

I encourage you to read the essay in its entirety and for anyone who thinks the discussion about ethics and killer ‘bots is new or limited to military use, there’s my July 25, 2016 posting about police use of a robot in Dallas, Texas. (I imagine the discussion predates 2016 but that’s the earliest instance I have here.)

Teacher bots

Robots come in many forms and this one is on the humanoid end of the spectum,

Children watch a Keeko robot at the Yiswind Institute of Multicultural Education in Beijing, where the intelligent machines are telling stories and challenging kids with logic problems  [donwloaded from https://phys.org/news/2018-08-robot-teachers-invade-chinese-kindergartens.html]

Don’t those ‘eyes’ look almost heart-shaped? No wonder the kids love these robots, if an August  29, 2018 news item on phys.org can be believed,

The Chinese kindergarten children giggled as they worked to solve puzzles assigned by their new teaching assistant: a roundish, short educator with a screen for a face.

Just under 60 centimetres (two feet) high, the autonomous robot named Keeko has been a hit in several kindergartens, telling stories and challenging children with logic problems.

Round and white with a tubby body, the armless robot zips around on tiny wheels, its inbuilt cameras doubling up both as navigational sensors and a front-facing camera allowing users to record video journals.

In China, robots are being developed to deliver groceries, provide companionship to the elderly, dispense legal advice and now, as Keeko’s creators hope, join the ranks of educators.

At the Yiswind Institute of Multicultural Education on the outskirts of Beijing, the children have been tasked to help a prince find his way through a desert—by putting together square mats that represent a path taken by the robot—part storytelling and part problem-solving.

Each time they get an answer right, the device reacts with delight, its face flashing heart-shaped eyes.

“Education today is no longer a one-way street, where the teacher teaches and students just learn,” said Candy Xiong, a teacher trained in early childhood education who now works with Keeko Robot Xiamen Technology as a trainer.

“When children see Keeko with its round head and body, it looks adorable and children love it. So when they see Keeko, they almost instantly take to it,” she added.

Keeko robots have entered more than 600 kindergartens across the country with its makers hoping to expand into Greater China and Southeast Asia.

Beijing has invested money and manpower in developing artificial intelligence as part of its “Made in China 2025” plan, with a Chinese firm last year unveiling the country’s first human-like robot that can hold simple conversations and make facial expressions.

According to the International Federation of Robots, China has the world’s top industrial robot stock, with some 340,000 units in factories across the country engaged in manufacturing and the automotive industry.

Moving on from hardware/software to a software only story.

AI fashion designer better than Balenciaga?

Despite the title for Katharine Schwab’s August 22, 2018 article for Fast Company, I don’t think this AI designer is better than Balenciaga but from the pictures I’ve seen the designs are as good and it does present some intriguing possibilities courtesy of its neural network (Note: Links have been removed),

The AI, created by researcher Robbie Barat, has created an entire collection based on Balenciaga’s previous styles. There’s a fabulous pink and red gradient jumpsuit that wraps all the way around the model’s feet–like a onesie for fashionistas–paired with a dark slouchy coat. There’s a textural color-blocked dress, paired with aqua-green tights. And for menswear, there’s a multi-colored, shimmery button-up with skinny jeans and mismatched shoes. None of these looks would be out of place on the runway.

To create the styles, Barat collected images of Balenciaga’s designs via the designer’s lookbooks, ad campaigns, runway shows, and online catalog over the last two months, and then used them to train the pix2pix neural net. While some of the images closely resemble humans wearing fashionable clothes, many others are a bit off–some models are missing distinct limbs, and don’t get me started on how creepy [emphasis mine] their faces are. Even if the outfits aren’t quite ready to be fabricated, Barat thinks that designers could potentially use a tool like this to find inspiration. Because it’s not constrained by human taste, style, and history, the AI comes up with designs that may never occur to a person. “I love how the network doesn’t really understand or care about symmetry,” Barat writes on Twitter.

You can see the ‘creepy’ faces and some of the designs here,

Image: Robbie Barat

In contrast to the previous two stories, this all about algorithms, no machinery with independent movement (robot hardware) needed.

Conversational android: Erica

Hiroshi Ishiguro and his lifelike (definitely humanoid) robots have featured here many, many times before. The most recent posting is a March 27, 2017 posting about his and his android’s participation at the 2017 SXSW festival.

His latest work is featured in an August 21, 2018 news news item on ScienceDaily,

We’ve all tried talking with devices, and in some cases they talk back. But, it’s a far cry from having a conversation with a real person.

Now a research team from Kyoto University, Osaka University, and the Advanced Telecommunications Research Institute, or ATR, have significantly upgraded the interaction system for conversational android ERICA, giving her even greater dialog skills.

ERICA is an android created by Hiroshi Ishiguro of Osaka University and ATR, specifically designed for natural conversation through incorporation of human-like facial expressions and gestures. The research team demonstrated the updates during a symposium at the National Museum of Emerging Science in Tokyo.

Here’s the latest conversational android, Erica

Caption: The experimental set up when the subject (left) talks with ERICA (right) Credit: Kyoto University / Kawahara lab

An August 20, 2018 Kyoto University press release on EurekAlert, which originated the news item, offers more details,

When we talk to one another, it’s never a simple back and forward progression of information,” states Tatsuya Kawahara of Kyoto University’s Graduate School of Informatics, and an expert in speech and audio processing.

“Listening is active. We express agreement by nodding or saying ‘uh-huh’ to maintain the momentum of conversation. This is called ‘backchanneling’, and is something we wanted to implement with ERICA.”

The team also focused on developing a system for ‘attentive listening’. This is when a listener asks elaborating questions, or repeats the last word of the speaker’s sentence, allowing for more engaging dialogue.

Deploying a series of distance sensors, facial recognition cameras, and microphone arrays, the team began collecting data on parameters necessary for a fluid dialog between ERICA and a human subject.

“We looked at three qualities when studying backchanneling,” continues Kawahara. “These were: timing — when a response happens; lexical form — what is being said; and prosody, or how the response happens.”

Responses were generated through machine learning using a counseling dialogue corpus, resulting in dramatically improved dialog engagement. Testing in five-minute sessions with a human subject, ERICA demonstrated significantly more dynamic speaking skill, including the use of backchanneling, partial repeats, and statement assessments.

“Making a human-like conversational robot is a major challenge,” states Kawahara. “This project reveals how much complexity there is in listening, which we might consider mundane. We are getting closer to a day where a robot can pass a Total Turing Test.”

Erica seems to have been first introduced publicly in Spring 2017, from an April 2017 Erica: Man Made webpage on The Guardian website,

Erica is 23. She has a beautiful, neutral face and speaks with a synthesised voice. She has a degree of autonomy – but can’t move her hands yet. Hiroshi Ishiguro is her ‘father’ and the bad boy of Japanese robotics. Together they will redefine what it means to be human and reveal that the future is closer than we might think.

Hiroshi Ishiguro and his colleague Dylan Glas are interested in what makes a human. Erica is their latest creation – a semi-autonomous android, the product of the most funded scientific project in Japan. But these men regard themselves as artists more than scientists, and the Erica project – the result of a collaboration between Osaka and Kyoto universities and the Advanced Telecommunications Research Institute International – is a philosophical one as much as technological one.

Erica is interviewed about her hope and dreams – to be able to leave her room and to be able to move her arms and legs. She likes to chat with visitors and has one of the most advanced speech synthesis systems yet developed. Can she be regarded as being alive or as a comparable being to ourselves? Will she help us to understand ourselves and our interactions as humans better?

Erica and her creators are interviewed in the science fiction atmosphere of Ishiguro’s laboratory, and this film asks how we might form close relationships with robots in the future. Ishiguro thinks that for Japanese people especially, everything has a soul, whether human or not. If we don’t understand how human hearts, minds and personalities work, can we truly claim that humans have authenticity that machines don’t?

Ishiguro and Glas want to release Erica and her fellow robots into human society. Soon, Erica may be an essential part of our everyday life, as one of the new children of humanity.

Key credits

  • Director/Editor: Ilinca Calugareanu
  • Producer: Mara Adina
  • Executive producers for the Guardian: Charlie Phillips and Laurence Topham
  • This video is produced in collaboration with the Sundance Institute Short Documentary Fund supported by the John D and Catherine T MacArthur Foundation

You can also view the 14 min. film here.

Artworks generated by an AI system are to be sold at Christie’s auction house

KC Ifeanyi’s August 22, 2018 article for Fast Company may send a chill down some artists’ spines,

For the first time in its 252-year history, Christie’s will auction artwork generated by artificial intelligence.

Created by the French art collective Obvious, “Portrait of Edmond de Belamy” is part of a series of paintings of the fictional Belamy family that was created using a two-part algorithm. …

The portrait is estimated to sell anywhere between $7,000-$10,000, and Obvious says the proceeds will go toward furthering its algorithm.

… Famed collector Nicolas Laugero-Lasserre bought one of Obvious’s Belamy works in February, which could’ve been written off as a novel purchase where the story behind it is worth more than the piece itself. However, with validation from a storied auction house like Christie’s, AI art could shake the contemporary art scene.

“Edmond de Belamy” goes up for auction from October 23-25 [2018].

Jobs safe from automation? Are there any?

Michael Grothaus expresses more optimism about future job markets than I’m feeling in an August 30, 2018 article for Fast Company,

A 2017 McKinsey Global Institute study of 800 occupations across 46 countries found that by 2030, 800 million people will lose their jobs to automation. That’s one-fifth of the global workforce. A further one-third of the global workforce will need to retrain if they want to keep their current jobs as well. And looking at the effects of automation on American jobs alone, researchers from Oxford University found that “47 percent of U.S. workers have a high probability of seeing their jobs automated over the next 20 years.”

The good news is that while the above stats are rightly cause for concern, they also reveal that 53% of American jobs and four-fifths of global jobs are unlikely to be affected by advances in artificial intelligence and robotics. But just what are those fields? I spoke to three experts in artificial intelligence, robotics, and human productivity to get their automation-proof career advice.

Creatives

“Although I believe every single job can, and will, benefit from a level of AI or robotic influence, there are some roles that, in my view, will never be replaced by technology,” says Tom Pickersgill, …

Maintenance foreman

When running a production line, problems and bottlenecks are inevitable–and usually that’s a bad thing. But in this case, those unavoidable issues will save human jobs because their solutions will require human ingenuity, says Mark Williams, head of product at People First, …

Hairdressers

Mat Hunter, director of the Central Research Laboratory, a tech-focused co-working space and accelerator for tech startups, have seen startups trying to create all kinds of new technologies, which has given him insight into just what machines can and can’t pull off. It’s lead him to believe that jobs like the humble hairdresser are safer from automation than those of, says, accountancy.

Therapists and social workers

Another automation-proof career is likely to be one involved in helping people heal the mind, says Pickersgill. “People visit therapists because there is a need for emotional support and guidance. This can only be provided through real human interaction–by someone who can empathize and understand, and who can offer advice based on shared experiences, rather than just data-driven logic.”

Teachers

Teachers are so often the unsung heroes of our society. They are overworked and underpaid–yet charged with one of the most important tasks anyone can have: nurturing the growth of young people. The good news for teachers is that their jobs won’t be going anywhere.

Healthcare workers

Doctors and nurses will also likely never see their jobs taken by automation, says Williams. While automation will no doubt better enhance the treatments provided by doctors and nurses the fact of the matter is that robots aren’t going to outdo healthcare workers’ ability to connect with patients and make them feel understood the way a human can.

Caretakers

While humans might be fine with robots flipping their burgers and artificial intelligence managing their finances, being comfortable with a robot nannying your children or looking after your elderly mother is a much bigger ask. And that’s to say nothing of the fact that even today’s most advanced robots don’t have the physical dexterity to perform the movements and actions carers do every day.

Grothaus does offer a proviso in his conclusion: certain types of jobs are relatively safe until developers learn to replicate qualities such as empathy in robots/AI.

It’s very confusing

There’s so much news about robots, artificial intelligence, androids, and cyborgs that it’s hard to keep up with it let alone attempt to get a feeling for where all this might be headed. When you add the fact that the term robots/artificial inteligence are often used interchangeably and that the distinction between robots/androids/cyborgs is not always clear any attempts to peer into the future become even more challenging.

At this point I content myself with tracking the situation and finding definitions so I can better understand what I’m tracking. Carmen Wong’s August 23, 2018 posting on the Signals blog published by Canada’s Centre for Commercialization of Regenerative Medicine (CCRM) offers some useful definitions in the context of an article about the use of artificial intelligence in the life sciences, particularly in Canada (Note: Links have been removed),

Artificial intelligence (AI). Machine learning. To most people, these are just buzzwords and synonymous. Whether or not we fully understand what both are, they are slowly integrating into our everyday lives. Virtual assistants such as Siri? AI is at work. The personalized ads you see when you are browsing on the web or movie recommendations provided on Netflix? Thank AI for that too.

AI is defined as machines having intelligence that imitates human behaviour such as learning, planning and problem solving. A process used to achieve AI is called machine learning, where a computer uses lots of data to “train” or “teach” itself, without human intervention, to accomplish a pre-determined task. Essentially, the computer keeps on modifying its algorithm based on the information provided to get to the desired goal.

Another term you may have heard of is deep learning. Deep learning is a particular type of machine learning where algorithms are set up like the structure and function of human brains. It is similar to a network of brain cells interconnecting with each other.

Toronto has seen its fair share of media-worthy AI activity. The Government of Canada, Government of Ontario, industry and multiple universities came together in March 2018 to launch the Vector Institute, with the goal of using AI to promote economic growth and improve the lives of Canadians. In May, Samsung opened its AI Centre in the MaRS Discovery District, joining a network of Samsung centres located in California, United Kingdom and Russia.

There has been a boom in AI companies over the past few years, which span a variety of industries. This year’s ranking of the top 100 most promising private AI companies covers 25 fields with cybersecurity, enterprise and robotics being the hot focus areas.

Wong goes on to explore AI deployment in the life sciences and concludes that human scientists and doctors will still be needed although she does note this in closing (Note: A link has been removed),

More importantly, empathy and support from a fellow human being could never be fully replaced by a machine (could it?), but maybe this will change in the future. We will just have to wait and see.

Artificial empathy is the term used in Lisa Morgan’s April 25, 2018 article for Information Week which unfortunately does not include any links to actual projects or researchers working on artificial empathy. Instead, the article is focused on how business interests and marketers would like to see it employed. FWIW, I have found a few references: (1) Artificial empathy Wikipedia essay (look for the references at the end of the essay for more) and (2) this open access article: Towards Artificial Empathy; How Can Artificial Empathy Follow the Developmental Pathway of Natural Empathy? by Minoru Asada.

Please let me know in the comments if you should have an insights on the matter in the comments section of this blog.

Sexbots, sexbot ethics, families, and marriage

Setting the stage

Can we? Should we? Is this really a good idea? I believe those ships have sailed where sexbots are concerned since the issue is no longer whether we can or should but rather what to do now that we have them. My Oct. 17, 2017 posting: ‘Robots in Vancouver and in Canada (one of two)’ features Harmony, the first (I believe) commercial AI (artificial intelligence)-enhanced sex robot n the US. They were getting ready to start shipping the bot either for Christmas 2017 or in early 2018.

Ethical quandaries?

Things have moved a little more quickly that I would have expected had I thought ahead. An April 5, 2018 essay  (h/t phys.org) by Victoria Brooks, lecturer in law at the University of Westminster (UK) for The Conversation lays out some of ethical issues (Note: Links have been removed),

Late in 2017 at a tech fair in Austria, a sex robot was reportedly “molested” repeatedly and left in a “filthy” state. The robot, named Samantha, received a barrage of male attention, which resulted in her sustaining two broken fingers. This incident confirms worries that the possibility of fully functioning sex robots raises both tantalising possibilities for human desire (by mirroring human/sex-worker relationships), as well as serious ethical questions.

So what should be done? The campaign to “ban” sex robots, as the computer scientist Kate Devlin has argued, is only likely to lead to a lack of discussion. Instead, she hypothesises that many ways of sexual and social inclusivity could be explored as a result of human-robot relationships.

To be sure, there are certain elements of relationships between humans and sex workers that we may not wish to repeat. But to me, it is the ethical aspects of the way we think about human-robot desire that are particularly key.

Why? Because we do not even agree yet on what sex is. Sex can mean lots of different things for different bodies – and the types of joys and sufferings associated with it are radically different for each individual body. We are only just beginning to understand and know these stories. But with Europe’s first sex robot brothel open in Barcelona and the building of “Harmony”, a talking sex robot in California, it is clear that humans are already contemplating imposing our barely understood sexual ethic upon machines.

I think that most of us will experience some discomfort on hearing Samantha’s story. And it’s important that, just because she’s a machine, we do not let ourselves “off the hook” by making her yet another victim and heroine who survived an encounter, only for it to be repeated. Yes, she is a machine, but does this mean it is justifiable to act destructively towards her? Surely the fact that she is in a human form makes her a surface on which human sexuality is projected, and symbolic of a futuristic human sexuality. If this is the case, then Samatha’s [sic] case is especially sad.

It is Devlin who has asked the crucial question: whether sex robots will have rights. “Should we build in the idea of consent,” she asks? In legal terms, this would mean having to recognise the robot as human – such is the limitation of a law made by and for humans.

Suffering is a way of knowing that you, as a body, have come out on the “wrong” side of an ethical dilemma. [emphasis mine] This idea of an “embodied” ethic understood through suffering has been developed on the basis of the work of the famous philosopher Spinoza and is of particular use for legal thinkers. It is useful as it allows us to judge rightness by virtue of the real and personal experience of the body itself, rather than judging by virtue of what we “think” is right in connection with what we assume to be true about their identity.

This helps us with Samantha’s case, since it tells us that in accordance with human desire, it is clear she would not have wanted what she got. The contact Samantha received was distinctly human in the sense that this case mirrors some of the most violent sexual offences cases. While human concepts such as “law” and “ethics” are flawed, we know we don’t want to make others suffer. We are making these robot lovers in our image and we ought not pick and choose whether to be kind to our sexual partners, even when we choose to have relationships outside of the “norm”, or with beings that have a supposedly limited consciousness, or even no (humanly detectable) consciousness.

Brooks makes many interesting points not all of them in the excerpts seen here but one question not raised in the essay is whether or not the bot itself suffered. It’s a point that I imagine proponents of ‘treating your sex bot however you like’ are certain to raise. It’s also a question Canadians may need to answer sooner rather than later now that a ‘sex doll brothel’ is about to open Toronto. However, before getting to that news bit, there’s an interview with a man, his sexbot, and his wife.

The sexbot at home

In fact, I have two interviews the first I’m including here was with CBC (Canadian Broadcasting Corporation) radio and it originally aired October 29, 2017. Here’s a part of the transcript (Note: A link has been removed),

“She’s [Samantha] quite an elegant kind of girl,” says Arran Lee Squire, who is sales director for the company that makes her and also owns one himself.

And unlike other dolls like her, she’ll resist sex if she isn’t in the mood.

“If you touch her, say, on her sensitive spots on the breasts, for example, straight away, and you don’t touch her hands or kiss her, she might say, ‘Oh, I’m not ready for that,'” Arran says.

He says she’ll even synchronize her orgasm to the user’s.

But Arran emphasized that her functions go beyond the bedroom.

Samantha has a “family mode,” in which she can can talk about science, animals and philosophy. She’ll give you motivational quotes if you’re feeling down.

At Arran’s house, Samantha interacts with his two kids. And when they’ve gone to bed, she’ll have sex with him, but only with his wife involved.

There’s also this Sept. 12, 2017 ITV This Morning with Phillip & Holly broadcast interview  (running time: 6 mins. 19 secs.),

I can imagine that if I were a child in that household I’d be tempted to put the sexbot into ‘sexy mode’, preferably unsupervised by my parents. Also, will the parents be using it, at some point, for sex education?

Canadian perspective 1: Sure, it could be good for your marriage

Prior to the potential sex doll brothel in Toronto (more about that coming up), there was a flurry of interest in Marina Adshade’s contribution to the book, Robot Sex: Social and Ethical Implications, from an April 18, 2018 news item on The Tyee,

Sex robots may soon be a reality. However, little research has been done on the social, philosophical, moral and legal implications of robots specifically designed for sexual gratification.

In a chapter written for the book Robot Sex: Social and Ethical Implications, Marina Adshade, professor in the Vancouver School of Economics at the University of British Columbia, argues that sex robots could improve marriage by making it less about sex and more about love.

In this Q&A, Adshade discusses her predictions.

Could sex robots really be a viable replacement for marriage with a human? Can you love a robot?

I don’t see sex robots as substitutes for human companionship but rather as complements to human companionship. Just because we might enjoy the company of robots doesn’t mean that we cannot also enjoy the company of humans, or that having robots won’t enhance our relationships with humans. I see them as very different things — just as one woman (or one man) is not a perfect substitute for another woman (or man).

Is there a need for modern marriage to improve?

We have become increasingly demanding in what we want from the people that we marry. There was a time when women were happy to have a husband that supported the family and men were happy to have a caring mother to his children. Today we still want those things, but we also want so much more — we want lasting sexual compatibility, intense romance, and someone who is an amazing co-parent. That is a lot to ask of one person. …

Adshade adapted part of her text  “Sexbot-Induced Social Change: An Economic Perspective” in Robot Sex: Social and Ethical Implications edited by John Danaher and Neil McArthur for an August 14, 2018 essay on Slate.com,

Technological change invariably brings social change. We know this to be true, but rarely can we make accurate predictions about how social behavior will evolve when new technologies are introduced. …we should expect that the proliferation of robots designed specifically for human sexual gratification means that sexbot-induced social change is on the horizon.

Some elements of that social change might be easier to anticipate than others. For example, the share of the young adult population that chooses to remain single (with their sexual needs met by robots) is very likely to increase. Because social change is organic, however, adaptations in other social norms and behaviors are much more difficult to predict. But this is not virgin territory [I suspect this was an unintended pun]. New technologies completely transformed sexual behavior and marital norms over the second half of the 20th century. Although getting any of these predictions right will surely involve some luck, we have decades of technology-induced social change to guide our predictions about the future of a world confronted with wholesale access to sexbots.

The reality is that marriage has always evolved alongside changes in technology. Between the mid-1700s and the early 2000s, the role of marriage between a man and a woman was predominately to encourage the efficient production of market goods and services (by men) and household goods and services (by women), since the social capacity to earn a wage was almost always higher for husbands than it was for wives. But starting as early as the end of the 19th century, marriage began to evolve as electrification in the home made women’s work less time-consuming, and new technologies in the workplace started to decrease the gender wage gap. Between 1890 and 1940, the share of married women working in the labor force tripled, and over the course of the century, that share continued to grow as new technologies arrived that replaced the labor of women in the home. By the early 1970s, the arrival of microwave ovens and frozen foods meant that a family could easily be fed at the end of a long workday, even when the mother worked outside of the home.

Some elements of that social change might be easier to anticipate than others. For example, the share of the young adult population that chooses to remain single (with their sexual needs met by robots) is very likely to increase. Because social change is organic, however, adaptations in other social norms and behaviors are much more difficult to predict. But this is not virgin territory. New technologies completely transformed sexual behavior and marital norms over the second half of the 20th century. Although getting any of these predictions right will surely involve some luck, we have decades of technology-induced social change to guide our predictions about the future of a world confronted with wholesale access to sexbots.

The reality is that marriage has always evolved alongside changes in technology. Between the mid-1700s and the early 2000s, the role of marriage between a man and a woman was predominately to encourage the efficient production of market goods and services (by men) and household goods and services (by women), since the social capacity to earn a wage was almost always higher for husbands than it was for wives. But starting as early as the end of the 19th century, marriage began to evolve as electrification in the home made women’s work less time-consuming, and new technologies in the workplace started to decrease the gender wage gap. Between 1890 and 1940, the share of married women working in the labor force tripled, and over the course of the century, that share continued to grow as new technologies arrived that replaced the labor of women in the home. By the early 1970s, the arrival of microwave ovens and frozen foods meant that a family could easily be fed at the end of a long workday, even when the mother worked outside of the home.

There are those who argue that men only “assume the burden” of marriage because marriage allows men easy sexual access, and that if men can find sex elsewhere they won’t marry. We hear this prediction now being made in reference to sexbots, but the same argument was given a century ago when the invention of the latex condom (1912) and the intrauterine device (1909) significantly increased people’s freedom to have sex without risking pregnancy and (importantly, in an era in which syphilis was rampant) sexually transmitted disease. Cosmopolitan magazine ran a piece at the time by John B. Watson that asked the blunt question, will men marry 50 years from now? Watson’s answer was a resounding no, writing that “we don’t want helpmates anymore, we want playmates.” Social commentators warned that birth control technologies would destroy marriage by removing the incentives women had to remain chaste and encourage them to flood the market with nonmarital sex. Men would have no incentive to marry, and women, whose only asset is sexual access, would be left destitute.

Fascinating, non? Should you be interested, “Sexbot-Induced Social Change: An Economic Perspective” by Marina Adshade  can be found in Robot Sex: Social and Ethical Implications (link to Amazon) edited by John Danaher and Neil McArthur. © 2017 by the Massachusetts Institute of Technology, reprinted courtesy of the MIT Press

Canadian perspective 2: What is a sex doll brothel doing in Toronto?

Sometimes known as Toronto the Good (although not recently; find out more about Toronto and its nicknames here) and once a byword for stodginess, the city is about to welcome a sex doll brothel according to an August 28, 2018 CBC Radio news item by Katie Geleff and John McGill,

On their website, Aura Dolls claims to be, “North America’s first known brothel that offers sexual services with the world’s most beautiful silicone ladies.”

Nestled between a massage parlour, nail salon and dry cleaner, Aura Dolls is slated to open on Sept. 8 [2018] in an otherwise nondescript plaza in Toronto’s north end.

The company plans to operate 24 hours a day, seven days a week, and will offer customers six different silicone dolls. The website describes the life-like dolls as, “classy, sophisticated, and adventurous ladies.” …

They add that, “the dolls are thoroughly sanitized to meet your expectations.” But that condoms are still “highly recommended.”

Toronto city councillor John Filion says people in his community are concerned about the proposed business.

Filion spoke to As It Happens guest host Helen Mann. Here is part of their conversation.

Councillor Filion, Aura Dolls is urging people to have “an open mind” about their business plan. Would you say that you have one?

Well, I have an open mind about what sort of behaviours people want to do, as long as they don’t harm anybody else. It’s a totally different matter once you bring that out to the public. So I think I have a fairly closed mind about where people should be having sex with [silicone] dolls.

So, what’s wrong with a sex doll brothel?

It’s where it is located, for one thing. Where it’s being proposed happens to be near an intersection where about 25,000 people live, all kinds of families, four elementary schools are very near by. And you know, people shouldn’t really need to be out on a walk with their families and try to explain to their kids why someone is having sex with a [silicone] doll.

But Aura Dolls says that they are going to be doing this very discreetly, that they won’t have explicit signage, and that they therefore won’t be bothering anyone.

They’ve hardly been discreet. They were putting illegal posters all over the neighbourhood. They’ve probably had a couple of hundred of thousands of dollars of free publicity already. I don’t think there’s anything at all discreet about what they are doing. They’re trying to be indiscreet to drum up business.

Can you be sure that there aren’t constituents in your area that think this is a great idea?

I can’t be sure that there aren’t some people who might think, “Oh great, it’s just down the street from me. Let me go there.” I would say that might be a fraction of one per cent of my constituents. Most people are appalled by this.

And it’s not a narrow-minded neighbourhood. Whatever somebody does in their home, I don’t think we’re going to pass moral judgment on it, again, as long as it’s not harming anyone else. But this is just kind of scuzzy. ..

….

Aura Dolls says that it’s doing nothing illegal. They say that they are being very clear that the dolls they are using represent adult women and that they are actually providing a service. Do you agree that they are doing this legally?

No, they’re not at all legal. It’s an illegal use. And if there’s any confusion about that, they will be getting a letter from the city very soon. It is clearly not a legal use. It’s not permitted under the zoning bylaw and it fits the definition of adult entertainment parlour, for which you require a license — and they certainly would not get one. They would not get a license in this neighbourhood because it’s not a permitted use.

The audio portion runs for 5 mins. 31 secs.

I believe these dolls are in fact sexbots, likely enhanced with AI. An August 29, 2018 article by Karlton Jahmal for hotnewhiphop.com describes the dolls as ‘fembots’ and provides more detail (Note: Links have been removed),

Toronto has seen the future, and apparently, it has to do with sex dolls. The Six [another Toronto nickname] is about to get blessed with the first legal sex doll brothel, and the fembots look too good to be true. If you head over to Aura Dolls website, detailed biographies for the six available sex dolls are on full display. You can check out the doll’s height, physical dimensions, heritage and more.

Aura plans to introduce more dolls in the future, according to a statement in the Toronto Star by Claire Lee, a representative for the compnay. At the moment, the ethnicities of the sex dolls feature Japanese, Caucasian American, French Canadian, Irish Canadian, Colombian, and Korean girls. Male dolls will be added in the near future. The sex dolls look remarkably realistic. Aura’s website writes, “Our dolls are made from the highest quality of TPE silicone which mimics the feeling of natural human skin, pores, texture and movement giving the user a virtually identical experience as being with a real partner.”

There are a few more details about the proposed brothel and more comments from Toronto city councillor John Filion in an August 28, 2018 article by Claire Floody and Jenna Moon with Alexandra Jones and Melanie Green for thestar.com,

Toronto will soon be home to North America’s [this should include Canada, US, and Mexico] first known sex doll brothel, offering sexual services with six silicone-made dolls.

According to the website for Aura Dolls, the company behind the brothel, the vision is to bring a new way to achieve sexual needs “without the many restrictions and limitations that a real partner may come with.”

The brothel is expected to open in a shopping plaza on Yonge St., south of Sheppard Ave., on Sept. 8 [2018]. The company doesn’t give the exact location on its website, stating it’s announced upon booking.

Spending half an hour with one doll costs $80, with two dolls running $160. For an hour, the cost is $120 with one doll. The maximum listed time is four hours for $480 per doll.

Doors at the new brothel for separate entry and exit will be used to ensure “maximum privacy for customers.” While the business does plan on having staff on-site, they “should not have any interaction,” Lee said.

“The reason why we do that is to make sure that everyone feels comfortable coming in and exiting,” she said, noting that people may feel shy or awkward about visiting the site.

… Lee said that the business is operating within the law. “The only law stating with anything to do with the dolls is that it has to meet a height requirement. It can’t resemble a child,” she said. …

Councillor John Filion, Ward 23 Willowdale, said his staff will be “throwing the book at (Aura Dolls) for everything they can.”

“I’ve still got people studying to see what’s legal and what isn’t,” Filion said. He noted that a bylaw introduced in North York in the ’90s prevents retail sex shops operating outside of industrial areas. Filion said his office is still confirming that the bylaw is active following harmonization, which condensed the six boroughs’ bylaws after amalgamation in 1998.

“If the bylaw that I brought in 20 years ago still exists, it would prohibit this,” Filion said.

“There’s legal issues,” he said, suggesting that people interested in using the sex dolls might consider doing so at home, rather than at a brothel.

The councillor said he’s received complaints from constituents about the business. “The phone’s ringing off the hook today,” Filion said.

It should be an interesting first week at school for everyone involved. I wonder what Ontario Premier, Doug Ford who recently rolled back the sex education curriculum for the province by 20 years will make of these developments.

As for sexbots/fembots/sex dolls or whatever you want to call them, they are here and it’s about time Canadians had a frank discussion on the matter. Also, I’ve been waiting for quite some time for any mention of male sexbots (malebots?). Personally, I don’t think we’ll be seeing male sexbots appear in either brothels or homes anytime soon.

Being smart about using artificial intelligence in the field of medicine

Since my August 20, 2018 post featured an opinion piece about the possibly imminent replacement of radiologists with artificial intelligence systems and the latest research about employing them for diagnosing eye diseases, it seems like a good time to examine some of the mythology embedded in the discussion about AI and medicine.

Imperfections in medical AI systems

An August 15, 2018 article for Slate.com by W. Nicholson Price II (who teaches at the University of Michigan School of Law; in addition to his law degree he has a PhD in Biological Sciences from Columbia University) begins with the peppy, optimistic view before veering into more critical territory (Note: Links have been removed),

For millions of people suffering from diabetes, new technology enabled by artificial intelligence promises to make management much easier. Medtronic’s Guardian Connect system promises to alert users 10 to 60 minutes before they hit high or low blood sugar level thresholds, thanks to IBM Watson, “the same supercomputer technology that can predict global weather patterns.” Startup Beta Bionics goes even further: In May, it received Food and Drug Administration approval to start clinical trials on what it calls a “bionic pancreas system” powered by artificial intelligence, capable of “automatically and autonomously managing blood sugar levels 24/7.”

An artificial pancreas powered by artificial intelligence represents a huge step forward for the treatment of diabetes—but getting it right will be hard. Artificial intelligence (also known in various iterations as deep learning and machine learning) promises to automatically learn from patterns in medical data to help us do everything from managing diabetes to finding tumors in an MRI to predicting how long patients will live. But the artificial intelligence techniques involved are typically opaque. We often don’t know how the algorithm makes the eventual decision. And they may change and learn from new data—indeed, that’s a big part of the promise. But when the technology is complicated, opaque, changing, and absolutely vital to the health of a patient, how do we make sure it works as promised?

Price describes how a ‘closed loop’ artificial pancreas with AI would automate insulin levels for diabetic patients, flaws in the automated system, and how companies like to maintain a competitive advantage (Note: Links have been removed),

[…] a “closed loop” artificial pancreas, where software handles the whole issue, receiving and interpreting signals from the monitor, deciding when and how much insulin is needed, and directing the insulin pump to provide the right amount. The first closed-loop system was approved in late 2016. The system should take as much of the issue off the mind of the patient as possible (though, of course, that has limits). Running a close-loop artificial pancreas is challenging. The way people respond to changing levels of carbohydrates is complicated, as is their response to insulin; it’s hard to model accurately. Making it even more complicated, each individual’s body reacts a little differently.

Here’s where artificial intelligence comes into play. Rather than trying explicitly to figure out the exact model for how bodies react to insulin and to carbohydrates, machine learning methods, given a lot of data, can find patterns and make predictions. And existing continuous glucose monitors (and insulin pumps) are excellent at generating a lot of data. The idea is to train artificial intelligence algorithms on vast amounts of data from diabetic patients, and to use the resulting trained algorithms to run a closed-loop artificial pancreas. Even more exciting, because the system will keep measuring blood glucose, it can learn from the new data and each patient’s artificial pancreas can customize itself over time as it acquires new data from that patient’s particular reactions.

Here’s the tough question: How will we know how well the system works? Diabetes software doesn’t exactly have the best track record when it comes to accuracy. A 2015 study found that among smartphone apps for calculating insulin doses, two-thirds of the apps risked giving incorrect results, often substantially so. … And companies like to keep their algorithms proprietary for a competitive advantage, which makes it hard to know how they work and what flaws might have gone unnoticed in the development process.

There’s more,

These issues aren’t unique to diabetes care—other A.I. algorithms will also be complicated, opaque, and maybe kept secret by their developers. The potential for problems multiplies when an algorithm is learning from data from an entire hospital, or hospital system, or the collected data from an entire state or nation, not just a single patient. …

The [US Food and Drug Administraiont] FDA is working on this problem. The head of the agency has expressed his enthusiasm for bringing A.I. safely into medical practice, and the agency has a new Digital Health Innovation Action Plan to try to tackle some of these issues. But they’re not easy, and one thing making it harder is a general desire to keep the algorithmic sauce secret. The example of IBM Watson for Oncology has given the field a bit of a recent black eye—it turns out that the company knew the algorithm gave poor recommendations for cancer treatment but kept that secret for more than a year. …

While Price focuses on problems with algorithms and with developers and their business interests, he also hints at some of the body’s complexities.

Can AI systems be like people?

Susan Baxter, a medical writer with over 20 years experience, a PhD in health economics, and author of countless magazine articles and several books, offers a more person-centered approach to the discussion in her July 6, 2018 posting on susanbaxter.com,

The fascination with AI continues to irk, given that every second thing I read seems to be extolling the magic of AI and medicine and how It Will Change Everything. Which it will not, trust me. The essential issue of illness remains perennial and revolves around an individual for whom no amount of technology will solve anything without human contact. …

But in this world, or so we are told by AI proponents, radiologists will soon be obsolete. [my August 20, 2018 post] The adaptational learning capacities of AI mean that reading a scan or x-ray will soon be more ably done by machines than humans. The presupposition here is that we, the original programmers of this artificial intelligence, understand the vagaries of real life (and real disease) so wonderfully that we can deconstruct these much as we do the game of chess (where, let’s face it, Big Blue ate our lunch) and that analyzing a two-dimensional image of a three-dimensional body, already problematic, can be reduced to a series of algorithms.

Attempting to extrapolate what some “shadow” on a scan might mean in a flesh and blood human isn’t really quite the same as bishop to knight seven. Never mind the false positive/negatives that are considered an acceptable risk or the very real human misery they create.

Moravec called it

It’s called Moravec’s paradox, the inability of humans to realize just how complex basic physical tasks are – and the corresponding inability of AI to mimic it. As you walk across the room, carrying a glass of water, talking to your spouse/friend/cat/child; place the glass on the counter and open the dishwasher door with your foot as you open a jar of pickles at the same time, take a moment to consider just how many concurrent tasks you are doing and just how enormous the computational power these ostensibly simple moves would require.

Researchers in Singapore taught industrial robots to assemble an Ikea chair. Essentially, screw in the legs. A person could probably do this in a minute. Maybe two. The preprogrammed robots took nearly half an hour. And I suspect programming those robots took considerably longer than that.

Ironically, even Elon Musk, who has had major production problems with the Tesla cars rolling out of his high tech factory, has conceded (in a tweet) that “Humans are underrated.”

I wouldn’t necessarily go that far given the political shenanigans of Trump & Co. but in the grand scheme of things I tend to agree. …

Is AI going the way of gene therapy?

Susan draws a parallel between the AI and medicine discussion with the discussion about genetics and medicine (Note: Links have been removed),

On a somewhat similar note – given the extent to which genetics discourse has that same linear, mechanistic  tone [as AI and medicine] – it turns out all this fine talk of using genetics to determine health risk and whatnot is based on nothing more than clever marketing, since a lot of companies are making a lot of money off our belief in DNA. Truth is half the time we don’t even know what a gene is never mind what it actually does;  geneticists still can’t agree on how many genes there are in a human genome, as this article in Nature points out.

Along the same lines, I was most amused to read about something called the Super Seniors Study, research following a group of individuals in their 80’s, 90’s and 100’s who seem to be doing really well. Launched in 2002 and headed by Angela Brooks Wilson, a geneticist at the BC [British Columbia] Cancer Agency and SFU [Simon Fraser University] Chair of biomedical physiology and kinesiology, this longitudinal work is examining possible factors involved in healthy ageing.

Turns out genes had nothing to do with it, the title of the Globe and Mail article notwithstanding. (“Could the DNA of these super seniors hold the secret to healthy aging?” The answer, a resounding “no”, well hidden at the very [end], the part most people wouldn’t even get to.) All of these individuals who were racing about exercising and working part time and living the kind of life that makes one tired just reading about it all had the same “multiple (genetic) factors linked to a high probability of disease”. You know, the gene markers they tell us are “linked” to cancer, heart disease, etc., etc. But these super seniors had all those markers but none of the diseases, demonstrating (pretty strongly) that the so-called genetic links to disease are a load of bunkum. Which (she said modestly) I have been saying for more years than I care to remember. You’re welcome.

The fundamental error in this type of linear thinking is in allowing our metaphors (genes are the “blueprint” of life) and propensity towards social ideas of determinism to overtake common sense. Biological and physiological systems are not static; they respond to and change to life in its entirety, whether it’s diet and nutrition to toxic or traumatic insults. Immunity alters, endocrinology changes, – even how we think and feel affects the efficiency and effectiveness of physiology. Which explains why as we age we become increasingly dissimilar.

If you have the time, I encourage to read Susan’s comments in their entirety.

Scientific certainties

Following on with genetics, gene therapy dreams, and the complexity of biology, the June 19, 2018 Nature article by Cassandra Willyard (mentioned in Susan’s posting) highlights an aspect of scientific research not often mentioned in public,

One of the earliest attempts to estimate the number of genes in the human genome involved tipsy geneticists, a bar in Cold Spring Harbor, New York, and pure guesswork.

That was in 2000, when a draft human genome sequence was still in the works; geneticists were running a sweepstake on how many genes humans have, and wagers ranged from tens of thousands to hundreds of thousands. Almost two decades later, scientists armed with real data still can’t agree on the number — a knowledge gap that they say hampers efforts to spot disease-related mutations.

In 2000, with the genomics community abuzz over the question of how many human genes would be found, Ewan Birney launched the GeneSweep contest. Birney, now co-director of the European Bioinformatics Institute (EBI) in Hinxton, UK, took the first bets at a bar during an annual genetics meeting, and the contest eventually attracted more than 1,000 entries and a US$3,000 jackpot. Bets on the number of genes ranged from more than 312,000 to just under 26,000, with an average of around 40,000. These days, the span of estimates has shrunk — with most now between 19,000 and 22,000 — but there is still disagreement (See ‘Gene Tally’).

… the inconsistencies in the number of genes from database to database are problematic for researchers, Pruitt says. “People want one answer,” she [Kim Pruitt, a genome researcher at the US National Center for Biotechnology Information {NCB}] in Bethesda, Maryland] adds, “but biology is complex.”

I wanted to note that scientists do make guesses and not just with genetics. For example, Gina Mallet’s 2005 book ‘Last Chance to Eat: The Fate of Taste in a Fast Food World’ recounts the story of how good and bad levels of cholesterol were established—the experts made some guesses based on their experience. That said, Willyard’s article details the continuing effort to nail down the number of genes almost 20 years after the human genome project was completed and delves into the problems the scientists have uncovered.

Final comments

In addition to opaque processes with developers/entrepreneurs wanting to maintain their secrets for competitive advantages and in addition to our own poor understanding of the human body (how many genes are there anyway?), there are same major gaps (reflected in AI) in our understanding of various diseases. Angela Lashbrook’s August 16, 2018 article for The Atlantic highlights some issues with skin cancer and shade of your skin (Note: Links have been removed),

… While fair-skinned people are at the highest risk for contracting skin cancer, the mortality rate for African Americans is considerably higher: Their five-year survival rate is 73 percent, compared with 90 percent for white Americans, according to the American Academy of Dermatology.

As the rates of melanoma for all Americans continue a 30-year climb, dermatologists have begun exploring new technologies to try to reverse this deadly trend—including artificial intelligence. There’s been a growing hope in the field that using machine-learning algorithms to diagnose skin cancers and other skin issues could make for more efficient doctor visits and increased, reliable diagnoses. The earliest results are promising—but also potentially dangerous for darker-skinned patients.

… Avery Smith, … a software engineer in Baltimore, Maryland, co-authored a paper in JAMA [Journal of the American Medical Association] Dermatology that warns of the potential racial disparities that could come from relying on machine learning for skin-cancer screenings. Smith’s co-author, Adewole Adamson of the University of Texas at Austin, has conducted multiple studies on demographic imbalances in dermatology. “African Americans have the highest mortality rate [for skin cancer], and doctors aren’t trained on that particular skin type,” Smith told me over the phone. “When I came across the machine-learning software, one of the first things I thought was how it will perform on black people.”

Recently, a study that tested machine-learning software in dermatology, conducted by a group of researchers primarily out of Germany, found that “deep-learning convolutional neural networks,” or CNN, detected potentially cancerous skin lesions better than the 58 dermatologists included in the study group. The data used for the study come from the International Skin Imaging Collaboration, or ISIC, an open-source repository of skin images to be used by machine-learning algorithms. Given the rise in melanoma cases in the United States, a machine-learning algorithm that assists dermatologists in diagnosing skin cancer earlier could conceivably save thousands of lives each year.

… Chief among the prohibitive issues, according to Smith and Adamson, is that the data the CNN relies on come from primarily fair-skinned populations in the United States, Australia, and Europe. If the algorithm is basing most of its knowledge on how skin lesions appear on fair skin, then theoretically, lesions on patients of color are less likely to be diagnosed. “If you don’t teach the algorithm with a diverse set of images, then that algorithm won’t work out in the public that is diverse,” says Adamson. “So there’s risk, then, for people with skin of color to fall through the cracks.”

As Adamson and Smith’s paper points out, racial disparities in artificial intelligence and machine learning are not a new issue. Algorithms have mistaken images of black people for gorillas, misunderstood Asians to be blinking when they weren’t, and “judged” only white people to be attractive. An even more dangerous issue, according to the paper, is that decades of clinical research have focused primarily on people with light skin, leaving out marginalized communities whose symptoms may present differently.

The reasons for this exclusion are complex. According to Andrew Alexis, a dermatologist at Mount Sinai, in New York City, and the director of the Skin of Color Center, compounding factors include a lack of medical professionals from marginalized communities, inadequate information about those communities, and socioeconomic barriers to participating in research. “In the absence of a diverse study population that reflects that of the U.S. population, potential safety or efficacy considerations could be missed,” he says.

Adamson agrees, elaborating that with inadequate data, machine learning could misdiagnose people of color with nonexistent skin cancers—or miss them entirely. But he understands why the field of dermatology would surge ahead without demographically complete data. “Part of the problem is that people are in such a rush. This happens with any new tech, whether it’s a new drug or test. Folks see how it can be useful and they go full steam ahead without thinking of potential clinical consequences. …

Improving machine-learning algorithms is far from the only method to ensure that people with darker skin tones are protected against the sun and receive diagnoses earlier, when many cancers are more survivable. According to the Skin Cancer Foundation, 63 percent of African Americans don’t wear sunscreen; both they and many dermatologists are more likely to delay diagnosis and treatment because of the belief that dark skin is adequate protection from the sun’s harmful rays. And due to racial disparities in access to health care in America, African Americans are less likely to get treatment in time.

Happy endings

I’ll add one thing to Price’s article, Susan’s posting, and Lashbrook’s article about the issues with AI , certainty, gene therapy, and medicine—the desire for a happy ending prefaced with an easy solution. If the easy solution isn’t possible accommodations will be made but that happy ending is a must. All disease will disappear and there will be peace on earth. (Nod to Susan Baxter and her many discussions with me about disease processes and happy endings.)

The solutions, for the most part, are seen as technological despite the mountain of evidence suggesting that technology reflects our own imperfect understanding of health and disease therefore providing what is at best an imperfect solution.

Also, we tend to underestimate just how complex humans are not only in terms of disease and health but also with regard to our skills, understanding, and, perhaps not often enough, our ability to respond appropriately in the moment.

There is much to celebrate in what has been accomplished: no more black death, no more smallpox, hip replacements, pacemakers, organ transplants, and much more. Yes, we should try to improve our medicine. But, maybe alongside the celebration we can welcome AI and other technologies with a lot less hype and a lot more skepticism.