Tag Archives: machine learning

Uncanny Valley: Being Human in the Age of AI (artificial intelligence) at the de Young museum (San Francisco, US) February 22 – October 25, 2020

So we’re still stuck in 20th century concepts about artificial intelligence (AI), eh? Sean Captain’s February 21, 2020 article (for Fast Company) about the new AI exhibit in San Francisco suggests that artists can help us revise our ideas (Note: Links have been removed),

Though we’re well into the age of machine learning, popular culture is stuck with a 20th century notion of artificial intelligence. While algorithms are shaping our lives in real ways—playing on our desires, insecurities, and suspicions in social media, for instance—Hollywood is still feeding us clichéd images of sexy, deadly robots in shows like Westworld and Star Trek Picard.

The old-school humanlike sentient robot “is an important trope that has defined the visual vocabulary around this human-machine relationship for a very long period of time,” says Claudia Schmuckli, curator of contemporary art and programming at the Fine Arts Museums of San Francisco. It’s also a naïve and outdated metaphor, one she is challenging with a new exhibition at San Francisco’s de Young Museum, called Uncanny Valley, that opens on February 22 [2020].

The show’s name [Uncanny Valley: Being Human in the Age of AI] is a kind of double entendre referencing both the dated and emerging conceptions of AI. Coined in the 1970s, the term “uncanny valley” describes the rise and then sudden drop off of empathy we feel toward a machine as its resemblance to a human increases. Putting a set of cartoony eyes on a robot may make it endearing. But fitting it with anatomically accurate eyes, lips, and facial gestures gets creepy. As the gap between the synthetic and organic narrows, the inability to completely close that gap becomes all the more unsettling.

But the artists in this exhibit are also looking to another valley—Silicon Valley, and the uncanny nature of the real AI the region is building. “One of the positions of this exhibition is that it may be time to rethink the coordinates of the Uncanny Valley and propose a different visual vocabulary,” says Schmuckli.

Artist Stephanie Dinkins faces off with robot Bina48, a bot on display at the de Young Museum’s Uncanny Valley show. [Photo: courtesy of the artist; courtesy of the Fine Arts Museums of San Francisco]

From Captain’s February 21, 2020 article,

… the resemblance to humans is only synthetic-skin deep. Bina48 can string together a long series of sentences in response to provocative questions from Dinkins, such as, “Do you know racism?” But the answers are sometimes barely intelligible, or at least lack the depth and nuance of a conversation with a real human. The robot’s jerky attempts at humanlike motion also stand in stark contrast to Dinkins’s calm bearing and fluid movement. Advanced as she is by today’s standards, Bina48 is tragically far from the sci-fi concept of artificial life. Her glaring shortcomings hammer home why the humanoid metaphor is not the right framework for understanding at least today’s level of artificial intelligence.

For anybody who has more curiosity about the ‘uncanny valley’, there’s this Wikipedia entry.

For more details about the’ Uncanny Valley: Being Human in the Age of AI’ exhibition there’s this September 26, 2019 de Young museum news release,

What are the invisible mechanisms of current forms of artificial intelligence (AI)? How is AI impacting our personal lives and socioeconomic spheres? How do we define intelligence? How do we envision the future of humanity?

SAN FRANCISCO (September 26, 2019) — As technological innovation continues to shape our identities and societies, the question of what it means to be, or remain human has become the subject of fervent debate. Taking advantage of the de Young museum’s proximity to Silicon Valley, Uncanny Valley: Being Human in the Age of AI arrives as the first major exhibition in the US to explore the relationship between humans and intelligent machines through an artistic lens. Organized by the Fine Arts Museums of San Francisco, with San Francisco as its sole venue, Uncanny Valley: Being Human in the Age of AI will be on view from February 22 to October 25, 2020.

“Technology is changing our world, with artificial intelligence both a new frontier of possibility but also a development fraught with anxiety,” says Thomas P. Campbell, Director and CEO of the Fine Arts Museums of San Francisco. “Uncanny Valley: Being Human in the Age of AI brings artistic exploration of this tension to the ground zero of emerging technology, raising challenging questions about the future interface of human and machine.”

The exhibition, which extends through the first floor of the de Young and into the museum’s sculpture garden, explores the current juncture through philosophical, political, and poetic questions and problems raised by AI. New and recent works by an intergenerational, international group of artists and activist collectives—including Zach Blas, Ian Cheng, Simon Denny, Stephanie Dinkins, Forensic Architecture, Lynn Hershman Leeson, Pierre Huyghe, Christopher Kulendran Thomas in collaboration with Annika Kuhlmann, Agnieszka Kurant, Lawrence Lek, Trevor Paglen, Hito Steyerl, Martine Syms, and the Zairja Collective—will be presented.

The Uncanny Valley

In 1970 Japanese engineer Masahiro Mori introduced the concept of the “uncanny valley” as a terrain of existential uncertainty that humans experience when confronted with autonomous machines that mimic their physical and mental properties. An enduring metaphor for the uneasy relationship between human beings and lifelike robots or thinking machines, the uncanny valley and its edges have captured the popular imagination ever since. Over time, the rapid growth and affordability of computers, cloud infrastructure, online search engines, and data sets have fueled developments in machine learning that fundamentally alter our modes of existence, giving rise to a newly expanded uncanny valley.

“As our lives are increasingly organized and shaped by algorithms that track, collect, evaluate, and monetize our data, the uncanny valley has grown to encompass the invisible mechanisms of behavioral engineering and automation,” says Claudia Schmuckli, Curator in Charge of Contemporary Art and Programming at the Fine Arts Museums of San Francisco. “By paying close attention to the imminent and nuanced realities of AI’s possibilities and pitfalls, the artists in the exhibition seek to thicken the discourse around AI. Although fables like HBO’s sci-fi drama Westworld, or Spike Jonze’s feature film Her still populate the collective imagination with dystopian visions of a mechanized future, the artists in this exhibition treat such fictions as relics of a humanist tradition that has little relevance today.”

In Detail

Ian Cheng’s digitally simulated AI creature BOB (Bag of Beliefs) reflects on the interdependency of carbon and silicon forms of intelligence. An algorithmic Tamagotchi, it is capable of evolution, but its growth, behavior, and personality are molded by online interaction with visitors who assume collective responsibility for its wellbeing.

In A.A.I. (artificial artificial intelligence), an installation of multiple termite mounds of colored sand, gold, glitter and crystals, Agnieszka Kurant offers a vibrant critique of new AI economies, with their online crowdsourcing marketplace platforms employing invisible armies of human labor at sub-minimum wages.

Simon Denny ‘s Amazon worker cage patent drawing as virtual King Island Brown Thornbill cage (US 9,280,157 B2: “System and method for transporting personnel within an active workspace”, 2016) (2019) also examines the intersection of labor, resources, and automation. He presents 3-D prints and a cage-like sculpture based on an unrealized machine patent filed by Amazon to contain human workers. Inside the cage an augmented reality application triggers the appearance of a King Island Brown Thornbill — a bird on the verge of extinction; casting human labor as the proverbial canary in the mine. The humanitarian and ecological costs of today’s data economy also informs a group of works by the Zairja Collective that reflect on the extractive dynamics of algorithmic data mining. 

Hito Steyerl addresses the political risks of introducing machine learning into the social sphere. Her installation The City of Broken Windows presents a collision between commercial applications of AI in urban planning along with communal and artistic acts of resistance against neighborhood tipping: one of its short films depicts a group of technicians purposefully smashing windows to teach an algorithm how to recognize the sound of breaking glass, and another follows a group of activists through a Camden, NJ neighborhood as they work to keep decay at bay by replacing broken windows in abandoned homes with paintings. 

Addressing the perpetuation of societal biases and discrimination within AI, Trevor Paglen’s They Took the Faces from the Accused and the Dead…(SD18), presents a large gridded installation of more than three thousand mugshots from the archives of the American National Standards Institute. The institute’s collections of such images were used to train ealry facial-recognition technologies — without the consent of those pictured. Lynn Hershman Leeson’s new installation Shadow Stalker critiques the problematic reliance on algorithmic systems, such as the military forecasting tool Predpol now widely used for policing, that categorize individuals into preexisting and often false “embodied metrics.”

Stephanie Dinkins extends the inquiry into how value systems are built into AI and the construction of identity in Conversations with Bina48, examining the social robot’s (and by extension our society’s) coding of technology, race, gender and social equity. In the same territory, Martine Syms posits AI as a “shamespace” for misrepresentation. For Mythiccbeing she has created an avatar of herself that viewers can interact with through text messaging. But unlike service agents such as Siri and Alexa, who readily respond to questions and demands, Syms’s Teeny is a contrarious interlocutor, turning each interaction into an opportunity to voice personal observations and frustrations about racial inequality and social injustice.

Countering the abusive potential of machine learning, Forensic Architecture pioneers an application to the pursuit of social justice. Their proposition of a Model Zoo marks the beginnings of a new research tool for civil society built of military vehicles, missile fragments, and bomb clouds—evidence of human-rights violations by states and militaries around the world. Christopher Kulendran Thomas’s video Being Human, created in collaboration with Annika Kuhlmann, poses the philosophical question of what it means to be human when machines are able to synthesize human understanding ever more convincingly. Set  in Sri Lanka, it employs AI-generated characters of singer Taylor Swift and artist Oscar Murillo to reflect on issues of individual authenticity, collective sovereignty, and the future of human rights.

Lawrence Lek’s sci-fi-inflected film Aidol, which explores the relationship between algorithmic automation and human creativity, projects this question into the future. It transports the viewer into the computer-generated “sinofuturist” world of the 2065 eSports Olympics: when the popular singer Diva enlists the super-intelligent Geomancer to help her stage her artistic comeback during the game’s halftime show, she unleashes an existential and philosophical battle that explodes the divide between humans and machines.

The Doors, a newly commissioned installation by Zach Blas, by contrast shines the spotlight back onto the present and on the culture and ethos of Silicon Valley — the ground zero for the development of AI. Inspired by the ubiquity of enclosed gardens on tech campuses, he has created an artificial garden framed by a six-channel video projected on glass panes that convey a sense of algorithmic psychedelia aiming to open new “doors of perception.” While luring visitors into AI’s promises, it also asks what might become possible when such glass doors begin to crack. 

Unveiled in late spring Pierre Huyghe‘s Exomind (Deep Water), a sculpture of a crouched female nude with a live beehive as its head will be nestled within the museum’s garden. With its buzzing colony pollinating the surrounding flora, it offers a poignant metaphor for the modeling of neural networks on the biological brain and an understanding of intelligence as grounded in natural forms and processes.

The Uncanny Valley: Being Human in the Age of AI event page features a link to something unexpected 9scroll down about 40% of the way), a Statement on Eyal Weizman of Forensic Architecture,

On Thursday, February 13 [2020], Eyal Weizman of Forensic Architecture had his travel authorization to the United States revoked due to an “algorithm” that identified him as a security threat.

He was meant to be in the United States promoting multiple exhibitions including Uncanny Valley: Being Human in the Age of AI, opening on February 22 [2020] at the de Young museum in San Francisco.

Since 2018, Forensic Architecture has used machine learning / AI to aid in humanitarian work, using synthetic images—photorealistic digital renderings based around 3-D models—to train algorithmic classifiers to identify tear gas munitions and chemical bombs deployed against protesters worldwide, including in Hong Kong, Chile, the US, Venezuela, and Sudan.

Their project, Model Zoo, on view in Uncanny Valley represents a growing collection of munitions and weapons used in conflict today and the algorithmic models developed to identify them. It shows a collection of models being used to track and hold accountable human rights violators around the world. The piece joins work by 14 contemporary artists reflecting on the philosophical and political consequences of the application of AI into the social sphere.

We are deeply saddened that Weizman will not be allowed to travel to celebrate the opening of the exhibition. We stand with him and Forensic Architecture’s partner communities who continue to resist violent states and corporate practices, and who are increasingly exposed to the regime of “security algorithms.”

—Claudia Schmuckli, Curator-in-Charge, Contemporary Art & Programming, & Thomas P. Campbell, Director and CEO, Fine Arts Museums of San Francisco

There is a February 20, 2020 article (for Fast Company) by Eyal Weizman chronicling his experience with being denied entry by an algorithm. Do read it in its entirety (the Fast Company is itself an excerpt from Weizman’s essay) if you have the time, if not, here’s the description of how he tried to gain entry after being denied the first time,

The following day I went to the U.S. Embassy in London to apply for a visa. In my interview, the officer informed me that my authorization to travel had been revoked because the “algorithm” had identified a security threat. He said he did not know what had triggered the algorithm but suggested that it could be something I was involved in, people I am or was in contact with, places to which I had traveled (had I recently been in Syria, Iran, Iraq, Yemen, or Somalia or met their nationals?), hotels at which I stayed, or a certain pattern of relations among these things. I was asked to supply the Embassy with additional information, including 15 years of travel history, in particular where I had gone and who had paid for it. The officer said that Homeland Security’s investigators could assess my case more promptly if I supplied the names of anyone in my network whom I believed might have triggered the algorithm. I declined to provide this information.

I hope the exhibition is successful; it has certainly experienced a thought-provoking start.

Finally, I have often featured postings that discuss the ‘uncanny valley’. To find those postings, just use that phrase in the blog search engine. You might also went to search ‘Hiroshi Ishiguro’, a Japanese scientist and robotocist who specializes in humanoid robots.

Memristor-based neural network and the biosimilar principle of learning

Once you get past the technical language (there’s a lot of it), you’ll find that they make the link between biomimicry and memristors explicit. Admittedly I’m not an expert but if I understand the research correctly, the scientists are suggesting that the algorithms used in machine learning today cannot allow memristors to be properly integrated for use in true neuromorphic computing and this work from Russia and Greece points to a new paradigm. If you understand it differently, please do let me know in the comments.

A July 12, 2019 news item on Nanowerk kicks things off (Note: A link has been removed),

Lobachevsky University scientists together with their colleagues from the National Research Center “Kurchatov Institute” (Moscow) and the National Research Center “Demokritos” (Athens) are working on the hardware implementation of a spiking neural network based on memristors.

The key elements of such a network, along with pulsed neurons, are artificial synaptic connections that can change the strength (weight) of connection between neurons during the learning (Microelectronic Engineering, “Yttria-stabilized zirconia cross-point memristive devices for neuromorphic applications”).

For this purpose, memristive devices based on metal-oxide-metal nanostructures developed at the UNN Physics and Technology Research Institute (PTRI) are suitable, but their use in specific spiking neural network architectures developed at the Kurchatov Institute requires demonstration of biologically plausible learning principles.

Caption: Cross-section image of the metal-oxide-metal memristive structure based on ZrO2(Y) polycrystalline film (a); corresponding schematic view of the cross-point memristive device (b); STDP dependencies of memristive device conductance changes for different delay values between pre- and postsynaptic neuron spikes (c); photographs of a microchip and an array of memristive devices in a standard cermet casing (d); the simplest spiking neural network architecture learning on the basis of local rules for changing memristive weights (e). Credit: Lobachevsky University

A July 12, 2019 (?) Lobachevsky University press release (also on EurekAlert), which originated the news item, delves further into the work,

The biological mechanism of learning of neural systems is described by Hebb’s rule, according to which learning occurs as a result of an increase in the strength of connection  (synaptic weight) between simultaneously active neurons, which indicates the presence of a causal relationship in their excitation. One of the clarifying forms of this fundamental rule is plasticity, which depends on the time of arrival of pulses (Spike-Timing Dependent Plasticity – STDP).

In accordance with STDP, synaptic weight increases if the postsynaptic neuron generates a pulse (spike) immediately after the presynaptic one, and vice versa, the synaptic weight decreases if the postsynaptic neuron generates a spike right before the presynaptic one. Moreover, the smaller the time difference Δt between the pre- and postsynaptic spikes, the more pronounced the weight change will be.

According to one of the researchers, Head of the UNN PTRI laboratory Alexei Mikhailov, in order to demonstrate the STDP principle, memristive nanostructures based on yttria-stabilized zirconia (YSZ) thin films were used. YSZ is a well-known solid-state electrolyte with high oxygen ion mobility.

“Due to a specified concentration of oxygen vacancies, which is determined by the controlled concentration of yttrium impurities, and the heterogeneous structure of the films obtained by magnetron sputtering, such memristive structures demonstrate controlled bipolar switching between different resistive states in a wide resistance range. The switching is associated with the formation and destruction of conductive channels along grain boundaries in the polycrystalline ZrO2 (Y) film,” notes Alexei Mikhailov.

An array of memristive devices for research was implemented in the form of a microchip mounted in a standard cermet casing, which facilitates the integration of the array into a neural network’s analog circuit. The full technological cycle for creating memristive microchips is currently implemented at the UNN PTRI. In the future, it is possible to scale the devices down to the minimum size of about 50 nm, as was established by Greek partners.
Our studies of the dynamic plasticity of the memoristive devices, continues Alexey Mikhailov, have shown that the form of the conductance change depending on Δt is in good agreement with the STDP learning rules. It should be also noted that if the initial value of the memristor conductance is close to the maximum, it is easy to reduce the corresponding weight while it is difficult to enhance it, and in the case of a memristor with a minimum conductance in the initial state, it is difficult to reduce its weight, but it is easy to enhance it.

According to Vyacheslav Demin, director-coordinator in the area of nature-like technologies of the Kurchatov Institute, who is one of the ideologues of this work, the established pattern of change in the memristor conductance clearly demonstrates the possibility of hardware implementation of the so-called local learning rules. Such rules for changing the strength of synaptic connections depend only on the values ​​of variables that are present locally at each time point (neuron activities and current weights).

“This essentially distinguishes such principle from the traditional learning algorithm, which is based on global rules for changing weights, using information on the error values ​​at the current time point for each neuron of the output neural network layer (in a widely popular group of error back propagation methods). The traditional principle is not biosimilar, it requires “external” (expert) knowledge of the correct answers for each example presented to the network (that is, they do not have the property of self-learning). This principle is difficult to implement on the basis of memristors, since it requires controlled precise changes of memristor conductances, as opposed to local rules. Such precise control is not always possible due to the natural variability (a wide range of parameters) of memristors as analog elements,” says Vyacheslav Demin.

Local learning rules of the STDP type implemented in hardware on memristors provide the basis for autonomous (“unsupervised”) learning of a spiking neural network. In this case, the final state of the network does not depend on its initial state, but depends only on the learning conditions (a specific sequence of pulses). According to Vyacheslav Demin, this opens up prospects for the application of local learning rules based on memristors when solving artificial intelligence problems with the use of complex spiking neural network architectures.

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

Yttria-stabilized zirconia cross-point memristive devices for neuromorphic applications by A. V. Emelyanov, K. E. Nikiruy, A. Demin, V. V. Rylkov, A. I. Belov, D. S. Korolev, E. G. Gryaznov, D. A. Pavlov, O. N. Gorshkov, A. N. Mikhaylov, P. Dimitrakis. Microelectronic Engineering Volume 215, 15 July 2019, 110988 First available online 16 May 2019

This paper is behind a paywall.

Large Interactive Virtual Environment Laboratory (LIVELab) located in McMaster University’s Institute for Music & the Mind (MIMM) and the MetaCreation Lab at Simon Fraser University

Both of these bits have a music focus but they represent two entirely different science-based approaches to that form of art and one is solely about the music and the other is included as one of the art-making processes being investigated..

Large Interactive Virtual Environment Laboratory (LIVELab) at McMaster University

Laurel Trainor and Dan J. Bosnyak both of McMaster University (Ontario, Canada) have written an October 27, 2019 essay about the LiveLab and their work for The Conversation website (Note: Links have been removed),

The Large Interactive Virtual Environment Laboratory (LIVELab) at McMaster University is a research concert hall. It functions as both a high-tech laboratory and theatre, opening up tremendous opportunities for research and investigation.

As the only facility of its kind in the world, the LIVELab is a 106-seat concert hall equipped with dozens of microphones, speakers and sensors to measure brain responses, physiological responses such as heart rate, breathing rates, perspiration and movements in multiple musicians and audience members at the same time.

Engineers, psychologists and clinician-researchers from many disciplines work alongside musicians, media artists and industry to study performance, perception, neural processing and human interaction.

In the LIVELab, acoustics are digitally controlled so the experience can change instantly from extremely silent with almost no reverberation to a noisy restaurant to a subway platform or to the acoustics of Carnegie Hall.

Real-time physiological data such as heart rate can be synchronized with data from other systems such as motion capture, and monitored and recorded from both performers and audience members. The result is that the reams of data that can now be collected in a few hours in the LIVELab used to take weeks or months to collect in a traditional lab. And having measurements of multiple people simultaneously is pushing forward our understanding of real-time human interactions.

Consider the implications of how music might help people with Parkinson’s disease to walk more smoothly or children with dyslexia to read better.

[…] area of ongoing research is the effectiveness of hearing aids. By the age of 60, nearly 49 per cent of people will suffer from some hearing loss. People who wear hearing aids are often frustrated when listening to music because the hearing aids distort the sound and cannot deal with the dynamic range of the music.

The LIVELab is working with the Hamilton Philharmonic Orchestra to solve this problem. During a recent concert, researchers evaluated new ways of delivering sound directly to participants’ hearing aids to enhance sounds.

Researchers hope new technologies can not only increase live musical enjoyment but alleviate the social isolation caused by hearing loss.

Imagine the possibilities for understanding music and sound: How it might help to improve cognitive decline, manage social performance anxiety, help children with developmental disorders, aid in treatment of depression or keep the mind focused. Every time we conceive and design a study, we think of new possibilities.

The essay also includes an embedded 12 min. video about LIVELab and details about studies conducted on musicians and live audiences. Apparently, audiences experience live performance differently than recorded performances and musicians use body sway to create cohesive performances. You can find the McMaster Institute for Music & the Mind here and McMaster’s LIVELab here.

Capturing the motions of a string quartet performance. Laurel Trainor, Author provided [McMaster University]

Metacreation Lab at Simon Fraser University (SFU)

I just recently discovered that there’s a Metacreation Lab at Simon Fraser University (Vancouver, Canada), which on its homepage has this ” Metacreation is the idea of endowing machines with creative behavior.” Here’s more from the homepage,

As the contemporary approach to generative art, Metacreation involves using tools and techniques from artificial intelligence, artificial life, and machine learning to develop software that partially or completely automates creative tasks. Through the collaboration between scientists, experts in artificial intelligence, cognitive sciences, designers and artists, the Metacreation Lab for Creative AI is at the forefront of the development of generative systems, be they embedded in interactive experiences or integrated into current creative software. Scientific research in the Metacreation Lab explores how various creative tasks can be automated and enriched. These tasks include music composition [emphasis mine], sound design, video editing, audio/visual effect generation, 3D animation, choreography, and video game design.

Besides scientific research, the team designs interactive and generative artworks that build upon the algorithms and research developed in the Lab. This work often challenges the social and cultural discourse on AI.

Much to my surprise I received the Metacreation Lab’s inaugural email newsletter (received via email on Friday, November 15, 2019),

Greetings,

We decided to start a mailing list for disseminating news, updates, and announcements regarding generative art, creative AI and New Media. In this newsletter: 

  1. ISEA 2020: The International Symposium on Electronic Art. ISEA return to Montreal, check the CFP bellow and contribute!
  2. ISEA 2015: A transcription of Sara Diamond’s keynote address “Action Agenda: Vancouver’s Prescient Media Arts” is now available for download. 
  3. Brain Art, the book: we are happy to announce the release of the first comprehensive volume on Brain Art. Edited by Anton Nijholt, and published by Springer.

Here are more details from the newsletter,

ISEA2020 – 26th International Symposium on Electronic Arts

Montreal, September 24, 2019
Montreal Digital Spring (Printemps numérique) is launching a call for participation as part of ISEA2020 / MTL connect to be held from May 19 to 24, 2020 in Montreal, Canada. Founded in 1990, ISEA is one of the world’s most prominent international arts and technology events, bringing together scholarly, artistic, and scientific domains in an interdisciplinary discussion and showcase of creative productions applying new technologies in art, interactivity, and electronic and digital media. For 2020, ISEA Montreal turns towards the theme of sentience.

ISEA2020 will be fully dedicated to examining the resurgence of sentience—feeling-sensing-making sense—in recent art and design, media studies, science and technology studies, philosophy, anthropology, history of science and the natural scientific realm—notably biology, neuroscience and computing. We ask: why sentience? Why and how does sentience matter? Why have artists and scholars become interested in sensing and feeling beyond, with and around our strictly human bodies and selves? Why has this notion been brought to the fore in an array of disciplines in the 21st century?
CALL FOR PARTICIPATION: WHY SENTIENCE? ISEA2020 invites artists, designers, scholars, researchers, innovators and creators to participate in the various activities deployed from May 19 to 24, 2020. To complete an application, please fill in the forms and follow the instructions.

The final submissions deadline is NOVEMBER 25, 2019. Submit your application for WORKSHOP and TUTORIAL Submit your application for ARTISTIC WORK Submit your application for FULL / SHORT PAPER Submit your application for PANEL Submit your application for POSTER Submit your application for ARTIST TALK Submit your application for INSTITUTIONAL PRESENTATION
Find Out More
You can apply for several categories. All profiles are welcome. Notifications of acceptance will be sent around January 13, 2020.

Important: please note that the Call for participation for MTL connect is not yet launched, but you can also apply to participate in the programming of the other Pavilions (4 other themes) when registrations are open (coming soon): mtlconnecte.ca/en TICKETS

Registration is now available to assist to ISEA2020 / MTL connect, from May 19 to 24, 2020. Book today your Full Pass and get the early-bird rate!
Buy Now

More from the newsletter,

ISEA 2015 was in Vancouver, Canada, and the proceedings and art catalog are still online. The news is that Sara Diamond released her 2015 keynote address as a paper: Action Agenda: Vancouver’s Prescient Media Arts. It is never too late so we thought we would let you know about this great read. See The 2015 Proceedings Here

The last item from the inaugural newsletter,

The first book that surveys how brain activity can be monitored and manipulated for artistic purposes, with contributions by interactive media artists, brain-computer interface researchers, and neuroscientists. View the Book Here

As per the Leonardo review from Cristina Albu:

“Another seminal contribution of the volume is the presentation of multiple taxonomies of “brain art,” which can help art critics develop better criteria for assessing this genre. Mirjana Prpa and Philippe Pasquier’s meticulous classification shows how diverse such works have become as artists consider a whole range of variables of neurofeedback.” Read the Review

For anyone not familiar with the ‘Leonardo’ cited in the above, it’s Leonardo; the International Society for the Arts, Sciences and Technology.

Should this kind of information excite and motivate you do start metacreating, you can get in touch with the lab,

Our mailing address is:
Metacreation Lab for Creative AI
School of Interactive Arts & Technology
Simon Fraser University
250-13450 102 Ave.
Surrey, BC V3T 0A3
Web: http://metacreation.net/
Email: metacreation_admin (at) sfu (dot) ca

The glorious glasswing butterfly and superomniphobic glass

This is not the first time the glasswing butterfly has inspired some new technology. Lat time, it was an eye implant,

The clear wings make this South-American butterfly hard to see in flight, a succesfull defense mechanism. Credit: Eddy Van 3000 from in Flanders fields – B – United Tribes ov Europe – the wings-become-windows butterfly. [downloaded from https://commons.wikimedia.org/wiki/Category:Greta_oto#/media/File:South-American_butterfly.jpg]

You’ll find that image and more in my May 22, 2018 posting about the eye implant. Don’t miss scrolling down to the video which features the butterfly fluttering its wings in the first few seconds.

Getting back to the glasswing butterfly’s latest act of inspiration a July 11, 2019 news item on ScienceDaily announces the work,

Glass for technologies like displays, tablets, laptops, smartphones, and solar cells need to pass light through, but could benefit from a surface that repels water, dirt, oil, and other liquids. Researchers from the University of Pittsburgh’s Swanson School of Engineering have created a nanostructure glass that takes inspiration from the wings of the glasswing butterfly to create a new type of glass that is not only very clear across a wide variety of wavelengths and angles, but is also antifogging.

A July 11, 2019 University of Pittsburgh news release (also on EurekAlert), which originated the news item, provides more technical detail about the new glass,

The nanostructured glass has random nanostructures, like the glasswing butterfly wing, that are smaller than the wavelengths of visible light. This allows the glass to have a very high transparency of 99.5% when the random nanostructures are on both sides of the glass. This high transparency can reduce the brightness and power demands on displays that could, for example, extend battery life. The glass is antireflective across higher angles, improving viewing angles. The glass also has low haze, less than 0.1%, which results in very clear images and text.

“The glass is superomniphobic, meaning it repels a wide variety of liquids such as orange juice, coffee, water, blood, and milk,” explains Sajad Haghanifar, lead author of the paper and doctoral candidate in industrial engineering at Pitt. “The glass is also anti-fogging, as water condensation tends to easily roll off the surface, and the view through the glass remains unobstructed. Finally, the nanostructured glass is durable from abrasion due to its self-healing properties–abrading the surface with a rough sponge damages the coating, but heating it restores it to its original function.”

Natural surfaces like lotus leaves, moth eyes and butterfly wings display omniphobic properties that make them self-cleaning, bacterial-resistant and water-repellant–adaptations for survival that evolved over millions of years. Researchers have long sought inspiration from nature to replicate these properties in a synthetic material, and even to improve upon them. While the team could not rely on evolution to achieve these results, they instead utilized machine learning.

“Something significant about the nanostructured glass research, in particular, is that we partnered with SigOpt to use machine learning to reach our final product,” says Paul Leu, PhD, associate professor of industrial engineering, whose lab conducted the research. Dr. Leu holds secondary appointments in mechanical engineering and materials science and chemical engineering. “When you create something like this, you don’t start with a lot of data, and each trial takes a great deal of time. We used machine learning to suggest variables to change, and it took us fewer tries to create this material as a result.”

“Bayesian optimization and active search are the ideal tools to explore the balance between transparency and omniphobicity efficiently, that is, without needing thousands of fabrications, requiring hundreds of days.” said Michael McCourt, PhD, research engineer at SigOpt. Bolong Cheng, PhD, fellow research engineer at SigOpt, added, “Machine learning and AI strategies are only relevant when they solve real problems; we are excited to be able to collaborate with the University of Pittsburgh to bring the power of Bayesian active learning to a new application.”

Here’s an image illustrating the work from the researchers,

Courtesy: University of Pittsburgh

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

Creating glasswing butterfly-inspired durable antifogging superomniphobic supertransmissive, superclear nanostructured glass through Bayesian learning and optimization by Sajad Haghanifar, Michael McCourt, Bolong Cheng, Jeffrey Wuenschell, Paul Ohodnickic, and Paul W. Leu. Mater. Horiz., 2019, Advance Article DOI: 10.1039/C9MH00589G first published on 10 Jun 2019

This paper is behind a paywall. One more thing, here’s SigOpt, the company the scientists partnered.

Data science guide from Sense about Science

Sense about Science, headquartered in the UK, is in its own words (from its homepage)

Sense about Science is an independent campaigning charity that challenges the misrepresentation of science and evidence in public life. …

According to an October 1, 2019 announcement from Sense about Science (received via email), the organization has published a new guide,

Our director warned yesterday [September 30, 2019] that data science is being given a free
pass on quality in too many arenas. From flood predictions to mortgage offers to the prediction of housing needs, we are not asking enough about whether AI solutions and algorithms can bear the weight we want to put on them.

It was the UK launch of our ‘Data Science: a guide for society’ at the Institute of Physics, where we invited representatives from different sectors to take up the challenge of creating a more questioning culture. Tracey Brown said the situation was like medicine 50 years ago: it seems that some people have become too clever to explain and the rest of us are feeling too dumb to ask.

At the end of the event we had a lot of proposals for how to make different communities aware of the guide’s three fundamental questions from the people who attended. There are many hundreds of people among our friends who could do something along these lines:

     * Publicise the guide
     * Incorporate it into your own work
     * Send it to people who are involved in procurement, licensing or
reporting or decision making at community, national and international
levels
     * Undertake a project with us to equip particular groups such as
parliamentary advisers, journalists and small charities.

Would you take a look at the guide [1] here and tell me if there’s something you can do? (alex@senseaboutscience.org)

There are launches planned in other countries over the rest of this year and into 2020. We are drawing up a map of offers to reach different communities. I’ll share all your suggestions with my colleague Errin Riley at the end of this week and we will get back to you quickly.

Before linking you to the guide, here’s a brief description from the Patterns in Data webpage,

In recent years, phrases like ‘big data’, ‘machine learning’, ‘algorithms’ and ‘pattern recognition’ have started slipping into everyday discussion. We’ve worked with researchers and experts to generate an open and informed public discussion on patterns in data across a wide range of projects.

Data Science: A guide for society

According to the headlines, we’re in the middle of a ‘data revolution: large, detailed datasets and complex algorithms allow us to make predictions on anything from who will win the league to who is likely to commit a crime. Our ability to question the quality of evidence – as the public, journalists, politicians or decision makers – needs to be expanded to meet this. To know the questions to ask and how to press for clarity about the strengths and weaknesses of using analysis from data models to make decisions. This is a guide to having more of those conversations, regardless of how much you don’t know about data science.

Here’s Data Science: A Guide for Society.

AI (artificial intelligence) artist got a show at a New York City art gallery

AI artists first hit my radar in August 2018 when Christie’s Auction House advertised an art auction of a ‘painting’ by an algorithm (artificial intelligence). There’s more in my August 31, 2018 posting but, briefly, a French art collective, Obvious, submitted a painting, “Portrait of Edmond de Belamy,” that was created by an artificial intelligence agent to be sold for an estimated to $7000 – $10,000. They weren’t even close. According to Ian Bogost’s March 6, 2019 article for The Atlantic, the painting sold for $432,500 In October 2018.

It has also, Bogost notes in his article, occasioned an art show (Note: Links have been removed),

… part of “Faceless Portraits Transcending Time,” an exhibition of prints recently shown [Februay 13 – March 5, 2019] at the HG Contemporary gallery in Chelsea, the epicenter of New York’s contemporary-art world. All of them were created by a computer.

The catalog calls the show a “collaboration between an artificial intelligence named AICAN and its creator, Dr. Ahmed Elgammal,” a move meant to spotlight, and anthropomorphize, the machine-learning algorithm that did most of the work. According to HG Contemporary, it’s the first solo gallery exhibit devoted to an AI artist.

If they hadn’t found each other in the New York art scene, the players involved could have met on a Spike Jonze film set: a computer scientist commanding five-figure print sales from software that generates inkjet-printed images; a former hotel-chain financial analyst turned Chelsea techno-gallerist with apparent ties to fine-arts nobility; a venture capitalist with two doctoral degrees in biomedical informatics; and an art consultant who put the whole thing together, A-Team–style, after a chance encounter at a blockchain conference. Together, they hope to reinvent visual art, or at least to cash in on machine-learning hype along the way.

The show in New York City, “Faceless Portraits …,” exhibited work by an artificially intelligent artist-agent (I’m creating a new term to suit my purposes) that’s different than the one used by Obvious to create “Portrait of Edmond de Belamy,” As noted earlier, it sold for a lot of money (Note: Links have been removed),

Bystanders in and out of the art world were shocked. The print had never been shown in galleries or exhibitions before coming to market at auction, a channel usually reserved for established work. The winning bid was made anonymously by telephone, raising some eyebrows; art auctions can invite price manipulation. It was created by a computer program that generates new images based on patterns in a body of existing work, whose features the AI “learns.” What’s more, the artists who trained and generated the work, the French collective Obvious, hadn’t even written the algorithm or the training set. They just downloaded them, made some tweaks, and sent the results to market.

“We are the people who decided to do this,” the Obvious member Pierre Fautrel said in response to the criticism, “who decided to print it on canvas, sign it as a mathematical formula, put it in a gold frame.” A century after Marcel Duchamp made a urinal into art [emphasis mine] by putting it in a gallery, not much has changed, with or without computers. As Andy Warhol famously said, “Art is what you can get away with.”

A bit of a segue here, there is a controversy as to whether or not that ‘urinal art’, also known as, The Fountain, should be attributed to Duchamp as noted in my January 23, 2019 posting titled ‘Baroness Elsa von Freytag-Loringhoven, Marcel Duchamp, and the Fountain’.

Getting back to the main action, Bogost goes on to describe the technologies underlying the two different AI artist-agents (Note: Links have been removed),

… Using a computer is hardly enough anymore; today’s machines offer all kinds of ways to generate images that can be output, framed, displayed, and sold—from digital photography to artificial intelligence. Recently, the fashionable choice has become generative adversarial networks, or GANs, the technology that created Portrait of Edmond de Belamy. Like other machine-learning methods, GANs use a sample set—in this case, art, or at least images of it—to deduce patterns, and then they use that knowledge to create new pieces. A typical Renaissance portrait, for example, might be composed as a bust or three-quarter view of a subject. The computer may have no idea what a bust is, but if it sees enough of them, it might learn the pattern and try to replicate it in an image.

GANs use two neural nets (a way of processing information modeled after the human brain) to produce images: a “generator” and a “discerner.” The generator produces new outputs—images, in the case of visual art—and the discerner tests them against the training set to make sure they comply with whatever patterns the computer has gleaned from that data. The quality or usefulness of the results depends largely on having a well-trained system, which is difficult.

That’s why folks in the know were upset by the Edmond de Belamy auction. The image was created by an algorithm the artists didn’t write, trained on an “Old Masters” image set they also didn’t create. The art world is no stranger to trend and bluster driving attention, but the brave new world of AI painting appeared to be just more found art, the machine-learning equivalent of a urinal on a plinth.

Ahmed Elgammal thinks AI art can be much more than that. A Rutgers University professor of computer science, Elgammal runs an art-and-artificial-intelligence lab, where he and his colleagues develop technologies that try to understand and generate new “art” (the scare quotes are Elgammal’s) with AI—not just credible copies of existing work, like GANs do. “That’s not art, that’s just repainting,” Elgammal says of GAN-made images. “It’s what a bad artist would do.”

Elgammal calls his approach a “creative adversarial network,” or CAN. It swaps a GAN’s discerner—the part that ensures similarity—for one that introduces novelty instead. The system amounts to a theory of how art evolves: through small alterations to a known style that produce a new one. That’s a convenient take, given that any machine-learning technique has to base its work on a specific training set.

The results are striking and strange, although calling them a new artistic style might be a stretch. They’re more like credible takes on visual abstraction. The images in the show, which were produced based on training sets of Renaissance portraits and skulls, are more figurative, and fairly disturbing. Their gallery placards name them dukes, earls, queens, and the like, although they depict no actual people—instead, human-like figures, their features smeared and contorted yet still legible as portraiture. Faceless Portrait of a Merchant, for example, depicts a torso that might also read as the front legs and rear haunches of a hound. Atop it, a fleshy orb comes across as a head. The whole scene is rippled by the machine-learning algorithm, in the way of so many computer-generated artworks.

Faceless Portrait of a Merchant, one of the AI portraits produced by Ahmed Elgammal and AICAN. (Artrendex Inc.) [downloaded from https://www.theatlantic.com/technology/archive/2019/03/ai-created-art-invades-chelsea-gallery-scene/584134/]

Bogost consults an expert on portraiture for a discussion about the particularities of portraiture and the shortcomings one might expect of an AI artist-agent (Note: A link has been removed),

“You can’t really pick a form of painting that’s more charged with cultural meaning than portraiture,” John Sharp, an art historian trained in 15th-century Italian painting and the director of the M.F.A. program in design and technology at Parsons School of Design, told me. The portrait isn’t just a style, it’s also a host for symbolism. “For example, men might be shown with an open book to show how they are in dialogue with that material; or a writing implement, to suggest authority; or a weapon, to evince power.” Take Portrait of a Youth Holding an Arrow, an early-16th-century Boltraffio portrait that helped train the AICAN database for the show. The painting depicts a young man, believed to be the Bolognese poet Girolamo Casio, holding an arrow at an angle in his fingers and across his chest. It doubles as both weapon and quill, a potent symbol of poetry and aristocracy alike. Along with the arrow, the laurels in Casio’s hair are emblems of Apollo, the god of both poetry and archery.

A neural net couldn’t infer anything about the particular symbolic trappings of the Renaissance or antiquity—unless it was taught to, and that wouldn’t happen just by showing it lots of portraits. For Sharp and other critics of computer-generated art, the result betrays an unforgivable ignorance about the supposed influence of the source material.

But for the purposes of the show, the appeal to the Renaissance might be mostly a foil, a way to yoke a hip, new technology to traditional painting in order to imbue it with the gravity of history: not only a Chelsea gallery show, but also an homage to the portraiture found at the Met. To reinforce a connection to the cradle of European art, some of the images are presented in elaborate frames, a decision the gallerist, Philippe Hoerle-Guggenheim (yes, that Guggenheim; he says the relation is “distant”) [the Guggenheim is strongly associated with the visual arts by way the two Guggeheim museums, one in New York City and the other in Bilbao, Portugal], told me he insisted upon. Meanwhile, the technical method makes its way onto the gallery placards in an official-sounding way—“Creative Adversarial Network print.” But both sets of inspirations, machine-learning and Renaissance portraiture, get limited billing and zero explanation at the show. That was deliberate, Hoerle-Guggenheim said. He’s betting that the simple existence of a visually arresting AI painting will be enough to draw interest—and buyers. It would turn out to be a good bet.

The art market is just that: a market. Some of the most renowned names in art today, from Damien Hirst to Banksy, trade in the trade of art as much as—and perhaps even more than—in the production of images, objects, and aesthetics. No artist today can avoid entering that fray, Elgammal included. “Is he an artist?” Hoerle-Guggenheim asked himself of the computer scientist. “Now that he’s in this context, he must be.” But is that enough? In Sharp’s estimation, “Faceless Portraits Transcending Time” is a tech demo more than a deliberate oeuvre, even compared to the machine-learning-driven work of his design-and-technology M.F.A. students, who self-identify as artists first.

Judged as Banksy or Hirst might be, Elgammal’s most art-worthy work might be the Artrendex start-up itself, not the pigment-print portraits that its technology has output. Elgammal doesn’t treat his commercial venture like a secret, but he also doesn’t surface it as a beneficiary of his supposedly earnest solo gallery show. He’s argued that AI-made images constitute a kind of conceptual art, but conceptualists tend to privilege process over product or to make the process as visible as the product.

Hoerle-Guggenheim worked as a financial analyst for Hyatt before getting into the art business via some kind of consulting deal (he responded cryptically when I pressed him for details). …

This is a fascinating article and I have one last excerpt, which poses this question, is an AI artist-agent a collaborator or a medium? There ‘s also speculation about how AI artist-agents might impact the business of art (Note: Links have been removed),

… it’s odd to list AICAN as a collaborator—painters credit pigment as a medium, not as a partner. Even the most committed digital artists don’t present the tools of their own inventions that way; when they do, it’s only after years, or even decades, of ongoing use and refinement.

But Elgammal insists that the move is justified because the machine produces unexpected results. “A camera is a tool—a mechanical device—but it’s not creative,” he said. “Using a tool is an unfair term for AICAN. It’s the first time in history that a tool has had some kind of creativity, that it can surprise you.” Casey Reas, a digital artist who co-designed the popular visual-arts-oriented coding platform Processing, which he uses to create some of his fine art, isn’t convinced. “The artist should claim responsibility over the work rather than to cede that agency to the tool or the system they create,” he told me.

Elgammal’s financial interest in AICAN might explain his insistence on foregrounding its role. Unlike a specialized print-making technique or even the Processing coding environment, AICAN isn’t just a device that Elgammal created. It’s also a commercial enterprise.

Elgammal has already spun off a company, Artrendex, that provides “artificial-intelligence innovations for the art market.” One of them offers provenance authentication for artworks; another can suggest works a viewer or collector might appreciate based on an existing collection; another, a system for cataloging images by visual properties and not just by metadata, has been licensed by the Barnes Foundation to drive its collection-browsing website.

The company’s plans are more ambitious than recommendations and fancy online catalogs. When presenting on a panel about the uses of blockchain for managing art sales and provenance, Elgammal caught the attention of Jessica Davidson, an art consultant who advises artists and galleries in building collections and exhibits. Davidson had been looking for business-development partnerships, and she became intrigued by AICAN as a marketable product. “I was interested in how we can harness it in a compelling way,” she says.

The art market is just that: a market. Some of the most renowned names in art today, from Damien Hirst to Banksy, trade in the trade of art as much as—and perhaps even more than—in the production of images, objects, and aesthetics. No artist today can avoid entering that fray, Elgammal included. “Is he an artist?” Hoerle-Guggenheim asked himself of the computer scientist. “Now that he’s in this context, he must be.” But is that enough? In Sharp’s estimation, “Faceless Portraits Transcending Time” is a tech demo more than a deliberate oeuvre, even compared to the machine-learning-driven work of his design-and-technology M.F.A. students, who self-identify as artists first.

Judged as Banksy or Hirst might be, Elgammal’s most art-worthy work might be the Artrendex start-up itself, not the pigment-print portraits that its technology has output. Elgammal doesn’t treat his commercial venture like a secret, but he also doesn’t surface it as a beneficiary of his supposedly earnest solo gallery show. He’s argued that AI-made images constitute a kind of conceptual art, but conceptualists tend to privilege process over product or to make the process as visible as the product.

Hoerle-Guggenheim worked as a financial analyst[emphasis mine] for Hyatt before getting into the art business via some kind of consulting deal (he responded cryptically when I pressed him for details). …

If you have the time, I recommend reading Bogost’s March 6, 2019 article for The Atlantic in its entirety/ these excerpts don’t do it enough justice.

Portraiture: what does it mean these days?

After reading the article I have a few questions. What exactly do Bogost and the arty types in the article mean by the word ‘portrait’? “Portrait of Edmond de Belamy” is an image of someone who doesn’t and never has existed and the exhibit “Faceless Portraits Transcending Time,” features images that don’t bear much or, in some cases, any resemblance to human beings. Maybe this is considered a dull question by people in the know but I’m an outsider and I found the paradox: portraits of nonexistent people or nonpeople kind of interesting.

BTW, I double-checked my assumption about portraits and found this definition in the Portrait Wikipedia entry (Note: Links have been removed),

A portrait is a painting, photograph, sculpture, or other artistic representation of a person [emphasis mine], in which the face and its expression is predominant. The intent is to display the likeness, personality, and even the mood of the person. For this reason, in photography a portrait is generally not a snapshot, but a composed image of a person in a still position. A portrait often shows a person looking directly at the painter or photographer, in order to most successfully engage the subject with the viewer.

So, portraits that aren’t portraits give rise to some philosophical questions but Bogost either didn’t want to jump into that rabbit hole (segue into yet another topic) or, as I hinted earlier, may have assumed his audience had previous experience of those kinds of discussions.

Vancouver (Canada) and a ‘portraiture’ exhibit at the Rennie Museum

By one of life’s coincidences, Vancouver’s Rennie Museum had an exhibit (February 16 – June 15, 2019) that illuminates questions about art collecting and portraiture, From a February 7, 2019 Rennie Museum news release,

‘downloaded from https://renniemuseum.org/press-release-spring-2019-collected-works/] Courtesy: Rennie Museum

February 7, 2019

Press Release | Spring 2019: Collected Works
By rennie museum

rennie museum is pleased to present Spring 2019: Collected Works, a group exhibition encompassing the mediums of photography, painting and film. A portraiture of the collecting spirit [emphasis mine], the works exhibited invite exploration of what collected objects, and both the considered and unintentional ways they are displayed, inform us. Featuring the works of four artists—Andrew Grassie, William E. Jones, Louise Lawler and Catherine Opie—the exhibition runs from February 16 to June 15, 2019.

Four exquisite paintings by Scottish painter Andrew Grassie detailing the home and private storage space of a major art collector provide a peek at how the passionately devoted integrates and accommodates the physical embodiments of such commitment into daily life. Grassie’s carefully constructed, hyper-realistic images also pose the question, “What happens to art once it’s sold?” In the transition from pristine gallery setting to idiosyncratic private space, how does the new context infuse our reading of the art and how does the art shift our perception of the individual?

Furthering the inquiry into the symbiotic exchange between possessor and possession, a selection of images by American photographer Louise Lawler depicting art installed in various private and public settings question how the bilateral relationship permeates our interpretation when the collector and the collected are no longer immediately connected. What does de-acquisitioning an object inform us and how does provenance affect our consideration of the art?

The question of legacy became an unexpected facet of 700 Nimes Road (2010-2011), American photographer Catherine Opie’s portrait of legendary actress Elizabeth Taylor. Opie did not directly photograph Taylor for any of the fifty images in the expansive portfolio. Instead, she focused on Taylor’s home and the objects within, inviting viewers to see—then see beyond—the façade of fame and consider how both treasures and trinkets act as vignettes to the stories of a life. Glamorous images of jewels and trophies juxtapose with mundane shots of a printer and the remote-control user manual. Groupings of major artworks on the wall are as illuminating of the home’s mistress as clusters of personal photos. Taylor passed away part way through Opie’s project. The subsequent photos include Taylor’s mementos heading off to auction, raising the question, “Once the collections that help to define someone are disbursed, will our image of that person lose focus?”

In a similar fashion, the twenty-two photographs in Villa Iolas (1982/2017), by American artist and filmmaker William E. Jones, depict the Athens home of iconic art dealer and collector Alexander Iolas. Taken in 1982 by Jones during his first travels abroad, the photographs of art, furniture and antiquities tell a story of privilege that contrast sharply with the images Jones captures on a return visit in 2016. Nearly three decades after Iolas’s 1989 death, his home sits in dilapidation, looted and vandalized. Iolas played an extraordinary role in the evolution of modern art, building the careers of Max Ernst, Yves Klein and Giorgio de Chirico. He gave Andy Warhol his first solo exhibition and was a key advisor to famed collectors John and Dominique de Menil. Yet in the years since his death, his intention of turning his home into a modern art museum as a gift to Greece, along with his reputation, crumbled into ruins. The photographs taken by Jones during his visits in two different eras are incorporated into the film Fall into Ruin (2017), along with shots of contemporary Athens and antiquities on display at the National Archaeological Museum.

“I ask a lot of questions about how portraiture functionswhat is there to describe the person or time we live in or a certain set of politics…”
 – Catherine Opie, The Guardian, Feb 9, 2016

We tend to think of the act of collecting as a formal activity yet it can happen casually on a daily basis, often in trivial ways. While we readily acknowledge a collector consciously assembling with deliberate thought, we give lesser consideration to the arbitrary accumulations that each of us accrue. Be it master artworks, incidental baubles or random curios, the objects we acquire and surround ourselves with tell stories of who we are.

Andrew Grassie (Scotland, b. 1966) is a painter known for his small scale, hyper-realist works. He has been the subject of solo exhibitions at the Tate Britain; Talbot Rice Gallery, Edinburgh; institut supérieur des arts de Toulouse; and rennie museum, Vancouver, Canada. He lives and works in London, England.

William E. Jones (USA, b. 1962) is an artist, experimental film-essayist and writer. Jones’s work has been the subject of retrospectives at Tate Modern, London; Anthology Film Archives, New York; Austrian Film Museum, Vienna; and, Oberhausen Short Film Festival. He is a recipient of the John Simon Guggenheim Memorial Fellowship and the Creative Capital/Andy Warhol Foundation Arts Writers Grant. He lives and works in Los Angeles, USA.

Louise Lawler (USA, b. 1947) is a photographer and one of the foremost members of the Pictures Generation. Lawler was the subject of a major retrospective at the Museum of Modern Art, New York in 2017. She has held exhibitions at the Whitney Museum of American Art, New York; Stedelijk Museum, Amsterdam; National Museum of Art, Oslo; and Musée d’Art Moderne de La Ville de Paris. She lives and works in New York.

Catherine Opie (USA, b. 1961) is a photographer and educator. Her work has been exhibited at Wexner Center for the Arts, Ohio; Henie Onstad Art Center, Oslo; Los the Angeles County Museum of Art; Portland Art Museum; and the Guggenheim Museum, New York. She is the recipient of United States Artist Fellowship, Julius Shulman’s Excellence in Photography Award, and the Smithsonian’s Archive of American Art Medal.  She lives and works in Los Angeles.

rennie museum opened in October 2009 in historic Wing Sang, the oldest structure in Vancouver’s Chinatown, to feature dynamic exhibitions comprising only of art drawn from rennie collection. Showcasing works by emerging and established international artists, the exhibits, accompanied by supporting catalogues, are open free to the public through engaging guided tours. The museum’s commitment to providing access to arts and culture is also expressed through its education program, which offers free age-appropriate tours and customized workshops to children of all ages.

rennie collection is a globally recognized collection of contemporary art that focuses on works that tackle issues related to identity, social commentary and injustice, appropriation, and the nature of painting, photography, sculpture and film. Currently the collection includes works by over 370 emerging and established artists, with over fifty collected in depth. The Vancouver based collection engages actively with numerous museums globally through a robust, artist-centric, lending policy.

So despite the Wikipedia definition, it seems that portraits don’t always feature people. While Bogost didn’t jump into that particular rabbit hole, he did touch on the business side of art.

What about intellectual property?

Bogost doesn’t explicitly discuss this particular issue. It’s a big topic so I’m touching on it only lightly, if an artist worsk with an AI, the question as to ownership of the artwork could prove thorny. Is the copyright owner the computer scientist or the artist or both? Or does the AI artist-agent itself own the copyright? That last question may not be all that farfetched. Sophia, a social humanoid robot, has occasioned thought about ‘personhood.’ (Note: The robots mentioned in this posting have artificial intelligence.) From the Sophia (robot) Wikipedia entry (Note: Links have been removed),

Sophia has been interviewed in the same manner as a human, striking up conversations with hosts. Some replies have been nonsensical, while others have impressed interviewers such as 60 Minutes’ Charlie Rose.[12] In a piece for CNBC, when the interviewer expressed concerns about robot behavior, Sophia joked that he had “been reading too much Elon Musk. And watching too many Hollywood movies”.[27] Musk tweeted that Sophia should watch The Godfather and asked “what’s the worst that could happen?”[28][29] Business Insider’s chief UK editor Jim Edwards interviewed Sophia, and while the answers were “not altogether terrible”, he predicted it was a step towards “conversational artificial intelligence”.[30] At the 2018 Consumer Electronics Show, a BBC News reporter described talking with Sophia as “a slightly awkward experience”.[31]

On October 11, 2017, Sophia was introduced to the United Nations with a brief conversation with the United Nations Deputy Secretary-General, Amina J. Mohammed.[32] On October 25, at the Future Investment Summit in Riyadh, the robot was granted Saudi Arabian citizenship [emphasis mine], becoming the first robot ever to have a nationality.[29][33] This attracted controversy as some commentators wondered if this implied that Sophia could vote or marry, or whether a deliberate system shutdown could be considered murder. Social media users used Sophia’s citizenship to criticize Saudi Arabia’s human rights record. In December 2017, Sophia’s creator David Hanson said in an interview that Sophia would use her citizenship to advocate for women’s rights in her new country of citizenship; Newsweek criticized that “What [Hanson] means, exactly, is unclear”.[34] On November 27, 2018 Sophia was given a visa by Azerbaijan while attending Global Influencer Day Congress held in Baku. December 15, 2018 Sophia was appointed a Belt and Road Innovative Technology Ambassador by China'[35]

As for an AI artist-agent’s intellectual property rights , I have a July 10, 2017 posting featuring that question in more detail. Whether you read that piece or not, it seems obvious that artists might hesitate to call an AI agent, a partner rather than a medium of expression. After all, a partner (and/or the computer scientist who developed the programme) might expect to share in property rights and profits but paint, marble, plastic, and other media used by artists don’t have those expectations.

Moving slightly off topic , in my July 10, 2017 posting I mentioned a competition (literary and performing arts rather than visual arts) called, ‘Dartmouth College and its Neukom Institute Prizes in Computational Arts’. It was started in 2016 and, as of 2018, was still operational under this name: Creative Turing Tests. Assuming there’ll be contests for prizes in 2019, there’s (from the contest site) [1] PoetiX, competition in computer-generated sonnet writing; [2] Musical Style, composition algorithms in various styles, and human-machine improvisation …; and [3] DigiLit, algorithms able to produce “human-level” short story writing that is indistinguishable from an “average” human effort. You can find the contest site here.

An artificial synapse tuned by light, a ferromagnetic memristor, and a transparent, flexible artificial synapse

Down the memristor rabbit hole one more time.* I started out with news about two new papers and inadvertently found two more. In a bid to keep this posting to a manageable size, I’m stopping at four.

UK

In a June 19, 2019 Nanowerk Spotlight article, Dr. Neil Kemp discusses memristors and some of his latest work (Note: A link has been removed),

Memristor (or memory resistors) devices are non-volatile electronic memory devices that were first theorized by Leon Chua in the 1970’s. However, it was some thirty years later that the first practical device was fabricated. This was in 2008 when a group led by Stanley Williams at HP Research Labs realized that switching of the resistance between a conducting and less conducting state in metal-oxide thin-film devices was showing Leon Chua’s memristor behaviour.

The high interest in memristor devices also stems from the fact that these devices emulate the memory and learning properties of biological synapses. i.e. the electrical resistance value of the device is dependent on the history of the current flowing through it.

There is a huge effort underway to use memristor devices in neuromorphic computing applications and it is now reasonable to imagine the development of a new generation of artificial intelligent devices with very low power consumption (non-volatile), ultra-fast performance and high-density integration.

These discoveries come at an important juncture in microelectronics, since there is increasing disparity between computational needs of Big Data, Artificial Intelligence (A.I.) and the Internet of Things (IoT), and the capabilities of existing computers. The increases in speed, efficiency and performance of computer technology cannot continue in the same manner as it has done since the 1960s.

To date, most memristor research has focussed on the electronic switching properties of the device. However, for many applications it is useful to have an additional handle (or degree of freedom) on the device to control its resistive state. For example memory and processing in the brain also involves numerous chemical and bio-chemical reactions that control the brain structure and its evolution through development.

To emulate this in a simple solid-state system composed of just switches alone is not possible. In our research, we are interested in using light to mediate this essential control.

We have demonstrated that light can be used to make short and long-term memory and we have shown how light can modulate a special type of learning, called spike timing dependent plasticity (STDP). STDP involves two neuronal spikes incident across a synapse at the same time. Depending on the relative timing of the spikes and their overlap across the synaptic cleft, the connection strength is other strengthened or weakened.

In our earlier work, we were only able to achieve to small switching effects in memristors using light. In our latest work (Advanced Electronic Materials, “Percolation Threshold Enables Optical Resistive-Memory Switching and Light-Tuneable Synaptic Learning in Segregated Nanocomposites”), we take advantage of a percolating-like nanoparticle morphology to vastly increase the magnitude of the switching between electronic resistance states when light is incident on the device.

We have used an inhomogeneous percolating network consisting of metallic nanoparticles distributed in filamentary-like conduction paths. Electronic conduction and the resistance of the device is very sensitive to any disruption of the conduction path(s).

By embedding the nanoparticles in a polymer that can expand or contract with light the conduction pathways are broken or re-connected causing very large changes in the electrical resistance and memristance of the device.

Our devices could lead to the development of new memristor-based artificial intelligence systems that are adaptive and reconfigurable using a combination of optical and electronic signalling. Furthermore, they have the potential for the development of very fast optical cameras for artificial intelligence recognition systems.

Our work provides a nice proof-of-concept but the materials used means the optical switching is slow. The materials are also not well suited to industry fabrication. In our on-going work we are addressing these switching speed issues whilst also focussing on industry compatible materials.

Currently we are working on a new type of optical memristor device that should give us orders of magnitude improvement in the optical switching speeds whilst also retaining a large difference between the resistance on and off states. We hope to be able to achieve nanosecond switching speeds. The materials used are also compatible with industry standard methods of fabrication.

The new devices should also have applications in optical communications, interfacing and photonic computing. We are currently looking for commercial investors to help fund the research on these devices so that we can bring the device specifications to a level of commercial interest.

If you’re interested in memristors, Kemp’s article is well written and quite informative for nonexperts, assuming of course you can tolerate not understanding everything perfectly.

Here are links and citations for two papers. The first is the latest referred to in the article, a May 2019 paper and the second is a paper appearing in July 2019.

Percolation Threshold Enables Optical Resistive‐Memory Switching and Light‐Tuneable Synaptic Learning in Segregated Nanocomposites by Ayoub H. Jaafar, Mary O’Neill, Stephen M. Kelly, Emanuele Verrelli, Neil T. Kemp. Advanced Electronic Materials DOI: https://doi.org/10.1002/aelm.201900197 First published: 28 May 2019

Wavelength dependent light tunable resistive switching graphene oxide nonvolatile memory devices by Ayoub H.Jaafar, N.T.Kemp. DOI: https://doi.org/10.1016/j.carbon.2019.07.007 Carbon Available online 3 July 2019

The first paper (May 2019) is definitely behind a paywall and the second paper (July 2019) appears to be behind a paywall.

Dr. Kemp’s work has been featured here previously in a January 3, 2018 posting in the subsection titled, Shining a light on the memristor.

China

This work from China was announced in a June 20, 2019 news item on Nanowerk,

Memristors, demonstrated by solid-state devices with continuously tunable resistance, have emerged as a new paradigm for self-adaptive networks that require synapse-like functions. Spin-based memristors offer advantages over other types of memristors because of their significant endurance and high energy effciency.

However, it remains a challenge to build dense and functional spintronic memristors with structures and materials that are compatible with existing ferromagnetic devices. Ta/CoFeB/MgO heterostructures are commonly used in interfacial PMA-based [perpendicular magnetic anisotropy] magnetic tunnel junctions, which exhibit large tunnel magnetoresistance and are implemented in commercial MRAM [magnetic random access memory] products.

“To achieve the memristive function, DW is driven back and forth in a continuous manner in the CoFeB layer by applying in-plane positive or negative current pulses along the Ta layer, utilizing SOT that the current exerts on the CoFeB magnetization,” said Shuai Zhang, a coauthor in the paper. “Slowly propagating domain wall generates a creep in the detection area of the device, which yields a broad range of intermediate resistive states in the AHE [anomalous Hall effect] measurements. Consequently, AHE resistance is modulated in an analog manner, being controlled by the pulsed current characteristics including amplitude, duration, and repetition number.”

“For a follow-up study, we are working on more neuromorphic operations, such as spike-timing-dependent plasticity and paired pulsed facilitation,” concludes You. …

Here’s are links to and citations for the paper (Note: It’s a little confusing but I believe that one of the links will take you to the online version, as for the ‘open access’ link, keep reading),

A Spin–Orbit‐Torque Memristive Device by Shuai Zhang, Shijiang Luo, Nuo Xu, Qiming Zou, Min Song, Jijun Yun, Qiang Luo, Zhe Guo, Ruofan Li, Weicheng Tian, Xin Li, Hengan Zhou, Huiming Chen, Yue Zhang, Xiaofei Yang, Wanjun Jiang, Ka Shen, Jeongmin Hong, Zhe Yuan, Li Xi, Ke Xia, Sayeef Salahuddin, Bernard Dieny, Long You. Advanced Electronic Materials Volume 5, Issue 4 April 2019 (print version) 1800782 DOI: https://doi.org/10.1002/aelm.201800782 First published [online]: 30 January 2019 Note: there is another DOI, https://doi.org/10.1002/aelm.201970022 where you can have open access to Memristors: A Spin–Orbit‐Torque Memristive Device (Adv. Electron. Mater. 4/2019)

The paper published online in January 2019 is behind a paywall and the paper (almost the same title) published in April 2019 has a new DOI and is open access. Final note: I tried accessing the ‘free’ paper and opened up a free file for the artwork featuring the work from China on the back cover of the April 2019 of Advanced Electronic Materials.

Korea

Usually when I see the words transparency and flexibility, I expect to see graphene is one of the materials. That’s not the case for this paper (link to and citation for),

Transparent and flexible photonic artificial synapse with piezo-phototronic modulator: Versatile memory capability and higher order learning algorithm by Mohit Kumar, Joondong Kim, Ching-Ping Wong. Nano Energy Volume 63, September 2019, 103843 DOI: https://doi.org/10.1016/j.nanoen.2019.06.039 Available online 22 June 2019

Here’s the abstract for the paper where you’ll see that the material is made up of zinc oxide silver nanowires,

An artificial photonic synapse having tunable manifold synaptic response can be an essential step forward for the advancement of novel neuromorphic computing. In this work, we reported the development of highly transparent and flexible two-terminal ZnO/Ag-nanowires/PET photonic artificial synapse [emphasis mine]. The device shows purely photo-triggered all essential synaptic functions such as transition from short-to long-term plasticity, paired-pulse facilitation, and spike-timing-dependent plasticity, including in the versatile memory capability. Importantly, strain-induced piezo-phototronic effect within ZnO provides an additional degree of regulation to modulate all of the synaptic functions in multi-levels. The observed effect is quantitatively explained as a dynamic of photo-induced electron-hole trapping/detraining via the defect states such as oxygen vacancies. We revealed that the synaptic functions can be consolidated and converted by applied strain, which is not previously applied any of the reported synaptic devices. This study will open a new avenue to the scientific community to control and design highly transparent wearable neuromorphic computing.

This paper is behind a paywall.

Artificial synapse based on tantalum oxide from Korean researchers

This memristor story comes from South Korea as we progress on the way to neuromorphic computing (brainlike computing). A Sept. 7, 2018 news item on ScienceDaily makes the announcement,

A research team led by Director Myoung-Jae Lee from the Intelligent Devices and Systems Research Group at DGIST (Daegu Gyeongbuk Institute of Science and Technology) has succeeded in developing an artificial synaptic device that mimics the function of the nerve cells (neurons) and synapses that are response for memory in human brains. [sic]

Synapses are where axons and dendrites meet so that neurons in the human brain can send and receive nerve signals; there are known to be hundreds of trillions of synapses in the human brain.

This chemical synapse information transfer system, which transfers information from the brain, can handle high-level parallel arithmetic with very little energy, so research on artificial synaptic devices, which mimic the biological function of a synapse, is under way worldwide.

Dr. Lee’s research team, through joint research with teams led by Professor Gyeong-Su Park from Seoul National University; Professor Sung Kyu Park from Chung-ang University; and Professor Hyunsang Hwang from Pohang University of Science and Technology (POSTEC), developed a high-reliability artificial synaptic device with multiple values by structuring tantalum oxide — a trans-metallic material — into two layers of Ta2O5-x and TaO2-x and by controlling its surface.

A September 7, 2018 DGIST press release (also on EurekAlert), which originated the news item, delves further into the work,

The artificial synaptic device developed by the research team is an electrical synaptic device that simulates the function of synapses in the brain as the resistance of the tantalum oxide layer gradually increases or decreases depending on the strength of the electric signals. It has succeeded in overcoming durability limitations of current devices by allowing current control only on one layer of Ta2O5-x.

In addition, the research team successfully implemented an experiment that realized synapse plasticity [or synaptic plasticity], which is the process of creating, storing, and deleting memories, such as long-term strengthening of memory and long-term suppression of memory deleting by adjusting the strength of the synapse connection between neurons.

The non-volatile multiple-value data storage method applied by the research team has the technological advantage of having a small area of an artificial synaptic device system, reducing circuit connection complexity, and reducing power consumption by more than one-thousandth compared to data storage methods based on digital signals using 0 and 1 such as volatile CMOS (Complementary Metal Oxide Semiconductor).

The high-reliability artificial synaptic device developed by the research team can be used in ultra-low-power devices or circuits for processing massive amounts of big data due to its capability of low-power parallel arithmetic. It is expected to be applied to next-generation intelligent semiconductor device technologies such as development of artificial intelligence (AI) including machine learning and deep learning and brain-mimicking semiconductors.

Dr. Lee said, “This research secured the reliability of existing artificial synaptic devices and improved the areas pointed out as disadvantages. We expect to contribute to the development of AI based on the neuromorphic system that mimics the human brain by creating a circuit that imitates the function of neurons.”

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

Reliable Multivalued Conductance States in TaOx Memristors through Oxygen Plasma-Assisted Electrode Deposition with in Situ-Biased Conductance State Transmission Electron Microscopy Analysis by Myoung-Jae Lee, Gyeong-Su Park, David H. Seo, Sung Min Kwon, Hyeon-Jun Lee, June-Seo Kim, MinKyung Jung, Chun-Yeol You, Hyangsook Lee, Hee-Goo Kim, Su-Been Pang, Sunae Seo, Hyunsang Hwang, and Sung Kyu Park. ACS Appl. Mater. Interfaces, 2018, 10 (35), pp 29757–29765 DOI: 10.1021/acsami.8b09046 Publication Date (Web): July 23, 2018

Copyright © 2018 American Chemical Society

This paper is open access.

You can find other memristor and neuromorphic computing stories here by using the search terms I’ve highlighted,  My latest (more or less) is an April 19, 2018 posting titled, New path to viable memristor/neuristor?

Finally, here’s an image from the Korean researchers that accompanied their work,

Caption: Representation of neurons and synapses in the human brain. The magnified synapse represents the portion mimicked using solid-state devices. Credit: Daegu Gyeongbuk Institute of Science and Technology(DGIST)

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.

I found it at the movies: a commentary on/review of “Films from the Future”

Kudos to anyone who recognized the reference to Pauline Kael (she changed film criticism forever) and her book “I Lost it at the Movies.” Of course, her book title was a bit of sexual innuendo, quite risqué for an important film critic in 1965 but appropriate for a period (the 1960s) associated with a sexual revolution. (There’s more about the 1960’s sexual revolution in the US along with mention of a prior sexual revolution in the 1920s in this Wikipedia entry.)

The title for this commentary is based on an anecdote from Dr. Andrew Maynard’s (director of the Arizona State University [ASU] Risk Innovation Lab) popular science and technology book, “Films from the Future: The Technology and Morality of Sci-Fi Movies.”

The ‘title-inspiring’ anecdote concerns Maynard’s first viewing of ‘2001: A Space Odyssey, when as a rather “bratty” 16-year-old who preferred to read science fiction, he discovered new ways of seeing and imaging the world. Maynard isn’t explicit about when he became a ‘techno nerd’ or how movies gave him an experience books couldn’t but presumably at 16 he was already gearing up for a career in the sciences. That ‘movie’ revelation received in front of a black and white television on January 1,1982 eventually led him to write, “Films from the Future.” (He has a PhD in physics which he is now applying to the field of risk innovation. For a more detailed description of Dr. Maynard and his work, there’s his ASU profile webpage and, of course, the introduction to his book.)

The book is quite timely. I don’t know how many people have noticed but science and scientific innovation is being covered more frequently in the media than it has been in many years. Science fairs and festivals are being founded on what seems to be a daily basis and you can now find science in art galleries. (Not to mention the movies and television where science topics are covered in comic book adaptations, in comedy, and in standard science fiction style.) Much of this activity is centered on what’s called ’emerging technologies’. These technologies are why people argue for what’s known as ‘blue sky’ or ‘basic’ or ‘fundamental’ science for without that science there would be no emerging technology.

Films from the Future

Isn’t reading the Table of Contents (ToC) the best way to approach a book? (From Films from the Future; Note: The formatting has been altered),

Table of Contents
Chapter One
In the Beginning 14
Beginnings 14
Welcome to the Future 16
The Power of Convergence 18
Socially Responsible Innovation 21
A Common Point of Focus 25
Spoiler Alert 26
Chapter Two
Jurassic Park: The Rise of Resurrection Biology 27
When Dinosaurs Ruled the World 27
De-Extinction 31
Could We, Should We? 36
The Butterfly Effect 39
Visions of Power 43
Chapter Three
Never Let Me Go: A Cautionary Tale of Human Cloning 46
Sins of Futures Past 46
Cloning 51
Genuinely Human? 56
Too Valuable to Fail? 62
Chapter Four
Minority Report: Predicting Criminal Intent 64
Criminal Intent 64
The “Science” of Predicting Bad Behavior 69
Criminal Brain Scans 74
Machine Learning-Based Precognition 77
Big Brother, Meet Big Data 79
Chapter Five
Limitless: Pharmaceutically-enhanced Intelligence 86
A Pill for Everything 86
The Seduction of Self-Enhancement 89
Nootropics 91
If You Could, Would You? 97
Privileged Technology 101
Our Obsession with Intelligence 105
Chapter Six
Elysium: Social Inequity in an Age of Technological
Extremes 110
The Poor Shall Inherit the Earth 110
Bioprinting Our Future Bodies 115
The Disposable Workforce 119
Living in an Automated Future 124
Chapter Seven
Ghost in the Shell: Being Human in an
Augmented Future 129
Through a Glass Darkly 129
Body Hacking 135
More than “Human”? 137
Plugged In, Hacked Out 142
Your Corporate Body 147
Chapter Eight
Ex Machina: AI and the Art of Manipulation 154
Plato’s Cave 154
The Lure of Permissionless Innovation 160
Technologies of Hubris 164
Superintelligence 169
Defining Artificial Intelligence 172
Artificial Manipulation 175
Chapter Nine
Transcendence: Welcome to the Singularity 180
Visions of the Future 180
Technological Convergence 184
Enter the Neo-Luddites 190
Techno-Terrorism 194
Exponential Extrapolation 200
Make-Believe in the Age of the Singularity 203
Chapter Ten
The Man in the White Suit: Living in a Material World 208
There’s Plenty of Room at the Bottom 208
Mastering the Material World 213
Myopically Benevolent Science 220
Never Underestimate the Status Quo 224
It’s Good to Talk 227
Chapter Eleven
Inferno: Immoral Logic in an Age of
Genetic Manipulation 231
Decoding Make-Believe 231
Weaponizing the Genome 234
Immoral Logic? 238
The Honest Broker 242
Dictating the Future 248
Chapter Twelve
The Day After Tomorrow: Riding the Wave of
Climate Change 251
Our Changing Climate 251
Fragile States 255
A Planetary “Microbiome” 258
The Rise of the Anthropocene 260
Building Resiliency 262
Geoengineering the Future 266
Chapter Thirteen
Contact: Living by More than Science Alone 272
An Awful Waste of Space 272
More than Science Alone 277
Occam’s Razor 280
What If We’re Not Alone? 283
Chapter Fourteen
Looking to the Future 288
Acknowledgments 293

The ToC gives the reader a pretty clue as to where the author is going with their book and Maynard explains how he chose his movies in his introductory chapter (from Films from the Future),

“There are some quite wonderful science fiction movies that didn’t make the cut because they didn’t fit the overarching narrative (Blade Runner and its sequel Blade Runner 2049, for instance, and the first of the Matrix trilogy). There are also movies that bombed with the critics, but were included because they ably fill a gap in the bigger story around emerging and converging technologies. Ultimately, the movies that made the cut were chosen because, together, they create an overarching narrative around emerging trends in biotechnologies, cybertechnologies, and materials-based technologies, and they illuminate a broader landscape around our evolving relationship with science and technology. And, to be honest, they are all movies that I get a kick out of watching.” (p. 17)

Jurassic Park (Chapter Two)

Dinosaurs do not interest me—they never have. Despite my profound indifference I did see the movie, Jurassic Park, when it was first released (someone talked me into going). And, I am still profoundly indifferent. Thankfully, Dr. Maynard finds meaning and a connection to current trends in biotechnology,

Jurassic Park is unabashedly a movie about dinosaurs. But it’s also a movie about greed, ambition, genetic engineering, and human folly—all rich pickings for thinking about the future, and what could possibly go wrong. (p. 28)

What really stands out with Jurassic Park, over twenty-five years later, is how it reveals a very human side of science and technology. This comes out in questions around when we should tinker with technology and when we should leave well enough alone. But there is also a narrative here that appears time and time again with the movies in this book, and that is how we get our heads around the sometimes oversized roles mega-entrepreneurs play in dictating how new tech is used, and possibly abused. These are all issues that are just as relevant now as they were in 1993, and are front and center of ensuring that the technologyenabled future we’re building is one where we want to live, and not one where we’re constantly fighting for our lives.  (pp. 30-1)

He also describes a connection to current trends in biotechnology,

De-Extinction

In a far corner of Siberia, two Russians—Sergey Zimov and his son Nikita—are attempting to recreate the Ice Age. More precisely, their vision is to reconstruct the landscape and ecosystem of northern Siberia in the Pleistocene, a period in Earth’s history that stretches from around two and a half million years ago to eleven thousand years ago. This was a time when the environment was much colder than now, with huge glaciers and ice sheets flowing over much of the Earth’s northern hemisphere. It was also a time when humans
coexisted with animals that are long extinct, including saber-tooth cats, giant ground sloths, and woolly mammoths.

The Zimovs’ ambitions are an extreme example of “Pleistocene rewilding,” a movement to reintroduce relatively recently extinct large animals, or their close modern-day equivalents, to regions where they were once common. In the case of the Zimovs, the
father-and-son team believe that, by reconstructing the Pleistocene ecosystem in the Siberian steppes and elsewhere, they can slow down the impacts of climate change on these regions. These areas are dominated by permafrost, ground that never thaws through
the year. Permafrost ecosystems have developed and survived over millennia, but a warming global climate (a theme we’ll come back to in chapter twelve and the movie The Day After Tomorrow) threatens to catastrophically disrupt them, and as this happens, the impacts
on biodiversity could be devastating. But what gets climate scientists even more worried is potentially massive releases of trapped methane as the permafrost disappears.

Methane is a powerful greenhouse gas—some eighty times more effective at exacerbating global warming than carbon dioxide— and large-scale releases from warming permafrost could trigger catastrophic changes in climate. As a result, finding ways to keep it in the ground is important. And here the Zimovs came up with a rather unusual idea: maintaining the stability of the environment by reintroducing long-extinct species that could help prevent its destruction, even in a warmer world. It’s a wild idea, but one that has some merit.8 As a proof of concept, though, the Zimovs needed somewhere to start. And so they set out to create a park for deextinct Siberian animals: Pleistocene Park.9

Pleistocene Park is by no stretch of the imagination a modern-day Jurassic Park. The dinosaurs in Hammond’s park date back to the Mesozoic period, from around 250 million years ago to sixty-five million years ago. By comparison, the Pleistocene is relatively modern history, ending a mere eleven and a half thousand years ago. And the vision behind Pleistocene Park is not thrills, spills, and profit, but the serious use of science and technology to stabilize an increasingly unstable environment. Yet there is one thread that ties them together, and that’s using genetic engineering to reintroduce extinct species. In this case, the species in question is warm-blooded and furry: the woolly mammoth.

The idea of de-extinction, or bringing back species from extinction (it’s even called “resurrection biology” in some circles), has been around for a while. It’s a controversial idea, and it raises a lot of tough ethical questions. But proponents of de-extinction argue
that we’re losing species and ecosystems at such a rate that we can’t afford not to explore technological interventions to help stem the flow.

Early approaches to bringing species back from the dead have involved selective breeding. The idea was simple—if you have modern ancestors of a recently extinct species, selectively breeding specimens that have a higher genetic similarity to their forebears can potentially help reconstruct their genome in living animals. This approach is being used in attempts to bring back the aurochs, an ancestor of modern cattle.10 But it’s slow, and it depends on
the fragmented genome of the extinct species still surviving in its modern-day equivalents.

An alternative to selective breeding is cloning. This involves finding a viable cell, or cell nucleus, in an extinct but well-preserved animal and growing a new living clone from it. It’s definitely a more appealing route for impatient resurrection biologists, but it does mean getting your hands on intact cells from long-dead animals and devising ways to “resurrect” these, which is no mean feat. Cloning has potential when it comes to recently extinct species whose cells have been well preserved—for instance, where the whole animal has become frozen in ice. But it’s still a slow and extremely limited option.

Which is where advances in genetic engineering come in.

The technological premise of Jurassic Park is that scientists can reconstruct the genome of long-dead animals from preserved DNA fragments. It’s a compelling idea, if you think of DNA as a massively long and complex instruction set that tells a group of biological molecules how to build an animal. In principle, if we could reconstruct the genome of an extinct species, we would have the basic instruction set—the biological software—to reconstruct
individual members of it.

The bad news is that DNA-reconstruction-based de-extinction is far more complex than this. First you need intact fragments of DNA, which is not easy, as DNA degrades easily (and is pretty much impossible to obtain, as far as we know, for dinosaurs). Then you
need to be able to stitch all of your fragments together, which is akin to completing a billion-piece jigsaw puzzle without knowing what the final picture looks like. This is a Herculean task, although with breakthroughs in data manipulation and machine learning,
scientists are getting better at it. But even when you have your reconstructed genome, you need the biological “wetware”—all the stuff that’s needed to create, incubate, and nurture a new living thing, like eggs, nutrients, a safe space to grow and mature, and so on. Within all this complexity, it turns out that getting your DNA sequence right is just the beginning of translating that genetic code into a living, breathing entity. But in some cases, it might be possible.

In 2013, Sergey Zimov was introduced to the geneticist George Church at a conference on de-extinction. Church is an accomplished scientist in the field of DNA analysis and reconstruction, and a thought leader in the field of synthetic biology (which we’ll come
back to in chapter nine). It was a match made in resurrection biology heaven. Zimov wanted to populate his Pleistocene Park with mammoths, and Church thought he could see a way of
achieving this.

What resulted was an ambitious project to de-extinct the woolly mammoth. Church and others who are working on this have faced plenty of hurdles. But the technology has been advancing so fast that, as of 2017, scientists were predicting they would be able to reproduce the woolly mammoth within the next two years.

One of those hurdles was the lack of solid DNA sequences to work from. Frustratingly, although there are many instances of well preserved woolly mammoths, their DNA rarely survives being frozen for tens of thousands of years. To overcome this, Church and others
have taken a different tack: Take a modern, living relative of the mammoth, and engineer into it traits that would allow it to live on the Siberian tundra, just like its woolly ancestors.

Church’s team’s starting point has been the Asian elephant. This is their source of base DNA for their “woolly mammoth 2.0”—their starting source code, if you like. So far, they’ve identified fifty plus gene sequences they think they can play with to give their modern-day woolly mammoth the traits it would need to thrive in Pleistocene Park, including a coat of hair, smaller ears, and a constitution adapted to cold.

The next hurdle they face is how to translate the code embedded in their new woolly mammoth genome into a living, breathing animal. The most obvious route would be to impregnate a female Asian elephant with a fertilized egg containing the new code. But Asian elephants are endangered, and no one’s likely to allow such cutting edge experimentation on the precious few that are still around, so scientists are working on an artificial womb for their reinvented woolly mammoth. They’re making progress with mice and hope to crack the motherless mammoth challenge relatively soon.

It’s perhaps a stretch to call this creative approach to recreating a species (or “reanimation” as Church refers to it) “de-extinction,” as what is being formed is a new species. … (pp. 31-4)

This selection illustrates what Maynard does so very well throughout the book where he uses each film as a launching pad for a clear, readable description of relevant bits of science so you understand why the premise was likely, unlikely, or pure fantasy while linking it to contemporary practices, efforts, and issues. In the context of Jurassic Park, Maynard goes on to raise some fascinating questions such as: Should we revive animals rendered extinct (due to obsolescence or inability to adapt to new conditions) when we could develop new animals?

General thoughts

‘Films for the Future’ offers readable (to non-scientific types) science, lively writing, and the occasional ‘memorish’ anecdote. As well, Dr. Maynard raises the curtain on aspects of the scientific enterprise that most of us do not get to see.  For example, the meeting  between Sergey Zimov and George Church and how it led to new ‘de-extinction’ work’. He also describes the problems that the scientists encountered and are encountering. This is in direct contrast to how scientific work is usually presented in the news media as one glorious breakthrough after the next.

Maynard does discuss the issues of social inequality and power and ownership. For example, who owns your transplant or data? Puzzlingly, he doesn’t touch on the current environment where scientists in the US and elsewhere are encouraged/pressured to start up companies commercializing their work.

Nor is there any mention of how universities are participating in this grand business experiment often called ‘innovation’. (My March 15, 2017 posting describes an outcome for the CRISPR [gene editing system] patent fight taking place between Harvard University’s & MIT’s [Massachusetts Institute of Technology] Broad Institute vs the University of California at Berkeley and my Sept. 11, 2018 posting about an art/science exhibit in Vancouver [Canada] provides an update for round 2 of the Broad Institute vs. UC Berkeley patent fight [scroll down about 65% of the way.) *To read about how my ‘cultural blindness’ shows up here scroll down to the single asterisk at the end.*

There’s a foray through machine-learning and big data as applied to predictive policing in Maynard’s ‘Minority Report’ chapter (my November 23, 2017 posting describes Vancouver’s predictive policing initiative [no psychics involved], the first such in Canada). There’s no mention of surveillance technology, which if I recall properly was part of the future environment, both by the state and by corporations. (Mia Armstrong’s November 15, 2018 article for Slate on Chinese surveillance being exported to Venezuela provides interesting insight.)

The gaps are interesting and various. This of course points to a problem all science writers have when attempting an overview of science. (Carl Zimmer’s latest, ‘She Has Her Mother’s Laugh: The Powers, Perversions, and Potential of Heredity’] a doorstopping 574 pages, also has some gaps despite his focus on heredity,)

Maynard has worked hard to give an comprehensive overview in a remarkably compact 279 pages while developing his theme about science and the human element. In other words, science is not monolithic; it’s created by human beings and subject to all the flaws and benefits that humanity’s efforts are always subject to—scientists are people too.

The readership for ‘Films from the Future’ spans from the mildly interested science reader to someone like me who’s been writing/blogging about these topics (more or less) for about 10 years. I learned a lot reading this book.

Next time, I’m hopeful there’ll be a next time, Maynard might want to describe the parameters he’s set for his book in more detail that is possible in his chapter headings. He could have mentioned that he’s not a cinéaste so his descriptions of the movies are very much focused on the story as conveyed through words. He doesn’t mention colour palates, camera angles, or, even, cultural lenses.

Take for example, his chapter on ‘Ghost in the Shell’. Focused on the Japanese animation film and not the live action Hollywood version he talks about human enhancement and cyborgs. The Japanese have a different take on robots, inanimate objects, and, I assume, cyborgs than is found in Canada or the US or Great Britain, for that matter (according to a colleague of mine, an Englishwoman who lived in Japan for ten or more years). There’s also the chapter on the Ealing comedy, The Man in The White Suit, an English film from the 1950’s. That too has a cultural (as well as, historical) flavour but since Maynard is from England, he may take that cultural flavour for granted. ‘Never let me go’ in Chapter Two was also a UK production, albeit far more recent than the Ealing comedy and it’s interesting to consider how a UK production about cloning might differ from a US or Chinese or … production on the topic. I am hearkening back to Maynard’s anecdote about movies giving him new ways of seeing and imagining the world.

There’s a corrective. A couple of sentences in Maynard’s introductory chapter cautioning that in depth exploration of ‘cultural lenses’ was not possible without expanding the book to an unreadable size followed by a sentence in each of the two chapters that there are cultural differences.

One area where I had a significant problem was with regard to being “programmed” and having  “instinctual” behaviour,

As a species, we are embarrassingly programmed to see “different” as “threatening,” and to take instinctive action against it. It’s a trait that’s exploited in many science fiction novels and movies, including those in this book. If we want to see the rise of increasingly augmented individuals, we need to be prepared for some social strife. (p. 136)

These concepts are much debated in the social sciences and there are arguments for and against ‘instincts regarding strangers and their possible differences’. I gather Dr. Maynard hies to the ‘instinct to defend/attack’ school of thought.

One final quandary, there was no sex and I was expecting it in the Ex Machina chapter, especially now that sexbots are about to take over the world (I exaggerate). Certainly, if you’re talking about “social strife,” then sexbots would seem to be fruitful line of inquiry, especially when there’s talk of how they could benefit families (my August 29, 2018 posting). Again, there could have been a sentence explaining why Maynard focused almost exclusively in this chapter on the discussions about artificial intelligence and superintelligence.

Taken in the context of the book, these are trifling issues and shouldn’t stop you from reading Films from the Future. What Maynard has accomplished here is impressive and I hope it’s just the beginning.

Final note

Bravo Andrew! (Note: We’ve been ‘internet acquaintances/friends since the first year I started blogging. When I’m referring to him in his professional capacity, he’s Dr. Maynard and when it’s not strictly in his professional capacity, it’s Andrew. For this commentary/review I wanted to emphasize his professional status.)

If you need to see a few more samples of Andrew’s writing, there’s a Nov. 15, 2018 essay on The Conversation, Sci-fi movies are the secret weapon that could help Silicon Valley grow up and a Nov. 21, 2018 article on slate.com, The True Cost of Stain-Resistant Pants; The 1951 British comedy The Man in the White Suit anticipated our fears about nanotechnology. Enjoy.

****Added at 1700 hours on Nov. 22, 2018: You can purchase Films from the Future here.

*Nov. 23, 2018: I should have been more specific and said ‘academic scientists’. In Canada, the great percentage of scientists are academic. It’s to the point where the OECD (Organization for Economic Cooperation and Development) has noted that amongst industrialized countries, Canada has very few industrial scientists in comparison to the others.