Tag Archives: Turing Test

Beer and wine reviews, the American Chemical Society’s (ACS) AI editors, and the Turing Test

Writing

An April 25, 2022 Dartmouth College news release by David Hirsch announces the AI reviewer, Note: Links have been removed,

In mid-2020, the computer science team of Keith Carlson, Allen Riddell and Dan Rockmore was stuck on a problem. It wasn’t a technical challenge. The computer code they had developed to write product reviews was working beautifully. But they were struggling with a practical question.
“Getting the code to write reviews was only the first part of the challenge,” says Carlson, Guarini ’21, a doctoral research fellow at the Tuck School of Business, “The remaining challenge was figuring out how and where it could be used.”
The original study took on two challenges: to design code that could write original, human-quality product reviews using a small set of product features and to see if the algorithm could be adapted to write “synthesis reviews” for products from a large number of existing reviews.
Review writing can be challenging because of the overwhelming number of products available. The team wanted to see if artificial intelligence was up to the task of writing opinionated text about vast product classes.
They focused on wine and beer reviews because of the extensive availability of material to train the algorithm. The relatively narrow vocabularies used to describe the products also makes it open to the techniques of AI systems and natural language processing tools.
The project was kickstarted by Riddell, a former fellow at the Neukom Institute for Computational Science, and developed with Carlson under the guidance of Rockmore, the William H. Neukom 1964 Distinguished Professor of Computational Science.
The code couldn’t taste the products, but it did ingest reams of written material. After training the algorithm on hundreds of thousands of published wine and beer reviews, the team found that the code could complete both tasks.
One result read: “This is a sound Cabernet. It’s very dry and a little thin in blackberry fruit, which accentuates the acidity and tannins. Drink up.”
Another read: “Pretty dark for a rosé, and full-bodied, with cherry, raspberry, vanilla and spice flavors. It’s dry with good acidity.”
“But now what?” Carlson explains as a question that often gnaws at scientists. The team wondered, “Who else would care?”
“I didn’t want to quit there,” says Rockmore. “I was sure that this work could be interesting to a wider audience.”
Sensing that the paper could have relevance in marketing, the team walked the study to Tuck Drive to see what others would think.
“Brilliant,” Praveen Kopalle, the Signal Companies’ Professor of Management at Tuck School of Business, recalls thinking when first reviewing the technical study.
Kopalle knew that the research was important. It could even “disrupt” the online review industry, a huge marketplace of goods and services.
“The paper has a lot of marketing applications, particularly in the context of online reviews where we can create reviews or descriptions of products when they may not already exist,” adds Kopalle. “In fact, we can even think about summarizing reviews for products and services as well.”
With the addition of Prasad Vana, assistant professor of business administration at Tuck, the team was complete. Vana reframed the technical feat of creating review-writing code into that of a market-friendly tool that can assist consumers, marketers, and professional reviewers.
“This is a sound Cabernet. It’s very dry and a little thin in blackberry fruit, which accentuates the acidity and tannins. Drink up.” Attribution: Artificial Intelligence review from Dartmouth project
The resulting research, published in International Journal of Research in Marketing, surveyed independent participants to confirm that the AI system wrote human-like reviews in both challenges.
“Using artificial intelligence to write and synthesize reviews can create efficiencies on both sides of the marketplace,” said Vana. “The hope is that AI can benefit reviewers facing larger writing workloads and consumers who have to sort through so much content about products.”
The paper also dwells on the ethical concerns raised by computer-generated content. It notes that marketers could get better acceptance by falsely attributing the reviews to humans. To address this, the team advocates for transparency when computer-generated text is used.
They also address the issue of computers taking human jobs. Code should not replace professional product reviewers, the team insists in the paper. The technology is meant to make the tasks of producing and reading the material more efficient. [emphasis mine]
“It’s interesting to imagine how this could benefit restaurants that cannot afford sommeliers or independent sellers on online platforms who may sell hundreds of products,” says Vana.
According to Carlson, the paper’s first author, the project demonstrates the potential of AI, the power of innovative thinking, and the promise of cross-campus collaboration.
“It was wonderful to work with colleagues with different expertise to take a theoretical idea and bring it closer to the marketplace,” says Carlson. “Together we showed how our work could change marketing and how people could use it. That could only happen with collaboration.”

A revised April 29, 2022 version was published on EurekAlert and some of the differences are interesting (to me, if no one else). As you see, there’s a less ‘friendly’ style and the ‘jobs’ issue has been approached differently. Note: Links have been removed,

Artificial intelligence systems can be trained to write human-like product reviews that assist consumers, marketers and professional reviewers, according to a study from Dartmouth College, Dartmouth’s Tuck School of Business, and Indiana University.
The research, published in the International Journal of Research in Marketing, also identifies ethical challenges raised by the use of the computer-generated content.
“Review writing is challenging for humans and computers, in part, because of the overwhelming number of distinct products,” said Keith Carlson, a doctoral research fellow at the Tuck School of Business. “We wanted to see how artificial intelligence can be used to help people that produce and use these reviews.”
For the research, the Dartmouth team set two challenges. The first was to determine whether a machine can be taught to write original, human-quality reviews using only a small number of product features after being trained on a set of existing content. Secondly, the team set out to see if machine learning algorithms can be used to write syntheses of reviews of products for which many reviews already exist.
“Using artificial intelligence to write and synthesize reviews can create efficiencies on both sides of the marketplace,” said Prasad Vana, assistant professor of business administration at Tuck School of Business. “The hope is that AI can benefit reviewers facing larger writing workloads and consumers that have to sort through so much content about products.”
The researchers focused on wine and beer reviews because of the extensive availability of material to train the computer algorithms. Write-ups of these products also feature relatively focused vocabularies, an advantage when working with AI systems.
To determine whether a machine could write useful reviews from scratch, the researchers trained an algorithm on about 180,000 existing wine reviews. Metadata tags for factors such as product origin, grape variety, rating, and price were also used to train the machine-learning system.
When comparing the machine-generated reviews against human reviews for the same wines, the research team found agreement between the two versions. The results remained consistent even as the team challenged the algorithms by changing the amount of input data that was available for reference.
The machine-written material was then assessed by non-expert study participants to test if they could determine whether the reviews were written by humans or a machine. According to the research paper, the participants were unable to distinguish between the human and AI-generated reviews with any statistical significance. Furthermore, their intent to purchase a wine was similar across human versus machine generated reviews of the wine.
Having found that artificial intelligence can write credible wine reviews, the research team turned to beer reviews to determine the effectiveness of using AI to write “review syntheses.” Rather than being trained to write new reviews, the algorithm was tasked with aggregating elements from existing reviews of the same product. This tested AI’s ability to identify and provide limited but relevant information about products based on a large volume of varying opinions.
“Writing an original review tests the computer’s expressive ability based on a relatively narrow set of data. Writing a synthesis review is a related but distinct task where the system is expected to produce a review that captures some of the key ideas present in an existing set of reviews for a product,” said Carlson, who conducted the research while a PhD candidate in computer science at Dartmouth.
To test the algorithm’s ability to write review syntheses, researchers trained it on 143,000 existing reviews of over 14,000 beers. As with the wine dataset, the text of each review was paired with metadata including the product name, alcohol content, style, and scores given by the original reviewers.
As with the wine reviews, the research used independent study participants to judge whether the machine-written summaries captured and summarized the opinions of numerous reviews in a useful, human-like manner.
According to the paper, the model was successful at taking the reviews of a product as input and generating a synthesis review for that product as output.
“Our modeling framework could be useful in any situation where detailed attributes of a product are available and a written summary of the product is required,” said Vana. “It’s interesting to imagine how this could benefit restaurants that cannot afford sommeliers or independent sellers on online platforms who may sell hundreds of products.”
Both challenges used a deep learning neural net based on transformer architecture to ingest, process and output review language.
According to the research team, the computer systems are not intended to replace professional writers and marketers, but rather to assist them in their work. A machine-written review, for instance, could serve as a time-saving first draft of a review that a human reviewer could then revise. [emphasis mine]
The research can also help consumers. Syntheses reviews—like those on beer in the study—can be expanded to the constellation of products and services in online marketplaces to assist people who have limited time to read through many product reviews.
In addition to the benefits of machine-written reviews, the research team highlights some of the ethical challenges presented by using computer algorithms to influence human consumer behavior.
Noting that marketers could get better acceptance of machine-generated reviews by falsely attributing them to humans, the team advocates for transparency when computer-generated reviews are offered.
“As with other technology, we have to be cautious about how this advancement is used,” said Carlson. “If used responsibly, AI-generated reviews can be both a productivity tool and can support the availability of useful consumer information.”
Researchers contributing to the study include Praveen Kopalle, Dartmouth’s Tuck School of Business; Allen Riddell, Indiana University, and Daniel Rockmore, Dartmouth College.

I wonder if the second news release was written by an AI agent.

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

Complementing human effort in online reviews: A deep learning approach to automatic content generation and review synthesis by Keith Carlson, Praveen K.Kopal, Allen Ridd, Daniel Rockmore, Prasad Vana. International Journal of Research in Marketing DOI: https://doi.org/10.1016/j.ijresmar.2022.02.004 Available online 12 February 2022 In Press, Corrected Proof

This paper is behind a paywall.

Daniel (Dan) Rockmore was mentioned here in a May 6, 2016 posting about a competition he’d set up through Dartmouth College,’s Neukom Institute. The competition, which doesn’t seem to have been run since 2018, was called Turing Tests in Creative Arts.

Editing

It seems the American Chemical Society (ACS) has decided to further automate some of its editing. From an April 28, 2022 Digital Science business announcement (also on EurekAlert) by David Ellis,

Writefull’s world-leading AI-based language services have been integrated into the American Chemical Society’s (ACS) Publications workflow.
In a partnership that began almost two years ago, ACS has now progressed to a full integration of Writefull’s application programming interfaces (APIs) for three key uses.
One of the world’s largest scientific societies, ACS publishes more than 300,000 research manuscripts in more than 60 scholarly journals per year.
Writefull’s proprietary AI technology is trained on millions of scientific papers using Deep Learning. It identifies potential language issues with written texts, offers solutions to those issues, and automatically assesses texts’ language quality. Thanks to Writefull’s APIs, its tech can be applied at all key points in the editorial workflows.
Writefull’s Manuscript Categorization API is now used by ACS before copyediting to automatically classify all accepted manuscripts by their language quality. Using ACS’s own classification criteria, the API assigns a level-of-edit grade to manuscripts at scale without editors having to open documents and review the text. After thorough benchmarking alongside human editors, Writefull reached more than 95% alignment in grading texts, significantly reducing the time ACS spends on manuscript evaluation.
The same Manuscript Categorization API is now part of ACS’s quality control program, to evaluate the language in manuscripts after copyediting.
Writefull’s Metadata API is also being used to automate aspects of manuscript review, ensuring that all elements of an article are complete prior to publication. The same API is used by Open Access publisher Hindawi as a pre-submission structural checks tool for authors.
Juan Castro, co-founder and CEO of Writefull, says: “Our partnership with the American Chemical Society over the past two years has been aimed at thoroughly vetting and shaping our services to meet ACS’s needs. Writefull’s AI-based language services empower publishers to increase their workflow efficiency and positively impact production costs, while also maintaining the quality and integrity of the manuscript.”
Digital Science is a technology company working to make research more efficient. We invest in, nurture and support innovative businesses and technologies that make all parts of the research process more open and effective. Our portfolio includes admired brands including Altmetric, Dimensions, Figshare, ReadCube, Symplectic, IFI CLAIMS, GRID, Overleaf, Ripeta and Writefull. We believe that together, we can help researchers make a difference. Visit www.digital-science.com and follow @digitalsci on Twitter.
Writefull is a technology startup that creates tools to help researchers improve their writing in English. The first version of the Writefull product allowed researchers to discover patterns in academic language, such as frequent word combinations and synonyms in context. The new version utilises Natural Language Processing and Deep Learning algorithms that will give researchers feedback on their full texts. Visit writefull.com and follow @writefullapp on Twitter.
The American Chemical Society (ACS) is a nonprofit organization chartered by the U.S. Congress. ACS’ mission is to advance the broader chemistry enterprise and its practitioners for the benefit of Earth and all its people. The Society is a global leader in promoting excellence in science education and providing access to chemistry-related information and research through its multiple research solutions, peer-reviewed journals, scientific conferences, eBooks and weekly news periodical Chemical & Engineering News. ACS journals are among the most cited, most trusted and most read within the scientific literature; however, ACS itself does not conduct chemical research. As a leader in scientific information solutions, its CAS division partners with global innovators to accelerate breakthroughs by curating, connecting and analyzing the world’s scientific knowledge. ACS’ main offices are in Washington, D.C., and Columbus, Ohio. Visit www.acs.org and follow @AmerChemSociety on Twitter.

So what?

An artificial intelligence (AI) agent being used for writing assignments is not new (see my July 16, 2014 posting titled, “Writing and AI or is a robot writing this blog?“). The argument that these agents will assist rather than replace (pick an occupation: e.g., writers, doctors, programmers, scientists, etc) is almost always used as scientists explain that AI agents will take over the boring work giving you (the human) more opportunities to do interesting work. The AI-written beer and wine reviews described here support at least part of the argument—for the time being.

It’s true that an AI agent can’t taste beer or wine but that can change as this August 8, 2019 article by Alice Johnston for CNN hints (Note: Links have been removed),

An artificial “tongue” that can taste minute differences between varieties of Scotch whisky could be the key to identifying counterfeit alcohol, scientists say.
Engineers from the universities of Glasgow and Strathclyde in Scotland created a device made of gold and aluminum and measured how it absorbed light when submerged in different kinds of whisky.
Analysis of the results allowed the scientists to identify the samples from Glenfiddich, Glen Marnoch and Laphroaig with more than 99% accuracy
…

BTW, my earliest piece on artificial tongues is a July 28, 2011 posting, “Bio-inspired electronic tongue replaces sommelier?,” about research in Spain.

For a contrast, this is the first time I can recall seeing anything about an artificial intelligence agent that edits and Writefall’s use at the ACS falls into the ‘doing all the boring work’ category and narrative quite neatly.

Having looked at a definition of the various forms of editing and core skills, I”m guessing that AI will take over every aspect (from the Editors’ Association of Canada, Definitions of Editorial Skills webpage),

CORE SKILLS
Structural Editing
Assessing and shaping draft material to improve its organization and content. Changes may be suggested to or drafted for the writer. Structural editing may include:
revising, reordering, cutting, or expanding material
writing original material
determining whether permissions are necessary for third-party material
recasting material that would be better presented in another form, or revising material for a different medium (such as revising print copy for web copy)
clarifying plot, characterization, or thematic elements
Also known as substantive editing, manuscript editing, content editing, or developmental editing.
Stylistic Editing
Editing to clarify meaning, ensure coherence and flow, and refine the language. It includes:
eliminating jargon, clichés, and euphemisms
establishing or maintaining the language level appropriate for the intended audience, medium, and purpose
adjusting the length and structure of sentences and paragraphs
establishing or maintaining tone, mood, style, and authorial voice or level of formality
Also known as line editing (which may also include copy editing).
Copy Editing
Editing to ensure correctness, accuracy, consistency, and completeness. It includes:
editing for grammar, spelling, punctuation, and usage
checking for consistency and continuity of mechanics and facts, including anachronisms, character names, and relationships
editing tables, figures, and lists
notifying designers of any unusual production requirements
developing a style sheet or following one that is provided
correcting or querying general information that should be checked for accuracy
It may also include:
marking levels of headings and the approximate placement of art
Canadianizing or other localizing
converting measurements
providing or changing the system of citations
editing indexes
obtaining or listing permissions needed
checking front matter, back matter, and cover copy
checking web links
Note that “copy editing” is often loosely used to include stylistic editing, structural editing, fact checking, or proofreading. Editors Canada uses it only as defined above.
Proofreading
Examining material after layout or in its final format to correct errors in textual and visual elements. The material may be read in isolation or against a previous version. It includes checking for:
adherence to design
minor mechanical errors (such as spelling mistakes or deviations from style sheet)
consistency and accuracy of elements in the material (such as cross-references, running heads, captions, web page heading tags, hyperlinks, and metadata)
It may also include:
distinguishing between printer’s, designer’s, or programmer’s errors and writer’s or editor’s alterations
copyfitting
flagging or checking locations of art
inserting page numbers or checking them against content and page references
Note that proofreading is checking a work after editing; it is not a substitute for editing.

I’m just as happy to get rid of ‘boring’ parts of my work as anyone else but that’s how I learned in the first place and I haven’t seen any discussion about the importance of boring, repetitive tasks for learning.

News from the Canadian Light Source (CLS), Canadian Science Policy Conference (CSPC) 2020, the International Symposium on Electronic Arts (ISEA) 2020, and HotPopRobot

Canadian Light Source (CLS) and modern art

Rita Letendre. Victoire [Victory], 1961. Oil on canvas, Overall: 202.6 × 268 cm. Art Gallery of Ontario. Gift of Jessie and Percy Waxer, 1974, donated by the Ontario Heritage Foundation, 1988. © Rita Letendre L74.8. Photography by Ian Lefebvre

This is one of three pieces by Rita Letendre that underwent chemical mapping according to an August 5, 2020 CLS news release by Victoria Martinez (also received via email),

Research undertaken at the Canadian Light Source (CLS) at the University of Saskatchewan was key to understanding how to conserve experimental oil paintings by Rita Letendre, one of Canada’s most respected living abstract artists.
The work done at the CLS was part of a collaborative research project between the Art Gallery of Ontario (AGO) and the Canadian Conservation Institute (CCI) that came out of a recent retrospective Rita Letendre: Fire & Light at the AGO. During close examination, Meaghan Monaghan, paintings conservator from the Michael and Sonja Koerner Centre for Conservation, observed that several of Letendre’s oil paintings from the fifties and sixties had suffered significant degradation, most prominently, uneven gloss and patchiness, snowy crystalline structures coating the surface known as efflorescence, and cracking and lifting of the paint in several areas.
Kate Helwig, Senior Conservation Scientist at the Canadian Conservation Institute, says these problems are typical of mid-20^th century oil paintings. “We focused on three of Rita Letendre’s paintings in the AGO collection, which made for a really nice case study of her work and also fits into the larger question of why oil paintings from that period tend to have degradation issues.”
Growing evidence indicates that paintings from this period have experienced these problems due to the combination of the experimental techniques many artists employed and the additives paint manufacturers had begun to use.
In order to determine more precisely how these factors affected Letendre’s paintings, the research team members applied a variety of analytical techniques, using microscopic samples taken from key points in the works.
“The work done at the CLS was particularly important because it allowed us to map the distribution of materials throughout a paint layer such as an impasto stroke,” Helwig said. The team used Mid-IR chemical mapping at the facility, which provides a map of different molecules in a small sample.
For example, chemical mapping at the CLS allowed the team to understand the distribution of the paint additive aluminum stearate throughout the paint layers of the painting Méduse. This painting showed areas of soft, incompletely dried paint, likely due to the high concentration and incomplete mixing of this additive.
The painting Victoire had a crumbling base paint layer in some areas and cracking and efflorescence at the surface in others. Infrared mapping at the CLS allowed the team to determine that excess free fatty acids in the paint were linked to both problems; where the fatty acids were found at the base they formed zing “soaps” which led to crumbling and cracking, and where they had moved to the surface they had crystallized, causing the snowflake-like efflorescence.
AGO curators and conservators interviewed Letendre to determine what was important to her in preserving and conserving her works, and she highlighted how important an even gloss across the surface was to her artworks, and the philosophical importance of the colour black in her paintings. These priorities guided conservation efforts, while the insights gained through scientific research will help maintain the works in the long term.
In order to restore the black paint to its intended even finish for display, conservator Meaghan Monaghan removed the white crystallization from the surface of Victoire, but it is possible that it could begin to recur. Understanding the processes that lead to this degradation will be an important tool to keep Letendre’s works in good condition.
“The world of modern paint research is complicated; each painting is unique, which is why it’s important to combine theoretical work on model paint systems with this kind of case study on actual works of art” said Helwig. The team hopes to collaborate on studying a larger cross section of Letendre’s paintings in oil and acrylic in the future to add to the body of knowledge.

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

Rita Letendre’s Oil Paintings from the 1960s: The Effect of Artist’s Materials on Degradation Phenomena by Kate Helwig, Meaghan Monaghan, Jennifer Poulin, Eric J. Henderson & Maeve Moriarty. Studies in Conservation (2020): 1-15 DOI: https://doi.org/10.1080/00393630.2020.1773055 Published online: 06 Jun 2020

This paper is behind a paywall.

Canadian Science Policy Conference (CSPC) 2020

The latest news from the CSPC 2020 (November 16 – 20 with preconference events from Nov. 1 -14) organizers is that registration is open and early birds have a deadline of September 27, 2020 (from an August 6, 2020 CSPC 2020 announcement received via email),

It’s time! Registration for the 12th Canadian Science Policy Conference (CSPC 2020) is open now. Early Bird registration is valid until Sept. 27th [2020].
CSPC 2020 is coming to your offices and homes:
Register for full access to 3 weeks of programming of the biggest science and innovation policy forum of 2020 under the overarching theme: New Decade, New Realities: Hindsight, Insight, Foresight.
2500+ Participants
300+ Speakers from five continents
65+ Panel sessions, 15 pre conference sessions and symposiums
50+ On demand videos and interviews with the most prominent figures of science and innovation policy
20+ Partner-hosted functions
15+ Networking sessions
15 Open mic sessions to discuss specific topics
The virtual conference features an exclusive array of offerings:
3D Lounge and Exhibit area
Advance access to the Science Policy Magazine, featuring insightful reflections from the frontier of science and policy innovation
Many more
Don’t miss this unique opportunity to engage in the most important discussions of science and innovation policy with insights from around the globe, just from your office, home desk, or your mobile phone.
Benefit from significantly reduced registration fees for an online conference with an option for discount for multiple ticket purchases
Register now to benefit from the Early Bird rate!

The preliminary programme can be found here. This year there will be some discussion of a Canadian synthetic biology roadmap, presentations on various Indigenous concerns (mostly health), a climate challenge presentation focusing on Mexico and social vulnerability and another on parallels between climate challenges and COVID-19. There are many presentations focused on COVID-19 and.or health.

There doesn’t seem to be much focus on cyber security and, given that we just lost two ice caps (see Brandon Spektor’s August 1, 2020 article [Two Canadian ice caps have completely vanished from the Arctic, NASA imagery shows] on the Live Science website), it’s surprising that there are no presentations concerning the Arctic.

International Symposium on Electronic Arts (ISEA) 2020

According to my latest information, the early bird rate for ISEA 2020 (Oct. 13 -18) ends on August 13, 2020. (My June 22, 2020 posting describes their plans for the online event.)

You can find registration information here.

Margaux Davoine has written up a teaser for the 2020 edition of ISEA in the form of an August 6, 2020 interview with Yan Breuleux. I’ve excerpted one bit,

Finally, thinking about this year’s theme [Why Sentience?], there might be something a bit ironic about exploring the notion of sentience (historically reserved for biological life, and quite a small subsection of it) through digital media and electronic arts. There’s been much work done in the past 25 years to loosen the boundaries between such distinctions: how do you imagine ISEA2020 helping in that?
The similarities shared between humans, animals, and machines are fundamental in cybernetic sciences. According to the founder of cybernetics Norbert Wiener, the main tenets of the information paradigm – the notion of feedback – can be applied to humans, animals as well as the material world. Famously, the AA predictor (as analysed by Peter Galison in 1994) can be read as a first attempt at human-machine fusion (otherwise known as a cyborg).
The infamous Turing test also tends to blur the lines between humans and machines, between language and informational systems. Second-order cybernetics are often associated with biologists Francisco Varela and Humberto Maturana. The very notion of autopoiesis (a system capable of maintaining a certain level of stability in an unstable environment) relates back to the concept of homeostasis formulated by Willam Ross [William Ross Ashby] in 1952. Moreover, the concept of “ecosystems” emanates directly from the field of second-order cybernetics, providing researchers with a clearer picture of the interdependencies between living and non-living organisms. In light of these theories, the absence of boundaries between animals, humans, and machines constitutes the foundation of the technosciences paradigm. New media, technological arts, virtual arts, etc., partake in the dialogue between humans and machines, and thus contribute to the prolongation of this paradigm. Frank Popper nearly called his book “Techno Art” instead of “Virtual Art”, in reference to technosciences (his editor suggested the name change). For artists in the technological arts community, Jakob von Uexkull’s notion of “human-animal milieu” is an essential reference. Also present in Simondon’s reflections on human environments (both natural and artificial), the notion of “milieu” is quite important in the discourses about art and the environment. Concordia University’s artistic community chose the concept of “milieu” as the rallying point of its research laboratories.
ISEA2020’s theme resonates particularly well with the recent eruption of processing and artificial intelligence technologies. For me, Sentience is a purely human and animal idea: machines can only simulate our ways of thinking and feeling. Partly in an effort to explore the illusion of sentience in computers, Louis-Philippe Rondeau, Benoît Melançon and I have established the Mimesis laboratory at NAD University. Processing and AI technologies are especially useful in the creation of “digital doubles”, “Vactors”, real-time avatar generation, Deep Fakes and new forms of personalised interactions.
I adhere to the epistemological position that the living world is immeasurable. Through their ability to simulate, machines can merely reduce complex logics to a point of understandability. The utopian notion of empathetic computers is an idea mostly explored by popular science-fiction movies. Nonetheless, research into computer sentience allows us to devise possible applications, explore notions of embodiment and agency, and thereby develop new forms of interaction. Beyond my own point of view, the idea that machines can somehow feel emotions gives artists and researchers the opportunity to experiment with certain findings from the fields of the cognitive sciences, computer sciences and interactive design. For example, in 2002 I was particularly marked by an immersive installation at Universal Exhibition in Neuchatel, Switzerland titled Ada: Intelligence Space. The installation comprised an artificial environment controlled by a computer, which interacted with the audience on the basis of artificial emotion. The system encouraged visitors to participate by intelligently analysing their movements and sounds. Another example, Louis-Philippe Demers’ Blind Robot (2012), demonstrates how artists can be both critical of, and amazed by, these new forms of knowledge. Additionally, the 2016 BIAN (Biennale internationale d’art numérique), organized by ELEKTRA (Alain Thibault) explored the various ways these concepts were appropriated in installation and interactive art. The way I see it, current works of digital art operate as boundary objects. The varied usages and interpretations of a particular work of art allow it to be analyzed from nearly every angle or field of study. Thus, philosophers can ask themselves: how does a computer come to understand what being human really is?
I have yet to attend conferences or exchange with researchers on that subject. Although the sheer number of presentation propositions sent to ISEA2020, I have no doubt that the symposium will be the ideal context to reflect on the concept of Sentience and many issues raised therein.

For the last bit of news.

HotPopRobot, one of six global winners of 2020 NASA SpaceApps COVID-19 challenge

I last wrote about HotPopRobot’s (Artash and Arushi with a little support from their parents) response to the 2020 NASA (US National Aeronautics and Space Administration) SpaceApps challenge in my July 1, 2020 post, Toronto COVID-19 Lockdown Musical: a data sonification project from HotPopRobot. (You’ll find a video of the project embedded in the post.)

Here’s more news from HotPopRobot’s August 4, 2020 posting (Note: Links have been removed),

Artash (14 years) and Arushi (10 years). Toronto.
We are excited to become the global winners of the 2020 NASA SpaceApps COVID-19 Challenge from among 2,000 teams from 150 countries. The six Global Winners will be invited to visit a NASA Rocket Launch site to view a spacecraft launch along with the SpaceApps Organizing team once travel is deemed safe. They will also receive an invitation to present their projects to NASA, ESA [European Space Agency], JAXA [Japan Aerospace Exploration Agency], CNES [Centre National D’Etudes Spatiales; France], and CSA [Canadian Space Agency] personnel. https://covid19.spaceappschallenge.org/awards
15,000 participants joined together to submit over 1400 projects for the COVID-19 Global Challenge that was held on 30-31 May 2020. 40 teams made to the Global Finalists. Amongst them, 6 teams became the global winners!
The 2020 SpaceApps was an international collaboration between NASA, Canadian Space Agency, ESA, JAXA, CSA,[sic] and CNES focused on solving global challenges. During a period of 48 hours, participants from around the world were required to create virtual teams and solve any of the 12 challenges related to the COVID-19 pandemic posted on the SpaceApps website. More details about the 2020 SpaceApps COVID-19 Challenge: https://sa-2019.s3.amazonaws.com/media/documents/Space_Apps_FAQ_COVID_.pdf
…
We have been participating in NASA Space Challenge for the last seven years since 2014. We were only 8 years and 5 years respectively when we participated in our very first SpaceApps 2014.
We have grown up learning more about space, tacking global challenges, making hardware and software projects, participating in meetings, networking with mentors and teams across the globe, and giving presentations through the annual NASA Space Apps Challenges. This is one challenge we look forward to every year.
It has been a fun and exciting journey meeting so many people and astronauts and visiting several fascinating places on the way! We hope more kids, youths, and families are inspired by our space journey. Space is for all and is yours to discover!
…

If you have the time, I recommend reading HotPopRobot’s August 4, 2020 posting in its entirety.

Will AI ‘artists’ be able to fool a panel judging entries the Neukom Institute Prizes in Computational Arts?

FrogHeart

Commentary about nanotech, science policy and communication, society, and the arts