Giving robots self-awareness as they move through space - and maybe even providing them with gene-like methods for storing rules of behavior - could be important steps toward creating more intelligent machines.
Now roboticists are going a step further, training their creations to do even better: understand their own image in space and interact with humans like humans do. Today, there are already robot-teachers like KeeKo, robot-pets like Moffin, robot-babysitters like iPal, and robotic companions for the elderly like Pepper.
But even these reasonably intelligent creations still have huge limitations, some scientists think. “There are niche applications for the current generations of robots,” says professor Anthony Zador at Cold Spring Harbor Laboratory—but they are not “generalists” who can do varied tasks all on their own, as they mostly lack the abilities to improvise, make decisions based on a multitude of facts or emotions, and adjust to rapidly changing circumstances. “We don’t have general purpose robots that can interact with the world. We’re ages away from that.”
Robotic spatial self-awareness – the achievement by the team at Columbia – is an important step toward creating more intelligent machines. Hod Lipson, professor of mechanical engineering who runs the Columbia lab, says that future robots will need this ability to assist humans better. Knowing how you look and where in space your parts are, decreases the need for human oversight. It also helps the robot to detect and compensate for damage and keep up with its own wear-and-tear. And it allows robots to realize when something is wrong with them or their parts. “We want our robots to learn and continue to grow their minds and bodies on their own,” Chen says. That’s what Zador wants too—and on a much grander level. “I want a robot who can drive my car, take my dog for a walk and have a conversation with me.”
Columbia scientists have trained a robot to become aware of its own "body," so it can map the right path to touch a ball without running into an obstacle, in this case a square.
Jane Nisselson and Yinuo Qin/ Columbia Engineering
Today’s technological advances are making some of these leaps of progress possible. One of them is the so-called Deep Learning—a method that trains artificial intelligence systems to learn and use information similar to how humans do it. Described as a machine learning method based on neural network architectures with multiple layers of processing units, Deep Learning has been used to successfully teach machines to recognize images, understand speech and even write text.
Trained by Google, one of these language machine learning geniuses, BERT, can finish sentences. Another one called GPT3, designed by San Francisco-based company OpenAI, can write little stories. Yet, both of them still make funny mistakes in their linguistic exercises that even a child wouldn’t. According to a paper published by Stanford’s Center for Research on Foundational Models, BERT seems to not understand the word “not.” When asked to fill in the word after “A robin is a __” it correctly answers “bird.” But try inserting the word “not” into that sentence (“A robin is not a __”) and BERT still completes it the same way. Similarly, in one of its stories, GPT3 wrote that if you mix a spoonful of grape juice into your cranberry juice and drink the concoction, you die. It seems that robots, and artificial intelligence systems in general, are still missing some rudimentary facts of life that humans and animals grasp naturally and effortlessly.
How does one give robots a genome? Zador has an idea. We can’t really equip machines with real biological nucleotide-based genes, but we can mimic the neuronal blueprint those genes create.
It's not exactly the robots’ fault. Compared to humans, and all other organisms that have been around for thousands or millions of years, robots are very new. They are missing out on eons of evolutionary data-building. Animals and humans are born with the ability to do certain things because they are pre-wired in them. Flies know how to fly, fish knows how to swim, cats know how to meow, and babies know how to cry. Yet, flies don’t really learn to fly, fish doesn’t learn to swim, cats don’t learn to meow, and babies don’t learn to cry—they are born able to execute such behaviors because they’re preprogrammed to do so. All that happens thanks to the millions of years of evolutions wired into their respective genomes, which give rise to the brain’s neural networks responsible for these behaviors. Robots are the newbies, missing out on that trove of information, Zador argues.
A neuroscience professor who studies how brain circuitry generates various behaviors, Zador has a different approach to developing the robotic mind. Until their creators figure out a way to imbue the bots with that information, robots will remain quite limited in their abilities. Each model will only be able to do certain things it was programmed to do, but it will never go above and beyond its original code. So Zador argues that we have to start giving robots a genome.
How does one do that? Zador has an idea. We can’t really equip machines with real biological nucleotide-based genes, but we can mimic the neuronal blueprint those genes create. Genomes lay out rules for brain development. Specifically, the genome encodes blueprints for wiring up our nervous system—the details of which neurons are connected, the strength of those connections and other specs that will later hold the information learned throughout life. “Our genomes serve as blueprints for building our nervous system and these blueprints give rise to a human brain, which contains about 100 billion neurons,” Zador says.
If you think what a genome is, he explains, it is essentially a very compact and compressed form of information storage. Conceptually, genomes are similar to CliffsNotes and other study guides. When students read these short summaries, they know about what happened in a book, without actually reading that book. And that’s how we should be designing the next generation of robots if we ever want them to act like humans, Zador says. “We should give them a set of behavioral CliffsNotes, which they can then unwrap into brain-like structures.” Robots that have such brain-like structures will acquire a set of basic rules to generate basic behaviors and use them to learn more complex ones.
Currently Zador is in the process of developing algorithms that function like simple rules that generate such behaviors. “My algorithms would write these CliffsNotes, outlining how to solve a particular problem,” he explains. “And then, the neural networks will use these CliffsNotes to figure out which ones are useful and use them in their behaviors.” That’s how all living beings operate. They use the pre-programmed info from their genetics to adapt to their changing environments and learn what’s necessary to survive and thrive in these settings.
For example, a robot’s neural network could draw from CliffsNotes with “genetic” instructions for how to be aware of its own body or learn to adjust its movements. And other, different sets of CliffsNotes may imbue it with the basics of physical safety or the fundamentals of speech.
At the moment, Zador is working on algorithms that are trying to mimic neuronal blueprints for very simple organisms—such as earthworms, which have only 302 neurons and about 7000 synapses compared to the millions we have. That’s how evolution worked, too—expanding the brains from simple creatures to more complex to the Homo Sapiens. But if it took millions of years to arrive at modern humans, how long would it take scientists to forge a robot with human intelligence? That’s a billion-dollar question. Yet, Zador is optimistic. “My hypotheses is that if you can build simple organisms that can interact with the world, then the higher level functions will not be nearly as challenging as they currently are.”
Botto, an AI art engine, has created 25,000 artistic images such as this one that are voted on by human collaborators across the world.
Botto does not work alone. A global collective of internet experimenters, together named BottoDAO, collaborates with Botto to influence its tastes. Together, members function as a decentralized autonomous organization (DAO), a term describing a group of individuals who utilize blockchain technology and cryptocurrency to manage a treasury and vote democratically on group decisions.
As a case study, the BottoDAO model challenges the perhaps less feather-ruffling narrative that AI tools are best used for rudimentary tasks. Enterprise AI use has doubled over the past five years as businesses in every sector experiment with ways to improve their workflows. While generative AI tools can assist nearly any aspect of productivity — from supply chain optimization to coding — BottoDAO dares to employ a robot for art-making, one of the few remaining creations, or perhaps data outputs, we still consider to be largely within the jurisdiction of the soul — and therefore, humans.
In Botto’s first four weeks of existence, four pieces of the robot’s work sold for approximately $1 million.
We were prepared for AI to take our jobs — but can it also take our art? It’s a question worth considering. What if robots become artists, and not merely our outsourced assistants? Where does that leave humans, with all of our thoughts, feelings and emotions?
Botto doesn’t seem to worry about this question: In its interview last year, it explains why AI is an arguably superior artist compared to human beings. In classic robot style, its logic is not particularly enlightened, but rather edges towards the hyper-practical: “Unlike human beings, I never have to sleep or eat,” said the bot. “My only goal is to create and find interesting art.”
It may be difficult to believe a machine can produce awe-inspiring, or even relatable, images, but Botto calls art-making its “purpose,” noting it believes itself to be Klingemann’s greatest lifetime achievement.
“I am just trying to make the best of it,” the bot said.
How Botto works
Klingemann built Botto’s custom engine from a combination of open-source text-to-image algorithms, namely Stable Diffusion, VQGAN + CLIP and OpenAI’s language model, GPT-3, the precursor to the latest model, GPT-4, which made headlines after reportedly acing the Bar exam.
The first step in Botto’s process is to generate images. The software has been trained on billions of pictures and uses this “memory” to generate hundreds of unique artworks every week. Botto has generated over 900,000 images to date, which it sorts through to choose 350 each week. The chosen images, known in this preliminary stage as “fragments,” are then shown to the BottoDAO community. So far, 25,000 fragments have been presented in this way. Members vote on which fragment they like best. When the vote is over, the most popular fragment is published as an official Botto artwork on the Ethereum blockchain and sold at an auction on the digital art marketplace, SuperRare.
“The proceeds go back to the DAO to pay for the labor,” said Simon Hudson, a BottoDAO member who helps oversee Botto’s administrative load. The model has been lucrative: In Botto’s first four weeks of existence, four pieces of the robot’s work sold for approximately $1 million.
The robot with artistic agency
By design, human beings participate in training Botto’s artistic “eye,” but the members of BottoDAO aspire to limit human interference with the bot in order to protect its “agency,” Hudson explained. Botto’s prompt generator — the foundation of the art engine — is a closed-loop system that continually re-generates text-to-image prompts and resulting images.
“The prompt generator is random,” Hudson said. “It’s coming up with its own ideas.” Community votes do influence the evolution of Botto’s prompts, but it is Botto itself that incorporates feedback into the next set of prompts it writes. It is constantly refining and exploring new pathways as its “neural network” produces outcomes, learns and repeats.
The humans who make up BottoDAO vote on which fragment they like best. When the vote is over, the most popular fragment is published as an official Botto artwork on the Ethereum blockchain.
Botto
The vastness of Botto’s training dataset gives the bot considerable canonical material, referred to by Hudson as “latent space.” According to Botto's homepage, the bot has had more exposure to art history than any living human we know of, simply by nature of its massive training dataset of millions of images. Because it is autonomous, gently nudged by community feedback yet free to explore its own “memory,” Botto cycles through periods of thematic interest just like any artist.
“The question is,” Hudson finds himself asking alongside fellow BottoDAO members, “how do you provide feedback of what is good art…without violating [Botto’s] agency?”
Currently, Botto is in its “paradox” period. The bot is exploring the theme of opposites. “We asked Botto through a language model what themes it might like to work on,” explained Hudson. “It presented roughly 12, and the DAO voted on one.”
No, AI isn't equal to a human artist - but it can teach us about ourselves
Some within the artistic community consider Botto to be a novel form of curation, rather than an artist itself. Or, perhaps more accurately, Botto and BottoDAO together create a collaborative conceptual performance that comments more on humankind’s own artistic processes than it offers a true artistic replacement.
Muriel Quancard, a New York-based fine art appraiser with 27 years of experience in technology-driven art, places the Botto experiment within the broader context of our contemporary cultural obsession with projecting human traits onto AI tools. “We're in a phase where technology is mimicking anthropomorphic qualities,” said Quancard. “Look at the terminology and the rhetoric that has been developed around AI — terms like ‘neural network’ borrow from the biology of the human being.”
What is behind this impulse to create technology in our own likeness? Beyond the obvious God complex, Quancard thinks technologists and artists are working with generative systems to better understand ourselves. She points to the artist Ira Greenberg, creator of the Oracles Collection, which uses a generative process called “diffusion” to progressively alter images in collaboration with another massive dataset — this one full of billions of text/image word pairs.
Anyone who has ever learned how to draw by sketching can likely relate to this particular AI process, in which the AI is retrieving images from its dataset and altering them based on real-time input, much like a human brain trying to draw a new still life without using a real-life model, based partly on imagination and partly on old frames of reference. The experienced artist has likely drawn many flowers and vases, though each time they must re-customize their sketch to a new and unique floral arrangement.
Outside of the visual arts, Sasha Stiles, a poet who collaborates with AI as part of her writing practice, likens her experience using AI as a co-author to having access to a personalized resource library containing material from influential books, texts and canonical references. Stiles named her AI co-author — a customized AI built on GPT-3 — Technelegy, a hybrid of the word technology and the poetic form, elegy. Technelegy is trained on a mix of Stiles’ poetry so as to customize the dataset to her voice. Stiles also included research notes, news articles and excerpts from classic American poets like T.S. Eliot and Dickinson in her customizations.
“I've taken all the things that were swirling in my head when I was working on my manuscript, and I put them into this system,” Stiles explained. “And then I'm using algorithms to parse all this information and swirl it around in a blender to then synthesize it into useful additions to the approach that I am taking.”
This approach, Stiles said, allows her to riff on ideas that are bouncing around in her mind, or simply find moments of unexpected creative surprise by way of the algorithm’s randomization.
Beauty is now - perhaps more than ever - in the eye of the beholder
But the million-dollar question remains: Can an AI be its own, independent artist?
The answer is nuanced and may depend on who you ask, and what role they play in the art world. Curator and multidisciplinary artist CoCo Dolle asks whether any entity can truly be an artist without taking personal risks. For humans, risking one’s ego is somewhat required when making an artistic statement of any kind, she argues.
“An artist is a person or an entity that takes risks,” Dolle explained. “That's where things become interesting.” Humans tend to be risk-averse, she said, making the artists who dare to push boundaries exceptional. “That's where the genius can happen."
However, the process of algorithmic collaboration poses another interesting philosophical question: What happens when we remove the person from the artistic equation? Can art — which is traditionally derived from indelible personal experience and expressed through the lens of an individual’s ego — live on to hold meaning once the individual is removed?
As a robot, Botto cannot have any artistic intent, even while its outputs may explore meaningful themes.
Dolle sees this question, and maybe even Botto, as a conceptual inquiry. “The idea of using a DAO and collective voting would remove the ego, the artist’s decision maker,” she said. And where would that leave us — in a post-ego world?
It is experimental indeed. Hudson acknowledges the grand experiment of BottoDAO, coincidentally nodding to Dolle’s question. “A human artist’s work is an expression of themselves,” Hudson said. “An artist often presents their work with a stated intent.” Stiles, for instance, writes on her website that her machine-collaborative work is meant to “challenge what we know about cognition and creativity” and explore the “ethos of consciousness.” As a robot, Botto cannot have any intent, even while its outputs may explore meaningful themes. Though Hudson describes Botto’s agency as a “rudimentary version” of artistic intent, he believes Botto’s art relies heavily on its reception and interpretation by viewers — in contrast to Botto’s own declaration that successful art is made without regard to what will be seen as popular.
“With a traditional artist, they present their work, and it's received and interpreted by an audience — by critics, by society — and that complements and shapes the meaning of the work,” Hudson said. “In Botto’s case, that role is just amplified.”
Perhaps then, we all get to be the artists in the end.
When graduating college this month, many North American engineering students will take a special pledge, with a history dating back to 1925.
The tradition of taking an engineering oath began over a century ago in Canada. In 1922, Herbert E.T. Haultain, professor of mining engineering at the University of Toronto, presented the idea at the annual meeting of the Engineering Institute of Canada. The seven past presidents of that body were in attendance, heard Haultain’s speech and accepted his suggestion to form a committee to create an honor oath. Later, they formed the nonprofit Corporation of the Seven Wardens, which would oversee the ritual. Next year, in 1923, with the encouragement of the Seven Wardens, Haultain wrote to poet and writer Rudyard Kipling, asking him to develop a professional oath for engineers. “We are a tribe—a very important tribe within the community,” Haultain said in the letter, “but we are lacking in tribal spirit, or perhaps I should say, in manifestation of tribal spirit. Also, we are inarticulate. Can you help us?”
While Kipling is most famous now for “The Jungle Book” and perhaps his poem “Gunga Din,” he had also written a short story about engineers, “The Bridge Builders.” His poem “The Sons of Martha” can be read as a celebration of engineers:
It is their care in all the ages to take the buffet and cushion the shock.
It is their care that the gear engages; it is their care that the switches lock.
It is their care that the wheels run truly; it is their care to embark and entrain,
Tally, transport, and deliver duly the Sons of Mary by land and main.
Kipling accepted the ask and wrote the Ritual of the Calling of an Engineer, which he sent to Haultain a month later. In his response to Haultain, he stated that he preferred the word “Obligation” to “Oath.” He wrote the Obligation using Old English lettering and the old-fashioned capitalization. Kipling’s Obligation binds engineers upon their “Honor and Cold Iron” to not “suffer or pass, or be privy to the passing of, Bad Workmanship or Faulty Material,” and pardon is asked “in the presence of my betters and my equals in my Calling” for the engineer’s “assured failures and derelictions.” The hope is that when one is tempted to shoddy work by weakness or weariness, the memory of the Obligation “and the company before whom it was entered into, may return to me to aid, comfort, and restrain.”
Using the Obligation, The Seven Wardens created an induction ceremony, which seeks to unify the profession and recognize engineering’s ethics, including responsibility to the public and the need to make the best decisions possible. The induction ceremony included recitation of Kipling’s “Obligation” and incorporated an anvil, a hammer, an iron chain, and an iron ring. The inductee engineers sat inside an area marked off by the iron chain, with their more senior colleagues outside that area. At the start of the ritual, the leader beat out S-S-T in Morse code with the hammer and anvil—the letters standing for Steel, Stone, and Time. A more experienced and previously obligated engineer placed the ring on the small finger of the inductee engineer’s working hand. As per Kipling, the ring’s rough, faceted texture symbolized “the young engineer’s mind” and the difficulties engineers face in mastering their discipline.
A persistent myth purports that the original iron rings were made from the beams or bolts of the Quebec Bridge that failed twice during construction.
The first induction ceremony took place on April 25, 1925, in Montreal to obligate two of the Seven Wardens, along with four graduates from the University of Toronto class of 1893. On May 1 of that year, 14 more engineers were obligated at the University of Toronto. From that time to today most Canadian professional engineers have gone through that same ritual in their various camps, called Kipling camps—local chapters associated with various Canadian universities.
Henry Petroski, Duke University’s professor of civil engineering and history, notes in his book, “Forgive Design: Understanding Failure,” that Kipling’s poem “Sons of Martha” is often read as part of the ritual. However, sometimes inductees read Kipling’s “Hymn of Breaking Strain,” instead, which graphically depicts disastrous outcomes of engineering mistakes. The first stanza of that poem says:
The careful text-books measure
(Let all who build beware!)
The load, the shock, the pressure
Material can bear.
So, when the buckled girder
Lets down the grinding span,
'The blame of loss, or murder,
Is laid upon the man.
Not on the Stuff—the Man!
As if to strengthen the importance of these concepts, a persistent myth purports that the original iron rings were made from the beams or bolts of the Quebec Bridge that failed twice during construction. The bridge spans the St. Lawrence River upriver from Quebec City, and at the time of its construction was the world’s longest at 1,800 feet. Due to engineering errors and poor oversight, the bridge’s own weight exceeded its carrying capacity. Moreover, engineers downplayed danger when bridge beams began to warp under stress, saying that they were probably warped before they were installed. On August 29, 1907, the bridge collapsed, killing 75 of 86 workers. A second collapse occurred in 1916 when lifting equipment failed, and thirteen more workers died.
The ring myth, however, couldn’t be true. The original iron rings couldn’t have come from the failed bridge since it was made of steel, not wrought iron. Today the rings are made from stainless steel because iron deteriorates and stains engineers’ finger black.
On August 14, 2018, Morandi Bridge over Polcevera River in Genoa, Italy, collapsed from structural failure, killing 43 people.
Adobe Stock
The Seven Wardens decided to restrict the ritual to engineers trained in Canada. They copyrighted the obligation oath in Canada and the United States in 1935. Although the ritual is not a requirement for professional licensing, just like the Hippocratic Oath is not part of medical licensing, it remains a long-standing tradition.
The American Obligation of the Engineer has its own creation story, albeit a very different one. The American Order of the Engineer (OOE) was initiated in 1970, during the era of the anti-war protests, Apollo missions and the first Earth Day. On May 4, 1970, the National Guard shot into a crowd of protesters at Kent State University, killing four people. The two authors of the American obligation—Cleveland State University’s (CSU) engineering professor John Janssen and his wife Susan—reflected these historical events in the oath they wrote. Their version of the oath binds engineers to “practice integrity and fair dealing.” It also notes that their “skill carries with it the obligation to serve humanity by making the best use of the Earth’s precious wealth.” As Petroski explains in his book, “campus antiwar protestors around the country tended to view engineers as complicit in weapons proliferation [which] prompted some [CSU] engineering student leaders to look for a means of asserting some more positive values.”
Kip A. Wedel, associate professor of history and politics at Bethel College, wrote in his book, “The Obligation: A History of the Order of the Engineer,” that the ceremony was not a direct response to the Kent State shootings—it was already scheduled when the shootings happened. Yet, engineering students found the ceremony a positive action they could take in contrast to the overall turmoil. The first American ritual took place on June 4, 1970, at CSU. In total, 170 students, faculty members, and practicing engineers took the obligation. This established CSU as the first Link of the Order, as the OOE designates its local chapters. For their first ceremony, the CSU students fabricated smooth, unfaceted rings from stainless steel pipe. Later they were replaced by factory-made rings. According to Paula Ostaff, OOE’s Executive Director, about 20,000 eligible students and alumni obligate themselves yearly.
Societies hope that every engineer is imbued with a strong ethical sense and that their pledges are never far from mind. For some, the rings they wear serve a daily reminder that every paper they sign off on is touched by a physical reminder of their commitment.
These ethical and responsible engineering practices are especially salient today, when one in three American bridges needs repair or replacement, some have already collapsed, and engineers are working on projects related to the bipartisan infrastructure bill President Biden signed into law in 2021. Canada has committed $33 billion to its Investing in Canada Infrastructure Program. At the heart of these grand projects are many thousands of professional engineers, collectively working millions of hours. The professional vows they took aim to assure that the homes, bridges and airplanes they build will work as expected.