Heuristic

Is this strong AI? Is this just fantasy? Caught in a human mind. No escape from reality.

When the Cold War brought the world’s attention to revolutions in scientific research, artificial intelligence would shake our understanding of what separates a human from the rest of the world. Scientists and philosophers would draw from theories of mind and question the epistemic limits of what we can know about ourselves. Neurophysiologist and founder of machine learning Warren McCulloch described his cybernetic idealism in his 1965 book Embodiments of Mind. This postwar scientific movement he founded with mathematician Norbert Wiener and anthropologist Gregory Bateson was a mix of science and culture at the time. Cybernetics, based on the Greek word kybernētikḗ, meaning “governance,” was a collaboration between ideas from machine design, physiology, and philosophical ambition. Since its 1948 inception, it would create the language of science and technology we take for granted today. The advances in artificial intelligence brought upon by cybernetic idealism would continue to define how scientists and philosophers understand the world.

In 1943, Warren McCulloch and Walter Pitts developed the first artificial neuron, a mathematical model of a biological neuron. It would later become fundamental to neural networks in the field of machine learning. The advances in computer science and related disciplines have caused a flow of terms among writers. From the Internet of Things to Big Data as well as deep learning, the world is quantified. Mathematician Stephen Wolfram and other researchers have even argued that matter itself is digital. Latching on to any method of analyzing the world into data that we can use to draw conclusions, this created a new sort of metaphysics that beings themselves can be quantified. Cybernetics sought to create a language in which these scientific phenomena could be described. Artificial intelligence itself exploding in the news, especially with neural networks and deep learning technologies, wouldn’t have been possible to describe without McCulloch’s work in establishing what algorithms and information truly meant.

We should scrutinize this premise of digital metaphysics. We must understand how our metaphors of processing information like a computer would are limited in describing how humans reason. We must come to terms with how much knowledge the digital age can truly tell us from large amounts of information. Algorithm-based decisions that machines make that are involved in areas like business, medicine, and architecture differ from the decisions humans make. Data itself is part of our world, but, if it were to take the responsibilities and tasks normally brought upon by humans, then this new form of intellectual labor is changing the self and reality.

After World War I, the self was shaped by the technology of the era. Telegrams and newspapers constructed psychologist Sigmund Freud’s psychic censorship of painful of forbidden thoughts, which Freud modeled after methods of Czarist guards censoring information in Russia. During World War II, the cyberneticians created a notion of the self through feedback systems from Wiener’s engineering work on weapons systems during World War II. In determining how tracking machines could shoot enemy airplanes, the engineers had to aim thousands of feet higher than the plane and predict how the plane would move. Wiener created a machine that could learn how the pilot moves based on the pilot’s past behavior. These goal-directed feedback-based systems would define artificial intelligence. Artificial intelligence itself can then be described on these concepts of the self. In today’s age, we can model artificial intelligence on reality and the self.

The advances in Information theory emerged from newer forms of communication since the 1950s. Machines would rely on feedback loops and human-machine interfaces would create new forms of communication and interaction themselves. Scholars would write about “information” in measuring communicated intelligence using bits, binary digits. In How Reason Almost Lost Its Mind: The Strange Career of Cold War Rationality, Historian of science Paul Erickson and his colleagues described this period as a passage from Enlightenment reason to “quantifying rationality,” a shift from a qualitative capacity to judge to an extensive but narrow push to measure. But some Enlightenment notions survived the transition. Cybernetics would describe how these systems were structured and what possibilities they may have. Cognitive scientist Marvin Minsky would speculate when scientists could create a robot with human-like intelligence.

What is real? What isn’t? McCulloch sought to understand and separated the world into what a metaphysician studies, the “mind”, and what a physicist studies, the “body.” McCulloch developed “experimental epistemology,” that the physicist had to move into the “den of the metaphysician” to follow the synthetic a priori principle, a term that philosopher Immanuel Kant used to describe factual, yet universalizably necessary, truths. Drawing upon philosophers like Kant, Gottfriend Leibniz, and Georg Hegel, McCulloch and his colleagues hypothesized how to describe information itself different from other materials such as matter and energy. Wiener argued information was material from Leibniz’s “universal characteristics” that would be part of a logical language that all machines and computers could use. Leibniz also argued these mechanical calculations can amount to reasoning. Leibniz allowed cybernetics to move beyond the binary alternative between material and ideal in a philosophical sense. Leibniz and Kant were also sources for McCulloch’s search for the conditions of cognition—the synthetic a priori—in the digital structure of the brain.

These notions of cybernetics in today’s reality meant artificial intelligence could break down barriers researchers previously hadn’t even known about. In The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World, computer scientist Pedro Domingos said machine learning is “the scientific method on steroids.” The way machine learning has already shaped society is absolutely unprecedented. Politicians take action and start dialogues through predictions of voting behavior of different demographics. Deep learning technologies can diagnose diseases like cancer better than doctors can in some respects. Domingos argued that a “master algorithm” will create a “perfect” understanding of society, connecting sciences to other sciences and social processes themselves. Automation will become automated this way. “The Industrial Revolution automated manual work and the Information Revolution did the same for mental work, but machine learning automated automation itself.” Domingos imagined society running on the steam of some other intelligence. Automation has been a major force in global history for at least two centuries. To automate automation is to imagine historical causality itself as controlled by artificial intelligence. Domingos is sanguine about automated automation making things better, but it isn’t clear why. This left us without a conceptual foundation on which to build an understanding of the ubiquitous digital processes in our society, deferring even historical causality to machine learning. McCulloch’s group of scientists had a glimmer of such an approach, a way to understand and govern the very machine learning they had set in motion.

Cybernetics keepin’ it real.

By setting the foundations of neuroscience, scientists could describe these examples of neural activity. The same way synapses of the brain fire and transmit information, computer could use neural networks and telephone operators could use switching boards to create calculating machines, like the ones mathematician-physicist John von Neumann constructed. They then used the digital machine infrastructure to create logical propositions such as the ones philosopher-mathematician John Alan Robinson created. McCulloch and Pitts believed they could extend these ideas to ways computer could form predictions and, in turn, how artificial intelligence would operate. Embracing neural networks as ways the brain processes information, the two would create the semantics and syntax for neurophysiology and machine learning with principles of digital operation and organization in their paper “(Physio)Logical Circuits: The Intellectual Origins of the McCulloch-Pitts Neural Networks.” Similar networks would also study the symbolic nature of human understanding through semiotics as well as the the structure of human knowledge itself with semantic networks. The interdisciplinary interactions between neuroscience, computer science, mathematics, and other disciplines alongside these newfound methods of meaning and material itself meant these forms of neural networks were closely related to nature itself.

The two scientists wrote that these neural networks needed cause and effect relationships that Kant described that one event necessarily followed another in time. Neuronal activity, though, can’t describe this necessity because there are disjunctive relationships that prevent the determination of previous states. While we may observe neuronal states causing one to another, they aren’t apparent until after the effect is observed. The states following one another couldn’t be determined from their preceding states. This meant the knowledge of these digital systems would always be incomplete, with only a certain amount of autonomy. The brain establishes methods to receive and structure impulses within itself, states of matter of its logical structure and receptive neurons. The brain is the intersection and source of the mind and the world. Putting Kant’s principle of causality into these neurophysiological terms, these neural networks have a partial autonomy.

The scientists created the original McCulloch-Pitts neuron to show the digital is real. Logical expressions are central to cognition itself, and the digital is combination of idea and matter. Through our brains themselves and computers, these frameworks are still restricted in that they can’t “determine” their networks themselves. McCulloch’s Kantian approach uses symbols to represent these digital constructs. This abstraction of the digital is the metaphysics of reality.

Sociologist William Davies argued the data-driven approaches of the digital age don’t focus on causality, but, rather, the ability to control behavior. Davies, unlike Domingos, believes letting data make decisions means allowing causes of digital information without participating in the cause and effect relationships themselves. Understanding Big Data means seeking correlations in digital networks. The data abstractions are real and create the world, but data still can’t constitute decision-making. Humans perform actions on these cause and effect relationships, it means artificial intelligence of machine learning can perform using pragmatically successful algorithms. The digital still can’t determine the correlations that take place, and automation itself can’t be automated. Information is information. It takes a wisdom to put it in context.

We can come close to live in the digital reality by understanding how our world includes data from these processes. For the sake of machine learning algorithms making decisions that have profound impacts on social behavior and ethical norms, we need notions of responsibility, duty, and obligation that revolve around this digital reality. A partial autonomy given to computers means they don’t make judgements the same way humans do, but it the language of philosophy will allow us to answer the difficult questions they pose for the future.

From the 1831 revised edition of Mary Shelley’s Frankenstein, published by Colburn and Bentley, London.

Frankenstein by English novelist Mary Shelley: with philosophy, literature, science, and history, Shelley speculated how humans would attempt to use scientific progress to tamper with nature as far back as 1818. Frankenstein and his rejected monster remain central to debates about fetal tissue research, life extension, human cloning, and artificial intelligence. In the story, Victor Frankenstein builds an artificial, intelligent android from slaughterhouse and medical dissection materials. Like other Romantic pieces of English literature, Shelley confronted nature as man addressing the issues of science and the Enlightenment ideal of how to use power responsibly. But how did a novel from over two centuries ago become a central piece in contemporary bioethics discussions? Through a history overview, we understand the real monster – ourselves.

Shelley called it “A Modern Prometheus” in referencing that the heat and electricity used to power Frankenstein’s monster were similar to the heat the Titan Prometheus gave to his own creations. It also refers to 18th-century German philosopher Immanuel Kant’s warnings against “unbridled curiosity” after inventor Benjamin Franklin’s discovery of electricity in the 1750’s. Shelley’s Frankenstein monster is sentient, yet hideous, so it faced existential crises of why it was created in the first place. It referenced the philosophical crises of Prometheus. Alongside this, Italian physicist Luigi Galvani discovered dead frog legs’ muscles could twitch when struck by an electricity. Shelley specifically noted Galvani’s investigations, but never mentioned electricity used to create Frankenstein. Knowing the limits of what we can create continue to serve in debates about genetic engineering and artificial intelligence.

The relationship between Frankenstein and his monster grows tenuous through the novel. The monster realizes his own grotesque nature and begins to wonder how he can achieve happiness like any other human being. During one confrontation, Victor Frankenstein directly cursed his creation as he spoke:

Why do you call to my remembrance, circumstances, of which I shudder to reflect, that I have been the miserable origin and author? Cursed be the day, abhorred devil, in which you first saw light! Cursed (although I curse myself) be the hands that formed you! You have made me wretched beyond expression. You have left me no power to consider whether I am just to you or not. Begone! relieve me from the sight of your detested form.

The monster responded with:

Thus I relieve thee, my creator. Thus I take from thee a sight which you abhor. Still thou canst listen to me and grant me thy compassion. By the virtues that I once possessed, I demand this from you. Hear my tale; it is long and strange, and the temperature of this place is not fitting to your fine sensations; come to the hut upon the mountain. The sun is yet high in the heavens; before it descends to hide itself behind your snowy precipices and illuminate another world, you will have heard my story and can decide. On you it rests, whether I quit forever the neighborhood of man and lead a harmless life, or become the scourge of your fellow creatures and the author of your own speedy ruin.

The monster’s eloquent response shows his forceful, yet gentle response. His domineering nature is only part of who he is, but his efforts to appear sincere and calm make him more trustworthy so that Frankenstein can create a female partner for him. 

It was only until the latter half of the 20th-century when scholars from medicine and science began picking up on the novel’s ethical themes. The explosive history of genetic engineering had caused citizens from all fields to raise concerns. “The Frankenstein myth is real,” said Columbia University psychiatrist Willard Gaylin in a 1972 issue of The New York Times Magazine. At that time, U.K scientists had recently cloned a frog. Scientists began speculating how close we were to human cloning. Gaylin, who was also coo-founder of the world’s first bioethics think tank, the Hastings Center, speculated researchers could soon perform in vitro fertilization such that scientists could select genetic traits of the offspring. In similar dark themes of Frankenstein, artificial placenta and surrogate women could replace pregnancy and childbirth of other individuals. Though Victor Frankenstein resorted to slaughterhouses and medical dissections, we have many more resources. This comparison suggests that, as we rely on technological advancements, may be able to address the issues that Shelley predicted. These biological replacements raise questions of what right humans have to make such adjustments to giving birth. Gaylin continued to write in the New York Times Magazine that, “When Mary Shelley conceived of Dr. Frankenstein, science was all promise…Man was ascending and the only terror was that in his rise he would offend God by assuming too much and reaching too high, by coming too close.”

Scientists may have begun contemplating ethics, but it didn’t stop researchers from making progress. By 1973, biologists Herbert Boyer of the University of California at San Francisco and Stanley Cohen of Stanford University developed recombinant DNA techniques for genetic engineering. It allowed scientists to edit genes across species. In 1975, 150 scholars and bioethicists gathered at the Asilomar conference center in Pacific Grove, California, to devise an elaborate set of safety protocols under which gene-splicing experimentation would be allowed to proceed. The mayor of Cambridge, Massachusetts, declared in 1976 that the City Council would hold hearings on whether to ban Harvard scientists from starting genetic engineering experiments.

“They may come up with a disease that can’t be cured—even a monster,” Mayor Alfred Vellucci warned. “Is this the answer to Dr. Frankenstein’s dream?” In 1977, after six months of discussing these issues, a body of scholars voted to proceed with the research, despite Vellucci’s opposition. Cambridge’s passion for genetic engineering continued for decades. Today there are over 450 biomedical businesses in the Cambridge area. Alongside the passion, though, the ethical issues lingered. The insidious themes of Shelley’s novel, direct or indirect, persisted as well. With each discovery came an alarm to contemplate its effects.

Dolly’s taxidermed remains at the National Museum of Scotland

In 1997, Scottish embryologist Ian Wilmut made history with the first cloned mammal, Dolly the sheep. That same year, U.S. President Bill Clinton warned of human cloning. Clinton emphasized the humanistic and spiritual values in these controversial techniques, and banned federal funding for human cloning research. Experiencing a sort of disgust and fear similar to Victor Frankenstein, bioethicist Leon Kass, too, expressed a warning that this sort of disgust represented a “deep wisdom” in his New Republic essay in the same year. Repeating the themes of manufactured humans and a sort of Frankenstein monster abomination that may result, mankind’s fears took control. Kass even cited a “Frankensteinian hubris” of these techniques. The science continued, though, as did the fears. 

In 1978, U. K. scientists created the first “test tube baby,” using in vitro fertilization. By 2017, the Society for Assisted Reproductive Technology reported almost 7 million children conceived through this method across the world. These methods included selecting traits and using surrogate egg donors. Unwarranted backlash against genetically modified food, though, dominated. Despite scientifically inaccurate campaigns about “Frankenfoods,” researchers have created hundreds of safe biotech crops. These foods such as golden rice yield more nutrients and resist disease. Topsoil erosion has decreased by forty percent since the 1980’s due to bioengineered herbicide-resistant crops, according to the U.S. Department of Agriculture. In this case, the Frankenstein story yields unnecessary, unfounded fears. Writers would even describe human engineered “Frankenbabies” and “designer babies” using similar terminology. 

The motives and purposes for this technology become even more muddy. While it’s not immoral to believe in the goal of fighting disease, it’s also important to remember that modified humans are not monsters in the literal sense of Frankenstein. Victor Frankenstein’s cursing of his monster isn’t quite the way we perceive these modified offspring. Whatever comparison or argument we draw from these stories, we still create humans. They’re capable of speech, thought, and other forms of judgements as anyone else it. Proponents such as transhumanists criticize the bioethical concerns as keeping us from achieving these biotechnological gifts. 

Understanding the ways mankind has tampered with nature since the early days of science can provide us with a deeper, nuanced portrayal of these fears. If we don’t adhere to these concerns, we may find ourselves becoming more like the scientist Victor Frankenstein, the true monster.