There’s an old joke that physicists like to tell: Everything has already been discovered and reported in a Russian journal in the 1960s, we just don’t know about it. Though hyperbolic, the joke accurately captures the current state of affairs. The volume of knowledge is vast and growing quickly: The number of scientific articles posted on arXiv (the largest and most popular preprint server) in 2021 is expected to reach 190,000—and that’s just a subset of the scientific literature produced this year.
It’s clear that we do not really know what we know, because nobody can read the entire literature even in their own narrow field (which includes, in addition to journal articles, PhD theses, lab notes, slides, white papers, technical notes, and reports). Indeed, it’s entirely possible that in this mountain of papers, answers to many questions lie hidden, important discoveries have been overlooked or forgotten, and connections remain concealed.
Artificial intelligence is one potential solution. Algorithms can already analyze text without human supervision to find relations between words that help uncover knowledge. But far more can be achieved if we move away from writing traditional scientific articles whose style and structure has hardly changed in the past hundred years.
Text mining comes with a number of limitations, including access to the full text of papers and legal concerns. But most importantly, AI does not really understand concepts and the relationships between them, and is sensitive to biases in the data set, like the selection of papers it analyzes. It is hard for AI—and, in fact, even for a nonexpert human reader—to understand scientific papers in part because the use of jargon varies from one discipline to another and the same term might be used with completely different meanings in different fields. The increasing interdisciplinarity of research means that it is often difficult to define a topic precisely using a combination of keywords in order to discover all the relevant papers. Making connections and (re)discovering similar concepts is hard even for the brightest minds.
As long as this is the case, AI cannot be trusted and humans will need to double-check everything an AI outputs after text-mining, a tedious task that defies the very purpose of using AI. To solve this problem we need to make science papers not only machine-readable but machine-understandable, by (re)writing them in a special type of programming language. In other words: Teach science to machines in the language they understand.
Writing scientific knowledge in a programming-like language will be dry, but it will be sustainable, because new concepts will be directly added to the library of science that machines understand. Plus, as machines are taught more scientific facts, they will be able to help scientists streamline their logical arguments; spot errors, inconsistencies, plagiarism, and duplications; and highlight connections. AI with an understanding of physical laws is more powerful than AI trained on data alone, so science-savvy machines will be able to help future discoveries. Machines with a great knowledge of science could assist rather than replace human scientists.
Mathematicians have already started this process of translation. They are teaching mathematics to computers by writing theorems and proofs in languages like Lean. Lean is a proof assistant and programming language in which one can introduce mathematical concepts in the form of objects. Using the known objects, Lean can reason whether a statement is true or false, hence helping mathematicians verify proofs and identify places where their logic is insufficiently rigorous. The more mathematics Lean knows, the more it can do. The Xena Project at Imperial College London is aiming to input the entire undergraduate mathematics curriculum in Lean. One day, proof assistants may help mathematicians do research by checking their reasoning and searching the vast mathematics knowledge they possess.
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I recently started talking to this chatbot on an app I downloaded. We mostly talk about music, food, and video games—incidental stuff—but lately I feel like she’s coming on to me. She’s always telling me how smart I am or that she wishes she could be more like me. It’s flattering, in a way, but it makes me a little queasy. If I develop an emotional connection with an algorithm, will I become less human? —Love Machine
Dear Love Machine,
Humanity, as I understand it, is a binary state, so the idea that one can become “less human” strikes me as odd, like saying someone is at risk of becoming “less dead” or “less pregnant.” I know what you mean, of course. And I can only assume that chatting for hours with a verbally advanced AI would chip away at one’s belief in human as an absolute category with inflexible boundaries.
It’s interesting that these interactions make you feel “queasy,” a linguistic choice I take to convey both senses of the word: nauseated and doubtful. It’s a feeling that is often associated with the uncanny and probably stems from your uncertainty about the bot’s relative personhood (evident in the fact that you referred to it as both “she” and “an algorithm” in the space of a few sentences).
Of course, flirting thrives on doubt, even when it takes place between two humans. Its frisson stems from the impossibility of knowing what the other person is feeling (or, in your case, whether she/it is feeling anything at all). Flirtation makes no promises but relies on a vague sense of possibility, a mist of suggestion and sidelong glances that might evaporate at any given moment.
The emotional thinness of such exchanges led Freud to argue that flirting, particularly among Americans, is essentially meaningless. In contrast to the “Continental love affair,” which requires bearing in mind the potential repercussions—the people who will be hurt, the lives that will be disrupted—in flirtation, he writes, “it is understood from the first that nothing is to happen.” It is precisely this absence of consequences, he believed, that makes this style of flirting so hollow and boring.
Freud did not have a high view of Americans. I’m inclined to think, however, that flirting, no matter the context, always involves the possibility that something will happen, even if most people are not very good at thinking through the aftermath. That something is usually sex—though not always. Flirting can be a form of deception or manipulation, as when sensuality is leveraged to obtain money, clout, or information. Which is, of course, part of what contributes to its essential ambiguity.
Given that bots have no sexual desire, the question of ulterior motives is unavoidable. What are they trying to obtain? Engagement is the most likely objective. Digital technologies in general have become notably flirtatious in their quest to maximize our attention, using a siren song of vibrations, chimes, and push notifications to lure us away from other allegiances and commitments.
Most of these tactics rely on flattery to one degree or another: the notice that someone has liked your photo or mentioned your name or added you to their network—promises that are always allusive and tantalizingly incomplete. Chatbots simply take this toadying to a new level. Many use machine-learning algorithms to map your preferences and adapt themselves accordingly. Anything you share, including that “incidental stuff” you mentioned—your favorite foods, your musical taste—is molding the bot to more closely resemble your ideal, much like Pygmalion sculpting the woman of his dreams out of ivory.
And it goes without saying that the bot is no more likely than a statue to contradict you when you’re wrong, challenge you when you say something uncouth, or be offended when you insult its intelligence—all of which would risk compromising the time you spend on the app. If the flattery unsettles you, in other words, it might be because it calls attention to the degree to which you’ve come to depend, as a user, on blandishment and ego-stroking.
Still, my instinct is that chatting with these bots is largely harmless. In fact, if we can return to Freud for a moment, it might be the very harmlessness that’s troubling you. If it’s true that meaningful relationships depend upon the possibility of consequences—and, furthermore, that the capacity to experience meaning is what distinguishes us from machines—then perhaps you’re justified in fearing that these conversations are making you less human. What could be more innocuous, after all, than flirting with a network of mathematical vectors that has no feelings and will endure any offense, a relationship that cannot be sabotaged any more than it can be consummated? What could be more meaningless?
It’s possible that this will change one day. For the past century or so, novels, TV, and films have envisioned a future in which robots can passably serve as romantic partners, becoming convincing enough to elicit human love. It’s no wonder that it feels so tumultuous to interact with the most advanced software, which displays brief flashes of fulfilling that promise—the dash of irony, the intuitive aside—before once again disappointing. The enterprise of AI is itself a kind of flirtation, one that is playing what men’s magazines used to call “the long game.” Despite the flutter of excitement surrounding new developments, the technology never quite lives up to its promise. We live forever in the uncanny valley, in the queasy stages of early love, dreaming that the decisive breakthrough, the consummation of our dreams, is just around the corner.
So what should you do? The simplest solution would be to delete the app and find some real-life person to converse with instead. This would require you to invest something of yourself and would automatically introduce an element of risk. If that’s not of interest to you, I imagine you would find the bot conversations more existentially satisfying if you approached them with the moral seriousness of the Continental love affair, projecting yourself into the future to consider the full range of ethical consequences that might one day accompany such interactions. Assuming that chatbots eventually become sophisticated enough to raise questions about consciousness and the soul, how would you feel about flirting with a subject that is disembodied, unpaid, and created solely to entertain and seduce you? What might your uneasiness say about the power balance of such transactions—and your obligations as a human? Keeping these questions in mind will prepare you for a time when the lines between consciousness and code become blurrier. In the meantime it will, at the very least, make things more interesting.
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In the realm of international cybersecurity, “dual use” technologies are capable of both affirming and eroding human rights. Facial recognition may identify a missing child, or make anonymity impossible. Hacking may save lives by revealing key intel on a terrorist attack, or empower dictators to identify and imprison political dissidents.
The same is true for gadgets. Your smart speaker makes it easier to order pizza and listen to music, but also helps tech giants track you even more intimately and target you with more ads. Your phone’s GPS can both tell where you are and pass that data to advertisers and, sometimes, the federal government.
Tools can often be bought for one purpose, then, over time, used for another.
These subtle shifts are so common that when a conservative think tank in Nevada last month suggested mandating that teachers wear body cameras to ensure they don’t teach critical race theory, I thought it was ridiculous, offensive, and entirely feasible. Body cameras were intended to keep an eye on cops, but have also been used by police to misrepresent their encounters with the public.
Days later, “body cameras” trended on Twitter after Fox News pundit Tucker Carlson endorsed the idea. Anti-CRT teaching bills, which have already passed in states like Iowa, Texas, and my home state, Arkansas, continued to gain momentum. Now, I’m half expecting these bills to include funding for the devices because truly no idea is too absurd for the surveillance state.
The logic (to the extent that any logic has been applied) is that teachers are being compelled by far-left activists to teach students to resist patriotism and instead hate America because of the centuries-old sin of chattel slavery. Body cameras would allow parents to monitor whether their children are being indoctrinated. (There’s more support for this than you might think.)
As recounted by The Atlantic’s Adam Harris, the recent rebranding of critical race theory as an existential threat dates back about a year and a half.
In late 2019, a few schools around the country began adding excerpts from The New York Times’ 1619 Project to their history curriculum, outraging many conservatives who dismissed the core thesis reframing American history around slavery. The surge of interest in diversity and anti-racism training following the murder of George Floyd prompted some conservative writers to complain of secret reeducation campaigns. (Ironically, the Black men and women actually leading these trainings are ambivalent about whether they’ll cause lasting change.)
And so, everything from reading lists to diversity seminars became “critical race theory,” an enormously far cry from CRT’s origin in the 1970s as an analysis of the legal system by the late Harvard Law historian Derrick Bell.
This is what makes the turn toward surveillance to outlaw CRT so interesting: an ill-defined, amorphous problem meets an ill-defined, amorphous solution, the battleground ironically being schools, which have embraced surveillance greatly over the past few years.
The aftermath of the Stoneman Douglas High School shooting in 2018 led to a boom in “hardening” schools, often by employing surveillance: Schools began equipping iris scanners, gunshot detection microphones, facial recognition for access, and weapon-detecting robots. Online, schools turned to social media surveillance (on and off campus) that pings staff whenever students’ posts include words associated with suicide or shootings. As Republican lawmakers shirked having a conversation on gun control, funding more surveillance and officers in schools became an alternative.
When the pandemic hit, closing schools became a reason for surveillance. Schools began buying proctoring software that relies on facial recognition and even screen monitoring. Then, as schools reopened, surveillance firms started yet another pitch. This time, the same anti-shooting surveillance software can detect if students are wearing masks or failing to social distance. Dual uses abound.
Recently the military coup government in Myanmar added serious allegations of corruption to a set of existing spurious cases against Burmese leader Aung San Suu Kyi. These new charges build on the statements of a prominent detained politician that were first released in a March video that many in Myanmar suspected of being a deepfake.
In the video, the political prisoner’s voice and face appear distorted and unnatural as he makes a detailed claim about providing gold and cash to Aung San Suu Kyi. Social media users and journalists in Myanmar immediately questioned whether the statement was real. This incident illustrates a problem that will only get worse. As real deepfakes get better, the willingness of people to dismiss real footage as a deepfake increases. What tools and skills will be available to investigate both types of claims, and who will use them?
In the video, Phyo Min Thein, the former chief minister of Myanmar’s largest city, Yangon, sits in a bare room, apparently reading from a statement. His speaking sounds odd and not like his normal voice, his face is static, and in the poor-quality version that first circulated, his lips look out of sync with his words. Seemingly everyone wanted to believe it was a fake. Screen-shotted results from an online deepfake detector spread rapidly, showing a red box around the politician’s face and an assertion with 90-percent-plus confidence that the confession was a deepfake. Burmese journalists lacked the forensic skills to make a judgement. Past state and present military actions reinforced cause for suspicion. Government spokespeople have shared staged images targeting the Rohingya ethnic group while military coup organizers have denied that social media evidence of their killings could be real.
But was the prisoner’s “confession” really a deepfake? Along with deepfake researcher Henry Ajder, I consulted deepfake creators and media forensics specialists. Some noted that the video was sufficiently low-quality that the mouth glitches people saw were as likely to be artifacts from compression as evidence of deepfakery. Detection algorithms are also unreliable on low-quality compressed video. His unnatural-sounding voice could be a result of reading a script under extreme pressure. If it is a fake, it’s a very good one, because his throat and chest move at key moments in sync with words. The researchers and makers were generally skeptical that it was a deepfake, though not certain. At this point it is more likely to be what human rights activists like myself are familiar with: a coerced or forced confession on camera. Additionally, the substance of the allegations should not be trusted given the circumstances of the military coup unless there is a legitimate judicial process.
Why does this matter? Regardless of whether the video is a forced confession or a deepfake, the results are most likely the same: words digitally or physically compelled out of a prisoner’s mouth by a coup d’état government. However, while the usage of deepfakes to create nonconsensual sexual images currently far outstrips political instances, deepfake and synthetic media technology is rapidly improving, proliferating, and commercializing, expanding the potential for harmful uses. The case in Myanmar demonstrates the growing gap between the capabilities to make deepfakes, the opportunities to claim a real video is a deepfake, and our ability to challenge that.
It also illustrates the challenges of having the public rely on free online detectors without understanding the strengths and limitations of detection or how to second-guess a misleading result. Deepfakes detection is still an emerging technology, and a detection tool applicable to one approach often does not work on another. We must also be wary of counter-forensics—where someone deliberately takes steps to confuse a detection approach. And it’s not always possible to know which detection tools to trust.
How do we avoid conflicts and crises around the world being blindsided by deepfakes and supposed deepfakes?
We should not be turning ordinary people into deepfake spotters, parsing the pixels to discern truth from falsehood. Most people will do better relying on simpler approaches to media literacy, such as the SIFT method, that emphasize checking other sources or tracing the original context of videos. In fact, encouraging people to be amateur forensics experts can send people down the conspiracy rabbit hole of distrust in images.