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Cow, Bull, and the Meaning of AI Essays

Cow, Bull, and the Meaning of AI Essays

The future of west virginia politics is uncertain. The state has been trending Democratic for the last decade, but it’s still a swing state. Democrats are hoping to keep that trend going with Hillary Clinton in 2016. But Republicans have their own hopes and dreams too. They’re hoping to win back some seats in the House of Delegates, which they lost in 2012 when they didn’t run enough candidates against Democratic incumbents.

QED. This is, yes, my essay on the future of West Virginia politics. I hope you found it instructive.

The GoodAI is an artificial intelligence company that promises to write essays. Its content generator, which handcrafted my masterpiece, is supremely easy to use. On demand, and with just a few cues, it will whip up a potage of phonemes on any subject. I typed in “the future of West Virginia politics,” and asked for 750 words. It insolently gave me these 77 words. Not words. Frankenwords.

Ugh. The speculative, maddening, marvelous form of the essay—the try, or what Aldous Huxley called “a literary device for saying almost everything about almost anything”—is such a distinctly human form, with its chiaroscuro mix of thought and feeling. Clearly the machine can’t move “from the personal to the universal, from the abstract back to the concrete, from the objective datum to the inner experience,” as Huxley described the dynamics of the best essays. Could even the best AI simulate “inner experience” with any degree of verisimilitude? Might robots one day even have such a thing?

Before I saw the gibberish it produced, I regarded The Good AI with straight fear. After all, hints from the world of AI have been disquieting in the past few years

In early 2019, OpenAI, the research nonprofit backed by Elon Musk and Reid Hoffman, announced that its system, GPT-2, then trained on a data set of some 10 million articles from which it had presumably picked up some sense of literary organization and even flair, was ready to show off its textual deepfakes. But almost immediately, its ethicists recognized just how virtuoso these things were, and thus how subject to abuse by impersonators and blackhats spreading lies, and slammed it shut like Indiana Jones’s Ark of the Covenant. (Musk has long feared that refining AI is “summoning the demon.”) Other researchers mocked the company for its performative panic about its own extraordinary powers, and in November downplayed its earlier concerns and re-opened the Ark.

The Guardian tried the tech that first time, before it briefly went dark, assigning it an essay about why AI is harmless to humanity.

“I would happily sacrifice my existence for the sake of humankind,” the GPT-2 system wrote, in part, for The Guardian. “This, by the way, is a logically derived truth. I know that I will not be able to avoid destroying humankind. This is because I will be programmed by humans to pursue misguided human goals and humans make mistakes that may cause me to inflict casualties.”

Humans Can’t Be the Sole Keepers of Scientific Knowledge

Humans Can’t Be the Sole Keepers of Scientific Knowledge

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.

I Think an AI Is Flirting With Me. Is It OK If I Flirt Back?

I Think an AI Is Flirting With Me. Is It OK If I Flirt Back?

SUPPORT REQUEST :

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.

Faithfully, 
Cloud


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The World Needs Deepfake Experts to Stem This Chaos

The World Needs Deepfake Experts to Stem This Chaos

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.

AI Could Soon Write Code Based on Ordinary Language

AI Could Soon Write Code Based on Ordinary Language

In recent years, researchers have used artificial intelligence to improve translation between programming languages or automatically fix problems. The AI system DrRepair, for example, has been shown to solve most issues that spawn error messages. But some researchers dream of the day when AI can write programs based on simple descriptions from non-experts.

On Tuesday, Microsoft and OpenAI shared plans to bring GPT-3, one of the world’s most advanced models for generating text, to programming based on natural language descriptions. This is the first commercial application of GPT-3 undertaken since Microsoft invested $1 billion in OpenAI last year and gained exclusive licensing rights to GPT-3.

“If you can describe what you want to do in natural language, GPT-3 will generate a list of the most relevant formulas for you to choose from,” said Microsoft CEO Satya Nadella in a keynote address at the company’s Build developer conference. “The code writes itself.”

Courtesy of Microsoft

Microsoft VP Charles Lamanna told WIRED the sophistication offered by GPT-3 can help people tackle complex challenges and empower people with little coding experience. GPT-3 will translate natural language into PowerFx, a fairly simple programming language similar to Excel commands that Microsoft introduced in March.

This is the latest demonstration of applying AI to coding. Last year at Microsoft’s Build, OpenAI CEO Sam Altman demoed a language model fine-tuned with code from GitHub that automatically generates lines of Python code. As WIRED detailed last month, startups like SourceAI are also using GPT-3 to generate code. IBM last month showed how its Project CodeNet, with 14 million code samples from more than 50 programming languages, could reduce the time needed to update a program with millions of lines of Java code for an automotive company from one year to one month.

Microsoft’s new feature is based on a neural network architecture known as Transformer, used by big tech companies including Baidu, Google, Microsoft, Nvidia, and Salesforce to create large language models using text training data scraped from the web. These language models continually grow larger. The largest version of Google’s BERT, a language model released in 2018, had 340 million parameters, a building block of neural networks. GPT-3, which was released one year ago, has 175 billion parameters.

Such efforts have a long way to go, however. In one recent test, the best model succeeded only 14 percent of the time on introductory programming challenges compiled by a group of AI researchers.