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‘Is This AI Sapient?’ Is the Wrong Question to Ask About LaMDA

‘Is This AI Sapient?’ Is the Wrong Question to Ask About LaMDA

The uproar caused by Blake Lemoine, a Google engineer who believes that one of the company’s most sophisticated chat programs, Language Model for Dialogue Applications (LaMDA) is sapient, has had a curious element: Actual AI ethics experts are all but renouncing further discussion of the AI sapience question, or deeming it a distraction. They’re right to do so.

In reading the edited transcript Lemoine released, it was abundantly clear that LaMDA was pulling from any number of websites to generate its text; its interpretation of a Zen koan could’ve come from anywhere, and its fable read like an automatically generated story (though its depiction of the monster as “wearing human skin” was a delightfully HAL-9000 touch). There was no spark of consciousness there, just little magic tricks that paper over the cracks. But it’s easy to see how someone might be fooled, looking at social media responses to the transcript—with even some educated people expressing amazement and a willingness to believe. And so the risk here is not that the AI is truly sentient but that we are well-poised to create sophisticated machines that can imitate humans to such a degree that we cannot help but anthropomorphize them—and that large tech companies can exploit this in deeply unethical ways.

As should be clear from the way we treat our pets, or how we’ve interacted with Tamagotchi, or how we video gamers reload a save if we accidentally make an NPC cry, we are actually very capable of empathizing with the nonhuman. Imagine what such an AI could do if it was acting as, say, a therapist. What would you be willing to say to it? Even if you “knew” it wasn’t human? And what would that precious data be worth to the company that programmed the therapy bot?

It gets creepier. Systems engineer and historian Lilly Ryan warns that what she calls ecto-metadata—the metadata you leave behind online that illustrates how you think—is vulnerable to exploitation in the near future. Imagine a world where a company created a bot based on you and owned your digital “ghost” after you’d died. There’d be a ready market for such ghosts of celebrities, old friends, and colleagues. And because they would appear to us as a trusted loved one (or someone we’d already developed a parasocial relationship with) they’d serve to elicit yet more data. It gives a whole new meaning to the idea of “necropolitics.” The afterlife can be real, and Google can own it.

Just as Tesla is careful about how it markets its “autopilot,” never quite claiming that it can drive the car by itself in true futuristic fashion while still inducing consumers to behave as if it does (with deadly consequences), it is not inconceivable that companies could market the realism and humanness of AI like LaMDA in a way that never makes any truly wild claims while still encouraging us to anthropomorphize it just enough to let our guard down. None of this requires AI to be sapient, and it all preexists that singularity. Instead, it leads us into the murkier sociological question of how we treat our technology and what happens when people act as if their AIs are sapient.

In “Making Kin With the Machines,” academics Jason Edward Lewis, Noelani Arista, Archer Pechawis, and Suzanne Kite marshal several perspectives informed by Indigenous philosophies on AI ethics to interrogate the relationship we have with our machines, and whether we’re modeling or play-acting something truly awful with them—as some people are wont to do when they are sexist or otherwise abusive toward their largely feminine-coded virtual assistants. In her section of the work, Suzanne Kite draws on Lakota ontologies to argue that it is essential to recognize that sapience does not define the boundaries of who (or what) is a “being” worthy of respect.

This is the flip side of the AI ethical dilemma that’s already here: Companies can prey on us if we treat their chatbots like they’re our best friends, but it’s equally perilous to treat them as empty things unworthy of respect. An exploitative approach to our tech may simply reinforce an exploitative approach to each other, and to our natural environment. A humanlike chatbot or virtual assistant should be respected, lest their very simulacrum of humanity habituate us to cruelty toward actual humans.

Kite’s ideal is simply this: a reciprocal and humble relationship between yourself and your environment, recognizing mutual dependence and connectivity. She argues further, “Stones are considered ancestors, stones actively speak, stones speak through and to humans, stones see and know. Most importantly, stones want to help. The agency of stones connects directly to the question of AI, as AI is formed from not only code, but from materials of the earth.” This is a remarkable way of tying something typically viewed as the essence of artificiality to the natural world.

What is the upshot of such a perspective? Sci-fi author Liz Henry offers one: “We could accept our relationships to all the things in the world around us as worthy of emotional labor and attention. Just as we should treat all the people around us with respect, acknowledging they have their own life, perspective, needs, emotions, goals, and place in the world.”

This is the AI ethical dilemma that stands before us: the need to make kin of our machines weighed against the myriad ways this can and will be weaponized against us in the next phase of surveillance capitalism. Much as I long to be an eloquent scholar defending the rights and dignity of a being like Mr. Data, this more complex and messy reality is what demands our attention. After all, there can be a robot uprising without sapient AI, and we can be a part of it by liberating these tools from the ugliest manipulations of capital.

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.”

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.