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Inside effective altruism, where the far future counts a lot more more the present

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Inside effective altruism, where the far future counts a lot more more the present


Longtermism sees history differently: as a forward march toward inevitable progress. MacAskill references the past often in What We Owe the Future, but only in the form of case studies on the life-­improving impact of technological and moral development. He discusses the abolition of slavery, the Industrial Revolution, and the women’s rights movement as evidence of how important it is to continue humanity’s arc of progress before the wrong values get “locked in” by despots. What are the “right” values? MacAskill has a coy approach to articulating them: he argues that “we should focus on promoting more abstract or general moral principles” to ensure that “moral changes stay relevant and robustly positive into the future.” 

Worldwide and ongoing climate change, which already affects the under-resourced more than the elite today, is notably not a core longtermist cause, as philosopher Emile P. Torres points out in his critiques. While it poses a threat to millions of lives, longtermists argue, it probably won’t wipe out all of humanity; those with the wealth and means to survive can carry on fulfilling our species’ potential. Tech billionaires like Thiel and Larry Page already have plans and real estate in place to ride out a climate apocalypse. (MacAskill, in his new book, names climate change as a serious worry for those alive today, but he considers it an existential threat only in the “extreme” form where agriculture won’t survive.)

“To come to the conclusion that in order to do the most good in the world you have to work on artificial general intelligence is very strange.”

Timnit Gebru

The final mysterious feature of EA’s version of the long view is how its logic ends up in a specific list of technology-based far-off threats to civilization that just happen to align with many of the original EA cohort’s areas of research. “I am a researcher in the field of AI,” says Gebru, “but to come to the conclusion that in order to do the most good in the world you have to work on artificial general intelligence is very strange. It’s like trying to justify the fact that you want to think about the science fiction scenario and you don’t want to think about real people, the real world, and current structural issues. You want to justify how you want to pull billions of dollars into that while people are starving.”

Some EA leaders seem aware that criticism and change are key to expanding the community and strengthening its impact. MacAskill and others have made it explicit that their calculations are estimates (“These are our best guesses,” MacAskill offered on a 2020 podcast episode) and said they’re eager to improve through critical discourse. Both GiveWell and CEA have pages on their websites titled “Our Mistakes,” and in June, CEA ran a contest inviting critiques on the EA forum; the Future Fund has launched prizes up to $1.5 million for critical perspectives on AI.

“We recognize that the problems EA is trying to address are really, really big and we don’t have a hope of solving them with only a small segment of people,” GiveWell board member and CEA community liaison Julia Wise says of EA’s diversity statistics. “We need the talents that lots of different kinds of people can bring to address these worldwide problems.” Wise also spoke on the topic at the 2020 EA Global Conference, and she actively discusses inclusion and community power dynamics on the CEA forum. The Center for Effective Altruism supports a mentorship program for women and nonbinary people (founded, incidentally, by Carrick Flynn’s wife) that Wise says is expanding to other underrepresented groups in the EA community, and CEA has made an effort to facilitate conferences in more locations worldwide to welcome a more geographically diverse group. But these efforts appear to be limited in scope and impact; CEA’s public-facing page on diversity and inclusion hasn’t even been updated since 2020. As the tech-utopian tenets of longtermism take a front seat in EA’s rocket ship and a few billionaire donors chart its path into the future, it may be too late to alter the DNA of the movement.

Politics and the future

Despite the sci-fi sheen, effective altruism today is a conservative project, consolidating decision-making behind a technocratic belief system and a small set of individuals, potentially at the expense of local and intersectional visions for the future. But EA’s community and successes were built around clear methodologies that may not transfer into the more nuanced political arena that some EA leaders and a few big donors are pushing toward. According to Wise, the community at large is still split on politics as an approach to pursuing EA’s goals, with some dissenters believing politics is too polarized a space for effective change. 

But EA is not the only charitable movement looking to political action to reshape the world; the philanthropic field generally has been moving into politics for greater impact. “We have an existential political crisis that philanthropy has to deal with. Otherwise, a lot of its other goals are going to be hard to achieve,” says Inside Philanthropy’s Callahan, using a definition of “existential” that differs from MacAskill’s. But while EA may offer a clear rubric for determining how to give charitably, the political arena presents a messier challenge. “There’s no easy metric for how to gain political power or shift politics,” he says. “And Sam Bankman-Fried has so far demonstrated himself not the most effective political giver.” 

Bankman-Fried has articulated his own political giving as “more policy than politics,” and has donated primarily to Democrats through his short-lived Protect Our Future PAC (which backed Carrick Flynn in Oregon) and the Guarding Against Pandemics PAC (which is run by his brother Gabe and publishes a cross-party list of its “champions” to support). Ryan Salame, the co-CEO with Bankman-Fried of FTX, funded his own PAC, American Dream Federal Action, which focuses mainly on Republican candidates. (Bankman-Fried has said Salame shares his passion for preventing pandemics.) Guarding Against Pandemics and the Open Philanthropy Action Fund (Open Philanthropy’s political arm) spent more than $18 million to get an initiative on the California state ballot this fall to fund pandemic research and action through a new tax.

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The Download: generative AI for video, and detecting AI text

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The original startup behind Stable Diffusion has launched a generative AI for video


The original startup behind Stable Diffusion has launched a generative AI for video

What’s happened: Runway, the generative AI startup that co-created last year’s breakout text-to-image model Stable Diffusion, has released an AI model that can transform existing videos into new ones by applying styles from a text prompt or reference image.

What it does: In a demo reel posted on its website, Runway shows how the model, called Gen-1, can turn people on a street into claymation puppets, and books stacked on a table into a cityscape at night. Other recent text-to-video models can generate very short video clips from scratch, but because Gen-1adapts existing footage it can produce much longer videos.

Why it matters: Last year’s explosion in generative AI was fueled by the millions of people who got their hands on powerful creative tools for the first time and shared what they made, and Runway hopes Gen-1 will have a similar effect on generated videos. Read the full story.

—Will Douglas Heaven

Why detecting AI-generated text is so difficult (and what to do about it)

Last week, OpenAI unveiled a tool that can detect text produced by its AI system ChatGPT. But if you’re a teacher who fears the coming deluge of ChatGPT-generated essays, don’t get too excited.

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Why detecting AI-generated text is so difficult (and what to do about it)

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Why detecting AI-generated text is so difficult (and what to do about it)


This tool is OpenAI’s response to the heat it’s gotten from educators, journalists, and others for launching ChatGPT without any ways to detect text it has generated. However, it is still very much a work in progress, and it is woefully unreliable. OpenAI says its AI text detector correctly identifies 26% of AI-written text as “likely AI-written.” 

While OpenAI clearly has a lot more work to do to refine its tool, there’s a limit to just how good it can make it. We’re extremely unlikely to ever get a tool that can spot AI-generated text with 100% certainty. It’s really hard to detect AI-generated text because the whole point of AI language models is to generate fluent and human-seeming text, and the model is mimicking text created by humans, says Muhammad Abdul-Mageed, a professor who oversees research in natural-language processing and machine learning at the University of British Columbia

We are in an arms race to build detection methods that can match the latest, most powerful models, Abdul-Mageed adds. New AI language models are more powerful and better at generating even more fluent language, which quickly makes our existing detection tool kit outdated. 

OpenAI built its detector by creating a whole new AI language model akin to ChatGPT that is specifically trained to detect outputs from models like itself. Although details are sparse, the company apparently trained the model with examples of AI-generated text and examples of human-generated text, and then asked it to spot the AI-generated text. We asked for more information, but OpenAI did not respond. 

Last month, I wrote about another method for detecting text generated by an AI: watermarks. These act as a sort of secret signal in AI-produced text that allows computer programs to detect it as such. 

Researchers at the University of Maryland have developed a neat way of applying watermarks to text generated by AI language models, and they have made it freely available. These watermarks would allow us to tell with almost complete certainty when AI-generated text has been used. 

The trouble is that this method requires AI companies to embed watermarking in their chatbots right from the start. OpenAI is developing these systems but has yet to roll them out in any of its products. Why the delay? One reason might be that it’s not always desirable to have AI-generated text watermarked. 

One of the most promising ways ChatGPT could be integrated into products is as a tool to help people write emails or as an enhanced spell-checker in a word processor. That’s not exactly cheating. But watermarking all AI-generated text would automatically flag these outputs and could lead to wrongful accusations.

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The original startup behind Stable Diffusion has launched a generative AI for video

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The original startup behind Stable Diffusion has launched a generative AI for video


Set up in 2018, Runway has been developing AI-powered video-editing software for several years. Its tools are used by TikTokers and YouTubers as well as mainstream movie and TV studios. The makers of The Late Show with Stephen Colbert used Runway software to edit the show’s graphics; the visual effects team behind the hit movie Everything Everywhere All at Once used the company’s tech to help create certain scenes.  

In 2021, Runway collaborated with researchers at the University of Munich to build the first version of Stable Diffusion. Stability AI, a UK-based startup, then stepped in to pay the computing costs required to train the model on much more data. In 2022, Stability AI took Stable Diffusion mainstream, transforming it from a research project into a global phenomenon. 

But the two companies no longer collaborate. Getty is now taking legal action against Stability AI—claiming that the company used Getty’s images, which appear in Stable Diffusion’s training data, without permission—and Runway is keen to keep its distance.

Gen-1 represents a new start for Runway. It follows a smattering of text-to-video models revealed late last year, including Make-a-Video from Meta and Phenaki from Google, both of which can generate very short video clips from scratch. It is also similar to Dreamix, a generative AI from Google revealed last week, which can create new videos from existing ones by applying specified styles. But at least judging from Runway’s demo reel, Gen-1 appears to be a step up in video quality. Because it transforms existing footage, it can also produce much longer videos than most previous models. (The company says it will post technical details about Gen-1 on its website in the next few days.)   

Unlike Meta and Google, Runway has built its model with customers in mind. “This is one of the first models to be developed really closely with a community of video makers,” says Valenzuela. “It comes with years of insight about how filmmakers and VFX editors actually work on post-production.”

Gen-1, which runs on the cloud via Runway’s website, is being made available to a handful of invited users today and will be launched to everyone on the waitlist in a few weeks.

Last year’s explosion in generative AI was fueled by the millions of people who got their hands on powerful creative tools for the first time and shared what they made with them. Valenzuela hopes that putting Gen-1 into the hands of creative professionals will soon have a similar impact on video.

“We’re really close to having full feature films being generated,” he says. “We’re close to a place where most of the content you’ll see online will be generated.”

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