In Silicon Valley, some of the brightest minds believe that a universal basic income (UBI) that guarantees people unrestricted cash payments will help them survive and thrive as advanced technologies wipe out more careers. as we know them, from administrative and creative positions: lawyers, journalists, artists, software engineers, to labor jobs. The idea has gained enough traction that dozens of guaranteed income programs have been launched in US cities since 2020.
However, even Sam Altman, the CEO of OpenAI and one of the most prominent proponents from UBI, doesn’t think it’s a complete solution. As he said during a Sit down earlier this year, “I think it’s a small part of the solution. I think it’s great. I think like [advanced artificial intelligence] participates more and more in the economy, we must distribute wealth and resources much more than we have and that will be important over time. But I don’t think that’s going to solve the problem. I don’t think that makes sense to people, I don’t think that means that people will stop trying to create and do new things and whatever. So I would consider it an enabling technology, but not a blueprint for society.”
The question posed is what a plan for society would look like in that case, and computer scientist Jaron Lanier, a founder in the field of virtual reality, writes in this week’s article New Yorker that “data dignity” could be a solution, if not he answer.
Here’s the basic premise: Right now, we mostly give our data away for free in exchange for free services. Lanier argues that it will be more important than ever that we stop doing this, that the “digital things” we depend on (social media in part, but also increasingly AI models like OpenAI’s GPT-4) “be connected to the humans”. who give them so much to ingest in the first place.
The idea is that people “get paid for what they create, even when it’s filtered and recombined through big models.”
The concept is not new, as Lanier first introduced the notion of data dignity in a 2018 Harvard Business Review article titled: “A plan for a better digital society.” As he wrote at the time with co-author and economist Glen Weyl, “[R]rhetoric from the tech sector suggests a coming wave of underemployment due to artificial intelligence (AI) and automation” and a “future in which people are increasingly treated as worthless and without economic agency.”
But the “rhetoric” of universal basic income advocates “leaves room for only two outcomes,” and they are extreme, they observed. “Either there will be mass poverty despite technological advances, or a lot of wealth will have to be brought under central national control through a social wealth fund to provide citizens with a universal basic income.”
But both “hyper-concentrate power and undermine or ignore the value of data creators,” the two wrote.
Of course, giving people the right amount of credit for their myriad contributions to everything in the world is no small challenge (even when one can imagine that AI audit startups promise to tackle the problem). Lanier acknowledges that even data dignity researchers can’t agree on how to unravel all that AI models have sucked up or how detailed an accounting should be attempted.
But he thinks, perhaps optimistically, that it could be done gradually. “The system would not necessarily account for the billions of people who have made environmental contributions to large models, those who have added to a model’s simulated proficiency with grammar, for example. [It] it could serve only the small number of special taxpayers that arise in a given situation.” Over time, however, “more people could be included, as intermediary rights organizations (unions, guilds, professional groups, etc.) begin to play a role.”
Of course, the most immediate challenge is the black-box nature of current AI tools, says Lanier, who believes that “systems need to be made more transparent. We need to get better at saying what’s going on inside them and why.”
While OpenAI had published at least some of its training data in previous years, it has since closed the kimono entirely. In fact, Greg Brockman told TechCrunch last month of GPT-4, its latest and most powerful big language model to date, that its training data came from a “variety of licensed, publicly created and available data sources, which may include publicly available personal information”, but he declined to offer anything more specific.
Like OpenAI fixed Following the release of GPT-4, there is too much harm in revealing too much. “Given the competitive landscape and security implications of large-scale models like GPT-4, this report does not contain further details on architecture (including model size), hardware, training computation, ensemble construction of data, the training method or the like”.
The same is true for all large language models today. Google’s Bard chatbot, for example, is based on the LaMDA language model, which trains on Internet content-based data sets called Infiniset, upon which little is knownalthough a year ago, the Google research team wrote which incorporated 2.97 billion documents and 1.12 billion dialogues with 13.39 billion statements.
OpenAI, whose technology in particular is spreading like wildfire, is already in the crosshairs of regulators due to its aversion to greater transparency. The Italian authority has blocked the use of ChatGPT, and French, German, Irish and Canadian data regulators are also investigating how it collects and uses data.
But as Margaret Mitchell, an AI researcher and chief ethics scientist at startup Hugging Face, who was previously Google’s co-head of AI ethics, says Tech Reviewit might actually be next to impossible at this point to identify people’s data and remove it from your models.
As the outlet explained: “The company could have saved itself a major headache by building in robust data record keeping early on, she says. Instead, it’s common in the AI industry to create data sets for AI models by scraping the web indiscriminately and then outsourcing the work of removing duplicate or irrelevant data points, filtering out unwanted stuff, and fixing typos. These methods, and the large size of the data set, mean that technology companies tend to have a very limited understanding of what has been used to train their models.”
That’s an obvious challenge to the proposal by Lanier, who calls Altman a “colleague and friend” in his New Yorker article.
Whether it makes it impossible is something that only time will tell.
There is certainly merit in wanting to give people ownership of their work; whether or not OpenAI and others had the right to crawl the entire Internet to feed their algorithms is already at the heart of numerous and wide copyright infringement lawsuits Against them.
The so-called dignity of data could also go a long way toward preserving human sanity over time, Lanier suggests in his fascinating New Yorker article.
While universal basic income “is tantamount to putting everyone on the dole to preserve the idea of black-box AI,” ending the “black-box nature of our current AI models” would make it easier to account for AI contributions. people, making them more likely to continue making contributions.
Importantly, Lanier adds, it might also help to “set up a new creative class instead of a new dependent class.”
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