As AI cuts jobs, a way to keep people financially afloat and motivated

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 cutting-edge technologies wipe out more careers like as we know them, from white collar and creative jobs – lawyers, journalists, artists, software engineers – to working roles. The idea has gained enough traction that dozens of guaranteed income programs have launched in US cities since 2020.

Yet even Sam Altman, CEO of OpenAI and one of UBI’s biggest proponents, doesn’t think it’s a complete solution. As he said at a meeting earlier this year, “I think that’s a small part of the solution. I think that’s great. I think as (advanced artificial intelligence) participates more and more in the economy, we should distribute a lot more wealth and resources than we have and that will be important over time. But I don’t think that will solve the problem. I don’t think it’s going to make sense to people, I don’t think it means people are going to completely stop trying to create and do new things and whatever. So I would consider this as an enabling technology, but not as a blueprint for society. »

The question posed is what a societal blueprint should look like then, and computer scientist Jaron Lanier, a founder in the field of virtual reality, writes in this week’s New Yorker that “data dignity” could be a solution, if not THE respond.

Here’s the basics: right now, we mostly give away our data for free in exchange for free services. Lanier argues that in the age of AI, should we stop doing this, that the powerful models currently making their way into society “are connected with the humans” who give them so much to ingest and learn in the first place.

The idea is that people “get paid for what they create, even when filtered and recombined” into something that is unrecognizable.

The concept isn’t entirely new, with Lanier first introducing the notion of data dignity in a 2018 Harvard Business Review article titled “A Blueprint for a Better Digital Society.”

As he wrote at the time with co-author and economist Glen Weyl, “(R)hetoric of the tech sector suggests a wave of underemployment to come due to artificial intelligence (AI) and automation”. But the predictions of UBI advocates “only leave room for two outcomes,” and they’re extreme, Lanier and Weyl observed. “Either there will be mass poverty despite advances in technology, or much of the wealth will have to be brought under central and national control through a social wealth fund to provide citizens with a universal basic income. “

The problem is that both “hyper-focus power and undermine or ignore the value of data creators,” the two wrote.

unravel my mind

Of course, giving people the right amount of credit for their countless contributions to all that exists in the world is no small challenge (although one can imagine AI audit startups promising to tackle the problem). Lanier acknowledges that even data-dignity researchers can’t agree on how to untangle all that AI models have absorbed or how granular an accounting should be.

But he thinks – perhaps optimistically – that it could happen gradually. “The system wouldn’t necessarily account for the billions of people who have made ambient contributions to large models, those who have added to a model’s simulated proficiency with grammar, for example. (It) might only deal with the small number of special contributors that emerge in a given situation. Over time, however, “more people could be included, as intermediary advocacy organizations – unions, guilds, professional groups, etc. – will start 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 telling what is going on inside them and why.

While OpenAI had at least released some of its training data in previous years, it has since shut down the kimono altogether. Indeed, Greg Brockman told TechCrunch last month of GPT-4, his latest and most powerful big language model to date, that his training data comes from a “variety of licensed data sources. , created and publicly available, which may include publicly available personal information. information,” but declined to offer anything more specific.

As OpenAI stated when it released GPT-4, there are too many downsides for the outfit to reveal more than it does. “Given both the competitive landscape and the security implications of large-scale models such as GPT-4, this report does not contain any further details on the architecture (including model size), hardware, training computation, dataset construction, training method, or the like.”

The same is true for all major language models today. Google’s Bard chatbot, for example, is based on the LaMDA language model, which is trained on internet content-based datasets called Infiniset. But not much else is known about it other than what Google’s research team wrote a year ago that at some point in the past it incorporated 2.97 billion documents and 1.12 billion dialogs with 13.39 billion statements.

Regulators are wondering what to do. OpenAI – whose technology in particular is spreading like wildfire – is already in the crosshairs of a growing number of countries, including the Italian authority, which has blocked the use of ChatGPT. French, German, Irish and Canadian data regulators are also investigating how it collects and uses data.

But as Margaret Mitchell, an AI researcher who was previously co-head of AI ethics at Google, told the Technology Review outlet, it might be nearly impossible at this point for these companies to identify individuals’ data and remove it from their models.

As the outlet explains: OpenAI “could have saved itself a huge headache by building in strong data curation from the start (according to Mitchell). Instead, it’s common in the software industry. ‘AI to create datasets for AI models by indiscriminately scraping the web, then outsourcing the work of removing duplicates or irrelevant data points, filtering out unwanted items, and fixing typos .

How to save a life

The fact that these tech companies actually have a limited understanding of what’s now in their models is an obvious challenge to Lanier’s “data dignity” proposition, which calls Altman a “colleague and friend” in his article. New Yorker.

Whether that makes it impossible is something only time will tell.

Certainly there is merit in wanting to give people ownership of their work, and frustration with the issue could certainly increase as the world is reshaped with these new tools.

Whether or not OpenAI and others have the right to scrape the entire internet to power its algorithms is already at the heart of many sweeping copyright infringement lawsuits against them.

But this so-called data dignity could also go a long way toward preserving human sanity over time, Lanier suggests in his fascinating New Yorker article.

According to him, the universal basic income “is like putting everyone out of work to preserve the idea of ​​artificial intelligence in a black box”. Meanwhile, ending the “black box nature of our current AI models” would make it easier to count people’s contributions, making them much more likely to continue making contributions.

Importantly, adds Lanier, it could also help “establish a new creative class instead of a new dependent class.” And who would you rather be part of?