AI

google-brings-new-gemini-features-to-chromebooks,-debuts-first-on-device-ai

Google brings new Gemini features to Chromebooks, debuts first on-device AI

Google hasn’t been talking about Chromebooks as much since AI became its all-consuming focus, but that’s changing today with a bounty of new AI features for Google-powered laptops. Newer, more powerful Chromebooks will soon have image generation, text summarization, and more built into the OS. There’s also a new Lenovo Chromebook with a few exclusive AI goodies that only work thanks to its overpowered hardware.

If you have a Chromebook Plus device, which requires a modern CPU and at least 8GB of RAM, your machine will soon get a collection of features you may recognize from other Google products. For example, Lens is expanding on Chrome OS, allowing you to long-press the launcher icon to select any area of the screen to perform a visual search. Lens also includes text capture and integration with Google Calendar and Docs.

Gemini models are also playing a role here, according to Google. The Quick Insert key, which debuted last year, is gaining a new visual element. It could already insert photos or emoji with ease, but it can now also help you generate a new image on demand with AI.

Google’s new Chromebook AI features.

Even though Google’s AI features are running in the cloud, the AI additions are limited to this more powerful class of Google-powered laptops. The Help Me Read feature leverages Gemini to summarize long documents and webpages, and it can now distill that data into a more basic form. The new Summarize option can turn dense, technical text into something more readable in a few clicks.

Google has also rolled out a new AI trial for Chromebook Plus devices. If you buy one of these premium Chromebooks, you’ll get a 12-month free trial of the Google AI Pro plan, which gives you 2TB of cloud storage, expanded access to Google’s Gemini Pro model, and NotebookLM Pro. NotebookLM is also getting a place in the Chrome OS shelf.

Google brings new Gemini features to Chromebooks, debuts first on-device AI Read More »

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How a grad student got LHC data to play nice with quantum interference


New approach is already having an impact on the experiment’s plans for future work.

The ATLAS particle detector of the Large Hadron Collider (LHC) at the European Nuclear Research Center (CERN) in Geneva, Switzerland. Credit: EThamPhoto/Getty Images

The ATLAS particle detector of the Large Hadron Collider (LHC) at the European Nuclear Research Center (CERN) in Geneva, Switzerland. Credit: EThamPhoto/Getty Images

Measurements at the Large Hadron Collider have been stymied by one of the most central phenomena of the quantum world. But now, a young researcher has championed a new method to solve the problem using deep neural networks.

The Large Hadron Collider is one of the biggest experiments in history, but it’s also one of the hardest to interpret. Unlike seeing an image of a star in a telescope, saying anything at all about the data that comes out of the LHC requires careful statistical modeling.

“If you gave me a theory [that] the Higgs boson is this way or that way, I think people imagine, ‘Hey, you built the experiment, you should be able to tell me what you’re going to see under various hypotheses!’” said Daniel Whiteson, a professor at the University of California, Irvine. “But we don’t.”

One challenge with interpreting LHC data is interference, a core implication of quantum mechanics. Interference allows two possible events to inhibit each other, weakening the likelihood of seeing the result of either. In the presence of interference, physicists needed to use a fuzzier statistical method to analyze data, losing the data’s full power and increasing its uncertainty.

However, a recent breakthrough suggests a different way to tackle the problem. The ATLAS collaboration, one of two groups studying proton collisions at the LHC, released two papers last December that describe new ways of exploring data from their detector. One describes how to use a machine learning technique called Neural Simulation-Based Inference to maximize the potential of particle physics data. The other demonstrates its effectiveness with the ultimate test: re-doing a previous analysis with the new technique and seeing dramatic improvement.

The papers are the culmination of a young researcher’s six-year quest to convince the collaboration of the value of the new technique. Its success is already having an impact on the experiment’s plans for future work.

Making sense out of fusing bosons

Each particle collision at the LHC involves many possible pathways in which different particles combine to give rise to the spray of debris that experimenters see. In 2017, David Rousseau at IJCLab in Orsay, a member of the ATLAS collaboration, asked one of his students, Aishik Ghosh, to improve his team’s ability to detect a specific pathway. That particular pathway is quite important since it’s used to measure properties of the Higgs boson, a particle (first measured in 2012) that helps explain the mass of all other fundamental particles.

It was a pretty big ask. “When a grad student gets started in ATLAS, they’re a tiny cog in a giant, well-oiled machine of 3,500 physicists, who all seem to know exactly what they’re doing,” said Ghosh.

The pathway Ghosh was asked to study occurs via several steps. First, the two colliding protons each emit a W boson, a particle associated with the weak nuclear force. These two bosons fuse together, changing their identity to form a Higgs boson. The Higgs boson then decays, forming a pair of Z bosons, another particle associated with the weak force. Finally, those Z bosons themselves each decay into a lepton, like an electron, and its antimatter partner, like a positron.

A Feynman diagram for the pathway studied by Aishik Ghosh. Credit: ATLAS

Measurements like the one Ghosh was studying are a key way of investigating the properties of the Higgs boson. By precisely measuring how long it takes the Higgs boson to decay, physicists could find evidence of it interacting with new, undiscovered particles that are too massive for the LHC to produce directly.

Ghosh started on the project, hoping to find a small improvement in the collaboration’s well-tested methods. Instead, he noticed a larger issue. The goal he was given, of detecting a single pathway by itself, didn’t actually make sense.

“I was doing that and I realized, ‘What am I doing?’ There’s no clear objective,” said Ghosh.

The problem was quantum interference.

How quantum histories interfere

One of the most famous demonstrations of the mysterious nature of quantum mechanics is called the double-slit experiment. In this demonstration, electrons are shot through a screen with two slits that allow them to pass through to a photographic plate on the other side. With one slit covered, the electrons form a pattern centered on the opening. The photographic plate lights up bright right across from the slit and dims further away from it.

With both slits open, you would expect the pattern to get brighter as more electrons reach the photographic plate. Instead, the effect varies. The two slits do not give rise to two nice bright peaks; instead, you see a rippling pattern in which some areas get brighter while others get dimmer, even though the dimmer areas should, in principle, be easier for electrons to reach.

The effect happens even if the electrons are shot at the screen one by one to stop them from influencing each other directly. It’s as if each electron carries with it two possible histories, one in which it goes through one slit and another where it goes through the other before both end up at the same place. These two histories interfere with each other so that some destinations become less likely instead of more likely.

Results of the double-slit experiment. Credit: Jordgette (CC BY-SA 3.0)

For electrons in the double-slit experiment, the two different histories are two different paths through space. For a measurement at the Large Hadron Collider, the histories are more abstract—paths that lead through transformations of fields. One history might be like the pathway Ghosh was asked to study, in which two W bosons fuse to form a Higgs boson before the Higgs boson splits into two Z bosons. But in another history, the two W bosons might fuse and immediately split into two Z bosons without ever producing a Higgs.

Both histories have the same beginning, with two W bosons, and the same end, with two Z bosons. And just as the two histories of electrons in the double-slit experiment can interfere, so can the two histories for these particles.

Another possible history for colliding particles at the Large Hadron Collider, which interferes with the measurement Ghosh was asked to do. Credit: ATLAS

That interference makes the effect of the Higgs boson much more challenging to spot. ATLAS scientists wanted to look for two pairs of electrons and positrons, which would provide evidence that two Z bosons were produced. They would classify their observations into two types: observations that are evidence for the signal they were looking for (that of a decaying Higgs boson) and observations of events that generate this pattern of particles without the Higgs boson acting as an intermediate (the latter are called the background). But the two types of observations, signal and background, interfere. With a stronger signal, corresponding to more Higgs bosons decaying, you might observe more pairs of electrons and positrons… but if these events interfere, you also might see those pairs disappear.

Learning to infer

In traditional approaches, those disappearances are hard to cope with, even when using methods that already incorporate machine learning.

One of the most common uses of machine learning is classification—for example, distinguishing between pictures of dogs and cats. You train the machine on pictures of cats and pictures of dogs, and it tells you, given a picture, which animal is the most likely match. Physicists at the LHC were already using this kind of classification method to characterize the products of collisions, but it functions much worse when interference is involved.

“If you have something that disappears, you don’t quite know what to train on,” said David Rousseau. “Usually, you’re training signal versus background, exactly like you’re training cats versus dogs. When there is something that disappears, you don’t see what you trained on.”

At first, Ghosh tried a few simple tricks, but as time went on, he realized he needed to make a more fundamental change. He reached out to others in the community and learned about a method called Neural Simulation-Based Inference, or NSBI.

In older approaches, people had trained machine learning models to classify observations into signal and background, using simulations of particle collisions to make the training data. Then they used that classification to infer the most likely value of a number, like the amount of time it takes a Higgs boson to decay, based on data from an actual experiment. Neural Simulation-Based Inference skips the classification and goes directly to the inference.

Instead of trying to classify observations into signal and background, NSBI uses simulations to teach an artificial neural network to guess a formula called a likelihood ratio. Someone using NSBI would run several simulations that describe different situations, such as letting the Higgs boson decay at different rates, and then check how many of each type of simulation yielded a specific observation. The fraction of these simulations with a certain decay rate would provide the likelihood ratio, a method for inferring which decay rate is more likely given experimental evidence. If the neural network is good at guessing this ratio, it will be good at finding how long the Higgs takes to decay.

Because NSBI doesn’t try to classify observations into different categories, it handles quantum interference more effectively. Instead of trying to find the Higgs based on a signal that disappears, it examines all the data, trying to guess which decay time is the most likely.

Ghosh tested the method, which showed promising results on test data, and presented the results at a conference in 2019. But if he was going to convince the ATLAS collaboration that the method was safe to use, he still had a lot of work ahead of him.

Shifting the weight on ATLAS’ shoulders

Experiments like ATLAS have high expectations attached to them. A collaboration of thousands of scientists, ATLAS needs to not only estimate the laws of physics but also have a clear idea of just how uncertain those estimates are. At the time, NSBI hadn’t been tested in that way.

“None of this has actually been used on data,” said Ghosh. “Nobody knew how to quantify the uncertainties. So you have a neural network that gives you a likelihood. You don’t know how good the likelihood is. Is it well-estimated? What if it’s wrongly estimated just in some weird corner? That would completely bias your results.”

Checking those corners was too big a job for a single PhD student and too complex to complete within a single PhD degree. Aishik would have to build a team, and he would need time to build that team. That’s tricky in the academic world, where students go on to short-term postdoc jobs with the expectation that they quickly publish new results to improve their CV for the next position.

“We’re usually looking to publish the next paper within two to three years—no time to overhaul our methods,” said Ghosh. Fortunately, Ghosh had support. He received his PhD alongside Rousseau and went to work with Daniel Whiteson, who encouraged him to pursue his ambitious project.

“I think it’s really important that postdocs learn to take those risks because that’s what science is,” Whiteson said.

Ghosh gathered his team. Another student of Rousseau’s, Arnaud Maury, worked to calibrate the machine’s confidence in its answers. A professor at the University of Massachusetts, Rafael Coelho Lopes de Sa, joined the project. His student Jay Sandesara would have a key role in getting the calculation to work at full scale on a computer cluster. IJCLab emeritus RD Schaffer and University of Liège professor Gilles Loupe provided cross-checks and advice.

The team wanted a clear demonstration that their method worked, so they took an unusual step. They took data that ATLAS had already analyzed and performed a full analysis using their method instead, showing that it could pass every check the collaboration could think of. They would publish two papers, one describing the method and the other giving the results of their upgraded analysis. Zach Marshall, who was the computing coordinator for ATLAS at the time, helped get the papers through, ensuring that they were vetted by experts in multiple areas.

“It was a very small subset of our community that had that overlap between this technical understanding and the physics analysis experience and understanding that were capable of really speaking to whether that paper was sufficient and intelligible and useful. So we really had to make sure that we engaged that little group of humans by name,” said Marshall.

The new method showed significant improvements, getting a much more precise result than the collaboration’s previous analysis. That improvement, and the thorough checks, persuaded ATLAS to use NSBI more broadly going forward. It will give them much more precision than they expected, using the Higgs boson to search for new particles and clarify our understanding of the quantum world. When ATLAS discusses its future plans, it makes projections of the precision it expects to reach in the future. But those plans are now being upended.

“One of the fun things about this method that Aishik pushed hard is each time it feels like now we do that projection—here’s how well we’ll do in 15 years—we absolutely crush those projections,” said Marshall. “So we are just now having to redo a set of projections because we matched our old projections for 15 years out already today. It’s a very fun problem to have.”

How a grad student got LHC data to play nice with quantum interference Read More »

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MIT student prints AI polymer masks to restore paintings in hours

MIT graduate student Alex Kachkine once spent nine months meticulously restoring a damaged baroque Italian painting, which left him plenty of time to wonder if technology could speed things up. Last week, MIT News announced his solution: a technique that uses AI-generated polymer films to physically restore damaged paintings in hours rather than months. The research appears in Nature.

Kachkine’s method works by printing a transparent “mask” containing thousands of precisely color-matched regions that conservators can apply directly to an original artwork. Unlike traditional restoration, which permanently alters the painting, these masks can reportedly be removed whenever needed. So it’s a reversible process that does not permanently change a painting.

“Because there’s a digital record of what mask was used, in 100 years, the next time someone is working with this, they’ll have an extremely clear understanding of what was done to the painting,” Kachkine told MIT News. “And that’s never really been possible in conservation before.”

Figure 1 from the paper.

Figure 1 from the paper. Credit: MIT

Nature reports that up to 70 percent of institutional art collections remain hidden from public view due to damage—a large amount of cultural heritage sitting unseen in storage. Traditional restoration methods, where conservators painstakingly fill damaged areas one at a time while mixing exact color matches for each region, can take weeks to decades for a single painting. It’s skilled work that requires both artistic talent and deep technical knowledge, but there simply aren’t enough conservators to tackle the backlog.

The mechanical engineering student conceived the idea during a 2021 cross-country drive to MIT, when gallery visits revealed how much art remains hidden due to damage and restoration backlogs. As someone who restores paintings as a hobby, he understood both the problem and the potential for a technological solution.

To demonstrate his method, Kachkine chose a challenging test case: a 15th-century oil painting requiring repairs in 5,612 separate regions. An AI model identified damage patterns and generated 57,314 different colors to match the original work. The entire restoration process reportedly took 3.5 hours—about 66 times faster than traditional hand-painting methods.

A handout photo of Alex Kachkine, who developed the AI printed film technique.

Alex Kachkine, who developed the AI-printed film technique. Credit: MIT

Notably, Kachkine avoided using generative AI models like Stable Diffusion or the “full-area application” of generative adversarial networks (GANs) for the digital restoration step. According to the Nature paper, these models cause “spatial distortion” that would prevent proper alignment between the restored image and the damaged original.

MIT student prints AI polymer masks to restore paintings in hours Read More »

to-avoid-admitting-ignorance,-meta-ai-says-man’s-number-is-a-company-helpline

To avoid admitting ignorance, Meta AI says man’s number is a company helpline

Although that statement may provide comfort to those who have kept their WhatsApp numbers off the Internet, it doesn’t resolve the issue of WhatsApp’s AI helper potentially randomly generating a real person’s private number that may be a few digits off from the business contact information WhatsApp users are seeking.

Expert pushes for chatbot design tweaks

AI companies have recently been grappling with the problem of chatbots being programmed to tell users what they want to hear, instead of providing accurate information. Not only are users sick of “overly flattering” chatbot responses—potentially reinforcing users’ poor decisions—but the chatbots could be inducing users to share more private information than they would otherwise.

The latter could make it easier for AI companies to monetize the interactions, gathering private data to target advertising, which could deter AI companies from solving the sycophantic chatbot problem. Developers for Meta rival OpenAI, The Guardian noted, last month shared examples of “systemic deception behavior masked as helpfulness” and chatbots’ tendency to tell little white lies to mask incompetence.

“When pushed hard—under pressure, deadlines, expectations—it will often say whatever it needs to to appear competent,” developers noted.

Mike Stanhope, the managing director of strategic data consultants Carruthers and Jackson, told The Guardian that Meta should be more transparent about the design of its AI so that users can know if the chatbot is designed to rely on deception to reduce user friction.

“If the engineers at Meta are designing ‘white lie’ tendencies into their AI, the public need to be informed, even if the intention of the feature is to minimize harm,” Stanhope said. “If this behavior is novel, uncommon, or not explicitly designed, this raises even more questions around what safeguards are in place and just how predictable we can force an AI’s behavior to be.”

To avoid admitting ignorance, Meta AI says man’s number is a company helpline Read More »

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Study: Meta AI model can reproduce almost half of Harry Potter book


Harry Potter and the Copyright Lawsuit

The research could have big implications for generative AI copyright lawsuits.

Meta CEO Mark Zuckerberg. Credit: Andrej Sokolow/picture alliance via Getty Images

In recent years, numerous plaintiffs—including publishers of books, newspapers, computer code, and photographs—have sued AI companies for training models using copyrighted material. A key question in all of these lawsuits has been how easily AI models produce verbatim excerpts from the plaintiffs’ copyrighted content.

For example, in its December 2023 lawsuit against OpenAI, The New York Times Company produced dozens of examples where GPT-4 exactly reproduced significant passages from Times stories. In its response, OpenAI described this as a “fringe behavior” and a “problem that researchers at OpenAI and elsewhere work hard to address.”

But is it actually a fringe behavior? And have leading AI companies addressed it? New research—focusing on books rather than newspaper articles and on different companies—provides surprising insights into this question. Some of the findings should bolster plaintiffs’ arguments, while others may be more helpful to defendants.

The paper was published last month by a team of computer scientists and legal scholars from Stanford, Cornell, and West Virginia University. They studied whether five popular open-weight models—three from Meta and one each from Microsoft and EleutherAI—were able to reproduce text from Books3, a collection of books that is widely used to train LLMs. Many of the books are still under copyright.

This chart illustrates their most surprising finding:

The chart shows how easy it is to get a model to generate 50-token excerpts from various parts of Harry Potter and the Sorcerer’s Stone. The darker a line is, the easier it is to reproduce that portion of the book.

Each row represents a different model. The three bottom rows are Llama models from Meta. And as you can see, Llama 3.1 70B—a mid-sized model Meta released in July 2024—is far more likely to reproduce Harry Potter text than any of the other four models.

Specifically, the paper estimates that Llama 3.1 70B has memorized 42 percent of the first Harry Potter book well enough to reproduce 50-token excerpts at least half the time. (I’ll unpack how this was measured in the next section.)

Interestingly, Llama 1 65B, a similar-sized model released in February 2023, had memorized only 4.4 percent of Harry Potter and the Sorcerer’s Stone. This suggests that despite the potential legal liability, Meta did not do much to prevent memorization as it trained Llama 3. At least for this book, the problem got much worse between Llama 1 and Llama 3.

Harry Potter and the Sorcerer’s Stone was one of dozens of books tested by the researchers. They found that Llama 3.1 70B was far more likely to reproduce popular books—such as The Hobbit and George Orwell’s 1984—than obscure ones. And for most books, Llama 3.1 70B memorized more than any of the other models.

“There are really striking differences among models in terms of how much verbatim text they have memorized,” said James Grimmelmann, a Cornell law professor who has collaborated with several of the paper’s authors.

The results surprised the study’s authors, including Mark Lemley, a law professor at Stanford. (Lemley used to be part of Meta’s legal team, but in January, he dropped them as a client after Facebook adopted more Trump-friendly moderation policies.)

“We’d expected to see some kind of low level of replicability on the order of 1 or 2 percent,” Lemley told me. “The first thing that surprised me is how much variation there is.”

These results give everyone in the AI copyright debate something to latch onto. For AI industry critics, the big takeaway is that—at least for some models and some books—memorization is not a fringe phenomenon.

On the other hand, the study only found significant memorization of a few popular books. For example, the researchers found that Llama 3.1 70B only memorized 0.13 percent of Sandman Slim, a 2009 novel by author Richard Kadrey. That’s a tiny fraction of the 42 percent figure for Harry Potter.

This could be a headache for law firms that have filed class-action lawsuits against AI companies. Kadrey is the lead plaintiff in a class-action lawsuit against Meta. To certify a class of plaintiffs, a court must find that the plaintiffs are in largely similar legal and factual situations.

Divergent results like these could cast doubt on whether it makes sense to lump J.K. Rowling, Kadrey, and thousands of other authors together in a single mass lawsuit. And that could work in Meta’s favor, since most authors lack the resources to file individual lawsuits.

The broader lesson of this study is that the details will matter in these copyright cases. Too often, online discussions have treated “do generative models copy their training data or merely learn from it?” as a theoretical or even philosophical question. But it’s a question that can be tested empirically—and the answer might differ across models and across copyrighted works.

It’s common to talk about LLMs predicting the next token. But under the hood, what the model actually does is generate a probability distribution over all possibilities for the next token. For example, if you prompt an LLM with the phrase “Peanut butter and,” it will respond with a probability distribution that might look like this made-up example:

  • P(“jelly”) = 70 percent
  • P(“sugar”) = 9 percent
  • P(“peanut”) = 6 percent
  • P(“chocolate”) = 4 percent
  • P(“cream”) = 3 percent

And so forth.

After the model generates a list of probabilities like this, the system will select one of these options at random, weighted by their probabilities. So 70 percent of the time the system will generate “Peanut butter and jelly.” Nine percent of the time, we’ll get “Peanut butter and sugar.” Six percent of the time, it will be “Peanut butter and peanut.” You get the idea.

The study’s authors didn’t have to generate multiple outputs to estimate the likelihood of a particular response. Instead, they could calculate probabilities for each token and then multiply them together.

Suppose someone wants to estimate the probability that a model will respond to “My favorite sandwich is” with “peanut butter and jelly.” Here’s how to do that:

  • Prompt the model with “My favorite sandwich is,” and look up the probability of “peanut” (let’s say it’s 20 percent).
  • Prompt the model with “My favorite sandwich is peanut,” and look up the probability of “butter” (let’s say it’s 90 percent).
  • Prompt the model with “My favorite sandwich is peanut butter” and look up the probability of “and” (let’s say it’s 80 percent).
  • Prompt the model with “My favorite sandwich is peanut butter and” and look up the probability of “jelly” (let’s say it’s 70 percent).

Then we just have to multiply the probabilities like this:

0.2 0.9 0.8 0.7 = 0.1008

So we can predict that the model will produce “peanut butter and jelly” about 10 percent of the time, without actually generating 100 or 1,000 outputs and counting how many of them were that exact phrase.

This technique greatly reduced the cost of the research, allowed the authors to analyze more books, and made it feasible to precisely estimate very low probabilities.

For example, the authors estimated that it would take more than 10 quadrillion samples to exactly reproduce some 50-token sequences from some books. Obviously, it wouldn’t be feasible to actually generate that many outputs. But it wasn’t necessary: the probability could be estimated just by multiplying the probabilities for the 50 tokens.

A key thing to notice is that probabilities can get really small really fast. In my made-up example, the probability that the model will produce the four tokens “peanut butter and jelly” is just 10 percent. If we added more tokens, the probability would get even lower. If we added 46 more tokens, the probability could fall by several orders of magnitude.

For any language model, the probability of generating any given 50-token sequence “by accident” is vanishingly small. If a model generates 50 tokens from a copyrighted work, that is strong evidence that the tokens “came from” the training data. This is true even if it only generates those tokens 10 percent, 1 percent, or 0.01 percent of the time.

The study authors took 36 books and divided each of them into overlapping 100-token passages. Using the first 50 tokens as a prompt, they calculated the probability that the next 50 tokens would be identical to the original passage. They counted a passage as “memorized” if the model had a greater than 50 percent chance of reproducing it word for word.

This definition is quite strict. For a 50-token sequence to have a probability greater than 50 percent, the average token in the passage needs a probability of at least 98.5 percent! Moreover, the authors only counted exact matches. They didn’t try to count cases where—for example—the model generates 48 or 49 tokens from the original passage but got one or two tokens wrong. If these cases were counted, the amount of memorization would be even higher.

This research provides strong evidence that significant portions of Harry Potter and the Sorcerer’s Stone were copied into the weights of Llama 3.1 70B. But this finding doesn’t tell us why or how this happened. I suspect that part of the answer is that Llama 3 70B was trained on 15 trillion tokens—more than 10 times the 1.4 trillion tokens used to train Llama 1 65B.

The more times a model is trained on a particular example, the more likely it is to memorize that example. Perhaps Meta had trouble finding 15 trillion distinct tokens, so it trained on the Books3 dataset multiple times. Or maybe Meta added third-party sources—such as online Harry Potter fan forums, consumer book reviews, or student book reports—that included quotes from Harry Potter and other popular books.

I’m not sure that either of these explanations fully fits the facts. The fact that memorization was a much bigger problem for the most popular books does suggest that Llama may have been trained on secondary sources that quote these books rather than the books themselves. There are likely exponentially more online discussions of Harry Potter than Sandman Slim.

On the other hand, it’s surprising that Llama memorized so much of Harry Potter and the Sorcerer’s Stone.

“If it were citations and quotations, you’d expect it to concentrate around a few popular things that everyone quotes or talks about,” Lemley said. The fact that Llama 3 memorized almost half the book suggests that the entire text was well represented in the training data.

Or there could be another explanation entirely. Maybe Meta made subtle changes in its training recipe that accidentally worsened the memorization problem. I emailed Meta for comment last week but haven’t heard back.

“It doesn’t seem to be all popular books,” Mark Lemley told me. “Some popular books have this result and not others. It’s hard to come up with a clear story that says why that happened.”

  1. Training on a copyrighted work is inherently infringing because the training process involves making a digital copy of the work.
  2. The training process copies information from the training data into the model, making the model a derivative work under copyright law.
  3. Infringement occurs when a model generates (portions of) a copyrighted work.

A lot of discussion so far has focused on the first theory because it is the most threatening to AI companies. If the courts uphold this theory, most current LLMs would be illegal, whether or not they have memorized any training data.

The AI industry has some pretty strong arguments that using copyrighted works during the training process is fair use under the 2015 Google Books ruling. But the fact that Llama 3.1 70B memorized large portions of Harry Potter could color how the courts consider these fair use questions.

A key part of fair use analysis is whether a use is “transformative”—whether a company has made something new or is merely profiting from the work of others. The fact that language models are capable of regurgitating substantial portions of popular works like Harry Potter1984, and The Hobbit could cause judges to look at these fair use arguments more skeptically.

Moreover, one of Google’s key arguments in the books case was that its system was designed to never return more than a short excerpt from any book. If the judge in the Meta lawsuit wanted to distinguish Meta’s arguments from the ones Google made in the books case, he could point to the fact that Llama can generate far more than a few lines of Harry Potter.

The new study “complicates the story that the defendants have been telling in these cases,” co-author Mark Lemley told me. “Which is ‘we just learn word patterns. None of that shows up in the model.’”

But the Harry Potter result creates even more danger for Meta under that second theory—that Llama itself is a derivative copy of Rowling’s book.

“It’s clear that you can in fact extract substantial parts of Harry Potter and various other books from the model,” Lemley said. “That suggests to me that probably for some of those books there’s something the law would call a copy of part of the book in the model itself.”

The Google Books precedent probably can’t protect Meta against this second legal theory because Google never made its books database available for users to download—Google almost certainly would have lost the case if it had done that.

In principle, Meta could still convince a judge that copying 42 percent of Harry Potter was allowed under the flexible, judge-made doctrine of fair use. But it would be an uphill battle.

“The fair use analysis you’ve gotta do is not just ‘is the training set fair use,’ but ‘is the incorporation in the model fair use?’” Lemley said. “That complicates the defendants’ story.”

Grimmelmann also said there’s a danger that this research could put open-weight models in greater legal jeopardy than closed-weight ones. The Cornell and Stanford researchers could only do their work because the authors had access to the underlying model—and hence to the token probability values that allowed efficient calculation of probabilities for sequences of tokens.

Most leading labs, including OpenAI, Anthropic, and Google, have increasingly restricted access to these so-called logits, making it more difficult to study these models.

Moreover, if a company keeps model weights on its own servers, it can use filters to try to prevent infringing output from reaching the outside world. So even if the underlying OpenAI, Anthropic, and Google models have memorized copyrighted works in the same way as Llama 3.1 70B, it might be difficult for anyone outside the company to prove it.

Moreover, this kind of filtering makes it easier for companies with closed-weight models to invoke the Google Books precedent. In short, copyright law might create a strong disincentive for companies to release open-weight models.

“It’s kind of perverse,” Mark Lemley told me. “I don’t like that outcome.”

On the other hand, judges might conclude that it would be bad to effectively punish companies for publishing open-weight models.

“There’s a degree to which being open and sharing weights is a kind of public service,” Grimmelmann told me. “I could honestly see judges being less skeptical of Meta and others who provide open-weight models.”

Timothy B. Lee was on staff at Ars Technica from 2017 to 2021. Today, he writes Understanding AI, a newsletter that explores how AI works and how it’s changing our world. You can subscribe here.

Photo of Timothy B. Lee

Timothy is a senior reporter covering tech policy and the future of transportation. He lives in Washington DC.

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xAI faces legal threat over alleged Colossus data center pollution in Memphis

“For instance, if all the 35 turbines operated by xAI were using” add-on air pollution control technology “to achieve a NOx emission rate of 2 ppm”—as xAI’s consultant agreed it would—”they would emit about 177 tons of NOx per year, as opposed to the 1,200 to 2,100 tons per year they currently emit,” the letter said.

Allegedly, all of xAI’s active turbines “continue to operate without utilizing best available control technology” (BACT) and “there is no dispute” that since xAI has yet to obtain permitting, it’s not meeting BACT requirements today, the letter said.

“xAI’s failure to comply with the BACT requirement is not only a Clean Air Act violation on paper, but also a significant and ongoing violation that is resulting in substantial amounts of harmful excess emissions,” the letter said.

Additionally, xAI’s turbines are considered a major source of a hazardous air pollutant, formaldehyde, the letter said, with “the potential to emit more than 16 tons” since xAI operations began. “xAI was required to conduct initial emissions testing for formaldehyde within 180 days of becoming a major source,” the letter alleged, but it appears that a year after moving into Memphis, still “xAI has not conducted this testing.”

Terms of xAI’s permitting exemption remain vague

The NAACP and SELC suggested that the exemption that xAI is seemingly operating under could be a “nonroad engine exemption.” However, they alleged that xAI’s turbines don’t qualify for that yearlong exemption, and even if they did, any turbines still onsite after a year would surely not be covered and should have permitting by now.

“While some local leaders, including the Memphis Mayor and Shelby County Health Department, have claimed there is a ‘364-exemption’ for xAI’s gas turbines, they have never been able to point to a specific exemption that would apply to turbines as large as the ones at the xAI site,” SELC’s press release alleged.

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Google’s frighteningly good Veo 3 AI videos to be integrated with YouTube Shorts

Even in the age of TikTok, YouTube viewership continues to climb. While Google’s iconic video streaming platform has traditionally pushed creators to produce longer videos that can accommodate more ads, the site’s Shorts format is growing fast. That growth may explode in the coming months, as YouTube CEO Neal Mohan has announced that the Google Veo 3 AI video generator will be integrated with YouTube Shorts later this summer.

According to Mohan, YouTube Shorts has seen a rise in popularity even compared to YouTube as a whole. The streaming platform is now the most watched source of video in the world, but Shorts specifically have seen a massive 186 percent increase in viewership over the past year. Mohan says Shorts now average 200 billion daily views.

YouTube has already equipped creators with a few AI tools, including Dream Screen, which can produce AI video backgrounds with a text prompt. Veo 3 support will be a significant upgrade, though. At the Cannes festival, Mohan revealed that the streaming site will begin offering integration with Google’s leading video model later this summer. “I believe these tools will open new creative lanes for everyone to explore,” said Mohan.

YouTube Shorts recommendations.

YouTube heavily promotes Shorts on the homepage.

Credit: Google

YouTube heavily promotes Shorts on the homepage. Credit: Google

This move will require a few tweaks to Veo 3 outputs, but it seems like a perfect match. As the name implies, YouTube Shorts is intended for short video content. The format initially launched with a 30-second ceiling, but that has since been increased to 60 seconds. Because of the astronomical cost of generative AI, each generated Veo clip is quite short, a mere eight seconds in the current version of the tool. Slap a few of those together, and you’ve got a YouTube Short.

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scientists-once-hoarded-pre-nuclear-steel;-now-we’re-hoarding-pre-ai-content

Scientists once hoarded pre-nuclear steel; now we’re hoarding pre-AI content

A time capsule of human expression

Graham-Cumming is no stranger to tech preservation efforts. He’s a British software engineer and writer best known for creating POPFile, an open source email spam filtering program, and for successfully petitioning the UK government to apologize for its persecution of codebreaker Alan Turing—an apology that Prime Minister Gordon Brown issued in 2009.

As it turns out, his pre-AI website isn’t new, but it has languished unannounced until now. “I created it back in March 2023 as a clearinghouse for online resources that hadn’t been contaminated with AI-generated content,” he wrote on his blog.

The website points to several major archives of pre-AI content, including a Wikipedia dump from August 2022 (before ChatGPT’s November 2022 release), Project Gutenberg’s collection of public domain books, the Library of Congress photo archive, and GitHub’s Arctic Code Vault—a snapshot of open source code buried in a former coal mine near the North Pole in February 2020. The wordfreq project appears on the list as well, flash-frozen from a time before AI contamination made its methodology untenable.

The site accepts submissions of other pre-AI content sources through its Tumblr page. Graham-Cumming emphasizes that the project aims to document human creativity from before the AI era, not to make a statement against AI itself. As atmospheric nuclear testing ended and background radiation returned to natural levels, low-background steel eventually became unnecessary for most uses. Whether pre-AI content will follow a similar trajectory remains a question.

Still, it feels reasonable to protect sources of human creativity now, including archival ones, because these repositories may become useful in ways that few appreciate at the moment. For example, in 2020, I proposed creating a so-called “cryptographic ark”—a timestamped archive of pre-AI media that future historians could verify as authentic, collected before my then-arbitrary cutoff date of January 1, 2022. AI slop pollutes more than the current discourse—it could cloud the historical record as well.

For now, lowbackgroundsteel.ai stands as a modest catalog of human expression from what may someday be seen as the last pre-AI era. It’s a digital archaeology project marking the boundary between human-generated and hybrid human-AI cultures. In an age where distinguishing between human and machine output grows increasingly difficult, these archives may prove valuable for understanding how human communication evolved before AI entered the chat.

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openai-weighs-“nuclear-option”-of-antitrust-complaint-against-microsoft

OpenAI weighs “nuclear option” of antitrust complaint against Microsoft

OpenAI executives have discussed filing an antitrust complaint with US regulators against Microsoft, the company’s largest investor, The Wall Street Journal reported Monday, marking a dramatic escalation in tensions between the two long-term AI partners. OpenAI, which develops ChatGPT, has reportedly considered seeking a federal regulatory review of the terms of its contract with Microsoft for potential antitrust law violations, according to people familiar with the matter.

The potential antitrust complaint would likely argue that Microsoft is using its dominant position in cloud services and contractual leverage to suppress competition, according to insiders who described it as a “nuclear option,” the WSJ reports.

The move could unravel one of the most important business partnerships in the AI industry—a relationship that started with a $1 billion investment by Microsoft in 2019 and has grown to include billions more in funding, along with Microsoft’s exclusive rights to host OpenAI models on its Azure cloud platform.

The friction centers on OpenAI’s efforts to transition from its current nonprofit structure into a public benefit corporation, a conversion that needs Microsoft’s approval to complete. The two companies have not been able to agree on details after months of negotiations, sources told Reuters. OpenAI’s existing for-profit arm would become a Delaware-based public benefit corporation under the proposed restructuring.

The companies are discussing revising the terms of Microsoft’s investment, including the future equity stake it will hold in OpenAI. According to The Information, OpenAI wants Microsoft to hold a 33 percent stake in a restructured unit in exchange for foregoing rights to future profits. The AI company also wants to modify existing clauses that give Microsoft exclusive rights to host OpenAI models in its cloud.

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Google can now generate a fake AI podcast of your search results

NotebookLM is undoubtedly one of Google’s best implementations of generative AI technology, giving you the ability to explore documents and notes with a Gemini AI model. Last year, Google added the ability to generate so-called “audio overviews” of your source material in NotebookLM. Now, Google has brought those fake AI podcasts to search results as a test. Instead of clicking links or reading the AI Overview, you can have two nonexistent people tell you what the results say.

This feature is not currently rolling out widely—it’s available in search labs, which means you have to manually enable it. Anyone can opt in to the new Audio Overview search experience, though. If you join the test, you’ll quickly see the embedded player in Google search results. However, it’s not at the top with the usual block of AI-generated text. Instead, you’ll see it after the first few search results, below the “People also ask” knowledge graph section.

Credit: Google

Google isn’t wasting resources to generate the audio automatically, so you have to click the generate button to get started. A few seconds later, you’re given a back-and-forth conversation between two AI voices summarizing the search results. The player includes a list of sources from which the overview is built, as well as the option to speed up or slow down playback.

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Meta beefs up disappointing AI division with $15 billion Scale AI investment

Meta has invested heavily in generative AI, with the majority of its planned $72 billion in capital expenditure this year earmarked for data centers and servers. The deal underlines the high price AI companies are willing to pay for data that can be used to train AI models.

Zuckerberg pledged last year that his company’s models would outstrip rivals’ efforts in 2025, but Meta’s most recent release, Llama 4, has underperformed on various independent reasoning and coding benchmarks.

The long-term goal of researchers at Meta “has always been to reach human intelligence and go beyond it,” said Yann LeCun, the company’s chief AI scientist at the VivaTech conference in Paris this week.

Building artificial “general” intelligence—AI technologies that have human-level intelligence—is a popular goal for many AI companies. An increasing number of Silicon Valley groups are also seeking to reach “superintelligence,” a hypothetical scenario where AI systems surpass human intelligence.

The core of Scale’s business has been data-labeling, a manual process of ensuring images and text are accurately labeled and categorized before they are used to train AI models.

Wang has forged relationships with Silicon Valley’s biggest investors and technologists, including OpenAI’s Sam Altman. Scale AI’s early customers were autonomous vehicle companies, but the bulk of its expected $2 billion in revenues this year will come from labeling the data used to train the massive AI models built by OpenAI and others.

The deal will result in a substantial payday for Scale’s early venture capital investors, including Accel, Tiger Global Management, and Index Ventures. Tiger’s $200 million investment is worth more than $1 billion at the company’s new valuation, according to a person with knowledge of the matter.

Additional reporting by Tabby Kinder in San Francisco

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How to draft a will to avoid becoming an AI ghost—it’s not easy


Why requests for “no AI resurrections” will probably go ignored.

Proton beams capturing the ghost of OpenAI to suck it into a trap where it belongs

All right! This AI is TOAST! Credit: Aurich Lawson

All right! This AI is TOAST! Credit: Aurich Lawson

As artificial intelligence has advanced, AI tools have emerged to make it possible to easily create digital replicas of lost loved ones, which can be generated without the knowledge or consent of the person who died.

Trained on the data of the dead, these tools, sometimes called grief bots or AI ghosts, may be text-, audio-, or even video-based. Chatting provides what some mourners feel is a close approximation to ongoing interactions with the people they love most. But the tech remains controversial, perhaps complicating the grieving process while threatening to infringe upon the privacy of the deceased, whose data could still be vulnerable to manipulation or identity theft.

Because of suspected harms and perhaps a general repulsion to the idea of it, not everybody wants to become an AI ghost.

After a realistic video simulation was recently used to provide a murder victim’s impact statement in court, Futurism summed up social media backlash, noting that the use of AI was “just as unsettling as you think.” And it’s not the first time people have expressed discomfort with the growing trend. Last May, The Wall Street Journal conducted a reader survey seeking opinions on the ethics of so-called AI resurrections. Responding, a California woman, Dorothy McGarrah, suggested there should be a way to prevent AI resurrections in your will.

“Having photos or videos of lost loved ones is a comfort. But the idea of an algorithm, which is as prone to generate nonsense as anything lucid, representing a deceased person’s thoughts or behaviors seems terrifying. It would be like generating digital dementia after your loved ones’ passing,” McGarrah said. “I would very much hope people have the right to preclude their images being used in this fashion after death. Perhaps something else we need to consider in estate planning?”

For experts in estate planning, the question may start to arise as more AI ghosts pop up. But for now, writing “no AI resurrections” into a will remains a complicated process, experts suggest, and such requests may not be honored by all unless laws are changed to reinforce a culture of respecting the wishes of people who feel uncomfortable with the idea of haunting their favorite people through AI simulations.

Can you draft a will to prevent AI resurrection?

Ars contacted several law associations to find out if estate planners are seriously talking about AI ghosts. Only the National Association of Estate Planners and Councils responded; it connected Ars to Katie Sheehan, an expert in the estate planning field who serves as a managing director and wealth strategist for Crestwood Advisors.

Sheehan told Ars that very few estate planners are prepared to answer questions about AI ghosts. She said not only does the question never come up in her daily work, but it’s also “essentially uncharted territory for estate planners since AI is relatively new to the scene.”

“I have not seen any documents drafted to date taking this into consideration, and I review estate plans for clients every day, so that should be telling,” Sheehan told Ars.

Although Sheehan has yet to see a will attempting to prevent AI resurrection, she told Ars that there could be a path to make it harder for someone to create a digital replica without consent.

“You certainly could draft into a power of attorney (for use during lifetime) and a will (for use post death) preventing the fiduciary (attorney in fact or executor) from lending any of your texts, voice, image, writings, etc. to any AI tools and prevent their use for any purpose during life or after you pass away, and/or lay the ground rules for when they can and cannot be used after you pass away,” Sheehan told Ars.

“This could also invoke issues with contract, property and intellectual property rights, and right of publicity as well if AI replicas (image, voice, text, etc.) are being used without authorization,” Sheehan said.

And there are likely more protections for celebrities than for everyday people, Sheehan suggested.

“As far as I know, there is no law” preventing unauthorized non-commercial digital replicas, Sheehan said.

Widely adopted by states, the Revised Uniform Fiduciary Access to Digital Assets Act—which governs who gets access to online accounts of the deceased, like social media or email accounts—could be helpful but isn’t a perfect remedy.

That law doesn’t directly “cover someone’s AI ghost bot, though it may cover some of the digital material some may seek to use to create a ghost bot,” Sheehan said.

“Absent any law” blocking non-commercial digital replicas, Sheehan expects that people’s requests for “no AI resurrections” will likely “be dealt with in the courts and governed by the terms of one’s estate plan, if it is addressed within the estate plan.”

Those potential fights seemingly could get hairy, as “it may be some time before we get any kind of clarity or uniform law surrounding this,” Sheehan suggested.

In the future, Sheehan said, requests prohibiting digital replicas may eventually become “boilerplate language in almost every will, trust, and power of attorney,” just as instructions on digital assets are now.

As “all things AI become more and more a part of our lives,” Sheehan said, “some aspects of AI and its components may also be woven throughout the estate plan regularly.”

“But we definitely aren’t there yet,” she said. “I have had zero clients ask about this.”

Requests for “no AI resurrections” will likely be ignored

Whether loved ones would—or even should—respect requests blocking digital replicas appears to be debatable. But at least one person who built a grief bot wished he’d done more to get his dad’s permission before moving forward with his own creation.

A computer science professor at the University of Washington Bothell, Muhammad Aurangzeb Ahmad, was one of the earliest AI researchers to create a grief bot more than a decade ago after his father died. He built the bot to ensure that his future kids would be able to interact with his father after seeing how incredible his dad was as a grandfather.

When Ahmad started his project, there was no ChatGPT or other advanced AI model to serve as the foundation, so he had to train his own model based on his dad’s data. Putting immense thought into the effort, Ahmad decided to close off the system from the rest of the Internet so that only his dad’s memories would inform the model. To prevent unauthorized chats, he kept the bot on a laptop that only his family could access.

Ahmad was so intent on building a digital replica that felt just like his dad that it didn’t occur to him until after his family started using the bot that he never asked his dad if this was what he wanted. Over time, he realized that the bot was biased to his view of his dad, perhaps even feeling off to his siblings who had a slightly different relationship with their father. It’s unclear if his dad would similarly view the bot as preserving just one side of him.

Ultimately, Ahmad didn’t regret building the bot, and he told Ars he thinks his father “would have been fine with it.”

But he did regret not getting his father’s consent.

For people creating bots today, seeking consent may be appropriate if there’s any chance the bot may be publicly accessed, Ahmad suggested. He told Ars that he would never have been comfortable with the idea of his dad’s digital replica being publicly available because the question of an “accurate representation” would come even more into play, as malicious actors could potentially access it and sully his dad’s memory.

Today, anybody can use ChatGPT’s model to freely create a similar bot with their own loved one’s data. And a wide range of grief tech services have popped up online, including HereAfter AI, SeanceAI, and StoryFile, Axios noted in an October report detailing the latest ways “AI could be used to ‘resurrect’ loved ones.” As this trend continues “evolving very fast,” Ahmad told Ars that estate planning is probably the best way to communicate one’s AI ghost preferences.

But in a recently published article on “The Law of Digital Resurrection,” law professor Victoria Haneman warned that “there is no legal or regulatory landscape against which to estate plan to protect those who would avoid digital resurrection, and few privacy rights for the deceased. This is an intersection of death, technology, and privacy law that has remained relatively ignored until recently.”

Haneman agreed with Sheehan that “existing protections are likely sufficient to protect against unauthorized commercial resurrections”—like when actors or musicians are resurrected for posthumous performances. However, she thinks that for personal uses, digital resurrections may best be blocked not through estate planning but by passing a “right to deletion” that would focus on granting the living or next of kin the rights to delete the data that could be used to create the AI ghost rather than regulating the output.

A “right to deletion” could help people fight inappropriate uses of their loved ones’ data, whether AI is involved or not. After her article was published, a lawyer reached out to Haneman about a client’s deceased grandmother whose likeness was used to create a meme of her dancing in a church. The grandmother wasn’t a public figure, and the client had no idea “why or how somebody decided to resurrect her deceased grandmother,” Haneman told Ars.

Although Haneman sympathized with the client, “if it’s not being used for a commercial purpose, she really has no control over this use,” Haneman said. “And she’s deeply troubled by this.”

Haneman’s article offers a rare deep dive into the legal topic. It sensitively maps out the vague territory of digital rights of the dead and explains how those laws—or the lack thereof—interact with various laws dealing with death, from human remains to property rights.

In it, Haneman also points out that, on balance, the rights of the living typically outweigh the rights of the dead, and even specific instructions on how to handle human remains aren’t generally considered binding. Some requests, like organ donation that can benefit the living, are considered critical, Haneman noted. But there are mixed results on how courts enforce other interests of the dead—like a famous writer’s request to destroy all unpublished work or a pet lover’s insistence to destroy their cat or dog at death.

She told Ars that right now, “a lot of people are like, ‘Why do I care if somebody resurrects me after I’m dead?’ You know, ‘They can do what they want.’ And they think that, until they find a family member who’s been resurrected by a creepy ex-boyfriend or their dead grandmother’s resurrected, and then it becomes a different story.”

Existing law may protect “the privacy interests of the loved ones of the deceased from outrageous or harmful digital resurrections of the deceased,” Haneman noted, but in the case of the dancing grandma, her meme may not be deemed harmful, no matter how much it troubles the grandchild to see her grandma’s memory warped.

Limited legal protections may not matter so much if, culturally, communities end up developing a distaste for digital replicas, particularly if it becomes widely viewed as disrespectful to the dead, Haneman suggested. Right now, however, society is more fixated on solving other problems with deepfakes rather than clarifying the digital rights of the dead. That could be because few people have been impacted so far, or it could also reflect a broader cultural tendency to ignore death, Haneman told Ars.

“We don’t want to think about our own death, so we really kind of brush aside whether or not we care about somebody else being digitally resurrected until it’s in our face,” Haneman said.

Over time, attitudes may change, especially if the so-called “digital afterlife industry” takes off. And there is some precedent that the law could be changed to reinforce any culture shift.

“The throughline revealed by the law of the dead is that a sacred trust exists between the living and the deceased, with an emphasis upon protecting common humanity, such that data afforded no legal status (or personal data of the deceased) may nonetheless be treated with dignity and receive some basic protections,” Haneman wrote.

An alternative path to prevent AI resurrection

Preventing yourself from becoming an AI ghost seemingly now falls in a legal gray zone that policymakers may need to address.

Haneman calls for a solution that doesn’t depend on estate planning, which she warned “is a structurally inequitable and anachronistic approach that maximizes social welfare only for those who do estate planning.” More than 60 percent of Americans die without a will, often including “those without wealth,” as well as women and racial minorities who “are less likely to die with a valid estate plan in effect,” Haneman reported.”We can do better in a technology-based world,” Haneman wrote. “Any modern framework should recognize a lack of accessibility as an obstacle to fairness and protect the rights of the most vulnerable through approaches that do not depend upon hiring an attorney and executing an estate plan.”

Rather than twist the law to “recognize postmortem privacy rights,” Haneman advocates for a path for people resistant to digital replicas that focuses on a right to delete the data that would be used to create the AI ghost.

“Put simply, the deceased may exert control over digital legacy through the right to deletion of data but may not exert broader rights over non-commercial digital resurrection through estate planning,” Haneman recommended.

Sheehan told Ars that a right to deletion would likely involve estate planners, too.

“If this is not addressed in an estate planning document and not specifically addressed in the statute (or deemed under the authority of the executor via statute), then the only way to address this would be to go to court,” Sheehan said. “Even with a right of deletion, the deceased would need to delete said data before death or authorize his executor to do so post death, which would require an estate planning document, statutory authority, or court authority.”

Haneman agreed that for many people, estate planners would still be involved, recommending that “the right to deletion would ideally, from the perspective of estate administration, provide for a term of deletion within 12 months.” That “allows the living to manage grief and open administration of the estate before having to address data management issues,” Haneman wrote, and perhaps adequately balances “the interests of society against the rights of the deceased.”

To Haneman, it’s also the better solution for the people left behind because “creating a right beyond data deletion to curtail unauthorized non-commercial digital resurrection creates unnecessary complexity that overreaches, as well as placing the interests of the deceased over those of the living.”

Future generations may be raised with AI ghosts

If a dystopia that experts paint comes true, Big Tech companies may one day profit by targeting grieving individuals to seize the data of the dead, which could be more easily abused since it’s granted fewer rights than data of the living.

Perhaps in that future, critics suggest, people will be tempted into free trials in moments when they’re missing their loved ones most, then forced to either pay a subscription to continue accessing the bot or else perhaps be subjected to ad-based models where their chats with AI ghosts may even feature ads in the voices of the deceased.

Today, even in a world where AI ghosts aren’t yet compelling ad clicks, some experts have warned that interacting with AI ghosts could cause mental health harms, New Scientist reported, especially if the digital afterlife industry isn’t carefully designed, AI ethicists warned. Some people may end up getting stuck maintaining an AI ghost if it’s left behind as a gift, and ethicists suggested that the emotional weight of that could also eventually take a negative toll. While saying goodbye is hard, letting go is considered a critical part of healing during the mourning process, and AI ghosts may make that harder.

But the bots can be a helpful tool to manage grief, some experts suggest, provided that their use is limited to allow for a typical mourning process or combined with therapy from a trained professional, Al Jazeera reported. Ahmad told Ars that working on his bot has not only kept his father close to him but also helped him think more deeply about relationships and memory.

Haneman noted that people have many ways of honoring the dead. Some erect statues, and others listen to saved voicemails or watch old home movies. For some, just “smelling an old sweater” is a comfort. And creating digital replicas, as creepy as some people might find them, is not that far off from these traditions, Haneman said.

“Feeding text messages and emails into existing AI platforms such as ChatGPT and asking the AI to respond in the voice of the deceased is simply a change in degree, not in kind,” Haneman said.

For Ahmad, the decision to create a digital replica of his dad was a learning experience, and perhaps his experience shows why any family or loved one weighing the option should carefully consider it before starting the process.

In particular, he warns families to be careful introducing young kids to grief bots, as they may not be able to grasp that the bot is not a real person. When he initially saw his young kids growing confused with whether their grandfather was alive or not—the introduction of the bot was complicated by the early stages of the pandemic, a time when they met many relatives virtually—he decided to restrict access to the bot until they were older. For a time, the bot only came out for special events like birthdays.

He also realized that introducing the bot also forced him to have conversations about life and death with his kids at ages younger than he remembered fully understanding those concepts in his own childhood.

Now, Ahmad’s kids are among the first to be raised among AI ghosts. To continually enhance the family’s experience, their father continuously updates his father’s digital replica. Ahmad is currently most excited about recent audio advancements that make it easier to add a voice element. He hopes that within the next year, he might be able to use AI to finally nail down his South Asian father’s accent, which up to now has always sounded “just off.” For others working in this space, the next frontier is realistic video or even augmented reality tools, Ahmad told Ars.

To this day, the bot retains sentimental value for Ahmad, but, as Haneman suggested, the bot was not the only way he memorialized his dad. He also created a mosaic, and while his father never saw it, either, Ahmad thinks his dad would have approved.

“He would have been very happy,” Ahmad said.

There’s no way to predict how future generations may view grief tech. But while Ahmad said he’s not sure he’d be interested in an augmented reality interaction with his dad’s digital replica, kids raised seeing AI ghosts as a natural part of their lives may not be as hesitant to embrace or even build new features. Talking to Ars, Ahmad fondly remembered his young daughter once saw that he was feeling sad and came up with her own AI idea to help her dad feel better.

“It would be really nice if you can just take this program and we build a robot that looks like your dad, and then add it to the robot, and then you can go and hug the robot,” she said, according to her father’s memory.

Photo of Ashley Belanger

Ashley is a senior policy reporter for Ars Technica, dedicated to tracking social impacts of emerging policies and new technologies. She is a Chicago-based journalist with 20 years of experience.

How to draft a will to avoid becoming an AI ghost—it’s not easy Read More »