Sep 26 2024
Want AI that flags hateful content? Build it.

Humane Intelligence, an organization focused on evaluating AI systems, is launching a competition that challenges developers to create a computer vision model that can track hateful image-based propaganda online. Organized in partnership with the Nordic counterterrorism group Revontulet, the bounty program opens September 26. It is open to anyone, 18 or older, who wants to compete and promises $10,000 in prizes for the winners.

This is the second of a planned series of 10 “algorithmic bias bounty” programs from Humane Intelligence, a nonprofit that investigates the societal impact of AI and was launched by the prominent AI researcher Rumman Chowdhury in 2022. The series is supported by Google.org, Google’s philanthropic arm.

“The goal of our bounty programs is to, number one, teach people how to do algorithmic assessments,” says Chowdhury, “but also, number two, to actually solve a pressing problem in the field.” 

Its first challenge asked participants to evaluate gaps in sample data sets that may be used to train models—gaps that may specifically produce output that is factually inaccurate, biased, or misleading. 

The second challenge deals with tracking hateful imagery online—an incredibly complex problem. Generative AI has enabled an explosion in this type of content, and AI is also deployed to manipulate content so that it won’t be removed from social media. For example, extremist groups may use AI to slightly alter an image that a platform has already banned, quickly creating hundreds of different copies that can’t easily be flagged by automated detection systems. Extremist networks can also use AI to embed a pattern into an image that is undetectable to the human eye but will confuse and evade detection systems. It has essentially created a cat-and-mouse game between extremist groups and online platforms. 

The challenge asks for two different models. The first, a task for those with intermediate skills, is one that identifies hateful images; the second, considered an advanced challenge, is a model that attempts to fool the first one. “That actually mimics how it works in the real world,” says Chowdhury. “The do-gooders make one approach, and then the bad guys make an approach.” The goal is to engage machine-learning researchers on the topic of mitigating extremism, which may lead to the creation of new models that can effectively screen for hateful images.  

A core challenge of the project is that hate-based propaganda can be very dependent on its context. And someone who doesn’t have a deep understanding of certain symbols or signifiers may not be able to tell what even qualifies as propaganda for a white nationalist group. 

“If [the model] never sees an example of a hateful image from a part of the world, then it’s not going to be any good at detecting it,” says Jimmy Lin, a professor of computer science at the University of Waterloo, who is not associated with the bounty program.

This effect is amplified around the world, since many models don’t have a vast knowledge of cultural contexts. That’s why Humane Intelligence decided to partner with a non-US organization for this particular challenge. “Most of these models are often fine-tuned to US examples, which is why it’s important that we’re working with a Nordic counterterrorism group,” says Chowdhury.

Lin, though, warns that solving these problems may require more than algorithmic changes. “We have models that generate fake content. Well, can we develop other models that can detect fake generated content? Yes, that is certainly one approach to it,” he says. “But I think overall, in the long run, training, literacy, and education efforts are actually going to be more beneficial and have a longer-lasting impact. Because you’re not going to be subjected to this cat-and-mouse game.”

The challenge will run till November 7, 2024. Two winners will be selected, one for the intermediate challenge and another for the advanced; they will receive $4,000 and $6,000, respectively. Participants will also have their models reviewed by Revontulet, which may decide to add them to its current suite of tools to combat extremism. 

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Sep 25 2024
An AI script editor could help decide what films get made in Hollywood

Every day across Hollywood, scores of film school graduates and production assistants work as script readers. Their job is to find the diamonds in the rough from the 50,000 or so screenplays pitched each year and flag any worth pursuing further. Each script runs anywhere from 100 to 150 pages, and it can take half a day to read one and write up a “coverage,” or summary of the strengths and weaknesses. With only about 50 of these scripts selling in a given year, readers are trained to be ruthless. 

Now the film-focused tech company Cinelytic, which works with major studios like Warner Bros. and Sony Pictures to analyze film budgets and box office potential, aims to offer script feedback with generative AI. 

Today it launched a new tool called Callaia, which amateur writers and professional script readers alike can use to analyze scripts at $79 each. Using AI, it takes Callaia less than a minute to write its own coverage, which includes a synopsis, a list of comparable films, grades for areas like dialogue and originality, and actor recommendations. It also makes a recommendation on whether or not the film should be financed, giving it a rating of “pass,” “consider,” “recommend,” or “strongly recommend.” Though the foundation of the tool is built with ChatGPT’s API, the team had to coach the model on script-specific tasks like evaluating genres and writing a movie’s logline, which summarize the story in a sentence. 

“It helps people understand the script very quickly,” says Tobias Queisser, Cinelytic’s cofounder and CEO, who also had a career as a film producer. “You can look at more stories and more scripts, and not eliminate them based on factors that are detrimental to the business of finding great content.”

The idea is that Callaia will give studios a more analytical way to predict how a script may perform on the screen before spending on marketing or production. But, the company says, it’s also meant to ease the bottleneck that script readers create in the filmmaking process. With such a deluge to sort through, many scripts can make it to decision-makers only if they have a recognizable name attached. An AI-driven tool would democratize the script selection process and allow better scripts and writers to be discovered, Queisser says.

The tool’s introduction may further fuel the ongoing Hollywood debate about whether AI will help or harm its creatives. Since the public launch of ChatGPT in late 2022, the technology has drawn concern everywhere from writers’ rooms to special effects departments, where people worry that it will cheapen, augment, or replace human talent.  

In this case, Callaia’s success will depend on whether it can provide critical feedback as well as a human script reader can. 

That’s a challenge because of what GPT and other AI models are built to do, according to Tuhin Chakrabarty, a researcher who studied how well AI can analyze creative works during his PhD in computer science at Columbia University. In one of his studies, Chakrabarty and his coauthors had various AI models and a group of human experts—including professors of creative writing and a screenwriter—analyze the quality of 48 stories, 12 that appeared in the New Yorker and the rest of which were AI-generated. His team found that the two groups virtually never agreed on the quality of the works. 

“Whenever you ask an AI model about the creativity of your work, it is never going to say bad things,” Chakrabarty says. “It is always going to say good things, because it’s trained to be a helpful, polite assistant.”

Cinelytic CTO Dev Sen says this trait did present a hurdle in the design of Callaia, and that the initial output of the model was overly positive. That improved with time and tweaking. “We don’t necessarily want to be overly critical, but aim for a more balanced analysis that points out both strengths and weaknesses in the script,” he says. 

Vir Srinivas, an independent filmmaker whose film Orders from Above won Best Historical Film at Cannes in 2021, agreed to look at an example of Callaia’s output to see how well the AI model can analyze a script. I showed him what the model made of a 100-page script about a jazz trumpeter on a journey of self-discovery in San Francisco, which Cinelytic provided. Srinivas says that the coverage generated by the model didn’t go deep enough to present genuinely helpful feedback to a screenwriter.

“It’s approaching the script in too literal a sense and not a metaphorical one—something which human audiences do intuitively and unconsciously,” he says. “It’s as if it’s being forced to be diplomatic and not make any waves.”

There were other flaws, too. For example, Callaia predicted that the film would need a budget of just $5 to $10 million but also suggested that expensive A-listers like Paul Rudd would have been well suited for the lead role.

Cinelytic says it’s currently at work improving the actor recommendation component, and though the company did not provide data on how well its model analyzes a given script, Sen says feedback from 100 script readers who beta-tested the model was overwhelmingly positive. “Most of them were pretty much blown away, because they said that the coverages were on the order of, if not better than, the coverages they’re used to,” he says. 

Overall, Cinelytic is pitching Callaia as a tool meant to quickly provide feedback on lots of scripts, not to replace human script readers, who will still read and adjust the tool’s findings. Queisser, who is cognizant that whether AI can effectively write or edit creatively is hotly contested in Hollywood, is hopeful the tool will allow script readers to more quickly identify standout scripts while also providing an efficient source of feedback for writers.

“Writers that embrace our tool will have something that can help them refine their scripts and find more opportunities,” he says. “It’s positive for both sides.”

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Sep 25 2024
OpenAI released its advanced voice mode to more people. Here’s how to get it.

OpenAI is broadening access to Advanced Voice Mode, a feature of ChatGPT that allows you to speak more naturally with the AI model. It allows you to interrupt its responses midsentence, and it can sense and interpret your emotions from your tone of voice and adjust its responses accordingly. 

These features were teased back in May when OpenAI unveiled GPT-4o, but they were not released until July—and then just to an invite-only group. (At least initially, there seem to have been some safety issues with the model; OpenAI gave several Wired reporters access to the voice mode back in May, but the magazine reported that the company “pulled it the next morning, citing safety concerns.”) Users who’ve been able to try it have largely described the model as an impressively fast, dynamic, and realistic voice assistant—which has made its limited availability particularly frustrating to some other OpenAI users. 

Today is the first time OpenAI has promised to bring the new voice mode to a wide range of users. Here’s what you need to know.

What can it do? 

Though ChatGPT currently offers a standard voice mode to paid users, its interactions can be clunky. In the mobile app, for example, you can’t interrupt the model’s often long-winded responses with your voice, only with a tap on the screen. The new version fixes that, and also promises to modify its responses on the basis of the emotion it’s sensing from your voice. As with other versions of ChatGPT, users can personalize the voice mode by asking the model to remember facts about themselves. The new mode also has improved its pronunciation of words in non-English languages.

AI investor Allie Miller posted a demo of the tool in August, which highlighted a lot of the same strengths of OpenAI’s own release videos: The model is fast and adept at changing its accent, tone, and content to match your needs.

The update also adds new voices. Shortly after the launch of GPT-4o, OpenAI was criticized for the similarity between the female voice in its demo videos, named Sky, and that of Scarlett Johansson, who played an AI love interest in the movie Her. OpenAI then removed the voice. Now it has launched five new voices, named Arbor, Maple, Sol, Spruce, and Vale, which will be available in both the standard and advanced voice modes. MIT Technology Review has not heard them yet, but OpenAI says they were made using professional voice actors from around the world. “We interviewed dozens of actors to find those with the qualities of voices we feel people will enjoy talking to for hours—warm, approachable, inquisitive, with some rich texture and tone,” a company spokesperson says. 

Who can access it and when?

For now, OpenAI is rolling out access to Advanced Voice Mode to Plus users, who pay $20 per month for a premium version, and Team users, who pay $30 per month and have higher message limits. The next group to receive access will be those in the Enterprise and Edu tiers. The exact timing, though, is vague; an OpenAI spokesperson says the company will “gradually roll out access to all Plus and Team users and will roll out to Enterprise and Edu tiers starting next week.” The company hasn’t committed to a firm deadline for when all users in these categories will have access. A message in the ChatGPT app indicates that all Plus users will have access by “the end of fall.”

There are geographic limitations. The new feature is not yet available in the EU, the UK, Switzerland, Iceland, Norway, or Liechtenstein.

There is no immediate plan to release Advanced Voice Mode to free users. (The standard mode remains available to all paid users.)

What steps have been taken to make sure it’s safe?

As the company noted upon the initial release in July and again emphasized this week, Advanced Voice Mode has been safety-tested by external experts “who collectively speak a total of 45 different languages, and represent 29 different geographies.” The GPT-4o system card details how the underlying model handles issues like generating violent or erotic speech, imitating voices without their consent, or generating copyrighted content. 

Still, OpenAI’s models are not open-source. Compared with such models, which are more transparent about their training data and the “model weights” that govern how the AI produces responses, OpenAI’s closed-source models are harder for independent researchers to evaluate from the perspective of safety, bias, and harm.

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Sep 21 2024
Why virologists are getting increasingly nervous about bird flu

Bird flu has been spreading in dairy cows in the US—and the scale of the spread is likely to be far worse than it looks. In addition, 14 human cases have been reported in the US since March. Both are worrying developments, say virologists, who fear that the country’s meager response to the virus is putting the entire world at risk of another pandemic.

The form of bird flu that has been spreading over the last few years has been responsible for the deaths of millions of birds and tens of thousands of marine and land mammals. But infections in dairy cattle, first reported back in March, brought us a step closer to human spread. Since then, the situation has only deteriorated. The virus appears to have passed from cattle to poultry on multiple occasions. “If that virus sustains in dairy cattle, they will have a problem in their poultry forever,” says Thomas Peacock, a virologist at the Pirbright Institute in Woking, UK.

Worse, this form of bird flu that is now spreading among cattle could find its way back into migrating birds. It might have happened already. If that’s the case, we can expect these birds to take the virus around the world.

“It’s really troubling that we’re not doing enough right now,” says Seema Lakdawala, a virologist at the Emory University School of Medicine in Atlanta, Georgia. “I am normally very moderate in terms of my pandemic-scaredness, but the introduction of this virus into cattle is really troubling.”

Not just a flu for birds

Bird flu is so named because it spreads stably in birds. The type of H5N1 that has been decimating bird populations for the last few years was first discovered in the late 1990s. But in 2020, H5N1 began to circulate in Europe “in a big way,” says Peacock. The virus spread globally, via migrating ducks, geese, and other waterfowl. In a process that took months and years, the virus made it to the Americas, Africa, Asia, and eventually even Antarctica, where it was detected earlier this year.

And while many ducks and geese seem to be able to survive being infected with the virus, other bird species are much more vulnerable. H5N1 is especially deadly for chickens, for example—their heads swell, they struggle to breathe, and they experience extreme diarrhea. Seabirds like puffins and guillemots also seem to be especially susceptible to the virus, although it’s not clear why. Over the last few years, we’ve seen the worst ever outbreak of bird flu in birds. Millions of farmed birds have died, and an unknown number of wild birds—in the tens of thousands at the very least—have also succumbed. “We have no idea how many just fell into the sea and were never seen again,” says Peacock.

Alarmingly, animals that hunt and scavenge affected birds have also become infected with the virus. The list of affected mammals includes bears, foxes, skunks, otters, dolphins, whales, sea lions, and many more. Some of these animals appear to be able to pass the virus to other members of their species. In 2022, an outbreak of H5N1 in sea lions that started in Chile spread to Argentina and eventually to Uruguay and Brazil. At least 30,000 died. The sea lions may also have passed the virus to nearby elephant seals in Argentina, around 17,000 of which have succumbed to the virus.

This is bad news—not just for the affected animals, but for people, too. It’s not just a bird flu anymore. And when a virus can spread in other mammals, it’s a step closer to being able to spread in humans. That is even more likely when the virus spreads in an animal that people tend to spend a lot of time interacting with.

This is partly why the virus’s spread in dairy cattle is so troubling. The form of the virus that is spreading in cows is slightly different from the one that had been circulating in migrating birds, says Lakdawala. The mutations in this virus have likely enabled it to spread more easily among the animals.

Evidence suggests that the virus is spreading through the use of shared milking machinery within cattle herds. Infected milk can contaminate the equipment, allowing the virus to infect the udder of another cow. The virus is also spreading between herds, possibly by hitching a ride on people that work on multiple farms, or via other animals, or potentially via airborne droplets.

Milk from infected cows can look thickened and yogurt-like, and farmers tend to pour it down drains. This ends up irrigating farms, says Lakdawala. “Unless the virus is inactivated, it just remains infectious in the environment,” she says. Other animals could be exposed to the virus this way.

Hidden infections

So far, 14 states have reported a total of 208 infected cattle herds. Some states have reported only one or two cases among their cattle. But this is extremely unlikely to represent the full picture, given how rapidly the virus is spreading among herds in states that are doing more testing, says Peacock. In Colorado, where state-licensed dairy farms that sell pasteurized milk are required to submit milk samples for weekly testing, 64 herds have been reported to be affected. Neighboring Wyoming, which does not have the same requirements, has reported only one affected herd.

We don’t have a good idea of how many people have been infected either, says Lakdawala. The official count from the CDC is 14 people since April 2024, but testing is not routine, and because symptoms are currently fairly mild in people, we’re likely to be missing a lot of cases.

“It’s very frustrating, because there are just huge gaps in the data that’s coming out,” says Peacock. “I don’t think it’s unfair to say that a lot of outside observers don’t think this outbreak is being taken particularly seriously.”

And the virus is already spreading from cows back into wild birds and poultry, says Lakdawala: “There is definitely a concern that the virus is going to [become more widespread] in birds and cattle … but also other animals that ruminate, like goats.”

It may already be too late to rid America’s cattle herds of the bird flu virus. If it continues to circulate, it could become stable in the population. This is what has happened with flu in pigs around the world. That could also spell disaster—not only would the virus represent a constant risk to humans and other animals that come into contact with the cows, but it could also evolve over time. We can’t predict how this evolution might take shape, but there’s a chance the result could be a form of the virus that is better at spreading in people or causing fatal infections.

So far, it is clear that the virus has mutated but hasn’t yet acquired any of these more dangerous mutations, says Michael Tisza, a bioinformatics scientist at Baylor College of Medicine in Houston. That being said, Tisza and his colleagues have been looking for the virus in wastewater from 10 cities in Texas—and they have found H5N1 in all of them.

Tisza and his colleagues don’t know where this virus is coming from—whether it’s coming from birds, milk, or infected people, for example. But the team didn’t find any signal of the virus in wastewater during 2022 or 2023, when there were outbreaks in migratory birds and poultry. “In 2024, it’s been a different story,” says Tisza. “We’ve seen it a lot.”

Together, the evidence that the virus is evolving and spreading among mammals, and specifically cattle, has put virologists on high alert. “This virus is not causing a human pandemic right now, which is great,” says Tisza. “But it is a virus of pandemic potential.”

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Sep 21 2024
How AI can help spot wildfires

This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, sign up here.

In February 2024, a broken utility pole brought down power lines near the small town of Stinnett, Texas. In the following weeks, the fire reportedly sparked by that equipment grew to burn over 1 million acres, the biggest wildfire in the state’s history.

Anything from stray fireworks to lightning strikes can start a wildfire. While it’s natural for many ecosystems to see some level of fire activity, the hotter, drier conditions brought on by climate change are fueling longer fire seasons with larger fires that burn more land.

This means that the need to spot wildfires earlier is becoming ever more crucial, and some groups are turning to technology to help. My colleague James Temple just wrote about a new effort from Google to fund an AI-powered wildfire-spotting satellite constellation. Read his full story for the details, and in the meantime, let’s dig into how this project fits into the world of fire-detection tech and some of the challenges that lie ahead.

The earliest moments in the progression of a fire can be crucial. Today, many fires are reported to authorities by bystanders who happen to spot them and call emergency services. Technologies could help officials by detecting fires earlier, well before they grow into monster blazes.

One such effort is called FireSat. It’s a project from the Earth Fire Alliance, a collaboration between Google’s nonprofit and research arms, the Environmental Defense Fund, Muon Space (a satellite company), and others. This planned system of 52 satellites should be able to spot fires as small as five by five meters (about 16 feet by 16 feet), and images will refresh every 20 minutes.

These wouldn’t be the first satellites to help with wildfire detection, but many existing efforts can either deliver high-resolution images or refresh often—not both, as the new project is aiming to do.

A startup based in Germany, called OroraTech, is also working to launch new satellites that specialize in wildfire detection. The small satellites (around the size of a shoebox) will orbit close to Earth and use sensors that detect heat. The company’s long-term goal is to launch 100 of the satellites into space and deliver images every 30 minutes.

Other companies are staying on Earth, deploying camera stations that can help officials identify, confirm, and monitor fires. Pano AI is using high-tech camera stations to try to spot fires earlier. The company mounts cameras on high vantage points, like the tops of mountains, and spins them around to get a full 360-degree view of the surrounding area. It says the tech can spot wildfire activity within a 15-mile radius. The cameras pair up with algorithms to automatically send an alert to human analysts when a potential fire is detected.

Having more tools to help detect wildfires is great. But whenever I hear about such efforts, I’m struck by a couple of major challenges for this field. 

First, prevention of any sort can often be undervalued, since a problem that never happens feels much less urgent than one that needs to be solved.

Pano AI, which has a few camera stations deployed, points to examples in which its technology detected fires earlier than bystander reports. In one case in Oregon, the company’s system issued a warning 14 minutes before the first emergency call came in, according to a report given to TechCrunch.

Intuitively, it makes sense that catching a blaze early is a good thing. And modeling can show what might have happened if a fire hadn’t been caught early. But it’s really difficult to determine the impact of something that didn’t happen. These systems will need to be deployed for a long time, and researchers will need to undertake large-scale, systematic studies, before we’ll be able to say for sure how effective they are at preventing damaging fires. 

The prospect of cost is also a tricky piece of this for me to wrap my head around. It’s in the public interest to prevent wildfires that will end up producing greenhouse-gas emissions, not to mention endangering human lives. But who’s going to pay for that?

Each of PanoAI’s stations costs something like $50,000 per year. The company’s customers include utilities, which have a vested interest in making sure their equipment doesn’t start fires and watching out for blazes that could damage its infrastructure.

The electric utility Xcel, whose equipment allegedly sparked that fire in Texas earlier this year, is facing lawsuits over its role. And utilities can face huge costs after fires. Last year’s deadly blazes in Hawaii caused billions of dollars in damages, and Hawaiian Electric recently agreed to pay roughly $2 billion for its role in those fires. 

The proposed satellite system from the Earth Fire Alliance will cost more than $400 million all told. The group has secured about two-thirds of what it needs for the first phase of the program, which includes the first four launches, but it’ll need to raise a lot more money to make its AI-powered wildfire-detecting satellite constellation a reality.

Now read the rest of The Spark

Related reading

Read more about how an AI-powered satellite constellation can help spot wildfires faster here. 

Other companies are aiming to use balloons that will surf on wind currents to track fires. Urban Sky is deploying balloons in Colorado this year. 

Satellite images can also be used to tally up the damage and emissions caused by fires. Earlier this year I wrote about last year’s Canadian wildfires, which produced more emissions than the fossil fuels in most countries in 2023. 

Another thing

We’re just two weeks away from EmTech MIT, our signature event on emerging technologies. I’ll be on stage speaking with tech leaders on topics like net-zero buildings and emissions from Big Tech. We’ll also be revealing our 2024 list of Climate Tech Companies to Watch. 

For a preview of the event, check out this conversation I had with MIT Technology Review executive editor Amy Nordrum and editor in chief Mat Honan. You can register to join us on September 30 and October 1 at the MIT campus or online—hope to see you there!

Keeping up with climate  

The US Postal Service is finally getting its long-awaited electric vehicles. They’re funny-looking, and the drivers seem to love them already. (Associated Press)

→ Check out this timeline I made in December 2022 of the multi-year saga it took for the agency to go all in on EVs. (MIT Technology Review)

Microsoft is billing itself as a leader in AI for climate innovation. At the same time, the tech giant is selling its technology to oil and gas companies. Check out this fascinating investigation from my former colleague Karen Hao. (The Atlantic)

Imagine solar panels that aren’t affected by a cloudy day … because they’re in space. Space-based solar power sounds like a dream, but advances in solar tech and falling launch costs have proponents arguing that it’s a dream closer than ever to becoming reality. Many are still skeptical. (Cipher)

Norway is the first country with more EVs on the road than gas-powered cars. Diesel vehicles are still the most common, though. (Washington Post) 

The emissions cost of delivering Amazon packages keeps ticking up. A new report from Stand.earth estimates that delivery emissions have increased by 75% since just 2019. (Wired)

BYD has been dominant in China’s EV market. The company is working to expand, but to compete in the UK and Europe, it will need to win over wary drivers. (Bloomberg)

Some companies want to make air-conditioning systems in big buildings smarter to help cut emissions. Grid-interactive efficient buildings can cut energy costs and demand at peak hours. (Canary Media)

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Sep 21 2024
Flu season is coming—and so is the risk of an all-new bird flu

This article first appeared in The Checkup, MIT Technology Review’s weekly biotech newsletter. To receive it in your inbox every Thursday, and read articles like this first, sign up here.

September will soon be drawing to a close. The kids are back to school, and those of us in the Northern Hemisphere are experiencing the joys the end of summer brings: the cooling temperatures, the falling leaves, and, inevitably, the start of flu season.

I was reminded of that fact when my littlest woke me for an early-morning cuddle, sneezed into my face, and wiped her nose on my pajamas. I booked her flu vaccine the next morning.

In the US, the Centers for Disease Control and Prevention recommends the flu vaccine for everyone over six months old. This year, following the spread of the “bird flu” H5N1 in cattle, the CDC is especially urging dairy farm workers to get vaccinated. At the end of July, the organization announced a $10 million plan to deliver free flu shots to people who work with livestock.

The goal is not only to protect those workers from seasonal flu, but to protect us all from a potentially more devastating consequence: the emergence of a new form of flu that could trigger another pandemic. That hasn’t happened yet, but unfortunately, it’s looking increasingly possible.

First, it’s worth noting that flu viruses experience subtle changes in their genetic makeup all the time. This allows the virus to evolve rapidly, and it is why flu vaccines need to be updated every year, depending on which form of the virus is most likely to be circulating.

More dramatic genetic changes can take place when multiple flu viruses infect a single animal. The genome of a flu virus is made up of eight segments. When two different viruses end up in the same cell, they can swap segments with each other.

These swapping events can create all-new viruses. It’s impossible to predict exactly what will result, but there’s always a chance that the new virus will be easily spread or cause more serious disease than either of its predecessors.

The fear is that farm workers who get seasonal flu could also pick up bird flu from cows. Those people could become unwitting incubators for deadly new flu strains and end up passing them on to the people around them. “That is exactly how we think pandemics start,” says Thomas Peacock, a virologist at the Pirbright Institute in Woking, UK.

The virus responsible for the 2009 swine flu pandemic is thought to have come about this way. Its genome suggested it had resulted from the genetic reassortment of a mix of flu viruses, including some thought to largely infect pigs and others that originated in birds. Viruses with genes from both a human flu and a bird flu are thought to have been responsible for pandemics in 1918, 1957, and 1968, too.

The CDC is hoping that vaccinating these individuals against seasonal flu might lower the risk of history repeating. But unfortunately, it’s not an airtight solution. For a start, not everyone will get vaccinated. Around 45% of US agricultural workers are undocumented migrants, a group that tends to have low vaccination rates. 

Even if every farm worker were to be vaccinated, not all of them would be fully protected against getting sick with flu. The flu vaccine used in the US in 2019-2020 was 39% effective, but the one used in the 2004-2005 flu season was only 10% effective.

“It’s not a bad idea, but I don’t think it can get anywhere close to mitigating the underlying risk,” says Peacock.

I last reported on bird flu in February 2023. Back then, the virus was decimating bird populations, but there were no signs that it was making the jump to mammals, and it didn’t appear to be posing a risk to humans. “We don’t need to panic about a bird flu pandemic—yet,” was my conclusion at the time. Today, the picture is different. After speaking to virologists and scientists who are trying to track the spread of the current bird flu, I’ll admit that I am much more concerned about the potential for another pandemic.

The main advice for people who don’t work on farms is to avoid raw milk and dead animals, both of which could be harboring the virus. For the most part, we’re reliant on government agencies to monitor and limit the spread of this virus. And the limited actions that have been taken to date don’t exactly inspire much confidence.

“The barn door’s already open,” says Peacock. “This virus is already out and about.”

Now read the rest of The Checkup

Read more from MIT Technology Review’s archive

We don’t know how many dairy herds in the US are infected with H5N1 as the virus continues to spread. It could end up sticking around in farms forever, virologists told me earlier this week.

Manufacturing flu vaccines is a slow process that relies on eggs. But scientists hope mRNA flu vaccines could offer a quicker, cheaper, and more effective alternative.

Some flu vaccines are already made without eggs. One makes use of a virus synthesized in insect cells. Egg-free vaccines might even work better than those made using eggs, as Cassandra Willyard reported earlier this year.

Chickens are especially vulnerable to H5N1. Some scientists are exploring ways to edit the animals’ genes to make them more resilient to the virus, as Abdullahi Tsanni reported last year.

From around the web

Microplastics are everywhere. They even get inside our brains, possibly via our noses. (JAMA Network Open)

The majority of face transplants survive for at least 10 years, research has found. Of the 50 first face transplants, which were carried out across 11 countries, 85% survived for five years, and 74% for 10 years. (JAMA Surgery)

Don’t throw away that placenta! The organ holds clues to health and disease, and instead of being disposed of after birth, it should be carefully studied instead, scientists say. (Trends in Molecular Medicine)

In June, the drug lenacapavir was shown to be 100% effective at preventing HIV in women and adolescent girls. But while the drug was tested on women in Africa, it remains unavailable to most of them. (STAT)

We’re still getting to grips with what endometriosis is, how it works, and how to treat it. Women with the condition appear to have differences in their brain’s gray matter that can’t be explained by pelvic pain alone. (Human Reproduction)

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Sep 21 2024
AI models let robots carry out tasks in unfamiliar environments

It’s tricky to get robots to do things in environments they’ve never seen before. Typically, researchers need to train them on new data for every new place they encounter, which can become very time-consuming and expensive.

Now researchers have developed a series of AI models that teach robots to complete basic tasks in new surroundings without further training or fine-tuning. The five AI models, called robot utility models (RUMs), allow machines to complete five separate tasks—opening doors and drawers, and picking up tissues, bags, and cylindrical objects—in unfamiliar environments with a 90% success rate. 

The team, consisting of researchers from New York University, Meta, and the robotics company Hello Robot, hopes its findings will make it quicker and easier to teach robots new skills while helping them function within previously unseen domains. The approach could make it easier and cheaper to deploy robots in our homes.

“In the past, people have focused a lot on the problem of ‘How do we get robots to do everything?’ but not really asking ‘How do we get robots to do the things that they do know how to do—everywhere?’” says Mahi Shafiullah, a PhD student at New York University who worked on the project. “We looked at ‘How do you teach a robot to, say, open any door, anywhere?’”

Teaching robots new skills generally requires a lot of data, which is pretty hard to come by. Because robotic training data needs to be collected physically—a time-consuming and expensive undertaking—it’s much harder to build and scale training databases for robots than it is for types of AI like large language models, which are trained on information scraped from the internet.

To make it faster to gather the data essential for teaching a robot a new skill, the researchers developed a new version of a tool it had used in previous research: an iPhone attached to a cheap reacher-grabber stick, the kind typically used to pick up trash. 

The team used the setup to record around 1,000 demonstrations in 40 different environments, including homes in New York City and Jersey City, for each of the five tasks—some of which had been gathered as part of previous research. Then they trained learning algorithms on the five data sets to create the five RUM models.

These models were deployed on Stretch, a robot consisting of a wheeled unit, a tall pole, and a retractable arm holding an iPhone, to test how successfully they were able to execute the tasks in new environments without additional tweaking. Although they achieved a completion rate of 74.4%, the researchers were able to increase this to a 90% success rate when they took images from the iPhone and the robot’s head-mounted camera,  gave them to OpenAI’s recent GPT-4o LLM model, and asked it if the task had been completed successfully. If GPT-4o said no, they simply reset the robot and tried again.

A significant challenge facing roboticists is that training and testing their models in lab environments isn’t representative of what could happen in the real world, meaning research that helps machines to behave more reliably in new settings is much welcomed, says Mohit Shridhar, a research scientist specializing in robotic manipulation who wasn’t involved in the work. 

“It’s nice to see that it’s being evaluated in all these diverse homes and kitchens, because if you can get a robot to work in the wild in a random house, that’s the true goal of robotics,” he says.

The project could serve as a general recipe to build other utility robotics models for other tasks, helping to teach robots new skills with minimal extra work and making it easier for people who aren’t trained roboticists to deploy future robots in their homes, says Shafiullah.

“The dream that we’re going for is that I could train something, put it on the internet, and you should be able to download and run it on a robot in your home,” he says.

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Sep 19 2024
Why we need an AI safety hotline

In the past couple of years, regulators have been caught off guard again and again as tech companies compete to launch ever more advanced AI models. It’s only a matter of time before labs release another round of models that pose new regulatory challenges. We’re likely just weeks away, for example, from OpenAI’s release of ChatGPT-5, which promises to push AI capabilities further than ever before. As it stands, it seems there’s little anyone can do to delay or prevent the release of a model that poses excessive risks.

Testing AI models before they’re released is a common approach to mitigating certain risks, and it may help regulators weigh up the costs and benefits—and potentially block models from being released if they’re deemed too dangerous. But the accuracy and comprehensiveness of these tests leaves a lot to be desired. AI models may “sandbag” the evaluation—hiding some of their capabilities to avoid raising any safety concerns. The evaluations may also fail to reliably uncover the full set of risks posed by any one model. Evaluations likewise suffer from limited scope—current tests are unlikely to uncover all the risks that warrant further investigation. There’s also the question of who conducts the evaluations and how their biases may influence testing efforts. For those reasons, evaluations need to be used alongside other governance tools. 

One such tool could be internal reporting mechanisms within the labs. Ideally, employees should feel empowered to regularly and fully share their AI safety concerns with their colleagues, and they should feel those colleagues can then be counted on to act on the concerns. However, there’s growing evidence that, far from being promoted, open criticism is becoming rarer in AI labs. Just three months ago, 13 former and current workers from OpenAI and other labs penned an open letter expressing fear of retaliation if they attempt to disclose questionable corporate behaviors that fall short of breaking the law. 

How to sound the alarm

In theory, external whistleblower protections could play a valuable role in the detection of AI risks. These could protect employees fired for disclosing corporate actions, and they could help make up for inadequate internal reporting mechanisms. Nearly every state has a public policy exception to at-will employment termination—in other words, terminated employees can seek recourse against their employers if they were retaliated against for calling out unsafe or illegal corporate practices. However, in practice this exception offers employees few assurances. Judges tend to favor employers in whistleblower cases. The likelihood of AI labs’ surviving such suits seems particularly high given that society has yet to reach any sort of consensus as to what qualifies as unsafe AI development and deployment. 

These and other shortcomings explain why the aforementioned 13 AI workers, including ex-OpenAI employee William Saunders, called for a novel “right to warn.” Companies would have to offer employees an anonymous process for disclosing risk-related concerns to the lab’s board, a regulatory authority, and an independent third body made up of subject-matter experts. The ins and outs of this process have yet to be figured out, but it would presumably be a formal, bureaucratic mechanism. The board, regulator, and third party would all need to make a record of the disclosure. It’s likely that each body would then initiate some sort of investigation. Subsequent meetings and hearings also seem like a necessary part of the process. Yet if Saunders is to be taken at his word, what AI workers really want is something different. 

When Saunders went on the Big Technology Podcast to outline his ideal process for sharing safety concerns, his focus was not on formal avenues for reporting established risks. Instead, he indicated a desire for some intermediate, informal step. He wants a chance to receive neutral, expert feedback on whether a safety concern is substantial enough to go through a “high stakes” process such as a right-to-warn system. Current government regulators, as Saunders says, could not serve that role. 

For one thing, they likely lack the expertise to help an AI worker think through safety concerns. What’s more, few workers will pick up the phone if they know it’s a government official on the other end—that sort of call may be “very intimidating,” as Saunders himself said on the podcast. Instead, he envisages being able to call an expert to discuss his concerns. In an ideal scenario, he’d be told that the risk in question does not seem that severe or likely to materialize, freeing him up to return to whatever he was doing with more peace of mind. 

Lowering the stakes

What Saunders is asking for in this podcast isn’t a right to warn, then, as that suggests the employee is already convinced there’s unsafe or illegal activity afoot. What he’s really calling for is a gut check—an opportunity to verify whether a suspicion of unsafe or illegal behavior seems warranted. The stakes would be much lower, so the regulatory response could be lighter. The third party responsible for weighing up these gut checks could be a much more informal one. For example, AI PhD students, retired AI industry workers, and other individuals with AI expertise could volunteer for an AI safety hotline. They could be tasked with quickly and expertly discussing safety matters with employees via a confidential and anonymous phone conversation. Hotline volunteers would have familiarity with leading safety practices, as well as extensive knowledge of what options, such as right-to-warn mechanisms, may be available to the employee. 

As Saunders indicated, few employees will likely want to go from 0 to 100 with their safety concerns—straight from colleagues to the board or even a government body. They are much more likely to raise their issues if an intermediary, informal step is available.

Studying examples elsewhere

The details of how precisely an AI safety hotline would work deserve more debate among AI community members, regulators, and civil society. For the hotline to realize its full potential, for instance, it may need some way to escalate the most urgent, verified reports to the appropriate authorities. How to ensure the confidentiality of hotline conversations is another matter that needs thorough investigation. How to recruit and retain volunteers is another key question. Given leading experts’ broad concern about AI risk, some may be willing to participate simply out of a desire to lend a hand. Should too few folks step forward, other incentives may be necessary. The essential first step, though, is acknowledging this missing piece in the puzzle of AI safety regulation. The next step is looking for models to emulate in building out the first AI hotline. 

One place to start is with ombudspersons. Other industries have recognized the value of identifying these neutral, independent individuals as resources for evaluating the seriousness of employee concerns. Ombudspersons exist in academia, nonprofits, and the private sector. The distinguishing attribute of these individuals and their staffers is neutrality—they have no incentive to favor one side or the other, and thus they’re more likely to be trusted by all. A glance at the use of ombudspersons in the federal government shows that when they are available, issues may be raised and resolved sooner than they would be otherwise.

This concept is relatively new. The US Department of Commerce established the first federal ombudsman in 1971. The office was tasked with helping citizens resolve disputes with the agency and investigate agency actions. Other agencies, including the Social Security Administration and the Internal Revenue Service, soon followed suit. A retrospective review of these early efforts concluded that effective ombudspersons can meaningfully improve citizen-government relations. On the whole, ombudspersons were associated with an uptick in voluntary compliance with regulations and cooperation with the government. 

An AI ombudsperson or safety hotline would surely have different tasks and staff from an ombudsperson in a federal agency. Nevertheless, the general concept is worthy of study by those advocating safeguards in the AI industry. 

A right to warn may play a role in getting AI safety concerns aired, but we need to set up more intermediate, informal steps as well. An AI safety hotline is low-hanging regulatory fruit. A pilot made up of volunteers could be organized in relatively short order and provide an immediate outlet for those, like Saunders, who merely want a sounding board.

Kevin Frazier is an assistant professor at St. Thomas University College of Law and senior research fellow in the Constitutional Studies Program at the University of Texas at Austin.

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Sep 19 2024
Why OpenAI’s new model is such a big deal

This story is from The Algorithm, our weekly newsletter on AI. To get it in your inbox first, sign up here.

Last weekend, I got married at a summer camp, and during the day our guests competed in a series of games inspired by the show Survivor that my now-wife and I orchestrated. When we were planning the games in August, we wanted one station to be a memory challenge, where our friends and family would have to memorize part of a poem and then relay it to their teammates so they could re-create it with a set of wooden tiles. 

I thought OpenAI’s GPT-4o, its leading model at the time, would be perfectly suited to help. I asked it to create a short wedding-themed poem, with the constraint that each letter could only appear a certain number of times so we could make sure teams would be able to reproduce it with the provided set of tiles. GPT-4o failed miserably. The model repeatedly insisted that its poem worked within the constraints, even though it didn’t. It would correctly count the letters only after the fact, while continuing to deliver poems that didn’t fit the prompt. Without the time to meticulously craft the verses by hand, we ditched the poem idea and instead challenged guests to memorize a series of shapes made from colored tiles. (That ended up being a total hit with our friends and family, who also competed in dodgeball, egg tosses, and capture the flag.)    

However, last week OpenAI released a new model called o1 (previously referred to under the code name “Strawberry” and, before that, Q*) that blows GPT-4o out of the water for this type of purpose. 

Unlike previous models that are well suited for language tasks like writing and editing, OpenAI o1 is focused on multistep “reasoning,” the type of process required for advanced mathematics, coding, or other STEM-based questions. It uses a “chain of thought” technique, according to OpenAI. “It learns to recognize and correct its mistakes. It learns to break down tricky steps into simpler ones. It learns to try a different approach when the current one isn’t working,” the company wrote in a blog post on its website.

OpenAI’s tests point to resounding success. The model ranks in the 89th percentile on questions from the competitive coding organization Codeforces and would be among the top 500 high school students in the USA Math Olympiad, which covers geometry, number theory, and other math topics. The model is also trained to answer PhD-level questions in subjects ranging from astrophysics to organic chemistry. 

In math olympiad questions, the new model is 83.3% accurate, versus 13.4% for GPT-4o. In the PhD-level questions, it averaged 78% accuracy, compared with 69.7% from human experts and 56.1% from GPT-4o. (In light of these accomplishments, it’s unsurprising the new model was pretty good at writing a poem for our nuptial games, though still not perfect; it used more Ts and Ss than instructed to.)

So why does this matter? The bulk of LLM progress until now has been language-driven, resulting in chatbots or voice assistants that can interpret, analyze, and generate words. But in addition to getting lots of facts wrong, such LLMs have failed to demonstrate the types of skills required to solve important problems in fields like drug discovery, materials science, coding, or physics. OpenAI’s o1 is one of the first signs that LLMs might soon become genuinely helpful companions to human researchers in these fields. 

It’s a big deal because it brings “chain-of-thought” reasoning in an AI model to a mass audience, says Matt Welsh, an AI researcher and founder of the LLM startup Fixie. 

“The reasoning abilities are directly in the model, rather than one having to use separate tools to achieve similar results. My expectation is that it will raise the bar for what people expect AI models to be able to do,” Welsh says.

That said, it’s best to take OpenAI’s comparisons to “human-level skills” with a grain of salt, says Yves-Alexandre de Montjoye, an associate professor in math and computer science at Imperial College London. It’s very hard to meaningfully compare how LLMs and people go about tasks such as solving math problems from scratch.

Also, AI researchers say that measuring how well a model like o1 can “reason” is harder than it sounds. If it answers a given question correctly, is that because it successfully reasoned its way to the logical answer? Or was it aided by a sufficient starting point of knowledge built into the model? The model “still falls short when it comes to open-ended reasoning,” Google AI researcher François Chollet wrote on X.

Finally, there’s the price. This reasoning-heavy model doesn’t come cheap. Though access to some versions of the model is included in premium OpenAI subscriptions, developers using o1 through the API will pay three times as much as they pay for GPT-4o—$15 per 1 million input tokens in o1, versus $5 for GPT-4o. The new model also won’t be most users’ first pick for more language-heavy tasks, where GPT-4o continues to be the better option, according to OpenAI’s user surveys. 

What will it unlock? We won’t know until researchers and labs have the access, time, and budget to tinker with the new mode and find its limits. But it’s surely a sign that the race for models that can outreason humans has begun. 

Now read the rest of The Algorithm

Deeper learning

Chatbots can persuade people to stop believing in conspiracy theories

Researchers believe they’ve uncovered a new tool for combating false conspiracy theories: AI chatbots. Researchers from MIT Sloan and Cornell University found that chatting about a conspiracy theory with a large language model (LLM) reduced people’s belief in it by about 20%—even among participants who claimed that their beliefs were important to their identity. 

Why this matters: The findings could represent an important step forward in how we engage with and educate people who espouse such baseless theories, says Yunhao (Jerry) Zhang, a postdoc fellow affiliated with the Psychology of Technology Institute who studies AI’s impacts on society. “They show that with the help of large language models, we can—I wouldn’t say solve it, but we can at least mitigate this problem,” he says. “It points out a way to make society better.” Read more from Rhiannon Williams here.

Bits and bytes

Google’s new tool lets large language models fact-check their responses

Called DataGemma, it uses two methods to help LLMs check their responses against reliable data and cite their sources more transparently to users. (MIT Technology Review)

Meet the radio-obsessed civilian shaping Ukraine’s drone defense 

Since Russia’s invasion, Serhii “Flash” Beskrestnov has become an influential, if sometimes controversial, force—sharing expert advice and intel on the ever-evolving technology that’s taken over the skies. His work may determine the future of Ukraine, and wars far beyond it. (MIT Technology Review)

Tech companies have joined a White House commitment to prevent AI-generated sexual abuse imagery

The pledges, signed by firms like OpenAI, Anthropic, and Microsoft, aim to “curb the creation of image-based sexual abuse.” The companies promise to set limits on what models will generate and to remove nude images from training data sets where possible.  (Fortune)

OpenAI is now valued at $150 billion

The valuation arose out of talks it’s currently engaged in to raise $6.5 billion. Given that OpenAI is becoming increasingly costly to operate, and could lose as much as $5 billion this year, it’s tricky to see how it all adds up. (The Information)

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Sep 19 2024
There are more than 120 AI bills in Congress right now

More than 120 bills related to regulating artificial intelligence are currently floating around the US Congress.

They’re pretty varied. One aims to improve knowledge of AI in public schools, while another is pushing for model developers to disclose what copyrighted material they use in their training.  Three deal with mitigating AI robocalls, while two address biological risks from AI. There’s even a bill that prohibits AI from launching a nuke on its own.

The flood of bills is indicative of the desperation Congress feels to keep up with the rapid pace of technological improvements. “There is a sense of urgency. There’s a commitment to addressing this issue, because it is developing so quickly and because it is so crucial to our economy,” says Heather Vaughan, director of communications for the US House of Representatives Committee on Science, Space, and Technology.

Because of the way Congress works, the majority of these bills will never make it into law. But simply taking a look at all the different bills that are in motion can give us insight into policymakers’ current preoccupations: where they think the dangers are, what each party is focusing on, and more broadly, what vision the US is pursuing when it comes to AI and how it should be regulated.

That’s why, with help from the Brennan Center for Justice, which created a tracker with all the AI bills circulating in various committees in Congress right now, MIT Technology Review has taken a closer look to see if there’s anything we can learn from this legislative smorgasbord. 

As you can see, it can seem as if Congress is trying to do everything at once when it comes to AI. To get a better sense of what may actually pass, it’s useful to look at what bills are moving along to potentially become law. 

A bill typically needs to pass a committee, or a smaller body of Congress, before it is voted on by the whole Congress. Many will fall short at this stage, while others will simply be introduced and then never spoken of again. This happens because there are so many bills presented in each session, and not all of them are given equal consideration. If the leaders of a party don’t feel a bill from one of its members can pass, they may not even try to push it forward. And then, depending on the makeup of Congress, a bill’s sponsor usually needs to get some members of the opposite party to support it for it to pass. In the current polarized US political climate, that task can be herculean. 

Congress has passed legislation on artificial intelligence before. Back in 2020, the National AI Initiative Act was part of the Defense Authorization Act, which invested resources in AI research and provided support for public education and workforce training on AI.

And some of the current bills are making their way through the system. The Senate Commerce Committee pushed through five AI-related bills at the end of July. The bills focused on authorizing the newly formed US AI Safety Institute (AISI) to create test beds and voluntary guidelines for AI models. The other bills focused on expanding education on AI, establishing public computing resources for AI research, and criminalizing the publication of deepfake pornography. The next step would be to put the bills on the congressional calendar to be voted on, debated, or amended.

“The US AI Safety Institute, as a place to have consortium building and easy collaboration between corporate and civil society actors, is amazing. It’s exactly what we need,” says Yacine Jernite, an AI researcher at Hugging Face.

The progress of these bills is a positive development, says Varun Krovi, executive director of the Center for AI Safety Action Fund. “We need to codify the US AI Safety Institute into law if you want to maintain our leadership on the global stage when it comes to standards development,” he says. “And we need to make sure that we pass a bill that provides computing capacity required for startups, small businesses, and academia to pursue AI.”

Following the Senate’s lead, the House Committee on Science, Space, and Technology just passed nine more bills regarding AI on September 11. Those bills focused on improving education on AI in schools, directing the National Institute of Standards and Technology (NIST) to establish guidelines for artificial-intelligence systems, and expanding the workforce of AI experts. These bills were chosen because they have a narrower focus and thus might not get bogged down in big ideological battles on AI, says Vaughan.

“It was a day that culminated from a lot of work. We’ve had a lot of time to hear from members and stakeholders. We’ve had years of hearings and fact-finding briefings on artificial intelligence,” says Representative Haley Stevens, one of the Democratic members of the House committee.

Many of the bills specify that any guidance they propose for the industry is nonbinding and that the goal is to work with companies to ensure safe development rather than curtail innovation. 

For example, one of the bills from the House, the AI Development Practices Act, directs NIST to establish “voluntary guidance for practices and guidelines relating to the development … of AI systems” and a “voluntary risk management framework.” Another bill, the AI Advancement and Reliability Act, has similar language. It supports “the development of voluntary best practices and technical standards” for evaluating AI systems. 

“Each bill contributes to advancing AI in a safe, reliable, and trustworthy manner while fostering the technology’s growth and progress through innovation and vital R&D,” committee chairman Frank Lucas, an Oklahoma Republican, said in a press release on the bills coming out of the House.

“It’s emblematic of the approach that the US has taken when it comes to tech policy. We hope that we would move on from voluntary agreements to mandating them,” says Krovi.

Avoiding mandates is a practical matter for the House committee. “Republicans don’t go in for mandates for the most part. They generally aren’t going to go for that. So we would have a hard time getting support,” says Vaughan. “We’ve heard concerns about stifling innovation, and that’s not the approach that we want to take.” When MIT Technology Review asked about the origin of these concerns, they were attributed to unidentified “third parties.” 

And fears of slowing innovation don’t just come from the Republican side. “What’s most important to me is that the United States of America is establishing aggressive rules of the road on the international stage,” says Stevens. “It’s concerning to me that actors within the Chinese Communist Party could outpace us on these technological advancements.”

But these bills come at a time when big tech companies have ramped up lobbying efforts on AI. “Industry lobbyists are in an interesting predicament—their CEOs have said that they want more AI regulation, so it’s hard for them to visibly push to kill all AI regulation,” says David Evan Harris, who teaches courses on AI ethics at the University of California, Berkeley. “On the bills that they don’t blatantly try to kill, they instead try to make them meaningless by pushing to transform the language in the bills to make compliance optional and enforcement impossible.”

“A [voluntary commitment] is something that is also only accessible to the largest companies,” says Jernite at Hugging Face, claiming that sometimes the ambiguous nature of voluntary commitments allows big companies to set definitions for themselves. “If you have a voluntary commitment—that is, ‘We’re going to develop state-of-the-art watermarking technology’—you don’t know what state-of-the-art means. It doesn’t come with any of the concrete things that make regulation work.”

“We are in a very aggressive policy conversation about how to do this right, and how this carrot and stick is actually going to work,” says Stevens, indicating that Congress may ultimately draw red lines that AI companies must not cross.

There are other interesting insights to be gleaned from looking at the bills all together. Two-thirds of the AI bills are sponsored by Democrats. This isn’t too surprising, since some House Republicans have claimed to want no AI regulations, believing that guardrails will slow down progress.

The topics of the bills (as specified by Congress) are dominated by science, tech, and communications (28%), commerce (22%), updating government operations (18%), and national security (9%). Topics that don’t receive much attention include labor and employment (2%), environmental protection (1%), and civil rights, civil liberties, and minority issues (1%).

The lack of a focus on equity and minority issues came into view during the Senate markup session at the end of July. Senator Ted Cruz, a Republican, added an amendment that explicitly prohibits any action “to ensure inclusivity and equity in the creation, design, or development of the technology.” Cruz said regulatory action might slow US progress in AI, allowing the country to fall behind China.

On the House side, there was also a hesitation to work on bills dealing with biases in AI models. “None of our bills are addressing that. That’s one of the more ideological issues that we’re not moving forward on,” says Vaughan.

The lead Democrat on the House committee, Representative Zoe Lofgren, told MIT Technology Review, “It is surprising and disappointing if any of my Republican colleagues have made that comment about bias in AI systems. We shouldn’t tolerate discrimination that’s overt and intentional any more than we should tolerate discrimination that occurs because of bias in AI systems. I’m not really sure how anyone can argue against that.”

After publication, Vaughan clarified that “[Bias] is one of the bigger, more cross-cutting issues, unlike the narrow, practical bills we considered that week. But we do care about bias as an issue,” and she expects it to be addressed within an upcoming House Task Force report.

One issue that may rise above the partisan divide is deepfakes. The Defiance Act, one of several bills addressing them, is cosponsored by a Democratic senator, Amy Klobuchar, and a Republican senator, Josh Hawley. Deepfakes have already been abused in elections; for example, someone faked Joe Biden’s voice for a robocall to tell citizens not to vote. And the technology has been weaponized to victimize people by incorporating their images into pornography without their consent. 

“I certainly think that there is more bipartisan support for action on these issues than on many others,” says Daniel Weiner, director of the Brennan Center’s Elections & Government Program. “But it remains to be seen whether that’s going to win out against some of the more traditional ideological divisions that tend to arise around these issues.” 

Although none of the current slate of bills have resulted in laws yet, the task of regulating any new technology, and specifically advanced AI systems that no one entirely understands, is difficult. The fact that Congress is making any progress at all may be surprising in itself. 

“Congress is not sleeping on this by any stretch of the means,” says Stevens. “We are evaluating and asking the right questions and also working alongside our partners in the Biden-Harris administration to get us to the best place for the harnessing of artificial intelligence.”

Update: We added further comments from the Republican spokesperson.

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