“This new AI technology—it’s very interesting to learn how it works and understand it more,” says 10-year-old Luca, a young AI model maker.

Luca is one of the first kids to try Little Language Models, a new application from Manuj and Shruti Dhariwal, two PhD researchers at MIT’s Media Lab, that helps children understand how AI models work—by getting to build small-scale versions themselves. 

The program is a way to introduce the complex concepts that make modern AI models work without droning on about them in a theoretical lecture. Instead, kids can see and build a visualization of the concepts in practice, which helps them get to grips with them.

“What does it mean to have children see themselves as being builders of AI technologies and not just users?” says Shruti.

The program starts out by using a pair of dice to demonstrate probabilistic thinking, a system of decision-making that accounts for uncertainty. Probabilistic thinking underlies the LLMs of today, which predict the most likely next word in a sentence. By teaching a concept like it, the program can help to demystify the workings of LLMs for kids and assist them in understanding that sometimes the model’s choices are not perfect but the result of a series of probabilities. 

Students can modify each side of the dice to whatever variable they want. And then they can change how likely each side is to come up when you roll them. Luca thinks it would be “really cool” to incorporate this feature into the design of a Pokémon-like game he is working on. But it can also demonstrate some crucial realities about AI.

Let’s say a teacher wanted to educate students about how bias comes up in AI models. The kids could be told to create a pair of dice and then set each side to a hand of a different skin color. At first, they could set the probability of a white hand at 100%, reflecting a hypothetical situation where there are only images of white people in the data set. When the AI is asked to generate a visual, it produces only white hands.

Then the teacher can have the kids increase the percentage of other skin colors, simulating a more diverse data set. The AI model now produces hands of varying skin colors.

“It was interesting using Little Language Models, because it makes AI into something small [where the students] can grasp what’s going on,” says Helen Mastico, a middle school librarian in Quincy, Massachusetts, who taught a group of eighth graders to use the program.

“You start to see, ‘Oh, this is how bias creeps in,’” says Shruti. “It provides a rich context for educators to start talking about and for kids to imagine, basically, how these things scale to really big levels.”

They plan for the tool to be used around the world. Students will be able to upload their own data, monitored by their teacher. “[Students] can also add their own sounds, images, and backdrops that represent their culture,” says Manuj. 

The Dhariwals have also implemented a tool where kids can play around with more advanced concepts like Markov chains, where a preceding variable influences what comes after it. For example, a child could build an AI that creates random houses made from Lego bricks. The child can dictate that if the AI uses a red brick first, the percentage of yellow brick coming next is set much higher.

“The best way to support young people as creative learners is through helping them work on projects based on their passions,” says the Dhariwals’ PhD advisor Mitch Resnick, co-creator of Scratch, the most famous program in the world for teaching kids to code. “And that’s what Little Language Models does. It lets children take these new ideas and put them to use in creative ways.”

Little Language Models may fill a hole in the current educational landscape. “There is a real lack of playful resources and tools that teach children about data literacy and about AI concepts creatively,” says Emma Callow, a learning experience designer who works with educators and schools on implementing new ways to teach kids about technology. “Schools are more worried about safety, rather than the potential to use AI. But it is progressing in schools, and people are starting to kind of use it,” she says. “There is a space for education to change.”

Little Language Models is rolling out on the Dhariwals’ online education platform, coco.build, in mid-November, and they’re trialing the program at various schools over the next month. 

Luca’s mom, Diana, hopes the chance to experiment with it will serve him well. “It’s experiences like this that will teach him about AI from a very young age and help him use it in a wiser way,” she says.

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.

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.

“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.

Roblox plans to roll out a generative AI tool that will let creators make whole 3D scenes just using text prompts, it announced today. 

Once it’s up and running, developers on the hugely popular online game platform will be able to simply write “Generate a race track in the desert,” for example, and the AI will spin one up. Users will also be able to modify scenes or expand their scope—say, to change a daytime scene to night or switch the desert for a forest. 

Although developers can already create similar scenes like this manually in the platform’s creator studio, Roblox claims its new generative AI model will make the changes happen in a fraction of the time. It also claims that it will give developers with minimal 3D art skills the ability to craft more compelling environments. The firm didn’t give a specific date for when the tool will be live.

Developers are already excited. “Instead of sitting and doing it by hand, now you can test different approaches,” says Marcus Holmström, CEO of The Gang, a company that builds some of the top games on Roblox.  “For example, if you’re going to build a mountain, you can do different types of mountains, and on the fly, you can change it. Then we would tweak it and fix it manually so it fits. It’s going to save a lot of time.”

Roblox’s new tool works by “tokenizing” the 3D blocks that make up its millions of in-game worlds, or treating them as units that can be assigned a numerical value on the basis of how likely they are to come next in a sequence. This is similar to the way in which a large language model handles words or fractions of words. If you put “The capital of France is …” into a large language model like GPT-4, for example, it assesses what the next token is most likely to be. In this case, it would be “Paris.” Roblox’s system handles 3D blocks in much the same way to create the environment, block by most likely next block. 

Finding a way to do this has been difficult, for a couple of reasons. One, there’s far less data for 3D environments than there is for text. To train its models, Roblox has had to rely on user-generated data from creators as well as external data sets. 

“Finding high-quality 3D information is difficult,” says Anupam Singh, vice president of AI and growth engineering at Roblox. “Even if you get all the data sets that you would think of, being able to predict the next cube requires it to have literally three dimensions, X, Y, and Z.”

The lack of 3D data can create weird situations, where objects appear in unusual places—a tree in the middle of your racetrack, for example. To get around this issue, Roblox will use a second AI model that has been trained on more plentiful 2D data, pulled from open-source and licensed data sets, to check the work of the first one. 

Basically, while one AI is making a 3D environment, the 2D model will convert the new environment to 2D and assess whether or not the image is logically consistent. If the images don’t make sense and you have, say, a cat with 12 arms driving a racecar, the 3D AI generates a new block again and again until the 2D AI “approves.”

Roblox game designers will still need to be involved in crafting fun game environments for the platform’s millions of players, says Chris Totten, an associate professor in the animation game design program at Kent State University. “A lot of level generators will produce something that’s plain and flat. You need a human guiding hand,” he says. “It’s kind of like people trying to do an essay with ChatGPT for a class. It is also going to open up a conversation about what does it mean to do good, player-responsive level design?”

The new tool is part of Roblox’s push to integrate AI into all its processes. The company currently has 250 AI models live. One AI analyzes voice chat in real time and screens for bad language, instantly issuing reprimands and possible bans for repeated infractions.

Roblox plans to open-source its 3D foundation model so that it can be modified and used as a basis for innovation. “We’re doing it in open source, which means anybody, including our competitors, can use this model,” says Singh. 

Getting it into as many hands as possible also opens creative possibilities for developers who are not as skilled at creating Roblox environments. “There are a lot of developers that are working alone, and for them, this is going to be a game changer, because now they don’t have to try to find someone else to work with,” says Holmström.