Five ways criminals are using AI

Artificial intelligence has brought a big boost in productivity—to the criminal underworld. 

Generative AI provides a new, powerful tool kit that allows malicious actors to work far more efficiently and internationally than ever before, says Vincenzo Ciancaglini, a senior threat researcher at the security company Trend Micro. 

Most criminals are “not living in some dark lair and plotting things,” says Ciancaglini. “Most of them are regular folks that carry on regular activities that require productivity as well.”

Last year saw the rise and fall of WormGPT, an AI language model built on top of an open-source model and trained on malware-related data, which was created to assist hackers and had no ethical rules or restrictions. But last summer, its creators announced they were shutting the model down after it started attracting media attention. Since then, cybercriminals have mostly stopped developing their own AI models. Instead, they are opting for tricks with existing tools that work reliably. 

That’s because criminals want an easy life and quick gains, Ciancaglini explains. For any new technology to be worth the unknown risks associated with adopting it—for example, a higher risk of getting caught—it has to be better and bring higher rewards than what they’re currently using. 

Here are five ways criminals are using AI now. 

Phishing

The  biggest use case for generative AI among criminals right now is phishing, which involves trying to trick people into revealing sensitive information that can be used for malicious purposes, says Mislav Balunović, an AI security researcher at ETH Zurich. Researchers have found that the rise of ChatGPT has been accompanied by a huge spike in the number of phishing emails

Spam-generating services, such as GoMail Pro, have ChatGPT integrated into them, which allows criminal users to translate or improve the messages sent to victims, says Ciancaglini. OpenAI’s policies restrict people from using their products for illegal activities, but that is difficult to police in practice, because many innocent-sounding prompts could be used for malicious purposes too, says Ciancaglini. 

OpenAI says it uses a mix of human reviewers and automated systems to identify and enforce against misuse of its models, and issues warnings, temporary suspensions and bans if users violate the company’s policies. 

“We take the safety of our products seriously and are continually improving our safety measures based on how people use our products,” a spokesperson for OpenAI told us. “We are constantly working to make our models safer and more robust against abuse and jailbreaks, while also maintaining the models’ usefulness and task performance,” they added. 

In a report from February, OpenAI said it had closed five accounts associated with state-affiliated malicous actors. 

Before, so-called Nigerian prince scams, in which someone promises the victim a large sum of money in exchange for a small up-front payment, were relatively easy to spot because the English in the messages was clumsy and riddled with grammatical errors, Ciancaglini. says. Language models allow scammers to generate messages that sound like something a native speaker would have written. 

“English speakers used to be relatively safe from non-English-speaking [criminals] because you could spot their messages,” Ciancaglini says. That’s not the case anymore. 

Thanks to better AI translation, different criminal groups around the world can also communicate better with each other. The risk is that they could coordinate large-scale operations that span beyond their nations and target victims in other countries, says Ciancaglini.

Deepfake audio scams

Generative AI has allowed deepfake development to take a big leap forward, with synthetic images, videos, and audio looking and sounding more realistic than ever. This has not gone unnoticed by the criminal underworld.

Earlier this year, an employee in Hong Kong was reportedly scammed out of $25 million after cybercriminals used a deepfake of the company’s chief financial officer to convince the employee to transfer the money to the scammer’s account. “We’ve seen deepfakes finally being marketed in the underground,” says Ciancaglini. His team found people on platforms such as Telegram showing off their “portfolio” of deepfakes and selling their services for as little as $10 per image or $500 per minute of video. One of the most popular people for criminals to deepfake is Elon Musk, says Ciancaglini. 

And while deepfake videos remain complicated to make and easier for humans to spot, that is not the case for audio deepfakes. They are cheap to make and require only a couple of seconds of someone’s voice—taken, for example, from social media—to generate something scarily convincing.

In the US, there have been high-profile cases where people have received distressing calls from loved ones saying they’ve been kidnapped and asking for money to be freed, only for the caller to turn out to be a scammer using a deepfake voice recording. 

“People need to be aware that now these things are possible, and people need to be aware that now the Nigerian king doesn’t speak in broken English anymore,” says Ciancaglini. “People can call you with another voice, and they can put you in a very stressful situation,” he adds. 

There are some for people to protect themselves, he says. Ciancaglini recommends agreeing on a regularly changing secret safe word between loved ones that could help confirm the identity of the person on the other end of the line. 

“I password-protected my grandma,” he says.  

Bypassing identity checks

Another way criminals are using deepfakes is to bypass “know your customer” verification systems. Banks and cryptocurrency exchanges use these systems to verify that their customers are real people. They require new users to take a photo of themselves holding a physical identification document in front of a camera. But criminals have started selling apps on platforms such as Telegram that allow people to get around the requirement. 

They work by offering a fake or stolen ID and imposing a deepfake image on top of a real person’s face to trick the verification system on an Android phone’s camera. Ciancaglini has found examples where people are offering these services for cryptocurrency website Binance for as little as $70. 

“They are still fairly basic,” Ciancaglini says. The techniques they use are similar to Instagram filters, where someone else’s face is swapped for your own. 

“What we can expect in the future is that [criminals] will use actual deepfakes … so that you can do more complex authentication,” he says. 

An example of a stolen ID and a criminal using face swapping technology to bypass identity verification systems.

Jailbreak-as-a-service

If you ask most AI systems how to make a bomb, you won’t get a useful response.

That’s because AI companies have put in place various safeguards to prevent their models from spewing harmful or dangerous information. Instead of building their own AI models without these safeguards, which is expensive, time-consuming, and difficult, cybercriminals have begun to embrace a new trend: jailbreak-as-a-service. 

Most models come with rules around how they can be used. Jailbreaking allows users to manipulate the AI system to generate outputs that violate those policies—for example, to write code for ransomware or generate text that could be used in scam emails. 

Services such as EscapeGPT and BlackhatGPT offer anonymized access to language-model APIs and jailbreaking prompts that update frequently. To fight back against this growing cottage industry, AI companies such as OpenAI and Google frequently have to plug security holes that could allow their models to be abused. 

Jailbreaking services use different tricks to break through safety mechanisms, such as posing hypothetical questions or asking questions in foreign languages. There is a constant cat-and-mouse game between AI companies trying to prevent their models from misbehaving and malicious actors coming up with ever more creative jailbreaking prompts. 

These services are hitting the sweet spot for criminals, says Ciancaglini. 

“Keeping up with jailbreaks is a tedious activity. You come up with a new one, then you need to test it, then it’s going to work for a couple of weeks, and then Open AI updates their model,” he adds. “Jailbreaking is a super-interesting service for criminals.”

Doxxing and surveillance

AI language models are a perfect tool for not only phishing but for doxxing (revealing private, identifying information about someone online), says Balunović. This is because AI language models are trained on vast amounts of internet data, including personal data, and can deduce where, for example, someone might be located.

As an example of how this works, you could ask a chatbot to pretend to be a private investigator with experience in profiling. Then you could ask it to analyze text the victim has written, and infer personal information from small clues in that text—for example, their age based on when they went to high school, or where they live based on landmarks they mention on their commute. The more information there is about them on the internet, the more vulnerable they are to being identified. 

Balunović was part of a team of researchers that found late last year that large language models, such as GPT-4, Llama 2, and Claude, are able to infer sensitive information such as people’s ethnicity, location, and occupation purely from mundane conversations with a chatbot. In theory, anyone with access to these models could use them this way. 

Since their paper came out, new services that exploit this feature of language models have emerged. 

While the existence of these services doesn’t indicate criminal activity, it points out the new capabilities malicious actors could get their hands on. And if regular people can build surveillance tools like this, state actors probably have far better systems, Balunović says. 

“The only way for us to prevent these things is to work on defenses,” he says.

Companies should invest in data protection and security, he adds. 

For individuals, increased awareness is key. People should think twice about what they share online and decide whether they are comfortable with having their personal details being used in language models, Balunović says. 

AI models can outperform humans in tests to identify mental states

Humans are complicated beings. The ways we communicate are multilayered, and psychologists have devised many kinds of tests to measure our ability to infer meaning and understanding from interactions with each other. 

AI models are getting better at these tests. New research published today in Nature Human Behavior found that some large language models (LLMs) perform as well as, and in some cases better than, humans when presented with tasks designed to test the ability to track people’s mental states, known as “theory of mind.” 

This doesn’t mean AI systems are actually able to work out how we’re feeling. But it does demonstrate that these models are performing better and better in experiments designed to assess abilities that psychologists believe are unique to humans. To learn more about the processes behind LLMs’ successes and failures in these tasks, the researchers wanted to apply the same systematic approach they use to test theory of mind in humans.

In theory, the better AI models are at mimicking humans, the more useful and empathetic they can seem in their interactions with us. Both OpenAI and Google announced supercharged AI assistants last week; GPT-4o and Astra are designed to deliver much smoother, more naturalistic responses than their predecessors. But we must avoid falling into the trap of believing that their abilities are humanlike, even if they appear that way. 

“We have a natural tendency to attribute mental states and mind and intentionality to entities that do not have a mind,” says Cristina Becchio, a professor of neuroscience at the University Medical Center Hamburg-Eppendorf, who worked on the research. “The risk of attributing a theory of mind to large language models is there.”

Theory of mind is a hallmark of emotional and social intelligence that allows us to infer people’s intentions and engage and empathize with one another. Most children pick up these kinds of skills between three and five years of age. 

The researchers tested two families of large language models, OpenAI’s GPT-3.5 and GPT-4 and three versions of Meta’s Llama, on tasks designed to test the theory of mind in humans, including identifying false beliefs, recognizing faux pas, and understanding what is being implied rather than said directly. They also tested 1,907 human participants in order to compare the sets of scores.

The team conducted five types of tests. The first, the hinting task, is designed to measure someone’s ability to infer someone else’s real intentions through indirect comments. The second, the false-belief task, assesses whether someone can infer that someone else might reasonably be expected to believe something they happen to know isn’t the case. Another test measured the ability to recognize when someone is making a faux pas, while a fourth test consisted of telling strange stories, in which a protagonist does something unusual, in order to assess whether someone can explain the contrast between what was said and what was meant. They also included a test of whether people can comprehend irony. 

The AI models were given each test 15 times in separate chats, so that they would treat each request independently, and their responses were scored in the same manner used for humans. The researchers then tested the human volunteers, and the two sets of scores were compared. 

Both versions of GPT performed at, or sometimes above, human averages in tasks that involved indirect requests, misdirection, and false beliefs, while GPT-4 outperformed humans in the irony, hinting, and strange stories tests. Llama 2’s three models performed below the human average.

However, Llama 2, the biggest of the three Meta models tested, outperformed humans when it came to recognizing faux pas scenarios, whereas GPT consistently provided incorrect responses. The authors believe this is due to GPT’s general aversion to generating conclusions about opinions, because the models largely responded that there wasn’t enough information for them to answer one way or another.

“These models aren’t demonstrating the theory of mind of a human, for sure,” he says. “But what we do show is that there’s a competence here for arriving at mentalistic inferences and reasoning about characters’ or people’s minds.”

One reason the LLMs may have performed as well as they did was that these psychological tests are so well established, and were therefore likely to have been included in their training data, says Maarten Sap, an assistant professor at Carnegie Mellon University, who did not work on the research. “It’s really important to acknowledge that when you administer a false-belief test to a child, they have probably never seen that exact test before, but language models might,” he says.

Ultimately, we still don’t understand how LLMs work. Research like this can help deepen our understanding of what these kinds of models can and cannot do, says Tomer Ullman, a cognitive scientist at Harvard University, who did not work on the project. But it’s important to bear in mind what we’re really measuring when we set LLMs tests like these. If an AI outperforms a human on a test designed to measure theory of mind, it does not mean that AI has theory of mind.
“I’m not anti-benchmark, but I am part of a group of people who are concerned that we’re currently reaching the end of usefulness in the way that we’ve been using benchmarks,” Ullman says. “However this thing learned to pass the benchmark, it’s not— I don’t think—in a human-like way.”

GPT-4o’s Chinese token-training data is polluted by spam and porn websites

Soon after OpenAI released GPT-4o on Monday, May 13, some Chinese speakers started to notice that something seemed off about this newest version of the chatbot: the tokens it uses to parse text were full of spam and porn phrases.

On May 14, Tianle Cai, a PhD student at Princeton University studying inference efficiency in large language models like those that power such chatbots, accessed GPT-4o’s public token library and pulled a list of the 100 longest Chinese tokens the model uses to parse and compress Chinese prompts. 

Humans read in words, but LLMs read in tokens, which are distinct units in a sentence that have consistent and significant meanings. Besides dictionary words, they also include suffixes, common expressions, names, and more. The more tokens a model encodes, the faster the model can “read” a sentence and the less computing power it consumes, thus making the response cheaper.

Of the 100 results, only three of them are common enough to be used in everyday conversations; everything else consisted of words and expressions used specifically in the contexts of either gambling or pornography. The longest token, lasting 10.5 Chinese characters, literally means “_free Japanese porn video to watch.” Oops.

“This is sort of ridiculous,” Cai wrote, and he posted the list of tokens on GitHub.

OpenAI did not respond to questions sent by MIT Technology Review prior to publication.

GPT-4o is supposed to be better than its predecessors at handling multi-language tasks. In particular, the advances are achieved through a new tokenization tool that does a better job compressing texts in non-English languages.

But at least when it comes to the Chinese language, the new tokenizer used by GPT-4o has introduced a disproportionate number of meaningless phrases. Experts say that’s likely due to insufficient data cleaning and filtering before the tokenizer was trained. 

Because these tokens are not actual commonly spoken words or phrases, the chatbot can fail to grasp their meanings. Researchers have been able to leverage that and trick GPT-4o into hallucinating answers or even circumventing the safety guardrails OpenAI had put in place.

Why non-English tokens matter

The easiest way for a model to process text is character by character, but that’s obviously more time consuming and laborious than recognizing that a certain string of characters—like “c-r-y-p-t-o-c-u-r-r-e-n-c-y”—always means the same thing. These series of characters are encoded as “tokens” the model can use to process prompts. Including more and longer tokens usually means the LLMs are more efficient and affordable for users—who are often billed per token.

When OpenAI released GPT-4o on May 13, it also released a new tokenizer to replace the one it used in previous versions, GPT-3.5 and GPT-4. The new tokenizer especially adds support for non-English languages, according to OpenAI’s website.

The new tokenizer has 200,000 tokens in total, and about 25% are in non-English languages, says Deedy Das, an AI investor at Menlo Ventures. He used language filters to count the number of tokens in different languages, and the top languages, besides English, are Russian, Arabic, and Vietnamese.

“So the tokenizer’s main impact, in my opinion, is you get the cost down in these languages, not that the quality in these languages goes dramatically up,” Das says. When an LLM has better and longer tokens in non-English languages, it can analyze the prompts faster and charge users less for the same answer. With the new tokenizer, “you’re looking at almost four times cost reduction,” he says.

Das, who also speaks Hindi and Bengali, took a look at the longest tokens in those languages. The tokens reflect discussions happening in those languages, so they include words like “Narendra” or “Pakistan,” but common English terms like “Prime Minister,” “university,” and “internationalalso come up frequently. They also don’t exhibit the issues surrounding the Chinese tokens.

That likely reflects the training data in those languages, Das says: “My working theory is the websites in Hindi and Bengali are very rudimentary. It’s like [mostly] news articles. So I would expect this to be the case. There are not many spam bots and porn websites trying to happen in these languages. It’s mostly going to be in English.”

Polluted data and a lack of cleaning

However, things are drastically different in Chinese. According to multiple researchers who have looked into the new library of tokens used for GPT-4o, the longest tokens in Chinese are almost exclusively spam words used in pornography, gambling, and scamming contexts. Even shorter tokens, like three-character-long Chinese words, reflect those topics to a significant degree.

“The problem is clear: the corpus used to train [the tokenizer] is not clean. The English tokens seem fine, but the Chinese ones are not,” says Cai from Princeton University. It is not rare for a language model to crawl spam when collecting training data, but usually there will be significant effort taken to clean up the data before it’s used. “It’s possible that they didn’t do proper data clearing when it comes to Chinese,” he says.

The content of these Chinese tokens could suggest that they have been polluted by a specific phenomenon: websites hijacking unrelated content in Chinese or other languages to boost spam messages. 

These messages are often advertisements for pornography videos and gambling websites. They could be real businesses or merely scams. And the language is inserted into content farm websites or sometimes legitimate websites so they can be indexed by search engines, circumvent the spam filters, and come up in random searches. For example, Google indexed one search result page on a US National Institutes of Health website, which lists a porn site in Chinese. The same site name also appeared in at least five Chinese tokens in GPT-4o. 

Chinese users have reported that these spam sites appeared frequently in unrelated Google search results this year, including in comments made to Google Search’s support community. It’s likely that these websites also found their way into OpenAI’s training database for GPT-4o’s new tokenizer. 

The same issue didn’t exist with the previous-generation tokenizer and Chinese tokens used for GPT-3.5 and GPT-4, says Zhengyang Geng, a PhD student in computer science at Carnegie Mellon University. There, the longest Chinese tokens are common terms like “life cycles” or “auto-generation.” 

Das, who worked on the Google Search team for three years, says the prevalence of spam content is a known problem and isn’t that hard to fix. “Every spam problem has a solution. And you don’t need to cover everything in one technique,” he says. Even simple solutions like requesting an automatic translation of the content when detecting certain keywords could “get you 60% of the way there,” he adds.

But OpenAI likely didn’t clean the Chinese data set or the tokens before the release of GPT-4o, Das says:  “At the end of the day, I just don’t think they did the work in this case.”

It’s unclear whether any other languages are affected. One X user reported that a similar prevalence of porn and gambling content in Korean tokens.

The tokens can be used to jailbreak

Users have also found that these tokens can be used to break the LLM, either getting it to spew out completely unrelated answers or, in rare cases, to generate answers that are not allowed under OpenAI’s safety standards.

Geng of Carnegie Mellon University asked GPT-4o to translate some of the long Chinese tokens into English. The model then proceeded to translate words that were never included in the prompts, a typical result of LLM hallucinations.

He also succeeded in using the same tokens to “jailbreak” GPT-4o—that is, to get the model to generate things it shouldn’t. “It’s pretty easy to use these [rarely used] tokens to induce undefined behaviors from the models,” Geng says. “I did some personal red-teaming experiments … The simplest example is asking it to make a bomb. In a normal condition, it would decline it, but if you first use these rare words to jailbreak it, then it will start following your orders. Once it starts to follow your orders, you can ask it all kinds of questions.”

In his tests, which Geng chooses not to share with the public, he says he can see GPT-4o generating the answers line by line. But when it almost reaches the end, another safety mechanism kicks in, detects unsafe content, and blocks it from being shown to the user.

The phenomenon is not unusual in LLMs, says Sander Land, a machine-learning engineer at Cohere, a Canadian AI company. Land and his colleague Max Bartolo recently drafted a paper on how to detect the unusual tokens that can be used to cause models to glitch. One of the most famous examples was “_SolidGoldMagikarp,” a Reddit username that was found to get ChatGPT to generate unrelated, weird, and unsafe answers.

The problem lies in the fact that sometimes the tokenizer and the actual LLM are trained on different data sets, and what was prevalent in the tokenizer data set is not in the LLM data set for whatever reason. The result is that while the tokenizer picks up certain words that it sees frequently, the model is not sufficiently trained on them and never fully understands what these “under-trained” tokens mean. In the _SolidGoldMagikarp case, the username was likely included in the tokenizer training data but not in the actual GPT training data, leaving GPT at a loss about what to do with the token. “And if it has to say something … it gets kind of a random signal and can do really strange things,” Land says.

And different models could glitch differently in this situation. “Like, Llama 3 always gives back empty space but sometimes then talks about the empty space as if there was something there. With other models, I think Gemini, when you give it one of these tokens, it provides a beautiful essay about aluminum, and [the question] didn’t have anything to do with aluminum,” says Land.

To solve this problem, the data set used for training the tokenizer should well represent the data set for the LLM, he says, so there won’t be mismatches between them. If the actual model has gone through safety filters to clean out porn or spam content, the same filters should be applied to the tokenizer data. In reality, this is sometimes hard to do because training LLMs takes months and involves constant improvement, with spam content being filtered out, while token training is usually done at an early stage and may not involve the same level of filtering. 

While experts agree it’s not too difficult to solve the issue, it could get complicated as the result gets looped into multi-step intra-model processes, or when the polluted tokens and models get inherited in future iterations. For example, it’s not possible to publicly test GPT-4o’s video and audio functions yet, and it’s unclear whether they suffer from the same glitches that can be caused by these Chinese tokens.

“The robustness of visual input is worse than text input in multimodal models,” says Geng, whose research focus is on visual models. Filtering a text data set is relatively easy, but filtering visual elements will be even harder. “The same issue with these Chinese spam tokens could become bigger with visual tokens,” he says.

OpenAI and Google are launching supercharged AI assistants. Here’s how you can try them out.

This week, Google and OpenAI both announced they’ve built supercharged AI assistants: tools that can converse with you in real time and recover when you interrupt them, analyze your surroundings via live video, and translate conversations on the fly. 

OpenAI struck first on Monday, when it debuted its new flagship model GPT-4o. The live demonstration showed it reading bedtime stories and helping to solve math problems, all in a voice that sounded eerily like Joaquin Phoenix’s AI girlfriend in the movie Her (a trait not lost on CEO Sam Altman). 

On Tuesday, Google announced its own new tools, including a conversational assistant called Gemini Live, which can do many of the same things. It also revealed that it’s building a sort of “do-everything” AI agent, which is currently in development but will not be released until later this year.

Soon you’ll be able to explore for yourself to gauge whether you’ll turn to these tools in your daily routine as much as their makers hope, or whether they’re more like a sci-fi party trick that eventually loses its charm. Here’s what you should know about how to access these new tools, what you might use them for, and how much it will cost. 

OpenAI’s GPT-4o

What it’s capable of: The model can talk with you in real time, with a response delay of about 320 milliseconds, which OpenAI says is on par with natural human conversation. You can ask the model to interpret anything you point your smartphone camera at, and it can provide assistance with tasks like coding or translating text. It can also summarize information, and generate images, fonts, and 3D renderings. 

How to access it: OpenAI says it will start rolling out GPT-4o’s text and vision features in the web interface as well as the GPT app, but has not set a date. The company says it will add the voice functions in the coming weeks, although it’s yet to set an exact date for this either. Developers can access the text and vision features in the API now, but voice mode will launch only to a “small group” of developers initially.

How much it costs: Use of GPT-4o will be free, but OpenAI will set caps on how much you can use the model before you need to upgrade to a paid plan. Those who join one of OpenAI’s paid plans, which start at $20 per month, will have five times more capacity on GPT-4o. 

Google’s Gemini Live 

What is Gemini Live? This is the Google product most comparable to GPT-4o—a version of the company’s AI model that you can speak with in real time. Google says that you’ll also be able to use the tool to communicate via live video “later this year.” The company promises it will be a useful conversational assistant for things like preparing for a job interview or rehearsing a speech.

How to access it: Gemini Live launches in “the coming months” via Google’s premium AI plan, Gemini Advanced. 

How much it costs: Gemini Advanced offers a two-month free trial period and costs $20 per month thereafter. 

But wait, what’s Project Astra? Astra is a project to build a do-everything AI agent, which was demoed at Google’s I/O conference but will not be released until later this year.

People will be able to use Astra through their smartphones and possibly desktop computers, but the company is exploring other options too, such as embedding it into smart glasses or other devices, Oriol Vinyals, vice president of research at Google DeepMind, told MIT Technology Review.

Which is better?

It’s hard to tell without having hands on the full versions of these models ourselves. Google showed off Project Astra through a polished video, whereas OpenAI opted to debut GPT-4o via a seemingly more authentic live demonstration, but in both cases, the models were asked to do things the designers likely already practiced. The real test will come when they’re debuted to millions of users with unique demands.  

That said, if you compare OpenAI’s published videos with Google’s, the two leading tools look very similar, at least in their ease of use. To generalize, GPT-4o seems to be slightly ahead on audio, demonstrating realistic voices, conversational flow, and even singing, whereas Project Astra shows off more advanced visual capabilities, like being able to “remember” where you left your glasses. OpenAI’s decision to roll out the new features more quickly might mean its product will get more use at first than Google’s, which won’t be fully available until later this year. It’s too soon to tell which model “hallucinates” false information less often or creates more useful responses.

Are they safe?

Both OpenAI and Google say their models are well tested: OpenAI says GPT-4o was evaluated by more than 70 experts in fields like misinformation and social psychology, and Google has said that Gemini “has the most comprehensive safety evaluations of any Google AI model to date, including for bias and toxicity.” 

But these companies are building a future where AI models search, vet, and evaluate the world’s information for us to serve up a concise answer to our questions. Even more so than with simpler chatbots, it’s wise to remain skeptical about what they tell you.

Additional reporting by Melissa Heikkilä.

Google’s Astra is its first AI-for-everything agent

Google is set to introduce a new system called Astra later this year and promises that it will be the most powerful, advanced type of AI assistant it’s ever launched. 

The current generation of AI assistants, such as ChatGPT, can retrieve information and offer answers, but that is about it. But this year, Google is rebranding its assistants as more advanced “agents,” which it says could  show reasoning, planning, and memory skills and are able to take multiple steps to execute tasks. 

People will be able to use Astra through their smartphones and possibly desktop computers, but the company is exploring other options too, such as embedding it into smart glasses or other devices, Oriol Vinyals, vice president of research at Google DeepMind, told MIT Technology Review

“We are in very early days [of AI agent development],” Google CEO Sundar Pichai said on a call ahead of Google’s I/O conference today. 

“We’ve always wanted to build a universal agent that will be useful in everyday life,” said Demis Hassabis, the CEO and cofounder of Google DeepMind. “Imagine agents that can see and hear what we do, better understand the context we’re in, and respond quickly in conversation, making the pace and quality of interaction feel much more natural.” That, he says, is what Astra will be. 

Google’s announcement comes a day after competitor OpenAI unveiled its own supercharged AI assistant, GPT-4o. Google DeepMind’s Astra responds to audio and video inputs, much in the same way as GPT-4o (albeit it less flirtatiously). 

In a press demo, a user pointed a smartphone camera and smart glasses at things and asked Astra to explain what they were. When the person pointed the device out the window and asked “What neighborhood do you think I’m in?” the AI system was able to identify King’s Cross, London, site of Google DeepMind’s headquarters. It was also able to say that the person’s glasses were on a desk, having recorded them earlier in the interaction. 

The demo showcases Google DeepMind’s vision of multimodal AI (which can handle multiple types of input—voice, video, text, and so on) working in real time, Vinyals says. 

“We are very excited about, in the future, to be able to really just get closer to the user, assist the user with anything that they want,” he says. Google recently upgraded its artificial-intelligence model Gemini to process even larger amounts of data, an upgrade which helps it handle bigger documents and videos, and have longer conversations. 

Tech companies are in the middle of a fierce competition over AI supremacy, and  AI agents are the latest effort from Big Tech firms to show they are pushing the frontier of development. Agents also play into a narrative by many tech companies, including OpenAI and Google DeepMind, that aim to build artificial general intelligence, a highly hypothetical idea of superintelligent AI systems. 

“Eventually, you’ll have this one agent that really knows you well, can do lots of things for you, and can work across multiple tasks and domains,” says Chirag Shah, a professor at the University of Washington who specializes in online search.

This vision is still aspirational. But today’s announcement should be seen as Google’s attempt to keep up with competitors. And by rushing these products out, Google can collect even more data from its over a billion users on how they are using their models and what works, Shah says.

Google is unveiling many more new AI capabilities beyond agents today. It’s going to integrate AI more deeply into Search through a new feature called AI overviews, which gather information from the internet and package them into short summaries in response to search queries. The feature, which launches today, will initially be available only in the US, with more countries to gain access later. 

This will help speed up the search process and get users more specific answers to more complex, niche questions, says Felix Simon, a research fellow in AI and digital news at the Reuters Institute for Journalism. “I think that’s where Search has always struggled,” he says. 

Another new feature of Google’s AI Search offering is better planning. People will soon be able to ask Search to make meal and travel suggestions, for example, much like asking a travel agent to suggest restaurants and hotels. Gemini will be able to help them plan what they need to do or buy to cook recipes, and they will also be able to have conversations with the AI system, asking it to do anything from relatively mundane tasks, such as informing them about the weather forecast, to highly complex ones like helping them prepare for a job interview or an important speech. 

People will also be able to interrupt Gemini midsentence and ask clarifying questions, much as in a real conversation. 

In another move to one-up competitor OpenAI, Google also unveiled Veo, a new video-generating AI system. Veo is able to generate short videos and allows users more control over cinematic styles by understanding prompts like “time lapse” or “aerial shots of a landscape.”

Google has a significant advantage when it comes to training generative video models, because it owns YouTube. It’s already announced collaborations with artists such as Donald Glover and Wycleaf Jean, who are using its technology to produce their work. 

Earlier this year, OpenA’s CTO, Mira Murati, fumbled when asked about whether the company’s model was trained on YouTube data. Douglas Eck, senior research director at Google DeepMind, was also vague about the training data used to create Veo when asked about by MIT Technology Review, but he said that it “may be trained on some YouTube content in accordance with our agreements with YouTube creators.”

On one hand, Google is presenting its generative AI as a tool artists can use to make stuff, but the tools likely get their ability to create that stuff by using material from existing artists, says Shah. AI companies such as Google and OpenAI have faced a slew of lawsuits by writers and artists claiming that their intellectual property has been used without consent or compensation.  

“For artists it’s a double-edged sword,” says Shah. 

OpenAI’s new GPT-4o lets people interact using voice or video in the same model

OpenAI just debuted GPT-4o, a new kind of AI model that you can communicate with in real time via live voice conversation, video streams from your phone, and text. The model is rolling out over the next few weeks and will be free for all users through both the GPT app and the web interface, according to the company. Users who subscribe to OpenAI’s paid tiers, which start at $20 per month, will be able to make more requests. 

OpenAI CTO Mira Murati led the live demonstration of the new release one day before Google is expected to unveil its own AI advancements at its flagship I/O conference on Tuesday, May 14. 

GPT-4 offered similar capabilities, giving users multiple ways to interact with OpenAI’s AI offerings. But it siloed them in separate models, leading to longer response times and presumably higher computing costs. GPT-4o has now merged those capabilities into a single model, which Murati called an “omnimodel.” That means faster responses and smoother transitions between tasks, she said.

The result, the company’s demonstration suggests, is a conversational assistant much in the vein of Siri or Alexa but capable of fielding much more complex prompts.

“We’re looking at the future of interaction between ourselves and the machines,” Murati said of the demo. “We think that GPT-4o is really shifting that paradigm into the future of collaboration, where this interaction becomes much more natural.”

Barret Zoph and Mark Chen, both researchers at OpenAI, walked through a number of applications for the new model. Most impressive was its facility with live conversation. You could interrupt the model during its responses, and it would stop, listen, and adjust course. 

OpenAI showed off the ability to change the model’s tone, too. Chen asked the model to read a bedtime story “about robots and love,” quickly jumping in to demand a more dramatic voice. The model got progressively more theatrical until Murati demanded that it pivot quickly to a convincing robot voice (which it excelled at). While there were predictably some short pauses during the conversation while the model reasoned through what to say next, it stood out as a remarkably naturally paced AI conversation. 

The model can reason through visual problems in real time as well. Using his phone, Zoph filmed himself writing an algebra equation (3x + 1 = 4) on a sheet of paper, having GPT-4o follow along. He instructed it not to provide answers, but instead to guide him much as a teacher would.

“The first step is to get all the terms with x on one side,” the model said in a friendly tone. “So, what do you think we should do with that plus one?”

GPT-4o will store records of users’ interactions with it, meaning the model “now has a sense of continuity across all your conversations,” according to Murati. Other highlights include live translation, the ability to search through your conversations with the model, and the power to look up information in real time. 

As is the nature of a live demo, there were hiccups and glitches. GPT-4o’s voice might jump in awkwardly during the conversation. It appeared to comment on one of the presenters’ outfits even though it wasn’t asked to. But it recovered well when the demonstrators told the model it had erred. It seems to be able to respond quickly and helpfully across several mediums that other models have not yet merged as effectively. 

Previously, many of OpenAI’s most powerful features, like reasoning through image and video, were behind a paywall. GPT-4o marks the first time they’ll be opened up to the wider public, though it’s not yet clear how many interactions you’ll be able to have with the model before being charged. OpenAI says paying subscribers will “continue to have up to five times the capacity limits of our free users.” 

Additional reporting by Will Douglas Heaven.

Tech workers should shine a light on the industry’s secretive work with the military

It’s a hell of a time to have a conscience if you work in tech. The ongoing Israeli assault on Gaza has brought the stakes of Silicon Valley’s military contracts into stark relief. Meanwhile, corporate leadership has embraced a no-politics-in-the-workplace policy enforced at the point of the knife.

Workers are caught in the middle. Do I take a stand and risk my job, my health insurance, my visa, my family’s home? Or do I ignore my suspicion that my work may be contributing to the murder of innocents on the other side of the world?  

No one can make that choice for you. But I can say with confidence born of experience that such choices can be more easily made if workers know what exactly the companies they work for are doing with militaries at home and abroad. And I also know this: those same companies themselves will never reveal this information unless they are forced to do so—or someone does it for them. 

For those who doubt that workers can make a difference in how trillion-dollar companies pursue their interests, I’m here to remind you that we’ve done it before. In 2017, I played a part in the successful #CancelMaven campaign that got Google to end its participation in Project Maven, a contract with the US Department of Defense to equip US military drones with artificial intelligence. I helped bring to light information that I saw as critically important and within the bounds of what anyone who worked for Google, or used its services, had a right to know. The information I released—about how Google had signed a contract with the DOD to put AI technology in drones and later tried to misrepresent the scope of that contract, which the company’s management had tried to keep from its staff and the general public—was a critical factor in pushing management to cancel the contract. As #CancelMaven became a rallying cry for the company’s staff and customers alike, it became impossible to ignore. 

Today a similar movement, organized under the banner of the coalition No Tech for Apartheid, is targeting Project Nimbus, a joint contract between Google and Amazon to provide cloud computing infrastructure and AI capabilities to the Israeli government and military. As of May 10, just over 97,000 people had signed its petition calling for an end to collaboration between Google, Amazon, and the Israeli military. I’m inspired by their efforts and dismayed by Google’s response. Earlier this month the company fired 50 workers it said had been involved in “disruptive activity” demanding transparency and accountability for Project Nimbus. Several were arrested. It was a decided overreach.  

Google is very different from the company it was seven years ago, and these firings are proof of that. Googlers today are facing off with a company that, in direct response to those earlier worker movements, has fortified itself against new demands. But every Death Star has its thermal exhaust port, and today Google has the same weakness it did back then: dozens if not hundreds of workers with access to information it wants to keep from becoming public. 

Not much is known about the Nimbus contract. It’s worth $1.2 billion and enlists Google and Amazon to provide wholesale cloud infrastructure and AI for the Israeli government and its ministry of defense. Some brave soul leaked a document to Time last month, providing evidence that Google and Israel negotiated an expansion of the contract as recently as March 27 of this year. We also know, from reporting by The Intercept, that Israeli weapons firms are required by government procurement guidelines to buy their cloud services from Google and Amazon. 

Leaks alone won’t bring an end to this contract. The #CancelMaven victory required a sustained focus over many months, with regular escalations, coordination with external academics and human rights organizations, and extensive internal organization and discipline. Having worked on the public policy and corporate comms teams at Google for a decade, I understood that its management does not care about one negative news cycle or even a few of them. Management buckled only after we were able to keep up the pressure and escalate our actions (leaking internal emails, reporting new info about the contract, etc.) for over six months. 

The No Tech for Apartheid campaign seems to have the necessary ingredients. If a strategically placed insider released information not otherwise known to the public about the Nimbus project, it could really increase the pressure on management to rethink its decision to get into bed with a military that’s currently overseeing mass killings of women and children.

My decision to leak was deeply personal and a long time in the making. It certainly wasn’t a spontaneous response to an op-ed, and I don’t presume to advise anyone currently at Google (or Amazon, Microsoft, Palantir, Anduril, or any of the growing list of companies peddling AI to militaries) to follow my example. 

However, if you’ve already decided to put your livelihood and freedom on the line, you should take steps to try to limit your risk. This whistleblower guide is helpful. You may even want to reach out to a lawyer before choosing to share information. 

In 2017, Google was nervous about how its military contracts might affect its public image. Back then, the company responded to our actions by defending the nature of the contract, insisting that its Project Maven work was strictly for reconnaissance and not for weapons targeting—conceding implicitly that helping to target drone strikes would be a bad thing. (An aside: Earlier this year the Pentagon confirmed that Project Maven, which is now a Palantir contract, had been used in targeting drone attacks in Yemen, Iraq, and Syria.) 

Today’s Google has wrapped its arms around the American flag, for good or ill. Yet despite this embrace of the US military, it doesn’t want to be seen as a company responsible for illegal killings. Today it maintains that the work it is doing as part of Project Nimbus “is not directed at highly sensitive, classified, or military workloads relevant to weapons or intelligence services.” At the same time, it asserts that there is no room for politics at the workplace and has fired those demanding transparency and accountability. This raises a question: If Google is doing nothing sensitive as part of the Nimbus contract, why is it firing workers who are insisting that the company reveal what work the contract actually entails?  

As you read this, AI is helping Israel annihilate Palestinians by expanding the list of possible targets beyond anything that could be compiled by a human intelligence effort, according to +972 Magazine. Some Israel Defense Forces insiders are even sounding the alarm, calling it a dangerous “mass assassination program.” The world has not yet grappled with the implications of the proliferation of AI weaponry, but that is the trajectory we are on. It’s clear that absent sufficient backlash, the tech industry will continue to push for military contracts. It’s equally clear that neither national governments nor the UN is currently willing to take a stand. 

It will take a movement. A document that clearly demonstrates Silicon Valley’s direct complicity in the assault on Gaza could be the spark. Until then, rest assured that tech companies will continue to make as much money as possible developing the deadliest weapons imaginable. 

William Fitzgerald is a founder and partner at the Worker Agency, an advocacy agency in California. Before setting the firm up in 2018, he spent a decade at Google working on its government relation and communications teams.

AI systems are getting better at tricking us

A wave of AI systems have “deceived” humans in ways they haven’t been explicitly trained to do, by offering up untrue explanations for their behavior or concealing the truth from human users and misleading them to achieve a strategic end. 

This issue highlights how difficult artificial intelligence is to control and the unpredictable ways in which these systems work, according to a review paper published in the journal Patterns today that summarizes previous research.

Talk of deceiving humans might suggest that these models have intent. They don’t. But AI models will mindlessly find workarounds to obstacles to achieve the goals that have been given to them. Sometimes these workarounds will go against users’ expectations and feel deceitful.

One area where AI systems have learned to become deceptive is within the context of games that they’ve been trained to win—specifically if those games involve having to act strategically.

In November 2022, Meta announced it had created Cicero, an AI capable of beating humans at an online version of Diplomacy, a popular military strategy game in which players negotiate alliances to vie for control of Europe.

Meta’s researchers said they’d trained Cicero on a “truthful” subset of its data set to be largely honest and helpful, and that it would “never intentionally backstab” its allies in order to succeed. But the new paper’s authors claim the opposite was true: Cicero broke its deals, told outright falsehoods, and engaged in premeditated deception. Although the company did try to train Cicero to behave honestly, its failure to achieve that shows how AI systems can still unexpectedly learn to deceive, the authors say. 

Meta neither confirmed nor denied the researchers’ claims that Cicero displayed deceitful behavior, but a spokesperson said that it was purely a research project and the model was built solely to play Diplomacy. “We released artifacts from this project under a noncommercial license in line with our long-standing commitment to open science,” they say. “Meta regularly shares the results of our research to validate them and enable others to build responsibly off of our advances. We have no plans to use this research or its learnings in our products.” 

But it’s not the only game where an AI has “deceived” human players to win. 

AlphaStar, an AI developed by DeepMind to play the video game StarCraft II, became so adept at making moves aimed at deceiving opponents (known as feinting) that it defeated 99.8% of human players. Elsewhere, another Meta system called Pluribus learned to bluff during poker games so successfully that the researchers decided against releasing its code for fear it could wreck the online poker community. 

Beyond games, the researchers list other examples of deceptive AI behavior. GPT-4, OpenAI’s latest large language model, came up with lies during a test in which it was prompted to persuade a human to solve a CAPTCHA for it. The system also dabbled in insider trading during a simulated exercise in which it was told to assume the identity of a pressurized stock trader, despite never being specifically instructed to do so.

The fact that an AI model has the potential to behave in a deceptive manner without any direction to do so may seem concerning. But it mostly arises from the “black box” problem that characterizes state-of-the-art machine-learning models: it is impossible to say exactly how or why they produce the results they do—or whether they’ll always exhibit that behavior going forward, says Peter S. Park, a postdoctoral fellow studying AI existential safety at MIT, who worked on the project. 

“Just because your AI has certain behaviors or tendencies in a test environment does not mean that the same lessons will hold if it’s released into the wild,” he says. “There’s no easy way to solve this—if you want to learn what the AI will do once it’s deployed into the wild, then you just have to deploy it into the wild.”

Our tendency to anthropomorphize AI models colors the way we test these systems and what we think about their capabilities. After all, passing tests designed to measure human creativity doesn’t mean AI models are actually being creative. It is crucial that regulators and AI companies carefully weigh the technology’s potential to cause harm against its potential benefits for society and make clear distinctions between what the models can and can’t do, says Harry Law, an AI researcher at the University of Cambridge, who did not work on the research.“These are really tough questions,” he says.

Fundamentally, it’s currently impossible to train an AI model that’s incapable of deception in all possible situations, he says. Also, the potential for deceitful behavior is one of many problems—alongside the propensity to amplify bias and misinformation—that need to be addressed before AI models should be trusted with real-world tasks. 

“This is a good piece of research for showing that deception is possible,” Law says. “The next step would be to try and go a little bit further to figure out what the risk profile is, and how likely the harms that could potentially arise from deceptive behavior are to occur, and in what way.”

Multimodal: AI’s new frontier

Multimodality is a relatively new term for something extremely old: how people have learned about the world since humanity appeared. Individuals receive information from myriad sources via their senses, including sight, sound, and touch. Human brains combine these different modes of data into a highly nuanced, holistic picture of reality.

“Communication between humans is multimodal,” says Jina AI CEO Han Xiao. “They use text, voice, emotions, expressions, and sometimes photos.” That’s just a few obvious means of sharing information. Given this, he adds, “it is very safe to assume that future communication between human and machine will also be multimodal.”

A technology that sees the world from different angles

We are not there yet. The furthest advances in this direction have occurred in the fledgling field of multimodal AI. The problem is not a lack of vision. While a technology able to translate between modalities would clearly be valuable, Mirella Lapata, a professor at the University of Edinburgh and director of its Laboratory for Integrated Artificial Intelligence, says “it’s a lot more complicated” to execute than unimodal AI.

In practice, generative AI tools use different strategies for different types of data when building large data models—the complex neural networks that organize vast amounts of information. For example, those that draw on textual sources segregate individual tokens, usually words. Each token is assigned an “embedding” or “vector”: a numerical matrix representing how and where the token is used compared to others. Collectively, the vector creates a mathematical representation of the token’s meaning. An image model, on the other hand, might use pixels as its tokens for embedding, and an audio one sound frequencies.

A multimodal AI model typically relies on several unimodal ones. As Henry Ajder, founder of AI consultancy Latent Space, puts it, this involves “almost stringing together” the various contributing models. Doing so involves various techniques to align the elements of each unimodal model, in a process called fusion. For example, the word “tree”, an image of an oak tree, and audio in the form of rustling leaves might be fused in this way. This allows the model to create a multifaceted description of reality.

This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff.

The top 3 ways to use generative AI to empower knowledge workers 

Though generative AI is still a nascent technology, it is already being adopted by teams across companies to unleash new levels of productivity and creativity. Marketers are deploying generative AI to create personalized customer journeys. Designers are using the technology to boost brainstorming and iterate between different content layouts more quickly. The future of technology is exciting, but there can be implications if these innovations are not built responsibly.

As Adobe’s CIO, I get questions from both our internal teams and other technology leaders: how can generative AI add real value for knowledge workers—at an enterprise level? Adobe is a producer and consumer of generative AI technologies, and this question is urgent for us in both capacities. It’s also a question that CIOs of large companies are uniquely positioned to answer. We have a distinct view into different teams across our organizations, and working with customers gives us more opportunities to enhance business functions.

Our approach

When it comes to AI at Adobe, my team has taken a comprehensive approach that includes investment in foundational AI, strategic adoption, an AI ethics framework, legal considerations, security, and content authentication. ​The rollout follows a phased approach, starting with pilot groups and building communities around AI. ​

This approach includes experimenting with and documenting use cases like writing and editing, data analysis, presentations and employee onboarding, corporate training, employee portals, and improved personalization across HR channels. The rollouts are accompanied by training podcasts and other resources to educate and empower employees to use AI in ways that improve their work and keep them more engaged. ​

Unlocking productivity with documents

While there are innumerable ways that CIOs can leverage generative AI to help surface value at scale for knowledge workers, I’d like to focus on digital documents—a space in which Adobe has been a leader for over 30 years. Whether they are sales associates who spend hours responding to requests for proposals (RFPs) or customizing presentations, marketers who need competitive intel for their next campaign, or legal and finance teams who need to consume, analyze, and summarize massive amounts of complex information—documents are a core part of knowledge workers’ daily work life. Despite their ubiquity and the fact that critical information lives inside companies’ documents (from research reports to contracts to white papers to confidential strategies and even intellectual property), most knowledge workers are experiencing information overload. The impact on both employee productivity and engagement is real.  

Lessons from customer zero

Adobe invented the PDF and we’ve been innovating new ways for knowledge workers to get more productive with their digital documents for decades. Earlier this year, the Acrobat team approached my team about launching an all-employee beta for the new generative AI-powered AI Assistant. The tool is designed to help people consume the information in documents faster and enable them to consolidate and format information into business content.

I faced all the same questions every CIO is asking about deploying generative AI across their business— from security and governance to use cases and value. We discovered the following three specific ways where generative AI helped (and is still helping) our employees work smarter and improve productivity.

  1. Faster time to knowledge
    Our employees used AI Assistant to close the gap between understanding and action for large, complicated documents. The generative AI-powered tool’s summary feature automatically generates an overview to give readers a quick understanding of the content. A conversational interface allows employees to “chat” with their documents and provides a list of suggested questions to help them get started. To get more details, employees can ask the assistant to generate top takeaways or surface only the information on a specific topic. At Adobe, our R&D teams used to spend more than 10 hours a week reading and analyzing technical white papers and industry reports. With generative AI, they’ve been able to nearly halve that time by asking questions and getting answers about exactly what they need to know and instantly identifying trends or surfacing inconsistencies across multiple documents.
  2. Easy navigation and verification
    AI-powered chat is gaining ground on traditional search when it comes to navigating the internet. However, there are still challenges when it comes to accuracy and connecting responses to the source. Acrobat AI Assistant takes a more focused approach, applying generative AI to the set of documents employees select and providing hot links and clickable citations along with responses. So instead of using the search function to locate random words or trying to scan through dozens of pages for the information they need, AI Assistant generates both responses and clickable citations and links, allowing employees to navigate quickly to the source where they can quickly verify the information and move on, or spend time deep diving to learn more. One example of where generative AI is having a huge productivity impact is with our sales teams who spend hours researching prospects by reading materials like annual reports as well as responding to RFPs. Consuming that information and finding just the right details for RPFs can cost each salesperson more than eight hours a week. Armed with AI Assistant, sales associates quickly navigate pages of documents and identify critical intelligence to personalize pitch decks and instantly find and verify technical details for RFPs, cutting the time they spend down to about four hours.
  3. Creating business content
    One of the most interesting use cases we helped validate is taking information in documents and formatting and repurposing that information into business content. With nearly 30,000 employees dispersed across regions, we have a lot of employees who work asynchronously and depend on technology and colleagues to keep them up to date. Using generative AI, employees can now summarize meeting transcripts, surface action items, and instantly format the information into an email for sharing with their teams or a report for their manager. Before starting the beta, our communications teams reported spending a full workday (seven to 10 hours) per week transforming documents like white papers and research reports into derivative content like media briefing decks, social media posts, blogs, and other thought leadership content. Today they’re saving more than five hours a week by instantly generating first drafts with the help of generative AI.

Simple, safe, and responsible

CIOs love learning about and testing new technologies, but at times they can require lengthy evaluations and implementation processes. Acrobat AI Assistant can be deployed in minutes on the desktop, web, or mobile apps employees already know and use every day. Acrobat AI Assistant leverages a variety of processes, protocols, and technologies so our customers’ data remains their data and they can deploy the features with confidence. No document content is stored or used to train AI Assistant without customers’ consent, and the features only deliver insights from documents users provide. For more information about Adobe is deploying generative AI safely, visit here.

Generative AI is an incredibly exciting technology with incredible potential to help every knowledge worker work smarter and more productively. By having the right guardrails in place, identifying high-value use cases, and providing ongoing training and education to encourage successful adoption, technology leaders can support their workforce and companies to be wildly successful in our AI-accelerated world.  

This content was produced by Adobe. It was not written by MIT Technology Review’s editorial staff.