Category: Science & Tech

“We believe a democratic vision for AI is essential to unlocking its full potential and ensuring its benefits are broadly shared,” OpenAI wrote, echoing similar language in the White House memo. “We believe democracies should continue to take the lead in AI development, guided by values like freedom, fairness, and respect for human rights.” 

It offered a number of ways OpenAI could help pursue that goal, including efforts to “streamline translation and summarization tasks, and study and mitigate civilian harm,” while still prohibiting its technology from being used to “harm people, destroy property, or develop weapons.” Above all, it was a message from OpenAI that it is on board with national security work. 

The new policies emphasize “flexibility and compliance with the law,” says Heidy Khlaaf, a chief AI scientist at the AI Now Institute and a safety researcher who authored a paper with OpenAI in 2022 about the possible hazards of its technology in contexts including the military. The company’s pivot “ultimately signals an acceptability in carrying out activities related to military and warfare as the Pentagon and US military see fit,” she says.

Amazon, Google, and OpenAI’s partner and investor Microsoft have competed for the Pentagon’s cloud computing contracts for years. Those companies have learned that working with defense can be incredibly lucrative, and OpenAI’s pivot, which comes as the company expects $5 billion in losses and is reportedly exploring new revenue streams like advertising, could signal that it wants a piece of those contracts. Big Tech’s relationships with the military also no longer elicit the outrage and scrutiny that they once did. But OpenAI is not a cloud provider, and the technology it’s building stands to do much more than simply store and retrieve data. With this new partnership, OpenAI promises to help sort through data on the battlefield, provide insights about threats, and help make the decision-making process in war faster and more efficient. 

OpenAI’s statements on national security perhaps raise more questions than they answer. The company wants to mitigate civilian harm, but for which civilians? Does contributing AI models to a program that takes down drones not count as developing weapons that could harm people?

“Defensive weapons are still indeed weapons,” Khlaaf says. They “can often be positioned offensively subject to the locale and aim of a mission.”

Beyond those questions, working in defense means that the world’s foremost AI company, which has had an incredible amount of leverage in the industry and has long pontificated about how to steward AI responsibly, will now work in a defense-tech industry that plays by an entirely different set of rules. In that system, when your customer is the US military, tech companies do not get to decide how their products are used. 

The global food system is responsible for roughly 25% to 35% of all human-caused greenhouse gas emissions today (depending on how you tally them up), and much of that comes from animal agriculture. Alternative food sources could help feed the world while cutting climate pollution.

As climate change pushes weather conditions to new extremes, it’s going to be harder to grow food, says LanzaTech CEO Jennifer Holmgren. The company’s current specialty, sucking up waste gases and transforming them into ethanol, is mostly used today in places like steel mills and landfills.

The process the company uses to make ethanol relies on a bacterium that can be found in the guts of rabbits. LanzaTech grows the microbes in reactors, on a diet consisting of gases including carbon monoxide, carbon dioxide, and hydrogen. As they grow, they produce ethanol, which can then be funneled into processes that transform the ethanol into chemicals like ethylene or fuels.

A by-product of that process is tons of excess microbes. In LanzaTech’s existing plants where ethanol is the primary product, operators generally need to harvest bacteria from the reactors, since they multiply over time. When the excess bacteria are harvested and dried, the resulting powder is high in protein. Some plants using LanzaTech’s technology in China are already selling the protein product to feed fish, poultry, and pigs.

Now, LanzaTech is expanding its efforts. The company has identified a new microbe, one they hope to make the star of future plants. Cupriavidus necator can be found in soil and water, and it’s something of a protein machine. The company says that after growing, harvesting, and drying the microbes, the resulting powder is more than 85% protein and could be added to all sorts of food products, for either humans or animals.

“The web is a collection of data, but it’s a mess,” says Exa cofounder and CEO Will Bryk. “There’s a Joe Rogan video over here, an Atlantic article over there. There’s no organization. But the dream is for the web to feel like a database.”

Websets is aimed at power users who need to look for things that other search engines aren’t great at finding, such as types of people or companies. Ask it for “startups making futuristic hardware” and you get a list of specific companies hundreds long rather than hit-or-miss links to web pages that mention those terms. Google can’t do that, says Bryk: “There’s a lot of valuable use cases for investors or recruiters or really anyone who wants any sort of data set from the web.”

Things have moved fast since MIT Technology Review broke the news in 2021 that Google researchers were exploring the use of large language models in a new kind of search engine. The idea soon attracted fierce critics. But tech companies took little notice. Three years on, giants like Google and Microsoft jostle with a raft of buzzy newcomers like Perplexity and OpenAI, which launched ChatGPT Search in October, for a piece of this hot new trend.

Exa isn’t (yet) trying to out-do any of those companies. Instead, it’s proposing something new. Most other search firms wrap large language models around existing search engines, using the models to analyze a user’s query and then summarize the results. But the search engines themselves haven’t changed much. Perplexity still directs its queries to Google Search or Bing, for example. Think of today’s AI search engines as a sandwich with fresh bread but stale filling.

More than keywords

Exa provides users with familiar lists of links but uses the tech behind large language models to reinvent how search itself is done. Here’s the basic idea: Google works by crawling the web and building a vast index of keywords that then get matched to users’ queries. Exa crawls the web and encodes the contents of web pages into a format known as embeddings, which can be processed by large language models.

Embeddings turn words into numbers in such a way that words with similar meanings become numbers with similar values. In effect, this lets Exa capture the meaning of text on web pages, not just the keywords.

Large language models use embeddings to predict the next words in a sentence. Exa’s search engine predicts the next link. Type “startups making futuristic hardware” and the model will come up with (real) links that might follow that phrase.

Exa’s approach comes at cost, however. Encoding pages rather than indexing keywords is slow and expensive. Exa has encoded some billion web pages, says Bryk. That’s tiny next to Google, which has indexed around a trillion. But Bryk doesn’t see this as a problem: “You don’t have to embed the whole web to be useful,” he says. (Fun fact: “exa” means a 1 followed by 18 0s and “googol” means a 1 followed by 100 0s.)

Every year, MIT Technology Review recognizes 35 young innovators who are doing pioneering work across a range of technical fields including biotechnology, materials science, artificial intelligence, computing, and more. 

We’re now taking nominations for our 2025 list and you can submit one here. The process takes just a few minutes. Nominations will close at 11:59 PM ET on January 20, 2025. You can nominate yourself or someone you know, based anywhere in the world. The only rule is that the nominee must be under the age of 35 on October 1, 2025.  

Yet, difficulty successfully deploying generative AI continues to hamper progress. Companies know that generative AI could transform their businesses—and that failing to adopt will leave them behind—but they are faced with hurdles during implementation. This leaves two-thirds of business leaders dissatisfied with progress on their AI deployments. And while, in Q3 2023, 79% of companies said they planned to deploy generative AI projects in the next year, only 5% reported having use cases in production in May 2024. 

“We’re just at the beginning of figuring out how to productize AI deployment and make it cost effective,” says Rowan Trollope, CEO of Redis, a maker of real-time data platforms and AI accelerators. “The cost and complexity of implementing these systems is not straightforward.”

Estimates of the eventual GDP impact of generative AI range from just under $1 trillion to a staggering $4.4 trillion annually, with projected productivity impacts comparable to those of the Internet, robotic automation, and the steam engine. Yet, while the promise of accelerated revenue growth and cost reductions remains, the path to get to these goals is complex and often costly. Companies need to find ways to efficiently build and deploy AI projects with well-understood components at scale, says Trollope.

Download the full report.

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.