The new model makes progress on technical issues that have plagued AI image generators for years. While most have been great at creating fantastical images or realistic deepfakes, they've been terrible at something called binding, which refers to the ability to identify certain objects correctly and put them in their proper place (like a sign that says "hot dogs" properly placed above a food cart, not somewhere else in the image). It was only a few years ago that models started to succeed at things like "Put the red cube on top of the blue cube," a feature that is essential for any creative professional use of AI. Generators also struggle with text generation, typically creating distorted jumbles of letter shapes that look more like captchas than readable text. The model is able to generate 12 discrete graphics within a single image--like a cat emoji or a lightning bolt--and place them in proper order.

There might be a way to get out of this moral and scientific deadlock. Recent advances in biotechnology now provide a pathway to producing living human bodies without the neural components that allow us to think, be aware, or feel pain. Many will find this possibility disturbing, but if researchers and policymakers can find a way to pull these technologies together, we may one day be able to create "spare" bodies, both human and nonhuman. These could revolutionize medical research and drug development, greatly reducing the need for animal testing, rescuing many people from organ transplant lists, and allowing us to produce more effective drugs and treatments. A few weeks ago, when I was at the digital rights conference RightsCon in Taiwan, I watched in real time as civil society organizations from around the world, including the US, grappled with the loss of one of the biggest funders of global digital rights work: the United States government.

This imbalance between supply and demand is the underlying cause of the organ shortage crisis, with more than 100,000 patients currently waiting for a solid organ transplant in the US alone. It also forces us to rely heavily on animals in medical research, a practice that can't replicate major aspects of human physiology and makes it necessary to inflict harm on sentient creatures. In addition, the safety and efficacy of any experimental drug must still be confirmed in clinical trials on living human bodies. These costly trials risk harm to patients, can take a decade or longer to complete, and make it through to approval less than 15% of the time. There might be a way to get out of this moral and scientific deadlock.

As a result, some policymakers and business leaders--in Europe, in particular--are reconsidering their reliance on US-based tech and asking whether they can quickly spin up better, homegrown alternatives. This is particularly true for AI. One of the clearest examples of this is in social media. Yasmin Curzi, a Brazilian law professor who researches domestic tech policy, put it to me this way: "Since Trump's second administration, we cannot count on [American social media platforms] to do even the bare minimum anymore." Social media content moderation systems--which already use automation and are also experimenting with deploying large language models to flag problematic posts--are failing to detect gender-based violence in places as varied as India, South Africa, and Brazil.

AI agents have set the tech industry abuzz. Unlike chatbots, these groundbreaking new systems can navigate multiple applications to execute complex tasks, like scheduling meetings or shopping online, in response to simple user commands. As agents become more capable, a crucial question emerges: How much control are we willing to surrender, and at what cost? Who doesn't want assistance with cumbersome work or tasks there's no time for? But this vision for AI agents brings significant risks that might be overlooked in the rush toward greater autonomy.

These developments mark a major advance in artificial intelligence: systems designed to operate in the digital world without direct human oversight. Who doesn't want assistance with cumbersome work or tasks there's no time for? Agent assistance could soon take many different forms, such as reminding you to ask a colleague about their kid's basketball tournament or finding images for your next presentation. Within a few weeks, they'll probably be able to make presentations for you. For people with hand mobility issues or low vision, agents could complete tasks online in response to simple language commands.

The researchers found some intriguing differences between how men and women respond to using ChatGPT. After using the chatbot for four weeks, female study participants were slightly less likely to socialize with people than their male counterparts who did the same. Meanwhile, participants who interacted with ChatGPT's voice mode in a gender that was not their own for their interactions reported significantly higher levels of loneliness and more emotional dependency on the chatbot at the end of the experiment. OpenAI plans to submit both studies to peer-reviewed journals. Chatbots powered by large language models are still a nascent technology, and it's difficult to study how they affect us emotionally.

Recorded on March 20, 2025 AI Chatbots Have Joined the Chat Speakers: Rachel Courtland, commissioning editor, Rhiannon Williams, news reporter, and Eileen Guo, features & investigations reporter. Chatbots are quickly changing how we connect to each other and ourselves. But are these changes for the better? How should they be monitored and regulated? Hear from MIT…

Where can you find lasers, electric guitars, and racks full of novel batteries, all in the same giant room? This week, the answer was the 2025 ARPA-E Energy Innovation Summit just outside Washington, DC. Energy innovation can take many forms, and the variety in energy research was on display at the summit. ARPA-E, part of the US Department of Energy, provides funding for high-risk, high-reward research projects. The summit gathers projects the agency has funded, along with investors, policymakers, and journalists.

Data-sharing remains limited and companies across the value chain have vastly different needs and capabilities. There are also few standards and data governance protocols in place, and more talent and skills are needed to keep pace with the technological wave. All the same, progress is being made and the potential for AI in the food sector is huge. Predictive analytics are accelerating R&D cycles in crop and food science. AI reduces the time and resources needed to experiment with new food products and turns traditional trial-and-error cycles into more efficient data-driven discoveries.