On a commercial thoroughfare in old town Pasadena, California, a stone's throw from NASA's Jet Propulsion Laboratory (JPL), you'll find the Neon Retro Arcade. Among its collection of vintage video games is the 1979 Atari classic Asteroids, in which a pixelated spaceship shoots down a barrage of space rocks to stave off fatal collisions. After long days of work at JPL, Amy Mainzer used to rack up high scores on that console. "It was a hoot," she says. It was also apt, considering she oversees a space mission designed to spot dangerous asteroids before they crash into Earth. That mission, the Near-Earth Object (NEO) Surveyor, was conceived in the early 2000s and finally got the green light in 2022. Its components are now being built, tested, and assembled in clean rooms across the United States ahead of its planned launch in September 2027. "We're in the thick of building everything," says Mainzer, NEO Surveyor's principal investigator and now an astronomer at the University of California, Los Angeles (UCLA).

As SARS-CoV-2 radiated across the planet in 2020, epidemiologists scrambled to predict its spread--and its deadly consequences. Often, they turned to models that not only simulate viral transmission and hospitalization rates, but can also predict the effect of interventions: masks, vaccines, or travel bans. But in addition to being computationally intensive, models in epidemiology and other disciplines can be black boxes: millions of lines of legacy code subject to finicky tunings by operators at research organizations scattered around the world. They don't always provide clear guidance. "The models that are used are often kind of brittle and nonexplainable," says Erica Briscoe, who was a program manager for the Automating Scientific Knowledge Extraction and Modeling (ASKEM) project at the Defense Advanced Research Projects Agency (DARPA).

In the past 20 years, mice with glowing cables sprouting from their heads have become a staple of neuroscience. They reflect the rise of optogenetics, in which neurons are engineered to contain light-sensitive proteins called opsins, allowing pulses of light to turn them on or off. The method has powered thousands of basic experiments into the brain circuits that drive behavior and underlie disease. As this research tool matured, hopes arose for using it as a treatment, too. Compared with the electrical or magnetic brain stimulation approaches already in use, optogenetics offers a way to more precisely target and manipulate the exact cell types underlying brain disorders.

We recruited participants through two online platforms, CloudResearch and Bovitz, targeting US residents over 18 years old who self-identified as either Republican or Democrat and were active users of X (SM section S1.1). Qualified individuals were invited to complete a screening task, which included installing a browser extension that analyzed their X feed. To ensure the interventions could have a meaningful impact on participants' feeds, only those with at least 5% of posts related to politics or social issues were invited to participate. Figure S1 summarizes the recruitment funnel, including the number of individuals at each stage of the process. Participants were not instructed to use X in any particular way, but they received daily reminders if they had not used the platform that day.

We experimentally demonstrated different SKMs to perform machine learning analysis over complex, visible to mid-infrared (MIR) incident spectra, with the readout photocurrent directly providing the final inference. This process mathematically resembles the kernel machine algorithms typically used for digital machine learning, making each photodetector a spectral kernel machine. It can "sniff-and-seek," conceptually inspired by retriever dogs, learning from examples to recognize spectral features within a complex scene. We experimentally demonstrated different SKMs to perform diverse tasks, including visible-band image segmentation, nanoscale-thickness metrology of wafer oxide layers, discrimination of natural and artificial leaves, and identification of leaf hydration levels, achieving high accuracy under blind testing. For the MIR band, an electrically tunable bipolar photodiode made of bP-MoS2 heterostructure was operated at room temperature to identify chemicals and quantify mixture concentrations that were indistinguishable in the visible bands.

Social media platforms' opaque feed-ranking algorithms, which are designed to maximize engagement, may contribute to political polarization, body image, mental health, and other social issues. However, the evidence has rarely enabled conclusions about causality because external researchers need platforms' permission to experimentally intervene. Collaborations with platforms also involve trade-offs that undermine researchers' intellectual autonomy. Piccardi et al. circumvented this problem with a browser extension that intercepted feeds on X/Twitter in real time using a large language model (LLM) (see the Perspective by Allen and Tucker). Liberals and conservatives were randomly assigned to conditions where the LLM re-ranked feeds to up-rank or down-rank the visibility of hostile political content.

Social media is an important source of political information, yet there is little external oversight of platforms' ever-changing algorithms and policies. This opacity presents a major problem: Conducting a real-world experiment on the causal effects of platform features generally requires the collaboration of the platform being studied, which rarely happens, and even when it does, future platform changes may invalidate prior findings. The authors introduce a methodological paradigm for testing the effect of social media on partisan animosity without platform collaboration by reranking users' existing feeds using large language models (LLMs) and a browser extension. They find that changing the visibility of polarizing content can influence people's feelings about opposing partisans. Social media is in a period of upheaval.

At a recent conference on mathematics in the age of automated proofs, mathematician and Fields Medalist Akshay Venkatesh presented “How do we talk to our students about AI?'' He quoted an email he'd received from a young student who asked, “Do you believe that mathematics is worth being studied in a world in which a machine can answer everything for you? What do you believe would be the 'job’ of a mathematician in this world?” Venkatesh framed AI as an opportunity to correct what he called an “essential gap that has opened between the practice of mathematics and our values.” Mathematician William Thurston has explained these values by writing, “mathematics is not about numbers, equations, computations, or algorithms: it is about understanding.” But Venkatesh argued that the record on this is terrible, lamenting that “for a typical paper or talk, very few of us understand it.” He is not alone in thinking that something is wrong with the current state of mathematics research.

Case study 1 considers vocal production learning, an organism's capacity to enlarge and modify its repertoire of vocalizations based on auditory experience. This ability is crucial for learning spoken language and limited in nonhuman primates but has emerged in other branches of the evolutionary tree, including subsets of birds, bats, elephants, cetaceans, and pinnipeds. Bringing together data from molecular investigations of speech and language disorders, genetic manipulations in animal models, and studies of ancient DNA, this case study demonstrates how ancient genetic and neural infrastructures may have been modified and recombined to enable distinctive human capacities. Case study 2 examines the emergence of linguistic structure, a defining property of human language, using data from real-world cases of emergence (e.g., homesign and emerging sign languages); experiments recreating cultural evolution in the lab; and comparative studies of nonhuman animals, including songbirds and primates. This case study highlights the importance of transmission and interaction, suggesting that emergence of structure involves a combination of biological, cognitive, and cultural conditions: Although some (or all) traits are shared with other species, their combination may be specific to humans.

Pacific Grove, California--Pop a few human stem cells into culture, provide the right molecular signals, and before long a mock cerebral cortex or a cerebellum knockoff could be floating in the medium. These neural, or brain, organoids, typically just a few millimeters across, are not "brains in a dish," as some journalists have described them. But they are becoming ever more sophisticated and true to life, capturing more of the brain's cellular and structural intricacy. "It's surprising how far this [area] has advanced in the last year," says John Evans, a sociologist at the University of California San Diego who follows the research and public opinions on it. That progress has allowed researchers to delve deeper into how the human brain develops, functions, and goes awry in diseases, but it has also sharpened ethical questions.