What is unique about AI is also what is most feared and celebrated--its ability to match some of our own skills, and then to go further, accomplishing what humans cannot. AI's capacity to model itself on human behavior has become its defining feature. Yet behind every advance in machine learning and large language models are, in fact, people--both the often obscured human labor that makes large language models safer to use, and the individuals who make critical decisions on when and how to best use this technology. Reporting on people and influence is what TIME does best. That led us to the TIME100 AI.

Fox News correspondent Gillian Turner has the latest on the president's focus amid calls for an impeachment inquiry on'Special Report.' The top Republican on the Senate Energy Committee will warn Thursday against allowing U.S. artificial intelligence capabilities to fall into China's hands when the panel meets for a hearing on the topic. Senators returned to Capitol Hill just days ago after spending the month of August in their home states. AI is expected to be a prominent topic for lawmakers as they race to get ahead of the rapidly advancing technology. It's also the topic at the heart of Thursday's hearing led by Energy Committee Chair Joe Manchin, D-W.Va., and ranking member John Barrasso, R-Wyo., that aims to examine how AI has affected the U.S. energy sector and how the federal government can stay competitive in that lane.

Looking at this new picture from NASA's Perseverance rover, you'd be forgiven for thinking there's something fishy afoot on the Red Planet. That's because the car-sized robot has snapped an image of two separate boulders resembling a shark fin and a crab claw. The US space agency shared this latest discovery on X (formerly known as Twitter), prompting a wave of replies from space fans who joked that the crab-like rock was the remains of the'Almighty Great Cosmic Crab'. Others said the'claw' looked more like a coffee bean or the head of a turtle'digging a hole for its eggs', while some quipped that the shark fin might actually be the'back plates' of a Stegosaurus. The photos, which were taken last month, are the latest example of a phenomenon known as pareidolia -- where the human brain wants to make sense of what the eyes see so creates a meaning which isn't real. Peculiar: NASA's Perseverance rover has snapped images of two separate boulders resembling a shark fin and a crab claw Most famously with Mars, this happened in 1976 when NASA's Viking 1 spacecraft captured an image of what looked like a face carved into the surface of the Red Planet.

Governments around the world are rushing to embrace the algorithms that breathed some semblance of intelligence into ChatGPT, apparently enthralled by the enormous economic payoff expected from the technology. Two new reports out this week show that nation-states are also likely rushing to adapt the same technology into weapons of misinformation, in what could become a troubling AI arms race between great powers. Researchers at RAND, a nonprofit think tank that advises the United States government, point to evidence of a Chinese military researcher who has experience with information campaigns publicly discussing how generative AI could help such work. One research article, from January 2023, suggests using large language models such as a fine-tuned version of Google's BERT, a precursor to the more powerful and capable language models that power chatbots like ChatGPT. "There's no evidence of it being done right now," says William Marcellino, an AI expert and senior behavioral and social scientist at RAND, who contributed to the report. He and others at RAND are alarmed at the prospect of influence campaigns getting new scale and power thanks to generative AI. "Coming up with a system to create millions of fake accounts that purport to be Taiwanese, or Americans, or Germans, that are pushing a state narrative--I think that it's qualitatively and quantitatively different," Marcellino says.

Fox News correspondent Alex Hogan has more on the provocative military action near Taiwan on "Special Report." The U.S. Air Force's development of a pilotless aircraft run by artificial intelligence (AI) has the potential to give American forces the upper hand in any conflict, but it also raises ethical questions about how such powerful technology should be deployed on the battlefield. "This technology is something we'll need for the future of defense," Phil Siegel, an AI expert and the founder of the Center For Advanced Preparedness and Threat Response Simulation, told Fox News Digital. Siegel's comments come as the Air Force continues development of XQ-58A Valkyrie experimental aircraft, an artificial intelligence-run stealth platform that the U.S. hopes can provide a relatively inexpensive weapon that can be used to limit losses to manned planes and pilots in a conflict with a near-peer rival such as China. WHAT IS ARTIFICIAL INTELLIGENCE (AI)? The XQ-58A Valkyrie demonstrates the separation of the ALTIUS-600 small unmanned aircraft system in a test at the U.S. Army Yuma Proving Ground test range in Arizona on March 26, 2021.

At least five Ukrainian combat drones have been downed over Russian territory as Kyiv continues with a pledge to bring Moscow's war in Ukraine back to Russia. Two drones were shot down on approach to Bryansk city in Russia's southwest, two were shot down over the southern Rostov region, and one was intercepted near the capital, Moscow, Russian officials and state news agencies reported early on Thursday. One person was injured and several vehicles damaged when one drone was shot down and crashed in the city of Rostov-on-Don in the early hours of Thursday, according to Russia's state-run TASS news agency. "According to verified information, air defence systems shot down two unmanned aerial vehicles," Rostov's regional Governor Vasily Golubev said, according to TASS. A separate news report said that buildings were also damaged in Rostov-on-Don due to falling debris from the destroyed drone.

The state of California has entered the frenzied and at times confusing race among governments around the world to both regulate and harness the technology known as generative artificial intelligence. On Wednesday morning, Gov. Gavin Newsom signed Executive Order N-12-23, a 2,500-word directive that instructs state agencies to examine how AI might threaten the security and privacy of California residents, while also authorizing state employees to experiment with AI tools and try integrating them into the state's operations. Generative AI "is a new technology and requires a new class of responsibility," Newsom said in an interview. "There's a Pandora's box being opened here, and we just want it done in a safe way.

Elastic geophysical properties (such as P- and S-wave velocities) are of great importance to various subsurface applications like CO$_2$ sequestration and energy exploration (e.g., hydrogen and geothermal). Elastic full waveform inversion (FWI) is widely applied for characterizing reservoir properties. In this paper, we introduce $\mathbf{\mathbb{E}^{FWI}}$, a comprehensive benchmark dataset that is specifically designed for elastic FWI. $\mathbf{\mathbb{E}^{FWI}}$ encompasses 8 distinct datasets that cover diverse subsurface geologic structures (flat, curve, faults, etc). The benchmark results produced by three different deep learning methods are provided. In contrast to our previously presented dataset (pressure recordings) for acoustic FWI (referred to as OpenFWI), the seismic dataset in $\mathbf{\mathbb{E}^{FWI}}$ has both vertical and horizontal components. Moreover, the velocity maps in $\mathbf{\mathbb{E}^{FWI}}$ incorporate both P- and S-wave velocities. While the multicomponent data and the added S-wave velocity make the data more realistic, more challenges are introduced regarding the convergence and computational cost of the inversion. We conduct comprehensive numerical experiments to explore the relationship between P-wave and S-wave velocities in seismic data. The relation between P- and S-wave velocities provides crucial insights into the subsurface properties such as lithology, porosity, fluid content, etc. We anticipate that $\mathbf{\mathbb{E}^{FWI}}$ will facilitate future research on multiparameter inversions and stimulate endeavors in several critical research topics of carbon-zero and new energy exploration. All datasets, codes and relevant information can be accessed through our website at https://efwi-lanl.github.io/

Hyperparameter optimization (HPO) is an important step in machine learning (ML) model development, but common practices are archaic -- primarily relying on manual or grid searches. This is partly because adopting advanced HPO algorithms introduces added complexity to the workflow, leading to longer computation times. This poses a notable challenge to ML applications, as suboptimal hyperparameter selections curtail the potential of ML model performance, ultimately obstructing the full exploitation of ML techniques. In this article, we present a two-step HPO method as a strategic solution to curbing computational demands and wait times, gleaned from practical experiences in applied ML parameterization work. The initial phase involves a preliminary evaluation of hyperparameters on a small subset of the training dataset, followed by a re-evaluation of the top-performing candidate models post-retraining with the entire training dataset. This two-step HPO method is universally applicable across HPO search algorithms, and we argue it has attractive efficiency gains. As a case study, we present our recent application of the two-step HPO method to the development of neural network emulators for aerosol activation. Although our primary use case is a data-rich limit with many millions of samples, we also find that using up to 0.0025% of the data (a few thousand samples) in the initial step is sufficient to find optimal hyperparameter configurations from much more extensive sampling, achieving up to 135-times speedup. The benefits of this method materialize through an assessment of hyperparameters and model performance, revealing the minimal model complexity required to achieve the best performance. The assortment of top-performing models harvested from the HPO process allows us to choose a high-performing model with a low inference cost for efficient use in global climate models (GCMs).

Publicly available data from open sources (e.g., United States Census Bureau (Census), World Health Organization (WHO), Intergovernmental Panel on Climate Change (IPCC)) are vital resources for policy makers, students and researchers across different disciplines. Combining data from different sources requires the user to reconcile the differences in schemas, formats, assumptions, and more. This data wrangling is time consuming, tedious and needs to be repeated by every user of the data. Our goal with Data Commons (DC) is to help make public data accessible and useful to those who want to understand this data and use it to solve societal challenges and opportunities. We do the data processing and make the processed data widely available via standard schemas and Cloud APIs. Data Commons is a distributed network of sites that publish data in a common schema and interoperate using the Data Commons APIs. Data from different Data Commons can be joined easily. The aggregate of these Data Commons can be viewed as a single Knowledge Graph. This Knowledge Graph can then be searched over using Natural Language questions utilizing advances in Large Language Models. This paper describes the architecture of Data Commons, some of the major deployments and highlights directions for future work.