This is Atlantic Intelligence, an eight-week series in which The Atlantic's leading thinkers on AI will help you understand the complexity and opportunities of this groundbreaking technology. Earlier this year, The Atlantic published a story by Gary Marcus, a well-known AI expert who has agitated for the technology to be regulated, both in his Substack newsletter and before the Senate. Marcus argued that "this is a moment of immense peril," and that we are teetering toward an "information-sphere disaster, in which bad actors weaponize large language models, distributing their ill-gotten gains through armies of ever more sophisticated bots." I was interested in following up with Marcus given recent events. In the past six weeks, we've seen an executive order from the Biden administration focused on AI oversight; chaos at the influential company OpenAI; and this Wednesday, the release of Gemini, a GPT competitor from Google.

Why is the little ol' Competition & Markets Authority, a UK regulator, inserting itself into the entertaining and important – but distant – drama at San Francisco-based OpenAI? Even if the CMA finds eventually that Microsoft, another US company, is pulling the strings at Sam Altman's show, what could it actually do? Doesn't it all paint the UK as an unfriendly place for tech investment, notwithstanding Rishi Sunak's eagerness to host AI summits and conduct cosy chats with Elon Musk? All fair questions, and the CMA should brace for more in that vein. It is indeed slightly odd that the UK regulator is the first out of traps in wondering, albeit in a preliminary manner, if Microsoft has gained effective control over OpenAI and, if it has, whether that amounts to a problem. But there is another way to look at developments: thank goodness a regulator somewhere is seeking clarity about what just occurred at OpenAI.

The Assured and Trustworthy Human-centered AI (ATHAI) symposium was held as part of the AAAI Fall Symposium Series in Arlington, VA from October 25-27, 2023. The symposium brought together three groups of stakeholders from industry, academia, and government to discuss issues related to AI assurance in different domains ranging from healthcare to defense. The symposium drew over 50 participants and consisted of a combination of invited keynote speakers, spotlight talks, and interactive panel discussions. On Day 1, the symposium kicked off with a keynote by Professor Missy Cummings (George Mason University) titled "Developing Trustworthy AI: Lessons Learned from Self-driving Cars". Missy shared important lessons learned from her time at the National Highway Traffic Safety Administration (NHTSA) and interacting with the autonomous vehicle industry.

We haven't always been right (RIP, Baxter), but we've often been early to spot important areas of progress (we put natural-language processing on our very first list in 2001; today this technology underpins large language models and generative AI tools like ChatGPT). Every year, our reporters and editors nominate technologies that they think deserve a spot, and we spend weeks debating which ones should make the cut. Here are some of the technologies we didn't pick this time--and why we've left them off, for now. Alzmeiher's patients have long lacked treatment options. Several new drugs have now been proved to slow cognitive decline, albeit modestly, by clearing out harmful plaques in the brain.

Fox News host Bret Baier has more on U.S. and its allies efforts to increase semiconductor manufacturing on'Special Report.' The world could face another chip shortage as companies and nations seek to lead the way with artificial intelligence (AI) development, having seemingly made few changes after the impacts of the 2021 supply chain crisis, experts said. "The answer is different for different segments of the semiconductor industry and the chip economy," Gregory C. Allen, the director of the Wadhwani Center for AI and Advanced Technologies for the Center for Strategic and International Studies, told Fox News Digital. "Companies that make these chips are building out additional capacity to a different extent and in different market niches," he said, adding that while the world is "headed to an oversupply of certain types of chips," there is "already a shortage" of more advanced chips, reflected in the "extraordinary cost of each of these chips." China enacted a series of extreme lockdown measures, known as "zero-COVID," to combat the coronavirus pandemic, which required cities to shut down and test every resident after officials detected just a few positive cases.

Manila, Philippines – Leila de Lima was released from detention last month into what the former Philippines senator calls "a whole new world". In 2016, then-President Rodrigo Duterte promised to "destroy" de Lima, one of the loudest critics of his deadly drug war. The president's supporters began targeting the first-term senator and former human rights commissioner – ridiculing her for an alleged romantic affair with her driver, and accusing her of involvement in drug trafficking. In February 2017, she was arrested on drug charges she denies and that international observers have said are politically motivated. "I had this deep sense of disbelief," de Lima told Al Jazeera. "I never thought that Mr Duterte would go to that extent, that length, of jailing me. I thought it would just be daily vilification, personal attacks, attacks against my womanhood."

Extrapolation remains a grand challenge in deep neural networks across all application domains. We propose an operator learning method to solve time-dependent partial differential equations (PDEs) continuously and with extrapolation in time without any temporal discretization. The proposed method, named Diffusion-inspired Temporal Transformer Operator (DiTTO), is inspired by latent diffusion models and their conditioning mechanism, which we use to incorporate the temporal evolution of the PDE, in combination with elements from the transformer architecture to improve its capabilities. Upon training, DiTTO can make inferences in real-time. We demonstrate its extrapolation capability on a climate problem by estimating the temperature around the globe for several years, and also in modeling hypersonic flows around a double-cone. We propose different training strategies involving temporal-bundling and sub-sampling and demonstrate performance improvements for several benchmarks, performing extrapolation for long time intervals as well as zero-shot super-resolution in time.

In recent years, groundbreaking advancements in natural language processing have culminated in the emergence of powerful large language models (LLMs), which have showcased remarkable capabilities across a vast array of domains, including the understanding, generation, and translation of natural language, and even tasks that extend beyond language processing. In this report, we delve into the performance of LLMs within the context of scientific discovery, focusing on GPT-4, the state-of-the-art language model. Our investigation spans a diverse range of scientific areas encompassing drug discovery, biology, computational chemistry (density functional theory (DFT) and molecular dynamics (MD)), materials design, and partial differential equations (PDE). Evaluating GPT-4 on scientific tasks is crucial for uncovering its potential across various research domains, validating its domain-specific expertise, accelerating scientific progress, optimizing resource allocation, guiding future model development, and fostering interdisciplinary research. Our exploration methodology primarily consists of expert-driven case assessments, which offer qualitative insights into the model's comprehension of intricate scientific concepts and relationships, and occasionally benchmark testing, which quantitatively evaluates the model's capacity to solve well-defined domain-specific problems. Our preliminary exploration indicates that GPT-4 exhibits promising potential for a variety of scientific applications, demonstrating its aptitude for handling complex problem-solving and knowledge integration tasks. Broadly speaking, we evaluate GPT-4's knowledge base, scientific understanding, scientific numerical calculation abilities, and various scientific prediction capabilities.

Climate change and global warming have been trending topics worldwide since the Eco-92 conference. However, little progress has been made in reducing greenhouse gases (GHGs). The problems and challenges related to emissions are complex and require a concerted and comprehensive effort to address them. Emissions reporting is a critical component of GHG reduction policy and is therefore the focus of this work. The main goal of this work is two-fold: (i) to propose an emission reporting evaluation model to leverage emissions reporting overall quality and (ii) to use artificial intelligence (AI) to support the initiatives that improve emissions reporting. Thus, this work presents an Emissions Reporting Maturity Model (ERMM) for examining, clustering, and analysing data from emissions reporting initiatives to help the cities to deal with climate change and global warming challenges. The Performance Indicator Development Process (PIDP) proposed in this work provides ways to leverage the quality of the available data necessary for the execution of the evaluations identified by the ERMM. Hence, the PIDP supports the preparation of the data from emissions-related databases, the classification of the data according to similarities highlighted by different clustering techniques, and the identification of performance indicator candidates, which are strengthened by a qualitative analysis of selected data samples. Thus, the main goal of ERRM is to evaluate and classify the cities regarding the emission reporting processes, pointing out the drawbacks and challenges faced by other cities from different contexts, and at the end to help them to leverage the underlying emissions-related processes and emissions mitigation initiatives.

Convolutional neural network (CNN) inference using fully homomorphic encryption (FHE) is a promising private inference (PI) solution due to the capability of FHE that enables offloading the whole computation process to the server while protecting the privacy of sensitive user data. Prior FHE-based CNN (HCNN) work has demonstrated the feasibility of constructing deep neural network architectures such as ResNet using FHE. Despite these advancements, HCNN still faces significant challenges in practicality due to the high computational and memory overhead. To overcome these limitations, we present HyPHEN, a deep HCNN construction that incorporates novel convolution algorithms (RAConv and CAConv), data packing methods (2D gap packing and PRCR scheme), and optimization techniques tailored to HCNN construction. Such enhancements enable HyPHEN to substantially reduce the memory footprint and the number of expensive homomorphic operations, such as ciphertext rotation and bootstrapping. As a result, HyPHEN brings the latency of HCNN CIFAR-10 inference down to a practical level at 1.4 seconds (ResNet-20) and demonstrates HCNN ImageNet inference for the first time at 14.7 seconds (ResNet-18).