Plus: Trump has climbed down from his plan for the US to take Greenland. Yann LeCun's new venture is a contrarian bet against large language models Yann LeCun is a Turing Award recipient and a top AI researcher, but he has long been a contrarian figure in the tech world. He believes that the industry's current obsession with large language models is wrong-headed and will ultimately fail to solve many pressing problems. Instead, he thinks we should be betting on world models--a different type of AI that accurately reflects the dynamics of the real world. Perhaps it's no surprise, then, that he recently left Meta, where he had served as chief scientist for FAIR (Fundamental AI Research), the company's influential research lab that he founded. LeCun sat down with MIT Technology Review in an exclusive online interview from his Paris apartment to discuss his new venture, life after Meta, the future of artificial intelligence, and why he thinks the industry is chasing the wrong ideas.

You live in a house you designed and built yourself. You rely on the sun for power, heat your home with a woodstove, and farm your own fish and vegetables. This is the life of Marcin Jakubowski, the 53-year-old founder of Open Source Ecology, an open collaborative of engineers, producers, and builders developing what they call the Global Village Construction Set (GVCS). It's a set of 50 machines--everything from a tractor to an oven to a circuit maker--that are capable of building civilization from scratch and can be reconfigured however you see fit. It's all part of his ethos that life-changing technology should be available to all, not controlled by a select few. What it's like to find yourself in the middle of a conspiracy theory Last week, we held a subscribers-only Roundtables discussion exploring how to cope in this new age of conspiracy theories.

Lithium plating during fast charging is a critical degradation mechanism that accelerates capacity fade and can trigger catastrophic safety failures. Recent work has identified a distinctive dQ/dV peak above 4.0 V as a reliable signature of plating onset; however, conventional methods for computing dQ/dV rely on finite differencing with filtering, which amplifies sensor noise and introduces bias in peak location. In this paper, we propose a Gaussian Process (GP) framework for lithium plating detection by directly modeling the charge-voltage relationship Q(V) as a stochastic process with calibrated uncertainty. Leveraging the property that derivatives of GPs remain GPs, we infer dQ/dV analytically and probabilistically from the posterior, enabling robust detection without ad hoc smoothing. The framework provides three key benefits: (i) noise-aware inference with hyperparameters learned from data, (ii) closed-form derivatives with credible intervals for uncertainty quantification, and (iii) scalability to online variants suitable for embedded BMS. Experimental validation on Li-ion coin cells across a range of C-rates (0.2C-1C) and temperatures (0-40°C) demonstrates that the GP-based method reliably detects plating peaks under low-temperature, high-rate charging, while correctly reporting no peaks in baseline cases. The concurrence of GP-identified differential peaks, reduced charge throughput, and capacity fade measured via reference performance tests confirms the method's accuracy and robustness, establishing a practical pathway for real-time lithium plating detection.

The Download: extracting lithium, and what we still don't know about Sora On a bright afternoon in August, the shore of Utah's Great Salt Lake looks like something out of a science fiction film set in a scorching alien world. This otherworldly scene is the test site for a company called Lilac Solutions, which is developing a technology it says will shake up the United States' efforts to pry control over the global supply of lithium, the so-called "white gold" needed for electric vehicles and batteries, away from China. The startup is in a race to commercialize a new, less environmentally-damaging way to extract lithium from rocks. If everything pans out, it could significantly increase domestic supply at a crucial moment for the nation's lithium extraction industry. Last week OpenAI released Sora, a TikTok-style app that presents an endless feed of exclusively AI-generated videos, each up to 10 seconds long. The app allows you to create a "cameo" of yourself--a hyperrealistic avatar that mimics your appearance and voice--and insert other peoples' cameos into your own videos (depending on what permissions they set).

Fast and reliable validation of novel designs in complex physical systems such as batteries is critical to accelerating technological innovation. However, battery research and development remain bottlenecked by the prohibitively high time and energy costs required to evaluate numerous new design candidates, particularly in battery prototyping and life testing. Despite recent progress in data-driven battery lifetime prediction, existing methods require labeled data of target designs to improve accuracy and cannot make reliable predictions until after prototyping, thus falling far short of the efficiency needed to enable rapid feedback for battery design. Here, we introduce Discovery Learning (DL), a scientific machine-learning paradigm that integrates active learning, physics-guided learning, and zero-shot learning into a human-like reasoning loop, drawing inspiration from learning theories in educational psychology. DL can learn from historical battery designs and actively reduce the need for prototyping, thus enabling rapid lifetime evaluation for unobserved material-design combinations without requiring additional data labeling. To test DL, we present 123 industrial-grade large-format lithium-ion pouch cells, spanning eight material-design combinations and diverse cycling protocols. Trained solely on public datasets of small-capacity cylindrical cells, DL achieves 7.2% test error in predicting the average cycle life under unknown device variability. This results in savings of 98% in time and 95% in energy compared to industrial practices. This work highlights the potential of uncovering insights from historical designs to inform and accelerate the development of next-generation battery technologies. DL represents a key advance toward efficient data-driven modeling and helps realize the promise of machine learning for accelerating scientific discovery and engineering innovation.

Large Language Models (LLMs) have shown strong potential for tabular data generation by modeling textualized feature-value pairs. However, tabular data inherently exhibits sparse feature-level dependencies, where many feature interactions are structurally insignificant. This creates a fundamental mismatch as LLMs' self-attention mechanism inevitably distributes focus across all pairs, diluting attention on critical relationships, particularly in datasets with complex dependencies or semantically ambiguous features. To address this limitation, we propose GraDe (Graph-Guided Dependency Learning), a novel method that explicitly integrates sparse dependency graphs into LLMs' attention mechanism. GraDe employs a lightweight dynamic graph learning module guided by externally extracted functional dependencies, prioritizing key feature interactions while suppressing irrelevant ones. Our experiments across diverse real-world datasets demonstrate that GraDe outperforms existing LLM-based approaches by up to 12% on complex datasets while achieving competitive results with state-of-the-art approaches in synthetic data quality. Our method is minimally intrusive yet effective, offering a practical solution for structure-aware tabular data modeling with LLMs.

As demand for high-purity lithium surges with the growth of the electric vehicle (EV) industry, cost-effective extraction from lower-grade North American sources like the Smackover Formation is critical. These resources, unlike high-purity South American brines, require innovative purification techniques to be economically viable. Continuous crystallization is a promising method for producing battery-grade lithium carbonate, but its optimization is challenged by a complex parameter space and limited data. This study introduces a Human-in-the-Loop (HITL) assisted active learning framework to optimize the continuous crystallization of lithium carbonate. By integrating human expertise with data-driven insights, our approach accelerates the optimization of lithium extraction from challenging sources. Our results demonstrate the framework's ability to rapidly adapt to new data, significantly improving the process's tolerance to critical impurities like magnesium from the industry standard of a few hundred ppm to as high as 6000 ppm. This breakthrough makes the exploitation of low-grade, impurity-rich lithium resources feasible, potentially reducing the need for extensive pre-refinement processes. By leveraging artificial intelligence, we have refined operational parameters and demonstrated that lower-grade materials can be used without sacrificing product quality. This advancement is a significant step towards economically harnessing North America's vast lithium reserves, such as those in the Smackover Formation, and enhancing the sustainability of the global lithium supply chain.

The performance of battery materials is determined by their composition and the processing conditions employed during commercial-scale fabrication, where raw materials undergo complex processing steps with various additives to yield final products. As the complexity of these parameters expands with the development of industry, conventional one-factor-at-a-time (OFAT) experiment becomes old fashioned. While domain expertise aids in parameter optimization, this traditional approach becomes increasingly vulnerable to cognitive limitations and anthropogenic biases as the complexity of factors grows. Herein, we introduce an iterative machine learning (ML) framework that integrates active learning to guide targeted experimentation and facilitate incremental model refinement. This method systematically leverages comprehensive experimental observations, including both successful and unsuccessful results, effectively mitigating human-induced biases and alleviating data scarcity. Consequently, it significantly accelerates exploration within the high-dimensional design space. Our results demonstrate that active-learning-driven experimentation markedly reduces the total number of experimental cycles necessary, underscoring the transformative potential of ML-based strategies in expediting battery material optimization.

The state of health (SOH) of lithium-ion batteries (LIBs) is crucial for ensuring the safe and reliable operation of electric vehicles. Nevertheless, the prevailing SOH estimation methods often have limited generalizability. This paper introduces a data-driven approach for estimating the SOH of LIBs, which is designed to improve generalization. We construct a hybrid model named ACLA, which integrates the attention mechanism, convolutional neural network (CNN), and long short-term memory network (LSTM) into the augmented neural ordinary differential equation (ANODE) framework. This model employs normalized charging time corresponding to specific voltages in the constant current charging phase as input and outputs the SOH as well as remaining useful of life. The model is trained on NASA and Oxford datasets and validated on the TJU and HUST datasets. Compared to the benchmark models NODE and ANODE, ACLA exhibits higher accuracy with root mean square errors (RMSE) for SOH estimation as low as 1.01% and 2.24% on the TJU and HUST datasets, respectively.

Battery degradation is a major challenge in electric vehicles (EV) and energy storage systems (ESS). However, most degradation investigations focus mainly on estimating the state of charge (SOC), which fails to accurately interpret the cells' internal degradation mechanisms. Differential capacity analysis (DCA) focuses on the rate of change of cell voltage about the change in cell capacity, under various charge/discharge rates. This paper developed a battery cell degradation testing model that used two types of lithium-ions (Li-ion) battery cells, namely lithium nickel cobalt aluminium oxides (LiNiCoAlO2) and lithium iron phosphate (LiFePO4), to evaluate internal degradation during loading conditions. The proposed battery degradation model contains distinct charge rates (DCR) of 0.2C, 0.5C, 1C, and 1.5C, as well as discharge rates (DDR) of 0.5C, 0.9C, 1.3C, and 1.6C to analyze the internal health and performance of battery cells during slow, moderate, and fast loading conditions. Besides, this research proposed a model that incorporates the Extended Kalman Filter (EKF), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) networks to validate experimental data. The proposed model yields excellent modelling results based on mean squared error (MSE), and root mean squared error (RMSE), with errors of less than 0.001% at DCR and DDR. The peak identification technique (PIM) has been utilized to investigate battery health based on the number of peaks, peak position, peak height, peak area, and peak width. At last, the PIM method has discovered that the cell aged gradually under normal loading rates but deteriorated rapidly under fast loading conditions. Overall, LiFePO4 batteries perform more robustly and consistently than (LiNiCoAlO2) cells under varying loading conditions.