Hydrosilicon

Multivariate Canadian Downscaled Climate Scenarios for CMIP6 (CanDCS- M6)

Multivariate Canadian Downscaled Climate Scenarios for CMIP6 (CanDCS- M6)
Stephen R. Sobie1 | Dhouha Ouali1 | Charles L. Curry1,2 | Francis W. Zwiers1,3
1Pacific Climate Impacts Consortium, University of Victoria, Victoria, British Columbia, Canada 2School of Earth and Ocean Sciences, University of Victoria, Victoria, British Columbia, Canada 3Nanjing University of Information Science and Technology, Nanjing, China
This study presents a new dataset, Multivariate Canadian Downscaled Climate Scenarios for CMIP6 (CanDCS-M6), which provides statistically downscaled simulations of global climate models from the Sixth Coupled Model Intercomparison Project (CMIP6). The authors developed a new multivariate downscaling method called N-dimensional Multivariate Bias Correction (MBCn) to improve the representation of compound climate events, which involve interactions between multiple climate variables. This dataset uses PCIC-Blend, a new calibration dataset that combines existing gridded observational datasets to provide a more accurate representation of precipitation and temperature conditions across Canada. The authors evaluated the performance of MBCn compared to other downscaling methods and found that MBCn outperforms other methods, particularly in capturing dependencies between variables, which is essential for simulating compound climate events. The CanDCS-M6 dataset provides daily simulations of precipitation, maximum temperature, and minimum temperature at a high resolution for 26 global climate models, covering the historical period (1950–2014) and three future Shared Socioeconomic Pathways (SSPs) representing different future emissions scenarios. This dataset is intended to facilitate climate impact assessments, hydrologic modelling, and analysis tools for presenting climate projections for Canada.

Artificial Intelligence, Scientific Discovery, and Product Innovation

Artificial Intelligence, Scientific Discovery, and Product Innovation*
Aidan Toner-Rodgers†
MIT
November 6, 2024
This paper studies the impact of artificial intelligence on innovation, exploiting the randomized introduction of a new materials discovery technology to 1,018 scientists in the R&D lab of a large U.S. firm. AI-assisted researchers discover 44% more materials, resulting in a 39% increase in patent filings and a 17% rise in downstream product in-novation. These compounds possess more novel chemical structures and lead to more radical inventions. However, the technology has strikingly disparate effects across the productivity distribution: while the bottom third of scientists see little benefit, the output of top researchers nearly doubles. Investigating the mechanisms behind these results, I show that AI automates 57% of “idea-generation” tasks, reallocating researchers to the new task of evaluating model-produced candidate materials. Top scientists leverage their domain knowledge to prioritize promising AI suggestions, while others waste significant resources testing false positives. Together, these findings demonstrate the potential of AI-augmented research and highlight the complemen-tarity between algorithms and expertise in the innovative process. Survey evidence reveals that these gains come at a cost, however, as 82% of scientists report reduced satisfaction with their work due to decreased creativity and skill underutilization.

Scaling transformer neural networks for medium range weather forcasting

https://arxiv.org/abs/2312.03876 by Nguyen et al

AI-empowered next-generation multiscale climate modelling for mitigation and adaptation by Eyring et al. 2024

Summary
This research paper from Nature Geoscience proposes a novel approach to climate modelling that leverages artificial intelligence (AI) to enhance existing models. The authors argue that current Earth system models (ESMs) are limited by their coarse resolution and reliance on parameterizations for subgrid-scale processes, leading to systematic errors in climate projections. They propose a multiscale approach, which combines high-resolution climate models with improved coarser-resolution hybrid ESMs, which incorporate essential Earth system processes and feedbacks. This integration, they suggest, can be achieved by implementing machine learning (ML) algorithms to represent these subgrid-scale processes. The paper further calls for modernized infrastructures to support this approach, including increased use of Earth observations and operationalized policy-relevant simulations. This vision aims to create a step change in climate modelling, allowing for more accurate and timely projections to inform mitigation and adaptation strategies in a rapidly changing world.
https://www.nature.com/articles/s41561-024-01527-w

AI News 2024-10-16

This weeks AI News

AI News for 2024-10-11

AI News 2024-10-08

AI News Digest: October 7-8, 2024
This digest summarises key information extracted from AI News email newsletter excerpts and various Discord server discussions covering October 7th and 8th, 2024.
AI News ([AINews] The AI Nobel Prize)
Headline News: This section highlights the awarding of the 2024 Nobel Prize in Physics to John J. Hopfield and Geoffrey E. Hinton for their foundational work on artificial neural networks. It includes links to the citation, memes, and reactions from the AI and physics communities.
Sponsored Content: This section features Zep, a low-latency memory layer for AI agents and assistants, highlighting their new community edition and linking to their GitHub repository.

Horizons Securing copper supply: no China, no energy transition

Author(s):
Nick Pickens,
 
 
 
 
 
 
 
 
 
 
Robin Griffin,
 
 
 
 
 
 
 
 
 
 
Eleni Joannides,
 
 
 
 
 
 
Zhifei Liu

AI News - Oct 4th, 2024

AI News Research Assistant: Table of Contents for October 4, 2024
I. AI Model Developments & Releases
Contextual Document Embeddings: cde-small-v1: Introduces a new embedding model, cde-small-v1, highlighting its efficient performance in contextual batching despite its smaller size compared to competitors.
John X. Morris, Alexander M. Rush
https://arxiv.org/abs/2410.02525
https://huggingface.co/jxm/cde-small-v1
Gemma 2 2b-it: An Underrated SLM: Explores the capabilities of Gemma 2 2b-it, a small language model that punches above its weight class in zero-shot reasoning, few-shot learning, and coding tasks.
XTC Sampler: Reducing GPTisms in LLM Outputs: Introduces the XTC Sampler for llama.cpp, a new method aimed at reducing repetitive phrases ("GPTisms") and improving the creativity and coherence of LLM-generated text.
Aphrodite Engine's Custom FPx Quantization: Examines the performance of Aphrodite Engine's custom FPx quantization, showcasing its superiority over INT8 quantization and potential memory savings compared to FP16.
Salesforce Releases xLAM-1b: Announces the release of Salesforce's xLAM-1b model, achieving impressive 70% accuracy in function calling, surpassing the performance of GPT 3.5.
Phi-3 Mini Updated with Function Calling: Covers the release of an updated Phi-3 Mini model featuring function calling capabilities, positioning it as a competitor to Mistral-7b v3.
Nvidia Drops GPT-4 Rival: Announces the arrival of a new, massive, and open AI model from Nvidia, positioned as a direct competitor to OpenAI's GPT-4.
Gemini 1.5 Flash-8B Released: Details the release of Google's Gemini 1.5 Flash-8B, highlighting its cost-effectiveness with a price of $0.0375 per million tokens while maintaining strong performance.
II. Technical Advancements & Research
Tool Calling in Open-Source LLMs: Provides an introductory guide to tool calling in LLMs, outlining the process of integrating external tools and functions to enhance LLM capabilities and build agentic AI systems.
Multimodal Learning Advancements: Discusses a new research paper from Google DeepMind demonstrating how data curation through joint example selection can accelerate multimodal learning.
MInference for Long-Context Inference: Introduces Microsoft's MInference, a technique enabling accurate inference with up to millions of tokens, significantly enhancing long-context task handling.
Scaling Synthetic Data Creation: Examines a paper focusing on scaling synthetic data creation using a vast dataset of 1 billion web-curated personas to generate diverse and robust training data.
Exact Volume Rendering for NeRFs: Features a research paper achieving real-time exact volume rendering for NeRFs (Neural Radiance Fields) at 30FPS@720p, resulting in highly detailed and 3D-consistent outputs.
TorchAO for PyTorch Model Optimization: Introduces the new torchao library for PyTorch, enabling quantization and low-bit datatypes to optimize model performance and reduce memory consumption.
SageAttention: A Faster Attention Mechanism: Showcases SageAttention, a new quantization method that significantly speeds up attention mechanisms, achieving 2.1x speedups over FlashAttention2 and 2.7x over xformers without compromising accuracy.
VinePPO: Improved RL for LLM Reasoning: Details the VinePPO algorithm, a refinement of Proximal Policy Optimization (PPO) specifically designed to address credit assignment issues in LLM reasoning tasks.
Minimalist RNNs for Efficient Training: Explores the resurgence of Recurrent Neural Networks (RNNs) with the introduction of minimalist LSTMs and GRUs. By eliminating hidden state dependencies, these models train dramatically faster, challenging the dominance of Transformers in sequence modeling.
III. AI Industry & Applications
Controversy over Chinese AI Models: Discusses the controversy surrounding the use of Chinese-developed AI models, like Qwen 2.5, in conservative industries. Concerns range from data security and potential espionage to the impact of perceived risks on business operations.
iPhone Photo Style LoRA for Stable Diffusion: Highlights a new LoRA (Low-Rank Adaptation) fine-tuning technique for Stable Diffusion's Flux model that replicates the aesthetics of iPhone photography, improving the realism of generated images.
High Demand for Nvidia's Blackwell AI Chip: Reports on the soaring demand for Nvidia's next-generation AI chip, Blackwell, with major tech companies vying for access to this powerful hardware.
OpenAI Discourages Funding Competitors: Reveals OpenAI's efforts to dissuade investors from backing specific AI competitors, raising concerns about potential monopolistic practices in the rapidly evolving AI landscape.
Meta Unveils Movie Gen: Announces the launch of Meta's Movie Gen, a groundbreaking suite of AI models capable of generating high-quality images, videos, and synchronized audio from text prompts. Movie Gen promises to revolutionize video creation and personalized content generation.
New LLM Leaderboard for Finance: Introduces a new LLM leaderboard specifically designed to evaluate model performance in financial tasks. OpenAI's GPT-4, Meta's Llama 3.1, and Alibaba's Qwen emerge as early leaders in this specialized domain.
Luma AI for 3D Modeling: Explores the capabilities of Luma AI, a platform generating significant interest for its ability to create lifelike 3D models compatible with popular platforms like Unity and Unreal Engine.
IV. Tools, Platforms & Community Updates
OpenAI's Canvas Tool: Introduces OpenAI's Canvas tool, designed to streamline coding workflows with integrated features that minimize scrolling and enhance editing capabilities.
OpenRouter Free Model Limitations: Discusses the limitations of OpenRouter's free AI models, which impose strict account-wide limits on message usage, prompting discussions about the need for more flexible paid options.
Salamandra On-Device AI Demo: Highlights the impressive capabilities of the Salamandra on-device AI demo, demonstrating the growing interest and advancements in on-device AI applications.
MusicGen iOS App Developments: Covers updates on the MusicGen iOS app, including new features like noise cancellation for input audio and refined audio integration for an enhanced user experience.
Unsloth AI for LLM Fine-Tuning: Discusses Unsloth AI's tools and techniques for efficient LLM fine-tuning, enabling faster training with reduced VRAM consumption compared to traditional methods.
lm-evaluation-harness Seeks Contributors: Announces a call for contributions to the lm-evaluation-harness project, inviting developers to help integrate new LLM evaluations and address existing bugs.
LM Studio Updates & Issues: Covers the latest updates and reported issues with LM Studio, including a new UI for the Collections feature, memory leak problems with specific versions, and discrepancies in displayed pricing information.
Faster-Whisper for Audio Transcription: Introduces Faster-Whisper, a new tool outperforming Whisper-Turbo in audio transcription speed on certain hardware configurations.
Discussions on IREE Adoption: Explores the potential adoption timeline for IREE (Intermediate Representation Execution Environment), a technology for serving AI models at scale.

A 485-million-year history of Earth’s surface temperature

Source: Judd, E. J. et al. A 485-million-year history of Earth’s surface temperature. Science 385, eadk3705 (2024).