AI Techniques For Accurate Data Predictions

You might have seen news from our Google DeepMind colleagues lately on GenCast, which is changing the game of weather forecasting by building state-of-the-art weather models using AI. Some of our teams started to wonder – can we apply similar techniques to the notoriously compute-intensive challenge of climate modeling? General circulation models (GCMs) are a critical part of climate modeling, focused on the physical aspects of the climate system, such as temperature, pressure, wind, and ocean currents. Traditional GCMs, while powerful, can struggle with precipitation – and our teams wanted to see if AI could help. Our team released a paper and data on our AI-based GCM, building on our Nature paper from last year - specifically, now predicting precipitation with greater accuracy than prior state of the art. The new paper on NeuralGCM introduces 𝗺𝗼𝗱𝗲𝗹𝘀 𝘁𝗵𝗮𝘁 𝗹𝗲𝗮𝗿𝗻 𝗳𝗿𝗼𝗺 𝘀𝗮𝘁𝗲𝗹𝗹𝗶𝘁𝗲 𝗱𝗮𝘁𝗮 𝘁𝗼 𝗽𝗿𝗼𝗱𝘂𝗰𝗲 𝗺𝗼𝗿𝗲 𝗿𝗲𝗮𝗹𝗶𝘀𝘁𝗶𝗰 𝗿𝗮𝗶𝗻 𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗼𝗻𝘀. Kudos to Janni Yuval, Ian Langmore, Dmitrii Kochkov, and Stephan Hoyer! Here's why this is a big deal: 𝗟𝗲𝘀𝘀 𝗕𝗶𝗮𝘀, 𝗠𝗼𝗿𝗲 𝗔𝗰𝗰𝘂𝗿𝗮𝗰𝘆: These new models have less bias, meaning they align more closely with actual observations – and we see this both for forecasts up to 15 days, and also for 20-year projections (in which sea surface temperatures and sea ice were fixed at historical values, since we don’t yet have an ocean model). NeuralGCM forecasts are especially performant around extremes, which are especially important in understanding climate anomalies, and can predict rain patterns throughout the day with better precision. 𝗖𝗼𝗺𝗯𝗶𝗻𝗶𝗻𝗴 𝗔𝗜, 𝗦𝗮𝘁𝗲𝗹𝗹𝗶𝘁𝗲 𝗜𝗺𝗮𝗴𝗲𝗿𝘆, 𝗮𝗻𝗱 𝗣𝗵𝘆𝘀𝗶𝗰𝘀: The model combines a learned physics model with a dynamic differentiable core to leverage both physics and AI methods, with the model trained directly on satellite-based precipitation observations. 𝗢𝗽𝗲𝗻 𝗔𝗰𝗰𝗲𝘀𝘀 𝗳𝗼𝗿 𝗘𝘃𝗲𝗿𝘆𝗼𝗻𝗲! This is perhaps the most exciting news! The team has made their pre-trained NeuralGCM model checkpoints (including their awesome new precipitation models) available under a CC BY-SA 4.0 license. Anyone can use and build upon this cutting-edge technology! https://lnkd.in/gfmAx_Ju 𝗪𝗵𝘆 𝗧𝗵𝗶𝘀 𝗠𝗮𝘁𝘁𝗲𝗿𝘀: Accurate predictions of precipitation are crucial for everything from water resource management and flood mitigation to understanding the impacts of climate change on agriculture and ecosystems. Check out the paper to learn more:  https://lnkd.in/geqaNTRP

A poor demand forecast destroys profits and cash. This infographic shows 7 forecasting techniques, pros, cons, & when to use: 1️⃣ Moving Average ↳ Averages historical demand over a specified period to smooth out trends ↳ Pros: simple to calculate and understand  ↳ Cons: lag effect; may not respond well to rapid changes ↳ When: short-term forecasting where trends are relatively stable 2️⃣ Exponential Smoothing ↳ Weights recent demand more heavily than older data ↳ Pros: responds faster to recent changes; easy to implement ↳ Cons: requires selection of a smoothing constant ↳ When: when recent data is more relevant than older data 3️⃣ Triple Exponential Smoothing  ↳ Adds components for trend & seasonality ↳ Pros: handles data with both trend and seasonal patterns ↳ Cons: requires careful parameter tuning ↳ When: when data has both trend and seasonal variations 4️⃣ Linear Regression ↳ Models the relationship between dependent and independent variables ↳ Pros: provides a clear mathematical relationship ↳ Cons: assumes a linear relationship ↳ When: when the relationship between variables is linear 5️⃣ ARIMA ↳ Combines autoregression, differencing, and moving averages ↳ Pros: versatile; handles a variety of time series data patterns ↳ Cons: complex; requires parameter tuning and expertise ↳ When: when data exhibits autocorrelation and non-stationarity 6️⃣ Delphi Method ↳ Expert consensus is gathered and refined through multiple rounds ↳ Pros: leverages expert knowledge; useful for long-term forecasting ↳ Cons: time-consuming; subjective and may introduce bias ↳ When: historical data is limited or unavailable, low predictability 7️⃣ Neural Networks ↳ Uses AI to model complex relationships in data ↳ Pros: can capture nonlinear relationships; adaptive and flexible ↳ Cons: requires large data sets; can be a "black box" with less interpretability ↳ When: for complex, non-linear data patterns and large data sets Any others to add?

With so many options for building AI systems based on LLMs, I found Databricks' guide by Jonathan Frankle to be a helpful resource covering when and how to apply different methods, including Prompt Engineering, In-Context Learning, Retrieval-Augmented Generation (RAG), Fine-Tuning, and Pre-Training. 1. Prompt Engineering (Including In-Context Learning) Involves crafting and structuring input prompts to guide a model’s output. This includes providing examples within the prompt (in-context learning) to influence how the model generates responses. Pros: - No need for modifying the model. - Quick to implement and cost-effective. - Flexible, can include providing examples to improve the model’s understanding. Cons: - Limited control over output quality, especially for specialized tasks. - Requires expertise in creating effective prompts and examples. - Performance improvements may be limited compared to fine-tuning. Use Case: Suitable for quickly adapting a model to new tasks or obtaining better results without additional training, especially when providing relevant examples within the prompt. 2. Retrieval-Augmented Generation (RAG) Combines the model's responses with relevant external data retrieved from a database to provide more accurate and contextually relevant answers. Pros: - Enhances the model’s responses by incorporating up-to-date or domain-specific information. - Cost-effective compared to training or fine-tuning. - Versatile and can be combined with other techniques like fine-tuning. Cons: - The quality of the output depends on the relevance of the retrieved data. - More complex to implement due to the need for a reliable retrieval system. Use Case: Best when specific, accurate, and context-rich responses are needed. 3. Fine-Tuning Adjusting a pre-trained model’s parameters by training it on a specific, smaller dataset to tailor it to a particular task or domain. Pros: - Highly customizable for specific tasks. - Can significantly improve the model’s accuracy on specialized tasks. Cons: - Resource-intensive and time-consuming. - Risk of overfitting, leading to a model that may not generalize well. Use Case: Suitable for scenarios requiring high accuracy in a specialized domain, where the investment in additional training is justified. 4. Pre-Training Training a model from scratch or continuing the training of a model on a large dataset to provide a strong foundational understanding before fine-tuning. Pros: - Provides control over the model’s foundational knowledge. - Allows the creation of highly specialized models tailored to specific needs. Cons: - Extremely resource-intensive and time-consuming. - Requires extensive datasets and computational power. Use Case: Best when a highly specialized model is needed, or when existing models do not meet the required criteria, and there are sufficient resources to build a model from the ground up. https://lnkd.in/gbF_3e_F

Excited to showcase the latest publication on our platform about Conformal Prediction - a powerful method that provides confidence intervals for machine learning predictions. In high-stakes environments, like healthcare or finance, knowing how certain a model is about its predictions is crucial. This introductory guide offers: - Introduction to Conformal Prediction and its importance in modern ML - Detailed explanation of the calibration process for classification tasks - Extension of Conformal Prediction to regression problems - Discussion on designing effective score functions for various ML tasks The beauty of Conformal Prediction lies in its ability to provide statistically rigorous confidence measures for any pre-trained model, without making distributional assumptions about the data. This publication offers valuable insights for data scientists, ML engineers, and researchers looking to improve the reliability of their models. It's particularly relevant for those working in fields where understanding prediction uncertainty is crucial. Check out the full publication here: https://lnkd.in/gWptMwAh We would love to hear about your use-cases and experience with prediction intervals. Have you explored conformal prediction before? Let us know! About Ready Tensor: We are a platform for AI publications aimed at AI/ML developers and practitioners. We welcome contributions from the community and look forward to sharing more cutting-edge insights in AI and ML. #MachineLearning #DataScience #ConformalPrediction #AI #UncertaintyQuantification #ModelReliability #ReadyTensor #ShowcaseYourAI