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After announcing GraphCast on Arxiv in December 2022, Google DeepMind has finally introduced the open-source AI weather forecasting model. It claims to provide unparalleled accuracy and speed in predicting global weather conditions up to 10 days in advance.
This AI model is constructed on machine learning and graph neural networks, managing over a million grid points globally to predict various atmospheric and surface variables. It is trained on decades of weather data, combining AI with traditional weather forecasting techniques.
GraphCast outperforms the conventional Global High-Resolution Forecast (GRAF) system in accuracy, especially in predicting tropospheric conditions.
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GraphCast’s potential for early detection of severe weather events is significant, offering better preparedness and potentially saving lives. In September this year, it accurately predicted Hurricane Lee’s landfall in Nova Scotia nine days in advance, showcasing its superiority over traditional models.
Impacting Human Life
While tech giants like OpenAI are pursuing AGI, Google is taking a different approach by actively working towards building AI models that directly impact human lives in areas such as healthcare, shopping, climate, pollution, and more.
Google, particularly through its DeepMind division, is pioneering AI in climate science with various innovative weather models. Earlier this month, Google Research and DeepMind launched weather forecasting model MetNet-3, for high-resolution predictions up to 24 hours ahead for a larger set of core variables, including precipitation, surface temperature, wind speed and direction, and dew point.
Notably, the team’s FourCastNet, stands out as the first open-sourced AI weather model, focusing on medium-range forecasting with enhanced accuracy due to its advanced techniques. Additionally, DGMR, developed in collaboration with the UK Met Office, is a nowcasting tool, particularly effective in predicting imminent rainfall, outperforming existing methods.
However, Google is not only betting big on climate but also venturing into areas like healthcare, shopping, designing and more.
For instance, their protein folding model AlphaFold has been addressing serious health issues by aiding the drug development process for gene therapy, malaria vaccine, liver cancer medicine, combating neglected diseases, and more.
While the original AlphaFold was pivotal for predicting single-chain protein structures, the newest version, AlphaFold-latest, released two weeks ago, is even bigger and better. It can now anticipate structures from nearly all molecules in the Protein Data Bank (PDB)—a comprehensive database for 3D biological molecule structures—and has extended its capabilities to include small molecules, proteins, nucleic acids, and molecules with post-translational modifications.
Meanwhile, Google’s medical LLM, Med-PaLM 2, accurately identified murine genes containing causative genetic factors for biomedical traits like diabetes and cataract. While in the early stages, the findings highlight the promising role of LLMs in genetic and biomedical discovery, with ongoing efforts to develop a more scalable LLM-based genetic discovery pipeline and extend the research to rare diseases and humans.
Similarly, when it comes to managing traffic, Google’s ‘Project Green Light’ launched in 2021, now uses AI-powered features to optimise the placement of traffic signals based on Google Maps data. This aims to enhance traffic flow and decrease pollution levels at intersections. Early results suggest a potential 30% reduction in pollution.
In essence, Google’s AI application in India focuses on intelligent traffic signal management to mitigate environmental and urban planning challenges.
On the other hand, Google Maps also introduced another set of features for environmental monitoring. It now includes Solar, Air Quality, and Pollen APIs. Project Sunroof uses AI to assess rooftop solar potential, while the Air Quality API consolidates data for accurate air quality information, applicable in healthcare and transportation. The Pollen API offers localised pollen count data and predictions, aiding health-conscious decisions.
Open Sourcing is Key
Google is not only innovating solutions with real-world use cases, but also making them open source for others to build upon, fostering broader use and adaptation by weather agencies and researchers worldwide.
This model is part of a broader initiative by Google DeepMind and Google Research in AI-driven weather forecasting, contributing to our understanding of climate patterns and aiding in environmental challenges.
Both FourCastNet and GraphCast are open-sourced AI weather models. Even AlphaFold is open-sourced. BERT, one of the initial Transformer-based LLMs, and EfficientNet for computer vision are also open-sourced. Meta’s RoBERTa, Baidu’s Ernie, and HuggingFace’s DistilBERT are all built on BERT.
Google also has a range of open-source AI products, including TensorFlow and Keras, which are widely used for machine learning. The company also supports critical open-source initiatives like JAX, TFX, MLIR, KubeFlow, and Kubernetes.
However, its PaLM language model is not open. If not PaLM 2, Google should at least open source the first version, PaLM, to make it available to a wider range of researchers and developers, accelerating AI research and development. Additionally, it would provide equitable access to AI, reducing the digital divide and making AI more transparent and accountable.
This move could lead to the development of new medical diagnostic tools, AI-powered educational tools, and solutions for complex global challenges like climate change, energy, and transportation, ultimately contributing to a more sustainable and equitable future.