AI in Action 

From data to downpour, how AI is changing weather forecasting

Microsoft’s newly unveiled Aurora model is at the forefront of this transformation. Unlike traditional weather models that rely on complex physics and require hours of supercomputer processing, Aurora employs deep learning techniques, analyzing over a million hours of climate data to deliver 10-day global forecasts in under a minute on a single GPU. This efficiency makes it approximately 5,000 times faster than conventional methods.

Speed is only part of the story. Aurora is already outperforming leading systems in accuracy, especially for tropical cyclones and air quality. It’s efficient too. By requiring far less computer power, it’s dramatically cheaper to run and likely less energy intensive than traditional method, which makes it more sustainable.

If Microsoft makes Aurora widely available, this could open up cutting edge forecasting to smaller governments, nonprofits, and industries that previously couldn’t access this kind of modeling. As climate volatility grows, tools like Aurora could play a key role in how we prepare for, adapt to, and mitigate environmental risks.

Aurora represents a new class of AI foundation models trained not just on language or images, but on the physical world. It’s a glimpse of how foundation models are moving beyond screens and language into real-world systems that shape policy, safety, and daily life

Bits of Brilliance

What is a foundation model?

Imagine a traditional data center that stores and processes data based on predefined rules. Now imagine an advanced version, one that doesn’t just process instructions, but can generate original content, translate languages, or predict complex outcomes based on massive training. That’s what a foundation model is: a cognitive engine trained on broad, diverse datasets, capable of tackling many tasks across many domains.

Unlike older AI models built for a single job, foundation models are general purpose. Once trained, they can be fine-tuned or applied directly to a wide variety of tasks without having to build a new model every time. Think of them as a flexible base layer that can be adapted for everything from writing assistance to weather prediction.

Take GPT-4, for example. It’s the foundation model that powers ChatGPT. GPT-4 was trained on massive swaths of the internet including books, code, conversations, and more to understand patterns in language. When you interact with ChatGPT, you’re using an interface layered on top of that foundation model. It’s the same underlying engine that can summarize documents, draft emails, brainstorm ideas, and help debug code all without retraining. ChatGPT simply steers GPT-4 with your prompt

Foundation models are quickly becoming the infrastructure layer of modern AI. However, they still carry limitations: they require huge resources to train, may reflect the biases of their datasets, and can underperform in rare or unseen scenarios. That’s why researchers are exploring more modular and efficient alternatives like fine-tuned adapters or smaller domain-specific models.

Try This

We often trust AI summaries because they sound confident and complete. But what happens when we ask the model to reflect on what it might have missed?

Prompt to try:

“You just summarized this document/article. What information might be missing from your summary? What should I double-check before using it to make a decision?”

Use this after any summary you get from AI tools, whether it’s a news article, meeting transcript, or report.

It’s not about catching the model red-hande, it’s about building the habit of healthy skepticism, even when the answer sounds polished. That mindset is core to using AI tools wisely, not just conveniently.