From Conversation to Comprehension: How AI Is Learning to Understand the World

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For years, artificial intelligence has been synonymous with chatbots, content creation, and automation. However, now Big Tech is directing AI toward science, health, and the planet itself.

From Google’s efforts to predict floods and wildfires, to Microsoft’s experiments in drug discovery, to OpenAI’s newest push into biology, the world’s largest tech companies are beginning to use AI to make life easier, and better.

Floods, Fires, and a Smarter Map

This year, Google introduced Google Earth AI, a suite of geospatial AI models and datasets designed to help people, businesses, and governments address environmental challenges.

Think of it as a brain built into Google Earth, one that can predict floods before they happen, track wildfires in real time, and even guide city planners toward smarter, safer urban design.

Google said, “From flood alerts in Search and Maps to real-time wildfire detection, these models already help millions.” 

Now, with AlphaEarth Foundations, Google wants to make these insights accessible to everyone, so anyone from a small NGO to a national government can use AI to make better decisions for the planet.

From Protein Puzzles to a Playbook of Life

If Google Earth AI is about understanding the planet, DeepMind’s AlphaFold is about understanding life itself.

Proteins, the building blocks of all living things, are notoriously complex. It used to take scientists years (and very deep pockets) to map out just one protein’s 3D structure.

Then came AlphaFold. In 2020, it cracked the code, predicting protein structures in minutes, and with accuracy. As of today, it has mapped over 200 million protein structures, covering nearly every known protein in science.

Its database is open and free, already used by over two million researchers in 190 countries. What once took decades of lab work can now be accomplished in hours, accelerating research into diseases, new medicines, and even environmental solutions.

DeepMind hasn’t stopped there. Its AlphaTensor project used AI to discover entirely new mathematical algorithms, proving that the same models that once played chess and Go can now solve 50-year-old mathematical mysteries.

Sketching Medicines Before the Lab

Microsoft’s researchers have been busy, too, but their AI experiments could save lives.

Working with the Global Health Drug Discovery Institute (GHDDI), they built TamGen, a generative AI model that designs new drug molecules. Instead of testing millions of compounds one by one in the lab, TamGen imagines new molecular structures from scratch, identifying potential cures faster than ever before.

In early tests, the AI helped scientists discover new inhibitors for tuberculosis and coronavirus proteins in under five months, a process that typically takes years.

It’s a glimpse into how AI can bridge the gap between computation and chemistry, speeding up drug discovery for some of the world’s most pressing diseases.

A Telescope for the Microbiome

Meanwhile, Meta AI is taking a different route, into the hidden world of microbes.

In 2022, it introduced the ESM Metagenomic Atlas, a massive database revealing the structures of over 600 million proteins found in soil, oceans, and even inside the human body.

Most of these proteins were previously unknown, part of what scientists call the “dark matter” of the protein universe. Understanding them could unlock new ways to clean the environment, create renewable energy, and even discover new forms of life.

Meta trained a large AI language model,  not unlike those behind ChatGPT, to learn evolutionary patterns and predict protein structures 60 times faster than before. As a result, a public resource lets scientists anywhere explore the mysteries of the microscopic world. 

Forecasts Built for Decision Time

When it comes to climate, timing is everything. That’s why DTN, a global data and technology company specializing in weather, energy, and agriculture intelligence, is betting big on AI-powered forecasting.

In June, DTN achieved a breakthrough by successfully integrating an AI weather forecasting model built in collaboration with NVIDIA into its production environment. Running on NVIDIA Earth-2 and AWS, the system can track cyclones with unprecedented speed and precision.

This isn’t just about better predictions; it’s about faster, more reliable decision-making during high-impact weather events. From farmers and energy operators to logistics planners, everyone stands to gain.

DTN’s patent-pending model predicts cyclone tracks as an ensemble, offering richer insights into potential paths and risks. Combined with more than 25 public and proprietary weather models, this approach enables organizations to protect their assets, minimize business risk, and make informed decisions under pressure.

In short, DTN’s use of Earth-2 shows how AI, cloud computing, and domain expertise can converge to make weather prediction operationally intelligent and life-saving.

Drafting an AI Lab Partner for Science

Even OpenAI, known for ChatGPT and GPT-5, is venturing into science.

Last month, Kevin Weil, VP of OpenAI for Science, announced a new initiative aimed at “building the next great scientific instrument: an AI-powered platform that accelerates discovery.”

His goal is to bring together top academics and AI experts to see how generative models can help make sense of complex problems in physics, biology, and chemistry.

Weil shared early examples, from GPT-5 helping mathematicians improve optimization proofs, to custom models designing better versions of Nobel Prize-winning stem cell proteins.

Weil wrote, “Scientific discovery improves everything from the quality of our daily lives to national security. Few domains hold as much promise for improving lives as science.”

OpenAI has set a 2026 target for an “automated AI research intern” and a 2028 goal for a fuller automated researcher, positioning its science push as more than tooling.

A New Chapter for AI

What’s striking about these projects is that they’re about understanding. Whether it’s predicting floods, decoding proteins, or inventing new drugs, AI is beginning to act less like a gadget and more like a microscope, one that helps humanity see what was previously invisible.

Big Tech’s latest experiments remind us that the real power of AI might not lie in replacing human work, but in expanding human knowledge for the good.