AI can revolutionize science – Carnevalestoricocivitonico

Artificial intelligence (AI), one of science’s greatest achievements, is poised to go full circle to revolutionize science itself. As currently applied science, human ingenuity and new technologies offer deep insights into some of humanity’s greatest questions.

But while we often think of scientific progress as rapid and inexorable, some commentators believe that the pace of progress has slowed.

While the scientific community continues to debate the cause of this slowdown, much of today’s technology, from jet planes to manufacturing processes, is not significantly different from half a century ago. But now breakthroughs in areas of development such as artificial intelligence (AI) and quantum computing have dramatically accelerated the pace of scientific discoveries in areas such as Healthcare and Cyber security.

The influence of AI has spread to almost every field of science, from biology and chemistry to geology and physics. Many researchers who use AI techniques also enjoy a “citation premium,” meaning their papers become more influential among their peers. These breakthroughs are based on decades of collaboration between researchers, technologists, policy makers, civil society organizations and many people from across society. They offer a blueprint for how applying AI to science can dramatically improve people’s lives.

In recognition of this, The Royal Society – a community of many of the world’s most eminent scientists – is partnering with Google DeepMind to co-host the first AI for Science Forum. This event in London brings together the research community, policy makers and industry leaders to look at the transformative potential of AI to accelerate science and the role of public-private partnerships in innovation.

The forum is intended to consider how AI can be used to accelerate the pace of scientific progress, including research in the following areas:

Protein Structure Prediction: Experts have described demystifying protein folding as a “grand challenge” for decades. In 2022, Google DeepMind shared the predicted structures of 200 million proteins from their AlphaFold 2 model. Previously it typically took a year or more to determine the 3D structure of a single protein, AlphaFold can predict these shapes with remarkable accuracy in minutes.

It does so by releasing the protein structure predictions into a free database, which has enabled scientists around the world to accelerate progress in areas such as developing new drugs, fighting antibiotic resistance and tackling plastic pollution, and predicting the structure of life molecules.

Healthcare research on the human brain: Few things have held more mystery throughout time than the human brain. Developed over 10 years of research, Google has worked with other organizations to map a tiny piece of the human brain to a level of detail never before achieved. This project, released in 2024, revealed never-before-seen structures in the human brain. And the entire dataset, including AI-generated annotations for each cell, has been made publicly available to accelerate research.

Save lives with accurate flood forecasts: When Google’s flood forecasting project began in 2018, many believed it was impossible to accurately deliver large-scale flood forecasts, given the lack of data.
However, researchers were able to develop an AI model that achieves reliability in predicting extreme river events in ungauged watersheds at up to five days of lead time with reliability matching or exceeding nuut broadcasts (zero day lead time).

By 2024, Google Research expanded this coverage to 100 countries and 700 million people globally, improving the AI model to offer the same accuracy at seven days of lead time as the previous model had at five.

Detecting wildfires to help firefighters stop them: Forest fires are increasing in communities around the world due to warmer and drier climates. In 2024, Google Research is partnering with the US Forest Services, an AI model and a new global satellite constellation designed specifically to detect and track classroom-sized forest fires by providing higher-resolution images within 20 minutes. This will allow fire authorities to respond more quickly, potentially saving lives, property and natural resources.

Accurate Fast Weather Forecast: In 2023, Google DeepMind launched and open-sourced the model code for GraphCast, a machine learning research model that predicts weather conditions up to 10 days in advance more accurately and much faster than the industry’s gold-standard weather simulation system (HRES).

GraphCast can also predict the tracks of cyclones (and associated risks like flooding) with greater accuracy, accurately predicting Hurricane Lee to hit Nova Scotia three days ahead of traditional models.

Advanced Mathematical Reasoning: AI has always struggled with complex mathematics due to a lack of data and reasoning. Then, in 2024, Google announced DeepMind AlphaGeometry, an AI system that solved complex geometry problems at a level approaching a human Olympiad gold medalist, a breakthrough in AI performance and the pursuit of more advanced general AI systems.

The subsequent Gemini-trained model, AlphaGeometry 2, was combined with a new model AlphaProof, and together they solved 83% of all historical International Mathematical Olympiad (IMO) geometry problems from the past 25 years. By demonstrating AI’s growing ability to reason, and potentially solve problems beyond current human capabilities, this brought us closer to systems that can discover and verify new knowledge.

Using Quantum Computing to Predict Chemical Reactivity and Kinetics: Google researchers worked with UC Berkeley and Columbia University to perform the largest chemistry simulations to date on a quantum computer. The results, published in 2022, were not only competitive with classical methods, but they also did not require the burdensome error reduction typically associated with quantum computing.

The ability to run these simulations will offer even more accurate predictions of chemical reactivity and kinetics, which is a precursor to applying chemistry in new ways to help solve real-world challenges.

Accelerating Materials Science: In 2023, Google announced DeepMind Graph Networks for Materials Exploration (GNoME), a new AI tool that has already discovered 380,000 materials that are stable at low temperatures, according to simulations. At a time when our world is looking for new approaches to energy, process power and materials science, this work could pave the way for better solar cells, batteries and potential superconductors.

Additionally, to help this technology benefit everyone, Google DeepMind made GNoME’s most stable predictions available through the Materials Project in their open database.

Nuclear fusion and clean energy: Controlling and harnessing the energy that powers stars, including our own sun, has been beyond the realm of science. In 2022, Google DeepMind announced that they developed AI that can control the plasma in a nuclear fusion reactor autonomously. Working with the Swiss Plasma Center at EPFL, Google DeepMind built the first system that can autonomously stabilize and shape the plasma in a working fusion reactor, taking a critical step towards stable fusion and abundant clean energy for all.

Conclusion

New AI technology is influential in a range of industries. For individuals they provide an excellent opportunity to improve living conditions, while for companies they implement new technology in AI is the way to increase profits and improve the customer experience.

Photo: Unsplash

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