What Is Artificial Intelligence (AI)? Definition, Types, Goals, Challenges, and Trends in 2022
How Digital Biology Will Transform Drug Discovery And Beyond
The CEO of DeepMind has forecasted a fantastic shift in science: the rise of "digital biology." This emerging discipline,
But what exactly is digital biology, and how can AI transform fields as complex as biology and drug development? Think of it as a innovative way of viewing life itself—as an information system that can be analyzed, predicted, and even simulated. With tools like AlphaFold already making waves by solving the decades-old puzzle of protein folding, the possibilities seem endless.
What Is Digital Biology?TL;DR Key Takeaways :
Digital biology envisions biological systems as information-processing frameworks, where AI serves as a powerful "description language" to decode life's complexities. A prime example of this is DeepMind's AlphaFold, an AI system that has significantly advanced protein folding research. For decades, scientists grappled with the challenge of predicting protein structures, a critical task for understanding biological processes and developing treatments. AlphaFold solved this long-standing problem with unprecedented accuracy, offering insights that are already transforming drug discovery and disease research.
This achievement underscores how AI can enhance our understanding of life at the molecular level. By treating biological systems as data-rich environments, digital biology enables researchers to uncover patterns and mechanisms that were previously hidden. The success of AlphaFold demonstrates the potential of AI to bridge knowledge gaps and accelerate progress in fields that rely on molecular and cellular insights.
AI's Expanding Role in Scientific DiscoveryAI's applications in science extend far beyond protein folding, offering fantastic benefits across multiple disciplines. In drug development, for instance, AI can drastically reduce the time required to identify promising drug candidates. What once took years can now be accomplished in months or even weeks. By simulating biological systems, AI allows researchers to predict experimental outcomes with remarkable precision, minimizing the need for costly and time-consuming laboratory trials.
Beyond drug discovery, AI is addressing complex challenges in biology, such as modeling cellular mechanisms, simulating entire organisms, and analyzing genetic interactions. These capabilities are reshaping how science is conducted, allowing researchers to explore questions that were previously considered too complex or resource-intensive. AI's ability to process vast amounts of data and identify meaningful patterns is unlocking new opportunities for innovation in areas such as genomics, neuroscience, and environmental science.
Google Deepmind CEO's Prediction – Digital Biology
Uncover more insights about Artificial Intelligence (AI) in previous articles we have written.
When Is AI the Right Tool?AI is not a universal solution for all scientific problems, but it excels in specific scenarios where its strengths can be fully used. The most promising applications of AI in science share three defining characteristics:
In cases where real-world data is limited, researchers can generate synthetic datasets to train AI systems. This approach enables scientists to simulate scenarios that would otherwise remain inaccessible, further expanding the scope of AI's utility. By combining real and synthetic data, researchers can tackle problems that demand high levels of precision and adaptability.
Classical vs. Quantum Computing: Complementary ToolsWhile classical computing has been instrumental in advancing scientific research, it faces limitations when dealing with extreme computational demands. Quantum computing offers a complementary solution, using its unique ability to process information in fundamentally different ways. For example, Google's advancements in quantum systems, such as reducing error rates, highlight the potential of this technology to solve problems that are beyond the reach of classical methods.
However, classical computing remains indispensable for many applications, particularly those where existing algorithms efficiently model natural phenomena. Together, classical and quantum computing form a synergistic partnership, providing researchers with a diverse set of tools to address a wide range of scientific challenges. By integrating these technologies, scientists can push the boundaries of what is computationally possible, opening new avenues for discovery.
Broader Implications for ScienceThe integration of AI into scientific research is set to redefine numerous disciplines, extending far beyond biology. In materials science, AI can accelerate the discovery of new materials with desirable properties, such as superconductors or lightweight alloys. In physics, AI-driven models are helping researchers unravel the mysteries of the universe, from dark matter to the behavior of subatomic particles. In engineering, AI is optimizing designs for greater efficiency, sustainability, and performance.
AI's influence also extends to fields like complexity theory and information theory, where it is allowing researchers to analyze and model intricate systems with unprecedented accuracy. The rapid pace of AI innovation suggests that the coming decades will bring breakthroughs across a wide range of scientific domains. By automating routine tasks and enhancing analytical capabilities, AI is empowering scientists to focus on creative problem-solving and hypothesis generation.
Looking Ahead: A New FrontierThe prediction of a digital biology revolution underscores the fantastic potential of AI in reshaping science and technology. From solving protein folding with AlphaFold to accelerating drug discovery and exploring the possibilities of quantum computing, AI is unlocking new frontiers of innovation. As researchers continue to harness AI's capabilities, the boundaries of what is scientifically achievable will expand, paving the way for discoveries that were once considered unattainable.
This era of digital biology and AI-driven science represents a paradigm shift in how humanity understands and interacts with the natural world. By integrating AI into the fabric of scientific inquiry, researchers are not only solving existing problems but also uncovering entirely new questions to explore. The future of science lies at the intersection of human ingenuity and machine intelligence, promising a wealth of opportunities for discovery and progress.
Media Credit: Matthew Berman
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Google DeepMind has unveiled an AI model that's better at predicting the weather than the current best systems. The new model, dubbed GenCast, is published in Nature today.
This is the second AI weather model that Google has launched in just the past few months. In July, it published details of NeuralGCM, a model that combined AI with physics-based methods like those used in existing forecasting tools. That model performed similarly to conventional methods but used less computing power.
GenCast is different, as it relies on AI methods alone. It works sort of like ChatGPT, but instead of predicting the next most likely word in a sentence, it produces the next most likely weather condition. In training, it starts with random parameters, or weights, and compares that prediction with real weather data. Over the course of training, GenCast's parameters begin to align with the actual weather.
The model was trained on 40 years of weather data (1979 to 2018) and then generated a forecast for 2019. In its predictions, it was more accurate than the current best forecast, the Ensemble Forecast, ENS, 97% of the time, and it was better at predicting wind conditions and extreme weather like the path of tropical cyclones. Better wind prediction capability increases the viability of wind power, because it helps operators calculate when they should turn their turbines on and off. And better estimates for extreme weather can help in planning for natural disasters.
Google DeepMind isn't the only big tech firm that is applying AI to weather forecasting. Nvidia released FourCastNet in 2022. And in 2023 Huawei developed its Pangu-Weather model, which trained on 39 years of data. It produces deterministic forecasts—those providing a single number rather than a range, like a prediction that tomorrow will have a temperature of 30 °F or 0.7 inches of rainfall.
GenCast differs from Pangu-Weather in that it produces probabilistic forecasts—likelihoods for various weather outcomes rather than precise predictions. For example, the forecast might be "There is a 40% chance of the temperature hitting a low of 30 °F" or "There is a 60% chance of 0.7 inches of rainfall tomorrow." This type of analysis helps officials understand the likelihood of different weather events and plan accordingly.
These results don't mean the end of conventional meteorology as a field. The model is trained on past weather conditions, and applying them to the far future may lead to inaccurate predictions for a changing and increasingly erratic climate.
GenCast is still reliant on a data set like ERA5, which is an hourly estimate of various atmospheric variables going back to 1940, says Aaron Hill, an assistant professor at the School of Meteorology at the University of Oklahoma, who was not involved in this research. "The backbone of ERA5 is a physics-based model," he says.
In addition, there are many variables in our atmosphere that we don't directly observe, so meteorologists use physics equations to figure out estimates. These estimates are combined with accessible observational data to feed into a model like GenCast, and new data will always be required. "A model that was trained up to 2018 will do worse in 2024 than a model trained up to 2023 will do in 2024," says Ilan Price, researcher at DeepMind and one of the creators of GenCast.
In the future, DeepMind plans to test models directly using data such as wind or humidity readings to see how feasible it is to make predictions on observation data alone.
There are still many parts of forecasting that AI models still struggle with, like estimating conditions in the upper troposphere. And while the model may be good at predicting where a tropical cyclone may go, it underpredicts the intensity of cyclones, because there's not enough intensity data in the model's training.
The current hope is to have meteorologists working in tandem with GenCast. "There's actual meteorological experts that are looking at the forecast, making judgment calls, and looking at additional data if they don't trust a particular forecast," says Price.
Hill agrees. "It's the value of a human being able to put these pieces together that is significantly undervalued when we talk about AI prediction systems," he says. "Human forecasters look at way more information, and they can distill that information to make really good forecasts."
Google's DeepMind Tackles Weather Forecasting, With Great Performance
By some measures, AI systems are now competitive with traditional computing methods for generating weather forecasts. Because their training penalizes errors, however, the forecasts tend to get "blurry"—as you move further ahead in time, the models make fewer specific predictions since those are more likely to be wrong. As a result, you start to see things like storm tracks broadening and the storms themselves losing clearly defined edges.
But using AI is still extremely tempting because the alternative is a computational atmospheric circulation model, which is extremely compute-intensive. Still, it's highly successful, with the ensemble model from the European Centre for Medium-Range Weather Forecasts considered the best in class.
In a paper being released today, Google's DeepMind claims its new AI system manages to outperform the European model on forecasts out to at least a week and often beyond. DeepMind's system, called GenCast, merges some computational approaches used by atmospheric scientists with a diffusion model, commonly used in generative AI. The result is a system that maintains high resolution while cutting the computational cost significantly.
Ensemble forecastingTraditional computational methods have two main advantages over AI systems. The first is that they're directly based on atmospheric physics, incorporating the rules we know govern the behavior of our actual weather, and they calculate some of the details in a way that's directly informed by empirical data. They're also run as ensembles, meaning that multiple instances of the model are run. Due to the chaotic nature of the weather, these different runs will gradually diverge, providing a measure of the uncertainty of the forecast.
At least one attempt has been made to merge some of the aspects of traditional weather models with AI systems. An internal Google project used a traditional atmospheric circulation model that divided the Earth's surface into a grid of cells but used an AI to predict the behavior of each cell. This provided much better computational performance, but at the expense of relatively large grid cells, which resulted in relatively low resolution.
For its take on AI weather predictions, DeepMind decided to skip the physics and instead adopt the ability to run an ensemble.
Gen Cast is based on diffusion models, which have a key feature that's useful here. In essence, these models are trained by starting them with a mixture of an original—image, text, weather pattern—and then a variation where noise is injected. The system is supposed to create a variation of the noisy version that is closer to the original. Once trained, it can be fed pure noise and evolve the noise to be closer to whatever it's targeting.
In this case, the target is realistic weather data, and the system takes an input of pure noise and evolves it based on the atmosphere's current state and its recent history. For longer-range forecasts, the "history" includes both the actual data and the predicted data from earlier forecasts. The system moves forward in 12-hour steps, so the forecast for day three will incorporate the starting conditions, the earlier history, and the two forecasts from days one and two.
This is useful for creating an ensemble forecast because you can feed it different patterns of noise as input, and each will produce a slightly different output of weather data. This serves the same purpose it does in a traditional weather model: providing a measure of the uncertainty for the forecast.
For each grid square, GenCast works with six weather measures at the surface, along with six that track the state of the atmosphere and 13 different altitudes at which it estimates the air pressure. Each of these grid squares is 0.2 degrees on a side, a higher resolution than the European model uses for its forecasts. Despite that resolution, DeepMind estimates that a single instance (meaning not a full ensemble) can be run out to 15 days on one of Google's tensor processing systems in just eight minutes.
It's possible to make an ensemble forecast by running multiple versions of this in parallel and then integrating the results. Given the amount of hardware Google has at its disposal, the whole process from start to finish is likely to take less than 20 minutes. The source and training data will be placed on the GitHub page for DeepMind's GraphCast project. Given the relatively low computational requirements, we can probably expect individual academic research teams to start experimenting with it.
Measures of successDeepMind reports that GenCast dramatically outperforms the best traditional forecasting model. Using a standard benchmark in the field, DeepMind found that GenCast was more accurate than the European model on 97 percent of the tests it used, which checked different output values at different times in the future. In addition, the confidence values, based on the uncertainty obtained from the ensemble, were generally reasonable.
Past AI weather forecasters, having been trained on real-world data, are generally not great at handling extreme weather since it shows up so rarely in the training set. But GenCast did quite well, often outperforming the European model in things like abnormally high and low temperatures and air pressure (one percent frequency or less, including at the 0.01 percentile).
DeepMind also went beyond standard tests to determine whether GenCast might be useful. This research included projecting the tracks of tropical cyclones, an important job for forecasting models. For the first four days, GenCast was significantly more accurate than the European model, and it maintained its lead out to about a week.
One of DeepMind's most interesting tests was checking the global forecast of wind power output based on information from the Global Powerplant Database. This involved using it to forecast wind speeds at 10 meters above the surface (which is actually lower than where most turbines reside but is the best approximation possible) and then using that number to figure out how much power would be generated. The system beat the traditional weather model by 20 percent for the first two days and stayed in front with a declining lead out to a week.
The researchers don't spend much time examining why performance seems to decline gradually for about a week. Ideally, more details about GenCast's limitations would help inform further improvements, so the researchers are likely thinking about it. In any case, today's paper marks the second case where taking something akin to a hybrid approach—mixing aspects of traditional forecast systems with AI—has been reported to improve forecasts. And both those cases took very different approaches, raising the prospect that it will be possible to combine some of their features.
Nature, 2024. DOI: 10.1038/s41586-024-08252-9 (About DOIs).
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