Currently, the field of artificial intelligence is undergoing a profound transformation.
While public attention is focused on the interactive revolution sparked by large language models (LLM) and their exceptional ability to integrate, process, and efficiently present existing human knowledge, another more disruptive force—represented by Google DeepMind’s “AI for Science”—is quietly emerging, showcasing the potential to change the landscape of scientific research.
Demis Hassabis, CEO of Google DeepMind, stated in a recent CBS “60 Minutes” interview that AI may help eliminate all diseases in the next decade.
Unlike mainstream large language models that focus on learning, digesting, and reproducing known information, DeepMind’s exploration reflects a completely new path. From AlphaZero, AlphaFold to the recently emerging AlphaProof, the core goal of these landmark achievements is not merely to process and apply existing knowledge, but to serve as an engine driving scientific discoveries themselves.
The essence of science lies in breaking through known boundaries and creating entirely new cognitive frameworks and principles. DeepMind’s “AI for Science” directly applies the power of artificial intelligence to scientific exploration, aiming to push humanity toward breakthroughs in this cutting-edge field. Perhaps this signals the next critical trajectory for AI development and could even be the first to overcome the “knowledge boundary” limitations faced by large language models.
01
The Innovative Path of the Alpha Series: From Rules to Discovery
To better understand the unique route taken by DeepMind, we need to reconsider the essence of its “discovery” capabilities. The operation of the Alpha series models (such as AlphaZero and AlphaFold) differs fundamentally from large language models. They do not “imitate” or “predict” human answers by learning statistical patterns in vast amounts of text data. Instead, these models conduct deep exploration and innovation in a world defined by precise rules (such as the rules of Go, physical laws, mathematical axioms).
The core mechanism of the Alpha series typically combines deep learning (for pattern recognition and state evaluation) with reinforcement learning/search algorithms (such as Monte Carlo Tree Search):
Defining the problem space: Transforming complex problems (e.g., playing games, protein folding, theorem proving) into a vast search space with clear rules. Exploration and evaluation: The model explores countless possibilities within this space through simulation (e.g., self-play, simulating physical processes) or reasoning. It does not rely on “correct answers” from existing data but evaluates each exploration based on preset goals (such as winning a game, minimizing an energy function, or finding a proof path) and rules through trial-and-error learning. Discovering novel solutions: After extensive exploration and optimization, the model can discover solutions that transcend human intuition, even proposing ideas not present in human data. For example, the non-traditional Go strategies discovered by AlphaZero, or the highly accurate and previously unknown protein structures predicted by AlphaFold, are typical examples of this “emergent discovery” approach.
The core of this mechanism is reasoning and creation based on fundamental principles, rather than simply inducting and repeating existing knowledge. It is this approach that enables Alpha series models to demonstrate a unique advantage in breaking through human cognitive limits and generating genuinely new knowledge.
02
Why is DeepMind’s AI for Science Feasible?
Some perspectives suggest that DeepMind’s “AI for Science” path is feasible, possibly for the following reasons:
Milestone Success Verification: AlphaZero not only defeated the human Go champion but also introduced innovative strategies that inspired human players. AlphaFold’s achievements are even more tangible—its open-sourced AlphaFold protein structure database (AlphaFold DB) currently contains over 200 million protein structure predictions, used by over 1 million researchers worldwide, significantly accelerating life sciences and drug development. Multiple achievements have been validated by top journals such as Nature and Science, providing strong proof of the effectiveness and potential of this technological approach.
Natural Alignment with Scientific Problems and Methods: Many cutting-edge scientific challenges, such as finding new catalysts to improve green hydrogen production or designing new materials with specific properties (e.g., room-temperature superconductivity, efficient energy storage), are essentially about optimizing searches within a vast space of possibilities governed by physical and chemical rules. Traditional trial-and-error methods are inefficient, whereas Alpha series models excel at large-scale, rule-driven exploration. For instance, an AI model can evaluate millions of battery electrode material structures in just a few days to find the best balance between energy density and stability—something human efforts cannot match in speed and scale.
Knowledge Sources Beyond “Human Data”: Unlike large language models that depend on text and code created by humans, “AI for Science” can utilize more fundamental, broad knowledge sources—such as physical laws, chemical equations, biological pathways, and mathematical axioms. More importantly, models can autonomously generate vast amounts of training data through simulation environments like physical simulations or self-play, which to some extent avoids the data ceiling problem faced by large language models. (Of course, even simulated data presents a massive challenge in terms of building accurate and reliable environments.)
Computational Power and Resource Advantage: These models require astonishing computational resources. For example, AlphaFold’s training involved hundreds of TPU/GPU cores for several weeks. As its parent company, Google can provide ample computational resources and continuous R&D investment, which is a critical enabler for DeepMind to tackle these “hardcore” scientific challenges. Furthermore, Google has positioned “AI for Science” as a core strategic direction, ensuring long-term resource focus.
03
LLMs and AI for Science Complement Each Other, Opening a New Chapter for AI
Although large language models and DeepMind’s scientific discovery engine focus on different technical aspects, they are not in opposition or mutually replaceable; rather, they represent two complementary main lines of AI development, with significant synergistic potential:
The Role of Large Language Models: As powerful general intelligence interfaces and knowledge processing assistants, large language models excel at understanding natural language, integrating cross-domain information, assisting in coding, generating reports, inspiring creativity, and even acting as “translators” in interactions between users and complex scientific models. They can significantly enhance researchers’ efficiency in information processing, idea communication, and tool utilization.
The Role of AI for Science: As deep problem solvers and creators of new knowledge, scientific discovery engines focus on conducting deep searches and optimizations within well-defined complex systems, excelling at finding non-intuitive solutions and driving breakthroughs in specific scientific or engineering fields.
The future landscape of AI development could be one of co-evolution:
Researchers could use large language models to quickly sort literature, propose initial hypotheses, and even assist in writing simulation code. Subsequently, AI for Science (Alpha-type models) could rigorously simulate, calculate, or prove these hypotheses, conducting deep exploration and verification. Large language models could also help scientists understand and explain complex results generated by scientific discovery engines. In the future, hybrid AI systems that integrate both strengths might emerge, combining powerful understanding and interaction capabilities with deep reasoning and discovery in specific domains. Thus, these two technological paths can jointly advance AI into a further-reaching future.
04
Far-reaching Impact: AI for Science Is Poised to Reshape Multiple Industry Landscapes
It is foreseeable that the continued progress of “AI for Science” will drive massive demand for high-performance computing. This “discovery-type AI” will have sustained and enormous computational demands. Whether training complex reinforcement learning models or searching through vast possibility spaces, powerful computational capabilities are required. AlphaFold’s success has verified this, and in the future, as more complex scientific problems such as climate models, new material designs, cosmology simulations, and mathematical theorem exploration emerge, the demand for AI-driven high-performance computing chips will continue to rise.
Therefore, the growing demand for “AI for Science” will become a key driving force for the growth of the semiconductor industry.
Beyond the semiconductor industry, “AI for Science” will fundamentally change the R&D models and innovation speed across multiple industries. These advancements will significantly accelerate the R&D process in various sectors, drive technological innovation, and reshape entire industrial landscapes.
Biopharmaceuticals and Health: Beginning with AlphaFold, AI will revolutionize fields such as new drug target discovery, candidate drug molecule design, clinical trial optimization, and personalized precision medicine, significantly shortening R&D cycles, reducing costs, and contributing to the fight against major diseases such as cancer and Alzheimer’s.
Materials Science and Energy: AI will accelerate the discovery of new materials, such as more efficient solar cells, higher capacity battery electrodes, more environmentally friendly catalysts, and lighter, stronger structural materials, providing critical technological support for solving the energy crisis, environmental issues, and driving manufacturing upgrades.
Chemistry and Chemical Engineering: AI can optimize chemical reaction pathways, design more efficient and environmentally friendly production processes, and even discover new synthesis methods, driving the chemical industry toward a greener transformation.
Fundamental Scientific Research: AI will assist in proving complex theorems in mathematics (such as the AlphaProof project), help analyze particle collider data, simulate the evolution of the universe, explore quantum phenomena in physics, and improve climate science prediction models.
Engineering and Design: AI will optimize the aerodynamic design of spacecraft, improve chip layouts, enhance the efficiency of complex systems (such as logistics networks and power grid scheduling), and drive engineering technology innovation.
The core goal of “AI for Science” is to accelerate scientific discovery and achieve an exponential leap in human abilities to understand and transform the world. It is not only a significant supplement to the current AI technological landscape but could also become the key pathway for humanity to reach new fields of knowledge. As technology continues to advance, we will witness a completely new mode of knowledge creation, which will profoundly transform the way scientific research is conducted and drive significant breakthroughs in human cognition and technology across multiple domains.
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