Large language models (LLMs) have demonstrated remarkable adaptability, excelling in tasks ranging from summarizing documents and generating code to brainstorming innovative concepts. Now, these capabilities are being extended to address complex problems in mathematics and modern computing.
Introducing AlphaEvolve, an evolutionary coding agent driven by LLMs, designed for the purpose of algorithm discovery and optimization. AlphaEvolve combines the creative problem-solving that our Gemini models are capable of, with automated evaluators that verify the soundness of proposed solutions. It then uses an evolutionary framework to improve upon the most promising aspects.
AlphaEvolve has already improved Google’s data centers, chip design and AI training, including the training of the large language models which underlie it. It has also helped to design faster matrix multiplication algorithms and discover new solutions to mathematical problems, showcasing a potential for application in multiple areas.
Harnessing LLMs for Better Algorithm Design
In 2023 research was done which showed that LLMs are able to generate computer code which can then discover new proofs for scientific problems. AlphaEvolve expands upon this, and is capable of evolving entire codebases to develop much more complex algorithms.
AlphaEvolve uses multiple LLMs. Gemini Flash is used to maximize the amount of ideas explored. Gemini Pro can then provide more insightful suggestions. Together, these models propose computer programs which implement algorithmic solutions as code.
AlphaEvolve uses automated evaluation metrics to verify, run, and score the proposed programs. These metrics provide an objective, quantifiable assessment of each solution’s accuracy and quality. This makes AlphaEvolve particularly helpful in domains where progress can be clearly and systematically measured, for example in math and computing.
Optimizing the Computing Ecosystem
Over the past year, algorithms discovered by AlphaEvolve have been deployed across a variety of systems at Google. The impact of each of these is multiplied across the AI and computing infrastructure to build a more powerful and sustainable digital ecosystem for all users.
Specifically, AlphaEvolve has been used in:
Improving Data Center Scheduling
AlphaEvolve discovered a heuristic to orchestrate Google’s data centers more efficiently. This solution has been deployed for over a year and helps to recover around 0.7% of Google’s worldwide compute resources. This efficiency gain allows more tasks to be completed within the same computational footprint. AlphaEvolve’s solution leads to better performance and provides operational advantages due to the readability of the code.
Assisting in Hardware Design
AlphaEvolve came up with a Verilog rewrite which removed bits in an arithmetic circuit for matrix multiplication. The proposal had to pass advanced checks to confirm that the modified circuit maintained functional correctness. This proposal was integrated into an upcoming Tensor Processing Unit, Google’s custom AI accelerator. By suggesting modifications in the standard language of chip designers, AlphaEvolve ensures that AI and hardware engineers can collaborate to accelerate the design of specialized chips.
Enhancing AI Training and Inference
AlphaEvolve is able to improve AI performance and research velocity. It sped up a kernel in Gemini’s architecture by 23%, which led to a reduction in Gemini’s training time of around 1%. Also, AlphaEvolve reduces the engineering time required for kernel optimization, from weeks of expert effort to days of automated experiments, helping researchers to innovate faster.
Furthermore, AlphaEvolve can optimize low level GPU instructions. AlphaEvolve achieved a speedup of up to around 33% for the FlashAttention kernel implementation in Transformer-based AI models. Optimizations such as this allows experts to find performance bottlenecks and easily incorporate improvements into existing codebases.
Advancing Math and Algorithm Discovery
AlphaEvolve can also propose new approaches to complex mathematical problems. Given a minimal code skeleton for a computer program, AlphaEvolve designed components of a gradient-based optimization procedure that discovered algorithms for matrix multiplication.
AlphaEvolve’s procedure led to the finding of an algorithm to multiply 4x4 complex-valued matrices using 48 scalar multiplications, improving upon Strassen’s 1969 algorithm.
To investigate AlphaEvolve’s breadth, the system was applied to over 50 open problems in mathematical analysis, geometry, combinatorics, and number theory. In around 75% of cases, it rediscovered state-of-the-art solutions.
In 20% of cases, AlphaEvolve improved the previously best known solutions, and advanced the kissing number problem. This geometric challenge concerns the maximum number of non-overlapping spheres that touch a common unit sphere. AlphaEvolve discovered a configuration of 593 outer spheres and established a new lower bound in 11 dimensions.
A New Era of Algorithm Design with AlphaEvolve
The development of AlphaEvolve marks a significant step forward in automated algorithm design, showcasing the progression from discovering specialized algorithms for specific domains to tackling complex, real-world challenges. By leveraging the capabilities of LLMs and combining them with automated evaluation and evolutionary optimization techniques, AlphaEvolve opens up opportunities for innovation across diverse fields.
The potential applications of AlphaEvolve extend far beyond its current use cases, promising to impact a wide range of disciplines, including:
- Material Science: AlphaEvolve could be used to design new materials with enhanced properties, such as improved strength, conductivity, and durability.
- Drug Discovery: Algorithmically optimized drug candidates could accelerate the discovery process and lead to new treatments for a variety of diseases.
- Sustainability: AlphaEvolve can be applied to design more sustainable processes and technologies, such as optimizing energy consumption and reducing waste.
- Wider Technological and Business Applications: From optimizing supply chains to improving financial models, the potential applications of AlphaEvolve are vast and varied.
The development of AlphaEvolve represents a paradigm shift in how algorithms are designed, offering a powerful new tool for solving complex problems and driving innovation across a wide range of industries. As LLMs continue to evolve and improve, the capabilities of AlphaEvolve are expected to expand even further, paving the way for even greater advancements in the years to come.
Business applications of AlphaEvolve
- Data Analysis and Interpretation: In the realm of business, AlphaEvolve can be harnessed to analyze vast datasets, uncover hidden patterns, and generate actionable insights. By automating the process of data exploration and interpretation, businesses can make more informed decisions, improve their operational efficiency, and gain a competitive edge in an increasingly data-driven world.
- Financial Modeling and Risk Management: The financial industry relies heavily on sophisticated algorithms for tasks such as portfolio optimization, risk assessment, and fraud detection. AlphaEvolve can assist in the development of more accurate and robust financial models, enabling financial institutions to better manage risk, improve their investment strategies, and enhance their overall performance.
- Logistics and Supply Chain Optimization: In today’s globalized economy, businesses face the challenge of managing complex supply chains that span multiple countries and involve numerous stakeholders. AlphaEvolve can be used to optimize logistics operations, streamline supply chain processes, and reduce overall costs. By identifying bottlenecks, minimizing transportation delays, and improving inventory management, businesses can create more efficient and responsive supply chains that are better able to meet the demands of their customers.
- Personalized Healthcare: By analyzing patient data, including medical history, genetic information, and lifestyle factors, AlphaEvolve can help to identify individuals who are at risk of developing certain diseases. This allows healthcare providers to intervene early, provide personalized treatment plans, and improve patient outcomes. In addition, AlphaEvolve can also be used to optimize drug dosages, predict treatment responses, and identify potential adverse effects.
- Environmental Monitoring and Conservation: Environmental monitoring and conservation efforts rely on algorithms to analyze data collected from satellites, sensors, and other sources. AlphaEvolve can contribute to developing more sophisticated algorithms for monitoring air and water quality, tracking deforestation, and predicting the impacts of climate change. This real-time information can be used to make decisions, manage resources, and develop more effective conservation policies.
- Education and Personalized Learning: The education sector stands to benefit greatly from the application of AI-driven personalization techniques. AlphaEvolve can be leveraged to develop adaptive learning platforms that cater to the unique needs and learning styles of individual students. By analyzing student performance data, identifying knowledge gaps, and tailoring the learning content accordingly, AlphaEvolve can help to create a more engaging and effective learning experience for all students. Furthermore, AlphaEvolve can also be used to automate tasks such as grading and feedback, freeing up teachers to focus on more individualized instruction.
AlphaEvolve represents a new paradigm in algorithm design, offering a powerful tool for solving complex problems and driving innovation across a wide range of industries.
Enhancing Scientific Discovery
Beyond its industrial applications, AlphaEvolve holds tremendous promise for advancing scientific discovery in areas such as:
Genomics: By analyzing vast amounts of genomic data, AlphaEvolve can help identify genes that are associated with certain diseases, predict the effects of genetic mutations, and develop personalized gene therapies. The ability to efficiently process and interpret these complex datasets can significantly accelerate progress in understanding and treating genetic disorders.
Proteomics: Proteomics involves the study of the structure and function of proteins, which are the building blocks of cells. AlphaEvolve can be used to analyze protein interactions, predict protein folding patterns, and design new proteins with desired properties. This capability has implications for drug development, biotechnology, and materials science.
Materials Discovery: AlphaEvolve can be used to design new materials with enhanced properties and optimize experimental conditions for materials synthesis. The ability to predict material properties and identify promising candidates can significantly accelerate the discovery of novel materials for various applications. The use of machine learning algorithms optimized by AlphaEvolve allows for faster and more efficient exploration of the vast chemical space of potential materials.
Climate Modeling: AlphaEvolve can be used to develop more accurate and efficient climate models that can better predict the effects of climate change. These models can incorporate vast amounts of data from various sources, including satellites, sensors, and climate simulations. Improved climate models can provide valuable insights for policymakers, scientists, and businesses to make more informed decisions about climate change mitigation and adaptation strategies. For example, AlphaEvolve could optimize the parameterization of cloud formation processes in climate models, leading to more accurate predictions of future temperature changes.
Space Exploration: Algorithmic optimizations can be particularly useful for space exploration. Since every bit counts in space, more efficient compression algorithms that reduce the amount of data that needs to be transmitted would improve communications with probes traveling through space a great deal. Reducing power requirements for on-board computational tasks would also be useful. In space, more efficient use of a spaceship’s limited power is important for mission success. If AI helps a spaceship to travel using less power, the spaceship can travel farther, carry more instruments, and send more information back to Earth, which are vital when exploring space.
In sum, AlphaEvolve has wide application in business, science, and engineering, showcasing the transformative power of LLMs in algorithm design and optimization. Its capacity to enhance efficiency, accelerate research, and improve decision-making across various domains highlights its significance in driving innovation and progress in the years to come.