News from the AI & ML world
DeeperML - #algorithm
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@Google DeepMind Blog
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Google DeepMind has announced AlphaEvolve, an AI coding agent that enhances the problem-solving abilities of large language models (LLMs) like Gemini. Unveiled in May 2025, AlphaEvolve utilizes a blend of LLMs' creative generation capabilities with genetic algorithms, marking a significant advancement in AI's capacity to automate and optimize coding processes. This system is not merely a code generator but a self-evolving AI designed to discover novel and efficient algorithms through automated code generation and testing.
AlphaEvolve operates through a process akin to natural selection for software. It begins with an initial code, potentially a "skeleton" provided by a human, and iteratively refines it. The system intelligently crafts prompts for the underlying Gemini LLM, instructing it to act as a world-class expert in a specific domain, utilizing context from previous attempts. The LLM then generates a diverse pool of candidate solutions, exploring different approaches to the problem at hand. The AI agent has already demonstrated its prowess by improving Google’s data center energy efficiency by 0.7%, optimizing matrix multiplications to speed up Gemini’s training by 23%, and co-designing a portion of Google’s next AI chip. Furthermore, AlphaEvolve has tackled over 50 open problems in mathematics, even discovering a new lower bound for the 300-year-old kissing number problem in 11-dimensional space, surpassing the progress of expert mathematicians. This represents a significant stride toward production-grade AI agent engineering. Recommended read:
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@Google DeepMind Blog
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Google DeepMind has unveiled AlphaEvolve, an AI agent powered by Gemini, that is revolutionizing algorithm discovery and scientific optimization. This innovative system combines the creative problem-solving capabilities of large language models (LLMs) with automated evaluators to verify solutions and iteratively improve upon promising ideas. AlphaEvolve represents a significant leap in AI's ability to develop sophisticated algorithms for both scientific challenges and everyday computing problems, expanding upon previous work by evolving entire codebases rather than single functions.
AlphaEvolve has already demonstrated its potential by breaking a 56-year-old mathematical record, discovering a more efficient matrix multiplication algorithm that had eluded human mathematicians. The system leverages an ensemble of state-of-the-art large language models, including Gemini Flash and Gemini Pro, to propose and refine algorithmic solutions as code. These programs are then evaluated using automated metrics, providing an objective assessment of accuracy and quality. This approach makes AlphaEvolve particularly valuable in domains where progress can be clearly and systematically measured, such as math and computer science. The impact of AlphaEvolve extends beyond theoretical breakthroughs, with algorithms discovered by the system already deployed across Google's computing ecosystem. Notably, AlphaEvolve has enhanced the efficiency of Google's data centers, chip design, and AI training processes, including the training of the large language models underlying AlphaEvolve itself. It has also optimized a matrix multiplication kernel used to train Gemini models and found new solutions to open mathematical problems. By optimizing Google’s massive cluster management system, Borg, AlphaEvolve recovers an average of 0.7% of Google’s worldwide computing resources continuously, which translates to substantial cost savings. Recommended read:
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Stephen Ornes@Quanta Magazine
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Quanta Magazine
, medium.com
A novel quantum algorithm has demonstrated a speedup over classical computers for a significant class of optimization problems, according to a recent report. This breakthrough could represent a major advancement in harnessing the potential of quantum computers, which have long promised faster solutions to complex computational challenges. The new algorithm, known as decoded quantum interferometry (DQI), outperforms all known classical algorithms in finding good solutions to a wide range of optimization problems, which involve searching for the best possible solution from a vast number of choices.
Classical researchers have been struggling to keep up with this quantum advancement. Reports of quantum algorithms often spark excitement, partly because they can offer new perspectives on difficult problems. The DQI algorithm is considered a "breakthrough in quantum algorithms" by Gil Kalai, a mathematician at Reichman University. While quantum computers have generated considerable buzz, it has been challenging to identify specific problems where they can significantly outperform classical machines. This new algorithm demonstrates the potential for quantum computers to excel in optimization tasks, a development that could have broad implications across various fields. Recommended read:
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@ncatlab.org
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nLab
Microsoft has announced a significant breakthrough in quantum computing with its new Majorana 1 chip. This groundbreaking processor is built upon a novel "Topological Core" architecture and boasts a theoretical capacity of up to one million qubits. The chip leverages a new material called topoconductor, the world’s first topological conductor, which harnesses topological superconductivity to control Majorana particles. This innovative approach promises more stable and reliable qubits, the fundamental building blocks of quantum computers. Microsoft also claims the chip could potentially break down microplastics into harmless byproducts or create self-healing materials for applications in construction, manufacturing, and healthcare.
Microsoft's Majorana 1 chip represents a paradigm shift in quantum computing technology, a development with far-reaching implications for industries and cybersecurity. By using topological qubits, Majorana 1 is designed to be inherently more stable and less prone to errors than current qubit technologies. While Microsoft touts this development as progress and hopes quantum computing will be used to benefit humanity, some experts warn of its potential use as a new tool that could break existing encryption methods. Despite these potential risks, Microsoft is dedicated to developing a scalable quantum computing prototype which solidifies their role at the forefront of quantum innovation. Recommended read:
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