NishMath - #agi

Google DeepMind has introduced AlphaEvolve, a revolutionary AI coding agent designed to autonomously discover innovative algorithms and scientific solutions. This groundbreaking research, detailed in the paper "AlphaEvolve: A Coding Agent for Scientific and Algorithmic Discovery," represents a significant step towards achieving Artificial General Intelligence (AGI) and potentially even Artificial Superintelligence (ASI). AlphaEvolve distinguishes itself through its evolutionary approach, where it autonomously generates, evaluates, and refines code across generations, rather than relying on static fine-tuning or human-labeled datasets. AlphaEvolve combines Google’s Gemini Flash, Gemini Pro, and automated evaluation metrics.

AlphaEvolve operates using an evolutionary pipeline powered by large language models (LLMs). This pipeline doesn't just generate outputs—it mutates, evaluates, selects, and improves code across generations. The system begins with an initial program and iteratively refines it by introducing carefully structured changes. These changes take the form of LLM-generated diffs—code modifications suggested by a language model based on prior examples and explicit instructions. A diff in software engineering refers to the difference between two versions of a file, typically highlighting lines to be removed or replaced.

Google's AlphaEvolve is not merely another code generator, but a system that generates and evolves code, allowing it to discover new algorithms. This innovation has already demonstrated its potential by shattering a 56-year-old record in matrix multiplication, a core component of many machine learning workloads. Additionally, AlphaEvolve has reclaimed 0.7% of compute capacity across Google's global data centers, showcasing its efficiency and cost-effectiveness. AlphaEvolve imagined as a genetic algorithm coupled to a large language model.


References :

• LearnAI: Google’s AlphaEvolve Is Evolving New Algorithms — And It Could Be a Game Changer
• The Next Web: Article on The Next Web describing feats of DeepMind’s AI coding agent AlphaEvolve.
• Towards Data Science: A blend of LLMs' creative generation capabilities with genetic algorithms
• www.unite.ai: Google DeepMind has unveiled AlphaEvolve, an evolutionary coding agent designed to autonomously discover novel algorithms and scientific solutions. Presented in the paper titled “AlphaEvolve: A Coding Agent for Scientific and Algorithmic Discovery,†this research represents a foundational step toward Artificial General Intelligence (AGI) and even Artificial Superintelligence (ASI).
• learn.aisingapore.org: AlphaEvolve imagined as a genetic algorithm coupled to a large language model. Models have undeniably revolutionized how many of us approach coding, but they’re often more like a super-powered intern than a seasoned architect.
• AI News | VentureBeat: Google's AlphaEvolve is the epitome of a best-practice AI agent orchestration. It offers a lesson in production-grade agent engineering. Discover its architecture & essential takeaways for your enterprise AI strategy.
• Unite.AI: Google DeepMind has unveiled AlphaEvolve, an evolutionary coding agent designed to autonomously discover novel algorithms and scientific solutions.
• Last Week in AI: DeepMind introduced Alpha Evolve, a new coding agent designed for scientific and algorithmic discovery, showing improvements in automated code generation and efficiency.
• venturebeat.com: VentureBeat article about Google DeepMind's AlphaEvolve system.

Classification:

• HashTags: #AI #AGI #DeepMind
• Company: Google DeepMind
• Target: Researchers
• Product: AlphaEvolve
• Feature: AlphaEvolve
• Type: AI
• Severity: Informative

The ARC Prize Foundation has launched ARC-AGI-2, a new AI benchmark designed to challenge current foundation models and track progress towards artificial general intelligence (AGI). Building on the original ARC benchmark, ARC-AGI-2 blocks brute force techniques and introduces new tasks intended for next-generation AI systems. The goal is to evaluate real progress toward AGI by requiring models to reason abstractly, generalize from few examples, and apply knowledge in new contexts, tasks that are simple for humans but difficult for machines.

The Foundation has also announced the ARC Prize 2025, a competition running from March 26 to November 3, with a grand prize of $700,000 for a solution achieving an 85% score on the ARC-AGI-2 benchmark's private evaluation dataset. Early testing results show that even OpenAI's top models experienced a significant performance drop, with o3 falling from 75% to approximately 4% on ARC-AGI-2. This highlights how the new benchmark significantly raises the bar for AI tests, measuring general fluid intelligence rather than memorized skills.


References :

• RunPod Blog: The race toward artificial general intelligence isn't just happening behind closed doors at trillion-dollar tech companies. It's also unfolding in the open—in research labs, Discord servers, GitHub repos, and competitions like the ARC Prize. This year, the ARC Prize Foundation is back with ARC-AGI-2
• Data Phoenix: The ARC Prize Foundation has officially released the ARC-AGI-2 to challenge current foundation models and help track progress towards AGI. Additionally, the Foundation has opened the ARC Prize 2025, running from Mar 26 to Nov 3, with a $700K Grand Prize for an 85% scoring solution on the ARC-AGI-2.
• THE DECODER: The new AI benchmark ARC-AGI-2 significantly raises the bar for AI tests. While humans can easily solve the tasks, even highly developed AI systems such as OpenAI o3 clearly fail. The article appeared first on .
• eWEEK: The newest AI benchmark, ARC-AGI-2, builds on the first iteration by blocking brute force techniques and designing new tasks for next-gen AI systems. The post appeared first on .

Classification:

• HashTags: #ARCAGI2 #AGI #AIBenchmark
• Company: ARC Prize Foundation
• Target: AI Researchers
• Product: ARC-AGI-2
• Feature: AGI Benchmarking
• Type: AI
• Severity: Informative