AI Research

Self-Assembly Gets Automated in Reverse of ‘Game of Life’

Published

2 days ago

September 10, 2025

Alexander Mordvintsev showed me two clumps of pixels on his screen. They pulsed, grew and blossomed into monarch butterflies. As the two butterflies grew, they smashed into each other, and one got the worst of it; its wing withered away. But just as it seemed like a goner, the mutilated butterfly did a kind of backflip and grew a new wing like a salamander regrowing a lost leg.

Mordvintsev, a research scientist at Google Research in Zurich, had not deliberately bred his virtual butterflies to regenerate lost body parts; it happened spontaneously. That was his first inkling, he said, that he was onto something. His project built on a decades-old tradition of creating cellular automata: miniature, chessboard-like computational worlds governed by bare-bones rules. The most famous, the Game of Life, first popularized in 1970, has captivated generations of computer scientists, biologists and physicists, who see it as a metaphor for how a few basic laws of physics can give rise to the vast diversity of the natural world.

In 2020, Mordvintsev brought this into the era of deep learning by creating neural cellular automata, or NCAs. Instead of starting with rules and applying them to see what happened, his approach started with a desired pattern and figured out what simple rules would produce it. “I wanted to reverse this process: to say that here is my objective,” he said. With this inversion, he has made it possible to do “complexity engineering,” as the physicist and cellular-automata researcher Stephen Wolfram proposed in 1986 — namely, to program the building blocks of a system so that they will self-assemble into whatever form you want. “Imagine you want to build a cathedral, but you don’t design a cathedral,” Mordvintsev said. “You design a brick. What shape should your brick be that, if you take a lot of them and shake them long enough, they build a cathedral for you?”

Such a brick sounds almost magical, but biology is replete with examples of basically that. A starling murmuration or ant colony acts as a coherent whole, and scientists have postulated simple rules that, if each bird or ant follows them, explain the collective behavior. Similarly, the cells of your body play off one another to shape themselves into a single organism. NCAs are a model for that process, except that they start with the collective behavior and automatically arrive at the rules.

Alexander Mordvintsev created complex cell-based digital systems that use only neighbor-to-neighbor communication.

Courtesy of Alexander Mordvintsev

The possibilities this presents are potentially boundless. If biologists can figure out how Mordvintsev’s butterfly can so ingeniously regenerate a wing, maybe doctors can coax our bodies to regrow a lost limb. For engineers, who often find inspiration in biology, these NCAs are a potential new model for creating fully distributed computers that perform a task without central coordination. In some ways, NCAs may be innately better at problem-solving than neural networks.

Life’s Dreams

Mordvintsev was born in 1985 and grew up in the Russian city of Miass, on the eastern flanks of the Ural Mountains. He taught himself to code on a Soviet-era IBM PC clone by writing simulations of planetary dynamics, gas diffusion and ant colonies. “The idea that you can create a tiny universe inside your computer and then let it run, and have this simulated reality where you have full control, always fascinated me,” he said.

He landed a job at Google’s lab in Zurich in 2014, just as a new image-recognition technology based on multilayer, or “deep,” neural networks was sweeping the tech industry. For all their power, these systems were (and arguably still are) troublingly inscrutable. “I realized that, OK, I need to figure out how it works,” he said.

He came up with “deep dreaming,” a process that takes whatever patterns a neural network discerns in an image, then exaggerates them for effect. For a while, the phantasmagoria that resulted — ordinary photos turned into a psychedelic trip of dog snouts, fish scales and parrot feathers — filled the internet. Mordvintsev became an instant software celebrity.

Among the many scientists who reached out to him was Michael Levin of Tufts University, a leading developmental biologist. If neural networks are inscrutable, so are biological organisms, and Levin was curious whether something like deep dreaming might help to make sense of them, too. Levin’s email reawakened Mordvintsev’s fascination with simulating nature, especially with cellular automata.

Source link

AI Research

Artificial Intelligence Cheating | Nation

Published

1 hour ago

September 12, 2025

Jae C. Hong - AP

Artificial Intelligence Cheating | Nation | hjnews.com

We recognize you are attempting to access this website from a country belonging to the European Economic Area (EEA) including the EU which
enforces the General Data Protection Regulation (GDPR) and therefore access cannot be granted at this time.

For any issues, call 435-752-2121.

Source link

AI Research

Artificial Intelligence in Healthcare Market : A Study of

Published

2 hours ago

September 12, 2025

The Editors

Global Artificial Intelligence in Healthcare Market size was valued at USD 27.07 Bn in 2024 and is expected to reach USD 347.28 Bn by 2032, at a CAGR of 37.57%

Artificial Intelligence (AI) in healthcare is reshaping the industry by enabling faster diagnosis, personalized treatment, and enhanced operational efficiency. AI-driven tools such as predictive analytics, natural language processing, and medical imaging analysis are empowering physicians with deeper insights and decision support, reducing human error and improving patient outcomes. Moreover, AI is revolutionizing drug discovery, clinical trial optimization, and remote patient monitoring, making healthcare more proactive and accessible in both developed and emerging markets.

The adoption of AI in healthcare is also being accelerated by the rising demand for telemedicine, wearable health devices, and real-time data-driven solutions. From virtual health assistants to robotic surgery, AI is driving innovation across patient care and hospital management. However, challenges such as data privacy, ethical considerations, and regulatory frameworks remain crucial in ensuring responsible deployment. As AI continues to integrate with IoT, cloud, and big data platforms, it is set to create a connected healthcare ecosystem that prioritizes precision medicine and patient-centric solutions.

Get a sample of the report https://www.maximizemarketresearch.com/request-sample/21261/

Major companies profiled in the market report include

BP Target Neutral . JPMorgan Chase & Co. . Gold Standard Carbon Clear . South Pole Group . 3Degrees . Shell. EcoAct.

Research objectives:

The latest research report has been formulated using industry-verified data. It provides a detailed understanding of the leading manufacturers and suppliers engaged in this market, their pricing analysis, product offerings, gross revenue, sales network & distribution channels, profit margins, and financial standing. The report’s insightful data is intended to enlighten the readers interested in this business sector about the lucrative growth opportunities in the Artificial Intelligence in Healthcare market.

Get access to the full description of the report @ https://www.maximizemarketresearch.com/market-report/global-artificial-intelligence-ai-healthcare-market/21261/

It has segmented the global Artificial Intelligence in Healthcare market

by Offering

Hardware

Software

Services

by Technology

Machine Learning

Natural Language Processing

Context-Aware Computing

Computer Vision

Key Objectives of the Global Artificial Intelligence in Healthcare Market Report:

The report conducts a comparative assessment of the leading market players participating in the globalArtificial Intelligence in Healthcare

The report marks the notable developments that have recently taken place in the Artificial Intelligence in Healthcare industry

It details on the strategic initiatives undertaken by the market competitors for business expansion.

It closely examines the micro- and macro-economic growth indicators, as well as the essential elements of theArtificial Intelligence in Healthcaremarket value chain.

The repot further jots down the major growth prospects for the emerging market players in the leading regions of the market

Explore More Related Report @

Engineering, Procurement, and Construction Management (EPCM) Market https://www.maximizemarketresearch.com/market-report/engineering-procurement-and-construction-management-epcm-market/73131/

Global Turbomolecular Pumps Market

https://www.maximizemarketresearch.com/market-report/global-turbomolecular-pumps-market/20730/

Contact Maximize Market Research:

3rd Floor, Navale IT Park, Phase 2

Pune Bangalore Highway, Narhe,

Pune, Maharashtra 411041, India

sales@maximizemarketresearch.com

+91 96071 95908, +91 9607365656

About Maximize Market Research:

Maximize Market Research is a multifaceted market research and consulting company with professionals from several industries. Some of the industries we cover include medical devices, pharmaceutical manufacturers, science and engineering, electronic components, industrial equipment, technology and communication, cars and automobiles, chemical products and substances, general merchandise, beverages, personal care, and automated systems. To mention a few, we provide market-verified industry estimations, technical trend analysis, crucial market research, strategic advice, competition analysis, production and demand analysis, and client impact studies

This release was published on openPR.

Source link

AI Research

A Unified Model for Robot Interaction, Reasoning and Planning

Published

3 hours ago

September 12, 2025

Huang Fang, Mengxi Zhang, Heng Dong, Wei Li, Zixuan Wang, Qifeng Zhang, Xueyun Tian, Yucheng Hu, Hang Li

[Submitted on 1 Sep 2025 (v1), last revised 11 Sep 2025 (this version, v2)]

View a PDF of the paper titled Robix: A Unified Model for Robot Interaction, Reasoning and Planning, by Huang Fang and 8 other authors

View PDF

Abstract:We introduce Robix, a unified model that integrates robot reasoning, task planning, and natural language interaction within a single vision-language architecture. Acting as the high-level cognitive layer in a hierarchical robot system, Robix dynamically generates atomic commands for the low-level controller and verbal responses for human interaction, enabling robots to follow complex instructions, plan long-horizon tasks, and interact naturally with human within an end-to-end framework. Robix further introduces novel capabilities such as proactive dialogue, real-time interruption handling, and context-aware commonsense reasoning during task execution. At its core, Robix leverages chain-of-thought reasoning and adopts a three-stage training strategy: (1) continued pretraining to enhance foundational embodied reasoning abilities including 3D spatial understanding, visual grounding, and task-centric reasoning; (2) supervised finetuning to model human-robot interaction and task planning as a unified reasoning-action sequence; and (3) reinforcement learning to improve reasoning-action consistency and long-horizon task coherence. Extensive experiments demonstrate that Robix outperforms both open-source and commercial baselines (e.g., GPT-4o and Gemini 2.5 Pro) in interactive task execution, demonstrating strong generalization across diverse instruction types (e.g., open-ended, multi-stage, constrained, invalid, and interrupted) and various user-involved tasks such as table bussing, grocery shopping, and dietary filtering.