QUÉBEC ♡ IA / AI QUEBEC.AI — le fleuron souverain de l’intelligence artificielle québécoise. - Québec’s sovereign AI flagship. Une vision souveraine. Une ambition mondiale. - A sovereign vision. A global ambition. quebec.ai #QuebecIA #QuebecAI

GoalOS AGIALPHA Ascension is an experimental framework for a persistent, goal-seeking, self-improving intelligence system that accumulates capabilities, evidence, and economic value over time. GitHub : github.com/MontrealAI/goalos… #AGIALPHA

Our report "From AGI to ASI" is out!

Deux annonces majeures viennent renforcer le positionnement de Montréal en IA. @MistralAI a annoncé l’ouverture d’un bureau dans la métropole dès cet été. 🔗 lapresse.ca/affaires/techno/… @cohere a conclu un partenariat avec le gouvernement du Québec. 📷 ici.radio-canada.ca/nouvelle… 📷👏

People replace their phones every ~4 yrs. This means there are hundreds of millions of old phones discarded each year that are still perfectly usable as computing devices. @Google in collabration with @UCSD is exploring how to turn these old phones into cloud-computing “phone clusters”. Putting phones back in service in this way can directly reduce the environmental footprint of computing by avoiding the need for further raw material extraction, and taking advantage of the embodied carbon already incurred from manufacturing these devices, and modern phones actually are already quite powerful computers. Read more in the blog below ⬇️

GoalOS AGIALPHA Ascension is an experimental framework for a persistent, goal-seeking, self-improving intelligence system that accumulates capabilities, evidence, and economic value over time. GitHub: github.com/MontrealAI/goalos… #AGIALPHA

Guess what? local ai for everyone (: we did it local.ai

About 8 months ago, I warned that “Anthropic is running a sophisticated regulatory capture strategy based on fear-mongering.” This take was controversial at the time; now look how many people are saying it.

Can confirm we saw a strong spike in growth of token consumption for Codex over last 48 hours. Unusual when we don't launch something.

First results from Recursive on AI that improves AI! 🚀 ✨ 📈 Our automated AI research system incorporates principles from open-ended and AI-generating algorithms. 🧠 💡🌱🧬 It conducts key parts of the science loop: proposing ideas, implementing them, testing them, and picking the next ideas based on the data. 🔬 🔭 🧪⚗️ The same general system produces state-of-the-art results on three different problems (two on training language models, one on speeding up AI via kernel optimization). • NanoChat Autoresearch: 1.3x faster to reach the same loss than the best solution produced by an entire community of humans agents over months, and 1.8x faster than the initial hand-optimized solution • NanoGPT Speedrun: 3% speedup of a very efficient solution produced by entire community of humans agents over 2 years • GPU Kernel Optimization: 18% reduction in gap to theoretically optimal score on Nvidia’s SOL-ExecBench These are early tests of our system. We’re very excited for what the future holds! Post: recursive.com/articles/first… Great work by the amazing team at Recursive!

Scientific research is fundamental to advancing civilization and helping people globally to solve the most critical problems, from medicine to materials, from brain science to physics, and much beyond. This is only possible when scientists have access to the best tools of the time to conduct scientific research, including having access to AI-based tools.

GoalOS-native α‑AGI Ascension using AGIALPHA GitHub : github.com/MontrealAI/goalos… #AGIALPHA #AGIAscension

Silently sabotaging experiments in order to stultify scientific progress and protect a technology lead. Hmmm that sounds familiar... Welcome to "Sophanthropic".

[ The Businesses Of AGI ] AGI Agents 👾 are potential titans of global wealth. “To capture 10% of the $14 quadrillion AGI market, approximately 140,000 businesses of AGI would be needed.” By QUEBEC.AI: quebecartificialintelligence… #AGIFirst #MontrealAI #QuebecAI

AI 101 Masterclass 📖✨ By: QUEBEC.AI Website: quebec.ai #AI101 #MontrealAI #QuebecAI

If you’re not inside, you’re outside. #AGIClub

The Transformer scaled intelligence inside models. The next frontier may be scaling intelligence across organizations. I’m sharing my paper: AGI ALPHA: A Scalable Substrate for Intelligence Organizations The core thesis: AI progress will not be defined only by stronger models. It will be defined by systems that convert model capability into verified work, verified work into reusable capability, and reusable capability into productive capacity. AGI ALPHA proposes an organizational substrate above models: agents jobs validators tools memory markets settlement governance capacity allocation The point is not “more agents talking.” The point is proof-bearing machine labor. A job should be bounded. A tool action should be traced. A result should be validated. A capability should be reusable. A settlement should be auditable. A claim should require evidence. In this framing, intelligence is not merely benchmark performance. It is governed, evidence-producing, compounding institutional work. The paper’s central distinction: Transformer = scalable substrate for intelligence inside models AGI ALPHA = scalable substrate for intelligence across governed organizations This is also why the paper is claim-bounded. It does not claim achieved AGI, ASI, empirical SOTA, autonomous sovereignty, energy abundance, or civilization-scale capability. It proposes a testable architecture and evaluation program. The burden of proof is explicit: real tasks baselines ProofBundles replay logs validator reports cost and safety ledgers delayed outcomes independent reproduction No Evidence Docket, no strong empirical claim. That discipline matters. Because the future of agentic AI will not be won by systems that merely appear autonomous. It will be won by systems that can prove what they did, what it cost, what risks were controlled, what was learned, and whether the resulting capability compounds. AGI ALPHA is my attempt to formalize that substrate. A validator-gated, proof-bearing, RSI-governed architecture for turning machine intelligence into auditable machine labor — and eventually into reusable capability, infrastructure, science, compute, and useful energy capacity. Full credit: Vincent Boucher President, MONTREAL.AI and QUEBEC.AI Paper: AGI ALPHA: A Scalable Substrate for Intelligence Organizations github.com/MontrealAI/agialp… I’m attaching the first page because the thesis is worth reading directly. The next frontier is not just bigger models. It is institutions that make intelligence verifiable. #AGIAlpha #QuebecAI #SovereignAI #ArtificialIntelligence #AIResearch #Agents

GoalOS-native α‑AGI Ascension using AGIALPHA GitHub : github.com/MontrealAI/goalos… #AGIALPHA #AGIAscension

The Transformer scaled intelligence inside models. The next frontier may be scaling intelligence across organizations. I’m sharing my paper: AGI ALPHA: A Scalable Substrate for Intelligence Organizations The core thesis: AI progress will not be defined only by stronger models. It will be defined by systems that convert model capability into verified work, verified work into reusable capability, and reusable capability into productive capacity. AGI ALPHA proposes an organizational substrate above models: agents jobs validators tools memory markets settlement governance capacity allocation The point is not “more agents talking.” The point is proof-bearing machine labor. A job should be bounded. A tool action should be traced. A result should be validated. A capability should be reusable. A settlement should be auditable. A claim should require evidence. In this framing, intelligence is not merely benchmark performance. It is governed, evidence-producing, compounding institutional work. The paper’s central distinction: Transformer = scalable substrate for intelligence inside models AGI ALPHA = scalable substrate for intelligence across governed organizations This is also why the paper is claim-bounded. It does not claim achieved AGI, ASI, empirical SOTA, autonomous sovereignty, energy abundance, or civilization-scale capability. It proposes a testable architecture and evaluation program. The burden of proof is explicit: real tasks baselines ProofBundles replay logs validator reports cost and safety ledgers delayed outcomes independent reproduction No Evidence Docket, no strong empirical claim. That discipline matters. Because the future of agentic AI will not be won by systems that merely appear autonomous. It will be won by systems that can prove what they did, what it cost, what risks were controlled, what was learned, and whether the resulting capability compounds. AGI ALPHA is my attempt to formalize that substrate. A validator-gated, proof-bearing, RSI-governed architecture for turning machine intelligence into auditable machine labor — and eventually into reusable capability, infrastructure, science, compute, and useful energy capacity. Full credit: Vincent Boucher President, MONTREAL.AI and QUEBEC.AI Paper: AGI ALPHA: A Scalable Substrate for Intelligence Organizations github.com/MontrealAI/agialp… I’m attaching the first page because the thesis is worth reading directly. The next frontier is not just bigger models. It is institutions that make intelligence verifiable. #AGIAlpha #QuebecAI #SovereignAI #ArtificialIntelligence #AIResearch #AIAgents