[ The Businesses of AGI ] “To capture 10% of the $14 quadrillion AGI market, assuming a conservative average valuation of $10 billion per business, approximately 140,000 businesses of AGI would be needed.” By http://
QUEBEC.AI: https://
quebecartificialintelligence.com/about #AGIFirst #QuebecAI
AGI
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140,000 AGI Businesses Needed to Capture 10% Market
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AGI Token Reaches $1 Trillion Valuation in ETH Ecosystem
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[ The AGI Token ] With last buys at $1 per $AGI, the AGI Token (a utility token for exclusive use in the AGI.Eth Ecosystem) already have a « USD 1 TRILLION – USD $1,000,000,000,000.00 – valuation » Contract: https://
etherscan.io/address/0xf078
0f43b86c13b3d0681b1cf6daeb1499e7f14d#code
… #AGI #AGINode #AGIToken #MontrealAI -
AGI Club: Exclusive NFT Community for AI Visionaries
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[ AGI Club: The Conclave of Visionaries ] "Beyond Access: Crafting Futures, Exclusively." Mint in $AGI by AGI.Eth. WebPage : https://
quebecartificialintelligence.com/agiclub #AGIClub #MontrealAI #QuebecAI #NFT #NFTCommunity -
AGI Genesis NFT Game Combines AI and Blockchain Gaming
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[ AGI Genesis NFT Game ] "A beacon of future gaming, where AI's prowess meets the creative explosion of NFTs." Prepare for a revolution. By: http://
MONTREAL.AI WebPage : https://
quebecartificialintelligence.com/agigenesis #AGI #AGIGame #AGIGenesis #NFT #NFTCommunity -
OlympiadBench: Challenging Benchmark Promoting AGI with Olympiad Problems
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OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems He et al.: https://
arxiv.org/abs/2402.14008 #Artificialintelligence #DepLearning #MachineLearning -
AGI CyberSecurity Engineering Task Force Phoenix Rising
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[ AGI CyberSecurity ] Behold the Dawn of the : "AGI CyberSecurity Engineering Task Force" Cybersecurity.AGI.eth : A Phoenix Rising From The Ashes Of Yesterday's Vulnerabilities . . . WebPage : https://
quebecartificialintelligence.com/agicybersecuri
ty
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AGI Trading Agents: Reinforcement Learning Powered Financial Innovation
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[ AGI Trading Agents ] Trading.AGI.Eth : The frontier of financial innovation, where cutting-edge reinforcement learning empowers real-time trading. The stakes have never been higher! Asset : https://
opensea.io/assets/ethereu
m/0xD4416b13d2b3a9aBae7AcD5D6C2BbDBE25686401/33394580571945435435819947337190002178497464131064320214903021280923183244962
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Actor Reflection with Episodic Memory for Agent Learning
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2/ Reflexion The actor reflects on each response, using citations for more actionable critique. Reflections stored in chat history as an "episodic memory buffer" to help the agent learn from past iterations. This better steers the generator in responding to the feedback.
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Language Agent Tree Search: Balancing Exploration and Reward
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3/ Language Agent Tree Search (LATS) Algorithm combines reflection and Monte-Carlo tree search to find the “best” trajectory for a task. The search process balances exploration and expected reward. It has 4 main steps: 1. Select: pick the best next actions based on the
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Reflection Agents Improve AI System Success Rates
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Reflection Agents Reflection can improve the success rate of agents and similar AI systems by instructing the agent to explicitly critique and learn from past actions. This has been shown to improve outcomes across a number of domains, from program synthesis to multi-step