Enquanto #Bitcoin cai 0,5% #AGIX #OCEAN #FET consolidam alta | Crypto_is_the_name_of_game na Binance Square

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Enquanto #Bitcoin cai 0,5% #AGIX #OCEAN #FET consolidam alta de 5%! O que acham do poder dessas Altcoins ? 

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We are pleased to announce a strategic partnership with Elohim Technology to advance responsible AI innovation and develop AGI for the benefit of humanity. $AGIX

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$DOT 𝕏 Space on JAM w/ @gavofyork JAM is a protocol combining elements of both Polkadot & Ethereum, and is a potential replacement of the Polkadot Relay Chain 💡 Tune in to learn how JAM introduces L2 scalability without the need for rollup solutions ⚡️ #Polkadot #ethurum #layer2 #Binance

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mesmo o mercado operando neste momento em queda passageira, os ativos que ainda aparecem em alta mostrando poder de impulso e resistência a queda do BTC são da area de Inteligência Artificial , com isso, fica ainda mais claro que o forte da alt season já é o setor AI! #Ai #artificialinteligence #Layer2 #Binance

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clique e ganhe Red Packs Grátis 📈🎁🎁 Go Go Go

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$AGIX $FET $OCEAN Join us this Tuesday, May 7th, 2024, at 5 pm UTC for the first session of a special two-part SingularityNET's Technical Tuesdays mini-series dedicated to the latest advancements in the development of a unified experiential learning component for OpenCog Hyperon, our framework for #AGI at the human level and beyond. Session 1 - The implementation of NARS (Non-Axiomatic Reasoning System) in OpenCog Hyperon’s MeTTa language cognitive computations; - Integrating the AIRIS (Autonomous Intelligent Reinforcement Interpreted Symbolism) causality-based learning AI into Hyperon. Session 2 - Recreating experiential learning in Hyperon using ROCCA (Rational OpenCog Controlled Agent); - Porting fundamental components ROCCA requires from OpenCog classic to Hyperon, including forward and backward chaining, PLN (Probabilistic Logic Networks), and pattern mining. These advancements are part of our ongoing initiative to consolidate the strengths of several systems —ROCCA, NARS, OpenPsi, and AIRIS— to create a unified experiential learning component for Hyperon. This approach will allow AI models to: - Develop a goal-independent understanding of their environment through causal knowledge gained from planned and spontaneous interactions; - Explore their environment with increased efficiency using a curiosity model that prioritizes situations with high uncertainty, challenging their existing causal knowledge. Our preliminary findings indicate that this approach surpasses common Reinforcement Learning techniques in terms of data efficiency by orders of magnitude. To learn more, set your reminder for the livestream now on your preferred platform in X (tweeter )

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