Understanding AGI and Asi Development Trends

AI is evolving from 𝗿𝘂𝗹𝗲-𝗯𝗮𝘀𝗲𝗱 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 to 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗱𝗶𝗴𝗶𝘁𝗮𝗹 𝗽𝗲𝗿𝘀𝗼𝗻𝗮𝘀—but how far have we actually come? This framework breaks down AI agents into 𝗳𝗶𝘃𝗲 𝗹𝗲𝘃𝗲𝗹𝘀, showing the trajectory from basic automation to AI that could eventually act on our behalf.  𝗕𝗿𝗲𝗮𝗸𝗶𝗻𝗴 𝗗𝗼𝘄𝗻 𝘁𝗵𝗲 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 𝗘𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻:   🟠 𝗟𝗲𝘃𝗲𝗹 𝟬 (𝗡𝗼 𝗔𝗜): Traditional rule-based software, following deterministic steps—think UI-driven automation.   🟠 𝗟𝗲𝘃𝗲𝗹 𝟭 (𝗥𝘂𝗹𝗲-𝗕𝗮𝘀𝗲𝗱 𝗔𝗜): Executes 𝗽𝗿𝗲𝗱𝗲𝗳𝗶𝗻𝗲𝗱 𝘀𝘁𝗲𝗽𝘀 but lacks flexibility—e.g., early chatbots or IF-THEN automation.   🟠 𝗟𝗲𝘃𝗲𝗹 𝟮 (𝗜𝗟/𝗥𝗟-𝗕𝗮𝘀𝗲𝗱 𝗔𝗜): Uses 𝗱𝗲𝘁𝗲𝗿𝗺𝗶𝗻𝗶𝘀𝘁𝗶𝗰 𝘁𝗮𝘀𝗸 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 but still requires user-defined instructions.   🟢 𝗟𝗲𝘃𝗲𝗹 𝟯 (𝗟𝗟𝗠 + 𝗧𝗼𝗼𝗹𝘀): AI agents with 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝘁𝗮𝘀𝗸 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻, feedback loops, and decision-making capabilities. This is where today's 𝗮𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗔𝗜 𝗮𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝘁𝘀 are heading.   🟢 𝗟𝗲𝘃𝗲𝗹 𝟰 (𝗠𝗲𝗺𝗼𝗿𝘆 + 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗔𝘄𝗮𝗿𝗲𝗻𝗲𝘀𝘀): AI starts to 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝘂𝘀𝗲𝗿 𝗰𝗼𝗻𝘁𝗲𝘅𝘁, proactively assisting and personalizing actions. This is the 𝗻𝗲𝘅𝘁 𝗳𝗿𝗼𝗻𝘁𝗶𝗲𝗿 for AI-powered workflows.   𝗟𝗲𝘃𝗲𝗹 𝟱 (𝗧𝗿𝘂𝗲 𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗣𝗲𝗿𝘀𝗼𝗻𝗮): AI acts 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀𝗹𝘆, representing users in complex tasks with safety and reliability. This is the dream of 𝗔𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹 𝗚𝗲𝗻𝗲𝗿𝗮𝗹 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 (𝗔𝗚𝗜)—but we’re not there yet.  𝗪𝗵𝗲𝗿𝗲 𝗔𝗿𝗲 𝗪𝗲 𝗧𝗼𝗱𝗮𝘆?   ✅ 𝗦𝘂𝗽𝗲𝗿𝗵𝘂𝗺𝗮𝗻 𝗡𝗮𝗿𝗿𝗼𝘄 𝗔𝗜 (e.g., AlphaFold, AlphaZero) already exists.   ✅ 𝗘𝗺𝗲𝗿𝗴𝗶𝗻𝗴 𝗔𝗚𝗜 is progressing but lacks full autonomy.   🔜 𝗧𝗿𝘂𝗲 𝗔𝗚𝗜 & 𝗔𝗦𝗜? Still a distant goal, requiring breakthroughs in reasoning, memory, and adaptability.  𝗪𝗵𝗮𝘁 𝗧𝗵𝗶𝘀 𝗠𝗲𝗮𝗻𝘀 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗙𝘂𝘁𝘂𝗿𝗲:   - The 𝘀𝗵𝗶𝗳𝘁 𝗳𝗿𝗼𝗺 "𝗰𝗵𝗮𝗶𝗻𝘀 & 𝗳𝗹𝗼𝘄𝘀" 𝘁𝗼 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 is the next major evolution.   - AI with 𝗺𝗲𝗺𝗼𝗿𝘆, 𝗰𝗼𝗻𝘁𝗲𝘅𝘁, 𝗮𝗻𝗱 𝗽𝗿𝗼𝗮𝗰𝘁𝗶𝘃𝗲 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗺𝗮𝗸𝗶𝗻𝗴 will redefine how we work.   - The race to 𝗔𝗚𝗜 is about 𝘀𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆, 𝗮𝗱𝗮𝗽𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆, 𝗮𝗻𝗱 𝗿𝗲𝗱𝘂𝗰𝗶𝗻𝗴 𝗵𝘂𝗺𝗮𝗻 𝗼𝘃𝗲𝗿𝘀𝗶𝗴𝗵𝘁 in complex tasks.  𝗪𝗵𝗮𝘁 𝗱𝗼 𝘆𝗼𝘂 𝘁𝗵𝗶𝗻𝗸? How soon will we see AI agents that truly act as our digital counterparts?

𝗠𝗬 𝗪𝗘𝗘𝗞 𝗜𝗡 𝗔𝗜: 𝘼𝙂𝙄 𝙞𝙣 𝙩𝙬𝙤 𝙮𝙚𝙖𝙧𝙨? 𝙊𝙧 𝟮𝟬? 𝘾𝙖𝙥𝙖𝙗𝙞𝙡𝙞𝙩𝙮 𝙜𝙖𝙥𝙨 𝙩𝙚𝙡𝙡 𝙖 𝙡𝙤𝙣𝙜𝙚𝙧 𝙨𝙩𝙤𝙧𝙮    Headlines claim AGI could arrive by 2027. Venture capital is flowing. Firms are freezing hiring until “AI can’t do the task.” Yet among the scientists building the systems? No consensus—not on timelines, not even on what AGI 𝘪𝘴.   🔹𝗬𝗮𝗻𝗻 𝗟𝗲𝗖𝘂𝗻 (𝗠𝗲𝘁𝗮) calls AGI a continuum, not a finish line. Core capabilities like reasoning, long-term memory, and causal understanding remain research frontiers? Likely decades away. 🔹𝗗𝗲𝗺𝗶𝘀 𝗛𝗮𝘀𝘀𝗮𝗯𝗶𝘀 (𝗚𝗼𝗼𝗴𝗹𝗲 𝗗𝗲𝗲𝗽𝗠𝗶𝗻𝗱) is more bullish, but frames AGI as a progression of milestones—each demanding new governance and safety protocols. 🔹Meanwhile, 𝗢𝗽𝗲𝗻𝗔𝗜 is restructuring as a public-benefit corp to raise bigger war chests. This week it released a “7-Step Readiness Framework” for enterprises—mapping high-value use cases, guardrails, red-teaming, and incident response.   𝗪𝗵𝘆 𝗶𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀: If AGI is a journey, we must shift from chasing launch dates to rewiring continuously:   𝟭. 𝗖𝗮𝗽𝗶𝘁𝗮𝗹 & 𝗖𝗼𝗻𝘁𝗿𝗼𝗹. OpenAI’s hybrid structure—and growing scrutiny of its profit motives—signal that funding models and oversight will keep evolving. 𝟮. 𝗪𝗼𝗿𝗸𝗳𝗼𝗿𝗰𝗲 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆. Duolingo and Shopify treat AI as a talent layer; but if LeCun is right, human expertise will remain indispensable far longer than doomers predict. 𝟯. 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗣𝗹𝗮𝘆𝗯𝗼𝗼𝗸𝘀. OpenAI’s 7-step guide is a solid checklist: pilot, audit, secure, stress-test, train, govern, repeat. But only if embedded across every product sprint.   𝗕𝗼𝘁𝘁𝗼𝗺 𝗹𝗶𝗻𝗲: Whether AGI lands in two years or twenty, the winners will treat intelligence as an expanding frontier—updating structures, skills, and safeguards each quarter—rather than betting everything on a single finish line.   Are we bracing for an instant leap, or building the muscle to adapt as the frontier keeps moving?   𝗙𝗼𝗿 𝗮 𝗱𝗲𝗲𝗽𝗲𝗿 𝗱𝗶𝘃𝗲: • AGI 2027 forecast – VentureBeat: https://lnkd.in/etncFZGu • OpenAI for-profit debate – TIME: https://lnkd.in/eJC4kwDb • AGI mentorship – Fortune: https://lnkd.in/eVeRmN-k • OpenAI restructuring – FOX Business: https://lnkd.in/evHkH-hg • OpenAI’s “7-Step Readiness Framework”: https://lnkd.in/eBqJCufb • LeCun on AGI continuum – LessWrong: https://lnkd.in/euu5JMBF   • Hassabis on milestone path – TIME: https://lnkd.in/eRhdKq6G #AI #AGI #AIReadiness #Innovation #Leadership

📝 Announcing our paper that proposes a unified cognitive and computational framework for Artificial General Intelligence (AGI) -- going beyond token-level predictions -- one that emphasizes modular reasoning, memory, agentic behavior, and ethical alignment 🔹 𝐓𝐡𝐢𝐧𝐤𝐢𝐧𝐠 𝐁𝐞𝐲𝐨𝐧𝐝 𝐓𝐨𝐤𝐞𝐧𝐬: 𝐅𝐫𝐨𝐦 𝐁𝐫𝐚𝐢𝐧‑𝐈𝐧𝐬𝐩𝐢𝐫𝐞𝐝 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 𝐭𝐨 𝐂𝐨𝐠𝐧𝐢𝐭𝐢𝐯𝐞 𝐅𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧𝐬 𝐟𝐨𝐫 𝐀𝐫𝐭𝐢𝐟𝐢𝐜𝐢𝐚𝐥 𝐆𝐞𝐧𝐞𝐫𝐚𝐥 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 𝐚𝐧𝐝 𝐢𝐭𝐬 𝐒𝐨𝐜𝐢𝐞𝐭𝐚𝐥 𝐈𝐦𝐩𝐚𝐜𝐭 🔹 In collaboration with University of Central Florida, Cornell University, UT MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Toronto Metropolitan University, University of Oxford, Torrens University Australia, Obuda University, Amazon others. 🔹 Paper: https://lnkd.in/gqKUV4Mr ✍🏼 Authors: Rizwan Qureshi, Ranjan Sapkota, Abbas Shah, Amgad Muneer, Anas Zafar, Ashmal Vayani, Maged Shoman, PhD, Abdelrahman Eldaly, Kai Zhang, Ferhat Sadak, Shaina Raza, PhD, Xinqi Fan, Ravid Shwartz Ziv, Hong Yang, Vinija Jain, Aman Chadha, Manoj Karkee, @Jia Wu, Philip Torr, FREng, FRS, Seyedali Mirjalili ➡️ 𝐊𝐞𝐲 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬 𝐨𝐟 𝐓𝐡𝐢𝐧𝐤𝐢𝐧𝐠 𝐁𝐞𝐲𝐨𝐧𝐝 𝐓𝐨𝐤𝐞𝐧𝐬' 𝐂𝐨𝐠𝐧𝐢𝐭𝐢𝐯𝐞‑𝐂𝐨𝐦𝐩𝐮𝐭𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐀𝐆𝐈 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤: 🧠 𝐅𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤: Integrates cognitive neuroscience, psychology, and AI to define AGI via modular reasoning, persistent memory, agentic behavior, vision-language grounding, and embodied interaction. 🔗 𝐁𝐞𝐲𝐨𝐧𝐝 𝐓𝐨𝐤𝐞𝐧‑𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧: Critiques token-level models like GPT-4.5 and Claude 3.5, advocating for test-time adaptation, dynamic planning, and training-free grounding through retrieval-augmented agentic systems. 🚀 𝐑𝐨𝐚𝐝𝐦𝐚𝐩 𝐚𝐧𝐝 𝐂𝐨𝐧𝐭𝐫𝐢𝐛𝐮𝐭𝐢𝐨𝐧𝐬: Proposes a roadmap for AGI through neuro-symbolic learning, value alignment, multimodal cognition, and cognitive scaffolding for transparent, socially integrated systems.

Essential listening to understand the cutting edge of AI: François Chollet (top AI researcher, creator of Keras) & Mike Knoop (Zapier co-founder) on AGI Limits and their new lab Ndea * The Adaptation Gap: Exploring the limitations of current LLMs, particularly their struggles with adapting to unseen problems – areas where humans excel. * Redefining Intelligence: Moving beyond task-specific benchmarks to measure fluid intelligence * Promising Pathways: Program synthesis and test-time adaptation * ARC AGI 2 & ARC Prize 2025: The next iteration of the benchmark ('an IQ test for machines') and prize, aimed at stimulating innovation ARC Prize Foundation * The Ndea Vision: a new AGI lab to build AI capable of autonomous innovation and accelerating scientific discovery in verifiable domains. #AI #AGI #Podcast #MachineLearning #DeepLearning #TechTrends https://lnkd.in/einCQs3d