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Core Insights - The next stage of artificial intelligence (AI) development requires overcoming two major challenges: the Transformer architecture and the backpropagation algorithm [1][7][54] - The focus should shift from larger models to creating "living" models that possess native memory, autonomous learning, and continuous evolution capabilities [2][4][48] - This transition signifies a move from centralized cloud computing to decentralized learning, where each device can contribute to knowledge generation [3][5][70] Group 1: Hardware Awakening - The concept of "hardware awakening" suggests that devices can learn and adapt in real-time, transforming them from mere tools into active intelligent agents [4][64] - A multitude of such intelligent agents collaborating in the real world can lead to the emergence of collective intelligence [5][71] - The current reliance on the Transformer model limits the potential for true intelligence, as it does not facilitate autonomous learning or native memory [21][30][76] Group 2: Redefining Value - The future of AI will redefine the value of hardware, moving beyond traditional metrics like memory and processing power to focus on the co-creation of value between users and devices [64][66] - Users should pay for intelligence rather than token consumption, as the latter is seen as an inefficient model [15][19][21] - The emergence of devices with autonomous learning capabilities will enhance user experience and privacy, as data remains localized [68][69] Group 3: Collective Intelligence - Collective intelligence arises when each device possesses its own intelligence and can learn from the physical world, similar to human collaboration [71][76] - True intelligence is characterized by the ability to generate knowledge rather than merely disseminating it, which is a limitation of current large models [75][77] - The path to general artificial intelligence is through collective intelligence rather than the centralized model exemplified by companies like OpenAI [77]