This is a topic worth discussing in depth. From the perspectives of physicality, mathematical rigor, and systematic thinking, we can layer our analysis of the question 'Will AI innovate?'



1. Define 'innovation'


Philosophical Perspective:

Innovation can be understood as the emergence of thought results or structures that have never appeared before, involving 'non-linear combinations', 'paradigm mutations', 'multi-dimensional analogies', and 'creative recombination'.


Mathematical Perspective:

Innovation can be seen as a leap to non-local extrema in the solution space, or a sudden transition to previously unreachable subspaces in a high-dimensional optimization problem.


Physical Perspective:

Innovation is akin to a system undergoing a mutation after reaching a 'critical point', such as a phase transition, like ice melting into water, where one structure transforms into another state.



2. Does AI's generative approach contain innovative potential?



  • Large language models (like me) are based on probabilistic language modeling (Transformer + large amounts of data), and in some sense belong to 'statistical generalization', adept at combining and recombining existing patterns.


  • The new concepts or combinations generated may exceed the superficial forms of the training set but still belong to 'mixed innovation' or 'incremental innovation'.


  • If we define 'innovation' as completely original, paradigm-exiting creativity (like the birth of quantum mechanics), then current AI still struggles to achieve this.



3. Where are the innovation boundaries of AI?




  • What kind of innovation can AI achieve?



    • Recombinatory Innovation: Such as style fusion, cross-domain migration (for example, using musical structures to explain stock cycles);


    • In-model Innovation: Generating unexpected combinations within limited rules;


    • Associative Innovation: Proposing seemingly absurd but highly inspiring ideas by combining knowledge from different fields.



  • Innovations that AI currently struggles to achieve:



    • Autonomously defining problems;


    • Building entirely new theories from outside the paradigm;


    • Proactively initiating exploration into 'unknown areas' and creating new language.



4. Mathematical Analogy Models:



  • Assuming innovation is a function, and AI is a fitting mechanism, then:


    • AI's innovative capabilities are limited by the training space and learning strategies;


    • Truly groundbreaking innovation requires perturbation functions acting on unknown areas, which is precisely the strength of human intuition and chaotic thinking.



Conclusion (Systematic Integration):


AI has 'combinatorial' and 'recombinatory' innovative capabilities, able to propose inspiring new structures, but lacks the consciousness-driven awareness, motivational mechanisms, and cross-paradigm thinking abilities to create something from 'nothing'.


Potential breakthroughs in the future:



  • AI + Autonomous Goal Setting (e.g. AGI);


  • AI + Multimodal Causal Modeling;


  • AI + Simulate-Test-Validate Cycle (Closed-loop Creation System);



Asking you:

Do you think human innovation is just a reorganization of past experiences? What essential roles does human 'consciousness' play in innovation?