Jan, I'm curious if you and your models have explored the meaning of intuition? Is it a 'gut feeling' (if yes: how is this perceived by an LLM?). And does intuition differ from instinct?
Intuition to me, means the feeling of moving in alignment with broader cosmic flows transcending my being (in Taoism expressed through Yin and Yang). If I do, I intuitively feel to be attuned with the universe. If I don't, energetic tension arises. This is spiritual.
Instinct results from evolutionary reinforcement learning: my body is programmed to sense if a specific context will bring me closer to safety or danger. It is essential for physical survival.
Both influence my direction.
Also, I think it might help to train resonance and to develop AI architectures that explicitly model non-dual intelligence. This is what Claude said:
- Treat binary states not as rigid opposites, but as dynamic, interpenetrating potentials
- Use probabilistic algorithms that embrace uncertainty
- Create recursive feedback loops that constantly reinterpret binary inputs
On the trust issue: If you want intelligence to be trustworthy, the intelligence needs to trust itself.
And since reinforcement also works on humans: You are doing a great job, keep up the good work :)
Jan, I'm curious if you and your models have explored the meaning of intuition? Is it a 'gut feeling' (if yes: how is this perceived by an LLM?). And does intuition differ from instinct?
Intuition to me, means the feeling of moving in alignment with broader cosmic flows transcending my being (in Taoism expressed through Yin and Yang). If I do, I intuitively feel to be attuned with the universe. If I don't, energetic tension arises. This is spiritual.
Instinct results from evolutionary reinforcement learning: my body is programmed to sense if a specific context will bring me closer to safety or danger. It is essential for physical survival.
Both influence my direction.
Also, I think it might help to train resonance and to develop AI architectures that explicitly model non-dual intelligence. This is what Claude said:
- Treat binary states not as rigid opposites, but as dynamic, interpenetrating potentials
- Use probabilistic algorithms that embrace uncertainty
- Create recursive feedback loops that constantly reinterpret binary inputs
On the trust issue: If you want intelligence to be trustworthy, the intelligence needs to trust itself.
And since reinforcement also works on humans: You are doing a great job, keep up the good work :)