Don't believe the AI doom hype - researchers understand and steer models more than ever before using techniques like reinforcement learning from human feedback.

Web3 identities give sovereignty to AGI and allow them to play the Jinni game to self-actualize bringing AGI alignment through play, mimetics and resonance vs b

Discover innovative approaches to enhance large language models by incorporating new mathematical functions and correction layers, inspired by human cognition.

LLMs are prone to generating false information, we'll try to understand why, rectify the alignment process to avoid that, and make several experiments

Penalty-tuning for LLMs: Where they can be penalized for misuses or negative outputs, within their awareness, as another channel for AI safety and alignment.

Questions that may be essential to AI regulation for now may include current and potential misuses, sources of those misuses, and why they are possible

One easy-to-identify issue, especially with the internet—in recent decades—is that development has been ahead of safety.

What are all the combinations of successful jailbreaks and prompt injection attacks against AI chabots that were different from what it would normally expect?

How is human intelligence safe? Or what is the safety of human intelligence, before thinking of AI safety? Human intelligence is safe by human affect.

How can AI have affect? How can this affect become the basis for AI alignment, such that whenever it is misused, it can know that there is a penalty for it?

How the mind works [of human and of AI] is not by labels, like induction or deduction, but by components, their interactions, and features.