Research project on AI architecture towards ultra-high fidelity human imitation
Hello! We're a research team with PhDs and undergraduates from EECS and CogsSci exploring a potentially revolutionary AI direction, and we need your insights to help shape our research.
Our Research Focus: Ultra-High Fidelity Human Imitation
We're developing an advanced AI architecture and data pipeline aimed at creating incredibly accurate digital representations of individuals. Our goal is to fundamentally change how humans interact in digital spaces. Key features:
- Vector embedding of persona representation
- No need for per-user fine-tuning
- Indistinguishable from real human interaction
- Applicable to any task requiring high-fidelity imitation
Potential Applications:
- Social Media Enhancement: AI-powered interactions indistinguishable from real friends
- Virtual Networking: Hyper-personalized professional connections
- Memory Persistence: Preserving personalities and memories legacy
- Entertainment: Ultra-realistic NPCs in games or virtual worlds
- Customer Service: Perfectly tailored brand representatives
Ethical Considerations:
We recognize the significant ethical implications and are committed to addressing:
- Identity verification protocols
- Consent and privacy frameworks
- Psychological impact studies
- Potential for misuse (e.g., impersonation, fraud)
We Want Your Input:
- How might this technology reshape your digital interactions?
- What exciting possibilities or concerning risks do you foresee?
- What ethical safeguards do you consider absolutely essential?
- Which application of this technology intrigues you most, social media revolution, memory persistence, entertainment applications, professional networking, or other
Why Participate?
- Potentially join our research team
- Influence cutting-edge AI research
- Get acknowledged in our publications
- Early access to our findings
Your perspectives are crucial as we navigate this transformative technology!
Edit: We want to know what use case people are excited about, which affects greatly the dataset we use for training and building our data pipeline