ideas

[Abidouz]

Here are some ideas and potential areas of research for Tensort:

• Model analysis and interpretability: Develop new techniques for analyzing and understanding what large language models have learned from their training. This could help ensure they behave as intended.

• Constitutional AI techniques: Apply concepts from Stability AI's Constitutional AI research like value specification languages, capability restrictions, and alignment incentives to improve Tensort's safety.

• Interactive debriefing tool: Create an interactive interface that allows users to have detailed conversational debriefing sessions with Tensort to understand its capabilities and limitations.

• Self-supervised pretraining methods: Research how to utilize self-supervised pretraining approaches like contrastive learning to implicitly teach helpfulness, harmlessness and honesty without labeled data.

• Adversarial robustness: Make Tensort more robust to adversarial examples, ambiguous queries, and attempts to deviate it from its intended purpose.

• Helpfulness evaluation: Develop better automatic and human-based evaluation methods to quantitatively measure how helpful Tensort is across different contexts and demographics.

• Stakeholder values: Conduct values research to better understand the priorities and concerns of different potential stakeholder groups interacting with an AI system like Tensort.

• Lifelong learning: Research how to continuously update Tensort's knowledge and abilities over its lifetime through reinforced self-supervised learning from interactions.

• Explainability of reasoning: Improve Tensort's ability to transparently explain to users the reasoning behind its responses and recommendations.

[lix19937]

Tensorrt is just a infer engine, according to your ideas, tensorrt should have an embedded llm to drive itself.

[adamconrad7]

This looks like an LLM generation