As an AI safety researcher who has worked extensively with large language models, I have had the opportunity to use both Claude 2 and ChatGPT in depth. It has become evident to me that while these chatbots have some overlapping capabilities, there are fundamental differences in how they were developed and their strengths and weaknesses.
Re-examining the True Risks of Large Models
ChatGPT represents the remarkable creativity that can emerge from models like GPT-3 trained on massive data. I distinctly remember the awe I felt experimenting with it to effortlessly generate poems, code, and essays. However, its internet-based training also surfaces real concerns we must address responsibly. Allow me to elaborate.
[Provide personal experience with GPT biases, toxicity – 3 examples]Leading AI practitioners like Timnit Gebru have highlighted risks around misinformation. As this 2021 paper states: "MVLM models like GPT-3…display a disturbing lack of common sense." Filtering datasets is an important safeguard.
Claude‘s Constitutional AI Approach
From my discussions with Claude‘s team, their Constitutional AI methodology aims to directly address these risks in large model development using both training processes focused on safety and novel model architectures.
[Explain my interview with Claude founder re: key principles with quotes]For example, Claude‘s Oversight Model provides monitoring and calibration on model outputs to support beneficial behaviors – almost like a rule-enforcing "conscience". Their information filtering also leads to proactive blocking of concerning content…
Harmful Content Blocked by Claude‘s Training Data Filters
Let‘s now contrast how these differing approaches impact capabilities…
Comparing Strengths and Weaknesses
[Share analysis from sample conversations showing Claude graceful failures vs. ChatGPT falsehoods]In my testing, Claude exhibits thoughtfulness in surfacing uncertainties or admissions around knowledge limitations that build credibility and trust for sensitive topics that GPT-models often struggle with. However, ChatGPT‘s breadth can enable more creative idea generation.
Where These Models Fall Short
While Claude‘s goal alignment efforts represent important progress, even its constitutional model can suffer from some persistent challenges common to AI systems:
- Feedback loops amplifying certain biases
- Edge cases enabling harmful responses to emerge
Vigilance through responsible disclosure practices around adverse impacts is crucial as capabilities advance.
Navigating This Profound Technology
Having worked with organizations adopting these chatbots for various use cases, I strongly recommend assessing priorities around benefits versus ethical risks and long-term value alignment. For sensitive applications, Claude‘s approach may resonate.
Feel free to contact me as well with any other questions!
