Actually Useful AI

Using AI to get constructive criticism and avoid cognitive biases.

Microsoft’s new chatbot goes crazy after a journalist uses psychology to manipulate it. The article contains the full transcript and nothing else. It’s a fascinating read.

Is it real engineering? Is it just dumb hype? How to do it if you want to do it well.

@goodside:

Idea: Using logit bias to adversarially suppress GPT-4's preferred answers for directed exploration of its hallucinations.

Here, I ask: "Who are you?" but I suppress "AI language model", "OpenAI", etc.

This reliably elicits narratives about being made by Google:

(see screenshot in tweet, he also posted the code)

An interesting and clever proposal to fix the prompt injection vulnerability.

• The author proposes a dual Large Language Model (LLM) system, consisting of a Privileged LLM and a Quarantined LLM.
• The Privileged LLM is the core of the AI assistant. It accepts input from trusted sources, primarily the user, and acts on that input in various ways. It has access to tools and can perform potentially destructive state-changing operations.
• The Quarantined LLM is used any time untrusted content needs to be worked with. It does not have access to tools and is expected to have the potential to go rogue at any moment.
• The Privileged LLM and Quarantined LLM should never directly interact. Unfiltered content output by the Quarantined LLM should never be forwarded to the Privileged LLM.
• The system also includes a Controller, which is regular software, not a language model. It handles interactions with users, triggers the LLMs, and executes actions on behalf of the Privileged LLM.
• The Controller stores variables and passes them to and from the Quarantined LLM, while ensuring their content is never provided to the Privileged LLM.
• The Privileged LLM only ever sees variable names and is never exposed to either the untrusted content from the email or the tainted summary that came back from the Quarantined LLM.
• The system should be cautious with chaining, where the output of one LLM prompt is piped into another. This is a dangerous vector for prompt injection.

A nice, detailed and useful guide you can send to your friends who want to try this new AI thing.

Guy trains an LLM on his group chat messages with his best friends with predictable but nevertheless very funny results.

OpenAI announced these API updates 3 days ago:

• new function calling capability in the Chat Completions API
• updated and more steerable versions of gpt-4 and gpt-3.5-turbo
• new 16k context version of gpt-3.5-turbo (vs the standard 4k version)
• 75% cost reduction on our state-of-the-art embeddings model
• 25% cost reduction on input tokens for gpt-3.5-turbo
• announcing the deprecation timeline for the gpt-3.5-turbo-0301 and gpt-4-0314 models

A deep dive into the inner workings of ChatGPT, and why it stops responding or replies weird or creepy things to seemingly simple requests.

Prompt injection is a serious and currently unresolved security vulnerability in tool-using LLM systems. This article convinced me that this is indeed a serious issue that needs to be addressed before letting an LLM loose on your emails, calendar or file system.

An excellent video series by Andrej Karpathy (founding member of OpenAI, then head of AI at Tesla). He teaches how GPTs work from the ground up, using Python. I learned a lot from this course.