@Grady_Booch Good point. Practically though do we need AI to produce a feeling mind or is it enough if we accelerate, augment and scale up all knowledge work?
Knowledge work (analysis, design, planning, decision making, etc) is exhausting and slow. Let’s make it fast and productive.
# automating software engineering
In my mind, automating software engineering will look similar to automating driving. E.g. in self-driving the progression of increasing autonomy and higher abstraction looks something like:
1. first the human performs all driving actions manually
2. then the AI helps keep the lane
3. then it slows for the car ahead
4. then it also does lane changes and takes forks
5. then it also stops at signs/lights and takes turns
6. eventually you take a feature complete solution and grind on the quality until you achieve full self-driving.
There is a progression of the AI doing more and the human doing less, but still providing oversight. In Software engineering, the progression is shaping up similar:
1. first the human writes the code manually
2. then GitHub Copilot autocompletes a few lines
3. then ChatGPT writes chunks of code
4. then you move to larger and larger code diffs (e.g. Cursor copilot++ style, nice demo here https://t.co/u8ueY0mGxZ)
5....
Devin is an impressive demo of what perhaps follows next: coordinating a number of tools that a developer needs to string together to write code: a Terminal, a Browser, a Code editor, etc., and human oversight that moves to increasingly higher level of abstraction.
There is a lot of work not just on the AI part but also the UI/UX part. How does a human provide oversight? What are they looking at? How do they nudge the AI down a different path? How do they debug what went wrong? It is very likely that we will have to change up the code editor, substantially.
In any case, software engineering is on track to change substantially. And it will look a lot more like supervising the automation, while pitching in high-level commands, ideas or progression strategies, in English.
Good luck to the team!
Next-step prediction is beautiful because it encourages, as a model gets extremely good, learning the underlying process that produced that data.
That is, if a model can predict what comes next super well, it must be close to having discovered the "underlying truth" of its data.
Current architecture of research agent enables learning through Google Search, @pinecone, multiple agent feedback, and incremental draft improvement.
Based on @yoheinakajima babyAGI
Why "centrist" is often used incorrectly:
Imagine that liberalism (in the classic sense: free speech, free markets, equality of opportunity, etc.) is a house. There are lots of people in that house, but let's simplify it to liberal progressives and liberal conservatives.
1/
This weekend I built =GPT3(), a way to run GPT-3 prompts in Google Sheets.
It's incredible how tasks that are hard or impossible to do w/ regular formulas become trivial.
For example: sanitize data, write thank you cards, summarize product reviews, categorize feedback...
The article had an example that stumped GPT-3 (I tried it). In the sentence:
She poured water from the pitcher to the cup until it was FULL.
We know “it” refers to the cup, but...
She poured water from the pitcher to the cup until it was EMPTY.
We know “it” refers to the pitcher.
I have a fix for the social dilemma. Facebook should require paid subscriptions for a post to be viewed more than 500 times including re-shares. To “go viral”, you have to pay $2 per 1000 views. Dampens vitriol. FB gets a new revenue stream. More effective than moderation.