[ypcx]

And then, GPT-3 came along and rendered Cyc a wasted effort.

[CodeGlitch]

> And then, GPT-3 came along and rendered Cyc a wasted effort.

I'm yet to see GPT-3 do anything commercially important? Cyc on the other hand seems to have been used in a number of sectors. Not to downplay GPT-3 - it's cool tech that produces cool demos - Cyc just seems more like a tool rather than a toy.

[montenegrohugo]

GPT-3 has been used in a ton of commercially important applications.

To name a few:

- GitHub CoPilot (transformative imo)

- Markcopy.ai, jenni.ai, etc.... Tons of content generation and SEO tools startups

- AI Dungeon and such

- Plenty of chatbots

- It's super useful for all kinds of classification tasks too (as are all transformer models)

[CodeGlitch]

Thanks for the response, I'm not familiar with Markcopy.ai, jenni.ai, but the others are "toys" if we're being honest (although as I said - very cool toys). You wouldn't want to use GPT-3 to recommend drugs for a condition you feed it...would you? As far as I understand, this is the kind of problem Cyc is trying to solve with it's domain-specific rules.

edit: Cyc can also tell you why it gave the response it did - something that deep nets cannot do. This is important in many fields, otherwise you cannot trust any response it produces.

[naresh_xai]

Current set of tools in explainable AI do allow deep neural nets to tell why they gave the response that they did to a certain extent.

Contact me if you want to see it applied to text based DL models, time series based DL models or Image based DL models.

[goatlover]

Cyc was trying to encode common knowledge about the world in a bunch of rules. That goes well beyond what GPT-3 does with text.

[canjobear]

I bet you could use GPT-3 to extend the list of rules.

[ypcx]

GPT-3 learns these rules by itself.

[Planktonne]

No it doesn't. It generates text that looks like it has learnt common sense rules, which is both subtly and importantly different.

[ypcx]

How would you define "common sense rules"?

[Planktonne]

This Wikipedia article is a good start if you're curious: https://en.wikipedia.org/wiki/Commonsense_knowledge_(artific...

[ypcx]

Amazing how our own brain can delude us by creating a feeling that we understand something because (apparently) we have produced a clear definition of it.

Just a hint - each of these "facts" or "rules" mean slightly something else depending on the context they are used in. This context is not hard-encodable because it slightly mutates with every new information learned or forgotten.

[johnthescott]

can you automate your fate with gpt-3? that is where the money lives.

will sublime conclusions made by GPT-3 EVER be trusted if the reasoning to its conclusion is not understood by a human? perhaps the gestalt of gpt3 implies a meta gpt3 that could derive human grokkable explains of its "dumber" self. or maybe not.

[naresh_xai]

Current set of tools in explainable AI do allow deep neural nets to tell why they gave the response that they did to a certain extent. Contact me if you want to see it applied to text based DL models, time series based DL models or Image based DL models.