Natural intelligence is inexplicable and often produces sentences that are incoherent.
Bob went to the store to get apples for his restaurant. He needed to cook food for an important dish. Bob came back home, and cut the apples using a ________
Most human readers would think of the word "knife". However, GPT-3 might fill in the blank with the word "machete" or "sword". While these words grammatically make sense, they don't make sense in the wider context of the sentence. Admittedly, my example is a bit contrived, but if you read through enough text, you can find this type of strange writing from GPT-3. That is what I meant by incoherent.
Also, by "explainability" I'm referring to the ability of engineers to understand why a model decided to choose a particular word or phrase versus another (in my apocryphal example, this would mean understanding why the model chose "sword" instead of "knife").
Seems to have nailed it.
Here's the result:
--- Bob went to the store to get apples for his restaurant. He needed to cook food for an important dish. Bob came back home, and cut the apples using a
knife. He needed to cut the apple into pieces, so he could use them to make some tasty food.
Bob cut the apple, and put it inside a pot. He filled the pot with water, and put it on the stove. The stove was hot and started to cook the apple. ---
I still think there’s a lack of explainability to the whole model though, and I struggle to understand how we could continue improving these models without understanding how they fundamentally make their decisions.
That being said, after reading some more output from GPT-3, it is more coherent than I remembered.
It's not a scam, but I think that it is severely lacking. Not only does the model have very little explainability in its choices, but it often produces sentences that are incoherent.
The biggest obstacle to GPT-3 from what I can tell is context. If there was a more sophisticated approach to encoding context in deep networks like GPT-3 then perhaps it would be less disappointing.