Given the current rate of progress, we will have robots that can learn simple manual labor from human demonstrations (e.g. Youtube as a dataset, no I do not mean bimanual teleoperation) by the end of the decade.
When I show a lot of people GPT4 or Claude, some percentage of them jump right to "What year did Nixon get elected?" or "How tall is Barack Obama?" and then kind of shrug with a "Yeah, Siri could do that ten years ago" take.
Beyond that you have people who prompt things like "Make a stock market program that has tabs for stocks, and shows prices" or "How do you make web cookies". Prompts that even a human would struggle greatly with.
For the record, I use GPT4 and Claude, and both have dramatically boosted my output at work. They are powerful tools, you just have to get used to massaging good output from them.
This is the "You're not using it right" defense.
It's an LLM, it's supposed to understand human language queries. I shouldn't have to speak LLM to speak to an LLM.
Here’s a real scenario: A Citrix virtual desktop crashed because a recent critical security fix forced an upgrade of a shared DLL. The output is a really specific set of errors in a stack trace. I watched with my own two eyes an IT professional typed the following phrase into Google: “Why did my PC crash?”
Then he sat there and started reading through each result… including blog posts by random kids complaining about Windows XP.
I wish I could say this kind of thing is an isolated incident.
And even if it was, there’s plenty of people completely unintelligible in English too…
What type of usages are you testing? For general knowledge it hallucinates way less often, and for reasoning and coding and modifying its past code based on English instructions it is way, way better than GPT-3 in my experience.
Also, the difference is so big and so plainly visible that I guess people don’t know how to even answer someone saying they don’t see it. That’s why you get crickets.