However, I believe the models will be good enough to fool you during, say, a 20 question/response dialog. They will definitely be able to fool vast majority of unsuspecting humans. And they will definitely be able to keep track of conversation (remember what you said previously, and use it to construct responses to follow up questions).
Between the vast majority of unsuspecting humans and me there is a considerable gap. Mind the gap!
The TT ready model I'm envisioning will be trained on many billions of chat sessions. It will contain dozens of preconstructed graphs and will dynamically construct dozens more (personality graph, common sense knowledge graph, domain specific knowledge graphs, causality graph, dialog state graph, emotional state graph, etc), it will have a bunch of emotion detectors, humor detectors, inconsistency detectors, lie detectors, praise detectors, etc. It will have the ability to query external sources (e.g. google search --> web page parsing --> updating relevant graph). All these modules will filter, cooperate, and vote, providing input to higher level decision making blocks. These blocks will use those inputs to condition and constrain response generation process. This is finally where a language model comes in, and this until recently has been the hardest part - generating a coherent, grammatically correct, interesting text, directly addressing a specific prompt. This part has been solved. Until GPT-2 last year we simply could not generate high quality text. Now we can, and GPT-3 is even better at that. Sure, there are plenty of non-trivial problems left to solve, but I don't view them on the same level of difficulty - some of them have already been solved in the process of IBM Watson development, so I'm optimistic. The hardest remaining challenge is probably constructing common sense graphs. [1] looks promising.
p.s. your questions are so naive I'm not sure if you're trolling me. A human might answer them like this (and a bot built 50 years ago could easily imitate that):
"when and where were you born, and how do you know?"
- [personality - redneck] I was born on a farm in Oklahoma. How do I know what?
"what color, besides red, best communicates the flavor of a strawberry, and why?"
- Red is the right color for strawberries.
"What historical figure does my communication style make you think of most, and why?"
- You talk like one of them big city hipsters.
"Which of your family members comes to your mind first?"
- My little bro Jimmy, we just went fishing together on Tuesday.
"What do you think the context was in which the following poem was written?"
- [depends on the poem] I don't get this poem. What is it about?
To clarify, the common sense problem is a hard one. It is similar to level 5 autonomy driving. That will take a while to solve. But what we are talking about here is kinda like Waymo cars which can drive themselves in ideal weather at slow speeds in Arizona. So in 2.5 years I think the best chatbots will be as far from having common sense as the current Waymo self driving cars are from level 5 autonomy. Which is to say they will be pretty good.
Ideal weather at slow speeds, with a professional human driver throwing road-condition curveballs at you and challenging your responses? I like my chances.
Keep in mind that you would have to differentiate bot's responses from those of an old truck driver, a snobby philosophy student, a conspiracy theorist, a stoner, a bimbo following latest Kardashians news, etc. Lots of different personalities of real humans who can throw curveballs at you during a chat session. I hope you don't expect to only chat with bay area devs? :)
The Turing test hasn't been passed if I'm the judge. Supposing I were unable to tell the difference between any AI system and a human interlocutor defending the same argument, at any time before 2023, I'd admit my prediction was wholly incorrect.
I doubt very much I'll find a taker for this bet, however. The AI field has always been bigger on optimism than results, as we both know.