I need to remember their usage model:
""Anyone grieving for a loved one will soon be able to talk to them using artificial intelligence technology pioneered by a woman who lost her best friend.
"Eugenia Kuyda, co-founder of Luka, a US technology company, has used the latest in AI systems to create a virtual version of Roman Mazurenko, who was killed in a road accident last year, days before his 33rd birthday.
"She fed more than 8,000 lines of text messages sent by Mr Mazurenko into a Google programme that allows people to create their own “chatbots” — computer programmes designed to simulate conversation with human users.
"The chatbot responds to questions in language that mimics Mr Mazurenko’s speech patterns so that she and other friends can “talk” to him as part of the grieving process.
"Ms Kuyda, 29, said that any doubts she had about the technology were allayed when the bot responded to her first questions in a tone that sounded like her friend."
The point of all this is that someone else, interacting with my chatbot-Replika, will think they're talking to "me". But I'm a different person depending on to whom I'm talking. Not everyone gets my flippant sense of humour so occasionally I have to act serious.
That's going to be a tough one.
Then I thought: it's unlikely that Replika (the company) will build a personal back-story, a contextual knowledge-base to power inference and make "my" replies more intelligent.
So why not do it myself?
I could simply copy all those Q&A texts into a .txt file and process them via a program like Eliza plus an automated theorem-prover (ATP).
The easiest way to do this would be in Prolog. The Eliza part isn't so hard: I already implemented a simple version using "The Art of Prolog" by Sterling and Shapiro. Writing an ATP in Prolog would also be interesting (and is also covered in the book).
The strategy is this. Start with the natural language input from those forty texts per day, specifically the questions being asked of me. We can't do inference on natural language so we have to translate it into a formal language with inference rules. The simplest is first-order predicate calculus: the Prolog clause variant would work.
We need the additional knowledge base which I would hand-craft (based on my own text responses) plus some rules to guide conversational inference - getting from an input to producing an appropriate output. All this could be coded as Prolog rules. It's similar to writing a meta-interpreter for Prolog in Prolog, covered in the book.
Finally I would translate the clausal form of the output into natural language as "my" response. A better response than any that a superficial Eliza-chatbot-like architecture could give.
You may think, like me, that this is already sounding like a big project.
So then I think. No-one but me could ever use this system. (Which kind of undermines the point).
It would have to be embedded in something like SWI-Prolog which would need to be launched first and would only work on my own machine. Getting it to work consistently and robustly across the Internet is most likely impossible.
... and so my mind wanders off, to something more immediately gratifying ... .
Sorry if this all sounds so GOFAI. I have as much chance of running TensorFlow at home as fly ...