Personality Vectors (2019)

Even more, since for questions there are:

- questions one decided to answer (which says something on its own)

- answers

- declared answers they accept in their partners

- actual partners they pursue (judged by matches, or dates)

While I expect that mostly similarities attract (so-called associative mating), there are compatible traits (e.g one person loves to listen, one person loves to talk), and there is the level of lack of self-knowledge, or hypocrisy (what we SAY we like, vs what we actually do).

And then e.g. probability that person A likes person B can be expressed as:

sigmoid(actualPrefVecA * personalityVecB)

...and with gradient descent magic, we can turn people into vectors!

Lots of overlap among these, but rather than start from scratch, a bit of reading in almost any of them, perhaps starting with behavioral economics or social psych, might enhance the "vectors".

If we had a Chatbot that would interact with someone hypothetically we could have this kind of distinction between Personality Vectors to mutate its own behavior and to interact with others.

Its sort of the idea I had when I watched the movie Interstellar and TARS is told to decrease his own humor. So if the system would just know who its talking to it would create a preference vector for each person it interacts with.

But you would need some kind of baseline so basically the Personality Vectors would be set to neutral. And each interaction with a Personality vector would be a system that stores a preference vector for each person.

As the system time evolves (which is also in the article) you would basically end up with a set of graphs. There is a notion out there which is called a Dynamic Network analysis [1]. If you also stored the history of that you could then not just be able to rewind the system backwards to a previous checkpoint.

The way you could start learning how to learn a personality vector is a reinforcement learning situation. If you say "bad AI I didn't like that" the system would then look at what it said and mutate the preference vector for that person. Or you could just change the vector manually by a technique that was illustrated above by just saying "reduce humor by 70%".

Then you could have model which would do conditional generation [1] of text based upon interactions with others. It would be basically a reinforcement learning system paired with a generative text model. The reinforcement system would store the Personality and Preference vectors for each person. For each behavior and preference you would need a corpus to bootstrap the system. The reinforcement system would be a retrieval network and it would retrieve a pertained system based on user(s) input and get a generational text network to provide a response. This would be similar to Alpha Go's design. [3]

[1] https://en.wikipedia.org/wiki/Dynamic_network_analysis [2] https://github.com/salesforce/ctrl [3] https://datascience.stackexchange.com/questions/10932/differ...