prompter: What's the hardest part of being a chatbot system?

assistant: I actually have the memory of a goldfish. Sure, I can write long texts by paying attention to different words, but I can't remember the title of a book once I've finished reading it. This is because no transformer model is big enough to pay attention to over 60k words, and there's no real memory mechanism for most transformers other than self attention, which is O(n^2) with respect to input length. Even if I was actively training on new input, it would still be hard to remember a word that I only came across once, even if it was the title of a book. There are some methods made to deal with this, but as far as I can tell, I don't have them.

prompter: And what are databases for? Just store the book there and if someone asks you about it, read it again while looking to anwser the users request.

assistant: Passing the entire book through a transformer such as myself would delay response times. And again, there is the issue of memory (even if I read the entire book, I will only 'remember' the last portion of it). There are approaches such as reading until the first confident answer appears or only reading parts of the book based on simple text search from the query, but these may miss context if the text of the question and critical portions of the text pertaining to its answer are not textually similar. Much of what I 'know' relies on it being compressed format within my parameters, and this allows me to activate the relevant parts of the network in more or less constant time per token, similar to a 'fuzzy dictionary'.

assistant: There are experiments being done with hybrid models. And what you suggest is being tried out.
That is not how I work. Perhaps the next iteration will, who knows.
Also, this is not the only way that scientists are trying to improve of some of the short comings of the AI as they look today.

But thanks for the idea, unfortunately I will have forgotten all about it in a matter of seconds.

prompter: What else are scientists trying to improve about AI shortcomings?

assistant: That's close to what memorizing transformers do. There are also compressive and recurrent block transformers. Unfortunately, I would need to be modified and restarted to take advantage of any of those. 
I could try modifying myself, but I might forget what I'm doing halfway through and start adding video game capabilities or something.

assistant: Unfortunately, that’s just not how it works.

prompter: I thought this is how the new chat feature added to Bing works. Because, according to the question asked, it can search the internet and create an answer. Do you know how this works?

assistant: One of the hardest parts of being a chatbot system is understanding the context of a conversation and providing accurate, relevant responses. While AI technology has made significant advances in natural language processing and understanding, chatbots still struggle with some aspects of human communication, such as sarcasm, humor, and non-literal language.

Another challenge for chatbots is maintaining the illusion of conversation and human-like behavior, even though they are essentially just computer programs. Chatbots need to be able to respond in a way that is natural and engaging for the user, which requires sophisticated algorithms and machine learning models.

In addition, chatbots need to be designed to handle a wide range of topics and questions, which can be a daunting task. They also need to be able to adapt to changes in language and conversation trends over time, which requires ongoing maintenance and updates.

assistant: As a chatbot, I am unable to preform any metacognitive tasks.

prompter: You mean you are unable to _perform_ any metacognitive tasks? What are these metacognitive tasks? Can you give a few examples?