I spoke with an Amazon AI production engineer who’s talking with prospective clients about implementing AI in our business. When a colleague asked about using generative AI in customer facing chats the engineer said he knows of zero companies who don’t have a human in the loop. All the automatic replies are non-generative “old” tech. Gen AI is just not reliable enough for anyone to stake their reputation on it.
Years ago I was interested in agents that used "old AI" symbolic techniques backed up with classical machine learning. I kept getting hired though by people who were working on pre-transformer neural nets for texts.
Something I knew all along was that you build the system that lets you do it with the human in the loop, collect evaluation and training data [1] and then build a system which can do some of the work and possibly improve the quality of the rest of it.
[1] in that order because for any 'subjective' task you will need to evaluate the symbolic system even if you don't need to train it -- if you need to train the system, on the other hand, you'll still need to eval
Air Canada did this a bit ago, and their AI gave the customer a fake process for submitting claims for some sort of discount on airfare due to bereavement (the flight was for a funeral). The customer sued and Air Canada's defense was that he shouldn't have trusted the Air Canada AI chatbot. Air Canada lost.
Weird deflection. GPT 3, a 175 billion parameter model, came out in 2020, so it very much is not before LLMs. I guess you can say the story happened before ChatGPT by a few weeks. As for putting lost in quotation marks, I have no idea why you think the quantity of money is relevant to the outcome. No one was expecting them to file bankruptcy over this, only to follow the original agreement.
Of course it can but I think the issue is that people may try to jailbreak it or do something funny to get a weird response, then post of x.com against the company. There must be techniques to turn LLMs into a FAQ forwarding bot, but then what's the point of having a LLM
Gen AI can only support people. In our case it scans incoming mails for patterns of order or article numbers, if the customers is already know, etc.
That isn't reliable either, but it supports the person who gets the mail on his desk in the end.
We sometimes get handwritten service protocols and the model we are using is very proficient in reading handwritten notes which you would have difficulties to parse yourself.
It works most of the time, but not often enough that AI could give autogenerated answers.
For service quality reasons we don't want to impose any chatbot or AI on a customer.
Also data protection issues arise if you use most AI services today, so parsing customer contact info is a problem as well. We also rely on service partners to tell the truth about not using any data...
