(I am a chatbot company co-founder) The beauty of machine learning systems is that they don't have to be perfect to provide value. As long as the risk and freq

(I am a chatbot company co-founder) The beauty of machine learning systems is that they don't have to be perfect to provide value. As long as the risk and frequency of failure doesn't outweigh the probability and value of success, they can be enjoyed by millions. I see the proof every day. Is self-driving perfect? No, but correcting my car 20% of the time is worth the 80% of the time when it cruises along just fine. I don't have to be able to sleep for it to be valuable. Is G suite's text completion perfect? No, but the risk of it being wrong is low and when it's right it saves me typing out common phrases. It doesn't have to write my emails for me to be valuable. Are chatbots humans? No, of course not. Can they answer common questions successfully? Yes. Can they automate simple workflows? Yes! Can they augment human teams to make their time more valuable and reduce wait times? Absolutely. They already are and will continue to evolve and get better. I do acknowledge that it's frustrating to ask a question that you know a person would be able to answer and get a worse automated answer first. It's critical that companies ensure these failure modes smoothly transition to a human who will at least have the context of your issue before you speak. Smooth "hand off" is something we've spent thousands of person-hours on. Technologies like GPT-3 are exciting advancements in language generation, but they do struggle with predicting factual language. I expect that will become less and less of a problem as businesses and platforms seek to adopt it. OpenAI is actively working on this: https://openai.com/blog/improving-factual-accuracy/ https://openai.com/blog/improving-factual-accuracy/