A Neural Conversational Model

Read this paper from July of last year and it'll make more sense as to why this research is interesting: http://arxiv.org/pdf/1506.05869.pdf. Seeding a dataset with human labels, then generating new, better data is pretty cool. Similar to deep mind watching go games, then learning to play better than said games. Add to which, humans can't tell the difference between the human generated data and algo generated data. We're about 3 months from where you can bootstrap a system like microsofts with freely available code and data. From there, if "memory modules" start working better so models can "remember specific context" (it can already remember general context), you'll have a bot that's a pretty good model for passing the Turing test. I'm defining a practical Turing test as text-based, human gets 5 questions, other party replies 5 times, then human must predict: human/not human.

My personal "oh shit" moment was in 2015, when this paper came out: https://arxiv.org/abs/1506.05869 It showed me that a model trained only on movie subtitles data exhibited some (very primitive) reasoning. I have been working on Deep Learning and later LLMs ever since.

In 2015 Google claimed to have created a chat bot that can do common-sense reasoning and do tech support just by reading a sufficiently large dataset: https://arxiv.org/pdf/1506.05869v1.pdf

Exactly, because it has a shot. Writing that it doesn't have a chance is just effect of lack of knowledge in ML field. You can see some work that was already done in this research paper, before AlphaGo success. http://arxiv.org/pdf/1506.05869v1.pdf

It seems like an old way to solve the problem. I would be quite surprised if it gave good results compared to recent advances such as the LSTM-based paper “A Neural Conversational Model” [0]. In fact, I doubt it is even competitive compared to Cleverbot. It does try to compare itself with existing alternatives, but it does not characterize the pros and cons of each alternative and does not compare results. (I am not an expert, but did take classes on the subject of pattern recognition and machine learning.) [0]: http://arxiv.org/pdf/1506.05869v1.pdf

> If this is the state of the art in machine translation, how on Earth can you expect a machine that can converse on human level in three years? Here are 2 good papers--one from last year, one very recent--that might change your mind. https://arxiv.org/pdf/1506.05869.pdf https://arxiv.org/pdf/1605.06065v1.pdf Over the past 10 years (deep learning got going in ~2006, really), the state of the art has improved at an exponential rate, and that's not slowing down. There are plenty of reasons to be bullish. Or at least, now seems like a bad time to leave the field.

Simulating bad typing is only necessary to fake a human. Here, having bad typing when faking an AI is stranger. That said, AIs trained through the chat transcripts of a large number of conversations may produce mistakes. I remember reading a paper that gave good results that way, with the side-effect that it produces typing mistakes as a result. I cannot find that paper again, unfortunately. Edit: found it! http://arxiv.org/pdf/1506.05869v1.pdf