Going on 5 years ago, I wrote part 1. Now, finally, it’s time for the rest of the story.
Deep natural language understanding (NLU) is different than deep learning, as is deep reasoning. Deep learning facilities deep NLP and will facilitate deeper reasoning, but it’s deep NLP for knowledge acquisition and question answering that seems most critical for general AI. If that’s the case, we might call such general AI, “natural intelligence”.
Deep learning on its own delivers only the most shallow reasoning and embarrasses itself due to its lack of “common sense” (or any knowledge at all, for that matter!). DARPA, the Allen Institute, and deep learning experts have come to their senses about the limits of deep learning with regard to general AI.
General artificial intelligence requires all of it: deep natural language understanding, deep learning, and deep reasoning. The deep aspects are critical but no more so than knowledge (including “common sense”). Continue reading “Natural Intelligence”
The title is in tribute to Raj Reddy‘s classic talk about how it’s hard to wreck a nice beach.
I came across interesting work on higher order and semantic dependency parsing today:
- Turning on the Turbo: Fast Third-Order Non-Projective Turbo Parsers.
- Priberam: A turbo semantic parser with second order features
So I gave the software a try for the sentence I discussed in this post. The results discussed below were somewhat disappointing but not unexpected. So I tried a well know parser with similar results (also shown below).
There is no surprise here. Both parsers are marvels of machine learning and natural language processing technology. It’s just that understanding is far beyond the ken of even the best NLP. This may be obvious to some, but many are surprised given all the hype about Google and Watson and artificial intelligence or “deep learning” recently.