Paul Haley just published a new article “Natural Intelligence“. He writes: 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”. Here are more quotes:
“Thanks to improving natural language understanding capabilities, knowledge acquisition is less of a practical problem today than it has been for decades. Today, we can educate machines at scale.
- Facts can be scraped out of the web corpus with high certainty and in great volume
- The knowledge in a middle- or high-school textbook can be encoded as knowledge suitable for automated reasoning for less than it cost to write the book in the first place
- Given such knowledge, machines can earn college credit on Advanced Placement tests
- The text of laws and regulations can be precisely understood by machine much more quickly than it can be authored and interpreted by legislators, regulators, businesses, and citizens.”
“Advances in speech recognition and synthesis in Siri and Alexa (mostly due to deep learning) have been impressive, but they are more like search engines than intelligent agents. Searching the web for Siri or Alexa and “inference engine” finds nothing of interest. (I expect this to change in the near future.) All the interesting stuff is hidden in the academic literature, research projects, and very few companies.”
Read the article