4 ms·

I'm not so sure their technology is as futuristic as everyone thinks it is. If I had to take an educated guess, I would say it's some powerful AI that makes the

I'm not so sure their technology is as futuristic as everyone thinks it is. If I had to take an educated guess, I would say it's some powerful AI that makes their knowledge graph smarter. Currently Google's Knowledge Graph uses more structured data sets and depends on a mechanism like this: http://www.zachvanness.com/nanobird_relevancy_engine.pdf http://www.zachvanness.com/nanobird_relevancy_engine.pdf But the real challenge is to make the knowledge graph update in real time and take meaning from something as unstructured as a blog post or an email. And to do something like that requires some really unique AI. --mjn - I totally agree!

mjn 13y ago
Oh, I'm decidedly agnostic on whether it really is futuristic AI. Just there is some buzz around it, and some of the people involved in it are definitely legit, and have also involved themselves in the "AGI" community, which leads to such speculation (which they've pretty deliberately cultivated). That doesn't prove they've Solved AI for any sci-fi-ish notion of Solved AI. They could just have some good but in the big picture fairly modest knowledge-graph tech, or they could even have not-that-good knowledge-graph tech with great PR! Hard to say without knowing any details.
nl 13y ago
Deep learning is pretty impressive as a supplement to knowledge engineering approaches. Google's Deep Learning team were the people who developed the alogithm that discovered cats on YouTube (without training). Presumably this team had something that impressed them. The weakness to knowledge engineering approaches is that they tend to be fragile - they break badly with small holes in recorded knowledge. The IBM Watson team has a great video that showed how the different definitions of "fluid" and "liquid" meant a correct answer would have been missed if evidence collected in the answer verification phase of the DeepQA pipeline (no relation to Deep Learning) hadn't overridden it. Edit: Your(?) paper on your (?) relevancy engine is interesting. It seems like an application of skip-grams (which, ironically enough are heavily used by the DeepQA answer verification phase mentioned above).

mjn 13y ago

Oh, I'm decidedly agnostic on whether it really is futuristic AI. Just there is some buzz around it, and some of the people involved in it are definitely legit, and have also involved themselves in the "AGI" community, which leads to such speculation (which they've pretty deliberately cultivated). That doesn't prove they've Solved AI for any sci-fi-ish notion of Solved AI. They could just have some good but in the big picture fairly modest knowledge-graph tech, or they could even have not-that-good knowledge-graph tech with great PR! Hard to say without knowing any details.

nl 13y ago

Deep learning is pretty impressive as a supplement to knowledge engineering approaches. Google's Deep Learning team were the people who developed the alogithm that discovered cats on YouTube (without training). Presumably this team had something that impressed them. The weakness to knowledge engineering approaches is that they tend to be fragile - they break badly with small holes in recorded knowledge. The IBM Watson team has a great video that showed how the different definitions of "fluid" and "liquid" meant a correct answer would have been missed if evidence collected in the answer verification phase of the DeepQA pipeline (no relation to Deep Learning) hadn't overridden it. Edit: Your(?) paper on your (?) relevancy engine is interesting. It seems like an application of skip-grams (which, ironically enough are heavily used by the DeepQA answer verification phase mentioned above).