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This actually worked really well for me for factual information, for example, I asked "When was the World Trade Center Built?" and it gave me the correct date.

This actually worked really well for me for factual information, for example, I asked "When was the World Trade Center Built?" and it gave me the correct date. For a lot of factual stuff, it worked well. There are some catches with word similarities, for example, "What is the lifespan of a turtle?" worked fine, but "How long can a turtle live?" and various re-phrasings result in very different answers, most of them correct within the context of the content they were scraped from, but some resulted in no answers at all. Separately, the system doesn't handle preposterous questions very well, like "Who won the World Cup in 2009?" it says Italy won it, but I think it was some other thing referred to as a World Cup. There are a lot of questions on Quora about personal experience like what it's like to have a family member die of cancer or what it was like to be in the WTC during the 9/11 attacks, and questions about these kinds of experience return nothing for the most part, despite being available via other resources (it's not all locked into Quora). Very great first go, I'm really interested in seeing how this progresses. Like you say, if you could scrape a lot more data, this would produce a very interesting spectrum of information. It'd probably be good to scrape some of the "How to" sites too, I asked "How does a gasoline engine work?" and the responses were... funny? :)

fchollet 12y ago
Interesting insight, thank you! I think crowd-based approaches and machine-based approaches will always have very different strengths and weaknesses. The quality of algorithmic answers will generally (always?) be well below that of human-written ones, especially at first. However, in the long term I can see machine-based approaches "winning", simply because of their near-infinite scalability (and the sort of insight that emerges from enormous scale). Take web search: if started out with hand-curation in the portal era, until that got wiped out by algos and scrapers that could better manage the growing scale of the web (and exploit patterns only visible at scale, like PageRank). Human knowledge, including the sum of personal experiences of the people who express themselves on the web, is now at a size where it is no longer human-manageable. I see enormous potential here to extract, understand and navigate that knowledge using algorithms.
- codezero 12y ago
  Yep, I agree on the whole. The problem that I've seen in a lot of cases is that the resources that collect knowledge and display it on the web don't necessarily incentivize the knowledgeable to contribute or reward them for doing so in the best possible way. What you end up with is a high signal to noise for quality, factual content, and for things where facts are not even expected (politics, etc...) you often end up with a polarity that doesn't acknowledge the other side exists. Beyond all that there is a huge amount of sarcasm, humor and nuance to content that will be hard for a machine to figure out on its own, but hopefully you can find a way to separate the wheat from the chaff :)

fchollet 12y ago

Interesting insight, thank you! I think crowd-based approaches and machine-based approaches will always have very different strengths and weaknesses. The quality of algorithmic answers will generally (always?) be well below that of human-written ones, especially at first. However, in the long term I can see machine-based approaches "winning", simply because of their near-infinite scalability (and the sort of insight that emerges from enormous scale). Take web search: if started out with hand-curation in the portal era, until that got wiped out by algos and scrapers that could better manage the growing scale of the web (and exploit patterns only visible at scale, like PageRank). Human knowledge, including the sum of personal experiences of the people who express themselves on the web, is now at a size where it is no longer human-manageable. I see enormous potential here to extract, understand and navigate that knowledge using algorithms.

codezero 12y ago
Yep, I agree on the whole. The problem that I've seen in a lot of cases is that the resources that collect knowledge and display it on the web don't necessarily incentivize the knowledgeable to contribute or reward them for doing so in the best possible way. What you end up with is a high signal to noise for quality, factual content, and for things where facts are not even expected (politics, etc...) you often end up with a polarity that doesn't acknowledge the other side exists. Beyond all that there is a huge amount of sarcasm, humor and nuance to content that will be hard for a machine to figure out on its own, but hopefully you can find a way to separate the wheat from the chaff :)