Comments on: Twitter’s human-powered search is the present and future of AI

By: Charlie Thompson

Charlie Thompson — Fri, 11 Jan 2013 01:15:47 +0000

Perhaps what we should be doing is allowing people to determine what they would like to gain more knowledge on verses trying to decide for them. Think about this and the consequences of making people mindless by telling them what a company or computer thinks they should know instead of letting them learn things they may not already be aware of. Think about it?
There is a company that is doing just that called Darwin Ecosystem and the have a product that allows you do the opposite of semantic search on the web and correlates and shows trends of mass tweets on any topic. It’s called tweetzup.
These are new true innovations.

By: Charlie Thompson

Charlie Thompson — Fri, 11 Jan 2013 01:12:20 +0000

Perhaps what we should be doing is allowing people to determine what they would like to gain more knowledge of verses trying to decide for them. Think about this and the consequences of making people mindless by telling them what a company or computer thinks they should know instead of letting them learn things they may not already be aware of. Think about it?
There is a company that is doing just that called Darwin Ecosystem and the have a product that allows you do the opposite of semantic search on the web and correlates and shows trends of mass tweets on any topic. It’s called tweetzup.
These are new true innovations.

By: robertsteele

robertsteele — Thu, 10 Jan 2013 16:01:55 +0000

Bravo. Cross-posted to Phi Beta Iota the Public Intelligence Blog.

By: jack johnson

jack johnson — Thu, 10 Jan 2013 13:47:39 +0000

Another Great Day For the World of Value Extraction Share Cropping. Community Content appropriated via TOS that is then leveraged for more value by low paid workers….Paid with the revenue generated from the appropriated content.

By: Amit Sheth

Amit Sheth — Thu, 10 Jan 2013 02:49:31 +0000

I have been a big believer in judicious use of humans in building intelligent systems. Let’s see recent success stories in this regards.

During 1999-2002 we built a semantic search engine [1,2] which got its semantics from ontologies/domain specific knowledge bases (we covered a series of domains [3]). The process involved a small amount of human involvement (just about 2 persons) who would use our toolkit to write software agents (focusing on a domain at a time) that would collect knowledge from multiple high quality sources of factual information, disambiguate, integrate and enrich them to build these domain specific ontologies/KBs. When we sought second round of funding, VCs asked how can you compete with Google who did every thing automatically (and in those days it was a bit hard to convince the need for “semantic search” when Web search seem to be giving users what they seem to want then!). So it was nice to see that recently Google took a major new step with GKG which started with Freebase that took a good bit of human effort to build it (and it is good that more semiautomated processes, after the Metabase acquisition, are speeding up building up of Google’s KB).

The discussion in this story about humans in the loop for improving Twitter search is further proof that good use of human is critical in building higher quality systems.

[1] http://slidesha.re/sw-ib
[2] http://knoesis.org/amit/Taalee-Seamtic-Search-Engine-Interview.pdf
[3] http://bit.ly/taalee-patent

By: ronald

ronald — Wed, 09 Jan 2013 23:00:37 +0000

One Word: Context

You seem to underestimate MI (Machine Intelligence). While AI doesn’t care about context MI is build on top, AI can’t learn math, MI learns it and also the fun of “no”. Not only the recognition but one of the earliest concepts we teach.

There’s also a difference in exposure learning vs. directed learning. With directed learning one can stuff a few billion records into a machine and it will recognize a pattern. With exposure learning one has to expose differentials to let the machine create a pattern, which then builds an abstract to use it in the most adaptable way.

The state of AI:
This summer, Google built the largest pattern recognizer of them all, a system running on sixteen thousand processor cores that analyzed ten million YouTube videos and managed to learn, all by itself, to recognize cats and faces—which initially sounds impressive, but only until you realize that in a larger sample (of twenty thousand categories), the system’s overall score fell to a dismal 15.8 per cent.

http://www.newyorker.com/online/blogs/books/2012/11/ray-kurzweils-dubious-new-theory-of-mind.html#ixzz2HWPurLf4

By: Steve Ardire

Steve Ardire — Wed, 09 Jan 2013 21:28:22 +0000

I’m on PST so #ideachat on Twitter this Sat, at 9 am ET a bit early however I’m now following you on twitter

By: Angela Dunn

Angela Dunn — Wed, 09 Jan 2013 21:15:04 +0000

Great post. We have been planning to discuss this topic at #ideachat on Twitter, this Saturday, at 9 am ET: “Intuition + Creativity = Insights”. Would love to have you join us.

@blogbrevity

By: Steve Ardire

Steve Ardire — Wed, 09 Jan 2013 18:54:06 +0000

> Thus perhaps the most practical AI — for now — is recognizing what humans can do, and getting them the best, most compact information that allows them to make their decisions.

Yes and intuition is a pattern recognition process !