Archive for the ‘Human Cognition’ Category

Give me human editors and the New York Times

Friday, November 30th, 2012

Techmeme founder: Give me human editors and the New York Times by Jeff John Roberts.

From the post:

At the event in New York, which was hosted by media company Outbrain, Rivera explained to Business Insider’s Steve Kovach why algorithms will never be able to curate as effectively as humans.

“A lot of people who think they can go all the way with the automated approach fail to realize a news story has become obsolete,” said Rivera, explaining that an article can be quickly superseded even if it receives a million links or tweets.

This is why Rivera now relies on human editors to shepherd the headlines that bubble up and swat down the inappropriate ones. He argues any serious tech or political news provider will always have to do the same.

Rivera is also not enthused about social-based news platforms — sites like LinkedIn Today or Flipboard that assemble news stories based on what your friends are sharing on social media. Asked if Techmeme will offer a social-based news feed, Rivera said don’t count on it.

“People like to go to the New York Times and look at what’s on the front page because they have a lot of trust in what editors decide and they know other people read it. We want to do the same thing,” he said. “There’s value in being divorced from your friends … I’d rather see what’s on the front of the New York Times.”

Are you trapped in a social media echo chamber?

Escape with the New York Times.

I first saw this in a tweet by Peter Cooper.

Posted in Curation, Human Cognition | 1 Comment »

Humans Plus Computers Equals Better Crowdsourcing

Saturday, November 12th, 2011

Humans Plus Computers Equals Better Crowdsourcing by Karen Weise.

Business Week isn’t a place I frequent for technology news. This article may change my attitude about it. Not its editorial policy but its technical content, at least sometimes.

From the article.

Greek-born computer scientist Panagiotis Ipeirotis is developing technology that gets computers to help people work smarter, and vice versa

If computer scientist Panagiotis Ipeirotis were to write a profile of himself, he’d start by hiring people online to summarize the key concepts in his published papers. Then he’d write a program to download every word in his 187 blog entries and examine which posts visitors to the site read most. Ipeirotis, an associate professor at New York University’s Stern School of Business, would do all that because his research shows that pairing computer and human intelligence can unearth discoveries neither can find alone. Ipeirotis, 35, is an expert on crowdsourcing, a way to break down big projects into small tasks that many people perform online. He tries to find ways, as he puts it, of using computer databases to augment human inputs.

Ipeirotis describes a recent real-world success with Magnum Photos. The renowned photo agency had hundreds of thousands of images scanned into its digital archive that it couldn’t search because they weren’t tagged with keywords. So Magnum hired Tagasauris, a startup Ipeirotis co-founded, to begin annotating. As Tagasauris’s online workers typed in tags, its analytical software queried databases to make the descriptions more specific. For example, when workers tagged a photo with the word “chicken,” the software tried to clarify whether the worker meant the feathery animal, the raw meat, or the death-defying game.

I really like the line:

He tries to find ways, as he puts it, of using computer databases to augment human inputs.

Rather than either humans or computers trying to do any task along, divide it up so that each is doing stuff it does well. For example, if photos are sorted down to a few possible matches, why not ask a human? Or if you have thousands of records to roughly sort, why not ask a computer?

Augmenting human inputs is something topic maps do well. They provide access to content that may have been input differently than at present. They can also enhance human knowledge of the data structures that hold information, augmenting our knowledge there as well.

Posted in Crowd Sourcing, Human Cognition | No Comments »

Categorial Compositionality:….

Wednesday, September 7th, 2011

Categorial Compositionality: A Category Theory Explanation for the Systematicity of Human Cognition

Abstract:

Classical and Connectionist theories of cognitive architecture seek to explain systematicity (i.e., the property of human cognition whereby cognitive capacity comes in groups of related behaviours) as a consequence of syntactically and functionally compositional representations, respectively. However, both theories depend on ad hoc assumptions to exclude specific instances of these forms of compositionality (e.g. grammars, networks) that do not account for systematicity. By analogy with the Ptolemaic (i.e. geocentric) theory of planetary motion, although either theory can be made to be consistent with the data, both nonetheless fail to fully explain it. Category theory, a branch of mathematics, provides an alternative explanation based on the formal concept of adjunction, which relates a pair of structure-preserving maps, called functors. A functor generalizes the notion of a map between representational states to include a map between state transformations (or processes). In a formal sense, systematicity is a necessary consequence of a higher-order theory of cognitive architecture, in contrast to the first-order theories derived from Classicism or Connectionism. Category theory offers a re-conceptualization for cognitive science, analogous to the one that Copernicus provided for astronomy, where representational states are no longer the center of the cognitive universe—replaced by the relationships between the maps that transform them.

Categorial Compositionality II: Universal Constructions and a General Theory of (Quasi-)Systematicity in Human Cognition

Abstract:

A complete theory of cognitive architecture (i.e., the basic processes and modes of composition that together constitute cognitive behaviour) must explain the systematicity property—why our cognitive capacities are organized into particular groups of capacities, rather than some other, arbitrary collection. The classical account supposes: (1) syntactically compositional representations; and (2) processes that are sensitive to—compatible with—their structure. Classical compositionality, however, does not explain why these two components must be compatible; they are only compatible by the ad hoc assumption (convention) of employing the same mode of (concatenative) compositionality (e.g., prefix/postfix, where a relation symbol is always prepended/appended to the symbols for the related entities). Architectures employing mixed modes do not support systematicity. Recently, we proposed an alternative explanation without ad hoc assumptions, using category theory. Here, we extend our explanation to domains that are quasi-systematic (e.g., aspects of most languages), where the domain includes some but not all possible combinations of constituents. The central category-theoretic construct is an adjunction involving pullbacks, where the primary focus is on the relationship between processes modelled as functors, rather than the representations. A functor is a structure-preserving map (or construction, for our purposes). An adjunction guarantees that the only pairings of functors are the systematic ones. Thus, (quasi-)systematicity is a necessary consequence of a categorial cognitive architecture whose basic processes are functors that participate in adjunctions.

“Copernican revolution” may be a bit strong but these are interesting articles.

I am more sympathetic to the discussion of the short-falls of first-order theories than I am to their replacement with other theories. Although theories can make for entertaining reading.

Posted in Category Theory, Human Cognition | No Comments »