(Source: http://twitter.com/realsoftware/status/166112112484159489)

Mary Ann Williams slideshare
Totally Wired:
1. Findability - metadata
2. Memory - documentation
3. Search and retrival - consistency
Change Agent
Translator, Consensus maker, Task master, Social butterfly
5 helpful lessons

MUST invest in adequate metadata planning and implementation
1. we cannot accurately describe the benefits of using metadata in the platform with all the advantages that it offers.
2. users are unaware that they have been working with metadata ever since they started using a computer

Conditional random fields (CRFs) are a probabilistic framework for labeling and segmenting structured data, such as sequences, trees and lattices. The underlying idea is that of defining a conditional probability distribution over label sequences given a particular observation sequence, rather than a joint distribution over both label and observation sequences. The primary advantage of CRFs over hidden Markov models is their conditional nature, resulting in the relaxation of the independence assumptions required by HMMs in order to ensure tractable inference. Additionally, CRFs avoid the label bias problem, a weakness exhibited by maximum entropy Markov models (MEMMs) and other conditional Markov models based on directed graphical models. CRFs outperform both MEMMs and HMMs on a number of real-world tasks in many fields, including bioinformatics, computational linguistics and speech recognition.

Tool to share bibliographic metadata.

authors (or anyone?) can manually add back any data lost from their books in Goodreads

archive in the cloud

What's hard-to-parse about current-gen metadata, and how we can and can't make it better algorithmically. Not sure how I missed this one; it's fabulous.