With the increased focus within the discipline of eDiscovery on Technology-Assisted Review, three references are provided to help legal professionals establish a solid base of definitional and contextual information for considering machine learning.
Reference #1: Book: New Advances in Machine Learning. Chapter: Types of Machine Learning Algorithms. Author: Taiwo Oldipupo Ayodele (University of Portsmouth, United Kingdom).
Reference #2: Video: Lectures on Machine Learning. Lecturer: Andrew Nq (Director , Stanford Artificial Intelligence Lab, Stanford University).
Available via Video Series Link: https://class.coursera.org/ml/lecture/preview
Reference #3: Video Lectures on Machine Learning. Lecturer: Pedros Domingos (Professor of Computer Science & Engineering, University of Washington.
Available via Video Series Link: https://class.coursera.org/machlearning-001/lecture/preview
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Taken from a combination of public market sizing estimations as shared in leading electronic discovery reports, publications and posts over time, the following eDiscovery Market Size Mashup shares general worldwide market sizing considerations for both the software and service areas of the electronic discovery market for the years between 2012 and 2017.
The gathering and use of information to help achieve personal and professional objectives has been a task executed by individuals and organizations from the beginning of time. However, with the advent of tools and technologies that can greatly accelerate this gathering and use of information, it is increasingly important that one considers not only the positive things that can be accomplished from the greater understanding derived from increased information access, but also considers the potential dark side usage of this increased information access.
Just as there are many tasks in electronic discovery, many times there are multiple technologies and platforms involved in the complete electronic discovery process. When there are multiple technologies and platforms involved, data must be transferred from disparate technologies and/or platforms to other disparate technologies and/or platforms. This data transfer can be considered a risk factor that affects the overall electronic discovery process.
In today’s “sound-bite” environment in which professional organizations compete for client attention through a variety of conduits and communications, it is increasingly important for marketing and sales leaders to consider and coordinate the use of all communications and communications tools in order to maximize impact and influence on potential clients.
Beginning in early 2012 the topic of Technology-Assisted Review moved from expert-led explanations to mainstream mentions in legal community articles, opinions, surveys and reports. Provided for your research, review and consideration are a compilation of key headlines and links from online sources on the topic of Technology-Assisted Review from February, 2012, until now.
Updated: 9/16/2013 – Provided for your consideration and use are the in-progress results of the One-Question Provider Implementation Survey launched by ComplexDiscovery on 3/3/13. The results consist of survey answers harvested directly from the online survey form as completed by provider representatives.
Updated 7/23/2013: Provided for your consideration and use are the in-progress results of the Predictive Coding and Provider Survey launched by ComplexDiscovery on 2/10/13. The in-progress results consist of survey answers harvested directly from the online survey form as completed by provider representatives.
Based on a website review of leading providers in the electronic discovery arena, the following list provides a quick, non-all inclusive reference of firms that appear to have developed “technology assisted review” technology (one form of this being “predictive coding”) for their own and/or partner offerings.