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 software and services in the electronic discovery market for the years between 2015 and 2020.
When it comes to law, and legal review, we want an attorney’s hands on, or at least near the wheel at all times. Our Hybrid Multimodal approach includes an autopilot mode using active machine learning, but our attorneys are always responsible. They may allow the programmed AI to take over in some situations, and go hands free, much like autonomous parallel parking or highway driving, but they always control the journey.
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The Actionable Intelligence (@ActionableINT) Weekly "Quick 10" Corporate Risk Review provides in-house counsel with a weekly overview of ten significant...