Brainspace White Paper: Advanced Machine Learning Drives a User-Focused eDiscovery Experience
If you’ve found your way to our Brainspace White Paper, you are most likely no stranger to legal document review, machine learning, advanced analytics, or the eDiscovery world in general. By now you’ve probably experienced the benefits of using machines instead of or—as we like to do at BIA—in addition to humans for managed document review.
TAR (Technology Assisted Review) in its early days was limited in that the machine’s coding accuracy depended on the size and diversity of the initial, one-time loaded ‘seed set’. Eventually, TAR added CAL (Continuous Active Learning), which coded documents on an ongoing basis, enabling humans to “teach” the software to code the next document using intel garnered from previous coding decisions.
Brainspace takes the innovation even further. Using CMML (Continuous Multimodal Learning), the tool watches, learns from, and builds on the user’s every click and every tag. Since the machine is integrated into the natural review platform process, it is constantly upping its accuracy game as reviewers continue to code, even as new data is brought into the system at any point in the review.
Long before we wrote our Brainspace White Paper, BIA team members have been steadily accruing Brainspace certifications. This is not only a sign of our team’s commitment to keeping its finger on the pulse of ever-evolving technology in the eDiscovery industry, but it’s an indication of how much the tool has—and continues to—improve and inform the way we use machine learning and how we run our managed document reviews overall.
As of the writing of this post, 100% of our Project Management team and 75% of our Lit Tech team are Brainspace-certified. We couldn’t be prouder, and we’re excited to share in detail some of what we have learned about the tool’s history, differentiators, unique features, novel uses in court, and more. (To hear us talk about it, also check out our webinar on the Practical Uses of Brainspace.)
What you will learn from this white paper:
- The basics of how Machine Learning and TAR work
- The main differences between Machine Learning, TAR 1.0, TAR 2.0
- Brainspace’s use of Continuous Multimodal Learning and how it’s different
- Case studies that lay out the cost and time savings of utilizing Brainspace
- 3 ways to add Brainspace into your workflows and benefit from it
Fill out the form below to receive your copy of this Brainspace white paper today!