Like a failed communist state traditional machine learning centralises training of a model on a single machine. Centralising data in a single central location is not always possible for a variety of reasons such as slow network connections, and legal constraints. These limitations have produced a series of techniques that allow the decentralised training of a model. This collection of techniques is referred to as Federated Machine Learning. Read More
Daily Archives: October 17, 2019
Machine Learning Advances and Edge Computing Redefining IoT
Smart, connected products are changing the face of competition. That was the thesis of a formative 2014 article in Harvard Business Review that highlighted the transformative potential of information technology integrated into an array of products.
In the past five years, however, the seemingly straightforward words of “smart” and “connected” have become more enigmatic and, arguably, more loaded terms. And the meaning of those two terms has steadily evolved and continues to change. Five to ten years ago, a “smart” product was one with embedded sensors, processors and software. These days, to qualify as “smart,” a device needs to take advantage of some form of basic machine learning at a minimum. Read More
Artificial Intelligence Is on the Case in the Legal Profession
Artificial intelligence (AI) is, in fact, becoming a mainstay component of the legal profession. In some circumstances, this analytics-crunching technology is using algorithms and machine learning to do work that was previously done by entry-level lawyers. (What does that say about entry-level lawyers?)
Apparently, AI robot lawyers are here—and they’re not going away. Read More
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