Automated Machine Learning Drives Intelligent Business - InformationWeek

InformationWeek is part of the Informa Tech Division of Informa PLC

This site is operated by a business or businesses owned by Informa PLC and all copyright resides with them.Informa PLC's registered office is 5 Howick Place, London SW1P 1WG. Registered in England and Wales. Number 8860726.

IoT
IoT
Data Management // AI/Machine Learning
Commentary
10/30/2017
08:00 AM
Jen Underwood
Jen Underwood
Commentary
Connect Directly
Twitter
RSS

Automated Machine Learning Drives Intelligent Business

Even as enterprises explore how artificial intelligence can help their organizations and people discuss the relationship between humans and software, automated machine learning tools continue to simplify deployment across all layers.
1 of 2

Data Robot Reason Codes
Data Robot Reason Codes

1 of 2
Comment  | 
Print  | 
Comments
Newest First  |  Oldest First  |  Threaded View
MMRESdan
50%
50%
MMRESdan,
User Rank: Apprentice
10/30/2017 | 11:51:18 AM
Trust... liability
I think you overlook one of the primary reasons for "human in the loop" machine intelligence. Specifically, while having humans trust the machine's results is important, having the CFO trust the machine's results is critical. The CFO is worried about risk: what liability does the company face if the machine is wrong? That risk may be low if the software vendor accepts the liability, but that's rare: many software companies are too small to take on (or insure) the liability, especially for a highly configurable - and highly training-dependent - machine learning system. Having a human make the final call clearly deliniates who has liability... and leaves the CFO no worse off.

 

Also, much has been made about the "black box" of machine learning... some of it, at least, as FUD from traditional rules-based AI vendors whose systems are easier to analyze.  But for many applications, the question is not "By what mathematics did the machine come to that conclusion?" Rather, many applications need to know "What inputs drove the machine to that conclusion?"  That is, many applications simply need to know what aspects of what is on the other side of the black box are important.  The first question comes from rule-based AI... it comes from the need to fix rules that are malfunctioning. The latter question comes from users; in an ML environment, you simply train the system more if it's not getting the answers you want.

 

At MultiModel Research - a machine learning company - the above strongly shapes what we do. We believe systems will eventually learn to have better accuracy than humans - although that will be difficult to demonstrate. Once that happens, the CFO can relax.  Until then, we'll insist on a human in the loop!
News
COVID-19: Using Data to Map Infections, Hospital Beds, and More
Jessica Davis, Senior Editor, Enterprise Apps,  3/25/2020
Commentary
Enterprise Guide to Robotic Process Automation
Cathleen Gagne, Managing Editor, InformationWeek,  3/23/2020
Slideshows
How Startup Innovation Can Help Enterprises Face COVID-19
Joao-Pierre S. Ruth, Senior Writer,  3/24/2020
White Papers
Register for InformationWeek Newsletters
Video
Current Issue
IT Careers: Tech Drives Constant Change
Advances in information technology and management concepts mean that IT professionals must update their skill sets, even their career goals on an almost yearly basis. In this IT Trend Report, experts share advice on how IT pros can keep up with this every-changing job market. Read it today!
Slideshows
Flash Poll