deriving value from machine learning in business
TRANSCRIPT
In this TechEmergence Consensus, we contacted a total of 30 artificial intelligence executives and researchers to ask them about the criterion needed for a company to derive maximal value from the application of machine learning in business problems.
This slide deck displays the major trends of responses as well as some of the most poignant quotes from the recognized experts we spoke with.
Access to complete data sets and all quotes and answers from our Machine Learning in Business Consensus is available for free download as a spreadsheet or Google Sheet in the link below. This series includes: + Machine Learning Industry Predictions + Deriving Value From Machine Learning in Business + Misconceptions in Machine Learning + Applications of Machine Learning
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Download the complete response set below:
© TechEmergence Consensus July 2016
“What are the criterion needed for a company to derive maximal value from the application of machine learning in a business problem?”
Sufficient Data
Pick the Right Problem
Data Science Talent
Percentage of Responses
0 10 20 30 40 50
* Answers from the respondants were submitted in an open ended text format later categorized and sorted after submission by techemergence.com
We’ve selected three quotes from each of the major response categories. Beneath each quote is a link (if available) of our complete interview with this guest on the TechEmergence Podcast.
* These consensus answers were recorded seperately from our podcasts interviews, but most podcasts are focused on related topics around the ethical implications of emerging technologies.
SUFFICIENT DATA
“Quantity and quality of a company’s business data is vital when it comes to machine learning
application. The ability to automate and integrate such applications, once developed, with business
processes and workflows is also required.”
- Dr. Helgi Páll HelgasonVP of Operational Intelligence, Activity Stream
SUFFICIENT DATA
“Possession of large amounts of proprietary data relevant to their business is the key criterion for companies wanting to exploit machine learning.”
- Dr. Alexander D. Wissner-GrossFounder, President, and Chief Scientist, Gemedy, Inc.
SUFFICIENT DATA
“What is needed is an abundance of data, a well defined problem, and a large set of correct
answers to the problem. Additionally, ample lead time is needed to train the machine before
expecting any value.”
- Dr. Pieter J. MostermanSenior Research Scientist, MathWorks
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Listen to or read our full interview with Dr. Mosterman at techemergence.com:
PICK THE RIGHT PROBLEM
“Identify allocation problems that can scale in new ways when automated and optimized, and
identify tasks (such as UI/UX, data processing in existing products) that are relevant for success of the product, and have not been touched in the last 24 months: these are very likely to benefit
from incorporating recent progress.” - Dr. Joscha Bach
Research Scientist, MIT Media Lab
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Listen to or read our full interview with Dr. Bach at techemergence.com:
PICK THE RIGHT PROBLEM
“The best setting is an operational loop that is closed under automated processes. A classic
example would be product recommendations in e-commerce -- a virtual environment for an AI
agent to learn, through experimentation, how to up and cross sell.”
- Dr. Edward ChallisCo-Founder & CEO, re:infer
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PICK THE RIGHT PROBLEM
“The most promising opportunities for machine learning applications are those that elude easy
codification into rules, but which are nonetheless tedious for a human.”
- Dr. Richard DowneVP of Data Science, Casetext
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DATA SCIENCE TALENT
“A company should count with staff who are ex-perts in all stages of the data analysis pipeline.
This mostly includes experts not only in machine learning but also in the sensor and their mea-
sures, which will be used as features, e.g. sales forecasting needs a sales expert in the team not
only data scientists.”- Dr.-Ing. Aureli Soria-Frisch
R&D Neuroscience Manager, Starlab Barcelona SL
DATA SCIENCE TALENT
“Companies need to understand the foundations of machine learning, so they don’t apply it naively
and get bad results.”
- Dr. Bruce MacLennanAuthor, Associate Professor at University of Tennessee
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Listen to or read our full interview with Dr. MacLennan at techemergence.com:
DATA SCIENCE TALENT
“Good theoretical background in data science and artificial intelligence. Knowledge how to apply
theoretical frameworks into practical use.”
- Dr. Mika RautiainenCEO, Valossa Labs Oy
If you’ve enjoyed this presentation and you’d like to see the full dataset of responses, the consensus is freely available below:
>> CLICK HERE
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