deriving value from machine learning in business

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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

>> CLICK HERE

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

>> CLICK HERE

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

Thanks for viewing our presentation. If you’d like to stay ahead of the curve about cutting-edge research trends and insights in the field of artificial intelligence, be sure to stay connected on social media by clicking the icons below:

[email protected] | www.techemergence.com

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