In this TechEmergence Consensus, we contacted a total of 30 artificial intelligence executives and researchers to ask them about the biggest mis-conception that executives and businesspeople have in applyting machine learning to business opportunities.

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 do you believe to be the biggest misconception that executives and businesspeople have in applying machine learning to business opportunities?”

Wrong Expectations of Capabilities/Applications

Percentage of Responses

0 10 20 30 40 50

Underestimating Resources/Staff Needed

Technical Misunderstandings

Not Understanding What AI Is or Does

Other

* 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.

“WRONG EXPECTATIONS OF CAPABILITIES/APPLICATIONS

“There are two misconceptions. One, that ML can solve every-thing, like a magic box. The other is exactly the opposite, that ML is useless and can solve only toy like problems where the solution is obvious. I believe the truth is in the middle, ML is

very good at classification, but is bad at real time control, and motion planning, where continuous solution is required.”

- Dr. Amir ShapiroAssociate Professor, Ben Gurion University

“WRONG EXPECTATIONS OF CAPABILITIES/APPLICATIONS

“There is this misconception that with sufficient data you can train a machine to solve any task. At least with the cur-rent state of the field of machine learning there are types of

problems that are distinctly more or less suited for a machine learning solution.”

- Dr. Pieter J. MostermanChief Research Scientist, MathWorks

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Listen to or read our full interview with Dr. Mosterman at techemergence.com:

“WRONG EXPECTATIONS OF CAPABILITIES/APPLICATIONS

“That it will be costly (it is not at all if you have the data), complicated (most graduate in Computer Science can get you a long way), and risky (the evaluation technics are simple and

will tell you how your system compare to a human).”

- Dr. Philippe PasquierAssociate Professor, Simon Fraser University

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Listen to or read our full interview with Dr. Pasquier at techemergence.com:

“UNDERESTIMATING RESOURCES/STAFF NEEDED

“Many business people looking only for complete machine learning solutions and underestimate the role of the data

scientists on their team.”

- Dr. Timur LuguevCEO, Clevapi

“UNDERESTIMATING RESOURCES/STAFF NEEDED

“A common misconception is that machine learning and AI tools contain within themselves certain level of intelligence,

but they don’t. Machine learning and AI are only tools in hands of more or less skilled people, and solely the intellectual

capabilities of these people eventually make a difference between success and failure.”

- Dr. Danko NikolicSenior Professional: Data Science, CSC

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Listen to or read our full interview with Dr. Nikolic at techemergence.com:

“UNDERESTIMATING RESOURCES/STAFF NEEDED

“That it is straightforward to develop and train ML systems that solve real world problems. The amount of AI engineering

and training work required to bring ML systems to a useful level is greater than what is assumed by the industry

executives.”

- Dr. Mika RautiainenCEO, Valossa Labs Oy

“TECHNICAL MISUNDERSTANDINGS

“ML is purely correlative - and correlation does not imply causation. As many times as one sees this, the mistakes

made by people who don’t understand the difference leads to significant negative consequences for business.”

- Dr. James HendlerProfessor, Rensselaer Polytechnic Institute

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Listen to or read our full interview with Dr. Hendler at techemergence.com:

“TECHNICAL MISUNDERSTANDINGS

“General statements on classifier and techniquesperformance. The accuracy performance depends not only on

the technique itself but on the data set you are analyzing.”

- Dr.-Ing. Aureli Soria-FrischR&D Neuroscience Manager, Starlab Barcelona SL

“TECHNICAL MISUNDERSTANDINGS

“Contrary to popular misconception, the size and quality of training datasets tend to be significantly more important than

algorithm choice in applying machine learning to business opportunities.”

- Dr. Alexander D. Wissner-GrossFounder, President, and Chief Scientist, Gemedy, Inc.

“NOT UNDERSTANING WHAT AI IS OR DOES

“What people call Deep Learning are just a progressive improvement on Neural Networks, a field that has been

slashed as “done” just a few years ago. The misconception is that this is new: in reality, this is a decades long effort which

is now being notices thanks to hardware catching up.”

- Dr. Massimiliano VersacePresident & CEO, Neurala, Inc.

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Listen to or read our full interview with Dr. Versace at techemergence.com:

“NOT UNDERSTANING WHAT AI IS OR DOES

“The biggest misconception that they have around machine learning is believing that they understand what it is.

Executives are very likely to be aggressively misinformed.”

- Slater VictoroffCEO, indico

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Listen to or read our full interview with Slater at techemergence.com:

“NOT UNDERSTANING WHAT AI IS OR DOES

“Artificial Intelligence in the long run is not so much about robots and intelligent agents, but much broader: it is about handling complexity in new ways. We will not live next to AI applications, but inside of artificially intelligent systems.”

- Dr. Joscha BachResearch Scientist, MIT Media Lab

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Listen to or read our full interview with Dr. Bach at techemergence.com:

If you’ve enjoyed this presentation and you’d like to see the full dataset of responses, the consensus is freely available below:

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