The EMA Top 3 Enterprise Decision Guide for Artificial Intelligence and Machine Learning
This EMA Top 3 guide helps enterprises understand and plan their AI/ML strategy choices and product selections. The EMA Top 3 for AI/ML aims at demystifying the 10 key misconceptions of AI/ML. 10 Common Misconceptions
12 Comments
Lloris M
8/15/2018 08:47:41 am
Interesting point about the GPUs. How do I figure out if, which, and how many GPUs I need for my projects? How do I get to a future proof strategy?
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Torsten Volk (EMA)
8/15/2018 09:22:36 am
Great question. If and when to buy dedicated hardware such as GPUs for AI/ML depends on a few factors: 1) how large is your training set, 2) which algorithms are you using, 3) how quickly do you need to train your neural nets, e.g. for experimentation, staging, or production, 4) are you running automatic hyperparameterization or comparing multiple algorithms to find out which one minimizes the total error. In short, there is no global answer as this needs use-case specific investigation and experimentation.
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Matt Bartlett
8/15/2018 11:07:18 am
What's the #1 use case for AI in IT today? Where do you see the biggest future potential?
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Torsten Volk
8/16/2018 06:46:18 am
Great question, as I believe most of us think about AI/ML within IT and DevOps in a much too glamorous manner. What I mean by "glamorous" is that we tend to believe that AI/ML models are able to make meaningful decisions based on numerous complex considerations.
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Simon K.
8/21/2018 09:14:47 am
How close are we to having fully self driving cars without a human driver? What is still missing?
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Torsten Volk (EMA)
9/7/2018 11:29:05 am
The problem is that we are using a large number of micromodels to enable the self driving car. This means, that human engineers need to anticipate, simulate, and test for any kind of situation that could possibly occur. This can only be done through millions of miles of test driving and it is still not fail safe as traffic situations are complex and the so-called AI doesn't understand these situation in a human sense, so that it cannot evaluate its decision options based on ethics or legal liabilities to the driver or owner of the car. This is a gap that cannot be closed by throwing more hardware at the problem, but we need to figure out how to model human-type concept learning with AI.
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Finley
8/21/2018 09:17:07 am
Why can't the AI just observe what humans do and imitate that? Doesn't a self-driving car just learn from human drivers?
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Torsten Volk (EMA)
9/7/2018 11:32:41 am
Yes, the AI learns from the human driver. However, there are many things that you cannot learn just by observing. For example, the AI could not tell if certain bad driving habits lead to accidents, without getting into a large number of accidents first, so that it can identify the relevant input variables.
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ProfileThis Blog is all about demystifying artificial intelligence and machine learning (AI/ML) for enterprise use. The EMA team and outside experts will offer pragmatic advice to help you plan, prepare, and execute your AI/ML projects. Without becoming overly technical, this blog will provide perspective and a clear understanding of how ML/AI works and what results we can and cannot expect today. Archives
February 2019
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