10 Misconceptions in AI and ML in 2018

Artificial Intelligence and Machine Learning (AI/ML) are often regarded to be "mythical topics" with data scientists working their magic and half million dollar nVidia rigs making deep neural networks "come to live".

But then there is much sobering news
As outlined in my article on "applied AI/ML" the discipline is far from mature or even well understood by practitioners. This has led to a big divide between the understanding of experts, such as data scientists, and practitioners in DevOps, business, and IT ops groups

DevOps and AI
I have published numerous articles on the topic of how we can enable DevOps teams to incorporate AI/ML capabilities into their code. This is a difficult problem due to the experimental character of AI/ML and the very real release deadlines DevOps groups must adhere to today. Please dig into this article over at DevOpsAgenda.com to read my "Seven steps to move a DevOps team into the ML and AI world."

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

1. I need specialized GPUs as I cannot train neural networks on simple GPUs.
2. Once I provide the AI/ML algorithm with training data, it will do its magic all by itself.
3. I can train my AI/ML algorithm by looking over my IT or DevOps staff's shoulder.
4. AI/ML can radically reduce the number of alerts in DevOps and IT Op.
5. AI/ML is only for data scientists. Business users and developers need the help of these data scientists for their projects to be successful.
6. The limit of AI/ML is in the algorithm and infrastructure performance.
7. Today's AI/ML is lightyears ahead of where we were 10 or even 50 years ago.
8. Self-driving cars can make truly autonomous decisions, similar to human drivers.
9. AI/ML is coming closer and closer to how humans think.
10. The differentiation between AI/ML products lies in their underlying learning algorithms.