3 Limitations of Industrial AI [Infographic]

When it comes to Industrial AI, the capabilities afforded by machines that can learn, predict and improve what we do are seemingly endless. Like all amazing new technolgy though – there’s a catch. We need to be aware of the data, bias and technical limitations – and the role that humans still have to play, in particular data scientists – to make sure we get the most out of this transformatie new technology.

DATA LIMITATIONS

• Inaccurate data: Results can only be as good as the data that is imported. Inaccurate data, will produce inaccurate results
• Insufficient data: Most supervised learning AI models require large amounts of quality data stored in a data lake

BIAS LIMITATIONS

• Cognitive biases: Cognitive biases become problematic if used to select which data points to use and which to disregard
• Rapid Diagnosis: Human operators develop knowledge and intuition around process and equipment, this can lead to rapid diagnosis with potential for bias

TECHNICAL LIMITATIONS

• Explain-ability: Complex large AI models can make it hard to explain, in human terms, why it was reached
• Transfer Learning: Historically it has been difficult to carry experience forward into different circumstances, as AI models would need to be created.

THE NEED FOR DATA SCIENTISTS

The growth of AI and data science as critical innovation tools go hand in hand. The limitations of AI are where data scientists are needed most – to step in and handle the technical and data problems that AI can’t. It’s no wonder LinkedIn ranked ‘Data Scientist’ as the most promising role of the year in 2019..