Data scientists play a crucial role in addressing three key limitations of artificial intelligence: 1) inaccurate or insufficient data can produce inaccurate results from AI models; 2) cognitive biases in data selection and filtering can influence AI outcomes; and 3) the complexity of large AI models can make their decisions difficult to explain. Data scientists are needed to ensure high quality data collection and management, identify and mitigate cognitive biases, and help make AI reasoning more transparent - addressing limitations that AI currently cannot overcome on its own.

1. DATA
LIMITATIONS
BIAS
LIMITATIONS
Results can only be as good as the data
that is imported. Inaccurate data, will
produce inaccurate results
Inaccurate Data
Most supervised learning AI models
require large amounts of quality data
stored in a data lake
Insufficient Data
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..
Cognitive biases become problematic if
used to select which data points to use
and which to disregard
Cognitive Bias
Human operators develop knowledge
and intuition around process and
equipment, this can lead to rapid
diagnosis with potential for bias
Rapid Diagnosis
Complex large AI models can make it
hard to explain, in human terms, why it
was reached
Explain-ability
Historically it has been difficult to carry
experience forward into different
circumstances, as AI models would
need to be created.
Transfer Learning
TECHNICAL
LIMITATIONS
3 Limitations of
Industrial AI
AND THE NEED FOR
DATA SCIENTISTS
vroc.ai
THE IMPORTANT ROLE OF DATA SCIENTISTS