AI and ML – What’s the Difference?
Artificial Intelligence vs. Machine Learning, what’s the difference?
Both terms appear often in news and literature. While there are subtle differences, they are often used interchangeably. However, Artificial Intelligence (AI) refers to the artificial creation of human-like intelligence that can learn, analyze, perceive and process, while Machine Learning (ML) is the most common method of training AI using data with no explicit programming.
Artificial Intelligence can specifically refer to the use of Neural Networks, which are a class of mathematical algorithms that has proven successful at achieving human-level performance at specific tasks. While machine learning often includes a broader set of algorithms (e.g. SVM, Random Forest, GBT), in its broader definition, artificial intelligence includes these and any other model that can achieve human-like performance, like Deep Learning. However, machine learning has proven to be the best method of training AI for well operations, and AI includes whatever model works best to improve well performance for each specific problem.
Why is this important for you and your oil wells?
OspreyData has found it best to utilize the importance of human expertise and experience in deploying artificial intelligence to oil field operations. After modeling thousands of wells across different fields, OspreyData has learned how to address critical challenges in AI innovation for producers. The key is to fully engage the know-how of Petroleum Engineering experts to train and maintain AI. Machine Learning (ML) defines the approach of using large amounts of data to train AI. Expert-Guided Machine Learning (EGML) enables oil field experts to augment machine learning so AI learns from their experience to more effectively achieve business objectives.
Supervised learning has proven to be the best approach to machine learning. However, this requires accurate tagging and labeling of operational data which is not available in many operations. How can O&G operators overcome this essential missing piece? How can they efficiently gather and maintain this information after deploying AI? EGML defines the collaborative platform and processes that enable operators and engineers to provide labeled data and share insights with data scientists and other technicians in developing, deploying, and improving AI.
Over the next few weeks, we will be reviewing how AI can improve production operations. as we delve into three core EGML approaches that enable AI development and success:
- Helping experts create the labeled data that feeds AI training
- Enabling collaboration across disparate teams to train and validate AI
- Empowering operators to provide feedback to control and improve AI