As professionals become interested in using artificial intelligence (AI) for manufacturing, they will likely encounter information about probabilistic and deterministic types. Understanding the differences between them and the factors that make them more or less suitable for production-related applications will help individuals make more informed choices and achieve measurable results.
What is probabilistic AI?
This type of AI relies on statistical models to analyze data and provide predictions based on probabilities. People also refer to probabilistic AI as “statistical AI” or “machine learning.” It can perform well despite uncertainty and incomplete information, and these models improve their performance through use.
What is deterministic AI?
Deterministic AI uses predefined rules and logic. This means it will always produce the same output if the inputs remain unchanged. Those developing this type of AI often program it with if-then statements, instructing the algorithms to handle explicit situations consistently each time.
Pros of probabilistic AI
Leaders should know the advantages of probabilistic AI before implementing it.
1. Supports improved decision-making
Probabilistic AI can increase manufacturing leaders' confidence by indicating the likelihood of specific scenarios occurring. This is one of the primary reasons manufacturing leaders utilize this technology to minimize downtime. Executives can then make more realistic predictions that encourage them to invest in the most practical enhancements.
2. Shows good performance in changing environments
This type of AI also performs well in environments with high degrees of variability, including manufacturing plants. Because its capabilities improve with use, parties relying on this technology can expect that outcome with limited or no supervision.
3. Reveals customer trends
Probabilistic AI enables manufacturing brands to further understand of their customers by revealing their behaviors and preferences. Decision-makers can then take the information further by studying specifics, such as the likelihood of someone making a purchase after seeing a pop-up advertisement for a discount.
Cons of probabilistic AI
Probabilistic AI, like virtually anything else, has some downsides. Knowing what they are helps manufacturers anticipate them and adapt.
1. Requires high-quality data
The quality of probabilistic AI’s outcomes relates to the condition of the data fed into it. This means that leaders hoping to use this technology in a manufacturing setting should establish data governance and quality control measures to increase or maintain its usability.
2. Features complex models
Manufacturing leaders using AI for the first time may find probabilistic models overly complex, making them challenging to create and maintain. These decision-makers should weigh whether they have the in-house expertise to utilize these kinds of algorithms or are willing to budget for it.
Pros of deterministic AI
Manufacturers and others interested in automation can reap numerous benefits by utilizing deterministic AI.
1. Offers consistency
Deterministic AI provides its users with consistent results. That characteristic can be helpful if they intend to use it in a manufacturing application that has little or no variability. It’s also beneficial for a regulated or safety-critical environment. When the behavior is predictable, isolating faults is easier compared to systems that behave differently every time.
2. Enables transparency
Due to its rule-based architecture, deterministic AI delivers users with consistent and reliable outcomes. Some types offer explainability, showing how it reached certain conclusions for maximized transparency,
3. Reduces unexpected outputs
This technology has become popular in regulated Industries such as health care and finance. One reason is that it is less likely to produce unexpected outputs compared to other types of AI. By following precise guidelines, it can provide high accuracy.
Cons of deterministic AI
Understanding the adverse aspects of this technology helps manufacturers decide whether to choose it.
1. Provides limited flexibility for other applications
Deterministic AI never deviates outside the task its programmers design it to do. Although this results in high accuracy for those applications, it also means the algorithms have little flexibility for other tasks.
2. Lacks interpretation for situations outside training data
This type of AI cannot handle situations not covered in its training data. That limitation can make it unsuitable for some manufacturing environments, especially those featuring new or unusual situations.
When to use these types of AI in manufacturing
Although there are no universally applicable circumstances for using these types of AI in production settings, those interested in using it should consider the associated characteristics before proceeding.
When speaking about best practices for deploying probabilistic models in a pharmaceutical manufacturing setting, one expert advised using a structured, transparent and documented process when implementing them. He also recommended focusing on process improvement instead of just model performance. This kind of AI is best for variable environments and applications where users can tolerate some level of error.
Decision-makers should consider deterministic AI if their desired applications have clear sets of rules and procedures. This option works well when executives require high accuracy levels and intend to deploy the AI in relatively stable environments.
Implementing AI in manufacturing
Regardless of the chosen type of AI, users should select key performance indicators to measure before, during and after deployment. Tracking that data allows them to evaluate the results and determine whether to proceed.
