October 31, 2017

Industrial AI: 6 Questions to Ask Before Implementing Your Enterprise AI Strategy

Bonsai was founded in 2014 to reduce complexity and lower barriers that often make it difficult for software developers to program AI models.

Over time, we’ve learned that the most immediate value of AI becomes clear when it’s applied in specific use cases within specific industry verticals. Speaking with business leaders and executives, we continue to see an underserved market with a genuine need for more accessible machine learning technologies: enterprises with industrial control systems.

These enterprises work in a range of industries, including robotics, manufacturing, supply chain, energy and HVAC. All want to leverage AI technology to enhance operations of these systems, but the kinds of tools these execs want - tools that allow enterprises to scale their existing domain expertise, while optimizing their best people’s work with machine learning - simply don’t exist.  

That’s why the Bonsai Platform has been built to make industrial AI techniques more accessible to a team of subject matter experts, data scientists and developers within an organization. The machine learning tools and technologies best suited for industrial AI  - including deep reinforcement learning, simulations, and machine teaching - allow enterprises to combine human knowledge and cutting-edge AI to program intelligent control into real-world systems.

Five or six years ago, applying AI to industrial applications would have been premature. But the technology has progressed to the point where it can be used to solve more than just toy problems. With the right tools and algorithms, industrial AI applications can now accomplish critical tasks at scale in large organizations.

Do You Have an Industrial AI Application?

The AI use case spectrum is vast. Here are some ways to tell if industrial AI techniques are the right approach to solving your organization’s business problems:

  1. You’re dealing with complex problem spaces that are dynamic or unconstrained, such as robotics adapting to new circumstances or global supply chains confronting unforeseen changes in weather conditions.
  2. Subject matter expertise is a strategic asset or a competitive advantage, such as when you have someone in your organization whose domain expertise can be leveraged to optimize and/or scale automated systems.
  3. Explainability is essential. For example, when you’re operating complex physical systems with real risks involved, you need to actually “see” how the AI model is solving a problem, rather than simply trusting it to do the right thing.
  4. Simulations are the most viable training option. In many real-world scenarios, it’s far too costly and potentially dangerous to train AI models by trial and error or by relying on existing datasets. Simulations, on the other hand, enable you to create digital twins of your environments to train models for a wide range of possible scenarios.
  5. Reinforcement learning can be leveraged to improve the accuracy of predictive models.  Reinforcement learning algorithms do more than simply make predictions. With RL, you can actually train a model to deal with unpredictable conditions and unforeseen environments. RL trains models by taking a series of actions over and over, getting positive or negative rewards for each one.  
  6. Industrial AI gives you the tools for building intelligent control into your systems, without sacrificing visibility into critical processes.

We believe that industrial AI techniques will become the standard for dynamic production environments, complex autonomous systems and other scenarios in which continuous control and optimization are paramount.

To learn more about getting started with Industrial AI, and to learn more about the best-fit use cases, download our free whitepaper, “Artificial Intelligence for Industrial Applications”.

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