5 Features of Accessible AI

for Engineers and Scientists

What is making artificial intelligence (AI) more accessible? With the rapid growth of AI in industrial applications, barriers such as workforce skills and data quality are dissolving as simulations provide the training data AI requires. It’s getting easier to deploy to low-power, low-cost embedded devices, and reinforcement learning is being used in control systems. This slideshow explores the features of accessible AI, starting with the proliferation of AI models.

It’s getting easier to deploy to embedded devices, reinforcement learning is being adopted in industrial applications, and simulations are helping create synthetic failure data. This slideshow explores the features of accessible AI starting with the proliferation of AI models.

1. Broadening workforce skills with access to AI models for non-image domains

More engineers and scientists now have access to existing AI models and research from the community. While AI models were once mainly image-based, many can also incorporate data from other domains like sensors, text, and radar.

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2. Experts have domain-specific tools to work with data in complex systems

Engineers and scientists will greatly influence the success of a project because of their inherent knowledge of the data. With tools such as automated labeling, they can use their domain knowledge to rapidly curate large, high-quality datasets. The better the availability of high-quality data, the higher the likelihood of accuracy in an AI model.

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3. Deploying AI to embedded devices is getting easier

AI has typically used the 32-bit floating-point math available in high-performance computing systems. Recent advances in software tools now support AI inference models with different levels of fixed-point math. This enables the deployment of AI on those low-power devices and opens up a new frontier for engineers to incorporate AI. 

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4. Reinforcement learning (RL) is moving from gaming to real-world industrial applications

Reinforcement learning refers to reward-based algorithms trained to optimize a system for its environment. RL models are being used in control systems, where they generate simulation data for training, integrate agents into system simulations, and generate code for hardware.

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5. Simulation is opening doors to trustworthy synthetic failure data

Gathering data from anomalies or critical failure conditions from physical equipment can be expensive. Often, the best approach is to generate data from simulations representing failure behavior and use this data to train an AI model.

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AI is becoming more accessible every day through domain-specific tools, new and widely available algorithms, and synthetic data. Read more about how to get started.

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