Artificial intelligence, or AI, is a simulation of intelligent human behavior. It’s a computer or system designed to perceive its environment, understand its behaviors, and take action. Consider self-driving cars: AI-driven systems like these integrate AI algorithms, such as machine learning and deep learning, into complex environments that enable automation.

Why Does AI Matter?

That’s because AI is transforming engineering in nearly every industry and application area. Beyond automated driving, AI is also used in models that predict machine failure, indicating when they will require maintenance; health and sensor analytics such as patient monitoring systems; and robotic systems that learn and improve directly from experience.

Key Components to an AI Workflow

Success with AI requires more than training an AI model, especially in AI-driven systems that make decisions and take action. A solid AI workflow involves preparing the data, creating a model, designing the system on which the model will run, and deploying to hardware or enterprise systems.

Data Preparation – Taking raw data and making it useful for an accurate, efficient, and meaningful model is a critical step. In fact, it represents most of your AI effort.Data preparation requires domain expertise, such as experience in speech and audio signals, navigation and sensor fusion, image and video processing, and radar and lidar. Engineers in these fields are best suited to determine what the critical features of the data are, which are unimportant, and what rare events to consider. AI also involves prodigious amounts of data. Yet labeling data and images is tedious and time-consuming. Sometimes, you don’t have enough data, especially for safety-critical systems. Generating accurate synthetic data can improve your data sets. In both cases, automation is critical to meeting deadlines.

AI Modeling – Key factors for success in modeling AI systems are to: 1. Start with a complete set of algorithms and prebuilt models for machine learning, deep learning, reinforcement learning, and other AI techniques. 2. Use apps for productive design and analysis. 3. Work in an open ecosystem where AI tools like MATLAB®, PyTorch, and TensorFlow™ can be used together. 4. Manage compute complexity with GPU acceleration and scaling to parallel and cloud servers and on-premise data centers

System Design – AI models exist within a complete system. In automated driving systems, AI for perception must integrate with algorithms for localization and path planning and controls for braking, acceleration, and turning.

Deployment – AI models need to be deployed to CPUs, GPUs, and/or FPGAs in to final product, whether part of an embedded or edge device, enterprise system, or cloud. AI models running on the embedded or edge device provide the quick results needed in the field, while AI models running in enterprise systems and the cloud provide results from data collected across many devices. Frequently, AI models are deployed to a combination of these systems.

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