Five engineering AI trends for 2020

As AI starts to gather speed and practical uses become more apparent, here are five industry trends we are likely to see emerge throughout the coming year.

Artificial intelligence is no longer a theoretical concept. As it moves into mainstream industry, system complexity increases while costs are driven down. Here’s what 2020 is likely to bring.

Workforce skills and data quality barriers start to abate

As AI becomes more prevalent in industry, more engineers and scientists — not just data scientists — will work on AI projects. They now have access to existing deep learning models and accessible research from the community, which allows a more significant advantage than starting from scratch. While AI models were once majority image based, most are also incorporating more sensor data, including time-series data, text and radar.

Engineers and scientists will greatly influence the success of a project because of their inherent knowledge of the data, which is an advantage over data scientists not as familiar with the domain area. With tools such as automated labelling, they can use their domain knowledge to rapidly curate large, high-quality datasets. The more availability of high-quality data, the higher the likelihood of accuracy in an AI model, and therefore the higher likelihood for success.

The rise of AI-driven systems increases design complexity

As AI is trained to work with more sensor types (IMUs, Lidar, Radar, etc), engineers are driving AI into a wide range of systems, including autonomous vehicles, aircraft engines, industrial plants and wind turbines. These are complex, multidomain systems where behaviour of the AI model has a substantial impact on the overall system performance. In this world, developing an AI model is not the finish line, it is merely a step along the way.

Designers are looking to model-based design tools for simulation, integration and continuous testing of these AI-driven systems. Simulation enables designers to understand how the AI interacts with the rest of the system. Integration allows designers to try design ideas within a complete system context. Continuous testing allows designers to quickly find weaknesses in the AI training datasets or design flaws in other components. Model-based design represents an end-to-end workflow that tames the complexity of designing AI-driven systems.

AI becomes easier to deploy to low power, low cost embedded devices

AI has typically used 32-bit floating-point math as available in high-performance computing systems, including GPUs, clusters and data centres. This allowed for more accurate results and easier training of models, but it ruled out low-cost, low-power devices that use fixed-point math. 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, low-cost devices and opens up a new frontier for engineers to incorporate AI in their designs. Examples include low-cost electronic control units (ECUs) in vehicles and other embedded industrial applications.

Reinforcement learning moves from gaming to real-world industrial applications

In 2020, reinforcement learning (RL) will go from playing games to enabling real-world industrial applications particularly for automated driving, autonomous systems, control design and robotics. We’ll see successes where RL is used as a component to improve a larger system. Key enablers are easier tools for engineers to build and train RL policies, generate lots of simulation data for training, easy integration of RL agents into system simulation tools and code generation for embedded hardware. An example is improving driver performance in an autonomous driving system. AI can enhance the controller in this system by adding an RL agent to improve and optimise performance — such as faster speed, minimal fuel consumption or response time. This can be incorporated in a full autonomous driving system model that includes a vehicle dynamics model, an environment model, camera sensor models and image processing algorithms.

Simulation lowers a primary barrier to successful AI adoption — lack of data quality

Data quality is a top barrier to successful adoption of AI — per analyst surveys. Simulation will help lower this barrier in 2020. We know training accurate AI models requires lots of data. While you often have lots of data for normal system operation, what you really need is data from anomalies or critical failure conditions. This is especially true for predictive maintenance applications, such as accurately predicting remaining useful life for a pump on an industrial site. Since creating failure data from physical equipment would be destructive and expensive, the best approach is to generate data from simulations representing failure behaviour and use the synthesised data to train an accurate AI model. Simulation will quickly become a key enabler for AI-driven systems.

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