US software company MathWorks predicts that 2020 will see artificial intelligence (AI) play an increasingly visible role in a wide range of industries from industrial automation and medical devices to automotive and aerospace, write Paul Pilotte and Bruce Tannenbaum.

AI is everywhere. It's not just powering applications like smart assistants, machine translation, and automated driving, it's also giving engineers and scientists a set of techniques for tackling common tasks in new ways.

We see more and more companies exploring AI in systems they create in industries like automotive, aeronautics, industrial machinery, oil and gas, and electric utilities.

Uses of AI range from automating a process (for example, defect detection with visual inspection on an assembly line) to improving a system (for example, lane detection in an automated driving application).

And yet, according to recent estimates, while many organisations recognise the value and potential of AI, few are using it.

Many organisations are deterred by what they see as the overwhelming challenges of implementing AI such as the belief they need data science expertise to do AI, developing AI systems is time-consuming and expensive, and the lack of access to good quality data.

We believe that 2020 will address these challenges to emerge as the year of the 'AI-driven system', with rapid growth of AI expected across multiple industrial applications.

Traditional barriers such as workforce skills and data quality will start to abate as more engineers and scientists work on AI projects, and as simulators provide the training data that AI requires.

Additionally, new frontiers will drive growth as AI becomes easier to deploy to low power, low cost embedded devices, and as ‘reinforcement learning’ moves from its origins in gaming to its use in real-world industrial applications. In total, this welcome progress can be summarised into five major trends:

1.) 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 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.

2.) The rise of AI-driven systems increases design complexity

As AI is trained to work with more sensor types (IMUs, Lidar, Radar, and so on), 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.

3.) AI becomes easier to deploy to low power, low cost embedded devices

AI has typically used 32-bit floating-point maths 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 maths. Recent advances in software tools now support AI inference models with different levels of fixed-point maths.

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.

4.) Reinforcement Learning moves from gaming to real-world industrial applications

In 2020, reinforcement learning 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 Reinforcement Learning (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.

5.) 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.

Authors: Paul Pilotte, technical marketing lead, AI, machine learning, data science. He has more than 20 years of experience in technical marketing and development in technical computing, security software, data communications, and test-equipment markets. He currently is technical marketing manager at MathWorks focusing on MATLAB toolboxes for statistics, optimization and symbolic math. He holds a bachelor’s and master’s degrees in electrical engineering from MIT and an MBA from Babson College. Bruce Tannenbaum, technical marketing lead, vision, AI and IoT applications. He is the manager of technical marketing for vision, AI, and IoT applications at MathWorks. His background is in image processing and computer vision system design. Earlier in his career, he worked on digital cameras, inkjet MFPs, computer vision systems, and standardisation efforts for MPEG-4 and JPEG-2000. He holds an MBA from Babson College, an MSEE from University of Michigan, and a BSEE from Penn State.