familiar with the modelling and simulation needed to build the overall system. You then need to pay

attention to how AI is integrated and implemented with the system. There are tools that go across the whole system, so that implementation is done across the entire design flow – not just the AI modelling piece. Finally, there is the

interaction of the system. As you are developing your broad AI system you need to be smart about what the interactions are going to be. If I think about an automotive application, many vehicles tend to have a lane detection algorithm built-in which can help the driver steer back into the middle of the lane. There are many ways to do

that - you can instantaneously jerk it right back to the middle or you can bring it in gradually for a smoother ride. That is an example of interaction design where you have to pay attention to the control or the response that you give to the system in order to optimise it for either the person or environment that might be involved.

So what we see in AI is that while it is in its infancy, people tend to be focusing on just the AI piece and not the broader system. By designing the entire system, and not just the AI, organisations can deliver systems that are more impactful. For instance, if you’re designing an autonomous vehicle, the passengers in the vehicle should have a smooth ride. The vehicle systems that have AI integrated in them will be making decisions about how the vehicle should respond to the environment around it. The system might

need to brake, speed up, or turn the steering wheel – there are many different actions of the system that AI can help enhance or automate – but you need to collectively optimise the reaction for the overall system design and requirements.

How do you create a combined workflow? In the context of the bigger system we typically look at four main stages, data preparation, AI modelling, system design and deployment. Data preparation is the

collection or preparation of the data inputs to the system. Training accurate AI models requires lots of data. Often you have lots of data for normal system operation, but really want to predict anomalies or critical failure conditions. This is especially true for predictive maintenance applications. A failure condition, such as a seal leak in a pump, may rarely happen and can be destructive to produce data from physical equipment. You can use a model of the pump and then run simulations to produce signals representing failure behaviour, signals that can be used to train an AI model. You then have the design of the AI model itself. For this you need to make it easy for the domain experts to build AI models. This means making it easy to label data, split data for training and validation, select from algorithms that are best suited for the problem, speed up training with high performance computing hardware such as GPUs, and visualise and evaluate the performance of the model. The key here is to provide a guided and automated | @scwmagazine

”AI is taking applications and solutions that engineers have built and transforming them”

workflow so the domain expert can train an AI model while focusing on the application area and not intricacies of algorithm implementation or computer science. System design requires being able to naturally take the AI model you’ve chosen and naturally use it along with the system simulation and verification tools an engineer is accustomed to using. This makes it easy to integrate the AI model as a natural part of building a bigger system. Finally, there is the

interaction, it is really important to identify where and how that AI fits into the system and then the interaction itself. In the lane detection example, I noted that you might need to make a drastic change to get yourself back on course. That provides a suboptimal response. The engineer that designs that system from that broader perspective has the ability to ensure that the right response is occurring.

That is the challenge if you

just focus on AI – you miss the bigger system-level design that is necessary to occur.

How does AI change modelling and testing? When I think about augmenting or extending the way that people have traditionally done modelling and testing, it brings in new challenges.

One of those challenges is the creation of the data, the generation of synthetic data and being able to have a model of the system that can help to train the system or train the AI algorithm.

That brings a lot more simulation, where you are looking at the behaviour of the broader system. While people already do a lot of simulation with traditional environments we end up having to do even more. In order to train your algorithm, you generally need to scale up the number of runs you need to do and the volume of data that you need to process. This is where you start

to see integration with the cloud because people do not necessarily have the resources locally to be able to do that scaling for training purposes – or they do not have time to wait.

Is the cost of more simulation offset by the benefits? When done well AI can add significant value to the system. For instance, if I buy a car today, my family and I really value the safety systems in there, and many of those modern safety systems are made possible with AI. The value that it brings is

significant, but the trade-off of additional simulation for scaling, training and testing, in addition to upskilling teams to be knowledgable about using and integrating AI, is something that requires time and investment. I have not heard it to be a roadblock for people, more something that is essential to remain competitive and innovative while delivering the ever-smarter systems that we have all come to expect.

October/November 2019 Scientific Computing World 29

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