“Inside AI is machine learning, inside machine learning is deep learning and inside that is CNN. AI does not imply CNNs, that’s the hammer that people are using for things like images,” said Jos Martin, director of engineering at MathWorks, whose tools are widely used for AI development.
“I think of machine learning as taking data plus an idea and generating a model that replicates the model that the data represents,” he said. “Anything that you can phrase like that is machine learning, and it incorporates traditional machine learning, CNN as well as deep learning and spiking nets.”
There are multiple steps in the AI development process. "You go through two or three stages. There’s data preparation, often of gnarly data, then you then go through training, and analysis. Then you train lots of different types of models, which might fit best, which are the smallest or the fastest, and then you deploy the models.”
These stages can present a challenge for the AI toolflow.
“The whole intent of what we do is to make every stage is available in the tools. No matter what you want to do in data preparation and checking followed by the coding ability to code up the model and the way to deploy the training,” said Martin. “This is a whole flow and throughout that whole tool chain there might be point tools to deploy to an embedded target, or run a training loop in the middle.”
MathWorks is working with AI developer AImotive on a toolflow for automotive applications. AImotive already supplies the AI technology for Sony's prototype electric vehicle.
"We can see that there is a growing trend that OEMs want to own the toolchain itself, the integration and the architecture as well,” said Szabolcs Jánky, Product Manager of aiSim at AIMotive.