However, even Agile methodologies are challenged by the requirements of AI systems. For one, what exactly is being “delivered” in an AI project? You can say that the machine learning model is a deliverable, but it’s actually just an enabler of a deliverable, not providing any functionality in and of itself. In addition, if you dig deeper into machine learning models, what exactly is in the model? The model consists of algorithmic code plus training model data (if supervised), parameter settings, hyperparameter configuration data, and additional support logic and code that together comprises the model. Indeed, you can have the same algorithm with different training data and that would generate a different model, and you can have a different algorithm with the same training data and that would also generate a different model. So is the deliverable the algorithm, the training data, the model that aggregates them, the code that uses the model for a particular application, all of the above, none of the above? The answer is yes. As such, we need to consider additional approaches to augment Agile in ways that make them more AI-relevant.
Guided by brain-like ‘spiking’ computational frameworks, neuromorphic computing—brain-inspired computing for machine intelligence—promises to realize artificial intelligence while reducing the energy requirements of computing platforms. This interdisciplinary field began with the implementation of silicon circuits for biological neural routines, but has evolved to encompass the hardware implementation of algorithms with spike-based encoding and event-driven representations. Here we provide an overview of the developments in neuromorphic computing for both algorithms and hardware and highlight the fundamentals of learning and hardware frameworks. We discuss the main challenges and the future prospects of neuromorphic computing, with emphasis on algorithm–hardware codesign.
S4 Agtech, which offers risk management solutions for agriculture companies, migrated to Google Cloud to save money and time, as well as, scale databases and machine learning models faster. The company uses BigQuery as its data warehouse, and has dramatically reduced storage and processing costs by 80%, while providing customers their analytics results 25% faster. S4 has also gained more time for innovation in helping its customers de-risk crop production, including updating and improving algorithms.