Sergey Tarasov - stock.adobe.com

In the continuing theme of higher level tools to improve developing useful applications, today well visit feature engineering in a changing environment. Artificial intelligence (AI) is increasingly used to analyze data, and deep learning (DL) is one of the more complex aspects of AI. In multiple forums, Ive discussed the need to move past heavy reliance on not just pure coding, but even past the basic frameworks discussed by DL programmers. One of the keys to the complexity is figuring out the right data attributes, or features, which matter to any system. Its even more important in DL, both because of larger data sets and due to the less transparent nature to the inference engine over procedural code. As tricky as that is the first time, it needs to be a repeatable process, as environments change, and systems must change with them.

Defining the initial feature set is important, but its not the end of the game. While many people focus on DLs ability to change results based on more data, that still means the use of the same features. For instance, the features are fairly well known radiology. Its gaining more examples for training that matters, to see the variation of how those features appear. However, what is theres a new tumor? There might be a new feature that needs to be added to the mix. With supervised systems, thats easy to modify because you can provide labeled images with the features and the system can be retrained.

However, what about consumer taste? Features are defined, then the deep learning system looks for relationships between the different defined features and provides analysis. However, fashion changes over time. Imagine, for instance, a system defined when all pants had pleats. The question of whether or not pants should have pleats isnt an issue, so the designers did not train the system to analyze the existence of pleats. While the feature might be defined in the full data set, for performance issues the feature was not engineered into the engine.

Suddenly, theres a change. People start buying pants without pleats. That becomes something that consumers want. While that might be in the full dataset, the inference engine is not evaluating that variable because it is not a defined feature. The environment has changed. How can that be recognized, and the DL system changed?

SparkBeyond is a company working to address the problem. While the product works with initial feature engineering, the key advantage is that it helps with DevOps and other processes to work to keep DL driven applications current in changing environments.

What the companys platform does is analyze the base data being used by the DL systems. It is not AI itself, but leverages random forests (RF). This technique is a way of running multiple tests with different parameters. This is helped by the advances of cloud technologies and the ability to scale-out to multiple servers. Large numbers of decision trees can be analyzed, with new patterns being seen. The RF is one of the ways that machine learning has moved past a pure AI definition, as it can create insight far faster than other methods, identifying new classifications and relationships in large data sets.

The complexities of consumer behavior, and that of financial and other markets, is far more complex than that of pleats v no-pleats, its important to recognize and adapt to change as fast as possible. Changing environments are critical to analysis, said Mike Sterling, Director of Impact Management, SparkBeyond. Generating large volumes of hypotheses and models, and them testing them, is critical to identifying those changes in order to adapt deep learning systems to remain accurate in those environments.

Artificial intelligence does not exist on its own. It is a technology that fits into a larger solution to address a business issue. No market is stagnant while remaining relevant. How and when to update deep learning systems, as they are used in more and more places, is important. The ability to analyze the data sets is critical, both for initial feature engineering and as an ongoing process to keep the systems relevant and accurate.

I see this as one feature, if you will, of what will eventually become development suites similar to 4GL development in the 90s. It will take a few more years, but this step to incorporate more tools into the deep learning environment

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Deep Learning AI Needs Tools To Adapt To Changes In The Data Environment - Forbes