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PHOTO: Ryoji Iwata

COVID-19 has disrupted the way companies do business, forcing many to reconsider how they use complex algorithms and models for forecasting a variety of tasks, from sales to budgets to when certain items come back in stock. These are unprecedented times, so it’s  surprise that historical data is no longer a good predictor of the future.

Many have debated whether it’s best to tune and adjust existing models trained with historical data, or completely rebuild with a better understanding of today’s “new normal.” The answer will depend on how adaptive your models are and how fast they can learn from the recent data. Before making potentially business-altering decisions that could impact the customer experience, leaders need to take a step back and make sure they understand a common misconception: the difference between machine learning models and true AI models.

Understanding the Difference Between Static Machine Learning and True AI

It’s natural to want to draw causal conclusions from correlated events. Unsurprisingly, many blame COVID-19 for why "their forecasting models have stopped working.” In reality, the more likely cause has to do with the type of model used. More often than not, teams use machine learning models that do not learn constantly, as opposed to a true AI model, which can learn and refine its model continuously.

Making the distinction between machine learning and AI is key here: machine learning is only a part of AI, just as an engine is only a part of a car. A true AI model is capable of incorporating feedback data through learning loops to update the model every time it’s being used. Although machine learning models can be updated, this doesn’t happen automatically. Data scientists are required to collect more data to retrain and refine the model. Using true AI should give you a much better chance of adapting to current situations (along with some manual tuning).

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Making the Transition From Machine Learning to AI

You may be thinking: I’m using a machine learning model. How can I turn this into a true AI model? There are three steps business leaders need to consider as they make the leap from static machine learning models to true AI.

Step 1: Collect feedback and outcome data to complete the learning loops. For example, historical weather data can be used to predict the current weather, but without feedback data on how good its prediction is, it will be challenging for the model to course correct and refine itself to improve its performance automatically. Feedback data is crucial for the model to learn and adapt.

Step 2: Leverage feedback data to refine the model. If final outcome data isn’t collected in the first place, it’s unlikely your current model is equipped to leverage this data as input. Collecting the feedback is one thing. Now, data scientists have to modify the model to take advantage of this feedback data to improve it.

Step 3: Make the machine learning process more dynamic and automatic. The traditional machine learning process in an enterprise relies on data scientists to collect a new batch of data over a period of weeks or months, and then use it to retrain the model. By automating the model update process, the operating model can be updated more frequently, learn from new data, and adapt to new market conditions much faster.

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Common Challenges During the Transition

Each step in the transition to true AI poses challenges in different parts of the business.

Step 1: Overcoming business limitations is one of the biggest challenges to collecting feedback and outcome data. Companies often don’t collect larger amounts of data because it’s either expensive, or they don’t know how it can be used. Outcome and feedback data is crucial for AI, but are not often seen as mission-critical for business. While this data is useful to refine the model, it’s not needed for the model to operate under stable business conditions. Convincing business leaders to invest in instrumenting a new feedback collection mechanism and all the data science and engineering required for the proper usage of this data is usually a huge uphill battle.

Step 2: Most data science challenges occur when teams try to leverage feedback data to refine models as part of the transition to true AI. Data scientists have to extract the information from the feedback data and use it to refine and update the model. The model then needs to be revised to take feedback data as a standard input to the model. Although this sounds difficult, there are standard procedures and best practices in statistics and machine learning to accomplish this, as long as you have good data scientists on board.

Step 3: The final step, making the machine learning process more automatic, has its own challenges, though these are mostly engineering-related. These include engineering the feedback data pipeline to handle the speed and scale at which feedback data is generated, and making the machine learning engine more dynamic by giving it sufficient computing resources to retrain, update and redeploy the model in a timely fashion. When either part becomes a bottleneck, our model won’t be updated and therefore will not be able to adapt fast enough to the dynamic market condition.

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The Balancing Act of Tuning the Learning Rate

Once you've successfully transitioned to true AI, your model should be able to learn from new data and adapt. However, this doesn’t mean your AI will automatically adapt to current market conditions well. Learning takes time, and an AI’s learning rate will determine how well it adapts. Our AI wouldn’t be able to adapt fast enough if its learning rate is too slow, but we can’t just arbitrarily increase our AI’s learning rate either. If the learning rate is too fast, our AI’s output will become too noisy and therefore suboptimal. Setting the proper learning rate for our AI is a balancing act, and it’s where we could use a bit of manual tuning.

Refine vs. Rebuild

The final question remains, “Since historical data is no longer representative of the current situation, should we rebuild our model?” Even with the proper learning rate for our AI and its ability to learn from recent data, why keep the history if it’s not helping? My response to this would be don’t be so short-sighted. Just because the history is not useful now, doesn’t mean it won’t be in the future.

Refine the model instead of rebuilding it from scratch. One of the biggest lessons learned from COVID-19’s first wave is that businesses need to be agile to weather unforeseen crises. Companies need to arm themselves with the digital tools and processes that can make agility happen — and transitioning from static machine learning to true AI models will help. If history is indeed completely useless, our feedback data will tell us that through the learning loop and make our model ignore it. So, it never hurts to keep the historical data.

As the pandemic eases and our economy opens up, we will reach a point where the relevance and usefulness of our history could increase. Take these steps now to transition to a true AI model and future-proof your business for the next unexpected turn.