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Why Data is the Foundation of Artificial Intelligence

Data is critical to AI deployments. Odds are, if you’re working with AI, that statement isn’t a huge surprise. 93% of respondents surveyed in our annual State of AI and Machine Learning report said that high-quality training data is important to successful AI tech implementation. What might surprise you is that the importance of training data doesn’t stop after the initial deployment. Three out of four organizations report updating their models at least quarterly, meaning data isn’t just critical for launching successful AI, it’s a vital part of maintaining AI model success. Organizations across all industries are turning to artificial intelligence (AI) as an essential strategy for maximizing revenue, meeting customer needs, and gaining a competitive advantage. But to do this, organizations need to prioritize training data, too.

Of those who update their models at least quarterly, 40% report that lack of data or data management are the biggest roadblocks to AI success. So why is quality data so important to building AI and maintaining its effectiveness in production, and why has data become one of the biggest challenges for organizations embracing AI? To answer this, we have to look at why machines need data and how quality plays a critical part in determining AI model performance.

The Role of Data in AI

Let’s break it down. Identifying anything (a word, a sound, an image) requires examples. When you first learned what a car is, you probably had many examples of cars pointed out to you before you could identify one accurately each time. You were probably also told what a car wasn’t. A motorcycle isn’t a car. A semi-truck isn’t a car. Machines need examples, too. And those examples need to be labeled; after all, an image of a car is just a bunch of pixels from a machine’s perspective until a label is applied to it.

A machine needs a lot of examples before it knows what a car is. The greater the variety of cars a machine learns about, the more accurate that machine will be when it needs to identify whether there’s a car in a given image. But, if you show that machine ten thousand labeled images of Honda Civics and only a hundred images of other types of cars, that machine is only going to be able to identify Honda Civics with any accuracy. Or, if you forget to show images of Toyota Priuses, your machine will never identify a Prius in any image, anywhere. In either case, you’ll have a useless, biased algorithm on your hands.

This is why data quality standards are so important.

For an AI model to perform well, its training data must cover as many use cases and edge cases as possible of a given question. That training data must also be clean, complete, and accurately labeled.

Most importantly, it must be free of bias. Bias can be mitigated by widening the representation of each use case in the data, utilizing human-in-the-loop to check for bias throughout the model building and deployment process.

Ultimately, a model is only as good as the data it was trained on. A machine trained on data that was inaccurately labeled, missing several use cases, or containing insufficient representation of each use case, will reflect those gaps in its decision-making. The more breadth and depth to your data, the better your model will perform. 

Embracing Retraining

You may think that once you’ve deployed your AI model successfully, you’re all set. But the truth is, consistently retraining a model in production is not only recommended, it’s essential. Most organizations are actively updating their AI models in production at least quarterly. Why? Change is a constant, as is use case transformation and data drift.

Think about how customer demands in any major industry have progressed recently, often in the direction of greater personalization. As customer needs change, and new use cases emerge, models must accommodate these changes to remain useful. This requires retraining regularly with new data that reflects these new use cases.

Another set of changes to watch out for is model drift. Drift accounts for changing conditions, such as a new concept appearing suddenly or an old concept gradually changing to a new concept. Take the example of slang words: sometimes, new words pop into our cultural lexicon quite suddenly, other times the meaning of a word may change over time. Both would be instances of drift.

To combat the constantly changing environment we live in, organizations need to continuously supply their models with new training data to adjust and pivot. Without it, not only will models accuracy plunge, but the overall lifespan and ROI of the AI model will be drastically limited.

Further emphasizing the need for organizations to focus on high-quality data early on is the 25% increase in data types (such as text, image, video, audio, etc.) that organizations are using in 2020, compared to 2019 . With more complex data types, sourcing and labeling data becomes a bigger challenge for many teams. Accounting for more complex and resource-intensive data types, plus new use cases and drift will allow organizations to properly budget and plan for successful, scalable AI deployments. As organizations scale their deployments to larger audiences, investing in a steady stream of high-quality data throughout the lifecycle of their AI models will become critical.

Becoming an AI-first Organization

Whether you’re in the process of building your first AI model, or already in production, high-quality data is the foundation on which your model’s success depends. Invest in reliable training data and embrace retraining to enhance the accuracy of your model. Companies that hope to become AI-first organizations will be those who recognize the importance of high-quality data, and suffuse that into every decision they make when building and deploying AI.

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