Artificial intelligence has been catapulted to center stage in the tech industry over the last few years. ChatGPT shook the market with its launch in late 2022, and now generative AI is at the top of most users' minds when they hear the term "artificial intelligence." But AI is much more than that. AI software development has been around for decades, innovating the way we solve problems and increasing the capabilities of the technology we use every day.
AI Software Development and Its Unique Challenges
Projects integrating AI software development have their own unique set of challenges. At DAS Labs, we combine computer vision, machine learning, and artificial intelligence to create production-grade solutions, and we are thoroughly familiar with the hurdles that come with AI software development. Effective AI models need robust computational capabilities. When working with massive datasets and training a model to interpret large amounts of data in specific and nuanced ways, developers might run into challenges like data scarcity, model interpretability, and maintaining model accuracy over time.
Challenge 1: Data Scarcity
High-quality data is gold in the world of artificial intelligence and machine learning. The quality, quantity, and relevance of available data is one of the most crucial factors in effective AI software development. Without enough reliable data, the model's performance, accuracy, and reliability will suffer from the start. Data is how developers "teach" a model to detect patterns, make predictions, and improve over time via continuous training.
There are a few common strategies that experienced developers use to overcome data scarcity: synthetic data generation, data augmentation, utilizing public datasets, and data sharing collaborations. Synthetic data generation involves carefully creating new data points from existing ones with algorithms. When we do not know enough about the statistical properties of the data, data augmentation comes into play, artificially generating more data points by performing subtle changes to available data. For example, if we are working with images, rotating the originals by 90, 180, and 270 degrees increases the dataset size threefold. When the project truly requires real-world data, engineers might source publicly available datasets or form collaborative partnerships.
Challenge 2: Model Interpretability
The degree to which a person can understand why an AI model made a certain decision is called interpretability. Since AI and machine learning are becoming more prevalent in sensitive settings like healthcare and finance, many users need to understand the model's reasoning in order to trust it.
To help people understand the models they rely on, there are a few methods that prove helpful:
- Feature Importance: By ranking an algorithm's features based on their contribution to predictions, engineers can explain which features drive decision-making.
- Visualization Tools: Visual aids like partial dependence plots (PDPs), decision tree visualizations, and neural network graph tools help demystify a model's internal workings and show how inputs transform into outputs.
Challenge 3: Maintaining Model Accuracy Over Time
As the world changes, the data that a model was trained on might become outdated. This phenomenon is called "concept drift," and it happens when the statistical properties of the target variable change over time. When this occurs, the assumptions at the foundation of the AI model no longer align with reality, and results can become inconsistent.
The first step in combating concept drift is awareness and planning. AI is not a "set it and forget it" type of software. Once you understand the risk of neglecting maintenance, you can plan to maintain its relevance. We recommend a combination of these techniques:
- Continuous monitoring and model updating
- Automatic retraining and versioning
- Implementing feedback loops from end-users
Credible AI Development Partners Know These Challenges
When considering an AI software development company, intentionally vetting their experience with these challenges can help avoid pitfalls. At DAS Labs, planning for and responding to these challenges is a common part of our process.
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