What will the next few years look like in data science?
There’s no denying that AI is becoming more widely adopted within enterprises, but much of it is still base-level predictive analytics. We want to discuss some of the more advanced data science techniques that organizations could (and should) be using to elevate their strategy, many of which have been highlighted in analyst reports like Gartner’s 2022 AI Hype Cycle.
In this post, we’ll discuss how widely adopted cutting edge techniques are being used innovatively today and shed some light on other techniques that are worth consideration, too.
Become a More Innovative Enterprise with These 5 Cutting Edge Data Science Techniques
Let’s break down how the following cutting-edge data science techniques allow users to deliver business critical DSML systems for the future.
1. Computer Vision
We’ve referenced computer vision (CV) a couple of times in our annual trends posts—most recently in 2022—as it’s been in the spotlight for a few years. Computer vision enables systems to gain insight from images, videos, and other digital input, and it’s been instrumental in making futuristic technologies like self-driving cars a reality.
Use Cases
- BMW uses CV in the production line to identify components with manufacturing errors to sort out faulty parts, saving time and reducing costs
- Retailers leverage computer vision for smart store surveillance to monitor their shelves, keep track of inventory, and analyze high-traffic areas of the shop
- Manufacturers can transform the shop floor with CV, analyzing workers’ actions in a live video feed to identify any incompliance with pre-determined standards
2. Deep Learning
Deep learning algorithms are more scalable and powerful than typical machine learning models because they’re inherently more advanced and don’t require human intervention. Deep learning has taken over many integral business processes, driving transformational enterprise machine learning solutions like Amazon and Netflix’s recommendation engines.
Use Cases
- Researchers have leveraged deep learning to predict how ice forms almost perfectly, significantly improving climate forecasting, especially when it comes to rainy or snowy conditions
- Radiologists can use deep learning algorithms such as GANs to generate images that are so realistic, they can be used to train other models for detecting abnormalities in medical imaging
3. Natural Language Processing (NLP)
Natural language processing allows a system to understand and interpret natural language such as speech and text, which is essential for understanding customer sentiment as it relates to your brand and your product. NLP solutions are widespread, and their popularity is only expected to keep growing—their market is currently increasing at an annual rate of 29.4%.
Use Cases
- Global customer service teams use NLP to enable real-time translations, making it effortless to communicate with consumers around the world
- Legal teams utilize NLP for intelligent document analysis to ensure they’re adhering to compliance and auditing requirements
4. Explainable AI
On the surface, explainable AI (XAI) sounds simple—it’s the use of explanation methods (EMs) to describe how a machine learning model works to humans. Local EMs explain how a model made a specific decision whereas global EMs explain the overall behavior of a model.
However, explainable methods are extremely nuanced, and they aren’t a silver bullet. Complex deep learning algorithms in particular are still very difficult to fully comprehend.
As XAI continues to develop, so do its applications.
Use Cases
- A bank uses explainable AI for bias detection to ensure that a model being used to determine if an applicant should be granted a loan is making fair decisions that comply with national regulations
- Doctors apply XAI principles to ML models that determine if a patient is at risk of an illness, so they understand what pattern or risk factors the model identified
5. Causal AI
While explainable AI provides an explanation to a model’s output, causal AI focuses on the cause and effect of a prediction. Causal AI can be a powerful tool for determining which methods are working and which are not when trying to achieve a particular outcome, such as converting leads into customers.
While causal AI isn’t a new technique, it’s been getting a lot of attention lately, especially after Gartner named it one of the top 25 emerging technologies in the space.
Use Cases
- B2C organizations can utilize causal AI to accurately identify churn triggers and optimize customer retention
- App developers can leverage causal AI to identify anomalies across the service chain and remedy degrading app performance with precise root cause tracing
- Farmers can use causal AI to support data-driven decisions, such as enriching an algorithm that helps them predict crop yield to understand why crop yield will be lower this year and making recommendations to increase it
- Causal AI also helps ensure that their models avoid biased outcomes by distinguishing between correlation and causation
Wrapping Up
The AI market moves at an extremely fast pace, and data science techniques (especially those considered “cutting edge”) are constantly evolving and changing. By staying on top of trending DSML technologies, enterprises can unlock new capabilities and create a competitive advantage.
To successfully leverage these techniques, your team needs a data science platform that enables them to access cutting-edge methods and build trust & transparency into their models from the start.
Check out our latest data science webinar, How the New RapidMiner Helps Data Scientists Be Even More Impactful, to learn how RapidMiner helps your data scientists spend less time on tedious “grunt work” and more time leveraging state-of-the-art techniques.
