Machine learning is becoming the most sought-after subset of artificial intelligence (AI). As InsideBigData reported, 68% of chief technical officers (CTOs) are now using machine learning at their organizations, according to STX Next’s 2021 Global CTO Survey. (STX Next is Europe’s largest software development company specializing in Python.) The survey includes feedback from 500 global CTOs about their company’s tech stack, as well as what they hope to include in the future.
Some major findings from the survey include:
- 72% of participants predict machine learning will become the number-one technology in two to four years.
- A quarter of CTOs said they now use natural language processing, 22% incorporating pattern recognition, and 21% integrating deep learning technologies, which are all types of machine learning.
- 87% of organizations employ as many as five individuals in a dedicated AI, machine learning, or data science -related position. Furthermore, 15% of organizations currently house an AI department.
“It’s unsurprising to see machine learning as a definite leader when it comes to future technologies as its applications are becoming more widespread every day,” Łukasz Grzybowski, Head of Machine Learning & Data Engineering at STX Next, told Inside Big Data. “What’s less obvious is the skills that people will need to take full advantage of its growth and face the challenges that will arise alongside it. It’s important that CTOs and other leaders are wise to these challenges, and are willing to take the steps to increase their AI expertise in order to maintain their innovative edge.”
Study Reveals Importance of Testing Feature-Attribution Methods
While more popular than ever, machine learning technology is far from mature. For example, a neural network responsible for scanning large numbers of images (known as feature attribution) can mistake imperfections on the surface of an image, such as watermarks, for imperfections within the image. This can have devastating effects in situations like medical diagnoses.
As such, not all machine learning models are fully trustworthy. According to SciTechDaily, a group of researchers from MIT recently discovered a way to alter the original data in order to differentiate features associated with the actual model from those that are associated with the image. Their research showed that feature-attribution methods are good at finding anomalies, but not at detecting an anomaly’s absence.
For example, it could identify a watermark better than it could an image that didn’t contain one. This suggests that models that provide negative predictions are less trustworthy. Their research reveals that it is necessary to test feature-attribution methods before using them in real-world models, especially in critical situations like medical screenings.
“Researchers and practitioners may employ explanation techniques like feature-attribution methods to engender a person’s trust in a model, but that trust is not founded unless the explanation technique is first rigorously evaluated,” one of the researchers told SciTechDaily. “An explanation technique may be used to help calibrate a person’s trust in a model, but it is equally important to calibrate a person’s trust in the explanations of the model.”
Despite these challenges, machine learning will continue to mature and grow in popularity. In the meantime, it’s important for organizations that develop and deploy machine learning to be aware of potential flaws in the technology.
What Uses Do You Predict Machine Learning Will Have in Your Company?
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Editorial Team. (8 January 2022). 68% of CTOs have Implemented Machine Learning at their Organization. InsideBigData.
Zewe, Adam. (19 January 2022). Can We Tell if Machine-Learning AI Models Are Working Correctly? SciTechDaily.