Machine learning moves from movies to the mainstream

Machine learning might still be the stuff of sci-fi novels and Hollywood movies but for the vast majority of companies with a chief technical officer, it is now gaining traction, with more than two-thirds (68%) having implemented the technology at their firm.

This makes ML overwhelmingly the most popular subset of artificial intelligence, with others such as natural language processing (NLP), pattern recognition and deep learning also showing considerable growth.

Despite the popularity of AI and its various subsets, it is also clear that AI implementation is still in its early phases and there is progress to be made in recruiting the talent needed for its development.

In fact, nearly two-thirds (63%) of tech chiefs reported that they are not actively hiring AI talent and, of those who are, over half (50%) report facing recruitment challenges.

These are among the key findings from STX Next’s 2021 Global CTO Survey, for which the Python programming specialist gathered insights from 500 global CTOs about their organisation’s tech stack and what they are looking to add to it in the future.

Other insights include the fact that nearly three-quarters (72%) of tech chiefs identified machine learning as the most likely technology to come to prominence in the next two to four years, with nearly six in ten (57%) predicting the same for cloud computing.

A quarter (25%) of CTOs reported that they have implemented natural language processing, with a similar proportion (22%) implementing pattern recognition and 21% applying deep learning technologies.

Most businesses (87%) employ up to five people in a dedicated AI, machine learning or data science capacity.
However, just 15% currently have a dedicated AI department at their company, underlining that there is room for further development.

STX Next head of machine learning and data engineering Łukasz Grzybowski said: “The implementation of AI and its subsets in many companies is still in its early stages, as evidenced by the prevalence of small AI teams.

“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. 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.”

Deep learning is a good example of where there is plenty of room for progress to be made, Grzybowski reckons, insisting it is one of the fastest developing areas of AI, in particular when it comes to its application in natural language processing, natural language understanding, chatbots, and computer vision.

He maintains that many innovative companies are trying to use deep learning to process unstructured data such as images, sounds, and text.

However, Grzybowski points out that AI is still most commonly used to process structured data, which is evidenced by the high popularity of classical machine learning methods such as linear or logistic regression and decision trees.

He concluded: “To adapt AI to unstructured data, the technology will need to mature further. This is why initiatives such as machine learning operations (MLOps) have a major role to play, as long-term success will only be achieved when data scientists and operations professionals are all on the same page and fully committed to making AI and machine learning work for everyone.”