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As developments in machine learning (ML) are expected to progress at a phenomenal pace, it is set to become one of the most powerful tools for businesses to enhance productivity and drive innovation. While ML, one of the most popular artificial intelligence (AI) applications, holds a lot of promise for businesses, is the role of data scientist today already evolving in order to keep up with the change?
What Is Next in AI
Continued advances in AI will see autonomous systems perceive, learn, decide, and act on their own, but to ensure the effectiveness of these systems, the machine will need to be able to explain their decisions and actions to humans. This is so called explainable AI.
“In the future, many AI systems are going to interact with people, especially those who will take responsibilities, hence the reason why AI needs to be explainable, meaning that the behavior of the system needs to be easily expected and interpreted by people,” said Min Sun, Chief AI Scientist at Appier.
Sun also pointed out that in the future, AI is going to be less supervised, which means that it will require less human inputs, and be more creative.
Data science was previously concerned with time-consuming ML tasks, such as data wrangling and feature engineering, which could take up 80 percent of data scientist’s time, but such tasks can be automated sooner or later, according to Deloitte’s Technology, Media and Telecommunications Predictions 2018 report.
Such advances in AI will give data scientists more time to execute more complex tasks. However, it brings up a problem: a majority of data scientists doesn’t possess the required advanced machine learning skills, such as deep learning (DL), a subfield of ML.
The Impact of Machine Learning on Businesses
Previously, companies might have spent a lot of time doing guesswork based on consumer data gathered online and offline, which is usually fragmented and siloed. With an AI-based approach, brands are able to unify data across different channels for a holistic view and analysis of the audience and their conversion journey.
Machine learning and deep learning allow a computer to take in huge sets of data and not only predict the outcome, but also understand what the desired output should be. It can be integrated into many aspects of digital marketing, such as predicting consumer behavior and campaign outcomes, marketing automation, sophisticated buyer segmentation and sales forecasting.
With these technologies, businesses have a more efficient and cost-effective way to build trustworthy AI systems to be used by professionals and/or to be naturally interacted with human users, according to Hsuan-Tien Lin, Appier’s Chief Data Scientist.
So, it’s no surprise to see that businesses are increasingly catching up on the adoption of AI technology. According to the International Data Corporation (IDC), AI continues to be a key spending area for companies in the near future, with worldwide spending on cognitive and AI systems increasing 54.2 percent in 2018 to US$19.1 billion. That number could go up to US$52.2 billion in 2021, IDC predicted.
Bridging the Machine Learning Skills Gap
As more businesses look to adopt AI techniques like machine learning and deep learning, data scientists are urged to upskill, in order to keep up with the current trends. Rudina Seseri, Founder and Managing Partner at Glasswing Ventures, wrote in Forbes, “Data scientists – at least the successful ones – will evolve from their current roles to becoming machine learning experts or some other new category of expertise, yet to be given a name”.
Leading tech companies such as Google and Microsoft have already been offering relevant courses aiming to help bridge the talent gap. For example, Google not only made its ‘Machine Learning Crash Course’ available to the general public earlier this year as part of the company’s ‘Learn With Google AI’ initiative, it has also launched a machine learning specialization on Coursera, an online learning platform.
Andrew Ng, one of the world’s best-known AI experts, also launched a set of courses on deep learning through Coursera in 2017, hoping to help more people get up to speed on key developments in AI.
While technical skills will be foundation of the role of data scientists, it’s crucial for them to master human-centric skills too. Data scientists will need to develop a better understanding of the overarching business strategy and business challenges in real-world scenarios, in order to create solutions that can solve real problems.
Businesses are looking for a total solution, Sun pointed out. For instance, self-driving car manufacturers need a system consisting of perception, communication, decision-making and control. In the old days, each module was designed separately, but this has been transitioning to more jointly design since the fatal self-driving Uber crash, where the perception system identified the pedestrian, but the decision-making module failed to react.
“The ability for scientists to design a complete system consisting of multiple ML modules will become more and more important,” he said. “In the future, data scientists will need to have the modeling and analysis skills at the system-level to provide business people with the right total solution to the market.”