In the era of digital transformation, businesses are reporting machine learning (ML) their top business priority. Companies are rapidly beginning to rely on ML to streamline their processes, ultimately increasing efficiencies. While ML is being implemented across many industries, it’s only the start of this journey. In this Ask the Expert, we dive deeper in topics about ML with our expert, Sumeet Singh.
How is machine learning changing the way we work?
Machine learning is helping us make smarter decisions in the workplace. At the same time, ML is pushing the boundaries of human creativity and ingenuity. Aided with machine intelligence, we are able to make decisions that are not only qualitatively better, but also impossible a few years ago.
For example, ML has fundamentally changed how to perform information processing and reporting. Traditionally it was solely a human specialist’s responsibility to interpret the data. ML is changing that dynamic. Its going a step further by recommending actions and in some cases, executing those actions. This allows for human counterparts to make safer, more confident, and qualitatively better decisions.
We have already increased the efficiency and introduced time savings by using ML-based solutions. A lot of startups and business leaders alike are building a foundation based on ML and AI initiatives. The next wave of change is going to be a significant shift in the way we run businesses.
What is the value in integrating machine learning within the enterprise?
Human workforce is one of the most valuable asset in any enterprise. The value stream that flows out of the enterprise results from the decisions that are made by employees at every level. Integrating machine learning impacts this value chain at multiple levels:
- Qualitative impact: Since machine learning based outcomes result from processing and correlating large amounts of data, it lead to a faster and accurate execution cycle.
- Novel domains: Machine learning-based solutions are helping us make decisions which were not even possible a few years ago. It is creating extremely wide ranging impacts in areas like health diagnostics to save lives, material fatigue detection to prevent infrastructure crisis, improving crop yields, and drug discovery with genetic and molecular analysis, the list goes on.
Mostly, machine learning applications are freeing up the enterprise brain power and allowing them to focus on things that machines are not good at – the human intuition.
By investing in machine learning initiatives, the enterprise can unlock a lot of human potential.
How is the enterprise using machine learning today? What industries are using AI?
It’s hard for think of any major industry that is not using or planning to use machine learning. Some of the biggest investments are going to come from the usual suspects: fintech and insurance. They are closely followed by cross-industry and commerce-based applications. And finally, some of the most impactful ML initiatives will come from news, media, education, cyber security, and healthcare.
There is going to a be a significant increase in machine learning solutions that interact with the physical world. Automobile and IoT industries are on a path to changing how we use our transportation infrastructure and everyday machinery.
Enterprises are already investing and seeing results in HR, sales, marketing, and decision systems. In addition to these fields, there is a significant increase in workforce awareness and ML training programs. Everybody is trying to adjust to the realities of how ML is going to eventually cause a fundamental shift to the way we work.
What are the necessary tools companies need to begin their machine learning initiatives?
The most critical ingredients to begin your machine learning journey is not the technology. The technological and mathematical foundation is already in place and constantly maturing. Instead, a core belief that technology can help you move the ball forward should be at the heart of your initiatives. Once you have knocked skepticism off the list, you need 4 things:
- Simple problem: Smaller initiatives are easier to fund and help you create the necessary foundation and “muscle memory” to handle bigger challenges. It’s a cliche, but when it comes to machine learning, you must learn to walk before you can run.
- Thought diversity: Solutions that emerge from a diversified thought process are usually more meaningful and long lasting. Bringing people together will also allow you to expose a larger audience to the machine learning delivery cycle. Asking your data science team to work on their own and come up with intelligence for your business is probably not a good idea.
- Open platform: Select a technology platform that allows you to build atop community and open source knowledge. Choosing a closed platform is definitely going to hold you back. No one company or set of individuals can keep up with the pace of community development.
- Labelled Data: ML algorithms have a hunger for very large quantities of labelled/annotated data. It varies by the domain, but it usually forms the bulk of work. Start by putting a robust and economically feasible data acquisition and labeling strategy.
What do you predict for the future of machine learning?
Just like computers and the internet made their way into all walks of life, ML is going to be the next big thing to do so. The only difference is that it’s going to happen way faster than we could have ever imagined.
Over the next 5 years, we are going to see intelligent versions of everyday electronics and connected devices. Our interactions with these devices will start to become more natural and human-like. One of the most profound and positive impacts is going to be in the field of medicine and biology. Convergence of multiple technologies, aided with machine learning is already leading to new developments in fields ranging from molecular biological all the way to discoveries of new planets.
Last but not least, there is an optimism as well as fear associated with the rise of intelligent machines. I don’t know if machines are going to take over the planet, but when it comes to the progress and development, I stand in the optimistic camp. I believe that within 20 years, we will make the leap from machine that can do learned activities to machines that can exhibit intelligence. They will help us solve some of the biggest global challenges in economics, health, and science.