Welcome to the fourth industrial revolution—a world where artificial intelligence (AI) drives today’s technological disruption, blurring the lines between tangible and digital realities. Companies continue to shift toward AI and machine learning (ML) processes, and business leaders are quickly realizing the potential gains from investing in them. These gains include faster, smarter automation, predictive analytics, and new-and-improved ways to establish customer connections—among countless other possibilities.
According to the International Data Corporation (IDC), spending on AI and ML will grow from $12B in 2017 to $57.6B by 2021. Deloitte Global predicts the number of machine learning pilots and implementations will double in 2018 compared to 2017, and double again by 2020.
These predictions are beginning to play out. In 2018, the Massachusetts Institute of Technology announced its plan to create a new college (backed by a planned investment of $1 billion) dedicated to “educating the bilinguals of the future: people in fields like biology, chemistry, politics, history, and linguistics who are also skilled in the techniques of modern computing that can be applied to them.”
AI and ML strategies are rapidly evolving. No longer just an intangible concept, AI solutions have already entered our daily lives in the form of smart speakers, customer service chatbots, and autonomous vehicle features.
So, what is machine learning?
By offering a specific set of guidelines, scientists enable machines to create their own logic, thereby developing the ability to explore and analyze data on their own. The term “machine learning” helps to define this process. Machine learning is one of the main ways artificial intelligence is created.
What makes machine learning tick?
Think of algorithms as the rules machines are instructed to follow. Initially, machines are introduced to a set of data and “asked” to begin exploring that information. This introductory set of data is called training data. Once the machine has worked through its training data, it can begin recognizing patterns and even make decisions according to specific algorithms.
Some computers can even aim for specific goals and receive rewards upon meeting them. As this “learning” process evolves, computers are able to alter new inputs into outputs. These outputs might include: new data, labeled data, decisions, and more.
While ultimately machines might arrive at an operations state where human intervention is no longer necessary, we’re not there yet. According to a recent report from the McKinsey Global Institute, AI techniques require models to be retrained to match potential changing conditions, so training data must be refreshed frequently. McKinsey notes that in one-third of the cases, the model needs to be refreshed at least monthly, and almost one in four