Like “big data,” today’s “artificial intelligence” is all about new tools and approaches for making sense of and profiting from the ever-increasing flood of data. It is the most recent stage in the steady evolution of computer technology since the late 1940s, which has been driven by the increasingly sophisticated and varied use of the key product of computers—digital data.
Specifically, today’s “artificial intelligence” is the latest stage in the evolution of the fruitful marriage of computer engineering and statistics, of teaching computers to learn from data, or “machine learning.” What is frequently overlooked is that this has been—and will continue to be—an evolution in how humans teach machines to learn and how humans learn from automated processes to improve their work. In other words, we are witnessing today a new stage in the steady progress over the last seventy years in the scale and scope of augmented intelligence.
The recent success of the latest variant of machine learning, “deep learning,” in performing well on a number of new tasks for computers such as object identification, has led many observers to predict (yet again) the imminent arrival of machines endowed with human-like intelligence (or even “super-intelligence”) and the disappearance of many (or even all) jobs.
But the recent reports of the demise of human intelligence and the rise of the artificial kind are greatly exaggerated and completely miss the potential for the further advancement of augmented intelligence, of humans and machines working together.
A recent Forrester Research report outlines a framework for understanding how augmentation takes shape, breaking the tasks that workers perform into three categories, each of which grows increasingly machine-centric:
Providing information to help employees act: understanding processes and gaining access to real-time information are common forms of technology augmentation for employees today;
Taking on part of the workload: assisting in the execution of tasks in collaboration with human workers;
Taking over certain workloads: human workers manage and guide machines that take on repetitive, predictable actions.
Forrester notes that deep learning and other artificial intelligence-based bots—from Robotic Process Automation (RPA) to chatbots to intelligent agents to physical robots—are starting to make contributions to augmenting human work, but they work best in narrow, specific contexts.
For example, P&G has developed a bot named Lucy to help answer questions from employees about technical issues and benefits, increasing productivity in specific areas of the company by up to 30%. Wal-Mart has deployed about 500 bots throughout the company performing various tasks, from answering questions to helping employees sift through documents. AT&T has deployed about 1,000 bots, also performing a range of tasks from helping technicians activate equipment for customers to aggregating data for service orders and customer service reports.
Augmentation of existing work is not limited to internal processes. In many enterprises, bots are helping employees provide better service to customers. For example, KLM has developed a bot that answers passenger questions and helps them book a ticket and delivers booking confirmation, check-in reminders, boarding passes, and flight status updates. Domino’s Pizza has introduced multiple bots on different social media and workplace collaboration channels, allowing their customer to order pizzas and track their delivery.
Deep learning or advanced machine learning is helping enterprises make further progress in human-machine collaboration, using augmented intelligence to make employees more productive and delight their customers.