Human and artificial intelligence interaction are presenting prodigious and exciting technological opportunities for mutual development even in today’s current technological climate, but the real potential for mutual development is in the foreseeable future and beyond which has the potential to be mind-boggling. Artificial intelligence is becoming good at many “human” jobs diagnosing disease, translating languages, providing customer service and it’s improving fast. This is raising reasonable fears that AI will ultimately replace human workers throughout the economy. But that’s not the inevitable, or even most likely, outcome. Never before have digital tools been so responsive to us, nor we to our tools. While AI will radically alter how work gets done and who does it, the technology’s larger impact will be in complementing and augmenting human capabilities, not replacing them.
AI and human beings bring their own unique traits with each learning from the other, humans bring attributes including experience, values and judgement which can weave together with the enormous attributes that AI can contribute. AI and human interaction have the potential to develop and push the boundaries of not just technologies on earth but also space exploration.
Many companies have used AI to automate processes, but those that deploy it mainly to displace employees will see only short-term productivity gains. Research conducted by Harvard Business Review, involving 1,500 companies, they found that firms achieve the most significant performance improvements when humans and machines work together. Through such collaborative intelligence, humans and AI actively enhance each other’s complementary strengths: the leadership, teamwork, creativity, and social skills of the former, and the speed, scalability, and quantitative capabilities of the latter. What comes naturally to people can be tricky for machines, and what’s straightforward for machines (analyzing gigabytes of data) remains virtually impossible for humans. Business requires both kinds of capabilities.
To take full advantage of this collaboration, companies must understand how humans can most effectively augment machines, how machines can enhance what humans do best, and how to redesign business processes to support the partnership. Some guidelines to help companies achieve this and put the power of collaborative intelligence to work:
Humans Assisting Machines
Humans need to perform three crucial roles: They must train machines to perform certain tasks; explain the outcomes of those tasks, especially when the results are counterintuitive or controversial; and sustain the responsible use of machines (by, for example, preventing robots from harming humans).
Machine-learning algorithms must be taught how to perform the work they’re designed to do. In that effort, huge training data sets are amassed to teach machine-translation apps to handle idiomatic expressions, medical apps to detect disease, and recommendation engines to support financial decision making. In addition, AI systems must be trained how best to interact with humans. While organizations across sectors are now in the early stages of filling trainer roles, leading tech companies and research groups already have mature training staffs and expertise
Consider Microsoft’s AI assistant, Cortana. The bot required extensive training to develop just the right personality: confident, caring, and helpful but not bossy. Instilling those qualities took countless hours of attention by a team that included a poet, a novelist, and a playwright. Similarly, human trainers were needed to develop the personalities of Apple’s Siri and Amazon’s Alexa to ensure that they accurately reflected their companies’ brands. Siri, for example, has just a touch of sassiness, as consumers might expect from Apple.
AI assistants are now being trained to display even more complex and subtle human traits, such as sympathy. The start-up Koko, an offshoot of the MIT Media Lab, has developed technology that can help AI assistants seem to commiserate. For instance, if a user is having a bad day, the Koko system doesn’t reply with a canned response such as “I’m sorry to hear that.” Instead it may ask for more information and then offer advice to help the person see his issues in a different light. If he were feeling stressed, for instance, Koko might recommend thinking of that tension as a positive emotion that could be channelled into action.
As AIs increasingly reach conclusions through processes that are opaque (the so-called black-box problem), they require human experts in the field to explain their behavior to nonexpert users. These “explainers” are particularly important in evidence-based industries, such as law and medicine, where a practitioner needs to understand how an AI weighed inputs into, say, a sentencing or medical recommendation. Explainers are similarly important in helping insurers and law enforcement understand why an autonomous car took actions that led to an accident—or failed to avoid one. And explainers are becoming integral in regulated industries—indeed, in any consumer-facing industry where a machine’s output could be challenged as unfair, illegal, or just plain wrong. For instance, the European Union’s new General Data Protection Regulation (GDPR) gives consumers the right to receive an explanation for any algorithm-based decision, such as the rate offer on a credit card or mortgage. This is one area where AI will contribute to increased employment: Experts estimate that companies will have to create about 75,000 new jobs to administer the GDPR requirements.
In addition to having people who can explain AI outcomes, companies need “sustainers”, employees who continually work to ensure that AI systems are functioning properly, safely, and responsibly.
For example, an array of experts sometimes referred to as safety engineers focus on anticipating and trying to prevent harm by AIs. The developers of industrial robots that work alongside people have paid careful attention to ensuring that they recognize humans nearby and don’t endanger them. These experts may also review analysis from explainers when AIs do cause harm, as when a self-driving car is involved in a fatal accident.
Machines Assisting Humans
Smart machines are helping humans expand their abilities in three ways: They can amplify our cognitive strengths; interact with customers and employees to free us for higher-level tasks; and embody human skills to extend our physical capabilities.
Artificial intelligence can boost our analytic and decision-making abilities by providing the right information at the right time. But it can also heighten creativity. Consider how Autodesk’s Dreamcatcher AI enhances the imagination of even exceptional designers. A designer provides Dreamcatcher with criteria about the desired product. For example, a chair able to support up to 300 pounds, with a seat 18 inches off the ground, made of materials costing less than $75, and so on. She can also supply information about other chairs that she finds attractive. Dreamcatcher then churns out thousands of designs that match those criteria, often sparking ideas that the designer might not have initially considered. She can then guide the software, telling it which chairs she likes or doesn’t, leading to a new round of designs.
Throughout the iterative process, Dreamcatcher performs the myriad calculations needed to ensure that each proposed design meets the specified criteria. This frees the designer to concentrate on deploying uniquely human strengths: professional judgment and aesthetic sensibilities.
Human-machine collaboration enables companies to interact with employees and customers in novel, more effective ways. AI agents like Cortana, for example, can facilitate communications between people or on behalf of people, such as by transcribing a meeting and distributing a voice-searchable version to those who couldn’t attend. Such applications are inherently scalable—a single chatbot, for instance, can provide routine customer service to large numbers of people simultaneously, wherever they may be.
Many AIs, like Aida and Cortana, exist principally as digital entities, but in other applications the intelligence is embodied in a robot that augments a human worker. With their sophisticated sensors, motors, and actuators, AI-enabled machines can now recognize people and objects and work safely alongside humans in factories, warehouses, and laboratories.
In manufacturing, for example, robots are evolving from potentially dangerous and “dumb” industrial machines into smart, context-awarel “cobots.” A cobot arm might, for example, handle repetitive actions that require heavy lifting, while a person performs complementary tasks that require dexterity and human judgment, such as assembling a gear motor.
Most activities at the human-machine interface require people to do new and different things (such as train a chatbot) and to do things differently (use that chatbot to provide better customer service). So far, however, only a small number of the companies have begun to reimagine their business processes to optimize collaborative intelligence. Organizations that use machines merely to displace workers through automation will miss the full potential of AI. Such a strategy is misguided from the get-go. Tomorrow’s leaders will instead be those that embrace collaborative intelligence, transforming their operations, their markets, their industries, and most importantly their workforces.