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In the conversation around the application and adoption of artificial intelligence (AI) and cognitive technologies, two recurring types of solutions usually come up: AI solutions meant to work in conjunction with people to help them accomplish their tasks better, and AI solutions meant to function entirely independent of human intervention. In Cognilytica’s nomenclature, we’ve usually referred to the sorts of solutions where AI is helping people do their jobs better as “augmented intelligence” solutions while those meant to operate independently as “autonomous intelligence” solutions. However, increasingly we’ve been seeing reference to the term “assisted intelligence” solutions that attempt to split the hair between different types of AI solutions that are helping people do their jobs. Is the term “assisted intelligence” meaningful and is there a way to provide a clear delineation between “augmented” and “assisted” types of intelligence solutions?
The Various Definitions of Assisted Intelligence and How they Differ from Augmented Intelligence
The folks from PwC have been most visible in promoting the different definitions of augmented vs. assisted intelligence. From their perspective, they see a continuum of human-machine intelligence interaction ranging from situations where machines are basically repeating many of the tasks humans are already doing (assisted) to enabling humans to do more than they are currently capable of doing (augmented) to fully accomplishing tasks on their own without human intervention (autonomous). Others are defining the assisted — augmented — autonomous continuum as being one of control and decision-making. From this perspective, in assisted intelligence approaches, machines might be doing the action but humans are making the decisions, while with augmented intelligence, machines are doing the action but there’s collaborative human-machine decision-making, and in autonomous systems machines are making both the actions and decisions. Another group defines things even more simply: assisted intelligence improves what people and organizations are already doing, augmented intelligence enables organizations and people to do things they couldn’t otherwise do, and autonomous intelligence systems act on their own.
While these definitions make some sense, the challenge is applying these terms to the various real-world situations in which they might be used. For example, it’s fairly clear that a Level 5 Autonomous vehicle is exhibiting truly autonomous behavior, especially when there’s no control of the vehicle even possible. But what about Level 2 or Level 3 vehicles? There’s clearly some AI at work here keeping the vehicle in lane and managing various speed and navigation changes. Is this augmented or assisted? I suppose since the system is not providing any capabilities that a human can’t otherwise do, you could call this simply assisted. However, that means in autonomous vehicle situations there is no augmented intelligence role, from that definition.
In other situations, it gets more complex. Are machine-language powered solutions for online recommendation systems augmented or assisted or autonomous? I suppose people could be manually providing recommendations for products, but that’s hardly possible in the context of millions of customers and tons of website traffic. So are these AI solutions augmented or assisted or perhaps they’re autonomous? We tend to think of collaborative robots (cobots) as augmented intelligence because they’re giving humans skills and capabilities they don’t already have. But if they’re just being used to assemble widgets or move things from place to place are they really augmenting anything, or are they just assisting? As we can see the assisted vs. augmented vs. autonomous difference is sometimes entirely from the perspective of not what the AI-enabled system can do, but what it’s actually doing at that time.
Using the Terms to Define Roles and Boundaries for AI Systems
Using these terms are all about providing some expectation about what the role of the systems are and how we can understand where the human needs to be in the loop. In many ways, the terms are used to define the boundaries of what we expect the AI systems, and their human counterparts, to do. From that perspective, the terms can be useful in project management and ROI contexts. When an organization is looking to use cognitive technology as part of their processes, they should first look at the use case in which those AI technologies will provide the best impact and ROI. After deciding that, then they can look at the scope of what they want these systems to do, what they want their people to continue doing, and what the scope of control they will give to the system in each of the specific parts of the application and processes they will be employing the AI system. From this perspective, we can say that the AI solution is playing an assisted, augmented, or autonomous role in the solution, and this role might change over the course of how the cognitive technology is applied over time.
As part of an AI project management exercise, organizations need to determine the range and scope of what they want their cognitive technologies to do and provide. Just like in many non-AI project, project managers first need to identify the business need and required return. Then, they need to define the various aspects of data requirements and functionality to solve those business needs. What makes AI projects unique is that they also need to consider the human-machine dynamic. Which elements of the AI solution do they want to hand over entirely to machines to execute, and which things do they want to retain for their human resources? In the scope of these decisions, then, it makes a lot more sense to create an AI role matrix which can evolve over time. This matrix specifies that for a particular function or process, the AI system will be providing an assisted, augmented, or autonomous role, and defines specifically what will be under the operation and control of the machine, and what will be under the operation and control of the person. Doing so not only helps to delineate the capabilities of the system, but also helps to assuage any concerns people might have about these AI systems taking their jobs.