We are in the midst of a massive experiment in which the relationship between human intelligence and its computer-based analogs is changing at an accelerating pace. The process of both designing technologies such as machine learning (ML) to best meet people’s needs and to adapt ourselves to the new possibilities opened up by these tools is not entirely clear or predictable.The concept of coevolution from biology offers one framework for thinking about these changes. The essential idea is that two, or more, interacting species reciprocally affect each other's evolution. These relationships can range from symbiotic to predator versus prey. Humans have been coevolving with machines for a long time, but in certain areas, the balance of control is fundamentally shifting with advances in areas such as artificial intelligence. For example, we have literally been in the driver’s seat with cars, but that relationship is in the process of a role reversal. In the environments we’re creating, the ability of technology to evolve, in many cases, seems to be outstripping our own. There are many unknowns about how people evolve and adapt along with their processor-based counterparts, including:
- How might new generations of data analysis tools transform the ways people think about, and solve, problems?
- As ML and related technologies advance, how will that reshape, or remove, the role of human analysts?
- How will human-computer interactions and interfaces change as machines become better at mimicking human behavior?
- Coevolution can take many forms from adversarial to symbiotic. Will machines eat the proverbial lunch of many human analysts, propel them to a higher level of ability, or some combination of both?
- Some pairs of species form highly specialized relationships with each other that can be both a real advantage and a tremendous vulnerability. What are the risks of dependence and overspecialization?
The talk will examine the idea of coevolution in this context and what UX design and data science can do to help people adapt effectively to changing environments.