AI can’t steal your job if you work alongside it — here’s how we might collaborate
Even in the future, there will still be plenty of things humans can do better than bots
Checkmate mates
While chess has been used to illustrate AI-human competition, it also provides an example of collaborative intelligence. IBM’s Deep Blue beat the world champion, but did not render humans obsolete. Human chess players collaborating with AI have proven superior to both the best AI systems and human players.
And while such “freestyle” chess requires both excellent human skill and AI technology, the best results don’t come from simply combining the best AI with the best grandmaster. The process through which they collaborate is crucial.
So for many problems – particularly those that involve complex, variable and hard-to-define contexts – we’re likely to get better results if we design AI systems explicitly towork withhuman partners, and give humans the skills to interpret AI systems.
A simple example of how machines and people are already working together is found in the safety features of modern cars.Lane keep assisttechnology uses cameras to monitor lane markings and will adjust the steering if the car appears to be drifting out of its lane.
However, if it senses the driver is actively steering away, it will desist so the human remains in charge (and the AI continues to assist in the new lane). This combines the strengths of a computer, such as limitless concentration, with those of the human, such as knowing how to respond to unpredictable events.
There is potential to apply similar approaches to a range of other challenging problems. In cybersecurity settings, humans and computers could work together to identify which of the many threats from cybercriminals are the most urgent.
Similarly, in biodiversity science, collaborative intelligence can be used to make sense of massive numbers of specimens housed in biological collections.
Laying the foundations
We know enough about collaborative intelligence to say it has massive potential, but it’s a new field of research – and there are more questions than answers.
Through CSIRO’s CINTEL program we will explore how people and machines work and learn together, and how this way of collaborating can improve human work.Specifically, we will address four foundations of collaborative intelligence:
Robots reimagined
One of our projects will involve working with theCSIRO-basedrobotics and autonomous systems team to develop richer human-robot collaboration. Collaborative intelligence will enable humans and robots to respond to changes in real time and make decisions together.
For example,robotsare often used to explore environments that might be dangerous for humans, such as in rescue missions. In June,robots were sentto help in search and rescue operations, after a 12-storey condobuilding collapsed in Surfside, Florida.
Often, these missions are ill-defined, and humans must use their own knowledge and skills (such as reasoning, intuition, adaptation and experience) to identify what the robots should be doing. While developing a true human-robot team may initially be difficult, it’s likely to be more effective in the long term for complex missions.
This article byCecile Paris, Chief Research Scientist, Knowledge Discovery & Management,CSIROandAndrew Reeson, Economist, Data61,CSIRO, is republished fromThe Conversationunder a Creative Commons license. Read theoriginal article.
Story byThe Conversation
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