Paper: An Affordance-Based Framework for Human Computation and Human-Computer Collaboration by R. Jordan Crouser and Remco Chang
Summary:
This paper provides a survey of 49 papers on human-computer collaboration systems and interfaces. The authors highlight some affordances that arise from these collaborative systems and propose an affordance-based framework as a common language for understanding seemingly disparate branches of research and indicate unexplored avenues for future work. They discuss various systems, and provide extensions to these systems that provide human adaptability, and machine sensing. Finally, they conclude with a discussion of the utility of their framework in an increasingly collaborative world, and some complexity measures for visual analytics.
Reflection:
This paper focuses on some fundamental questions in mixed-initiative collaborations, such as how does one tell if a problem even benefits from a collaborative technique, and if so, who is the work delegated to? The paper also provides ways to evaluate complexity in different visual analytic setups, but raises more questions, such as what is the best way to evaluate work, and how can we account for individual differences? These suggestions and questions, however, only beget more questions. The nature of work is increasingly complex, requiring more unique ways to measure success that are application-specific. The paper tries to come up with a one-size-fits-all solution for this, but the solution ends up being more generic.
The paper also highlights the need for a more holistic evaluation approach. Typically, ML and AI research is focused solely on the performance of the model. However, this paper highlights the need to evaluate the performance of both the human and the system that they are collaborating with.
The paper talks about human-computer collaboration, mostly focused on visual analytics. There is still more work to be done in studying how applicable this framework is to physical human-computer interfaces, for example, an exoskeleton, a robot that makes cars, etc. Here, there are different abilities of humans and robots, which are not covered in the paper. Perhaps humans’ visual skills may be combined with a robots’ accuracy.
Questions:
- How might one apply this framework in the course of their class project?
- What about this framework is still/no longer applicable in the age of deep learning?
- Will AI ever surpass human creativity, audio linguistic abilities, and visuospatial thinking abilities? What does it mean to surpass human abilities?
- Is this framework applicable for cyber-physical systems? How does it differ?
Will AI ever surpass human creativity, audio linguistic abilities, and visuospatial thinking abilities? What does it mean to surpass human abilities?
I think that AI will definitely eventually surpass the intelligence of humans. I think that we are currently far off from that. But, with enough time, AI’s can definitely catch up. They’re getting smarter and smarter every year and we’re staying the same. I really think that the brain is no more than a very highly advanced computer. The structure of neurons is similar to that of a computer. So, I think there is no reason why a sufficiently advanced A could be better than humans at many tasks.
I think for AI’s to “surpass human abilities,” they would have to have a lower error rate than the average human.
Good catch on the need for holistic evaluation! I didn’t think of that when reading but you are right. I also think that the nature of affordances, at least the way the authors have framed it, can cover a broad range of things. It’s just that there haven’t been any categories defined yet. Human robot interaction can be brought under the umbrella if somebody defines the right set of categories, because the categories proposed in the paper all pertain to software.
I agree with the fact that the paper comes out with one-size-fits-all solution which is generic which is not preferred for such unique and complex problems. I am aware of various evaluation methods of AI based systems; however, I am completely new to the evaluation of humans and measuring the effectiveness of such collaborations. This would be interesting to learn. Additionally, your question on “Will AI ever surpass human creativity?” is interesting to think about. AI nowadays can recognize images effectively and it won’t be surprising that AI would surpass some basic human abilities. Nonetheless, that would require training data and humans would be tasked to do such labeling eventually. Hence it can be certain that for the near future it is not possible to eliminate humans-in-the-loop completely.