Image credit: Starsky Robotics via Medium
Starsky Robotics, a self-driving truck company, announced it is shutting down this year. In a medium post the CEO explains in-depth about the challenges that led to the decision and specifically:
The biggest [problem], however, is that supervised machine learning doesn’t live up to the hype.
The reason is the amount of data needed to marginally improve model performance scales exponentially. This leads to dramatic diminishing returns in AI development, and it makes tackling enough edge cases to guarantee safety a prohibtively expensive endeavor.
Another challenge Starsky faced was how most VCs are far more interested in flashy features that don’t work well in real scenarios than important but almost invisible improvements in safety. While the CEO concludes that he’d “love to be wrong,”
The current companies […] will continue to drain momentum over the next two years, followed by a few years with nearly no investment in the space, and (hopefully) another unmanned highway test for 5 years.
Researchers developed an AI model that predicts how likely a Covid-19 patient is to survive based on 3 biomarkers in blood samples. This model is used to help doctors make triage decisions, alerting them about high-risk individuals who should be treated earlier before they exhibit significant symptoms.
As with any data-driven technology being used in making life-death decisions, there are serious concerns. For example, biases in the data could lead to some patients being treated less, reducing their survival rate, which in turn reinforces the model’s belief that these patients have low survival rates:
This is a tool that can also be used to deprive patients of advanced age or with underlying conditions of the right to be treated because a computer has decided they have almost zero chance of survival
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