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As robotic systems become more autonomous, it gets less straightforward to determine liability when humans are harmed. This is an emerging challenge, with legal implications, in the field of surgical robotic systems. The iRobotSurgeon Survey explores public opinions about responsibility and liability in the area of surgical robotics.
As artificial intelligence becomes prevalent in society, a framework is needed to connect interpretability and trust in algorithm-assisted decisions, for a range of stakeholders.
The worldwide outbreak of COVID-19 has led to great tragedy and poses unprecedented challenges for countries’ healthcare systems. Data has become an important instrument in the global fight against the unprecedented spread of the virus. But how will we ensure a return to previous forms of data privacy once the pandemic subsides?
Martial Hebert is the director of the Robotics Institute and dean of the School of Computer Science at Carnegie Mellon University. We spoke with him at the O’Reilly AI Conference in New York in April 2019, where he delivered a keynote talk, ‘AI and the robotics revolution’, and in a follow-up conversation.
Generative deep learning can produce artificial, natural-looking images and other data, which has many promising applications in research — and in art. But the wide availability of generative models poses a challenge for society, which needs tools and best practices to distinguish between real and synthetic data.
In a recent workshop at the Conference on Neural Information Processing Systems (NeurIPS), future directions at the intersection of neuroscience and AI were considered. A panel discussion at the end of the day started with a provocative question: do we need AI to understand the brain?
Machine learning models have great potential in biomedical applications. A new platform called GradioHub offers an interactive and intuitive way for clinicians and biomedical researchers to try out models and test their reliability on real-world, out-of-training data.
A valid machine model is predictive, but a predictive model may not be valid. The gap between these two can be larger than many practitioners may expect.
Many high-level ethics guidelines for AI have been produced in the past few years. It is time to work towards concrete policies within the context of existing moral, legal and cultural values, say Andreas Theodorou and Virginia Dignum.
There is no shortage of opinions on the impact of artificial intelligence and deep learning. We invited authors of Comment and Perspective articles that we published in roughly the first half of 2019 to look back at the year and give their thoughts on how the issue they wrote about developed.
The reach of artificial intelligence technologies across all parts of society is steadily growing, but so is the awareness of how they can negatively impact human rights. As 2019 draws to a close, the trajectory of technological progress defined by big technology companies is meeting resistance.
A new open challenge tests whether algorithmic models can explain human brain activity in cognitive tasks and encourages interaction between researchers studying natural and artificial intelligence.
Artificial intelligence and machine learning are increasingly seen as key technologies for building more decentralized and resilient energy grids. However, researchers must consider the ethical and social implications of these developments.
Robots are making a transition into human environments, where they can directly interact with us, in shops, hospitals, schools and more. Transparency about robots’ capabilities and level of autonomy should be integrated into the design from the start.
The first Smart Cities Robotics Challenge, organized by the European Robotics League, took place from 18–21 September at the Centre:MK shopping centre in Milton Keynes. The competition tested the ability of robots to interact with humans in everyday tasks as well as with the digital infrastructure of a smart city.
