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The COVID-19 pandemic poses a historical challenge to society. The profusion of data requires machine learning to improve and accelerate COVID-19 diagnosis, prognosis and treatment. However, a global and open approach is necessary to avoid pitfalls in these applications.
In an unprecedented effort of scientific collaboration, researchers across fields are racing to support the response to COVID-19. Making a global impact with AI tools will require scalable approaches for data, model and code sharing; adapting applications to local contexts; and cooperation across borders.
The attention and resources of AI researchers have been captured by COVID-19. However, successful adoption of AI models in the fight against the pandemic is facing various challenges, including moving clinical needs as the epidemic progresses and the necessity to translate models to local healthcare situations.
Scientists have been getting concerned about the carbon footprint of international meetings and have been asking whether travelling to conferences is the best use of their time and funds. 2020 is turning out to be the year that many organizers decide to go virtual — and this was before COVID-19.
The Catholic Church is challenged by scientific and technological innovation but can help to integrate multiple voices in the ongoing dialogue regarding AI and machine ethics. In this context, a multidisciplinary working group brought together by the Church reflected on roboethics, explored the themes of embodiment, agency and intelligence.
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.
