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A research paper makes the most impact when its methods, data and code are available for others to use and build on. We highlight the benefits of good sharing practices with a new type of article, reusability reports.
Artificial intelligence can be defined as intelligence demonstrated by machines. But what counts as intelligence, and how intelligence is implemented in different kinds of machines, robots and software varies across disciplines and over time.
Robots can relieve humans of dangerous tasks. With the pandemic making physical contact potentially dangerous due to the risk of contagion, a new focus for robotic applications in healthcare has come into view.
The challenge of practically integrating an ethical and social approach in the development and implementation of AI needs to be urgently addressed, to help restore public trust in technology.
OpenAI released a beta version of its language model, GPT-3. As artificial writing permeates our lives, the challenge is how to think clearly about what it is and what impact it could have on society.
AI tools used in society often enhance inequality, affecting Black lives disproportionally. Addressing this issue will require more than technological solutions. Researchers and experts in the field are overwhelmingly white and need to engage and listen to those experiencing the harm.
Expectations are high for AI to help fight COVID-19. But before AI tools can make an impact, global collaboration and high-quality data and model sharing are needed.
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 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?
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?
