AI can help doctors tackle new problems.
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Researchers from New York University are designing AI algorithms to help predict COVID-19 outcomes.
Artificial intelligence can do what humans can’t – connect the dots across the majority of coronavirus research.
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The scientific community is churning out vast quantities of research about the coronavirus pandemic – far too much for researchers to absorb. An AI system aims to do the heavy lifting for them.
There’s a creeping conformity taking place on the web.
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Design bloggers have long had creeping suspicion, so a team of researchers decided to analyze the aesthetics of nearly 10,000 websites.
Job ads are 12% lower than expected this year.
LiDAR helps an autonomous vehicle ‘visualize’ what’s around it.
Yulong Can with data from Baidu Apollo
Driverless vehicles rely heavily on sensors to navigate the world. They're vulnerable to attack if bad actors trick them into 'seeing' things that aren't there, potentially leading to deadly crashes.
Connecting the dots.
Machine learning technology was first to sound the alarm about the new coronavirus. Its success illustrates how AI is boosting epidemiology.
We created a reading-machine that finds poetry hidden in plain sight in popular books. In doing so, we are exploring Natural Language Processing, Machine Learning and reading in a digitised world.
Social media platforms are increasingly offering mental health support tools to users. The jury is still out on whether it will do any good
Going for a run… with big data.
The use of online health platforms is on the rise, allowing us to track and share our personal data. While such platforms have promise, significant scientific, ethical and privacy questions remains.
Automated algorithms – not humans – are increasingly making decisions about who’s eligible for welfare benefits.
States are increasingly turning to machine learning and algorithms to detect fraud in food stamps, Medicaid and other welfare programs – despite little evidence of actual fraud.
Understanding how the computations in the brain go wrong could help scientists develop treatments for neurological disorders.
The knowledge produced in designing and developing artificial neural networks may provide new insights into how our brains work.
Wonders of the world.
Machines mostly innovate on narrow dataset, which limits their creativity.
Advancements in data analytics can prevent food wastage and save farmers from significant losses along the fresh produce value chain.
Traditional companies need to embrace high-quality data gathering to avoid being left behind by the next industrial revolution.
When algorithms make decisions with real-world consequences, they need to be fair.
A machine learning expert predicts a new balance between human and machine intelligence is on the horizon. For that to be good news, researchers need to figure out how to design algorithms that are fair.
Sharing electronic medical records broadly could identify trends as well as mistakes, but it also poses privacy concerns.
A project involving tens of millions of patient records poses ethical issues, even though patients could ultimately gain. Here's why privacy concerns are a hurdle.
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Beware those who say it is a solved problem.
Close-up on the circuitry of the Vesuvius quantum computer, announced in 2012 by the Canadian firm D-Wave Systems.
On October 23 Google announced that it built a quantum computer thousands of times faster than classic computers. This could have immense impacts on finance, cryptography and other fields.
Responding to the ever-growing amount of email can be a stress-inducing job task.
Google's Smart Compose feature is meant to help deal with the deluge of email, but does it increase the pressure to respond quicker?
Can a machine understand style? Facebook’s Fashion++ system recommends small changes to an outfit to make it more fashionable.
Algorithm-based apps can recommend clothes based on what other people have worn, but they have a long way to go before they understand fashion.