American workers adopted artificial intelligence into their work lives at a remarkable pace over the past few years, ...
News Medical on MSN
New AI learning tool bridges communication gaps between autistic and neurotypical people
People with autism have brains that are wired differently. This can make them especially strong in some areas-such as ...
Researchers identified a major decline in neural activity and retention when students used AI for writing. We need to empower students to leverage AI to nurture their potential as creative thinkers.
Scientists reveal how artificial intelligence can learn emotion concepts the way humans do, using bodily responses and context.
4don MSN
Google’s work in schools aims to create a ‘pipeline of future users,’ internal documents say
Newly filed internal documents show how Google viewed its work with schools as a way of turning children into lifelong ...
11don MSN
Lego's latest educational kit seeks to teach AI as part of computer science, not to build a chatbot
Last week at CES, Lego introduced its new Smart Play system, with a tech-packed Smart Brick that can recognize and interact ...
Boomer Anne Golberg, 73, teaches other seniors how to master the art of using an iPhone. She says the technology isn't as ...
With countless applications and a combination of approachability and power, Python is one of the most popular programming ...
Opinion
The Hechinger Report on MSNOpinion
OPINION: We cannot wait until high school or college to integrate computer science lessons
The future of work will demand fluency in both science and technology. From addressing climate change to designing ethical AI systems, tomorrow’s challenges will require interdisciplinary thinkers who ...
Artificial intelligence is transforming career and technical education and showing students real-world career pathways.
At a hackathon over the weekend, students worked with real brain data to create new tools for brain-machine systems. The post ...
Tech Xplore on MSN
Mistaken correlations: Why it's critical to move beyond overly aggregated machine-learning metrics
MIT researchers have identified significant examples of machine-learning model failure when those models are applied to data other than what they were trained on, raising questions about the need to ...
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