We know students learn science concepts better when their learning is embedded in real-world issues. But teachers are currently not well prepared to teach science in this way.
Philosophy is often ridiculed by scientists as being little more than armchair speculation. Stephen Hawking famously declared it “dead.” This is unfortunate because the scientific method itself is a manifestation of philosophical thought arising from the subdiscipline known as epistemology. Historically, science and philosophy have worked hand-in-glove to advance our understanding of the world. In fact, “science” went by the moniker “natural philosophy” for much of history.
Scientists perhaps should be a bit more grateful. Advances in social and political philosophy helped prevent some scientists who upset the established order from being executed — but that’s a discussion for another day. Here, we will examine three philosophical insights that directly led to advances in how science is performed.
“What makes science different from everything else?” is inherently a philosophical question. That means that philosophy helps define what science is. This is important because, to learn about the world, we need to be sure of the validity of our methods. For most of the history of Western philosophy, Aristotle’s ideas reigned supreme. While Aristotle’s idea of finding causes through science was largely based on deductive reasoning, experimentation was not seen as a vital part of science.
Can human-aware artificial intelligence help accelerate science? In this article, the authors incorporate the distribution of human expertise by training unsupervised models on simulated inferences cognitively accessible to experts and show that this substantially improves the models’ predictions of future discoveries, but also enables AI to generate high-value alternatives that complement human discoveries.
In today’s column, I am going to identify and explain the momentous pairing of both generative AI and data science. These two realms are each monumental in their own respective ways, thus they are worthy of rapt attention on a standalone basis individually. On top of that, when you connect the dots and bring them together as a working partnership, you have to admire and anticipate big changes that will arise, especially as the two fields collaboratively reinvent data strategies all told.
This is entirely tangible and real-world, not merely something abstract or obtuse.
I will first do a quick overview of generative AI. If you are already versed in generative AI, perhaps do a fast skim on this portion.
If you’ve found yourself befuddled by extraordinary scientific-sounding claims, you’re not alone. But this centuries-old lesson can help.
Students at the Bay Area’s best universities once dreamed of working for Apple, Google and Meta. Then the lay-offs happened | 1843 magazine.
With artificial intelligence poised to assist in profound scientific discoveries that will change the world, Cornell is leading a new $11.3 million center focused on human-AI collaboration that uses mathematics as a common language.
The Scientific Artificial Intelligence Center, or SciAI Center, is being launched with a grant from the Office of Naval Research and is led by Christopher J. Earls, professor of civil and environmental engineering at Cornell Engineering. Co-investigators include Nikolaos Bouklas, assistant professor of mechanical and aerospace engineering at Cornell Engineering; Anil Damle, assistant professor of computer science in the Cornell Ann S. Bowers College of Computing and Information Science; and Alex Townsend, associate professor of mathematics in the College of Arts and Sciences. All of the investigators are field faculty members of the Center for Applied Mathematics.
With the advance of AI systems – built with tangled webs of algorithms and trained on increasingly large sets of data – researchers fear AI’s inner workings will provide little insight into its uncanny ability to recognize patterns in data and make scientific predictions. Earls described it as a situation at odds with true scientific discovery.
To celebrate the completion of the James Webb Space Telescope’s first year of science operations, NASA has released a close-up image of the birth of sun-like stars.
The image, captured on Webb’s telescope is a small star-forming region in the Rho Ophiuchi cloud complex – the nearest star-forming region to Earth.
The region’s proximity at 390 light-years allows for a highly detailed close-up, with no foreground stars in the intervening space.
Dr. ryan brinkman-vice president and research director, dotmatics
Scientists have long been perceived and portrayed in films as old people in white lab coats perched at a bench full of bubbling fluorescent liquids. The present-day reality is quite different. Scientists are increasingly data jockeys in hoodies sitting before monitors analyzing enormous amounts of data. Modern-day labs are more likely composed of sterile rows of robots doing the manual handling of materials, and lab notebooks are now electronic, in massive data centers holding vast quantities of information. Today, scientific input comes from data pulled from the cloud, with algorithms fueling scientific discovery the way Bunsen burners once did.
Advances in technology, and especially instrumentation, enable scientists to collect and process data at an unprecedented scale. As a result, scientists are now faced with massive datasets that require sophisticated analysis techniques and computational tools to extract meaningful insights. This also presents significant challenges—how do you store, manage, and share these large datasets, as well as ensure that the data is of high quality and reliable?
Once dismissed as a new age fad, lucid dreaming has gained attention as a possible performance enhancer and therapeutic tool.
