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3 Advances in Philosophy That Made Science Better

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.

Accelerating science with human-aware artificial intelligence

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.

Generative AI And Data Science Have Mightily Paired Up To Reinvent Data Strategies, Exemplified Via Release Of OpenAI’s ChatGPT Code Interpreter

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.

Foundations Of Generative AI

Generative AI is the latest and hottest form of AI and has caught our collective devout attention for being seemingly fluent in undertaking online interactive dialoguing and producing essays that appear to be composed by the human hand. In brief, generative AI makes use of complex mathematical and computational pattern-matching that can mimic human compositions by having been data-trained on the text and other content found on the Internet. For my detailed elaboration on how this works see the link here.

New center merges math, AI to push frontiers of science

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.

NASA marks the James Webb Space Telescope’s first year of science with release of new image

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.

Data-Driven Science: How AI and Open Data will Revolutionize Scientific Discovery

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?

The Science of Sleep: How to Get Better Sleep Every Night

We all know the feeling of waking up groggy and exhausted, struggling to find the energy to tackle the day ahead. The key to breaking free from this cycle lies in understanding the science of sleep and adopting evidence-based strategies to enhance its quality. So, let’s explore the stages of sleeping and the role of circadian rhythms in regulating our sleep-wake cycles to transform your habits and embark on the journey to obtain better sleep every night!

Get Better Sleep Every Night: Understand the Science

Sleep is far from being a passive state of unconsciousness. On the contrary, it’s a complex process that plays a vital role in our physical and mental well-being. To improve our sleep quality, we must learn more about its stages.

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