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Category: education – Page 48

Over the past few years, we have taken a gigantic leap forward in our decades-long quest to build intelligent machines: the advent of the large language model, or LLM.

This technology, based on research that tries to model the human brain, has led to a new field known as generative AI — software that can create plausible and sophisticated text, images and computer code at a level that mimics human ability.

Businesses around the world have begun to experiment with the new technology in the belief it could transform media, finance, law and professional services, as well as public services such as education. The LLM is underpinned by a scientific development known as the transformer model, made by Google researchers in 2017.

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The use of artificial intelligence has played an important role in science teaching and learning. The purpose of this study was to fill a gap in the current review of research on AI in science education (AISE) in the early stage of education by systematically reviewing existing research in this area. This systematic review examined the trends and research foci of AI in the science of early stages of education. This review study employed a bibliometric analysis and content analysis to examine the characteristics of 76 studies on Artificial Intelligence in Science Education (AISE) indexed in Web of Science and Scopus from 2013 to 2023. The analytical tool CiteSpace was utilized for the analysis.

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The Impact of chatGPT and other large language models on physics research and education (2023)
Event organizers: Kevin Burdge, Joshua Borrow, Mark Vogelsberger.
Session 3: “The use of large language models in teaching/administration”

Capstone talk: “LLMs for Physics, and Physics for LLMs“
Speaker: Dr Stephen Wolfram (Wolfram Research)

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The more physicists use artificial intelligence and machine learning, the more important it becomes for them to understand why the technology works and when it fails.

The advent of ChatGPT, Bard, and other large language models (LLM) has naturally excited everybody, including the entire physics community. There are many evolving questions for physicists about LLMs in particular and artificial intelligence (AI) in general. What do these stupendous developments in large-data technology mean for physics? How can they be incorporated in physics? What will be the role of machine learning (ML) itself in the process of physics discovery?

Before I explore the implications of those questions, I should point out there is no doubt that AI and ML will become integral parts of physics research and education. Even so, similar to the role of AI in human society, we do not know how this new and rapidly evolving technology will affect physics in the long run, just as our predecessors did not know how transistors or computers would affect physics when the technologies were being developed in the early 1950s. What we do know is that the impact of AI/ML on physics will be profound and ever evolving as the technology develops.

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Study math for long enough and you will likely have cursed Pythagoras’s name, or said “praise be to Pythagoras” if you’re a bit of a fan of triangles.

But while Pythagoras was an important historical figure in the development of mathematics, he did not figure out the equation most associated with him (a2 + b2 = c2). In fact, there is an ancient Babylonian tablet (by the catchy name of IM 67118) which uses the Pythagorean theorem to solve the length of a diagonal inside a rectangle. The tablet, likely used for teaching, dates from 1770 BCE – centuries before Pythagoras was born in around 570 BCE.

Another tablet from around 1800–1600 BCE has a square with labeled triangles inside. Translating the markings from base 60 – the counting system used by ancient Babylonians – showed that these ancient mathematicians were aware of the Pythagorean theorem (not called that, of course) as well as other advanced mathematical concepts.

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For nearly five years, Austin Energy’s EVs for Schools program has provided access to electric vehicle charging infrastructure and related technology curriculum to more than 150 schools across Central Texas. Now, AE is gearing up for the rollout of its upgraded program, adapted to meet the changing landscape of EV technology.

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Educators just started recovering from the profound impact of the COVID-19 pandemic. Some studies assess that the learning loss may never be recovered. However, a new challenge has crashed on the shores of education — AI — which could be even more impactful. In this post, we look at the challenges that AI brings to education, some ideas, and steps being taken.

A look at the newly announced Generative AI guidance from UNESCO, and others. An explanation of what they mean for application.

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The 2 SOPS or 2nd Space Operations Squadron commander, Lt Col Robert Wray… More.

Of all the missions the Space Force performs daily for a grateful nation, there is none more ubiquitous and essential than GPS. Today’s soldiers and sailors depend on reliable, accurate, and secure GPS as much as they do any weapon they employ. Meanwhile, the rest of the world is just as dependent on GPS to enable basic mobility and underpins every other sector of the modern global economy. The criticality of secure global navigation and timing to both warfighting and the national economy makes it unique – we simply could not go a day without space. In so few words, GPS’ future is ground zero for the new space race.

The 2 SOPS or 2nd Space Operations Squadron commander, Lt Col Robert Wray reminds me that “14 of the 16 critical infrastructures designated by the Department of Homeland Security rely on 24/7 GPS to operate for the country.” But the newest GPS satellites in use today are the same school bus sized ones Gen. Hyten has lamented are, “juicy targets” for our adversaries – marvels of modern engineering, yes, but no longer sufficient to meet modern needs.

Alternatives to GPS, categorically called Global Navigation Satellite Systems (GNSS), are growing rapidly because the old GPS system we rely on offers neither the precision nor security needed in an increasingly autonomous, rule based, and precisely timed world. What exactly needs to change then, aside from smaller, faster satellites as technology becomes more efficient and readily available? There are major challenges with the current system that today’s Guardians are already working on. But to usher in a new and improved GPS capability, the government needs to adopt artificial intelligence and machine learning to enhance squadron operations, work to better integrate commercial software into current GPS constellation to get the most out of current capabilities, and continue to invest in the next generation of leaders. Private capital has begun aligning with companies aiming to solve these future deficiencies, in a race against pacing threats like China and Russia.

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