💵 Do We Really Need CERN’s $17 Billion Supercollider?

Hey there!

ChatGPT against climate change denial, the collider conundrum, and the secret ocean in space—welcome to the latest issue of Hypertextual!

17 Billion Dollar Question

CERN is moving forward with a $17-billion gamble to build a 91-kilometre supercollider under France and Switzerland. The feasibility study revealed “no technical or scientific showstoppers” that would prevent its construction. But there’s a catch: nobody knows what the Future Circular Collider will find. It’s like planning the most expensive surprise party in history, where the guest of honour might not even show up.

Eco-Friendly AI

In a recent study, researchers engaged participants in climate change discussions with GPT-3, an early iteration of ChatGPT technology. This research, involving over 3,000 people with varied political views, revealed that 25 per cent of climate change sceptics shifted towards accepting scientific consensus after their interactions. 

GPT-3 adjusted its dialogue based on the audience’s background: it discussed global warming’s negative impacts with conservatives and the less educated while offering eco-friendly tips to those already on board with the science. Impressively, 98 per cent of GPT-3’s responses were factually accurate.

To meet the demand for accurate and eco-conscious climate information, another research team launched ClimateGPT. This specialised chatbot, trained on a wide array of climate data, uses 12 times less energy than traditional models, addressing concerns over the hefty energy use associated with AI—a crucial move given estimates comparing ChatGPT’s daily consumption to that of 33,000 US households. ChatGPT also uses a half-litre of water for every prompt set!

Scientific One-Liners

• Damage caused by rising sea levels could cost the EU and UK economies up to 872 billion euros by 2100 under a high CO2 emissions scenario.

• By adding chemical data to GPT-3’s training, researchers turned it into a chemistry whiz, outperforming specialised algorithms and making AI more accessible to chemists.

• Archaeologists in Saqqara, Egypt, have discovered several ancient funerary masks dating back at least 1,800 years, indicating that such costly artefacts were accessible to up to 35 per cent of the population, not just the elite.

• Extreme weather influences mortgage payments. Stronger cyclones lead to a higher chance of missed or delayed payments, while heavy rainfall, particularly in flood-prone coastal areas, increases the risk of defaults and discourages early repayment.

• The Russian invasion of Ukraine disrupted the wheat market. Ukraine saw a 27% drop in local wheat prices, while global prices rose by 2%. The conflict resulted in a $1.4 billion loss for Ukrainian wheat producers.

Liquid Surprise

At just 400 kilometres wide and seemingly dormant, Mimas—one of Saturn’s moons, resembling the menacing Death Star—is the last place you’d expect to find water. Yet, here we are, with evidence of an ocean lurking beneath its icy surface, a surprising space twist that even George Lucas couldn’t have scripted.

This ocean is a relative newcomer to the cosmic scene, having formed within the last 25 million years. It’s very young compared to Earth’s oceans, which have been around for nearly 4 billion years. Researchers believe that, at some point in the not-so-distant past, a close encounter with other moons of Saturn caused gravitational tugs that generated enough heat inside Mimas to melt part of its interior, giving birth to the ocean.

The discovery of liquid water on a seemingly dull—nothing personal; it’s just too geologically inert—moon like Mimas suggests that oceans could be more common in the universe than previously thought. And here’s why this matters: wherever there’s water, there’s the potential for life. The interaction between ocean water and the rocky core of moons can create the chemical energy needed for life to thrive. Thus, the more moons like Mimas we find, the better our chances of discovering extraterrestrial life.

Integrating AI Into Teaching in Four Steps

Here are four tips from the University of the West of England for educators to weave AI into the learning experience:

• Harness AI for Personalised Learning: Imagine using AI to design health programs for expectant mothers, tweaking everything from who’s involved to how it’s run, ensuring it respects local culture and can last. Engaging with AI in discussions helps cement knowledge, as repetition boosts understanding.

• Critique AI Content Like a Pro: Students who dissect AI-crafted ideas become critics, not just users. Identifying biases, gaps, and whether the tone hits the mark teaches critical evaluation skills and accurately underscores the value of citing sources. 

• Think Outside the AI Box: Encourage students not just to accept AI’s word as gospel. Exploring different angles and solutions sharpens their ability to debate, decide, and defend their viewpoints. This process prepares them for real-world problem-solving.

• Reflect with Purpose: Use AI as a springboard for inquiry. Start with the basics (“What?”), dive into the mechanisms (“How?”), and explore the implications (“Why?”). 

Well, that was quite the journey! Thank you for sticking with me till the end. If you enjoyed it, don’t forget to hit like and share it with some friends. See you next time!

Elia Kabanov is a science writer covering the past, present, and future of technology (@metkere).

Illustration: Elia Kabanov feat. MidJourney. Photo: NASA.