13536 Archives - WTF Fun Facts

For centuries, sailors have whispered tales of monstrous rogue waves capable of splitting ships and damaging oil rigs. These maritime myths turned real with the documented 26-meter-high rogue wave at Draupner oil platform in 1995.

Fast forward to 2023, and researchers at the University of Copenhagen and the University of Victoria have harnessed the power of artificial intelligence (AI) to predict these oceanic giants. They’ve developed a revolutionary formula using data from over a billion waves spanning 700 years, transforming maritime safety.

Decoding Rogue Waves: A Data-Driven Approach

The quest to understand rogue waves led researchers to explore vast ocean data. They focused on rogue waves, twice the size of surrounding waves, and even the extreme ones over 20 meters high. By analyzing data from buoys across the US and its territories, they amassed more than a billion wave records, equivalent to 700 years of ocean activity.

Using machine learning, the researchers crafted an algorithm to identify rogue wave causes. They discovered that rogue waves occur more frequently than imagined, with about one monster wave daily at random ocean locations. However, not all are the colossal 20-meter giants feared by mariners.

AI as a New-Age Oceanographer

The study stands out for its use of AI, particularly symbolic regression. Unlike traditional AI methods that offer single predictions, this approach yields an equation. It’s akin to Kepler deciphering planetary movements from Tycho Brahe’s astronomical data, but with AI analyzing waves.

The AI examined over a billion waves and formulated an equation, providing a “recipe” for rogue waves. This groundbreaking method offers a transparent algorithm, aligning with physics laws, and enhances human understanding beyond the typical AI black box.

Contrary to popular belief that rogue waves stem from energy-stealing wave combinations, this research points to “linear superposition” as the primary cause. Known since the 1700s, this phenomenon occurs when two wave systems intersect, amplifying each other momentarily.

The study’s data supports this long-standing theory, offering a new perspective on rogue wave formation.

Towards Safer Maritime Journeys

This AI-driven algorithm is a boon for the shipping industry, constantly navigating potential dangers at sea. With approximately 50,000 cargo ships sailing globally, this tool enables route planning that accounts for the risk of rogue waves. Shipping companies can now use the algorithm for risk assessment and choose safer routes accordingly.

The research, algorithm, and utilized weather and wave data are publicly accessible. This openness allows entities like weather services and public authorities to calculate rogue wave probabilities easily. The study’s transparency in intermediate calculations sets it apart from typical AI models, enhancing our understanding of these oceanic phenomena.

The University of Copenhagen’s groundbreaking research, blending AI with oceanography, marks a significant advancement in our understanding of rogue waves. By transforming a massive wave database into a clear, physics-aligned equation, this study not only demystifies a long-standing maritime mystery but also paves the way for safer sea travels. The algorithm’s potential to predict these maritime monsters will be a crucial tool for the global shipping industry, heralding a new era of informed and safer ocean navigation.

— WTF fun facts

Source: “AI finds formula on how to predict monster waves” — ScienceDaily

The Enigma of Olfaction

While we’ve successfully translated light into sight and sound into hearing, decoding the intricate world of smell remains a challenge.

Olfaction, compared to our other senses, is mysterious, diverse, and deeply rooted in both emotion and memory. Knowing this, can we teach machines to interpret this elusive sense?

Digitizing Smell

A collaboration between the Monell Chemical Senses Center and the startup Osmo aimed to bridge the gap between airborne chemicals and our brain’s odor perception. Their objective was not just to understand the science of smell better but to make a machine proficient enough to describe, in human terms, what various chemicals smell like.

Osmo, with roots in Google’s advanced research division, embarked on creating a machine-learning model. The foundation of this model was an industry dataset, which detailed the molecular structures and scent profiles of 5,000 known odorants.

The idea? Feed the model a molecule’s shape and get a descriptive prediction of its smell.

That might sound simple, but the team had to make sure they could ensure the model’s accuracy.

The Litmus Test: Man vs. Machine

To validate the machine’s “sense of smell,” a unique test was devised.

A group of 15 panelists, trained rigorously using specialized odor kits, was tasked with describing 400 unique odors. The model then predicted descriptions for the same set.

Astonishingly, the machine’s predictions often matched or even outperformed individual human assessments, showcasing its unprecedented accuracy.

Machines That Can ‘Smell’ vs. Digitizing Smell

Beyond its core training, the model displayed unexpected capabilities. It accurately predicted odor strength, a feature it wasn’t explicitly trained for, and identified distinct molecules with surprisingly similar scents. This accomplishment suggests we’re inching closer to a world where machines can reliably “smell.”

But for now, that’s overstating it. The team has made a major leap towards digitizing smell. But machines don’t have senses. They can only replicate the kind of information our brains produce when we smell things. Of course, they don’t have any sense of enjoyment (or repulsion) at certain smells.

In any case, the Monell and Osmo collaboration has significantly advanced our journey in understanding and replicating the sense of smell. As we move forward, this research could revolutionize industries from perfumery to food and beyond.

WTF Fun Fact 13636 – AI and Rogue Waves