Lifeboat Foundation

Winter in the northern hemisphere is always a brutal reminder for the shipping industry that routing vessels efficiently is a big challenge. Winter storms bring low visibility conditions, freezing spray, and sea ice, all of which can lead to catastrophic results if not appropriately navigated, including lost cargo, damaged hulls and even potentially toppling a ship in the most extreme weather. But this January adds additional pressures to the sector with new and enacted regulations around greenhouse emissions and carbon usages. The beneficial news is that in both scenarios, weather intelligence can help those navigating the open seas better plan and safely and efficiently navigate these waters.

While most of us know that weather impacts nearly every aspect of shipping, we likely think of it in terms of safety of people and cargo. According to The Swedish Club 2020 loss prevention report, heavy weather is cited in half of all claims and contributes to 80% of the financial losses. Weather optimized routing uses real-time weather forecasts, oceanic data, and the vessel’s current position to keep captains at sea and voyage managers on land about changing conditions. If there is hazardous weather, most voyage routing algorithms can make numerous calculations in real time and provide one or more alternatives for a ship operator to optimize a route. While ultimately this may not be the most efficient route, it will likely be the safest route for current conditions.

Weather intelligence is also critical in evaluating, and potentially adjusting, greenhouse gas emissions based on vessel performance and fuel usage. The Carbon Intensity Indicator (CII) introduced in 2023 is a rating framework that evaluates how efficiently a ship transports goods or passengers from a carbon emissions standpoint. This is the first year that ships will be assigned a rating. The data from the previous year is used in an efficiency conversion ratio. Each ship is assigned an individual CII rating from A to E, with A being the best possible rank.

The equations that describe physical systems often assume that measurable features of the system—temperature or chemical potential, for example—can be known exactly. But the real world is messier than that, and uncertainty is unavoidable. Temperatures fluctuate, instruments malfunction, the environment interferes, and systems evolve over time.

LimX Dyamics claims CL-1 is one of the few humanoid robots around the world that achieves dynamic stair climbing based on real-time terrain perception.

The Chinese company claims that CL-1 stands out as one of the few models capable of dynamic stair climbing through real-time terrain perception among the global array of humanoid robots. This is achieved through sophisticated motion control and AI algorithms developed by LimX Dynamics, complemented by their proprietary high-performance actuators and hardware systems.

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Year 2014 Basically once the master qubit is found it could even lead to a sorta master algorithm. Also it could show who actually pulls the strings of reality.

Whatever the u-bit is, it rotates quickly (Image: Natalie Nicklin)

Our best theory of nature has imaginary numbers at its heart. Making quantum physics more real conjures up a monstrous entity pulling the universe’s strings

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A new, potentially revolutionary artificial intelligence framework called “Blackout Diffusion” generates images from a completely empty picture, meaning that the machine-learning algorithm, unlike other generative diffusion models, does not require initiating a “random seed” to get started. Blackout Diffusion, presented at the recent International Conference on Machine Learning (“Blackout Diffusion: Generative Diffusion Models in Discrete-State Spaces”), generates samples that are comparable to the current diffusion models such as DALL-E or Midjourney, but require fewer computational resources than these models.

“Generative modeling is bringing in the next industrial revolution with its capability to assist many tasks, such as generation of software code, legal documents and even art,” said Javier Santos, an AI researcher at Los Alamos National Laboratory and co-author of Blackout Diffusion. “Generative modeling could be leveraged for making scientific discoveries, and our team’s work laid down the foundation and practical algorithms for applying generative diffusion modeling to scientific problems that are not continuous in nature.”

A new generative AI model can create images from a blank frame. (Image: Los Alamos National Laboratory)

It is still unclear whether and how quantum computing might prove useful in solving known large-scale classical machine learning problems. Here, the authors show that variants of known quantum algorithms for solving differential equations can provide an advantage in solving some instances of stochastic gradient descent dynamics.

In fields such as physics and engineering, partial differential equations (PDEs) are used to model complex physical processes to generate insight into how some of the most complicated physical and natural systems in the world function.

To solve these difficult equations, researchers use high-fidelity numerical solvers, which can be very time consuming and computationally expensive to run. The current simplified alternative, data-driven surrogate models, compute the goal property of a solution to PDEs rather than the whole solution. Those are trained on a set of data that has been generated by the high-fidelity solver, to predict the output of the PDEs for new inputs. This is data-intensive and expensive because complex physical systems require a large number of simulations to generate enough data.

In a new paper, “Physics-enhanced deep surrogates for partial differential equations,” published in December in Nature Machine Intelligence, a new method is proposed for developing data-driven surrogate models for complex physical systems in such fields as mechanics, optics, thermal transport, fluid dynamics, physical chemistry, and climate models.

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German researchers are developing an algorithm to help decode ancient cuneiform tablets — including those containing the oldest known work of world literature.

Ancient poem: The Epic of Gilgamesh is a Babylonian poem first written in cuneiform characters on clay tablets around 4,000 years ago. It tells the story of Gilgamesh, the king of the city of Uruk, and his quest for immortality.

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