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Professor Carlos Duarte, Ph.D. is Distinguished Professor, Marine Science, and Executive Director, Coral Research \& Development Accelerator Platform (CORDAP — https://cordap.org/), Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST — https://www.kaust.edu.sa/en/study/fac…), in Saudi Arabia, as well as Chief Scientist of Oceans2050, OceanUS, and E1Series.

Prior to these roles Professor Duarte was Research Professor with the Spanish National Research Council (CSIC) and Director of the Oceans Institute at The University of Western Australia. He also holds honorary positions at the Arctic Research Center in Aarhus University, Denmark and the Oceans Institute at The University of Western Australia.

Professor Duarte’s research focuses on understanding the effects of global change in marine ecosystems and developing nature-based solutions to global challenges, including climate change, and developing evidence-based strategies to rebuild the abundance of marine life by 2050.

Building on his research showing mangroves, seagrasses and salt-marshes to be globally-relevant carbon sinks, Professor Duarte developed, working with different UN agencies, the concept of Blue Carbon, as a nature-based solution to climate change, which has catalyzed their global conservation and restoration.

Accurately forecasting weather remains a complex challenge due to the inherent uncertainty in atmospheric dynamics and the nonlinear nature of weather systems. As such, methodologies developed ought to reflect the most probable and potential outcomes, especially in high-stakes decision-making over disasters, energy management, and public safety. While numerical weather prediction (NWP) models offer probabilistic insights through ensemble forecasting, they are computationally expensive and prone to errors. Although ML models have been very promising in giving faster and more accurate predictions, they fail to represent forecast uncertainty, especially in extreme events. This makes ML-based models less useful in actual real-world applications.

The physics-based ensemble models, for example, the ENS from the European Centre for Medium-Range Weather Forecasts (ECMWF), rely on these simulations to produce probabilistic forecasts. These models properly represent the forecast distributions and joint spatiotemporal dependencies and require high computational resources and manual engineering. Conversely, the ML-based method, like GraphCast or FourCastNet, focuses only on deterministic forecasts and will minimize the errors in the mean outcome without considering any uncertainty. None of the attempts to generate probabilistic ensembles by MLWP produced realistic samples or competed with the accuracy of operational ensemble forecasts. Hybrid approaches like NeuralGCM embed ML-based parameterizations within traditional frameworks but have poor resolution and limited performance.

Researchers from Google DeepMind released GenCast, a probabilistic weather forecasting model that generates accurate and efficient ensemble forecasts. This machine learning model applies conditional diffusion models to produce stochastic trajectories of weather, such that the ensembles consist of the entire probability distribution of atmospheric conditions. In systematic ways, it creates forecast trajectories by using the prior states through autoregressive sampling and uses a denoising neural network, which is integrated with a graph-transformer processor on a refined icosahedral mesh. Utilizing 40 years of ERA5 reanalysis data, GenCast captures a rich set of weather patterns and provides high performance. This feature allows it to generate a 15-day global forecast at 0.25° resolution within 8 minutes, which is state-of-the-art ENS in terms of both skill and speed.

You might be keenly interested to know that this eagerness to produce responses is something tuned into AI. The AI maker has made various computational adjustments to get the AI to press itself to respond. Why so? Because people want answers. If they aren’t getting answers from the AI, they will go someplace else. That’s not good for the AI maker since they are courting views.

There is a ton of research taking place about AI hallucinations. It is one of the most pressing AI issues of our time.

AI hallucinations are considered a scourge on the future of generative AI and LLMs. Sadly, the state-of-the-art AI still has them, for example, see my analysis of OpenAI’s most advanced ChatGPT or new model o1 that still indeed emits AI hallucinations at the link here. They are like the energy bunny and seem to just keep running.

“This is a highly engineered design, but the fundamental concepts are fairly simple,” said Dr. Jie Yin. “And with only a single actuation input, our robot can navigate a complex vertical environment.”


What influence can marine life have on robotics? This is what a recent study published in Science Advances hopes to address as a team of researchers from the University of Virginia and North Carolina State University have developed the fastest swimming soft robot by taking cues from manta ray fins. This study holds the potential to help researchers, engineers, and scientists develop faster and more efficient swimming soft robots that can be used for a variety of purposes worldwide.

This study builds on a 2022 study conducted by this same team of researchers that explored swimming soft robots that exhibited butterfly strokes, achieving a then-record of 3.74 body lengths per second, along with demonstrating high power efficiency, low energy use, and high maneuverability. For this new study, the researchers developed fins used by manta rays with the goal of achieving greater results than before. The fins are flexible when not in use but become rigid when the researchers pumped air into the silicone body that encompasses the soft robot.

In the end, the researchers not only achieved low energy use and maneuverability, but also broke their own record of body lengths per second at 6.8. Additionally, the manta ray-inspired swimming soft robot was able to avoid obstacles, which was an improvement from their 2022 study.

The fastest animals are neither the largest, nor the smallest, but rather intermediately sized, though the mechanism for this is unknown. This study built predictive musculoskeletal simulations, scaled in mass from the size of a mouse to an elephant to understand the underlying mechanisms.

You deserve an explanation, so please don’t skip this 1-minute read. It’s Friday, December 6. Our fundraiser will soon be over, but we’re still short of our goal. If you’ve lost count of the number of times you’ve visited Wikipedia this year, we hope that means it’s given you at least $2.75 worth of knowledge. If everyone who finds Wikipedia useful gave just $2.75, we’d hit our goal in a few hours.

The internet we were promised—a place of free, collaborative, and accessible knowledge —is under constant threat. On Wikipedia, volunteers work together to create and verify the pages you rely on, supported by tools that undo vandalism within minutes, ensuring the information you seek is trustworthy.

Just 2% of our readers donate, so if you have given in the past and Wikipedia still provides you with $2.75 worth of knowledge, donate today. If you are undecided, remember any contribution helps. Thank you.