A new study finds algae, mussels, and starfish flock to munitions dumped in the Baltic Sea. Breakthroughs, discoveries, and DIY tips sent every weekday. As the ink dried on Germany's unconditional surrender on May 8, 1945, celebrations erupted across the world. People cheered, wept, and kissed in the streets as World War II finally came to an end in Europe. A few months later at the Potsdam Conference, Germany agreed to demilitarize and dismantle its once formidable army, leaving the nation with lots and lots of leftover munitions.

Robotic pool cleaners have revolutionized pool care, eliminating the back-breaking routine of manual vacuuming, scrubbing, and brushing. Today's models don't just clean -- they maintain a consistently pristine pool with minimal human effort. Technology in this space has advanced rapidly. The newest generation offers greater efficiency, longer runtimes, and pinpoint navigation for faster, more thorough results. Two standout examples are Aiper's Scuba X1 and Surfer S2, designed to work together for a complete, floor-to-surface clean.

Abstract: Many approaches to robot learning begin by inferring a reward function from a set of human demonstrations. To learn a good reward, it is necessary to determine which features of the environment are relevant before determining how these features should be used to compute reward. End-to-end methods for joint feature and reward learning (e.g., using deep networks or program synthesis techniques) often yield brittle reward functions that are sensitive to spurious state features. By contrast, humans can often generalizably learn from a small number of demonstrations by incorporating strong priors about what features of a demonstration are likely meaningful for a task of interest. How do we build robots that leverage this kind of background knowledge when learning from new demonstrations? This paper describes a method named ALGAE (Adaptive Language-Guided Abstraction from [Contrastive] Explanations) which alternates between using language models to iteratively identify human-meaningful features needed to explain demonstrated behavior, then standard inverse reinforcement learning techniques to assign weights to these features. Experiments across a variety of both simulated and real-world robot environments show that ALGAE learns generalizable reward functions defined on interpretable features using only small numbers of demonstrations. Importantly, ALGAE can recognize when features are missing, then extract and define those features without any human input - making it possible to quickly and efficiently acquire rich representations of user behavior.

In Texas, Texas A&M AgriLife Research scientists are using artificial intelligence to set a new world record for producing algae as a reliable, economic source for biofuel that can be used as an alternative fuel source for jet aircraft and other transportation needs. Joshua Yuan, AgriLife Research scientist, professor and chair of Synthetic Biology and Renewable Products in the Texas A&M College of Agriculture and Life Sciences Department of Plant Pathology and Microbiology, is leading the research project. "The commercialization of algal biofuel has been hindered by the relatively low yield and high harvesting cost," Yuan said. "The limited light penetration and poor cultivation dynamics both contributed to the low yield." Overcoming these challenges could enable viable algal biofuels to reduce carbon emissions, mitigate climate change, alleviate petroleum dependency and transform the bioeconomy, Yuan said.

Algae has such immense potential as a biofuel source that scientists have long been studying it for sustainable energy. They even created 3D printed artificial leaves out of algae to produce oxygen for our investigations of Mars. Now, scientists from Texas A&M AgriLife Research are using artificial intelligence to break a new world record for producing algae as a reliable biofuel source, so that a greener and more economical fuel source for jet aircraft and other kinds of transportation could be achieved. The research project is conducted by Joshua Yuan, PhD., and funded by the U.S. Department of Energy Fossil Energy Office. One of the major problems with algaes' prominence was their growth limitations due to mutual shading and the high cost of harvest.

Algae are a varied category of aquatic plant-like creatures. Phytoplankton is a term used to describe oceanic algae. These basic creatures generate energy from sunlight through photosynthesis, which allows them to manufacture carbohydrates, oils, and proteins. These can then be processed to produce a third-generation biofuel. Biofuel is any fuel derived from living things or living things' waste products (like fecal matter or urine).

A robot piloted by a ball of algae can swim through water and move around obstacles, powered only by photosynthesis. Neil Phillips at the University of the West of England, UK, and his colleagues wanted to build a robot with no electronic parts, meaning it wouldn't interfere with any electromagnetically sensitive measurement instruments. The team inserted a marimo, a ball of algae that forms naturally in freshwater currents, inside a 3D-printed plastic spherical shell equipped with vents.

It is hoped a new partnership will put Nelson on the map as place to study and advance artificial intelligence technology. Nelson Artificial Intelligence Institute (NAI) is re-locating to the Nelson Marlborough Institute of Technology's campus, in a move the organisations say will bolster opportunities to train in and develop the technology. Artificial intelligence is an area of software engineering where computers "learn" how to mimic human cognitive functions. Products under development at NAI – which set up in Nelson last year – aimed to help increase both efficiency and environmental sustainability in operations including aquaculture, and commercial fishing. They included a model to detect and classify microscopic algae that could help protect animals like shellfish.

A new artificial intelligence invention by Hypergiant Industries could prove to be the solution to the world's carbon dioxide problem. The company is launching the second generation Eos Bioreactor, currently still a prototype, that can be used to absorb excess carbon dioxide from the atmosphere and give out oxygen. Besides its ability to reduce environmental pollution, the new AI-based bioreactor also improves health. The excessive presence of carbon dioxide in the atmosphere has led to a steady rise in the average global temperatures over the years. A National Geographic report states that ocean levels will rise by up to 2.3 feet by 2050 due to melting glaciers.

Algae, that green scum often seen on the surface of ponds, and credited with harmful ocean algal blooms that kill ocean life might just hold an important key to addressing climate change. Algae, much like trees, uses carbon dioxide to conduct photosynthesis, sequestering CO2 as it grows. Hypergiant, an AI products and solutions company, is harnessing this unique power of algae in its latest technology, the EOS bio-reactor which uses AI to optimize algae growth and carbon sequestration. Its bio-reactor is built to hook up to HVAC systems found in large industrial buildings, skyscrapers and apartment buildings which are some of the biggest contributors to global warming from the CO2 emitted through their energy usage and air conditioning systems. The science is clear that we must not only cut our carbon emissions as a means to stop the irreversible harm of climate change and limit global warming but that we also need to take carbon out of the atmosphere to stay within the stated target 1.5 C of the Paris Climate Agreement.