A long-standing question in water research is the possibility that supercooled liquid water can undergo a liquid-liquid phase transition (LLT) into high- and low-density liquids. We used several complementary molecular simulation techniques to evaluate the possibility of an LLT in an ab initio neural network model of water trained on density functional theory calculations with the SCAN exchange correlation functional. We conclusively show the existence of a first-order LLT and an associated critical point in the SCAN description of water, representing the first definitive computational evidence for an LLT in water from first principles.

Things are becoming more advanced as technology advances, and machines now perform the majority of the manual work. The most often used home appliance is the washing machine for cloths. Modification and research in this field is essential since it pertains to the amount of time, water, and electricity required for washing. In this work, a Fuzzy Logic Controller has been developed for smart washing machines. The objective of this paper is to optimize the consumption of electricity, water, and detergent for washing machines. The type of dirt, volume of clothes, and type of cloth play a vital role in saving water, electricity, and detergent. However, none of the work on the Fuzzy Logic Controller provided a design procedure endowed with the specified inputs and outputs implemented in Python. In this paper, we used the Mamdani approach and created an algorithm based on multi-input multi-output. The algorithm is implemented in Python. The results of this simulation show that the washing machine provides better execution at a low computation cost.

Unique 4320*3264px Art made by Artificial Intelligence (AI) captures eyes and mind. Artists are using computer programs with machine learning algorithms to generate the images. We use AI to create abstract digital art paintings that would be impossible for humans to create on their own. Artificial intelligence art is a fascinating and rapidly growing field. Don t miss to own one of its early creations.

In 2017, a friend gave me some sourdough starter to make bread with, and ever since then, my life has changed. It sounds cheesy, but I discovered a hobby that has led me to buy almost 200 pounds of flour at a time (seriously), develop a biweekly pizza baking habit, and dream of what bread I'm going to make in the coming days! Because I spend a lot of time baking sourdough and experimenting with new formulas, I wanted to see if I could create an artificial intelligence-powered recipe generator that would predict something for me to make! One of my go-to websites for technique, tips and tricks has been the helpful bread baking forum, The Fresh Loaf, where people ask questions and post recipes. My idea was to scrape this website and get data to train a neural network to generate new bread recipes -- and that's what I did.

I can't count the number of animal stories that appeared in my timelines this year with comments like, "Everything is garbage, so here's this." There was the cat who was reunited with her family after the Camp Fire, in California, and the parrot who was adopted after getting kicked out of an animal shelter for swearing too saltily. Among the bears preparing for hibernation at Katmai National Park, a female named Beadnose became famous for being the most gloriously round. There was the baby raccoon who scaled a skyscraper in St. Paul, "Mission Impossible" style, stopping occasionally for naps in window ledges along the way. Stories from the animal world offer reliable moments of escapism--the ones we see in viral videos are usually cute, or tame, or strange and majestic, and glimpsed from a safe distance.

Leibniz's (1984) An Introduction to a Secret Encyclopedia includes the following marginal note: Principle of Physical Certainty: Everything which men have experienced always and in many ways will still happen: for example that iron sinks in water (Leibniz 1984). In our daily lives, we routinely use this principle. Thus, we know that we can pull with a string but not push with it; that a flower pot dropped from our balcony falls to the ground and breaks; that when we place a container of water on fire, water might boil after a while and overflow the container. The origin of such knowledge is a matter of constant debate. It is clear that we learn a great deal about the physical world as we grow up.

As a challenge problem for AI systems, I propose the use of hand-constructed multiple-choice tests, with problems that are easy for people but hard for computers. Specifically, I discuss techniques for constructing such problems at the level of a fourth-grade child and at the level of a high school student. For the fourth-grade-level questions, I argue that questions that require the understanding of time, of impossible or pointless scenarios, of causality, of the human body, or of sets of objects, and questions that require combining facts or require simple inductive arguments of indeterminate length can be chosen to be easy for people, and are likely to be hard for AI programs, in the current state of the art. For the high school level, I argue that questions that relate the formal science to the realia of laboratory experiments or of real-world observations are likely to be easy for people and hard for AI programs. I argue that these are more useful benchmarks than existing standardized tests such as the SATs or New York Regents tests.

A large tiger shark was seen Friday near the South Beach in Miami, Florida, swimming dangerously close to beachgoers, who had no idea a predator was floating inches away from them. Photographer Kenny Melendez was flying his drone above the South Beach waters at 8 a.m. EST on Nov. 24, hoping to get some scenic shots and show his cousin how drones work, when he noticed a sizeable shadow near a swimmer, Miami Herald reported. That is when he panned the drone camera close to the water, hovering it just 15 feet above the surface. As it turned out, the shadow was an enormous tiger shark that was swimming really close to the sandy beach of Miami. Melendez decided to let his drone follow the shark around for the next 10 minutes, as it swam up to unsuspecting beachgoers wading in the waters.

Hurtigruten cruise line introduces a new way to see what lurks beneath the world's most remote polar waters. On its expedition ships, the company is introducing an underwater drone that streams real-time video of orcas, leopard sharks, penguins and other creatures beneath the water. Or passengers can wear masks with digital displays that may make them feel like they're on a dive deep in the ocean Hurtigruten plans to start by outfitting two hybrid-powered ships -- the Roald Amundsen and the Fridtjof Nansen -- with the new underwater drone. "[W]ith underwater drones on our ships we can take our guests to areas less explored than the surface of Mars," company Chief Executive Daniel Skjeldam said in a statement.

This could one day be used as a blueprint for mini robotic surgeons or explorers. Each exoskeleton starts out as a sheet of plastic onto which the robot, known as Primer, rolls. One gives it the ability to roll, meaning it can move twice as fast as without the exoskeleton; another is shaped like a boat, letting it float on water and carry nearly twice its weight. "In the future, we imagine robots like this could become mini surgeons, squished into a pill that you swallow," says Daniela Rus at the Massachusetts Institute of Technology.