Modern laundry detergents work better in cold water. Breakthroughs, discoveries, and DIY tips sent every weekday. You may wash your clothes with hot water--many people do. That could be a mistake. Modern washing machines, and modern laundry detergent, are designed to work well at cold temperatures.

An autonomous drone with wheels can roll along the ground, only flying when needed to clear obstacles, which helps its battery last seven times longer, according to its developers. Rolling robots are efficient and can travel long distances, but cannot traverse big obstacles, while flying drones can get past large obstructions, but have limited range.

The brain is not only constrained by energy needed to fuel computation, but it is also constrained by energy needed to form memories. Experiments have shown that learning simple conditioning tasks already carries a significant metabolic cost. Yet, learning a task like MNIST to 95% accuracy appears to require at least 10^{8} synaptic updates. Therefore the brain has likely evolved to be able to learn using as little energy as possible. We explored the energy required for learning in feedforward neural networks. Based on a parsimonious energy model, we propose two plasticity restricting algorithms that save energy: 1) only modify synapses with large updates, and 2) restrict plasticity to subsets of synapses that form a path through the network. Combining these two methods leads to substantial energy savings while only incurring a small increase in learning time. In biology networks are often much larger than the task requires. In particular in that case, large savings can be achieved. Thus competitively restricting plasticity helps to save metabolic energy associated to synaptic plasticity. The results might lead to a better understanding of biological plasticity and a better match between artificial and biological learning. Moreover, the algorithms might also benefit hardware because in electronics memory storage is energetically costly as well.

Google's Nest Thermostat is on sale for just $90 for Black Friday, with a tidy $40 discount. That's among the lowest prices we've seen, and close to the lowest it has gone for so far. Like most smart home devices, the Nest Thermostat grants control via smartphone, tablet, laptop or even a smart display like Amazon's Echo Show or Google's Nest Hub (both of which are seeing significant Black Friday deals). Since it's programmable and capable of knowing when you're home, the thermostat can save energy by not overly heating or cooling an empty house. Google designed it to be easy to install, and most people can do it themselves.

Connected, automated vehicles promise to save energy and improve safety. Michigan Tech engineers propose a modeling framework for cooperative driving. Simulation results show that the cooperative automated eco-driving algorithm saves energy -- 7% under light traffic and 23% under heavy traffic.

It has been known for centuries that many fish swim in schools, with large groups moving in unison. But scientists have never fully known why, and have been unable to prove how this behaviour benefits them. Now, European researchers have used robotic fish to show it it is up to 13.5 per cent more efficient for fish to swim in a group than alone, allowing them to save energy. This had been the long-standing theory, but it had never been conclusively proven. European researchers used robotic fish to prove it is because they allow fish to save energy and up to 13.5 per cent more efficient than swimming alone Scientists from the Max Planck Institute of Animal Behavior (MPI-AB), the University of Konstanz, and Peking University, set about building a lifelike robot to test the hypothesis.

The high energy consumption of artificial neural networks' learning activities is one of the biggest hurdles for the broad use of Artificial Intelligence (AI), especially in mobile applications. One approach to solving this problem can be gleaned from knowledge about the human brain. Although it has the computing power of a supercomputer, it only needs 20 watts, which is only a millionth of the energy of a supercomputer. One of the reasons for this is the efficient transfer of information between neurons in the brain. Neurons send short electrical impulses (spikes) to other neurons--but, to save energy, only as often as absolutely necessary.

The e-prop learning method developed at Graz University of Technology forms the basis for drastically more energy-efficient hardware implementations of Artificial Intelligence. The high energy consumption of artificial neural networks' learning activities is one of the biggest hurdles for the broad use of Artificial Intelligence (AI), especially in mobile applications. One approach to solving this problem can be gleaned from knowledge about the human brain. Although it has the computing power of a supercomputer, it only needs 20 watts, which is only a millionth of the energy of a supercomputer. One of the reasons for this is the efficient transfer of information between neurons in the brain.

It is the first boat to be showcased at CES in Las Vegas, but the state-of-the-art Sea Ray SLX-R 400e is far from a traditional water vessel. The 40-foot yacht is equip with auto-docking capabilities and allows passengers to communicate with it using gestures and voice commands through a new'Future Helm'. It seats 22 people and comes with a lithium battery pack that can power the craft's high-performance engines in order to save energy. Steve Langlais, Sea Ray president, said: 'CES presents a unique opportunity to debut the new SLX-R 400e in front of an audience that will truly appreciate its beauty, capabilities and incredible suite of new technologies.' 'This pioneering new model, which will be available in 2020, showcases the kind of unique, advanced technologies that are worthy of the world's largest consumer electronics show.'

Artificial Intelligence (AI) is the branch of computer sciences that emphasizes the development of intelligence machines, thinking and working like humans. Today, Artificial Intelligence is a very popular subject that is widely discussed in the technology and business circles. Many experts and industry analysts argue that AI or machine learning is the future – but if we look around, we are convinced that it's not the future – it is the present. With the advancement in technology, we are already connected to AI in one way or the other – whether it is Siri, Watson or Alexa. Yes, the technology is in its initial phase and more and more companies are investing resources in machine learning, indicating a robust growth in AI products and apps in the near future.