A non-volatile silicon photonics switch based on phase-change materials actuated by graphene heaters shows a switching energy density that is within an order of magnitude of the fundamental thermodynamic limit.
On-chip training of machine learning algorithms is challenging for photonic devices. Here, the authors construct nonlinear mapping functions in silicon photonic circuits, and experimentally demonstrate on-chip bacterial foraging training for projection-based classification.
Neuromorphic photonics/electronics is the future of ultralow energy intelligent computing and artificial intelligence (AI). In recent years, inspired by the human brain, artificial neuromorphic devices have attracted extensive attention, especially in simulating visual perception and memory storage. Because of its advantages of high bandwidth, high interference immunity, ultrafast signal transmission and lower energy consumption, neuromorphic photonic devices are expected to realize real-time response to input data. In addition, photonic synapses can realize non-contact writing strategy, which contributes to the development of wireless communication.
The use of low-dimensional materials provides an opportunity to develop complex brain-like systems and low-power memory logic computers. For example, large-scale, uniform and reproducible transition metal dichalcogenides (TMDs) show great potential for miniaturization and low-power biomimetic device applications due to their excellent charge-trapping properties and compatibility with traditional CMOS processes. The von Neumann architecture with discrete memory and processor leads to high power consumption and low efficiency of traditional computing. Therefore, the sensor-memory fusion or sensor-memory-processor integration neuromorphic architecture system can meet the increasingly developing demands of big data and AI for low power consumption and high performance devices. Artificial synaptic devices are the most important components of neuromorphic systems. The performance evaluation of synaptic devices will help to further apply them to more complex artificial neural networks (ANN).
Chemical vapor deposition (CVD)-grown TMDs inevitably introduce defects or impurities, showed a persistent photoconductivity (PPC) effect. TMDs photonic synapses integrating synaptic properties and optical detection capabilities show great advantages in neuromorphic systems for low-power visual information perception and processing as well as brain memory.
Making pizza is not rocket science, but for this actual rocket scientist it is now. Benson Tsai is a former SpaceX employee who is now using his skills to launch a new venture: Stellar Pizza, a fully automated, mobile pizza delivery service. When a customer places an order on an app, an algorithm decides when to start making the pizza based on how long it will take to get to the delivery address. Inside Edition Digital’s Mara Montalbano has more.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
AOPEN ACE series raises the bar with scalable commercial computing, maintaining reliability at an affordable price — without sacrificing quality for quantity.
How groups of humans working together collaboratively should redistribute the wealth they create is a problem that has plagued philosophers, economists, and political scientists for years. A new study from DeepMind suggests AI may be able to make better decisions than humans.
AI is proving increasingly adept at solving complex challenges in everything from business to biomedicine, so the idea of using it to help design solutions to social problems is an attractive one. But doing so is tricky, because answering these kinds of questions requires relying on highly subjective ideas like fairness, justice, and responsibility.
For an AI solution to work it needs to align with the values of the society it is dealing with, but the diversity of political ideologies that exists today suggests that these are far from uniform. That makes it hard to work out what should be optimized for and introduces the danger of the developers’ values biasing the outcome of the process.
One consequence of this is there is no guarantee the clocks will tick at the same rate. In fact, many clocks will tick at different rates.
Even worse, the faster you travel relative to someone else, the slower your clock will tick compared to theirs.
Photosynthesis uses a series of chemical reactions to convert carbon dioxide, water, and sunlight into glucose and oxygen. The light-dependent stage comes first, and relies on sunlight to transfer energy to plants, which convert it to chemical energy. The light-independent stage (also called the Calvin Cycle) follows, when this chemical energy and carbon dioxide are used to form carbohydrate molecules (like glucose).
A research team from UC Riverside and the University of Delaware found a way to leapfrog over the light-dependent stage entirely, providing plants with the chemical energy they need to complete the Calvin Cycle in total darkness. They used an electrolysis to convert carbon dioxide and water into acetate, a salt or ester form of acetic acid and a common building block for biosynthesis (it’s also the main component of vinegar). The team fed the acetate to plants in the dark, finding they were able to use it as they would have used the chemical energy they’d get from sunlight.
They tried their method on several varieties of plants and measured the differences in growth efficiency as compared to regular photosynthesis. Green algae grew four times more efficiently, while yeast saw an 18-fold improvement.
