Lifeboat Foundation

A new reinforcement-learning algorithm has learned to optimize the placement of components on a computer chip to make it more efficient and less power-hungry.

3D Tetris: Chip placement, also known as chip floor planning, is a complex three-dimensional design problem. It requires the careful configuration of hundreds, sometimes thousands, of components across multiple layers in a constrained area. Traditionally, engineers will manually design configurations that minimize the amount of wire used between components as a proxy for efficiency. They then use electronic design automation software to simulate and verify their performance, which can take up to 30 hours for a single floor plan.

Time lag: Because of the time investment put into each chip design, chips are traditionally supposed to last between two and five years. But as machine-learning algorithms have rapidly advanced, the need for new chip architectures has also accelerated. In recent years, several algorithms for optimizing chip floor planning have sought to speed up the design process, but they’ve been limited in their ability to optimize across multiple goals, including the chip’s power draw, computational performance, and area.

Researchers are using AI to search satellite images for unexploded bombs dropped in Cambodia during the Vietnam War.

The system uses object recognition algorithms that detect the unique features of bomb craters, including their shapes, colors, textures, and sizes. These algorithms then scan satellite images for signals of the craters.

The Ohio State University team first used the system to find craters in a village in the province of Prey Veng, a heavily bombed area around 30 kilometers from the Vietnam border.

Over the last decade, artificial intelligence (AI) and its applications such as machine learning have gained pace to revolutionize many industries. As the world gathers more data, the computing power of hardware systems needs to grow in tandem. Unfortunately, we are facing a future where we will not be able to generate enough energy to power our computational needs.

“We hear a lot of predictions about AI ushering in the fourth industrial revolution. It is important for us to understand that the computing platforms of today will not be able to sustain at-scale implementations of AI algorithms on massive datasets. It is clear that we will have to rethink our approaches to computation on all levels: materials, devices and architecture. We are proud to present an update on two fronts in this work: materials and devices. Fundamentally, the devices we are demonstrating are a million times more power efficient than what exists today,” shared Professor Thirumalai Venky Venkatesan, the lead Principal Investigator of this project who is from the National University of Singapore (NUS).

In a paper published in Nature Nanotechnology on 23 March 2020, the researchers from the NUS Nanoscience and Nanotechnology Initiative (NUSNNI) reported the invention of a nanoscale device based on a unique material platform that can achieve optimal digital in-memory computing while being extremely energy efficient. The invention is also highly reproducable and durable, unlike conventional organic electronic devices.

When you’re a software and hardware engineer sometimes you need a little challenge. Engineer Robert Lucian Chiriac’s latest Raspberry Pi creation can detect license plates and read the characters with fairly decent accuracy. This is an involved project that relies on machine learning to properly interpret images from the camera into discernible text.

The primary license plate reading function is constructed using three separate applications (there are more used throughout the project, but these three are critical). Chiriac used the YOLOv3 object detection algorithm to create a bounding box around each license plate it detects from the camera input. The image within the bounding box is sent to CRAFT, a text detecting application. Once the location for each character in the plate has been identified, the information is passed along to CRNN to predict the actual text.

Chiriac mounted the Raspberry Pi, GPS module, 4G module and Pi camera to his car’s rear-view mirror with a 3D-printed unit he designed. The Pi camera is even adjustable with a ball-joint swivel mount.

“We should plan ahead,” warned physicist Stephen Hawking who died last March, 2018, and was buried next to Isaac Newton. “If a superior alien civilization sent us a text message saying, ‘We’ll arrive in a few decades,’ would we just reply, ‘OK, call us when you get here, we’ll leave the lights on’? Probably not, but this is more or less what has happened with AI.”

The memorial stone placed on top of Hawking’s grave included his most famous equation describing the entropy of a black hole. “Here Lies What Was Mortal Of Stephen Hawking,” read the words on the stone, which included an image of a black hole.

Continue reading “Stephen Hawking --‘Treating AI as Science Fiction Would Potentially Be Our Worst Mistake Ever‘” »

Using algorithms, scientists can simulate how different nanoparticles interact with the antigen component—and how well they activate the immune system.

Obstacle avoidance is a crucial piece of technology for drones, but commercially-available systems just aren’t fast enough for some situations. Now, engineers at the University of Zurich have developed a new system that gives drones such fast reflexes that they can play – and win at – dodgeball.

According to the researchers, most current obstacle avoidance systems take about 20 to 40 milliseconds to process changes in their surroundings. That’s fine for a drone gently approaching a building and finding its way inside, but it’s no match for fast-moving obstacles like birds or other drones. That makes navigation a problem in certain situations, like when there are a lot of drones together or in dynamic environments like disaster zones, or when a drone just needs to move fast.

Continue reading “Drone plays dodgeball to demo fast new obstacle detection system” »

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A research team from ITMO University, with the help of colleagues from MIPT (Russia) and Politecnico di Torino (Italy), has predicted a novel type of topological quantum state of two photons. Scientists have also applied a new, affordable experimental method for testing this prediction. The method relies on an analogy: Instead of expensive experiments with quantum systems of two or more entangled photons, the researchers have used resonant electric circuits of higher dimensionality described by similar equations. The obtained results can be useful for the engineering of optical chips and quantum computers without the need for expensive experiments. The research was published in Nature Communications.

Light plays a key role in modern information technologies: With its help, information is transmitted over large distances via optical fibers. In the future, scientists anticipate the invention of optical chips and computers that process information with the help of photons—light quanta—instead of electrons, as it is done today. This will decrease energy consumption, while also increasing the capabilities of computers. However, to turn these predictions into reality, fundamental and applied research of light behavior at the micro- and nanoscale is needed.

Continue reading “The imitation game: Scientists describe and emulate new quantum state of entangled photons” »

Clause density is something new to me but seems interesting as I know shores algorithm is the only thing that can hack systems.

Google is racing to develop quantum-enhanced processors that utilize quantum mechanical effects to one day dramatically increase the speed at which data can be processed.

In the near term, Google has devised new quantum-enhanced algorithms that operate in the presence of realistic noise. The so-called quantum approximate optimization algorithm, or QAOA for short, is the cornerstone of a modern drive towards noise-tolerant quantum-enhanced algorithm development.

Continue reading “Russian Scientists Break Google’s Quantum Algorithm” »