Lifeboat Foundation: Safeguarding Humanity
Toggle light / dark theme

Blog

Category: information science – Page 234

A new blood test that can detect methylation of DNA can accurately predict whether a person has any one of 50 cancers and where the tumour is growing.

The California-based healthcare company Grail, which developed the test, owns a large database of methylation patterns in cancerous and non-cancerous cell-free DNA. From that repository, a machine learning program was developed to analyse blood samples. The algorithm identified methylation changes that are classified as cancerous or non-cancerous, and it could even pinpoint the tissue of origin before the onset of symptoms.

Validation of the test was carried out by researchers from the US at the Mayo Clinic, Cleveland Clinic and Harvard medical school, working with colleagues at the Francis Crick Institute and University College London in the UK. In all, more than 15,000 volunteers from over 140 clinics in North America took part, and their samples revealed that this ‘liquid biopsy’ had a 0.7% false positive rate for cancer detection. The test was also able to predict the tissue that the cancer originated in with more than 90% accuracy. It performed best on 12 of the most common cancers, including ones that are most lethal and have no established screening paradigms such as pancreatic and ovarian cancers.

Read more | >

An artificial intelligence can accurately translate thoughts into sentences, at least for a limited vocabulary of 250 words. The system may bring us a step closer to restoring speech to people who have lost the ability because of paralysis.

Joseph Makin at the University of California, San Francisco, and his colleagues used deep learning algorithms to study the brain signals of four women as they spoke. The women, who all have epilepsy, already had electrodes attached to their brains to monitor seizures.

Read more | >

Scientists have taken a step forward in their ability to decode what a person is saying just by looking at their brainwaves when they speak.

They trained algorithms to transfer the brain patterns into sentences in real-time and with word error rates as low as 3%.

Previously, these so-called “brain-machine interfaces” have had limited success in decoding neural activity.

Read more | >

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.

Read more | >

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.

Read more | >

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.

Read more | >

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.

Read more | >

“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.

“I regard the brain as a computer,” observed Hawking, “which will stop working when its components fail. There is no heaven or afterlife for broken down computers; that is a fairy story for people afraid of the dark.”

Read more | >

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.

So for the new study, the researchers kitted out a quadcopter drone with cameras specially designed to detect fast movement, as well as new algorithms that made them even faster. This cut the reaction time down to just 3.5 milliseconds.

Read more | >