Newswise: NUS researchers develop AI-powered tool to map sustainable roofs globally

Newswise: NUS researchers develop AI-powered tool to map sustainable roofs globally. “Dr Filip Biljecki, Presidential Young Professor from the Department of Architecture at the National University of Singapore (NUS) School of Design and Environment, and NUS Master of Architecture graduate Mr Abraham Noah Wu developed an automated tool that uses satellite images to track how rooftops around the world adopt solar panels and/or vegetation. Known as Roofpedia, it uses a fully convolutional neural network (deep learning) which allows researchers and policymakers to study how cities worldwide are greening their rooftops and using them for photovoltaic installations.”

New York Times: A.I. Is Not What You Think

New York Times: A.I. Is Not What You Think. “When you hear about artificial intelligence, stop imagining computers that can do everything we can do but better. My colleague Cade Metz, who has a new book about A.I., wants us to understand that the technology is promising but has its downsides: It’s currently less capable than people, and it is being coded with human bias.”

The Register: Machine-learning model creates creepiest Doctor Who images yet – by scanning the brain of a super fan

The Register: Machine-learning model creates creepiest Doctor Who images yet – by scanning the brain of a super fan . “AI researchers have attempted to reconstruct scenes from Doctor Who by using machine-learning algorithms to convert brain scans into images. The wacky experiment is described in a paper released via bioRxiv. A bloke laid inside a functional magnetic resonance imaging (fMRI) machine, with his head clamped in place, and was asked to watch 30 episodes of the BBC’s smash-hit family sci-fi show while the equipment scanned his brain. These scans were then passed to a neural network.”

TechCrunch: MIT researchers develop a new ‘liquid’ neural network that’s better at adapting to new info

TechCrunch: MIT researchers develop a new ‘liquid’ neural network that’s better at adapting to new info . “A new type of neural network that’s capable of adapting its underlying behavior after the initial training phase could be the key to big improvements in situations where conditions can change quickly – like autonomous driving, controlling robots, or diagnosing medical conditions.”

Wired: This AI Could Go From ‘Art’ to Steering a Self-Driving Car

Wired: This AI Could Go From ‘Art’ to Steering a Self-Driving Car. “YOU’VE PROBABLY NEVER wondered what a knight made of spaghetti would look like, but here’s the answer anyway—courtesy of a clever new artificial intelligence program from OpenAI, a company in San Francisco. The program, DALL-E, released earlier this month, can concoct images of all sorts of weird things that don’t exist, like avocado armchairs, robot giraffes, or radishes wearing tutus. OpenAI generated several images, including the spaghetti knight, at WIRED’s request.”

MIT Technology Review: Tiny four-bit computers are now all you need to train AI

MIT Technology Review: Tiny four-bit computers are now all you need to train AI. “Deep learning is an inefficient energy hog. It requires massive amounts of data and abundant computational resources, which explodes its electricity consumption. In the last few years, the overall research trend has made the problem worse. Models of gargantuan proportions—trained on billions of data points for several days—are in vogue, and likely won’t be going away any time soon. Some researchers have rushed to find new directions, like algorithms that can train on less data, or hardware that can run those algorithms faster. Now IBM researchers are proposing a different one. Their idea would reduce the number of bits, or 1s and 0s, needed to represent the data—from 16 bits, the current industry standard, to only four.”

MIT News: System brings deep learning to “internet of things” devices

MIT News: System brings deep learning to “internet of things” devices. “Deep learning is everywhere. This branch of artificial intelligence curates your social media and serves your Google search results. Soon, deep learning could also check your vitals or set your thermostat. MIT researchers have developed a system that could bring deep learning neural networks to new — and much smaller — places, like the tiny computer chips in wearable medical devices, household appliances, and the 250 billion other objects that constitute the ‘internet of things’ (IoT).”

University of Amsterdam: Google Streetview shows social importance pedestrian friendly environment

University of Amsterdam: Google Streetview shows social importance pedestrian friendly environment. “With Google Streetview and Deep Learning, researchers at the University of Amsterdam and the University of Twente identified how the urban environment is linked to the vitality of social organisations and neighbourhoods. They conclude that, if an environment provides more space to pedestrians, this will be conducive to neighbourhood-based social organisations’ chances of survival.”

EurekAlert: How neural networks can help us gain a deeper understanding of financial markets

EurekAlert: How neural networks can help us gain a deeper understanding of financial markets. “A new research project at Aarhus University will use Bayesian Neural Networks to model, analyse and understand trading behaviour in the world of finance. The researchers behind the project expect the technology to revolutionise our understanding of financial data. For the first time ever, a team of artificial intelligence researchers and experts will use Bayesian Neural Networks (BNN), a kind of deep machine learning algorithm, to analyse, model and understand causal relationships within trading on global financial markets.”

Digital diagnosis: Why teaching computers to read medical records could help against COVID-19 (World Economic Forum)

World Economic Forum: Digital diagnosis: Why teaching computers to read medical records could help against COVID-19. “Every day, healthcare staff in a typical NHS hospital generate so much text it would take a human an age just to scroll through it, let alone read it. Using computers to analyse all this data is an obvious solution, but far from simple. What makes perfect sense to a human can be highly difficult for a computer to understand. Our team is using a form artificial intelligence to bridge this gap. By teaching computers how to comprehend human doctors’ notes, we’re hoping they’ll uncover insights on how to fight COVID-19 by finding patterns across many thousands of patients’ records.”