Lifeboat Foundation

Text-to-image generation is the hot algorithmic process right now, with OpenAI’s Craiyon (formerly DALL-E mini) and Google’s Imagen AIs unleashing tidal waves of wonderfully weird procedurally generated art synthesized from human and computer imaginations. On Tuesday, Meta revealed that it too has developed an AI image generation engine, one that it hopes will help to build immersive worlds in the Metaverse and create high digital art.

A lot of work into creating an image based on just the phrase, “there’s a horse in the hospital,” when using a generation AI. First the phrase itself is fed through a transformer model, a neural network that parses the words of the sentence and develops a contextual understanding of their relationship to one another. Once it gets the gist of what the user is describing, the AI will synthesize a new image using a set of GANs (generative adversarial networks).

Continue reading “Meta’s ‘Make-A-Scene’ AI blends human and computer imagination into algorithmic art” »

Deep learning techniques have recently proved to be highly promising for detecting cybersecurity attacks and determining their nature. Concurrently, many cybercriminals have been devising new attacks aimed at interfering with the functioning of various deep learning tools, including those for image classification and natural language processing.

Perhaps the most common among these attacks are adversarial attacks, which are designed to “fool” deep learning algorithms using data that has been modified, prompting them to classify it incorrectly. This can lead to the malfunctioning of many applications, biometric systems, and other technologies that operate through deep learning algorithms.

Several past studies have shown the effectiveness of different adversarial attacks in prompting deep neural networks (DNNs) to make unreliable and false predictions. These attacks include the Carlini & Wagner attack, the Deepfool attack, the fast gradient sign method (FGSM) and the Elastic-Net attack (ENA).

A new computer algorithm developed by the University of Toronto’s Parham Aarabi can store and recall information strategically—just like our brains.

The associate professor in the Edward S. Rogers Sr. department of electrical and computer engineering, in the Faculty of Applied Science & Engineering, has also created an experimental tool that leverages the new algorithm to help people with memory loss.

“Most people think of AI as more robot than human,” says Aarabi, whose framework is explored in a paper being presented this week at the IEEE Engineering in Medicine and Biology Society Conference in Glasgow. “I think that needs to change.”

Scientists have created an artificial intelligence that is able to think and learn like a baby.

The system is able to grasp the basic common sense rules of the world in the same way as humans can, the researchers who create it say.

The breakthrough could not only help advance AI research but also the ways we understand the human mind, scientists say.

The AI-powered robot is named “Polly” and will pollinate truss tomato plants in Costa’s tomato glasshouse facilities in Guyra, New South Wales.

In its commercial application, Costa wrote on its website that these robotic pollinators will drive between the rows, detect flowers that are ripe for pollination utilizing artificial intelligence, and then emit air pulses to vibrate the flowers in a certain way that mimics buzz pollination that is carried out by bumblebees.

Compared to using insects, like bees, and the human laborers that are occasionally required to aid with the growth of particular crops, pollination robots could provide future farmers with a major advantage, which is to improve productivity.

Senior Analyst, AI & Quantum Computing, Paul Smith-Goodson, dives in as in today’s world, many critical business decisions and customer-facing services rely on accurate machine learning insights. Today, he discusses Google’s latest A.I. benchmarking test dethroning NVIDIA’s.

Inspired by research into how infants learn, computer scientists have created a program that can pick up simple physical rules about the behaviour of objects — and express surprise when they seem to violate those rules. The results were published on 11 July in Nature Human Behaviour¹.

Developmental psychologists test how babies understand the motion of objects by tracking their gaze. When shown a video of, for example, a ball that suddenly disappears, the children express surprise, which researchers quantify by measuring how long the infants stare in a particular direction.

Luis Piloto, a computer scientist at Google-owned company DeepMind in London, and his collaborators wanted to develop a similar test for artificial intelligence (AI). The team trained a neural network — a software system that learns by spotting patterns in large amounts of data — with animated videos of simple objects such as cubes and balls.

The team claims to have given a robot self-awareness of its location in physical space, but others are sceptical.

In short, we’re buying so much stuff that it’s becoming hard to get that stuff where it needs to be without undue negative impacts on infrastructure, transit networks, traffic, and ultimately, quality of life. People aren’t going to stop buying stuff, so how can we plan for a future where there’s more stuff being moved and our streets aren’t overwhelmingly clogged with semis and delivery vans (and the polluting emissions that come with them)?

A Swiss company has an idea—one that’s pretty original, a bit wacky, and maybe excessive. Or maybe it’s brilliant; you be the judge.

Continue reading “Swiss Company to Build an Intercity Network of Tunnels for Robotic Cargo Pods” »