Lifeboat Foundation

Microsoft is determined to thrust “AI” into all of its products at the moment and Microsoft Designer is no exception. This supposedly AI-driven service — currently in preview — is meant to create stunning social media posts, flyers etc. from your written prompts alone. Sadly, it’s about as intelligent as a Big Mac.

This is sort-of fine for a two-for one drinks offer:

This is, at best, conceptual:

Continue reading “Microsoft Designer Is The Very Worst Example Of AI” »

A lightweight, customized mouse delivering maximum comfort and peak performance that fits snugly into your palm and your palm alone.

In this day and age, where we spend hours hunched over a computer, there is a case for everything being ergonomic.

Into this niche steps Formify, a team based out of Toronto with the belief that individualized design should be accessible to everyone.

No human intervention is required.

A research team led by Yan Zeng, a scientist at the Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab), has built a new material research laboratory where robots do the work and artificial intelligence (AI) can make routine decisions. This allows work to be conducted around the clock, thereby accelerating the pace of research.

Research facilities and instrumentation have come a long way over the years, but the nature of research remains the same. At the center of each experiment is a human doing the measurements, making sense of data, and deciding the next steps to be taken. At the A-Lab set up at Berkeley, the researchers led by Zeng want to break the current pace of research by using robotics and AI.

A recent study has suggested that transcriptomes may be the best method for classifying different types of brain disease.

OpenAI’s ChatGPT successor GPT-4 is showing signs of artificial general intelligence, claims a Microsoft study.

A central pillar of cosmology — the universe is the same everywhere and in all directions — is surviving a storm of possible evidence against it.

When we look at something, the different properties of the image are processed in different brain regions. But how does our brain make a coherent image out of such a fragmented representation? A new review by Pieter Roelfsema sheds light on two existing hypotheses in the field.

When we open our eyes, we immediately see what is there. The efficiency of our vision is a remarkable achievement of evolution. The introspective ease with which we perceive our visual surroundings masks the sophisticated machinery in our brain that supports visual perception. The image that we see is rapidly analyzed by a complex hierarchy of cortical and subcortical brain regions.

Neurons in low level brain regions extract basic features such as line orientation, depth and the color of local image elements. They send the information to several mid-level brain areas. Neurons in these areas code for other features, such as motion direction, color and shape fragments.

Do you remember learning to drive a car? You probably fumbled around for the controls, checked every mirror multiple times, made sure your foot was on the brake pedal, then ever-so-slowly rolled your car forward.

Fast forward to now and you’re probably driving places and thinking, “how did I even get here? I don’t remember the drive”. The task of driving, which used to take a lot of mental energy and concentration, has now become subconscious, automatic—habitual.

But how—and why—do you go from concentrating on a task to making it automatic?

Brains are like puzzles, requiring many nested and co-dependent pieces to function well. The brain is divided into areas, each containing many millions of neurons connected across thousands of synapses. These synapses, which enable communication between neurons, depend on even smaller structures: message-sending boutons (swollen bulbs at the branch-like tips of neurons), message-receiving dendrites (complementary branch-like structures for receiving bouton messages), and power-generating mitochondria. To create a cohesive brain, all these pieces must be accounted for.

However, in the aging brain, these pieces can get lost or altered, and no longer fit in the greater brain puzzle. A research team has now published a study in Frontiers in Aging Neuroscience on this topic.

“Fifty percent of people experience loss of working memory with old age, meaning their ability to hold and manipulate information in the short-term decreases,” says co-first author Courtney Glavis-Bloom, a senior staff scientist in Salk Institute Professor John Reynolds’s lab. “We set out to understand why some individuals maintain healthy working memory as they age, while others do not. In the process, we discovered a novel mechanism for the synaptic basis of cognitive impairment.”