Computational performance has followed Moore’s Law since the dawn of the computer age. Not any more.
Category: robotics/AI – Page 1,961
Physics Breakthrough as AI Successfully Controls Plasma in Nuclear Fusion Experiment
Successfully achieving nuclear fusion holds the promise of delivering a limitless, sustainable source of clean energy, but we can only realize this incredible dream if we can master the complex physics taking place inside the reactor.
For decades, scientists have been taking incremental steps towards this goal, but many challenges remain. One of the core obstacles is successfully controlling the unstable and super-heated plasma in the reactor – but a new approach reveals how we can do this.
In a joint effort by EPFL’s Swiss Plasma Center (SPC) and artificial intelligence (AI) research company DeepMind, scientists used a deep reinforcement learning (RL) system to study the nuances of plasma behavior and control inside a fusion tokamak – a donut-shaped device that uses a series of magnetic coils placed around the reactor to control and manipulate the plasma inside it.
Meta AI Researchers Upgrade Their Machine Learning-Based Image Segmentation Models For Better Virtual Backgrounds in Video Calls And Metaverse
When video chatting with colleagues, coworkers, or family, many of us have grown accustomed to using virtual backgrounds and background filters. It has been shown to offer more control over the surroundings, allowing fewer distractions, preserving the privacy of those around us, and even liven up our virtual presentations and get-togethers. However, Background filters don’t always work as expected or perform well for everyone.
Image segmentation is a computer vision process of separating the different components of a photo or video. It has been widely used to improve backdrop blurring, virtual backgrounds, and other augmented reality (AR) effects. Despite advanced algorithms, achieving highly accurate person segmentation seems challenging.
The model used for image segmentation tasks must be incredibly consistent and lag-free. Inefficient algorithms may result in bad experiences for the users. For instance, during a video conference, artifacts generated by erroneous segmentation output might easily confuse persons utilizing virtual background programs. More importantly, segmentation problems may result in unwanted exposure to people’s physical environments when applying backdrop effects.
Neuralink: Bridging The Gap Between Human & Machine
Neuralink! Also known as, Elon Musk’s other (other) project.
You’ve probably heard of Elon Musk’s plan to insert brain chips into the human brain, finally bridging the gap between human and machine. Academically, this is known as a brain-machine interface and it’s actually not that novel an idea!
In this video, I’ll refer to two key scientific studies, discuss the threat of AI technology, why Elon Musk wants to put a chip in your brain and whether or not it’s feasible.
Once you’re done watching the video, feel free to tell me in the comments whether you would ever consider getting the N1 (or whatever future iteration) chip.
Could this be the future of humanity?
Video Of The Rats & References On Substack.
Pushing The Boundaries Of AI Ethics Into The Topsy-Turvy World Of Radical Ethical AI, Potentially Exemplified Via The Use Case Of Vaunted AI-Based Self-Driving Cars
The rising visibility of Ethical AI or AI Ethics is doing great good, meanwhile some believe it isn’t enough and a semblance of embracing Radical Ethical AI is appearing. This is closely examined, including for AI-based self-driving cars.
Has the prevailing tenor and attention of today’s widely emerging semblance of AI Ethics gotten into a veritable rut? Some seem to decidedly think so.
Let’s unpack this. You might generally be aware that there has been a rising tide of interest in the ethical ramifications of AI. This is often referred to as either AI Ethics or Ethical AI, which we’ll consider herein those two monikers as predominantly equivalent and interchangeable (I suppose some might quibble about that assumption, but I’d like to suggest that we not get distracted by the potential differences, if any, for the purposes of this discussion).
Using machine learning to understand how brain cells work
For something so small, neurons can be quite complex—not only because there are billions of them in a brain, but because their function can be influenced by many factors, like their shape and genetic makeup.
A research team led by Daifeng Wang, a Waisman Center professor of biostatistics and medical informatics and computer sciences at the University of Wisconsin–Madison, is adapting machine learning and artificial intelligence techniques to better understand how a variety of traits together affect the way neurons work and behave.
Called manifold learning, the approach may help researchers better understand and even predict brain disorders by looking at specific neuronal properties. The Wang lab recently published its findings in two studies.