They aren’t clones. They’re xenobots, synthetic lifeforms made from clusters of cells.
Robots such as these, capable of making offshoots of themselves, could be a future tool for science and medicine.
Category: robotics/AI – Page 415
How Nvidia became an AI Giant
It all started at a Denny’s in San Jose in 1993. Three engineers—Jensen Huang, Chris Malachowsky and Curtis Priem—gathered at the diner in what is now the heart of Silicon Valley to discuss building a computer chip that would make graphics for video games faster and more realistic. That conversation, and the ones that followed, led to the founding of Nvidia, the tech company that soared through the ranks of the stock market to briefly top Microsoft as the most valuable company in the S&P 500 this week.
The company is now worth over $3.2 trillion, with its dominance as a chipmaker cementing Nvidia’s place as the poster child of the artificial intelligence boom—a moment that Huang, Nvidia’s CEO, has dubbed “the next industrial revolution.”
On a conference call with analysts last month, Huang predicted that the companies using Nvidia chips would build a new type of data center called “AI factories.”
Bioplausible Artificial Intelligence
Listen to this episode from The Futurists on Spotify. Monica Anderson returns to the Futurists to share a radical concept: future AI models based on Darwinism. The “AI epistemologist” shares provocative opinions about where the current crop of generative AI systems went wrong, and why generative AI is computationally expensive and energy intensive, and why scaling AI with hardware will not achieve general intelligence. Instead she offers a radical alternative: a design for machine intelligence that is inspired by biology, and in particular by the Darwinian process of selection. Topics include: why generative AI is not a plagiarism machine; syntax versus semantics and why AI needs both; there is only one algorithm for creativity; and how to construct an AI that consumes a million times less energy.
The Joy of Why: Will AI Ever Have Common Sense? on Apple Podcasts
How do you teach ChatGPT common sense? Train it on questions that adults would never think to ask. In this week’s “The Joy of Why,” computer scientist Yejin Choi talks with co-host Steven Strogatz about how training AI can mimic the “why-this, why-that” curiosity of a toddler.
Show The Joy of Why, Ep Will AI Ever Have Common Sense? — Jul 18, 2024.
Brain implant patient says OpenAI’s tech helps him communicate with family
A 64-year-old named Mark has spent the last year learning how to control devices like his laptop and phone using a brain implant. And thanks to OpenAI, it’s gotten a whole lot easier to do.
The neurotech startup Synchron said Thursday it’s using OpenAI’s latest artificial intelligence models to build a new generative chat feature for patients with its brain-computer interface, or BCI.
A BCI system decodes brain signals and translates them into commands for external technologies. Synchron’s model is designed to help people with paralysis communicate and maintain some independence by controlling smartphones, computers and other devices with their thoughts.
Many people think AI is already sentient — and that’s a big problem
A survey of people in the US has revealed the widespread belief that artificial intelligence models are already self-aware, which is very far from the truth.
Is OI the Key to AI?
It takes an incredible amount of energy to both train and operate artificial intelligence software, as we explored last week in The Bleeding Edge – AI’s Thirst for Power.
OpenAI’s GPT-4 generative AI, which powers its ChatGPT, required about 10 megawatts (MW) of electricity to train. That’s roughly equivalent to the power requirements of 10,000 average homes.
It’s also about 833,000 times the electricity required to power the human brain.
Visualization and Quantitative Evaluation of Functional Structures of Soybean Root Nodules via Synchrotron X-ray Imaging
Published in Plant Phenomics:Click the link to read the full article for free:
The efficiency of N2-fixation in legume–rhizobia symbiosis is a function of root nodule activity. Nodules consist of 2 functionally important tissues: (a) a central infected zone (CIZ), colonized by rhizobia bacteria, which serves as the site of N2-fixation, and (b) vascular bundles (VBs), serving as conduits for the transport of water, nutrients, and fixed nitrogen compounds between the nodules and plant. A quantitative evaluation of these tissues is essential to unravel their functional importance in N2-fixation. Employing synchrotron-based x-ray microcomputed tomography (SR-μCT) at submicron resolutions, we obtained high-quality tomograms of fresh soybean root nodules in a non-invasive manner. A semi-automated segmentation algorithm was employed to generate 3-dimensional (3D) models of the internal root nodule structure of the CIZ and VBs, and their volumes were quantified based on the reconstructed 3D structures. Furthermore, synchrotron x-ray fluorescence imaging revealed a distinctive localization of Fe within CIZ tissue and Zn within VBs, allowing for their visualization in 2 dimensions. This study represents a pioneer application of the SR-μCT technique for volumetric quantification of CIZ and VB tissues in fresh, intact soybean root nodules. The proposed methods enable the exploitation of root nodule’s anatomical features as novel traits in breeding, aiming to enhance N2-fixation through improved root nodule activity.