The International Energy Agency predicts that artificial intelligence will consume 10 times as much energy in 2026 as it did in 2023.
Optical neural networks, which use photons instead of electrons, have advantages over traditional systems. They also face major obstacles.
In a path-breaking study, researchers have created artificial neurological junctions called synapses using a mix of water and salt, paving the way for the development of advanced computers that mimic the performance of a human brain. The artificial brain cells, developed by the team from Utrecht University in the Netherlands and Sogang University in South Korea, use the same water and salt ingredients the brain uses.
The results of the study were published in the journal Proceedings of the National Academy of Sciences.
Scientists striving to develop energy efficient and high-performance computers often look up to human brains as a source of inspiration.
OpenAI CEO Sam Altman, who has said the 2013 Spike Jonze film is his favorite movie, invited comparisons by posting the word “Her” on X after the company announced the new ChatGPT version. But later, OpenAI executives denied any connection between Johansson and the new voice assistant.
Then the company suddenly dropped the voice.
In a post on X just before midnight Pacific time Sunday, OpenAI said the voice would be halted as it addresses “questions about how we chose the voices in ChatGPT.” A company spokeswoman would not provide further detail.
Posted in robotics/AI
OpenAI has introduced GPT-4o, in the form of a digital personal assistant that can interact using text and visuals 🤖
Here, we look back at GPT-4’s performance in human exams from a year ago for comparison.
From the archive:
A new Winows 11 feature that will remember what you did in your PC.
Microsoft has announced a new AI-powered feature for Windows 11 called ‘Recall,’ which records everything you do on your PC and lets you search through your historical activities.
Recall works like a photographic memory for your PC, letting you access everything you’ve seen or done on your computer in an organized way using queries in your native language.
With Recall, you can scroll through your timeline to find content from any app, website, or document you had opened. The feature utilizes snapshots to suggest actions based on what it recognizes, making it easy to return to specific emails in Outlook or the right chat in Teams.
Joints are an essential part in robotics, especially those that try to emulate the motion of (human) animals. Unlike the average automaton, animals are not outfitted with bearings and similar types of joints, but rather rely sometimes on ball joints and a lot on rolling contact joints (RCJs). These RCJs have the advantage of being part of the skeletal structure, making them ideal for compact and small joints. This is the conclusion that [Breaking Taps] came to as well while designing the legs for a bird-like automaton.
These RCJs do not just have the surfaces which contact each other while rotating, but also provide the constraints for how far a particular joint is allowed to move, both in the forward and backward directions as well as sideways. In the case of the biological version these contact surfaces are also coated with a constantly renewing surface to prevent direct bone-on-bone contact. The use of RCJs is rather common in robotics, with the humanoid DRACO 3 platform as detailed in a 2023 research article by [Seung Hyeon Bang] and colleagues in Frontiers in Robotics and AI.
The other aspect of RCJs is that they have to be restrained with a compliant mechanism. In the video [Breaking Taps] uses fishing line for this, but many more options are available. The ‘best option’ also depends on the usage and forces which the specific joint will be subjected to. For further reading on the kinematics in robotics and kin, we covered the book Exact Constraint: Machine Design Using Kinematic Principles by [Douglass L. Blanding] a while ago.
In 2006, Feifei Li aimed to address the limitations of AI algorithms, which were hampered by small, non-diverse datasets.
And the unbending faith! A behind-the-scenes look at the unlikely ingredients that fueled the 2000s AI boom.
Chat gpt 4 is really excellent in physics work aiding the user very well much like wolfram alpha has done.
Artificial intelligence (AI) technologies have been consistently influencing the progress of education for an extended period, with its impact becoming more significant especially after the launch of ChatGPT-3.5 at the end of November 2022. In the field of physics education, recent research regarding the performance of ChatGPT-3.5 in solving physics problems discovered that its problem-solving abilities were only at the level of novice students, insufficient to cause outstanding alarm in the field of physics education. However, the release of ChatGPT-4 presented substantial improvements in reasoning and conciseness. How does this translate to performance in solving physics problems, and what kind of impact might it have on education?
Brain-machine interfaces are devices that enable direct communication between a brain’s electrical activity and an external device such as a computer or a robotic limb that allows people to control machines using their thoughts.
In the vast and ever-evolving landscape of technology, neuromorphic computing emerges as a groundbreaking frontier, reminiscent of uncharted territories awaiting exploration. This novel approach to computation, inspired by the intricate workings of the human brain, offers a path to traverse the complex terrains of artificial intelligence (AI) and advanced data processing with unprecedented efficiency and agility.
Neuromorphic computing, at its core, is an endeavor to mirror the human brain’s architecture and functionality within the realm of computer engineering. It represents a significant shift from traditional computing methods, charting a course towards a future where machines not only compute but also learn and adapt in ways that are strikingly similar to the human brain. This technology deploys artificial neurons and synapses, creating networks that process information in a manner akin to our cognitive processes. The ultimate objective is to develop systems capable of sophisticated tasks, with the agility and energy efficiency that our brain exemplifies.
The genesis of neuromorphic computing can be traced back to the late 20th century, rooted in the pioneering work of researchers who sought to bridge the gap between biological brain functions and electronic computing. The concept gained momentum in the 1980s, driven by the vision of Carver Mead, a physicist who proposed the use of analog circuits to mimic neural processes. Since then, the field has evolved, fueled by advancements in neuroscience and technology, growing from a theoretical concept to a tangible reality with vast potential.
