ChatGPT creator OpenAI has developed internal tools for watermarking and tracking AI-generated content with 99.9 percent accuracy, theWall Street Journal reports — but is refusing to release it.
Effective tools for flagging AI-generated text could be useful in any number of situations, from cracking down on cheating students to sorting through the AI-generated sludge filling the web.
Which is why it’s so surprising that OpenAI, as the WSJ reports, has been quietly hanging onto tools that could do exactly that.
In the rapidly advancing field of artificial intelligence (AI), few voices resonate as profoundly as Dr Ben Goertzel’s. With a background in mathematics and decades of experience as an AI researcher, Ben’s insights into the future of AI and its convergence with human intelligence offer a compelling narrative.
This article goes into his perspectives as shared in the session IA générale: vers la singularité — avec Dr Ben Goertzel, exploring the technological singularity, the mainstreaming of transhumanist ideas, and the profound societal and philosophical implications of these advancements.
Ben Goertzel’s journey into AI began in the 1970s, when the concept of machines matching human intelligence was confined to science fiction. Today, however, we are on the brink of achieving this milestone. He expresses his excitement and trepidation about this development, highlighting the double-edged nature of such a revolutionary transformation.
Singularity net Ben goerzel discusses artificial and general intelligence and cosmist intelligence.
Dr. Ben Goertzel discusses artificial general, non-human and cosmist intelligences with Ed Keller at The Overview Effect Lectures, which is a series positioned as a survey of some of the key operational themes critical to post planetary and universal design.
Ed Keller’s Youtube Channel — / machinicphylum.
SingularityNET is a decentralized marketplace for artificial intelligence. We aim to create the world’s global brain with a full-stack AI solution powered by a decentralized protocol.
We gathered the leading minds in machine learning and blockchain to democratize access to AI technology. Now anyone can take advantage of a global network of AI algorithms, services, and agents.
Think and you’ll miss it: researchers at MIT claim to have successfully created analog synapses that are one million times faster than those in our human brains.
Just as digital processors need transistors, analog ones need programmable resistors. Once put into the right configuration, these resistors can be used to create a network of analog synapses and neurons, according to a press release.
These analog synapses aren’t just ultra-fast, they’re remarkably efficient, too. And that’s pretty important, because as digital neural networks grow more advanced and powerful, they require more and more energy, increasing their carbon footprint considerably.
Neuromorphic computers are devices that try to achieve reasoning capability by emulating a human brain. They are a different type of computer architecture that copies the physical characteristics and design principles of biological nervous systems. Although neuromorphic computations can be emulated, it’s very inefficient for classical computers to simulate. Typically new hardware is required.
The first neuromorphic computer at the scale of a full human brain is about to come online. It’s called DeepSouth, and will be finished in April 2024 at Western Sydney University. This computer should enable new research into how our brain actually functions, potentially leading to breakthroughs in how AI is created.
One important characteristic of this neuromorphic computer is that it’s constructed out of commodity hardware. Specifically, it’s built on top of FPGAs. This means it will be much easier for other organizations to copy the design. It also means that once AI starts self-improving, it can probably build new iterations of hardware quite easily. Instead of having to build factories from the ground up, leveraging existing digital technology allows all the existing infrastructure to be reused. This might have implications for how quickly we develop AGI, and how quickly superintelligence arises.
#ai #neuromorphic #computing.
A new supercomputer aims to closely mimic the human brain — it could help unlock the secrets of the mind and advance AI https://theconversation.com/a-new-sup…
As advances in AI and Machine Learning accelerate, the once-fictional idea of machines gaining Consciousness is becoming a pressing reality. This video explores the potential risks and questions how prepared Hue-BEings are for this new form of Consciousness. From self-driving cars to Intelligent machinery, we delve into the Evolution and implications of AI emulating Hue-BEing interactions. What type of Future will we all Build, Together?
A research team has demonstrated that analog hardware using ECRAM devices can maximize the computational performance of artificial intelligence, showcasing its potential for commercialization. Their research has been published in Science Advances.
The rapid advancement of AI technology, including applications like generative AI, has pushed the scalability of existing digital hardware (CPUs, GPUs, ASICs, etc.) to its limits. Consequently, there is active research into analog hardware specialized for AI computation.
Analog hardware adjusts the resistance of semiconductors based on external voltage or current and utilizes a cross-point array structure with vertically crossed memory devices to process AI computation in parallel. Although it offers advantages over digital hardware for specific computational tasks and continuous data processing, meeting the diverse requirements for computational learning and inference remains challenging.
Google’s Gemini 1.5 Pro revolutionizes AI with top LMSYS scores, outperforming GPT-4 and Claude-3.5, promising transformative impact across industries while raising ethical questions.
