Lifeboat Foundation

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.

Continue reading “General AI: Towards the Singularity — With Ben Goertzel” »

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.

Continue reading “Dr. Ben Goertzel Discusses Artificial General, Non-Human & Cosmist Intelligences” »

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.

Continue reading “Will neuromorphic computers accelerate AGI development?” »

697: The (Short) Path to Artificial General Intelligence — with Dr. Ben Goertzel.

696: Brain-Computer Interfaces and Neural Decoding — with Prof. Bob Knight.

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?

Telomerase gene therapy shows promising potential for treating pulmonary fibrosis and other diseases associated with short telomeres.

Continue reading “Telomerase gene therapy shows promising potential for treating pulmonary fibrosis” »

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.