Lifeboat Foundation

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.

This is a temporary transcription of Josha Bach’s talk on artificial intelligence philosophy at CCC 2023.

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Inspired by the map makers of the 20th Century, Imperial researchers have demonstrated a new way to identify copyright holders’ work in LLMs.

The technique was presented at the International Conference on Machine Learning in Vienna this week, and is detailed in this preprint on the arXiv server.

Generative AI is taking the world by storm, already transforming the day-to-day lives of millions of people.

An analysis of how rhinoceros beetles deploy and retract their hindwings shows that the process is passive, requiring no muscular activity. The findings, reported in Nature, could help improve the design of flying micromachines.

Among all flying insects, beetles demonstrate the most complex wing mechanisms, involving two sets of wings: a pair of hardened forewings called elytra and a set of delicate membranous hindwings. Although extensive research exists on the origami-like folds of their wings, little is known about how they deploy and retract their hindwings.

Continue reading “Beetle-inspired robots show improved flight capabilities” »