Lifeboat Foundation

In this special crossover episode of The Cognitive Revolution, Nathan Labenz joins Robert Wright of the Nonzero newsletter and podcast to explore pressing questions about AI development. They discuss the nature of understanding in large language models, multimodal AI systems, reasoning capabilities, and the potential for AI to accelerate scientific discovery. The conversation also covers AI interpretability, ethics, open-sourcing models, and the implications of US-China relations on AI development.

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The past decade has witnessed the great success of deep neural networks in various domains. However, deep neural networks are very resource-intensive in terms of energy consumption, data requirements, and high computational costs. With the recent increasing need for the autonomy of machines in the real world, e.g., self-driving vehicles, drones, and collaborative robots, exploitation of deep neural networks in those applications has been actively investigated. In those applications, energy and computational efficiencies are especially important because of the need for real-time responses and the limited energy supply. A promising solution to these previously infeasible applications has recently been given by biologically plausible spiking neural networks. Spiking neural networks aim to bridge the gap between neuroscience and machine learning, using biologically realistic models of neurons to carry out the computation. Due to their functional similarity to the biological neural network, spiking neural networks can embrace the sparsity found in biology and are highly compatible with temporal code. Our contributions in this work are: (i) we give a comprehensive review of theories of biological neurons; (ii) we present various existing spike-based neuron models, which have been studied in neuroscience; (iii) we detail synapse models; (iv) we provide a review of artificial neural networks; (v) we provide detailed guidance on how to train spike-based neuron models; (vi) we revise available spike-based neuron frameworks that have been developed to support implementing spiking neural networks; (vii) finally, we cover existing spiking neural network applications in computer vision and robotics domains. The paper concludes with discussions of future perspectives.

Keywords: spiking neural networks, biological neural network, autonomous robot, robotics, computer vision, neuromorphic hardware, toolkits, survey, review.

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Futurist Raymond Kurzweil predicts humans may soon live up to 1,000 years by merging biotechnology, AI, and nanobots.

Year 2020 Nanobots that take the plaque out of arteries is here. face_with_colon_three

Single-walled carbon nanotubes can restore phagocytotic properties of macrophages in artherosclerotic plaques to promote plaque clearance and combat artherosclerosis.

A star wiggling oddly around in space may be the signpost to one of the most sought-after objects in the galaxy.

Some 5,825 light-years from Earth, a red giant star has been spotted moving as though in a slow orbital dance with a binary companion. The problem? There’s absolutely no light coming from the place where the binary companion should be.

It gets more interesting. Based on the behavior of the red giant, astronomers led by Song Wang of the Chinese Academy of Sciences have determined that the mass of the invisible object is just 3.6 times the mass of the Sun. There’s only one thing it could be: a black hole, one with a petite size that’s smack bang in the middle of a mysterious void in the data known as the lower mass gap.

The main power of artificial intelligence is not in modeling what we already know, but in creating solutions that are new. Such solutions exist in extremely large, high-dimensional, and complex search spaces. Population-based search techniques, i.e. variants of evolutionary computation, are well suited to finding them. These techniques are also well positioned to take advantage of large-scale parallel computing resources, making creative AI through evolutionary computation the likely “next deep learning”

Researchers have created a disk-like nanostructure that dramatically improves light frequency conversion efficiency. This innovation in photonics combines material and optical resonances in a compact form, paving the way for advanced optical and photonic applications.

Scientists at Chalmers University of Technology, in Sweden, have for the first time succeeded in combining two major research fields in photonics by creating a nanoobject with unique optical qualities. Since the object is a thousand times thinner than a human hair, yet very powerful, the breakthrough has great potential in the development of efficient and compact nonlinear optical devices. “My feeling is that this discovery has a great potential,” says Professor Timur Shegai, who led the study at Chalmers.

Harnessing Light With Advanced Photonics.

Ribonucleic acid (RNA) is a vital biological molecule that plays a significant role in the genetics of organisms and is essential to the origin and evolution of life. Structurally similar to DNA, RNA carries out various biological functions, largely determined by its spatial conformation, i.e. the way the molecule folds in on itself.

Now, a paper published in the journal Proceedings of the National Academy of Sciences (PNAS) describes for the first time how the process of RNA folding at low temperatures may open up a novel perspective on primordial biochemistry and the evolution of life on the planet.

The study is led by Professor Fèlix Ritort, from the Faculty of Physics and the Institute of Nanoscience and Nanotechnology (IN2UB) of the University of Barcelona, and is also signed by UB experts Paolo Rissone, Aurélien Severino, and Isabel Pastor.