Lifeboat Foundation

Michael Levin’s 2019 paper “The Computational Boundary of a Self” is discussed. The main topics of conversation include Scale-Free Cognition, Surprise & Stress, and the Morphogenetic Field. Michael Levin is a scientist at Tufts University; his lab studies anatomical and behavioral decision-making at multiple scales of biological, artificial, and hybrid systems. He works at the intersection of developmental biology, artificial life, bioengineering, synthetic morphology, and cognitive science.

🚩The Computational Boundary of a Self: Developmental Bioelectricity Drives Multicellularity and Scale-Free Cognition (can read in browser or download as pdf)
https://www.frontiersin.org/articles/10.3389/fpsyg.2019.02688/full.

Continue reading “What are Cognitive Light Cones? (Michael Levin Interview)” »

Many current computational models that aim to simulate cortical and hippocampal modules of the brain depend on artificial neural networks. However, such classical or even deep neural networks are very slow, sometimes taking thousands of trials to obtain the final response with a considerable amount of error. The need for a large number of trials at learning and the inaccurate output responses are due to the complexity of the input cue and the biological processes being simulated. This article proposes a computational model for an intact and a lesioned cortico-hippocampal system using quantum-inspired neural networks. This cortico-hippocampal computational quantum-inspired (CHCQI) model simulates cortical and hippocampal modules by using adaptively updated neural networks entangled with quantum circuits. The proposed model is used to simulate various classical conditioning tasks related to biological processes. The output of the simulated tasks yielded the desired responses quickly and efficiently compared with other computational models, including the recently published Green model.

Several researchers have proposed models that combine artificial neural networks (ANNs) or quantum neural networks (QNNs) with various other ingredients. For example, Haykin (1999) and Bishop (1995) developed multilevel activation function QNNs using the quantum linear superposition feature (Bonnell and Papini, 1997).

The prime factorization algorithm of Shor was used to illustrate the basic workings of QNNs (Shor, 1994). Shor’s algorithm uses quantum computations by quantum gates to provide the potential power for quantum computers (Bocharov et al., 2017; Dridi and Alghassi, 2017; Demirci et al., 2018; Jiang et al., 2018). Meanwhile, the work of Kak (1995) focused on the relationship between quantum mechanics principles and ANNs. Kak introduced the first quantum network based on the principles of neural networks, combining quantum computation with convolutional neural networks to produce quantum neural computation (Kak, 1995; Zhou, 2010). Since then, a myriad of QNN models have been proposed, such as those of Zhou (2010) and Schuld et al. (2014).

OpenAI’s breakthrough consistency model will lead into image understanding to make GPT4 multimodal, providing next generation improvements with human-computer interaction, human-robot interaction, and even helping the disabled. Microsoft has already released a predecessor to GPT4 image understanding by with Visual ChatGPT, which is much more limited in its abilities.

Deep Learning AI Specialization: https://imp.i384100.net/GET-STARTED
AI Marketplace: https://taimine.com/

Continue reading “Visual GPT 4: Unveiling 3 Next Gen AI Abilities + NEW OpenAI Model” »

The agency will look at developing a standard policy for setting privacy rules on artificial intelligence.

AI-enabled language models are becoming commonplace of late, spearheaded by the disruption caused by OpenAI’s ChatGPT. In its wake, we have seen other technological players like Google and Microsoft scrambling to catch up to the competition by introducing their respective models to the public.

As a counterbalance, global authorities are doing due diligence to evolve a common framework to regulate the industry.

Continue reading “A task force set up” »

This has important implications for measuring the mass of the central black hole in M87.

Look at the image on the left and then the image on the right. They are by no means identical. But what if we told you that both the images are of the same object?

The object being a supermassive black hole.

Continue reading “We now have a better image of black hole M87, thanks to machine learning” »

A system for realizing many-photon quantum circuits is presented, comprising a programmable nanophotonic chip operating at room temperature, interfaced with a fully automated control system.

University of Oxford professor explains how conscious machines are possible.

Up next, The intelligence explosion: Nick Bostrom on the future of AI ► https://youtu.be/1WcpN4ds0iY

Continue reading “Superintelligence is possible | Oxford professor Michael Wooldridge” »

Grocery shopping will lead the way in the AI (artificial intelligence) revolution.

Brits aged 15 to 64 spend about 43 percent of all their work and study time on cooking cleaning and other jobs round the home such as looking after children or elderly relatives.

In the UK, working-age men spend around half as much time as working-age women do on them.

It was therefore shortly after the discovery of g that Spearman’s contemporary, Godfrey Thomson, proposed that the general factor represents a global network phenomenon 11, 12, 13. Thomson held that g emerges from the interaction among the many elements of the brain, which he referred to as neural arcs or bonds 14, 15. According to Thomson’s Sampling Theory of Mental Ability, each item on an achievement test samples a number of these bonds 11, 12, 13. He proposed that the degree of overlap among bonds accounted for the correlation between tests and the resulting positive manifold. Thus, Thomson’s theory was the first to show that Spearman’s discovery of the general factor of intelligence is consistent with a network perspective.

Thomson’s legacy can be found in modern psychological theories which posit that g originates from the mutual interactions among cognitive processes [16]. Individual differences in g are known to be influenced, for example, by language abilities 10, 17, which facilitate a wealth of cognitive, social, and affective processes through mutual interactions (i.e., reciprocal causation) [18]. The central idea of the Mutualism Model is that change or growth in one aspect of mental ability is (i) partially autonomous (owing to developmental maturation), and is also (ii) based on growth in other areas (owing to the mutual interaction between cognitive processes). By accounting for both the autonomous and interactive nature of cognitive processes, this model is able to explain individual differences in the general factor of intelligence – accounting for the positive manifold and the hierarchical pattern of correlations among tests [16].

Advances in network neuroscience have further sharpened Thomson’s notion of neural bonds, revealing principles of brain organization that support (i) the modularity of cognitive processes (enabling the autonomy of mental processes), and (ii) the dynamic reorganization of this modular architecture in the service of system-wide flexibility and adaptation (enabling mutual interactions between cognitive processes). The following sections review these principles of brain organization and introduce a Network Neuroscience Theory for understanding individual differences in the general factor of intelligence based on the small-world topology and network dynamics of the human brain. This framework relies upon formal concepts from network neuroscience and their application to understanding the neurobiological foundations of g.