Lifeboat Foundation

Neural networks biological and artificial.

Neural Networks have found applications across various domains due to their ability to learn from data and improve over time without human intervention. They can solve challenging problems that are hard or impossible to solve using traditional methods. Here are some of the examples of how neural networks and artificial neurons are used in real-world scenarios:

Voice assistants: Voice assistants like Siri and Alexa use neural networks to understand spoken language commands and questions. They use trained models based on artificial neurons processing vast datasets of speech and text data. They can also generate natural-sounding responses and perform various tasks, such as playing music, setting reminders, searching the web, etc.

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A team of scientists from Montana State University has provided the first experimental evidence that two new groups of microbes thriving in thermal features in Yellowstone National Park produce methane—a discovery that could one day contribute to the development of methods to mitigate climate change and provide insight into potential life elsewhere in our solar system.

A very relevant subject for research.

The world appears to contain diverse kinds of objects and systems—planets, tornadoes, trees, ant colonies, and human persons, to name but a few—characterized by distinctive features and behaviors. This casual impression is deepened by the success of the special sciences, with their distinctive taxonomies and laws characterizing astronomical, meteorological, chemical, botanical, biological, and psychological processes, among others. But there’s a twist, for part of the success of the special sciences reflects an effective consensus that the features of the composed entities they treat do not “float free” of features and configurations of their components, but are rather in some way(s) dependent on them.

Consider, for example, a tornado. At any moment, a tornado depends for its existence on dust and debris, and ultimately on whatever micro-entities compose it; and its properties and behaviors likewise depend, one way or another, on the properties and interacting behaviors of its fundamental components. Yet the tornado’s identity does not depend on any specific composing micro-entity or configuration, and its features and behaviors appear to differ in kind from those of its most basic constituents, as is reflected in the fact that one can have a rather good understanding of how tornadoes work while being entirely ignorant of particle physics.

At sea, biologists discovered microbial partners that together produce nitrogen, a nutrient essential for life. The pair are in the process of merging into a single organism.

The brain, arrives at the claim that it possesses a non-physical, subjective awareness and assigns a high degree of certainty to that extraordinary claim. The theory does not address how the brain might actually possess a non-physical essence. It is not a theory that deals in the non-physical. It is about the computations that cause a machine to make a claim and to assign a high degree of certainty to the claim. The theory is offered as a possible starting point for building artificial consciousness. Given current technology, it should be possible to build a machine that contains a rich internal model of what consciousness is, attributes that property of consciousness to itself and to the people it interacts with, and uses that attribution to make predictions about human behavior. Such a machine would “believe” it is conscious and act like it is conscious, in the same sense that the human machine believes and acts.

This article is part of a special issue on consciousness in humanoid robots. The purpose of this article is to summarize the attention schema theory (AST) of consciousness for those in the engineering or artificial intelligence community who may not have encountered previous papers on the topic, which tended to be in psychology and neuroscience journals. The central claim of this article is that AST is mechanistic, demystifies consciousness and can potentially provide a foundation on which artificial consciousness could be engineered. The theory has been summarized in detail in other articles (e.g., Graziano and Kastner, 2011; Webb and Graziano, 2015) and has been described in depth in a book (Graziano, 2013). The goal here is to briefly introduce the theory to a potentially new audience and to emphasize its possible use for engineering artificial consciousness.

The AST was developed beginning in 2010, drawing on basic research in neuroscience, psychology, and especially on how the brain constructs models of the self (Graziano, 2010, 2013; Graziano and Kastner, 2011; Webb and Graziano, 2015). The main goal of this theory is to explain how the brain, a biological information processor, arrives at the claim that it possesses a non-physical, subjective awareness and assigns a high degree of certainty to that extraordinary claim. The theory does not address how the brain might actually possess a non-physical essence. It is not a theory that deals in the non-physical. It is about the computations that cause a machine to make a claim and to assign a high degree of certainty to the claim. The theory is in the realm of science and engineering.

This video explores the 10 hypothetical stages of AI and their impact on humanity. Watch this next video about 20 emerging technologies of the future: • 20 Emerging Technologies That Will Ch…

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This is pretty impressive, they can move around individual cells. Video in comments:

Researchers at the HUN-REN Biological Research Centre, Szeged, have developed tiny tools to capture individual cells. According to their study published in the journal Advanced Materials, key innovations of using flexible microrobots is that they do not require any treatment of the cells to grab them and also allow the cells to be released after examination, enabling more efficient investigations than ever before.

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A digital “primordial soup” with no rules or direction can lead to the emergence of self-replicating artificial life forms, in an experiment that may hint at how biological life began on Earth.

By Matthew Sparkes