Lifeboat Foundation

The idea of the brain as a computer is everywhere. So much so we have forgotten it is a model and not the reality. It’s a metaphor that has lead some to believe that in the future they’ll be uploaded to the digital ether and thereby achieve immortality. It’s also a metaphor that garners billions of dollars in research funding every year. Yet researchers argue that when we dig down into our grey matter our biology is anything but algorithmic. And increasingly, critics contend that the model of the brain as computer is sending scientists (and their resources) nowhere fast. Is our attraction to the idea of the brain as computer an accident of current human technology? Can we find a better metaphor that might lead to a new paradigm?

If there’s one phrase the June 2024 U.S. presidential debate may entirely eliminate from the English vocabulary it’s that age is a meaningless number. Often attributed to boxer Muhammad Ali, who grudgingly retired at age 39, this centuries-old idea has had far-reaching consequences in global politics, as life expectancy more than doubled since the start of the 20th century, and presidents’ ages shifted upwards. We say “age is what we make of it” to ourselves and to policymakers, and think it’s a harmless way to dignify the aged. But how true is it? And if it isn’t true, why would we lie?

For centuries, we have confused our narrative of what aging should be with what its ruthless biology is. Yet pretending that biological age does not matter is at best myopic, and at worst, it’s a dangerous story to our governments, families, and economies. In just 11 years — between 2018 and 2029 — U.S. spending on Social Security and Medicare will more than double, from $1.3 trillion to $2.7 trillion per year. As we age, our odds of getting sick and dying by basically anything go up exponentially. If smoking increases our chances of getting cancer by a factor of 15, aging does so 100-fold. At age 65, less than 5% of people are diagnosed with Alzheimer’s. Beyond age 85, nearly half the population has some form of dementia. Biological aging is the biggest risk factor for most chronic diseases; it’s a neglected factor in global pandemics; and it even plays a role in rare diseases.

This explains why in hospitals, if there’s one marker next to a patient’s name, it’s their age. How many birthday candles we have blown out is an archaic surrogate marker of biological aging. Yet it’s the best we have. Chronological age is so telling of overall health that physicians everywhere rely on it for life-or-death decisions, from evaluating the risks of cancer screening to rationing hospital beds.

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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