Life is ever from life?

 What Is the Essence of Life? 

Eric Hedin

One often answers this question by characterizing living organisms with attributes such as reproduction, metabolism, interacting with their environment, or even the processing of information. Insights into the complex biomolecular machinery within living cells that allow them to perform these functions have exploded exponentially in recent years. Despite these advances, the truth about the essence of life continues to elude any approach that ignores metaphysical aspects manifest in even the simplest biological forms.

The signs of a paradigm shift towards a teleological view of life have emerged even within the mainstream academy. As Mind Matters News explains:

It turns out that evolution is much more teleological than has been historically supposed. Not only has the prior evidence for the non-teleology of evolution mainly been overturned, but new research has increasingly focused on the teleological and teleonomic causes that underlie much of what shapes the direction of evolution.

“Goal-directed” behavior is unsupported within pure naturalism, but it would be expected if living things were designed by an intelligent agent. The behavior of living creatures appears to transcend a mechanistic version of teleology and exhibits qualities that are consistent with the essence of life as an imparted quality quite untraceable to physical origin.

Cells as “Sentient Beings”

Recently, microbiological research has come to entertain the notion that individual cells, in cooperation with other cells, possess a form of consciousness.

An earlier paper by Shapiro claims that “cells are sentient beings.”1

Contemporary research in many laboratories on cell–cell signaling, symbiosis and pathogenesis show that bacteria utilise sophisticated mechanisms for intercellular communication and even have the ability to commandeer the basic cell biology of ‘higher’ plants and animals to meet their own needs. This remarkable series of observations requires us to revise basic ideas about biological information processing and recognise that even the smallest cells are sentient beings.

Shapiro further describes this growing recognition of cellular cognition as a Kuhnian paradigm shift to view life as “informatics.”

My own view is that we are witnessing a major paradigm shift in the life sciences in the sense that Kuhn (1962) described that process. Matter, the focus of classical molecular biology, is giving way to information as the essential feature used to understand how living systems work. Informatics rather than mechanics is now the key to explaining cell biology and cell activities.

Astrophysicist Adam Frank also describes life in terms of information usage:2

But there’s another and perhaps more all-encompassing way of understanding life that puts information front and center. In this view, what makes life special — what makes it different from all the other physical systems — is its ability to use information.

Recognizing Cellular Intelligence 

Tufts University biologist Michael Levin identifies attributes of living cells that are foreign to a purely naturalistic way of thinking about life. He cites features such as goal-oriented cooperation, behaving cleverly, “problem-solving competencies,” and acting as “competent agents with preferences, with goals, with various abilities to pursue those goals, and other types of problem-solving capacities.”

Levin excuses researchers’ earlier failure to recognize cellular intelligence, saying “we really are very bad at recognizing intelligence in unconventional embodiments where our basic expectations strain against this idea that there could be intelligence in something extremely small or extremely large.” It’s an interesting irony that this blind spot applies to Levin himself, who along with many others, “really are very bad at recognizing” the signatures of intelligent design found pervasively throughout the biological realm.

The Source of Intelligence

If intelligence manifests even at the single-cell level, it becomes scientifically relevant to inquire as to its source. Some observers recognize intelligence as having a metaphysical origin. According to a recent ERG working group email:

[T]here appears to be a hierarchical organizational metaphysical masterpiece that unfolds as you pull back the curtains on cellular life.

Are we seeing that awareness and the rudiments of intelligence are an inherent accompaniment to life itself? James Barham comments on Shapiro’s statements about cellular intelligence with a discussion of various views of vitalism.

Historically, the term has most often been associated with the idea that a supernatural ‘life force’ impinges on living matter from the outside.

Barham offers the opinion that this historical view of vitalism would in principle debar scientific investigation of “the nature of the difference between the living state of matter and inorganic matter.” However, knowing, for example, that an advanced microelectronic device was made by intelligent designers from another place need not, even in principle, undercut scientific investigation of the device to see how it works. Likewise, we can learn the processes of biochemical engineering by studying cells, believing that they were intentionally designed, with arguably greater success than by studying them under the misguided presupposition of materialism.

Barham states that vitalism can also “refer to the claim that living things have properties and causal powers arising from within that are more than the sum of the properties and powers of the inanimate parts of which they are composed.” Such a position subscribes to an “emergentist” view of living systems that defies the traditional reductionistic approach to science.

My Reasons for Suspicion

As a physicist, I am suspicious of any claim of new and extraordinary properties of matter that are inconsistent with established and experimentally verified workings of the forces of nature. To claim that sophisticated “emergent properties” arise from collections of fundamental particles in specific complex arrangements, beyond what could be predicted by the laws of physics, is unwarranted and has no more scientific credibility than an appeal to magic.

What is scientifically credible is to claim that complex, functional arrangements of matter can result from the action of intelligent designers (my laptop is a case in point). The complexity of the simplest living organism far exceeds that of the most advanced human-engineered device. Above, life was described by its ability to use information. One of the traditionally recognized attributes of living things, that explicitly relies upon the creation, storage, retrieval, and usage of information, is their ability to reproduce (in theory, given the right conditions, forever). The origin of self-replication cannot be explained naturally and to describe it as an emergent property of matter violates mathematical analysis and known laws of nature. 

Not All Things Are Possible Naturally!

However, to ascribe self-replication in living things to an intelligent designer is a conclusion consistent with our well-established understanding of nature’s abilities and limitations. If the origin of the physical process of self-replication exceeds the limits of nature, claiming that cellular intelligence arises naturally is clearly not a scientific conclusion. 

It is well within the purview of scientific investigation for a scientist to draw a conclusion of “natural” or “unnatural.” For example, when astronomers observe a star near the center of our galaxy moving on an elliptical path with no visible object to cause such an orbit, they don’t conclude that some new law of motion has emerged, superseding Newton’s first law of motion. They conclude instead, consistent with established laws of physics, that since this star isn’t moving in a straight line, an external force must be acting on it, such as the gravity of a supermassive blackhole. Further applications of known laws of physics allow astronomers to accurately calculate the mass of this black hole3, even without being able to see it visibly.

A Dry Well

Naturalism is a dry well when it comes to explaining any of the attributes of cellular cognizance. Rather than recognizing such unphysical attributes as consistent with a view of God as the author of life, naturalism seems to be dredging up debris from pantheism and extending it to universal consciousness. Denyse O’Leary comments on the appeal and shortcomings of this view:

Panpsychism recognizes the reality of consciousness in the world of life. That is its strength. That is why it is slowly making inroads against materialism (physicalism, eliminationism, etc.). However, it avoids grappling with the reality of an Intelligence that is not and cannot be a part of nature. That is its weakness.

The strength of the intelligent design explanation for life lies in its full-orbed ability to address all aspects of life — its origin, complex biochemistry, information focus, consciousness, and ultimate purpose. Alternative views, when examined within the limits of nature, can only explain the “return to dust” of living organisms that have ceased to live, but they fail to explain the essence of life.

The caterpillar has eaten darwinism's lunch?

 

The inspiration and creativity of actual intelligence vs. Running of algorithmic programs by artificial intelligence.

 Artificial General Intelligence: The Oracle Problem

William A Dembski

In computer science, oracles are external sources of information made available to otherwise self-contained algorithmic processes. Oracles are in effect “black boxes” that can produce a solution for any instance of a given problem, and then supply that solution to a computer program or algorithm. For example, an oracle that could provide tomorrow’s price for a given stock could be used in an algorithm that today — with phenomenal returns — executes buy-and-sell orders for that stock. Of course, no such oracle actually exists (or if it does, it is a closely guarded secret). 

The point of oracles in computer science is not whether they exist but whether they can help us study aspects of algorithms. Alan Turing proposed the idea of an oracle that supplies information external to an algorithm in his 1938 doctoral dissertation. Some oracles, like tomorrow’s stock predictor, cannot be represented algorithmically. Others can, but the problems they solve may be so computationally intensive that no real-world computer could solve them. The concept of an oracle is important in computer science for understanding the limits of computation.

“Sing, Goddess, of the Anger of Achilles”

Turing’s choice of the word “oracle” was not accidental. Historically, oracles have denoted sources of information where the sender of the information is divine and the receiver is human. The Oracle of Delphi stands out in this regard, but there’s much in antiquity that could legitimately count as oracular. Consider, for instance, the opening of Homer’s Iliad: “Sing, goddess, of the anger of Achilles, son of Peleus.” The goddess here is one of the muses, presumably Calliope, the muse of epic poetry. In the ancient world, the value of artistic expression derived from its divine inspiration. Of course, prophecy in the Bible also falls under this conception of the oracular, as does real-time divine guidance of the believer’s life (as described in Proverbs 3:5–6 and John 16:13). 

Many of us are convinced that we have received information from oracles that can’t be explained in terms of everyday communication among people or everyday operations of the mind. We use many words to describe this oracular flow of information: inspiration, intuition, creative insight, dreams, reverie, collective unconscious, etc. Sometimes the language used is blatantly oracular. Einstein, for instance, told his biographer Banesh Hoffmann, “Ideas come from God.” Because Einstein did not believe in a personal God (Einstein would sometimes say he believed in the God of Spinoza), Hoffmann interpreted Einstein’s remark metaphorically to mean, “You cannot command the idea to come. It will come when it’s good and ready.” 

The Greatest Mathematician of His Age

Now granted, computational reductionists will dismiss such oracular talk as misleading nonsense. Really, all the information is there in some form already in the computational systems that make up our minds, and even though we are not aware of how the information is being processed, it is being processed nonetheless in purely computational and mechanistic ways. Clearly, this is what computational reductionists are bound to say. But the testimony of people in which they describe themselves as receiving information from an oracular realm needs to be taken seriously, especially if we are talking about people of the caliber of Einstein. Consider, for instance, how Henri Poincaré (1854–1912) described the process by which he made one of his outstanding mathematical discoveries. Poincaré was the greatest mathematician of his age (in 1905 he was awarded the Bolyai Prize ahead of David Hilbert). Here is how he described his discovery:

For fifteen days I strove to prove that there could not be any functions like those I have since called Fuchsian functions. I was then very ignorant; every day I seated myself at my work table, stayed an hour or two, tried a great number of combinations and reached no results. One evening, contrary to my custom, I drank black coffee and could not sleep. Ideas rose in crowds; I felt them collide until pairs interlocked, so to speak, making a stable combination. By the next morning I had established the existence of a class of Fuchsian functions, those which come from the hypergeometric series; I had only to write out the results, which took but a few hours. Then I wanted to represent these functions by the quotient of two series; this idea was perfectly conscious and deliberate, the analogy with elliptic functions guided me. I asked myself what properties these series must have if they existed, and I succeeded without difficulty in forming the series I have called theta-Fuchsian.

Just at this time I left Caen, where I was then living, to go on a geologic excursion under the auspices of the school of mines. The changes of travel made me forget my mathematical work. Having reached Coutances, we entered an omnibus to go some place or other. At the moment when I put my foot on the step the idea came to me, without anything in my former thoughts seeming to have paved the way for it, that the transformations I had used to define the Fuchsian functions were identical with those of non-Euclidean geometry. I did not verify the idea; I should not have had time, as, upon taking my seat in the omnibus, I went on with a conversation already commenced, but I felt a perfect certainty. On my return to Caen, for conscience’ sake I verified the result at my leisure.

Again, the computational reductionist would contend that Poincaré’s mind was in fact merely operating as a computer. Accordingly, the crucial computations needed to resolve his theorems were going on in the background and then just happened to percolate into consciousness once the computations were complete. But the actual experience and self-understanding of thinkers like Einstein and Poincaré, in accounting for their bursts of creativity, is very different from what we expect of computation, which is to run a computer program until it yields an answer. Humanists reject such a view of human creativity. Joseph Campbell, in The Power of Myth, offered this rejoinder to computational reductionism: “Technology is not going to save us. Our computers, our tools, our machines are not enough. We have to rely on our intuition, our true being.” Of course, artists of all stripes have from ages past to the present invoked muses of one form or another as inspiring their work. 

A Clash of Worldviews?

Does this controversy over the role of oracles in human cognition therefore merely describe a clash of worldviews between a humanism that refuses to reduce our humanity to machines and a computational reductionism that embraces such a reduction? Is this controversy just a difference in viewpoints based on a difference in first principles? In fact, oracles pose a significant theoretical and evidential challenge to computational reductionism that goes well beyond a mere collision of worldviews. Computational reductionism faces a deep conceptual problem independent of any worldview controversy.

Computational reductionism faces an oracle problem. The problem may be described thus: Our most advanced artificial intelligence systems, which I’m writing about in this series about Artificial General Intelligence (AGI), require input of external information to keep them from collapsing in on themselves. This problem applies especially to large language models (LLMs) and their most advanced current incarnation, ChatGPT-4. I’m not talking here about the role of human agency in creating LLMs, which no one disputes. I’m not even talking here about all the humanly generated data that these neural networks ingest or all the subsequent training of these systems by humans. What I’m talking about here is that once all this work is done, these systems cannot simply be set loose and thrive on their own. They need continual propping up from our human intelligence. For LLMs, we are the oracles that make and continue to make them work. 

The Death Knell for AGI

The need for ongoing human intervention in these systems may seem counterintuitive. It is also the death knell for AGI. Because if AGI is to succeed, it must surpass human intelligence, which means it must be able to leave us behind in the dust, learning and growing on its own, thriving and basking in its own marvelous capabilities. Like Aristotle’s unmoved mover God, who does not think about humanity or anything other than himself because it is in the nature of God only to think about the highest thing, and the highest thing of all is God. Thus, the Aristotelian God spends all his time contemplating only himself. A full-fledged AGI would do likewise, not deigning to occupy itself with lesser matters. (As an aside, AGI believers might take comfort in an AGI being so self-absorbed that it would not bother to destroy humanity. But to the degree that flesh-and-blood humans are a threat, or even merely an annoyance, to an AGI, it may be motivated to kill us all so as not to be distracted from contemplating itself!)

Unlike the Aristotelian God, LLMs do not thrive without human oracles continually feeding them novel information. There are sound mathematical reasons for this. The neural networks that are the basis for LLMs reside in finite dimensional vector subspaces. Everything in these spaces can therefore be expressed as a linear combination of finitely many basis vectors. In fact, they are simplexes and the linear combinations are convex, implying convergence to a center of mass, a point of mediocrity. When neural networks output anything, they are thus outputting what’s inherent in these predetermined subspaces. In consequence, they can’t output anything fundamentally new. Worse yet, as they populate their memory with their own productions and thereafter try to learn by teaching themselves, they essentially engage in an act of self-cannibalism. In the end, these systems go bankrupt because intelligence by its nature requires novel insights and creativity, which is to say, an oracle. 

Research backs up this claim that LLMs run aground in the absence of oracular intervention, and specifically external information added by humans. This becomes clear from the abstract of a recent article titled “The Curse of Recursion: Training on Generated Data Makes Models Forget“:

GPT-2, GPT-3(.5) and GPT-4 demonstrated astonishing performance across a variety of language tasks… What will happen to GPT-{n} once LLMs contribute much of the language found online? We find that use of model-generated content in training causes irreversible defects in the resulting models, where tails of the original content distribution disappear. We refer to this effect as Model Collapse and show that it can occur in Variational Autoencoders, Gaussian Mixture Models and LLMs. We build theoretical intuition behind the phenomenon and portray its ubiquity amongst all learned generative models. We demonstrate that it has to be taken seriously if we are to sustain the benefits of training from large-scale data scraped from the web. Indeed, the value of data collected about genuine human interactions with systems will be increasingly valuable in the presence of content generated by LLMs in data crawled from the Internet.

Think of It This Way

LLMs like ChatGPT are limited by a fixed finite number of dimensions, but the creativity needed to make these artificial intelligence models thrive requires added dimensions. Creativity is always orthogonal to the status quo, and orthogonality, by being at right angles with the status quo, always adds new dimensions. Oracles add such creativity. Without oracles, artificial intelligence systems become solipsistic, turning in on themselves, rehashing only what is in them already, and eventually going bankrupt because they cannot supply the daily bread needed to sustain them. AGI’s oracle problem is therefore real and damning. 

But if AGI faces an oracle problem, don’t humans likewise face an oracle problem? Suppose AGIs require human oracles to thrive. Yet if oracles are so important for creativity, don’t humans need access to oracles as well? But how, asks the computational reductionist, does the external information needed for human intelligence to thrive get to us and into us? A purely mechanistic world is a solipsistic world with all its information internal and self-generated. On mechanistic principles, there’s no way for humans to have access to such oracles.

But why think that the world is mechanistic? Organisms, as we’ve seen, give no signs of being mechanisms. And physics allows for an informationally porous universe. Quantum indeterminacy, for instance, cannot rule out the input of information from transcendent sources. The simplest metaphor for understanding what’s at stake is the radio. If we listen to a symphony broadcast on the radio, we don’t think that the radio is generating the music we hear. Instead, the radio is a conduit for the music from another source. Humans are such conduits. And machines need to be such conduits (for ongoing human intelligent input) if they are to have any real value to us.