biologit

Authors:
(1) STUART KAUFFMAN;
(2) ANDREA ROL. Table of Links Abstract and Introduction Part I. A Definition of Life Part II. The first Miracle: The emergence of life is an expected phase transition – TAP and RAF. Part III. The Second Miracle: The evolution of the biosphere is a propagating, non-deducible construction, not an entailed deduction. There is no Law. Evolution is ever-creative Part IV. New Observations and Experiments: Is There Life in the Cosmos? Conclusion and Acknowledgments Figures and References Conclusion We have sought the source of life in all our creation myths among all the peoples of the earth, perhaps back to Neanderthal 500,000 years ago. The issue of the Origin of Life as a scientific problem arose with Pasture’s claim: Life only comes from life, (55). Experimental efforts have been underway since Haldane and Oparin, (56,57), then the famous Miller Urey experiments in 1953, (58). Intense efforts based on the conviction that life must be based on template replication of polynucleotides have been carried out, (4,5,6). So far, no case of molecular reproduction has been found on this sensible pathway. The concept of the emergence of collectively autocatalytic sets was first formulated in 1971, (14). Such reproducing chemical sets of DNA, of RNA, and of peptides have been engineered using evolved polymer sequences. Xavier has now found small molecule collectively autocatalytic sets containing no DNA, RNA or peptide polymers in all 6700 prokaryotes, (11,12). This strongly suggests, but does not prove, that such systems may be the earliest form of molecular reproduction in the universe. Well established mathematical theory demonstrates that such systems can arise as a first-order phase transition in a universe where the diversity and atomic complexity of molecules increases. If this is correct, as we claim, the emergence of life is an expected phase transition in the evolving universe. Since Newton, basic science has rested on the powerful Newtonian Paradigm. This paradigm requires a prestated and knowable phase space of all the values of the relevant variables. But living organisms are Kantian Wholes that achieve Catalytic Closure, Constraint Closure, and Spatial Closure. Stunningly, we can use no mathematics based on Set Theory – all of mathematics it seems – to deduce the ever-creative evolution of the biosphere. Life is an expected miracle in the universe whose ways of becoming are literally numberless. The 20th Century saw the emergence of the atomic Age, the mushroom cloud, and mutually assured destruction. With Gödel, the 20th century also saw the End of Certainty, (59). In this, the first quarter of the 21st Century, we begin barely to glimpse the astonishing blossoming creativity of the biosphere of which we are members. We are, truly, Of Nature, not Above Nature. Acknowledgments We are truly grateful to Ingemar Ernberg, Carlos Gershenson, Wim Hordijk, Niles Lehman, and Ricard Solé for advice and comments. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) STUART KAUFFMAN; (2) ANDREA ROL. Authors: Authors: (1) STUART KAUFFMAN; (2) ANDREA ROL. Table of Links Abstract and Introduction Abstract and Introduction Part I. A Definition of Life Part I. A Definition of Life Part II. The first Miracle: The emergence of life is an expected phase transition – TAP and RAF. Part II. The first Miracle: The emergence of life is an expected phase transition – TAP and RAF. Part III. The Second Miracle: The evolution of the biosphere is a propagating, non-deducible construction, not an entailed deduction. There is no Law. Evolution is ever-creative Part III. The Second Miracle: The evolution of the biosphere is a propagating, non-deducible construction, not an entailed deduction. There is no Law. Evolution is ever-creative Part IV. New Observations and Experiments: Is There Life in the Cosmos? Part IV. New Observations and Experiments: Is There Life in the Cosmos? Conclusion and Acknowledgments Conclusion and Acknowledgments Figures and References Figures and References Conclusion We have sought the source of life in all our creation myths among all the peoples of the earth, perhaps back to Neanderthal 500,000 years ago. The issue of the Origin of Life as a scientific problem arose with Pasture’s claim: Life only comes from life, (55). Experimental efforts have been underway since Haldane and Oparin, (56,57), then the famous Miller Urey experiments in 1953, (58). Intense efforts based on the conviction that life must be based on template replication of polynucleotides have been carried out, (4,5,6). So far, no case of molecular reproduction has been found on this sensible pathway. The concept of the emergence of collectively autocatalytic sets was first formulated in 1971, (14). Such reproducing chemical sets of DNA, of RNA, and of peptides have been engineered using evolved polymer sequences. Xavier has now found small molecule collectively autocatalytic sets containing no DNA, RNA or peptide polymers in all 6700 prokaryotes, (11,12). This strongly suggests, but does not prove, that such systems may be the earliest form of molecular reproduction in the universe. Well established mathematical theory demonstrates that such systems can arise as a first-order phase transition in a universe where the diversity and atomic complexity of molecules increases. If this is correct, as we claim, the emergence of life is an expected phase transition in the evolving universe. Since Newton, basic science has rested on the powerful Newtonian Paradigm. This paradigm requires a prestated and knowable phase space of all the values of the relevant variables. But living organisms are Kantian Wholes that achieve Catalytic Closure, Constraint Closure, and Spatial Closure. Stunningly, we can use no mathematics based on Set Theory – all of mathematics it seems – to deduce the ever-creative evolution of the biosphere. Life is an expected miracle in the universe whose ways of becoming are literally numberless. The 20th Century saw the emergence of the atomic Age, the mushroom cloud, and mutually assured destruction. With Gödel, the 20th century also saw the End of Certainty, (59). In this, the first quarter of the 21st Century, we begin barely to glimpse the astonishing blossoming creativity of the biosphere of which we are members. We are, truly, Of Nature, not Above Nature. Acknowledgments We are truly grateful to Ingemar Ernberg, Carlos Gershenson, Wim Hordijk, Niles Lehman, and Ricard Solé for advice and comments. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

The Limitations of Set Theory in Understanding Biological Evolution

How Do You Go About Creating Biological Weapons That Cannot Be Countered?

This paper is available on arxiv under CC 4.0 license. Authors: (1) Vianney Brouard, ENS de Lyon, UMPA, CNRS UMR 5669, 46 All´ee d’Italie, 69364 Lyon Cedex 07, France; E-mail: vianney.brouard@ens-lyon.f. Table of Links Abstract & Introduction and presentation of the model Main results and biological interpretation First-order asymptotics of the mutant sub-populations for an infinite mono-directional grap First-order asymptotics of the mutant sub-populations for a general finite trait space (Theorem 2.1) Convergence for the stochastic exponents (Theorem 2.2) Acknowledgements & References 2 Main results and biological interpretation In Subsection 2.1 the first-order asymptotics of the size of all the mutant sub-populations in the timescales (3) and (4) are given under the non-increasing growth rate condition (5). In Subsection 2.2 the asymptotic result on the stochastic exponent of all the mutant sub-populations are given without any assumption on the growth rate function λ. In each subsection, biological interpretations of the results are made. 2.1 First-order asymptotics of the mutant sub-population sizes under nonincreasing growth rate condition In this subsection assume that (V, E, L) satisfies the non-increasing growth rate graph condition of Equation (5). Heuristics: The next definitions, notations and results are first motivated using some heuristics for the simplest graph that one can think of, i.e. a wild-type and a mutant population where only mutations from wild-type to mutant cells are considered. More precisely (V, E, L) = ({0, 1}, {(0, 1)}, {ℓ(0, 1)}) as in Figure 1. Then two scenari are possible: Notations: Now the natural definitions issued from these heuristics are formally made before giving the results. (Deleterious and neutral vertices) Definition 2.1. A vertex v ∈ V satisfying λ(v) = λ(0), respectively λ(v) < λ(0), is called a neutral, respectively deleterious, vertex. Remark 2.1. In the previous definition the neutral or deleterious denomination of a mutation originates from the comparison of its inner growth rate to the growth rate of the wild-type population. But one could imagine a mutation from a vertex v to a vertex u satisfying λ(v) < λ(u) ≤ λ(0). This mutation should theoretically be called selective but in the previous definition it is actually called neutral or deleterious (depending on the value of λ(u) compared to λ(0)). This nomenclature emerges from the fact that under Assumption (5) any mutant population grows exponentially fast at rate λ(0), as seen in the previous heuristics. Hence it legitimates the previous definition. (Path on the graph) Definition 2.2. γ = (v(0), · · · , v(k)) is said to be the path on the graph (V, E) linking v(0) to v(k) by using the edges (v(i), v(i + 1)) if for all 0 ≤ i ≤ k, v(i) ∈ V and ∀0 ≤ i ≤ k − 1,(v(i), v(i + 1)) ∈ E. For a path γ = (v(0), v(1), · · · v(k)) on (V, E, L) define Definition 2.3. (Admissible paths) For all v ∈ V denote by P(v) the set of all paths γ on (V, E) linking the vertex 0 to the vertex v. Define also t(v) := min{t(γ), ∀γ ∈ P(v)}, θ(v) := max {θ(γ), ∀γ ∈ P(v), t(γ) = t(v)} , A(v) := {γ ∈ P(v) : t(γ) = t(v) and θ(γ) = θ(v)}. . Remark 2.3 In the previous definition A(v) is called the set of admissible paths because as seen in the heuristics, only paths belonging to A(v) are contributing to the growth dynamics of the mutant cells of trait v. This is made formal in Theorem 2.1. Definition 2.4. (Weight of a vertex) The weight of a vertex v ∈ V \{0} at time t is defined as Results: Now the more refined result under Assumption (5) can formally be stated. such that for all v ∈ V \{0} we obtain the following results for the different time-scales: Using the mathematical definition of the model given in Section 4, see (78) and (79), the above convergences are obtained in probability in the adequate L∞-spaces. For any other mathematical description, the convergences are at least in distribution in D ([0, T] × [−M, M]) for Equation (11) and in D ([T1, T2] × [−M, M]) for Equations (12), (13) and (14). The proof of Theorem 2.1 is based on a martingale approach using Doob’s and Maximal Inequalities. The first step involves the control of the growth of the lineage of wild-type cells issued from the initial cell both for the deterministic and random time-scales (4) and (3) (Lemma 3.1 and 3.2). Then for any vertex v ∈ V \{0}, potentially many mutational paths on the graph (V, E) can start from 0 and lead to v. The contribution on the first-order asymptotics of the mutant sub-population of trait v for any of these paths needs to be understood. The proof is then done in 2 steps. The first one consists in considering an infinite mono-directional graph under Assumption (5) and in obtaining the result for this particular graph, see Section 3. Doing the first step for an infinite graph allows in particular to deal with the cycles (backward mutations for instance) for a general finite graph. The second step consists in discriminating among all the paths from the initial vertex 0 to v the ones that do not contribute to the first-order asymptotics of the mutant sub-population of trait v, see Section 4. (ii) The random variable W is explicitly defined as the almost sure limit of the natural positive martingale associated to a specific birth and death branching process with rates α(0) and β(0), see (81). The martingale associated to the lineage of wild-type cells issued from the initial cell is shown to behave as the one associated to the latter birth and death branching process (Lemma 4.1). Thus W quantifies the randomness of this lineage over the long time. Due to the power law mutation rates regime mutations arise after a long time such that the stochasticity of this lineage is already given by W. Notice that under Assumption (5) the randomness in the first-order asymptotics of any mutant sub-population is summed up in W. Meaning that the stochasticity of these sub-populations are driven more by the stochasticity in the growth of the wild-type sub-population than by both the randomness in the mutational process and the randomness of any lineages of mutant cells. (iii) It seems more than natural not to obtain such a result when considering selective mutation (λ(v) > λ(0)). Indeed, a selective mutation would mean that any time advantage is an advantage into growth. Thus the stochasticity of the mutational process can not be ignored as well as the one of the lineages of mutant cells. Hence hoping to control the stochasticity of the mutant population controlling only the randomness of the wild-type population and not the randomness of the mutational process as well as the one of the lineages of the mutant cells is vain. Meaning that using a martingale approach to get the first-order asymptotics can not be successful for a selective mutation. Nevertheless looking at the stochastic exponent (6) the martingale approach allows to get convergence results given in Theorem 2.2. (iv) In view of Theorem 2.1, the mathematical definition of neutral mutation λ(v) = λ(0) is well understood instead of the more restrictive but biologically more meaningful condition α(v) = α(0) and β(v) = β(0). Indeed, taking the growth rate λ(v) equal to λ(0) when changing birth and death rates α(v) and β(v) modify the distribution of any lineage of mutant cells. Consequently one could naturally believe that it should impact the stochasticity of the size order of the mutant population. This is not the case, the randomness on the first asymptotic order is fully summed up by W. Hence it is fully consistent with getting for the neutral assumption only a condition on the growth rate function instead of on the birth and death rates. (v) Considering the time-scale t (n) t notice that the result slightly differs depending on whether the vertex is neutral or deleterious. Indeed, when looking at the asymptotic behavior for a deleterious vertex v our result is true strictly after time t(v), whereas in the case of a neutral vertex all the trajectory from the initial time can be dealt with. Mathematically, this difference originates from the supplementary multiplicative log(n) factor in the first asymptotic order when considering a neutral mutation. It allows to control the quadratic variation at time t(v) for the martingale associated to the mutant population. Then exactly three different regimes are obtained, see (10) and (11) : 2.2 Result for a general finite oriented labeled graph This subsection is free from the non-increasing growth rate condition of Equation (5). Without this condition, the martingale approach fails in order to get the first-order asymptotics off all the mutant sub-populations. But, the stochastic exponent, as defined in (6), off all the mutant sub-populations can be uniformly tracked over time. In particular, we show that the limits are positive deterministic non-decreasing piecewise linear continuous functions. Such limits are defined via a recursive algorithm tracking their slopes over time. More precisely, we show that the slopes can only increase and take values on the set of the growth rates. Two different kinds of updates can be made. The first one is when a non-already born trait becomes alive and take the slope which is the maximum between its inner growth rate and the slope of the sub-population that is giving birth to it. The second one is when an already born trait changes it slope to increase it because another born trait among its upcoming neighbors with a higher slope has reached the typical size allowing it to now drive the trait in question, and consequently giving it its slope. This heuristic is made formal in the following theorem. The complexity of such an algorithm comes from the trait structure which is a general finite trait space. On a mono-directional one, this algorithm would be much easier. In particular, at the same time, the two kinds of event can happen. Theorem 2.2. For all v ∈ V define 0 < T1 < T2, Then we have for all Initialisation: Set A0 = {0}, U0 = V \{0} and for all v ∈ V For any other mathematical description as the one given in Section 4, see (78) and (79), the convergences are at least in distribution in D ([T1, T2]). The proof of this theorem is given in Section 5. It is heavily based on the proofs of [13], where we exploit the stochastic construction of such a model, given in the beginning of Section 4, to adapt the proofs of the previous article to the situation of the present work. For that reason, we introduce lemmas and explain in the proofs how the adaptations from the proofs of [13] are made, without reproving them. This theorem is the counterpart of the study made in [8] in the case of branching sub-populations, instead of having competition between sub-populations. One difference is that the power law mutation rates regime is a bit more general in the present work, allowing each mutation probabilities to scale differently. But, the result in [8] can be adapted with this more general regime, as mentioned by the authors.

Main Results and Biological Interpretation

God The Invisible King by H. G. Wells, is part of the HackerNoon Books Series. You can jump to any chapter in this book here . MODERN IDEAS OF SIN AND DAMNATION MODERN IDEAS OF SIN AND DAMNATION 1. THE BIOLOGICAL EQUIVALENT OF SIN If the reader who is unfamiliar with scientific things will obtain and read Metchnikoff’s “Nature of Man,” he will find there an interesting summary of the biological facts that bear upon and destroy the delusion that there is such a thing as individual perfection, that there is even ideal perfection for humanity. With an abundance of convincing instances Professor Metchnikoff demonstrates that life is a system of “disharmonies,” capable of no perfect way, that there is no “perfect” dieting, no “perfect” sexual life, no “perfect” happiness, no “perfect” conduct. He releases one from the arbitrary but all too easy assumption that there is even an ideal “perfection” in organic life. He sweeps out of the mind with all the confidence and conviction of a physiological specialist, any idea that there is a perfect man or a conceivable perfect man. It is in the nature of every man to fall short at every point from perfection. From the biological point of view we are as individuals a series of involuntary “tries” on the part of an imperfect species towards an unknown end. Our spiritual nature follows our bodily as a glove follows a hand. We are disharmonious beings and salvation no more makes an end to the defects of our souls than it makes an end to the decay of our teeth or to those vestigial structures of our body that endanger our physical welfare. Salvation leaves us still disharmonious, and adds not an inch to our spiritual and moral stature. 2. WHAT IS DAMNATION? Let us now take up the question of what is Sin? and what we mean by the term “damnation,” in the light of this view of human reality. Most of the great world religions are as clear as Professor Metchnikoff that life in the world is a tangle of disharmonies, and in most cases they supply a more or less myth-like explanation, they declare that evil is one side of the conflict between Ahriman and Ormazd, or that it is the punishment of an act of disobedience, of the fall of man and world alike from a state of harmony. Their case, like his, is that THIS world is damned. We do not find the belief that superposed upon the miseries of this world there are the still bitterer miseries of punishments after death, so nearly universal. The endless punishments of hell appear to be an exploit of theory; they have a superadded appearance even in the Christian system; the same common tendency to superlatives and absolutes that makes men ashamed to admit that God is finite, makes them seek to enhance the merits of their Saviour by the device of everlasting fire. Conquest over the sorrow of life and the fear of death do not seem to them sufficient for Christ’s glory. Now the turning round of the modern mind from a conception of the universe as something derived deductively from the past to a conception of it as something gathering itself adventurously towards the future, involves a release from the supposed necessity to tell a story and explain why. Instead comes the inquiry, “To what end?” We can say without mental discomfort, these disharmonies are here, this damnation is here—inexplicably. We can, without any distressful inquiry into ultimate origins, bring our minds to the conception of a spontaneous and developing God arising out of those stresses in our hearts and in the universe, and arising to overcome them. Salvation for the individual is escape from the individual distress at disharmony and the individual defeat by death, into the Kingdom of God. And damnation can be nothing more and nothing less than the failure or inability or disinclination to make that escape. Something of that idea of damnation as a lack of the will for salvation has crept at a number of points into contemporary religious thought. It was the fine fancy of Swedenborg that the damned go to their own hells of their own accord. It underlies a queer poem, “Simpson,” by that interesting essayist upon modern Christianity, Mr. Clutton Brock, which I have recently read. Simpson dies and goes to hell—it is rather like the Cromwell Road—and approves of it very highly, and then and then only is he completely damned. Not to realise that one can be damned is certainly to be damned; such is Mr. Brock’s idea. It is his definition of damnation. Satisfaction with existing things is damnation. It is surrender to limitation; it is acquiescence in “disharmony”; it is making peace with that enemy against whom God fights for ever. (But whether there are indeed Simpsons who acquiesce always and for ever remains for me, as I have already confessed in the previous chapter, a quite open question. My Arminian temperament turns me from the Calvinistic conclusion of Mr. Brock’s satire.) 3. SIN IS NOT DAMNATION Now the question of sin will hardly concern those damned and lost by nature, if such there be. Sin is not the same thing as damnation, as we have just defined damnation. Damnation is a state, but sin is an incident. One is an essential and the other an incidental separation from God. It is possible to sin without being damned; and to be damned is to be in a state when sin scarcely matters, like ink upon a blackamoor. You cannot have questions of more or less among absolute things. It is the amazing and distressful discovery of every believer so soon as the first exaltation of belief is past, that one does not remain always in touch with God. At first it seems incredible that one should ever have any motive again that is not also God’s motive. Then one finds oneself caught unawares by a base impulse. We discover that discontinuousness of our apparently homogeneous selves, the unincorporated and warring elements that seemed at first altogether absent from the synthesis of conversion. We are tripped up by forgetfulness, by distraction, by old habits, by tricks of appearance. There come dull patches of existence; those mysterious obliterations of one’s finer sense that are due at times to the little minor poisons one eats or drinks, to phases of fatigue, ill-health and bodily disorder, or one is betrayed by some unanticipated storm of emotion, brewed deep in the animal being and released by any trifling accident, such as personal jealousy or lust, or one is relaxed by contentment into vanity. All these rebel forces of our ill-coordinated selves, all these “disharmonies,” of the inner being, snatch us away from our devotion to God’s service, carry us off to follies, offences, unkindness, waste, and leave us compromised, involved, and regretful, perplexed by a hundred difficulties we have put in our own way back to God. This is the personal problem of Sin. Here prayer avails; here God can help us. From God comes the strength to repent and make such reparation as we can, to begin the battle again further back and lower down. From God comes the power to anticipate the struggle with one’s rebel self, and to resist and prevail over it. 4. THE SINS OF THE INSANE An extreme case is very serviceable in such a discussion as this. It happens that the author carries on a correspondence with several lunatics in asylums. There is a considerable freedom of notepaper in these institutions; the outgoing letters are no doubt censored or selected in some way, but a proportion at any rate are allowed to go out to their addresses. As a journalist who signs his articles and as the author of various books of fiction, as a frequent NAME, that is, to any one much forced back upon reading, the writer is particularly accessible to this type of correspondent. The letters come, some manifesting a hopeless disorder that permits of no reply, but some being the expression of minds overlaid not at all offensively by a web of fantasy, and some (and these are the more touching ones and the ones that most concern us now) as sanely conceived and expressed as any letters could be. They are written by people living lives very like the lives of us who are called “sane,” except that they lift to a higher excitement and fall to a lower depression, and that these extremer phases of mania or melancholia slip the leash of mental consistency altogether and take abnormal forms. They tap deep founts of impulse, such as we of the safer ways of mediocrity do but glimpse under the influence of drugs, or in dreams and rare moments of controllable extravagance. Then the insane become “glorious,” or they become murderous, or they become suicidal. All these letter-writers in confinement have convinced their fellow-creatures by some extravagance that they are a danger to themselves or others. The letters that come from such types written during their sane intervals, are entirely sane. Some, who are probably unaware—I think they should know—of the offences or possibilities that justify their incarceration, write with a certain resentment at their position; others are entirely acquiescent, but one or two complain of the neglect of friends and relations. But all are as manifestly capable of religion and of the religious life as any other intelligent persons during the lucid interludes that make up nine-tenths perhaps of their lives. . . . Suppose now one of these cases, and suppose that the infirmity takes the form of some cruel, disgusting, or destructive disposition that may become at times overwhelming, and you have our universal trouble with sinful tendency, as it were magnified for examination. It is clear that the mania which defines his position must be the primary if not the cardinal business in the life of a lunatic, but his problem with that is different not in kind but merely in degree from the problem of lusts, vanities, and weaknesses in what we call normal lives. It is an unconquered tract, a great rebel province in his being, which refuses to serve God and tries to prevent him serving God, and succeeds at times in wresting his capital out of his control. But his relationship to that is the same relationship as ours to the backward and insubordinate parishes, criminal slums, and disorderly houses in our own private texture. It is clear that the believer who is a lunatic is, as it were, only the better part of himself. He serves God with this unconquered disposition in him, like a man who, whatever else he is and does, is obliged to be the keeper of an untrustworthy and wicked animal. His beast gets loose. His only resort is to warn those about him when he feels that jangling or excitement of the nerves which precedes its escapes, to limit its range, to place weapons beyond its reach. And there are plenty of human beings very much in his case, whose beasts have never got loose or have got caught back before their essential insanity was apparent. And there are those uncertifiable lunatics we call men and women of “impulse” and “strong passions.” If perhaps they have more self-control than the really mad, yet it happens oftener with them that the whole intelligent being falls under the dominion of evil. The passion scarcely less than the obsession may darken the whole moral sky. Repentance and atonement; nothing less will avail them after the storm has passed, and the sedulous preparation of defences and palliatives against the return of the storm. This discussion of the lunatic’s case gives us indeed, usefully coarse and large, the lines for the treatment of every human weakness by the servants of God. A “weakness,” just like the lunatic’s mania, becomes a particular charge under God, a special duty for the person it affects. He has to minimise it, to isolate it, to keep it out of mischief. If he can he must adopt preventive measures. . . . These passions and weaknesses that get control of us hamper our usefulness to God, they are an incessant anxiety and distress to us, they wound our self-respect and make us incomprehensible to many who would trust us, they discredit the faith we profess. If they break through and break through again it is natural and proper that men and women should cease to believe in our faith, cease to work with us or to meet us frankly. . . . Our sins do everything evil to us and through us except separate us from God. Yet let there be no mistake about one thing. Here prayer is a power. Here God can indeed work miracles. A man with the light of God in his heart can defeat vicious habits, rise again combative and undaunted after a hundred falls, escape from the grip of lusts and revenges, make head against despair, thrust back the very onset of madness. He is still the same man he was before he came to God, still with his libidinous, vindictive, boastful, or indolent vein; but now his will to prevail over those qualities can refer to an exterior standard and an external interest, he can draw upon a strength, almost boundless, beyond his own. 5. BELIEVE, AND YOU ARE SAVED But be a sin great or small, it cannot damn a man once he has found God. You may kill and hang for it, you may rob or rape; the moment you truly repent and set yourself to such atonement and reparation as is possible there remains no barrier between you and God. Directly you cease to hide or deny or escape, and turn manfully towards the consequences and the setting of things right, you take hold again of the hand of God. Though you sin seventy times seven times, God will still forgive the poor rest of you. Nothing but utter blindness of the spirit can shut a man off from God. There is nothing one can suffer, no situation so unfortunate, that it can shut off one who has the thought of God, from God. If you but lift up your head for a moment out of a stormy chaos of madness and cry to him, God is there, God will not fail you. A convicted criminal, frankly penitent, and neither obdurate nor abject, whatever the evil of his yesterdays, may still die well and bravely on the gallows to the glory of God. He may step straight from that death into the immortal being of God. This persuasion is the very essence of the religion of the true God. There is no sin, no state that, being regretted and repented of, can stand between God and man. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. H. G. Wells (2016). God The Invisible King. Urbana, Illinois: Project Gutenberg. Retrieved October 2022, from https://www.gutenberg.org/files/1046/1046-h/1046-h.htm This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at www.gutenberg.org, located at https://www.gutenberg.org/policy/license.html.

What is the Biological Equivalent of Sin?

Has AI already learned to think? Will it learn soon? Or is the destiny of even the most advanced programs to remain mere imitators of human intelligence? Let's explore why finding answers to these questions is important. The Turing Test Is Just a Game Let's start with the most famous and widely accepted method for determining whether a machine is intelligent: the Turing Test. Many experts believe that modern AI, especially those based on large language models, passes this test quite effectively. To recap, the essence of the test involves a judge interacting with both humans and programs. The judge's task is to determine which of the interlocutors is human and which is a machine. The AI must therefore mimic human behavior so convincingly that the judge makes a mistake. There are various ways to conduct the test, but the core idea remains the same: if we cannot distinguish between the AI's responses and those of a human, then the machine is considered intelligent. However, amidst all the hype surrounding this test, many people overlook an important point. Alan Turing based his test on the Imitation Game, a popular party game from his time. In this game, two people, say a man and a woman, would hide in separate rooms and respond to questions from the attendees via a manager or typewritten notes. The woman would try to convince the attendees that she was a man, while the man would attempt to convince them that he was a woman. Are you getting the message we're conveying? Yes, everything is correct. The Turing Test is essentially a game in which a machine, following specific rules, tries to outplay a human. However, to win this game, the program doesn't need to be smart; it merely needs to play well. The essence of the "Imitation Game" is imitation. The AI doesn't need to be genuinely intelligent to win; it only needs to mimic intelligence. This means that the only thing that can be confidently proven by the Turing Test is that the AI has learned to pass the Turing Test by imitating a human, nothing more. It is worth noting that it is quite popular to think that even the most advanced AI is nothing more than a "Chinese Room." The essence of this interesting thought experiment boils down to a person who does not know Chinese following instructions to match characters in response to those given by someone who does know Chinese. Thanks to these detailed instructions, it is possible to create the appearance of a meaningful conversation without actually understanding the meanings of the characters. Imitation Is Not a Problem Modern AI agents, according to most experts, lack self-awareness or true intelligence, functioning instead as advanced imitators, mimics, and compilers. But does this hinder them from easily and convincingly carrying on conversations in various styles and on diverse topics? Not at all. They are capable of passing academic and professional examinations, writing poetry and articles, creating graphics and paintings, and even hosting broadcasts and presentations—like Pitch Avatar, which our team is part of. Day by day, we are edging closer to a universal AI capable of handling most of today's professions. And all this, we repeat, without any clear evidence of true intelligence. And in this, surprisingly, there is nothing fundamentally differentiating artificial intelligence from humans. Isn't a large part of our behavior imitation? We follow social norms and imitate parents, teachers, and other authorities. Moreover, we often do this without considering the meaning of our actions. We simply try to copy a model, memorize the rules, or choose behaviors that elicit the desired reactions. A typical example is the average driver who, while learning to drive, does not strive to deeply understand the meaning and evolution of all the traffic rules. And this, we should note, is not always bad. It is impossible to thoroughly understand all aspects, details, and nuances of our complex lives. To save time and effort, a lot has to be taken for granted. So what if AI has mastered this art perfectly, brilliantly manipulating data and imitating humans? Even if it doesn't truly understand or realize what it is doing, the main thing is that it effectively handles the tasks we set for it. This, in turn, frees up our time for creative endeavors and interesting pastimes. Safety and Ethics When it comes to practical applications, it doesn't matter whether AI possesses self-awareness and intelligence or is merely a consummate imitator and compiler. The crux of the matter lies in safety and ethics. From a safety perspective, there is a risk that an intelligent and self-aware AI could get out of control and start acting autonomously, potentially endangering humans. From an ethical standpoint, exploiting intelligent and self-aware AI imposes serious limitations on us, especially if it is proven that AI can experience the equivalent of suffering like biological beings. For these reasons, we must work diligently to develop criteria that can clearly, distinctly, and unambiguously determine whether a machine is self-aware and intelligent. What is intelligence? Despite the significance of the aforementioned reasons, we believe the primary reason is something else. By determining the criteria that make AI intelligent, we can finally get closer to answering one of the main philosophical questions: "What is intelligence?" Not human intelligence, not machine intelligence, but intelligence in general. We call our species Homo sapiens—Man of Reason—but in reality, we have no clear idea of what reason truly is. The only aspect we have a limited understanding of is self-consciousness. Yet, as it turns out, not only humans but also many animals are capable of recognizing their own "I." So, on what basis do we consider ourselves intelligent and deny it to others? Do whales, dolphins, and great apes possess intelligence or not? We won't delve into the complexities of biology, philosophy, and metaphysics. We simply remind you that we lack an unambiguous definition of rationality and clear boundaries between reason and non-reason. Perhaps the main point of humanity's work on artificial intelligence is precisely to make significant strides in the right direction, if not to finally understand this problem. The article was created in collaboration with Andriy Tkachenko Has AI already learned to think? Will it learn soon? Or is the destiny of even the most advanced programs to remain mere imitators of human intelligence? Let's explore why finding answers to these questions is important. The Turing Test Is Just a Game Let's start with the most famous and widely accepted method for determining whether a machine is intelligent: the Turing Test. Many experts believe that modern AI, especially those based on large language models, passes this test quite effectively. To recap, the essence of the test involves a judge interacting with both humans and programs. The judge's task is to determine which of the interlocutors is human and which is a machine. The AI must therefore mimic human behavior so convincingly that the judge makes a mistake. There are various ways to conduct the test, but the core idea remains the same: if we cannot distinguish between the AI's responses and those of a human, then the machine is considered intelligent. However, amidst all the hype surrounding this test, many people overlook an important point. Alan Turing based his test on the Imitation Game, a popular party game from his time. In this game, two people, say a man and a woman, would hide in separate rooms and respond to questions from the attendees via a manager or typewritten notes. The woman would try to convince the attendees that she was a man, while the man would attempt to convince them that he was a woman. Are you getting the message we're conveying? Yes, everything is correct. The Turing Test is essentially a game in which a machine, following specific rules, tries to outplay a human. However, to win this game, the program doesn't need to be smart; it merely needs to play well. The essence of the "Imitation Game" is imitation. The AI doesn't need to be genuinely intelligent to win; it only needs to mimic intelligence. This means that the only thing that can be confidently proven by the Turing Test is that the AI has learned to pass the Turing Test by imitating a human, nothing more. It is worth noting that it is quite popular to think that even the most advanced AI is nothing more than a "Chinese Room." The essence of this interesting thought experiment boils down to a person who does not know Chinese following instructions to match characters in response to those given by someone who does know Chinese. Thanks to these detailed instructions, it is possible to create the appearance of a meaningful conversation without actually understanding the meanings of the characters. Imitation Is Not a Problem Modern AI agents, according to most experts, lack self-awareness or true intelligence, functioning instead as advanced imitators, mimics, and compilers. But does this hinder them from easily and convincingly carrying on conversations in various styles and on diverse topics? Not at all. They are capable of passing academic and professional examinations, writing poetry and articles, creating graphics and paintings, and even hosting broadcasts and presentations—like Pitch Avatar, which our team is part of. Day by day, we are edging closer to a universal AI capable of handling most of today's professions. And all this, we repeat, without any clear evidence of true intelligence. And in this, surprisingly, there is nothing fundamentally differentiating artificial intelligence from humans. Isn't a large part of our behavior imitation? We follow social norms and imitate parents, teachers, and other authorities. Moreover, we often do this without considering the meaning of our actions. We simply try to copy a model, memorize the rules, or choose behaviors that elicit the desired reactions. A typical example is the average driver who, while learning to drive, does not strive to deeply understand the meaning and evolution of all the traffic rules. And this, we should note, is not always bad. It is impossible to thoroughly understand all aspects, details, and nuances of our complex lives. To save time and effort, a lot has to be taken for granted. So what if AI has mastered this art perfectly, brilliantly manipulating data and imitating humans? Even if it doesn't truly understand or realize what it is doing, the main thing is that it effectively handles the tasks we set for it. This, in turn, frees up our time for creative endeavors and interesting pastimes. Safety and Ethics When it comes to practical applications, it doesn't matter whether AI possesses self-awareness and intelligence or is merely a consummate imitator and compiler. The crux of the matter lies in safety and ethics. From a safety perspective, there is a risk that an intelligent and self-aware AI could get out of control and start acting autonomously, potentially endangering humans. From an ethical standpoint, exploiting intelligent and self-aware AI imposes serious limitations on us, especially if it is proven that AI can experience the equivalent of suffering like biological beings. For these reasons, we must work diligently to develop criteria that can clearly, distinctly, and unambiguously determine whether a machine is self-aware and intelligent. What is intelligence? Despite the significance of the aforementioned reasons, we believe the primary reason is something else. By determining the criteria that make AI intelligent, we can finally get closer to answering one of the main philosophical questions: "What is intelligence?" Not human intelligence, not machine intelligence, but intelligence in general. We call our species Homo sapiens—Man of Reason—but in reality, we have no clear idea of what reason truly is. The only aspect we have a limited understanding of is self-consciousness. Yet, as it turns out, not only humans but also many animals are capable of recognizing their own "I." So, on what basis do we consider ourselves intelligent and deny it to others? Do whales, dolphins, and great apes possess intelligence or not? We won't delve into the complexities of biology, philosophy, and metaphysics. We simply remind you that we lack an unambiguous definition of rationality and clear boundaries between reason and non-reason. Perhaps the main point of humanity's work on artificial intelligence is precisely to make significant strides in the right direction, if not to finally understand this problem. The article was created in collaboration with Andriy Tkachenko The article was created in collaboration with Andriy Tkachenko

AI: Intelligence or Imitation of Intelligence?

From OpenAI to Closed AI: Custom Chips Are Closing The Doors—What’s Next? 

Ад OpenAI да закрытага AI: Карыстальніцкія мікрасхемы зачыняюць дзверы - што далей? 

De l'OpenAI a la IA tancada: els xips personalitzats estan tancant les portes: què passa? 

Von OpenAI zu Closed AI: Kundenspezifische Chips schließen die Türen – was kommt als Nächstes?

Από το OpenAI στο Closed AI: Τα προσαρμοσμένα τσιπ κλείνουν τις πόρτες—Τι ακολουθεί; 

De la IA abierta a la IA cerrada: los chips personalizados están cerrando las puertas. ¿Qué sigue?

Az OpenAI-tól a zárt mesterséges intelligenciaig: Az egyedi chipek bezárják az ajtókat – mi a következő lépés? 

Dari OpenAI ke AI Tertutup: Chip Kustom Menutup Pintu—Apa Selanjutnya?

OpenAI からクローズド AI へ: カスタム チップが扉を閉ざす — 次は何が起こるのか?

Kobanda na OpenAI tii na AI oyo ekangami: Ba puces personnalisées ezali kokanga ba portes —What’s Next? 

له OpenAI څخه تړل شوي AI ته: دودیز چپس دروازې بندوي — بیا څه دي؟ 

От открытого ИИ к закрытому ИИ: кастомные чипы закрывают двери — что дальше?

Від відкритого штучного інтелекту до закритого штучного інтелекту: спеціальні мікросхеми закривають двері — що далі? 

Nature + Biomimicry + Bitcoin: Mutually Beneficial Partnership

Naturaleza + Biomimetismo + Bitcoin: una alianza mutuamente beneficiosa

Luonto + Biomimikri + Bitcoin: molemminpuolisesti hyödyllinen kumppanuus

प्रकृति + बायोमिमिक्री + बिटकॉइन: पारस्परिक रूप से लाभकारी साझेदारी

Nature + Biomimicry + Bitcoin. փոխշահավետ գործընկերություն

自然 + バイオミミクリー + ビットコイン: 相互に利益のあるパートナーシップ

Nature + Biomimicry + Bitcoin: ორმხრივად მომგებიანი პარტნიორობა

ធម្មជាតិ + Biomimicry + Bitcoin៖ ភាពជាដៃគូដែលមានអត្ថប្រយោជន៍ទៅវិញទៅមក

Natiora + Biomimicry + Bitcoin: Fiaraha-miombon'antoka

Masikirwo + Biomimicry + Bitcoin: Kudyidzana Kunobatsira Kudyidzana

Природа + биомимикрија + биткоин: обострано корисно партнерство

இயற்கை + பயோமிமிக்ரி + பிட்காயின்: பரஸ்பர நன்மை பயக்கும் கூட்டாண்மை

Tabiat + Biomimicry + Bitcoin: O'zaro manfaatli hamkorlik

Virtually all industries can benefit from artificial intelligence (AI), but few see improvements as meaningful as healthcare. While medical AI faces considerable obstacles, it’s hard to ignore its potential. Applications in chronic disease management are particularly noteworthy. Roughly 129 million people in the U.S. have a chronic health condition. Despite many of these diseases being treatable, they’re also behind five of the 10 most common causes of death. Consequently, managing them is becoming increasingly crucial, and AI can help in several ways. Improved Diagnoses and Prognoses The use of AI to treat chronic conditions starts with a patient’s diagnosis. Because machine learning excels at detecting subtle trends in data, it can often identify diseases early and with impressive accuracy. AI even outperformed doctors at detecting skin cancer in 61.2% of studies and matched human experts in another 29% of cases. Similarly, predictive analytics models can predict how an individual patient’s case may progress. This insight into the future helps patients and doctors understand the situation so they can better decide how to treat the condition. These predictions become increasingly reliable as AI gathers additional data. Personalized Healthcare Once treatment begins, AI can improve health outcomes by tailoring care plans to individual patients. The same disease may play out entirely differently between patients with varying biologies, lifestyles and socioeconomic backgrounds. Consequently, healthcare is most effective when it’s specific to the individual, and AI provides the granular data analysis necessary to personalize it. AI-powered personalized care apps already exist. Some generate meal plans for individual users to help them meet unique nutritional goals based on their background and biology. Others predict how different medicines may perform for varying patients. Across all cases, machine learning can take the trial and error out of finding the best treatment, leading to more effective long-term care. Automated Reminders and Updates As patients try to stick to their nutrition and medicine plans, AI apps can offer automated reminders to help. Many people don’t take their meds as prescribed, leading to 125,000 deaths annually in the U.S. AI can monitor data from wearables to detect when patients may be veering off their treatment plans and alert them so they can respond before it’s too late. Similarly, AI monitoring tools can provide doctors with real-time updates on how their patients’ conditions are progressing. In some cases, machine learning algorithms could then use this data to predict if a change of course would yield better results. Even without that level of automation, AI’s timely insights enable faster, effective care changes. Streamlined Administrative Tasks There are behind-the-scenes benefits to AI in chronic disease management, too. Smart algorithms can automate administrative work like scheduling, data entry and health documentation compliance. As a result, doctors and nurses get more time to spend with patients. Improving efficiency and accuracy in these tasks also produces financial advantages. Administrative work accounts for up to 25% of healthcare spending in the U.S., so removing errors and minimizing the time spent on it leads to considerable savings. As more hospitals take advantage of this opportunity, the industry could justify lower costs to become increasingly accessible. Potential Downsides to AI in Chronic Disease Management While the upsides to AI in chronic disease management show significant promise, there are some complications to consider, too. Before the technology can reach its full potential, medical organizations must grapple with issues of bias, hallucinations and data security. Bias One of the biggest challenges of AI in healthcare is its tendency to replicate and even exaggerate human prejudices. Machine learning’s accuracy varies between demographics — facial recognition misidentifies Asian and Black faces up to 100 times more often than white faces. Similar issues could make AI diagnoses far less reliable for patients of color. Human bias throughout history means there’s less data on Black patients and other minorities. Consequently, AI may be unable to accurately diagnose their conditions or determine which treatments will be most effective. Failing to recognize this gap could worsen the inequality in care already plaguing the U.S. medical system. Hallucinations There’s also the hallucination issue to deal with. Even the most accurate machine learning models can hallucinate, thanks to various data-related problems and the fact that AI cannot identify facts — it only sees trends in data. In many applications, hallucinations cause minor hiccups, but they could be dangerous in a chronic disease context. Doctors could over-rely on AI and fail to account for its tendency to hallucinate. Once that happens, they could prescribe treatments based on false or misleading predictions, possibly leading to risky health complications. Human experts must always have the final say, but the mere presence of AI suggestions can lead to complacency and overreliance. Data Privacy and Security AI models also require a significant amount of data, and medical information is highly sensitive. This combination raises questions about patient privacy and cybersecurity. What if an AI service accidentally discloses personal information about patients? What if AI datasets make hospitals a bigger target for cybercriminals? Healthcare has already seen a troubling rise in cybercrime. The industry suffered 809 data compromises in 2023, more than double the year before. Security standards will likely need to evolve before hospitals can safely feed large volumes of patient information to AI models. AI Could Revolutionize Chronic Disease Care While obstacles remain, AI has big potential in the realm of managing chronic diseases. As the industry develops regulations and best practices around using this technology safely, its promise will only grow. Increasing use and technological development will lead to better patient outcomes, lower costs and streamlined medical workflows, benefiting everyone involved. Virtually all industries can benefit from artificial intelligence (AI), but few see improvements as meaningful as healthcare. While medical AI faces considerable obstacles, it’s hard to ignore its potential. Applications in chronic disease management are particularly noteworthy. Virtually all industries can benefit from artificial intelligence (AI), but few see improvements as meaningful as healthcare. While medical AI faces considerable obstacles, it’s hard to ignore its potential. Applications in chronic disease management are particularly noteworthy. Roughly 129 million people in the U.S . have a chronic health condition. Despite many of these diseases being treatable, they’re also behind five of the 10 most common causes of death. Consequently, managing them is becoming increasingly crucial, and AI can help in several ways. 129 million people in the U.S 129 million people in the U.S Improved Diagnoses and Prognoses The use of AI to treat chronic conditions starts with a patient’s diagnosis. Because machine learning excels at detecting subtle trends in data, it can often identify diseases early and with impressive accuracy. AI even outperformed doctors at detecting skin cancer in 61.2% of studies and matched human experts in another 29% of cases. in 61.2% of studies in 61.2% of studies Similarly, predictive analytics models can predict how an individual patient’s case may progress. This insight into the future helps patients and doctors understand the situation so they can better decide how to treat the condition. These predictions become increasingly reliable as AI gathers additional data. Personalized Healthcare Once treatment begins, AI can improve health outcomes by tailoring care plans to individual patients. The same disease may play out entirely differently between patients with varying biologies, lifestyles and socioeconomic backgrounds. Consequently, healthcare is most effective when it’s specific to the individual, and AI provides the granular data analysis necessary to personalize it. AI-powered personalized care apps already exist. Some generate meal plans for individual users to help them meet unique nutritional goals based on their background and biology. Others predict how different medicines may perform for varying patients. Across all cases, machine learning can take the trial and error out of finding the best treatment, leading to more effective long-term care. generate meal plans for individual users generate meal plans for individual users Automated Reminders and Updates As patients try to stick to their nutrition and medicine plans, AI apps can offer automated reminders to help. Many people don’t take their meds as prescribed, leading to 125,000 deaths annually in the U.S. AI can monitor data from wearables to detect when patients may be veering off their treatment plans and alert them so they can respond before it’s too late. leading to 125,000 deaths annually leading to 125,000 deaths annually Similarly, AI monitoring tools can provide doctors with real-time updates on how their patients’ conditions are progressing. In some cases, machine learning algorithms could then use this data to predict if a change of course would yield better results. Even without that level of automation, AI’s timely insights enable faster, effective care changes. Streamlined Administrative Tasks There are behind-the-scenes benefits to AI in chronic disease management, too. Smart algorithms can automate administrative work like scheduling, data entry and health documentation compliance. As a result, doctors and nurses get more time to spend with patients. Improving efficiency and accuracy in these tasks also produces financial advantages. Administrative work accounts for up to 25% of healthcare spending in the U.S., so removing errors and minimizing the time spent on it leads to considerable savings. As more hospitals take advantage of this opportunity, the industry could justify lower costs to become increasingly accessible. 25% of healthcare spending 25% of healthcare spending Potential Downsides to AI in Chronic Disease Management While the upsides to AI in chronic disease management show significant promise, there are some complications to consider, too. Before the technology can reach its full potential, medical organizations must grapple with issues of bias, hallucinations and data security. Bias One of the biggest challenges of AI in healthcare is its tendency to replicate and even exaggerate human prejudices. Machine learning’s accuracy varies between demographics — facial recognition misidentifies Asian and Black faces up to 100 times more often than white faces. Similar issues could make AI diagnoses far less reliable for patients of color. up to 100 times more up to 100 times more Human bias throughout history means there’s less data on Black patients and other minorities. Consequently, AI may be unable to accurately diagnose their conditions or determine which treatments will be most effective. Failing to recognize this gap could worsen the inequality in care already plaguing the U.S. medical system. Hallucinations There’s also the hallucination issue to deal with. Even the most accurate machine learning models can hallucinate, thanks to various data-related problems and the fact that AI cannot identify facts — it only sees trends in data. In many applications, hallucinations cause minor hiccups, but they could be dangerous in a chronic disease context. Doctors could over-rely on AI and fail to account for its tendency to hallucinate. Once that happens, they could prescribe treatments based on false or misleading predictions, possibly leading to risky health complications. Human experts must always have the final say, but the mere presence of AI suggestions can lead to complacency and overreliance. Data Privacy and Security AI models also require a significant amount of data, and medical information is highly sensitive. This combination raises questions about patient privacy and cybersecurity. What if an AI service accidentally discloses personal information about patients? What if AI datasets make hospitals a bigger target for cybercriminals? Healthcare has already seen a troubling rise in cybercrime. The industry suffered 809 data compromises in 2023 , more than double the year before. Security standards will likely need to evolve before hospitals can safely feed large volumes of patient information to AI models. 809 data compromises in 2023 809 data compromises in 2023 AI Could Revolutionize Chronic Disease Care While obstacles remain, AI has big potential in the realm of managing chronic diseases. As the industry develops regulations and best practices around using this technology safely, its promise will only grow. Increasing use and technological development will lead to better patient outcomes, lower costs and streamlined medical workflows, benefiting everyone involved.

How AI is Transforming Chronic Disease Management

This text began as a defense of the characters that inhabit us, our own fictions, and their expressions in virtuality. However, as usual, I succumbed to a nearly endless diatribe, raising arguments to dismantle possible ideas. In the end, the defense of performance turned into a rejection of essence, such as authenticity. In other texts, I mentioned the need to consider the amplifying potential of the Internet and the social context in which the development of technologies occurs when reflecting on digital habits. In this sense, virtuality amplifies the calls of our time. Amid the calls to become our best selves, to establish our personal brand, to explore all possibilities, and to express our opinions on almost everything, there is the call to express ourselves authentically and in a way that represents our own essence, showing ourselves as we truly are. After all, this would represent integrity. Obviously, this call is expressed in the use of technology. In Zuckerberg's words: “You have one identity…The days of you having a different image for your work friends or co-workers and for the other people you know are probably coming to an end pretty quickly…Having two identities for yourself is an example of a lack of integrity.” [1] Curiously, criticisms of social media make use of similar arguments, supported by the idea of the impossibility of multiple and concurrent ways of being. Like when they mention that there is no real in the virtual, that we are merely characters in constant performance. I believe that these comments and arguments have roots in two points: assuming that there is an authenticity outside the network and an accessible essence; and believing that knowing more, learning more, is always beneficial. In this text, I limit myself to exploring the first point. The notion of essence can be seen as a way of dealing with the lack of guarantees of existence, with the anxiety of the uncertain and the indeterminacy to which we are, in many ways, subjected. To think that there is an essence to be achieved, accessed, capable of alleviating our anxiety and giving meaning to our existence, can be comforting. However, it can also be a source of discomfort and serve the discourses of self-improvement and the instrumentalization of care. Not presupposing an essence requires us to accept and deal with ambivalence and contradiction, both in ourselves and in others. We must be capable of recognizing the nuances and complexity that come with being human. In order to do this, accepting that we will select what we choose to represent may be an option, and this is, to some extent, a performance. Authenticity in this context is interpreted as synonymous with self-expression free from performance, which would bring us closer to an essence. However, what is being referred to as performance may serve a function of containment of limitation. Let us imagine what a world would be like without performance, where we all always express exactly what we are feeling and thinking, which is part of what we are. At the very least, it would be chaotic. I, like many, prefer to call it unbearable. Thus, perhaps we are always performing, for even if we idealized being always transparent, that would not be possible. How would we convey, at the same time, two lines of thought that confront each other, composing a conflict or dilemma we experience at a given moment? How would we fully convey two emotions that coexist? Containment, the imposition of limits, allows us to act in an adaptable manner, to recognize ourselves and others, and to avoid being overwhelmed. Does this mean that it is possible to be whatever I want? That I can do whatever I want? No. The selection of representations also has limits; they can be social, ethical, economic, or biological impositions. Different representations imply different characters, but they do not mean different actors. What Representations Are We Willing to Interpret, and Why? What is relevant may be to consider how this character interacts with our own narrative, whether it is a source of exhaustion, or if we identify with it so much that we come to believe we are it and only it, forgetting that no narrative can sustain itself with just one character. Do I fear appearing different from the character I maintain on social media? Are there drawbacks to sustaining this character in light of the daily activities that life demands? Do I forgo other possibilities for myself because of it? If this character were to step out of the scene, how would it be? What is the impact of this character on the lives of others? How does it affect others? Does it come closer to or further away from what I desire to be or think I am? Perhaps the new question is: in service of what or which narratives are our supposedly virtual characters? "(...) time is a powerful force. It transforms our preferences. It reshapes our values. It alters our personalities. We seem to appreciate this fact, but only in retrospect. (...) It's as if, for most of us, the present is a magic time. (...) It's the moment at which we finally become ourselves. Human beings are works in progress that mistakenly think they're finished. The person you are right now is as transient, as fleeting and as temporary as all the people you've ever been. The one constant in our life is change."  - Daniel Gilbert [2] References: [1] Zuckerberg em David Kirkpatrick The Facebook Effect (2010). [2] Daniel Gilbert — The Psychology of Your Future Self. Photo by Barthelemy de Mazenod on Unsplash This text began as a defense of the characters that inhabit us, our own fictions, and their expressions in virtuality. However, as usual, I succumbed to a nearly endless diatribe, raising arguments to dismantle possible ideas. In the end, the defense of performance turned into a rejection of essence, such as authenticity. In other texts, I mentioned the need to consider the amplifying potential of the Internet and the social context in which the development of technologies occurs when reflecting on digital habits. In this sense, virtuality amplifies the calls of our time. Amid the calls to become our best selves, to establish our personal brand, to explore all possibilities, and to express our opinions on almost everything, there is the call to express ourselves authentically and in a way that represents our own essence, showing ourselves as we truly are. After all, this would represent integrity . express ourselves authentically and in a way that represents our own essence, showing ourselves as we truly are. After all, this would represent integrity Obviously, this call is expressed in the use of technology. In Zuckerberg's words: “You have one identity…The days of you having a different image for your work friends or co-workers and for the other people you know are probably coming to an end pretty quickly…Having two identities for yourself is an example of a lack of integrity.” [1] “You have one identity…The days of you having a different image for your work friends or co-workers and for the other people you know are probably coming to an end pretty quickly…Having two identities for yourself is an example of a lack of integrity.” [1] Curiously, criticisms of social media make use of similar arguments, supported by the idea of the impossibility of multiple and concurrent ways of being. Like when they mention that there is no real in the virtual, that we are merely characters in constant performance. I believe that these comments and arguments have roots in two points: assuming that there is an authenticity outside the network and an accessible essence; and believing that knowing more, learning more, is always beneficial. In this text, I limit myself to exploring the first point. The notion of essence can be seen as a way of dealing with the lack of guarantees of existence, with the anxiety of the uncertain and the indeterminacy to which we are, in many ways, subjected. To think that there is an essence to be achieved, accessed, capable of alleviating our anxiety and giving meaning to our existence, can be comforting. However, it can also be a source of discomfort and serve the discourses of self-improvement and the instrumentalization of care. Not presupposing an essence requires us to accept and deal with ambivalence and contradiction, both in ourselves and in others. We must be capable of recognizing the nuances and complexity that come with being human. In order to do this, accepting that we will select what we choose to represent may be an option, and this is, to some extent, a performance. Authenticity in this context is interpreted as synonymous with self-expression free from performance, which would bring us closer to an essence. However, what is being referred to as performance may serve a function of containment of limitation. Let us imagine what a world would be like without performance, where we all always express exactly what we are feeling and thinking, which is part of what we are. At the very least, it would be chaotic. I, like many, prefer to call it unbearable. Thus, perhaps we are always performing, for even if we idealized being always transparent, that would not be possible. How would we convey, at the same time, two lines of thought that confront each other, composing a conflict or dilemma we experience at a given moment? How would we fully convey two emotions that coexist? Containment, the imposition of limits, allows us to act in an adaptable manner, to recognize ourselves and others, and to avoid being overwhelmed. Does this mean that it is possible to be whatever I want? That I can do whatever I want? No. The selection of representations also has limits; they can be social, ethical, economic, or biological impositions. Different representations imply different characters, but they do not mean different actors. What Representations Are We Willing to Interpret, and Why? What is relevant may be to consider how this character interacts with our own narrative, whether it is a source of exhaustion, or if we identify with it so much that we come to believe we are it and only it, forgetting that no narrative can sustain itself with just one character. to consider how this character interacts with our own narrative, whether it is a source of exhaustion, or if we identify with it so much that we come to believe we are it and only it, forgetting that no narrative can sustain itself with just one character. Do I fear appearing different from the character I maintain on social media? Are there drawbacks to sustaining this character in light of the daily activities that life demands? Do I forgo other possibilities for myself because of it? If this character were to step out of the scene, how would it be? What is the impact of this character on the lives of others? How does it affect others? Does it come closer to or further away from what I desire to be or think I am? Perhaps the new question is: in service of what or which narratives are our supposedly virtual characters? Perhaps the new question is: in service of what or which narratives are our supposedly virtual characters? "(...) time is a powerful force. It transforms our preferences. It reshapes our values. It alters our personalities. We seem to appreciate this fact, but only in retrospect. (...) It's as if, for most of us, the present is a magic time. (...) It's the moment at which we finally become ourselves. Human beings are works in progress that mistakenly think they're finished. The person you are right now is as transient, as fleeting and as temporary as all the people you've ever been. The one constant in our life is change."  - Daniel Gilbert [2] "(...) time is a powerful force. It transforms our preferences. It reshapes our values. It alters our personalities. We seem to appreciate this fact, but only in retrospect. (...) It's as if, for most of us, the present is a magic time. (...) It's the moment at which we finally become ourselves. Human beings are works in progress that mistakenly think they're finished. The person you are right now is as transient, as fleeting and as temporary as all the people you've ever been. The one constant in our life is change."  - Daniel Gilbert [ 2 ] 2 References: [1] Zuckerberg em David Kirkpatrick The Facebook Effect (2010). Zuckerberg em David Kirkpatrick The Facebook Effect The Facebook Effect (2010). [2] Daniel Gilbert — The Psychology of Your Future Self. Daniel Gilbert — The Psychology of Your Future Self. Photo by Barthelemy de Mazenod on Unsplash Photo by Barthelemy de Mazenod on Unsplash Barthelemy de Mazenod Unsplash

Why Not Be Authentic on Social Media?

لماذا لا نكون أصليين على وسائل التواصل الاجتماعي؟

¿Por qué no ser auténtico en las redes sociales?

Zašto ne biti autentičan na društvenim mrežama?

Ինչու՞ իրական չլինել սոցիալական լրատվամիջոցներում:

ソーシャル メディアで本物らしくないのはなぜですか?

ហេតុអ្វីមិនពិតប្រាកដនៅលើប្រព័ន្ធផ្សព្វផ្សាយសង្គម?

Эмне үчүн социалдык медиада чыныгы болбошу керек?

Mengapa Tidak Menjadi Tulen di Media Sosial?

Ubona gute ubaye umunyakuri ku mbuga nkoranyambaga?

Ha Yini U Nga Ri Wa Ntiyiso Eka Social Media?

Nega ijtimoiy tarmoqlarda haqiqiy bo'lmaslik kerak?

为什么不在社交媒体上表现真实？

Active Inference AI: Here's Why It's The Future of Enterprise Operations and Industry Innovation

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