Feynman, a complicated legacy

Ethan Siegel is the author of the “Starts with a Bang” newsletter on BigThink, which is a great read and I recommend it to everyone interested in the latest developments in Astronomy, but also in Physics in general. He is also a facebook friend. In his Nov 1st newsletter Ethan addresses a question from one of his readers, which ends with: “It seems to me that you have a somewhat ambivalent relationship with R. Feynman. Is there a deeper reason for this?”. Ethan here gives a very detailed answer acknowledging Feynman’s scientific accomplishments before going on to highlight some of Feynman’s more controversial character aspects, and I certainly agree with his conclusion that “We can rightfully laud [Feynman] for his great accomplishments while still being critical of his unacceptable behaviors, and I would argue we have an obligation to share the full truth about Feynman, both the physicist and the human being, with subsequent generations of scientists and science-literate citizens.” But after reading the whole article, and not finding anything specific I could disagree with, I was left with a feeling that something was missing, and it took me a while to fully conceptualise the origins of my discomfort and put it down to words.

Feynman was born in 1918, was mostly active during the 40s to the 60s, and died in 1988 when he was 69 years old. He was successfully treated for abdominal cancer in 1980, but then the cancer came back with a vengeance in 1988, at which point it had gotten so bad that Feynman refused treatment. In many ways his social views were a product of his time, yet his patterns of behaviour were not predictably consistent. Ethan’s article does a good job of pointing out the negatives, when viewed anachronistically through a modern lens, but in leaving out the aspects of Feynman’s character that made him “curious”, ends up with an incomplete picture of who Feynman really was as a person.

Fundamentally, Feynman was an iconoclast, and it is through this lens that his character contradictions can be reconciled. He was also a prankster with  little regard for authority. While working for the Manhattan project, he famously amused himself by breaking into secure safes containing nuclear secrets—not to undermine the project, but to expose how lax security was. Sometimes he would even leave notes in the safes, like “I borrowed document no. LA4312–Feynman the safecracker.” He loved the arts and was an avid bongo player who also learned to paint for fun (even holding an exhibition under a pseudonym), occasionally having intense discussions about art vs. science with his artist/mentor/friend Jirayr “Jerry” Zorthian (check out the Ode to a Flower monologue). An older contemporary colleague of his from Caltech once told me that Feynman could sometimes be found smoking weed in the Professor’s common room, much to the chagrin of everyone else. He was also a regular at the local strip club, and knew all the girls working there. He would pick up an orange juice from the bar (by that time he wasn’t a big fan of alcohol), together with a bunch of napkins or place mats, and he would watch the show or just sit there and think, scribbling down equations on the napkins, alone or with company. This was the kind of environment he felt more at ease in and he actually did quite a number of his calculations in that place. When the county tried to close the place down on account of “uncovered breasts”, he was the bar’s only regular customer willing to come forward and testify publicly in court in defense of the bar. 

Such stories reveal Feynman as a gadfly—a horsefly, if you will—who delighted in seeing how the social and academic order would reconfigure itself when challenged. He cared little for social norms or accolades and famously eschewed honorary degrees and pomp. His devotion was to truth, inquiry, and the freedom to explore without inhibition.

Ethan’s article rightly discusses the biases prevalent in academia during Feynman’s time (and later) and how he sometimes mirrored those biases. Like most people of his time, it doesn’t seem like Feynman had carefully thought through the harmful implications of maintaining these problematic attitudes. Take, for example, a talk he gave in 1966 at the National Science Teachers Association. The topic he was asked to talk about was “What is Science?”, a title that he didn’t really like. It is a fantastic talk and I strongly encourage everyone to read the transcript, but it is also a product of its time. At some point during this talk Feynman says the following: 

“I listened to a conversation between two girls, and one was explaining that if you want to make a straight line, you see, you go over a certain number to the right for each row you go up–that is, if you go over each time the same amount when you go up a row, you make a straight line–a deep principle of analytic geometry! It went on. I was rather amazed. I didn’t realize the female mind was capable of understanding analytic geometry. She went on and said, “Suppose you have another line coming in from the other side, and you want to figure out where they are going to intersect.  Suppose on one line you go over two to the right for every one you go up, and the other line goes over three to the right for every one that it goes up, and they start twenty steps apart,” etc.–I was flabbergasted.  She figured out where the intersection was. It turned out that one girl was explaining to the other how to knit argyle socks.“ 

This passage clearly comes across as sexist, reflecting the prevalent attitudes of that time. However, what is more revealing about how Feynman thought, is what comes after it. Feynman doesn’t end there, but continues the thought in this fashion: “I, therefore, did learn a lesson: The female mind is capable of understanding analytic geometry. Those people who have for years been insisting (in the face of all obvious evidence to the contrary) that the male and female are equally capable of rational thought may have something. The difficulty may just be that we have never yet discovered a way to communicate with the female mind.” 

Feynman here seems to acknowledge the possibility that systemic issues, rather than innate differences, limited women’s participation in science. But he offers no solution to this problem and moves back to his main topic. He does not own it as his problem to solve for the whole of the country. Society for him is one thing, the scientific enterprise another, and he is primarily interested in the latter. 

Richard Feynman also had a younger sister, Joan. Although they were separated by nine years, Joan and Richard were close, as Joan was also very curious about how the world worked. Their mother was a sophisticated woman who had marched for women’s suffrage in her youth, but believed that women lacked the capacity to understand maths and physics. Despite that negative attitude at home, the young Richard encouraged Joan’s interest in science. From a very young age, he would train her to solve simple math problems and rewarded each correct answer by letting her tug on his hair while he made funny faces. By the time she was 5, Richard was hiring her for 2 cents a week to assist him in the electronics lab he’d built in his room. Joan grew up to become an astrophysicist, crediting her brother’s mentorship as a key influence. In his later years, Richard became acutely aware of the discrimination women faced in physics, because he saw how it affected his sister. For her part, Joan Feynman was awarded NASA’s Exceptional Science Achievement medal in 2002, for her continued support and encouragement for women to persevere and make their marks in science.

Feynman’s first marriage, to Arline Greenbaum, adds another layer of complexity. They were high-school sweethearts and by all accounts their love was profound and marked by mutual respect. Feynman wrote her heartfelt letters that revealed his deep admiration for her intellect and spirit. Arline was sick for a long time, even before their marriage, and eventually died of tuberculosis in 1945, while Richard was working on the Manhattan project. When she was near death, he rushed from Los Alamos to be by her side. You can read here a remarkable letter he wrote two years after Arlene’s death, where he pours out his heart. The letter was discovered in a stash of old letters by Feynman’s biographer James Gleick.

Richard Feynman got married again in 1952 to Mary Louise Bell. This second marriage was difficult, strained by differences in temperament and lifestyle choices, and ended in divorce. Mary had very conservative views and they quarrelled often. She was so fed up with his obsession with calculus and physics and reported that on several occasions, when she disturbed his calculations, which he would sometimes even do while he was lying in bed at night, or his bongo playing, he would fly into a rage. She filed for divorce in 1956. His third marriage, to Gweneth Howarth, who shared his enthusiasm for travelling and playfulness, was far more harmonious.

In the book “What do YOU care about what other people think?” Feynman recalls an incident where feminist protesters (led by a man, ironically) entered a hall and picketed a lecture he was about to make in San Francisco, holding up placards and handing out leaflets calling him a "sexist pig". As soon as he got up to speak, some of the protesters marched to the front of the lecture hall and, holding their placards signs high, started chanting “Feynman sexist pig!”. Instead of reacting defensively, Feynman addressed the protesters saying: “Perhaps, after all, it is good that you came. For women do indeed suffer from prejudice and discrimination in Physics, and your presence here today serves to remind us of these difficulties and the need to remedy them”.

Feynman’s attitudes certainly weren’t those of a consistent advocate for gender equality, as we might expect today, but they weren’t wholly regressive either. The idea of dismantling systemic barriers wasn’t part of his worldview, but he was not resistant to change and was willing to support those who defied convention.

Criticisms of Feynman’s legacy through the lens of presentism risks overlooking the full complexity of his character and how progressive some of his views were for his time. He was a complicated individual, whose brilliance was tempered by human imperfections.

He achieved remarkable things in his lifetime and inspired many physicists that came after him, both male and female. 

As with every figure who has left a mark on the landscape of history, fairness requires that we should be honest about who he was, acknowledging both his achievements and flaws, while considering the context of his time. His legacy cannot be flattened into an uncomplicated hero or villain narrative. 

Perhaps Feynman's most enduring legacy is to remind us that progress is born from questioning, curiosity, and the willingness to defy convention --all driven by the joy of discovery. To reduce such a complicated life to binary judgments, to refuse to celebrate it, pointing out warts and all, would be to forget why we study these figures at all—to question, to learn, and to grow.

The Great Misinterpretation: How Palestinians View Israel - Haviv Rettig Gur

This is a good lecture about some of the less discussed aspects of the history of Zionism and the historical development of the Palestinian perspective. Haviv Rettig Gur is political correspondent and senior analyst for The Times of Israel.

UIr-Fascism by Umberto Eco

On the 25th of April 1995, Umberto Eco delivered a speech with the title “Ur-fascism” at Columbia University, commemorating the liberation of Europe. Shortly after, it was published in The New York Review of Books. The text was conceived for an audience of American students and the speech was given during a time of heightened awareness of right-wing extremism, following the Oklahoma City bombing. Eco tailored his message for this context, linking anti-Fascist themes to contemporary issues, urging reflection on the dangers of resurgent totalitarian ideologies across the world.

In the text, Eco highlights the enduring and adaptable nature of fascism, which can appear in various forms without a strict ideology. Eco identifies 14 characteristics of what he terms "Ur-Fascism" or “eternal fascism”, a set of traits that cannot be regimented into a system, many of which are mutually exclusive and are typical of other forms of despotism or fanaticism. But all you need is one of them to be present, and a Fascist nebula will begin to coagulate. Here they are, in summary:

1. Cult of Tradition
   Fascism glorifies tradition, combining diverse, often contradictory elements into a single mythic past. This reliance on the past resists change and rational progress, promoting the belief that all truth has already been revealed. 

2. Rejection of Modernism
   Ur-Fascism sees Enlightenment values—rationalism, skepticism, and individualism—as corruptions of society. Though it may appear modern on the surface, fascism seeks to undo the intellectual and social advancements of modernity.

3. Action for Action’s Sake
   Fascists glorify action without thought. For them, reflection or hesitation is a weakness, and physical action is seen as inherently virtuous, fostering a culture of violence and immediate responses.

4. Disagreement is Treason
   Critical thinking and questioning are viewed as betrayal. Fascist ideology demands loyalty and submission, presenting any dissent as dangerous opposition to the collective unity.

5. Fear of Difference
   Ur-Fascism thrives on the fear of "others," exploiting anxieties about race, ethnicity, religion, or sexuality. Fascism builds its identity by defining enemies and positioning itself as the defender of purity.

6. Appeal to a Frustrated Middle Class
   Fascism targets a disillusioned middle class suffering from economic instability or political discontent, often promising to restore their lost status and identity by attacking perceived threats from both the elite and lower classes.

7. Obsession with a Plot
   Conspiracy theories are a staple of Ur-Fascism, fueling paranoia and a sense of being constantly under siege. Whether the "enemy" is internal or external, the fascist regime thrives on the notion of an omnipresent threat.

8. Enemies are Both Strong and Weak
   Fascism portrays its enemies as simultaneously overwhelming and weak. The enemy is powerful enough to threaten society, yet weak enough to be easily defeated, allowing fascism to justify its aggression.

9. Life is Permanent Warfare
   Fascism presents life as a continuous struggle, glorifying conflict and war as necessary for survival. Peace is undesirable because it undermines the fascist narrative of eternal battle against enemies.

10. Contempt for the Weak
    In fascist ideology, strength is celebrated, and weakness is despised. The strong are worthy of power, while the weak deserve their plight, reinforcing a hierarchical social order based on superiority.

11. Cult of Heroism
    Ur-Fascism idolizes the heroic death, promoting martyrdom and the sacrifice of life for the cause. In this worldview, heroism is not an exception but an expectation, with individuals urged to die for the nation or leader.

12. Machismo and Weaponry
    Fascist regimes glorify hyper-masculinity, with an emphasis on military prowess and physical domination. This machismo extends to weaponry, where violence becomes a surrogate for sexual power and authority.

13. Selective Populism
    Fascism claims to represent "the people" but only a specific, pure section. It manipulates the masses through emotional appeals while rejecting pluralism and the complexities of democracy, often through charismatic leadership.

14. Newspeak
    Fascist regimes simplify language to prevent critical thought. By reducing vocabulary and controlling discourse, they limit the tools for reasoning and debate, ensuring that the populace remains docile and uncritical.

He concludes the essay by pointing out that ur-fascism is still around us, sometimes in civilian clothes, and can return in the most innocent of guises. Our duty is to unmask it and to point the finger at each of its new forms - every day, in every part of the world. Freedom and liberation are never-ending tasks -- and this should not be forgotten.

Eίναι το σύμπαν παλαιότερο απ όσο νομίζουν οι επιστήμονες; Μάλλον όχι.

 https://www.lifo.gr/now/tech-science/sympan-einai-giraiotero-apo-oti-pisteyoyn-oi-epistimones

Θα προσπαθήσω να εξηγήσω γιατί τα πράγματα δέν είναι ακριβώς έτσι όπως τα παρουσιάζει το συγκεκρμένο, κατά τα άλλα καλό, άρθρο. Όσοι δέν έχετε χρόνο ή ορεξη να διαβάσετε τα πώς και τα γιατί, το βασικό συμπέρασμα απο αυτά που θα γράψω παρακάτω είναι οτι οι αστρονομική κοινότητα που κάνει έρευνα στο θέμα αμφισβητεί έντονα, για πολύ καλούς λόγους, οτι τα νέα δεδομένα επηρρεάζουν ουσιαστικά τους μεχρι τώρα υποaλογισμούς της ηλικίας του Σύμπαντος και δέν παίρνει και πολύ στα σοβαρά τη συγκεκριμένη επιστημονική δημοσίευση. Οι μετρήσεις και οι μελέτες βέβαια συνεχίζονται.

Πάμε λοιπόν να δούμε τί παίζει. Βαθιά ανάσα. Το άρθρο που τάραξε τα νερά και περιλήψεις του οποίου έφτασαν να τραβήξουν την προσοχή των δημοσιογράφων(1), είναι αυτό https://ui.adsabs.harvard.edu/abs/2023MNRAS.524.3385G/abstract. Μήν ανησυχείτε άν δέν καταλαβαίνετε γρί, έκανα παρουσίαση αυτού του άρθρου σε ένα σεμινάριο εδώ και το ξεκοκκαλίσαμε με τους συναδέλφους, οπότε σας καταλαβαίνω. Εμείς τουλάχιστον πληρωνόμαστε να μαζοχιζόμαστε έτσι. Δημοσιεύτηκε πέρσι απο έναν μόνο επιστήμονα, θεωρητικό, ονόματι Rajendra Gupta, και έχει μέχρι σήμερα συγκεντρώσει μόνο 13 αναφορές απο άλλα επιστημονικά άρθρα. Στην επιστημονική κοινότητα ακούγονται γρύλλοι δηλαδή παρ όλη την φασαρία που γίνεται στα media. Τί λέει το άρθρο; Οτι κάποιες καινούριες παρατηρήσεις γαλαξιών σε πολύ νεαρή ηλικία δεν ταιριάζουν με τις προβλέψεις των ώς τώρα επικρατέστερων μοντέλων της εξέλιξης του Συμπαντος(2). Γιατί; Γιατί αυτοί οι πολύ πρώιμοι γαλαξίες είναι ήδη τόσο ανεπτυγμένοι  που δέν μπορούμε να εξηγήσουμε το μέγεθός τους με αυτό το μοντέλο. Πότε πρόλαβαν να γίνουν τόσο μεγάλοι; Δέν υπάρχει αρκετός χρόνος. Άρα, λέει ο Gupta, το Σύμπαν πρέπει να είναι παλιότερο απ όσο νομίζουμε, αλλιώς δέν φτάνουν τα κάστανα. Νά, πάρτε κι εδώ λέει τους υπολογισμούς, ορίστε.

Το βασικό κόκκινο σημαιάκι (red flag) εδώ, για όσουν τουλάχιστον ασχολούνται με το θέμα, είναι οτι βασίζεται σε μοντέλα εξέλιξης γαλαξιών για να κριτικάρει κοσμολογικά μοντέλα. Δηλαδή, είναι λίγο σάν να προσπαθεί να βγάλει συμπεράσματα για το τί εστί Καστανιά, κοιτώντας τα τσόφλια. Τέλος πάντων, πάμε παρακάτω. Συνεχίζει μετά και λέει, πώς όμως μπορούμε να κάνουμε το Σύμπαν αρχαιότερο ωστε να υπάρχει αρκετός χρόνος να μεγαλώσουν αρκετά οι γαλαξίες και να είναι όπως τους βλέπουμε; Ανασύρει λοιπόν απο τα σκονισμένα κιτάπια μια παλιά θεωρία, αυτή του «κουρασμένου φωτός»,  απ τη δεκαετία του 1920. Τη δεκαετία δηλαδή που οι αστρονόμοι τρωγόντουσαν για το άν το σύμπαν είναι στατικό ή εάν διαστέλλεται. Η θεωρία του «κουρασμένου φωτός», οτι το φώς δηλαδή χάνει ενέργεια όσο μακρύτερα ταξιδεύει στο σύμπαν, αποτελούσε τμήμα της θεωρίας του στατικού σύμπαντος, και σταδιακά εγκαταλείφθηκε με την πάροδο των δεκαετιών γιατί αδυνατούσε να εξηγήσει τις παρατηρήσεις(3). Ο Gupta όμως έχει έναν άσσο στο μανίκι του: Μιά ιδέα του Dirac απο το 1937. Ποιά ιδέα; Τί κι άν οι κοσμολογικές σταθερές δέν είναι σταθερές, αλλά μεταβάλλονται με την πάροδο του χρόνου(4); Αυτές οι σταθερές λοιπόν δέν είναι πλέον σταθερές στο μοντέλο του Gupta, αλλά γίνονται μεταβλητές! Όσο περισσότερες μεταβλητές έχεις στο μοντέλο σου, τόσο πιο εύκολο είναι να το κάνεις να προσαρμοστεί στα δεδομένα. Ο John von Neumann (1903-1957), τουλάχιστον σύμφωνα με μια δήλωση του Fermi το 1953, θέλοντας να κριτικάρει την εισαγωγή επιπέον παραμέτρων για να ταιριάξει ένα μοντέλο με τα δεδομένα όταν δέν συντρέχουν απολύτως απαραίτητοι λόγοι, είχε πεί: «Με τέσσερις παραμέτρους σου περιγράφω έναν ολόκληρο ελέφαντα, με πέντε στον κάνω να κουνάει και την προβοσκίδα του». Άλλο κόκκινο σημαιάκι κι αυτό.

Πάμε τώρα λίγο να δούμε κάποια πρακτικά θέματα. Πρώτον, το θέμα της μορφολογίας αυτών των Γαλαξιών, πάνω στους οποίους βασίζεται η ανάλυση του Gupta. Έστω κι άν δεχθούμε οτι οι μάζες αυτών των Γαλαξιών είναι όπως ακριβώς μας δείχνουν αυτές οι νέες μετρήσεις, που είναι πολύ νωρίς ακόμα για τα πούμε κάτι τέτοιο με βεβαιότητα, οι πυκνότητές τους και η μορφολογία τους είναι συμβατές με τις τρέχουσες εκτιμήσεις, οπότε δέν φαίνεται να υπάρχει πρόβλημα απο αυτή την πλευρά. Δεύτερον, το «κουρασμένο φώς» δέν έχει μόνο θέμα με την ομοιογένεια της ακτινοβολίας υποβάθρου. Δέν μπορεί να τα βγάλει πέρα ούτε με το anisotropy, ούτε, φερ’ ειπείν, με το Sunyaev-Zeldovic effect (κοιτάξτε τα στην Wikipedia άν θέλετε να μάθετε περισσότερα). Κι όχι μόνο με αυτά, αλλά έχει και πρόβλημα με τα δεδομένα απο τον τομέα των SΝe Ia (Υπερκαινοφανών τύπου Ια). Δέν του βγαίνει, τί να κάνουμε.

Τέλοσπαντων, το τράβηξα πολύ και θα το κόψω εδώ. Τα καλά νέα είναι οτι υπάρχουν τρόποι να τσεκάρουμε άν όντως το Σύμπαν είναι παλαιότερο απ’ότι μέχρι σήμερα νομίζαμε. ‘Ενας απο αυτούς είναι να βρούμε πληθυσμό αστέρων με ηλικίες μεγαλύτερες απο την ηλικία του σύμπαντος, όπως την υπολογίζουμε σήμερα(6). Το τηλεσκόπιο JWST, όπως και άλλα που σχεδιάζονται στο μέλλον, έχουν τη δυνατότητα να κάνουν τέτοιες παρατηρήσεις, οπότε θα μπορέσουμε να μάθουμε.

Κάποια απο αυτά που έγραψα εδώ, και διάφορα άλλα πορβλήματα με την εργασία του Gupta, τα εξηγεί πολύ καλύτερα ο κοσμολόγος (και blogger) Ethan Siegel εδώ https://bigthink.com/starts-with-a-bang/universe-13-8-or-26-7-billion-years/

Αυτά. Πάω για καφέ.

(1) Γι αυτό κυρίως ευθύνεται μια εντελώς clickbait press release απο το Πανεπιστήμιο της Οττάβα, οπου δουλεύει ο Gupta.

(2) Συγκεκριμένα αναφέρεται στη στάνταρ ΛCDM κοσμολογία. Η ΛCDM κοσμολογία σίγουρα έχει κάποια προβλήματα αλλά βασίζεται και υποστηρίζεται απο πραγματικά τεράστιο όγκο παρατηρήσεων. Γκουγκάρετε ας πούμε “What observational evidence supports ΛCDM cosmology?”.

(3)  (ειδικότερα είχε μεγάλο πρόβλημα με την ακτνοβολία υποβάθρου).

(4) Συγκεκριμένα αναφέρεται στις σταθερές fine-structure constant α, gravitational constant G, και proton-to-electron mass ratio μ.

(5) “With four parameters I can fit an elephant, and with five I can make him wiggle his trunk.”

(6) Έχουν ήδη βρεθεί ορισμένα αστέρια με πολύ μεγάλες ηλικίες αλλά οι αβεβαιότητες μεταξύ ανταγωνιστικών μοντέλων είναι προς το παρόν μεγάλες.

Artificial Intelligence : Digital Utopia or Dystopian Nightmare?

“They shamelessly print, at negligible cost, material which may  inflame impressionable youths, while a true writer dies of hunger. Cure the plague which is doing away with the laws of all decency, and curb the printers. They persist in their sick vices, setting Tibullus in type, while a young girl reads Ovid to learn sinfulness. [...] Writing, which brings in gold for us, should be respected and held to be nobler than all goods, unless she has suffered degradation in the brothel of the printing presses.” So wrote Italian Benedictine monk Filippo de Strata in a letter to the Doge of Venice in 1490, complaining about the introduction of the printing press to the city.

["The Unrestrained Demon". An anti-electricity cartoon from 1889.]

Throughout history, each wave of technological innovation has been met with its own unique blend of curiosity and trepidation. Take, for example, the discovery of electricity; Initially regarded as little more than an amusing novelty, with public demonstrations of its effects ranging from Thomas Edison’s mildly entertaining electric pen to the more macabre spectacle of electrocuting animals, electricity also came to be feared as a potential danger to public health. As the decades passed, worries gradually subsided as the profound utility of electricity became apparent even to the staunchest skeptics, eventually establishing it as the bedrock of modern civilization. Consider another example: the Luddites, a group of textile workers in 19th-century England, who went about destroying weaving machinery to protest against job displacement, and who did not shy away from resorting to violence. 

These historical instances reflect an enduring concern: the anxiety accompanying new technologies and the fear that human skills will be rendered redundant. Similar concerns are directed towards the field of artificial intelligence (AI) today. 

AI has been with us for at least several decades, making great strides since the introduction of rules-based systems in the 60s, but it is only in the last few years that a significant milestone appears to have been achieved through a combination of large language models (LLMs), which are examples of Deep Learning processes, and the truly massive data sets that are used to train them. Although Artificial General Intelligence (AGI), which many consider to be the Holy Grail of AI research, still seems to be out of reach for now, narrow AI is routinely outperforming humans in several different highly specialised tasks.

So, what are we to do now in the face of AI's relentless march forward? Should we cross our fingers and hope for the best like new Pollyannas, or are we to become neo-Luddites, smashing away at every AI creation in the digital domain? The answer is neither. For one, these are still the early days and the technology is in its infancy. Granted, it has already demonstrated it can provide assistance in a number of different domains, but it often makes mistakes, hallucinates and is not particularly creative unless carefully steered by an expert user. 

An AI agent can provide seemingly insightful responses to questions about highly specialised subjects where an average person lacking the expertise would be unable to. Experts in particular topics, such as programmers, engineers, artists, scientists etc., have the necessary training to pose complex technical questions using highly specialised terminology and are able to understand the contextually specialised responses of the AI. These expert users are also able to identify problems and inadequacies in these responses and improve them through successive queries which, again, the average person is not in a position to do, due to lack of specialised training. 

On a practical level, an expert user is therefore able to employ the AI systems of today as semi-skilled collaborators, and to drive gradual improvements, seeking additional help as and when it is needed. It is only the experts, who have spent a lifetime honing their skills, that have the necessary know-how to push this technology to its limits, far beyond what a casual user is capable of.

For instance, suppose you compose music for a living, and that there exists an AI agent that can help with composing music. You could ask it to prepare a template for a theme that, even though it has yet to completely coalesce in your mind, you know that it must be in C minor and that the arrangement is reminiscent of Baroque works by, say, Telemann and Pergolesi. You guess the sound you have in mind is probably about 80% more similar to Telemann and only about 20% Pergolesi. Maybe there’s even a bit of Corelli in there but you are not sure. You know exactly which instruments you want to use, you know the harmony, etc. and you pass all this information to the AI asking it to give you a test theme. Maybe you don't like what it gives you. You ask it for variations until you find one that roughly matches what you have in your mind, or one that clicks and inspires you. Then you ask it to put all the notes on a staff, print the score and you edit the details making adjustments. Then you scan the score you worked on and send it back to the AI asking it to maybe improve the timing or change the speed of this or that note, until the result sufficiently satisfies you and is close to what you envision. In short, this collaboration with the AI will significantly simplify your work as a composer, while you remain the creative director of the entire process. 

Is this inappropriate? Consider Hans Zimmer, the famous film score composer, who can afford to employ a number of other composers, as well as orchestrators and sound engineers, to help him write and arrange the music for his movie scores. The use of AI could allow budget-constrained and less well-known composers to do something similar, and perhaps even to become competitive and get their music to reach new audiences.

All this will lead to a democratisation of the creative process and a creative inflation that will have a lasting impact across every professional field. To appreciate how this may play out, let us continue with our thought experiment in the music industry. Supply and competition will certainly be greater, with the marked difference that the playing field will be more level, with smaller composers now having the ability to challenge more established names. Output will exponentially increase, making it far more challenging to build and maintain a lasting reputation. In an ocean of mediocre compositions, the deciding factor will inevitably become the uniquely personal touch the composer imparts to their music. 

None of this is sufficient to conclude that fewer people will consider music as a viable career option, but it will most definitely affect how musicians build a career. It is also unlikely that people will suddenly stop wanting to learn how to master musical instruments, a difficult process which did not disappear even when synthesisers and electronic music were invented. There may even be increased interest in attending live events, such as concerts and recitals. 

Take the example of painting and photography. Photography did not destroy painting; it rejuvenated it. It redefined the meaning of the art of painting and as a bonus created the altogether new branch of artistic photography and related professions. When faithful representation in painting succumbed to the undisputed superiority of the precision of the photographic plate in the late 19th/early 20th century, the creator was freed from a strict adherence to realism, thus giving birth to modern art, and a new generation of groundbreaking artists came to the foreground: Picasso, Dalí, Monet, Manet, Kandinsky, Van Gogh, and many, many others, all mounted this new wave. 

There is not enough space here to elaborate on the very significant social knock-on effects these developments had; this is an exercise better left to historians. The bottom line is that it would be at least disingenuous to persist on the claim that photography destroyed painting, for the additional reason that realistic representations still remain an active branch of painting today. 

In today’s modern world, individuals who make a living exclusively in the field of creative painting, have a much easier time doing so compared to their predecessors in the 18th and 19th centuries, who could achieve little without the continued support of rich sponsors. Of course, we must concede that all this was made possible due to fundamental changes in social conditions for the better, but these very changes themselves were significantly influenced by the historical developments mentioned previously. It is hard to disentangle with a high degree of confidence exactly how all these trends fed on each other. Society huffed and puffed, blew the doors down, and replaced an obsolete structure with a more elaborate one.

In the numerical sciences, the pocket calculator, and later the computer, did not eliminate the need to learn algebra. They accelerated the ability to perform complex calculations to an incredible degree, but did not make the learning of the underlying mathematical rules and methods irrelevant. We still continue to teach these rules and methods all the way from elementary school to university. 

Where these technological developments have clearly made a difference, is in the fact that we now recognize that there are better mechanisms available to us for controlling the accuracy of our results and for minimising errors, and we employ them. No researcher today would expect their doctoral student to perform all calculations by hand, because of the comparatively greater likelihood of introducing small errors somewhere along the line, which can cost greatly, both in time wasted and increased frustration. What researchers are interested in, is the proper scientific analysis of their measurements.

In any case, the further development of AI is of such great importance that it now constitutes a strategic necessity for every country, so any discussion about impacts and limitations should start with this as a given. There are  problems and challenges that are indeed significant, but they are potentially solvable by adjusting existing socio-economic models or by introducing novel solutions, as has happened again and again in the past. As societies gradually grow more accustomed to these changes, they are better able to absorb cultural shocks and reorganise around new points of equilibrium. Perhaps the greatest challenges AI will bring are of a different nature. For example, how will we be able to prevent a race to the bottom when it comes to autonomous smart weapons, and how can we ethically align the goals of a generalised AI with human goals. AI is designed to find unique solutions to very specific questions, and these may not always be the answers we would hope for. This is especially true when the AI is faced with complicated ethical choices.

Now let us consider what AI can do for teaching and research. Yes, AI will eventually be able to solve standard school exercises, and explain all the intermediate steps in detail. That is, it will be able to provide specific solutions to well-formulated questions from a range of known types of problems. This suggests that the education system will need to adjust in response, and focus less on methodology and information gathering, which can easily be automated, and more on developing critical thinking and analysis skills. On the fringes of scientific research, we often don't even have well-formulated questions, nor do we know exactly what questions it would be best to ask, nor how exactly to interpret the results if there is insufficient data. Solutions are often multidimensional and not unique, and AI methods can help us navigate through a complex parameter space.

Many parents and teachers have raised concerns about the state of math literacy, primarily in the West, compared to previous decades. These concerns are raised with the unstated assumption of ceteris paribus, which is obviously somewhat of an issue, because the skills of students in many other subjects, many of which did not even exist a few decades ago, are on an entirely different level regarding the collection and utilisation of available information. Even if we concede that students today could be weaker in mathematics, the fact remains that their particular skills more than make up for it.

Granted, this is an issue that should concern us, but it is not nearly as catastrophic as it is usually made to sound. Compare, for example, the level of students in computational analysis and numerical methods in the 70s with what they are able to achieve today. No comparison. The number of students choosing STEM subjects at university shows a steady increase, although dropout rates, especially in Physics, remain high.

Are all concerns about the development and use of AI to be dismissed as mere scaremongering? Of course not; that would be naive and dangerous. There are many well-grounded legitimate concerns. On a societal level, we need to think hard about how to regulate the AI industry and ways must be found for professionals to be remunerated when their work has been used to train AI models. How exactly that might work in practice is yet to be determined, but we better start having these discussions now.

Sooner or later every field and every profession will be affected. Moravec’s “Landscape of human competence” gets progressively more flooded as time goes by. Highly creative professions, which until recently were thought out of reach for at least a few more decades, are already feeling the first splashes. There are now AI copilots that can be used to write code and software engineers are already trying them out. They use them with a mix of elation and worry. In my own scientific field, we increasingly rely on machine learning to look for patterns in massive data sets. There is just too much of it, and it would take decades to do it without the use of such tools. 

The steady march forward of AI will inevitably cause major disruptions in how society currently operates, and I strongly suspect that it will make the introduction of some sort of UBI unavoidable. We have a long way to go.