Tag: algorithms

Categories
AI: Diffusion Models Art and Artists

An Algorithm by Hand

He didn’t know what he was looking for the first time he walked into the Alhambra. You come in from the heat and the light hits the walls and for a moment you just stand there, your mind doing something it doesn’t have words for yet.

That was 1922. Escher was twenty-four years old, recently graduated. The Moorish artists who made these walls had been dead for six centuries. They had left no notes. No theory. Just the walls.

He came back in 1936. Some things you have to see twice.

What the artists in the Alhambra had discovered — without algebra, without proof, working in a tradition that forbade them from drawing a single living creature — was that there were exactly seventeen ways to tile an infinite plane with a repeating pattern. The Russian mathematician Fedorov would articulate this formally in 1891, centuries after the fact, the way mathematics sometimes chases art home and explains what it already knew.

Escher took the problem and made it harder. He asked: what if the edge between two tiles is also the outline of a fish? What if the sky is made of birds and the birds are made of sky? He would move a single line and the whole system would tremble. He did this for years. Revision after revision, in small notebooks, by hand.

There is a word for what he was doing. We just didn’t have it yet.

The word is algorithm.

An algorithm is a set of rules, followed in sequence, to solve a problem. We think of them as things that live in machines, in data centers drawing enough power to light a city. We think of them as fast. Escher’s algorithm was not fast.

He would begin with a grid. Hexagons, maybe, or the interlocking diamonds of a pattern he had traced from the Alhambra walls. Then he would ask the question that made everything hard: what lives here? Not what shape — what creature? What thing with a spine and a purpose and an outline that a human eye would recognize before the brain caught up?

The constraint was absolute. Every point on every edge had to satisfy two animals simultaneously. Change one line and you changed everything downstream, the way a single altered fact in a long investigation suddenly makes you reread everything you thought you knew.

He failed constantly. The notebooks are full of it. Half a lizard becoming nothing. A bird whose wing destroyed the fish below it. He would back up and try again, the way you back up on a road that has stopped being a road.

He was doing, neuron by neuron, what a diffusion model now does in milliseconds.

But here is the thing about milliseconds. They don’t leave notebooks.

Categories
Living Serendipity

The Infrastructure of Accident

I had a ham shack when I was in high school. A tight corner of my bedroom, a transceiver, an antenna wire running out through the window frame to somewhere up on the roof. Late nights mostly. The ritual of it: power on, headphones on, find a frequency, make sure it’s clear. Then send CQ. CQ. CQ. A call to no one in particular, to anyone, to whoever happened to be listening on that frequency at that moment anywhere on earth.

Sometimes nothing came back. Sometimes someone answered from a place I had no reason to expect — a voice, or rather a pattern of dots and dashes that resolved into a voice, from a callsign I didn’t recognize, from a grid square I’d have to look up on a map afterward. We would exchange signal reports and names and locations and often we talked longer. Our gear. What we did that day. Ordinary things, transmitted at forty words a minute across a great distance to a stranger I would never meet.

I did not know then that I was practicing something. I thought I was just playing radio.

We have decided, sometime since, that luck is a system. That serendipity is an architecture. That the people to whom good things happen have engineered the conditions for good things to happen, and that the people to whom good things do not happen have, at some level, failed to present the right surface to the world.

I am not sure when we decided this. Sometime after we stopped believing in fate and before we started believing in algorithms, in that narrow window when we still believed, provisionally, in ourselves.

The self-help literature on luck is a literature of verbs. Expand. Broadcast. Reframe. Sabotage your algorithms. The verbs are always active, always transitive, always aimed at a future in which the random becomes, retroactively, inevitable. You will look back and see the architecture. You will understand that the flight delay was an opportunity, that the canceled meeting was a gift, that the stranger in the adjacent seat was not a stranger at all but a node in a network you were already, without knowing it, building.

What the literature cannot account for is the canceled meeting that was simply a canceled meeting. The flight delay in which nothing happened except that you sat in a molded plastic chair in Terminal B and ate a sandwich that cost fourteen dollars and thought about everything you had not yet done. The stranger who remained a stranger.

I have been thinking about a used bookstore on Telegraph Avenue in Berkeley, at the corner of Dwight. Shakespeare & Co. It smelled the way all serious used bookstores smell — dust and possibility, which are not always different things. The shelves ran floor to ceiling and were not organized in any way that rewarded efficiency. You found things there the way you find things in dreams: without looking, and then suddenly they were in your hands.

I found a paperback copy of Slouching Towards Bethlehem there. Someone else’s margin notes in blue ink, a handwriting I did not recognize and have never been able to stop thinking about. Whoever they were, they had underlined the same sentences I would have underlined. They had written yes in the margin next to things I did not yet know I believed.

I have no way of knowing whether that was luck or whether I had simply been the kind of person who wandered into bookstores and stayed too long. The kind of person for whom that particular door was already, structurally, open.

Buildings have architects. Someone drew the plans. But I cannot find, looking back, the moment I became that person. I can only locate the book.

The word serendipity was coined in 1754 by Horace Walpole, who derived it from a Persian fairy tale about three princes of Serendip who were always making discoveries by accidents and luck, of things they were not in quest of. Accidents and luck. The word has always contained both. What the contemporary literature has done is quietly eliminate the accident and keep only the luck — reframed now as preparation, as readiness, as optimized openness. The princes were not prepared. They wandered.

Anymore we are often uncomfortable with just wandering. Wandering has no metrics. A waste of time.

There is a thing that happens when you pick up a physical newspaper, one you did not choose from a menu of personalized recommendations online but simply lifted from a rack at the library because it was there. You read stories you would never have clicked on while reading on an iPhone. Not because you lacked interest but because no algorithm had yet determined that you had it. The story finds you before the system can decide whether you are the kind of person who would want to be found.

I go to the library some days for precisely this reason. It is a considered refusal — the same one the princes of Serendip were practicing, though they had no word for what they were refusing. The library does not know what I clicked on last Tuesday. It cannot optimize my morning. It can only offer everything, indiscriminately, and trust me to wander.

Life feels richer on those days. I have tried to understand why and have arrived, after some time, at this: on those days the world is larger than my prior assumptions about it. That is not a small thing. That may, in fact, be the whole thing. Here comes the sun!

Shakespeare & Co. closed in June 2015, after fifty-one years on Telegraph Avenue. The owner said the past few months had been unsupportable. He taped a note to the door and served his last customer and locked up around eight in the evening and that was that. Someone who worked there was quoted saying that the serendipity of finding a book that changes your life doesn’t happen on Amazon. Indeed. He meant it as an elegy. The infrastructure of accident had to be built by someone. It had to be maintained. It had to be, on some Tuesday evening, locked for the last time.

The owner locked up around eight. He had served his last customer. There was nothing more to do.

The margin notes are still in the book.

Categories
Aging Citizens Band Radio History Living

The Static We Left Behind

There was a time when the airwaves crackled with a distinct, unpolished kind of magic. It wasn’t the curated broadcast of a corporate radio station, but the raw, spontaneous voices of strangers sharing the same lonely stretch of highway or suburban night. When I previously wrote about the rise and decline of CB radio, I didn’t fully anticipate how deeply the piece would resonate. The influx of emails, comments, and shared memories pointed to a singular, striking truth: we don’t just miss the hardware of the 1970s; we miss the serendipity of the connection it offered.

In the decades since the fiberglass whip antenna faded from the American automotive silhouette, our society has become infinitely more “connected.” We carry glass slabs in our pockets capable of reaching anyone, anywhere, in an instant. Yet, paradoxically, we often find ourselves feeling more profoundly isolated. The modern digital landscape is largely an algorithmic echo chamber, meticulously designed to feed us reflections of what we already know and who we already are.

CB radio, by contrast, was a geographic lottery. You turned the dial, adjusted the squelch, and were instantly thrust into a transient community composed entirely of whoever happened to be within your physical radius. It was messy, chaotic, occasionally absurd, and deeply human. It was a localized town square operating on a 27 MHz frequency.

“We traded the spontaneous for the scheduled. We swapped the local for the global… We traded the crackle of static for the endless, frictionless scroll of the feed.”

Reflecting on the quiet that eventually fell over Channel 19, it becomes clear that the decline of CB radio was more than just a technological shift—it was a cultural one. We traded the spontaneous for the scheduled. We swapped the local for the global, and the intimately anonymous for the hyper-public. We traded the crackle of static for the endless, frictionless scroll of the feed.

But the fundamental human impulse that fueled the CB craze never actually disappeared. The desire to reach out into the dark void and hear a human voice echo back—the spirit of “Breaker 1-9, is anyone out there?”—remains hardwired into our psychology. We see fragmented echoes of it today in late-night Reddit threads, in niche Discord servers, and in the fleeting, unscripted interactions of multiplayer gaming. We are all still, in our own ways, searching for a shared frequency.

Perhaps the true legacy of the CB radio isn’t a cautionary tale of obsolescence, but a gentle reminder. It reminds us that in our highly polished, curated digital world, there is still immense, undeniable value in the unscripted encounter. We haven’t lost the need to connect; we are simply navigating a world with too much noise and too few open channels.

Categories
AI Work

The Centaur’s Dilemma: What Chess Teaches Us About the AI Era

Note: this post was stimulated by a recent conversation between Dario Amedei and Ross Douthat.

In 1998, Garry Kasparov did something unexpected after his historic defeat to IBM’s Deep Blue: he teamed up with the machine. He pioneered “Centaur Chess,” a hybrid format where human intuition merges with cold, silicon calculation. The human acts as the executive, the engine as the raw horsepower. For a time, it was the highest level of chess ever played.

But there is a sobering lesson hidden in the evolution of this game. We are currently living through the workforce equivalent of the Centaur era, and history suggests our “hybrid honeymoon” won’t last forever.

Right now, we are in the augmentation phase. A junior copywriter or coder armed with a Large Language Model can suddenly produce work at a staggering pace. The AI acts as a great equalizer, much like a mediocre chess player with a strong engine beating a Grandmaster in the early 2000s. We are shifting into executive roles—prompting, curating, and orchestrating rather than creating from scratch.

However, in modern Centaur Chess, a chilling reality has emerged: human intervention now yields negative returns. The engines have become so impossibly advanced that when a human overrides Stockfish today, they are almost certainly making a mistake. The human loop, once the ultimate strategic advantage, has become a liability.

This is the “Grandmaster Floor” problem, and it is coming for the job market.

“Eventually, companies may view human oversight not as a ‘value add,’ but as an insurance cost they’d rather cut.”

We are seeing this fracture already. Pure “engine” industries—entry-level data analysis, logistical tracking, basic customer support—are rapidly phasing out the human element because human latency is a drag on the system. Yet, in fields requiring high-stakes moral judgment or empathy, like healthcare or law, the Centaur model remains deeply necessary.

This forces a deeply personal question: How do we stay relevant when the engine eventually solves the game?

The answer lies in recognizing the boundaries of the board. Chess is a closed, finite system. Human life and business are open, messy, and infinitely complex. The survival strategy isn’t to compete on calculation, but to double down on connection, empathy, and problem definition. AI is brilliant at providing the perfect answer, but it fundamentally lacks the soul to know which questions are worth asking.

In the future, the human touch won’t just be a necessity; it will be a luxury. The most valuable skill won’t be navigating the engine, but deciding where the engine should go.

A couple of considerations:

• Take an honest look at your daily work: how much of your time is spent “calculating” (tasks an engine will soon do better) versus “evaluating” (deciding what actually matters)?

• If the technical, process-driven aspects of your job were completely automated tomorrow, what uniquely human value—empathy, context, or connection—would you still bring to the table?

Categories
AI AI: Large Language Models medical

Stethoscopes and Statutes in the Age of AI

David Sparks (aka MacSparky), dropped a casual bombshell on a recent podcast, the kind of offhand remark that lodges in your mind like a burr on a sock.

Paraphrasing, he said something like: “AI seems to be a boon for doctors and a threat to lawyers.” He was commenting on how he’s observed that sense among the members of his MacSparky Labs community.

It’s the sort of statement that invites you to pause, tilt your head, and wonder what lies beneath.

Sparks, a lawyer himself who gave up his legal career a few years ago, knows one of those worlds intimately. His words carry the weight of someone who’s walked the halls of courthouses and squinted at screens late into the night.

So what’s he pointing out that the rest of us might miss?

Start with doctors. Medicine is a profession of patterns and particulars, a dance between the general and the specific. A patient walks in—say, a 52-year-old man with a cough that’s lingered too long. The doctor’s mind whirs: pneumonia? Bronchitis? Something rarer, like sarcoidosis? The human brain is a marvel at this, but it’s not infallible. Enter AI, with its tireless capacity to sift through terabytes of data—X-rays, lab results, decades of case studies—and spot the needle in the haystack. A tool like Harvey, an AI platform now making waves in medical research, can crunch genetic sequences or flag anomalies in real time, handing doctors a sharper lens. It’s not replacing the physician; it’s amplifying her reach. For doctors, AI is like a stethoscope that’s upgraded.

Lawyers, though, face a different challenge. Their craft is less about data and more about argument, a tapestry of precedent and persuasion woven over centuries. Sparks knows this: he’s stood before judges, parsing statutes, coaxing juries with a turn of phrase. But here’s the rub—much of lawyering is rote. Drafting contracts, reviewing discovery, chasing down case law—these are tasks of repetition, not revelation. AI can do them faster, cheaper, and with fewer coffee stains. Harvey, repurposed for legal work, joins programs like ROSS, built on IBM’s Watson, to scan legal databases in seconds, spitting out answers that once took associates hours to unearth. For the grunt work, AI is a scythe through wheat. The threat isn’t extinction but erosion—junior lawyers, the ones who cut their teeth on those late-night searches, might find the ladder’s lower rungs sawed off.

Yet law isn’t just mechanics; it’s theater. A machine can draft a motion, but can it read a juror’s furrowed brow? Can it pivot mid-trial when a witness veers off script?

Doctors heal with facts; lawyers win with stories. AI—Harvey or otherwise—might streamline the former, but the latter resists its grasp—for now. Sparks sees a fault line: medicine gains an important new partner, law sees a new rival.