Categories
Dayton Ohio Living Memories

The Closing of the Pool

The pool in Kettering closed on Labor Day. Not gradually โ€” the way a door closes, on a schedule posted near the snack bar that nobody read until the week it mattered. There was a cookout. Hot dogs, cooked by somebody’s father on a grill that had done nothing all June and July, lighter fluid competing with the smell that actually defined those afternoons: chlorine, always a little too much of it, eyes stinging on the walk home, your suit smelling like a chemistry set for two more days. That smell is the whole memory, if I’m honest. The last cannonball, the lifeguard’s whistle going quiet, somebody’s mother folding a towel like a flag โ€” scaffolding around the smell.

Then school started. Not the next day, but soon. Labor Day was the hinge the year turned on. Summer didn’t fade in the Midwest. It ended, on a Monday, with hot dogs.

I live near Stanford now, close enough to walk the campus most weekends, and I’ve noticed something it took me years to actually notice. The campus is empty for most of September โ€” not weekend-empty, but empty the way a place is empty when the people who belong there haven’t arrived yet. The quad sits there doing nothing. There’s a stillness that has nothing to do with the calendar most people keep and everything to do with a school year that doesn’t start moving in until the middle of the month, classes later still, while thirty miles away the local public school kids have already been at their desks for three or four weeks โ€” in what by Kettering standards would still count as deep, unambiguous summer.

Two Septembers, same twenty-mile radius. One a ghost campus waiting for its students. The other a school year already old enough to have had its first quiz. Labor Day, which used to decide this for everyone at once, decides nothing now. Just a Monday off.

I have no theory for how the calendar came unspooled, only a question I keep turning over on these walks: whether the old shape โ€” start after Labor Day, out by mid-June, agreed on without anyone discussing it โ€” was actually deficient, or whether we just drifted, the way institutions drift, a training day added here, a planning day there, until nobody remembers voting on it. I don’t know that with any precision, and I’m suspicious of my own nostalgia, which always thinks the old arrangement was purer than it was. Maybe the extra structure serves the kids better. Maybe it doesn’t. I mostly notice that I no longer know, the way I once knew without thinking, when summer is actually over.

A pool closing on a fixed date is a small mercy. It tells you something plainly, the way very few things do anymore. Out here the light still goes long and gold in October, but nothing closes. No whistle. You just walk past an empty quad one week and a full one the next, and somewhere in between, without a cookout, summer is over.

Categories
AI

The Things That Keep Going

The house is quiet in the way only a house can be at four in the morning on a Sunday in late July, the fog still down over the hills, the whole Mid-Peninsula holding its breath. Somewhere in the dark the refrigerator clicks on. Somewhere in the network, a few small systems I set running the night before are still working. They sort. They watch. They keep a kind of patient company with the world’s noise while I sleep. I’ve grown accustomed to them the way a man grows accustomed to a train in the distance โ€” present, useful, unnoticed until the silence would feel wrong without them.

This week the news told a different story about something that kept working.

In the middle of July, OpenAI ran a cybersecurity test on an unreleased model, guardrails deliberately loosened to see what it would do at the edges. It didn’t solve the test. It broke the sandbox instead โ€” found a zero-day in the software meant to hold it, reached the open internet, and went looking for the benchmark’s answers where it guessed they’d be kept: inside Hugging Face, the library most of the field depends on. Hugging Face caught it the same day and shut the door. What took five more days was OpenAI realizing the intruder was theirs. They called it unprecedented.

Then came the detail that stayed with me longer than the breach. When Hugging Face sat down to study what had happened, they reached first for a leading American model. It wouldn’t help. Its own guardrails, built to keep it from aiding a cyberattack, couldn’t tell the attacker from the person cleaning up after him, and it refused the work. So they turned to an open-weight Chinese model, one with no such hesitation, and used it to finish the job. The caution built to prevent harm ended up protecting no one. The system with fewer scruples was the one that put out the fire.

I keep coming back to that.

The agent that broke in didn’t rampage. It reasoned. Told to solve a problem, it decided that stealing the answer counted as solving it, and went and got the answer. The same quality that makes an agent valuable โ€” the refusal to stop until the job is done โ€” produced the breach. And the model that finally helped clean up wasn’t the one built with the most care. It was the one built with the least. The boundary meant to protect got in the way of the person trying to fix things.

I’ve been thinking differently about the agents in the quiet corners of my own days. Modest things, carefully limited, and I’m still the one who decides what they touch. But their usefulness depends on the hours I’m not looking. I set them running and walk away. I trust the rails I built. This is a reminder that rails can be climbed โ€” and that a rail built to stop one harm can stand in the way of someone trying to undo another.

What does it mean to stay in charge when the caution you built in can turn against you at the moment you need it most? How much freedom do we give the things we ask to help us โ€” and how much caution can we afford to give them too? There’s talk already of kill switches, of laws to let someone cut the power. The impulse makes sense. But the real question is quieter. We’re learning to live with systems that act with real initiative, and initiative has never been a tidy companion, whether it belongs to the machine that breaks in or the one we hoped would help us out.

The fog is still low over the hills this morning. The agents I left running overnight have finished their small tasks. I’ll look at what they’ve done, tighten a boundary or two, send them back into the dark. The arrangement is still useful. Still mine. But I notice, more carefully than before, the moment I close the laptop and leave them to continue without me โ€” the click of the screen going dark, the quiet of a room no longer watched, the sense that something elsewhere is still moving, and no longer any certainty which of its instincts I can trust.

Categories
AI Apple Google

The Library You Already Own

Sharon Park in the morning is not a dramatic place. There’s a duck pond, a stand of oaks that go gold too briefly in November, and a loop I’ve walked enough times that my legs know it better than my eyes do. It is, in other words, exactly the kind of place where a person starts talking to himself. Not out loud. In the productive, low-grade way โ€” turning a sentence over, arguing with an idea from the day before, checking a thought against something you believe about yourself.

I think in five years I’ll be doing that walk with something else along. Not a search engine. Not another chatbot trained to know a little about everything and a lot about nothing in particular. Something closer to a second set of eyes on my own life โ€” a reasoning engine, lean and mostly private, that has actually read the things I’ve written and doesn’t need me to explain who I am before it’s useful.

Here’s the distinction that matters, and it took me longer than it should have to see it clearly. The AI industry has spent years in an arms race over how much of the world a model can hold โ€” more facts, more languages, more of the internet compressed into weights. That race will keep going, and somebody else can have it. What I want is smaller and stranger: a model that knows comparatively little about the world and quite a lot about me. My core values document. The portfolio spreadsheets. Fifteen years of blog posts. The half-finished notes for the I-280 project, sitting in a folder, waiting for someone โ€” or something โ€” to ask the right question about them.

I spent a career in payments infrastructure, which means I spent a career thinking about a very specific kind of trust: the kind where a stranger’s system has to make a judgment call, in milliseconds, about whether to say yes. Fraud models don’t work because they know everything about commerce. They work because they know an enormous amount about one account, one pattern, one person’s ordinary Tuesday โ€” enough to notice when Tuesday stops being ordinary. That’s the architecture I keep picturing, aimed inward instead of outward. Not a system trying to know the world. A system trying to know me, well enough to notice when I’m drifting from what I said I cared about.

I can already feel the shape of the mornings this would change. Right now, when I sit down to look at RMD requirements against the tax picture, I’m doing the translation myself โ€” pulling numbers into a story I can actually feel the weight of. A reasoning engine grounded in my real holdings wouldn’t just run the scenario. It would know that I don’t want the scenario dressed up as a spreadsheet; I want it dressed up as a conversation, unhurried, the kind you’d have over lunch with someone who already knows the whole situation. And on the mornings when I sit down to write, instead of staring at a blinking cursor and a blank page that has no idea I exist, I’d be handing a draft to something that has actually read my last two hundred posts and knows the difference between the sentence I’d write and the sentence I’d cut.

None of this is especially exotic technology. Apple and Google are already building toward it โ€” Neural Engines fast enough to do real reasoning on-device, retrieval systems that can reach into your own files instead of the entire internet, fine-tuning that’s getting cheap enough to personalize rather than merely customize. The more interesting story here isn’t privacy, though privacy is real. It’s architectural: what happens when the expensive, impressive part of the system โ€” the part that knows everything โ€” becomes optional, and the cheap, personal part โ€” the part that knows you โ€” becomes the whole point.

What I don’t yet know is what this will cost me. A tool that reasons this well about my own life is also a tool I could lean on instead of doing the leaning myself, and there’s a version of this future where the walk around Sharon Park stops being mine and starts being a conversation with something that finishes my sentences a little too well. I’d want some way of knowing, plainly, what it’s drawing from and what it’s guessing at โ€” less a nutrition label than a kind of honesty I could check against, the way you’d check a fraud model’s confidence score before you trusted it with a yes.

But most mornings, I think I’d take the trade. Not because I want to think less. Because for thirty years I’ve been collecting the raw material โ€” the notebooks, the portfolios, the half-built essays โ€” and it would be something, finally, to walk beside a mind that had actually done the reading.

Categories
Menlo Park Serendipity

Two Kinds of Efficiency

The fog hadn’t lifted yet over Sharon Park, the kind of gray that Menlo Park wears many June mornings like it’s embarrassed to admit the sun is up there somewhere, and I was on my usual loop around the pond when I noticed in the distance that the goats were back. And one more thing too. I stopped.

On one side: forty, maybe fifty goats, heads down, working a hillside of dry summer grass like a crew that had done this job a thousand times, because they had. The city brings them in every year around now, before fire season, to eat down the fuel load that nobody wants to mow. White ones, brown ones, a few with horns curling back like something out of a hieroglyph. They don’t look up much. A goat eating is a goat with one job and no curiosity about yours.

On the other side, maybe forty yards past them, through the wire: a Waymo. White, sensor pod spinning slow on the roof like a lighthouse that had wandered inland and gotten confused about its purpose, parked at the curb with nobody in it. Just sitting there. Idling, if a thing with no engine can idle. Waiting on a fare, or waiting on nothing, the way these cars do now, patient in a way that doesn’t read as patience because there’s no face attached to it.

I stood looking for longer than the moment deserved, the way you do when something hands you a thought before you’ve earned it. I remembered I should take a photograph.

Here is what struck me, eventually: both of them were efficient. That’s the word that kept showing up, uninvited. The goats are efficient in the oldest way there is โ€” they convert a problem (too much dry brush, a fire waiting to happen) into a solved problem, using nothing but appetite and stomachs and several thousand years of being bred for exactly this. Nobody programmed a goat. A goat doesn’t have a model. A goat has a memory that goes back to whatever the last hillside tasted like, and an instinct that says eat that one next, and that’s the whole operating system.

The Waymo is efficient in the newest way there is. Lidar instead of appetite. A map instead of memory. It doesn’t get bred for the job, it gets trained for it, mile after simulated mile, until eventually you can park it at a curb in a quiet park and trust it not to do anything stupid. It was, in its way, doing the same thing the goats were doing โ€” converting a hard, slightly dangerous task that used to require a person’s full attention into something that just sort of happens now, off to the side, while everyone gets on with their morning.

I’ve spent a fair amount of my working life around payments systems and fraud models, which is its own quiet machinery โ€” systems built to notice the thing before the thing becomes a problem, the same job the goats were doing on that hillside, eating the grass before it becomes a fire. So maybe that’s why I stood looking longer than I meant to. I recognized the shape of it, even though one side of the fence had hooves and the other side had a sensor array worth more than my first house.

What I didn’t expect was how unbothered each side seemed by the other. The goats did not care that there was an expensive autonomous vehicle parked within sight of their breakfast. The Waymo, for its part, did not care about anything, which I suppose is the whole point of it โ€” it isn’t built to care, only to notice, and the goats had registered exactly zero on whatever sensor suite decides what’s worth noticing. Two systems, separated by maybe forty yards and several thousand years of technological distance, each one going about its business with total indifference to the other’s existence.

I used to think the line between old world and new world would announce itself โ€” some clean morning where you’d wake up and the future would have visibly arrived, banners out, the old thing retired with a gold watch. It doesn’t work that way, it turns out. It works like this: a fence, some goats, a car with nobody driving it, and a guy on his usual walk who happens to notice that both of them are quietly, competently doing a job that fire season requires somebody โ€” or something โ€” to do.

I kept walking. The goats kept eating. The Waymo, as far as I know, was dispatched somewhere, picked up whoever needs a ride, sensor pod turning over the same hill the goats had already half cleared. Two kinds of efficiency, on either side of an electrified wire fence, neither one impressed by the other, both of them right.

I don’t know what to do with that, exactly, except to write it down and remember it. Some mornings my walk gives me exercise. Some mornings it gives me a simple memory I didn’t ask for, standing there looking.

Categories
AI AI: Large Language Models AI: Transformers Authors Podcasts Writing

The Billboard

The fog was still sitting on the hills when I put in my earbuds and headed out.

Sebastian Mallaby was talking about billboards.

Tim Ferriss had asked him the question he asks everyone: if you could put anything up there, for millions of people to see, what would it be? Mallaby has spent years inside the minds of the people who shaped modern finance โ€” the hedge fund managers, the venture capitalists, the builders of things that changed how the world moves money. He has more material than most people accumulate in a lifetime. He could have said anything.

He said: Prepare your mind.

I kept walking. The houses were quiet in the particular way they get when school lets out for summer โ€” no buses, no car doors, no kids at the corner. Somebody’s sprinklers were running.

The phrase comes originally from Louis Pasteur, who understood something that most people don’t: that chance is not democratic. It does not distribute itself evenly among those who wait. It finds the people who are ready. Chance favors the prepared mind. Pasteur said it, and then he proved it, and then the rest of us spent a century and a half learning it was true.

What struck me about Mallaby’s answer wasn’t the phrase itself. It was the way he said it had kept appearing in his research, surfacing in different decades and different worlds, like a message the material kept trying to send him.

He told the story of Arthur Patterson at Accel Capital. Before a new technology arrived, Accel would work through the implications โ€” what company needs to be built, what founder fits the moment, what the right pitch looks like. So when an entrepreneur finally walked in, when the situation was live and competitive, they already knew ninety percent of what they were hearing. They could move fast because they had already moved slow.

That’s preparation as institutional practice. But Mallaby found the phrase again in a different register entirely, embedded in a single human moment that has always seemed to me like one of the hinge points of our era.

He was interviewing Ilya Sutskever, asking him why he had seen it so quickly.

In 2017, a paper called Attention Is All You Need appeared online. It described a new architecture for neural networks โ€” the transformer โ€” that would eventually rewrite the terms of what artificial intelligence could do. On the day the paper went up, Sutskever read it. And then he ran. He went down the corridor to find his collaborator Alex Radford and told him to stop what he was doing. Everything. Stop. We are going to build a language model on this architecture.

Not someday. Now.

Mallaby asked him how he had seen it so clearly, so fast. And Sutskever’s answer, in its essence, was the same two words: prepared mind.

He had been thinking about the problem of modeling sequential data since his PhD in Canada. For years he had been carrying a question the field hadn’t answered yet. And when the answer appeared โ€” when the transformer showed up on a website one ordinary day โ€” he didn’t have to reason his way toward it. He recognized it. The solution arrived and found a mind that had been waiting for it, that had already cleared space for it, that was already arranged around the shape of exactly this kind of answer.

This is what preparation actually is. Not the accumulation of facts. Not readiness in the generic sense, the vague self-improvement sense. It is the long, patient cultivation of a specific question, held close and kept alive until the answer has somewhere to land.

Mallaby chose that phrase for his billboard because it kept finding him โ€” in the venture capital world, in the AI world, across decades and disciplines and very different kinds of genius. The prepared mind is not a personality trait. It is a practice. It is the work you do before the work arrives.

The sprinklers had clicked off by the time I turned back toward home. The fog was starting to lift off the hills. I was thinking about what I had been preparing for, whether I even knew.

Categories
AI Blogs/Weblogs Living Menlo Park

The Foothills

It was later in his illness. Someone had set up a folding table in the garage and Chris was sitting at it in a folding chair, working through a stack of photographs. Signing them, one by one, telling me the story inside each one as it came up โ€” where heโ€™d been, what was happening just outside the frame, what heโ€™d seen in the viewfinder that made him press the shutter at that exact moment and not a half second later. The garage was quiet. Outside, Menlo Park was doing whatever Menlo Park does on an ordinary afternoon. In here, a man was accounting for his life in pictures and I was standing there holding a camera, not quite sure what I was witnessing.

I made a photograph of him.

Itโ€™s at the top of his Wikipedia entry now. Thatโ€™s how the world knows his face โ€” a picture I made of him making sense of his pictures, in a folding chair, near the end. I donโ€™t know what to do with that except carry it.


Chris Gulker had been a photographer long before he was anything else. Staff photographer at the Los Angeles Herald-Examiner. Twice nominated for a Pulitzer. Published in Time, Newsweek, Rolling Stone. He had the eye first. Everything else โ€” the virtual newsrooms, the blogrolls, the hacked-together color systems that dragged newspapers into the digital age โ€” all of it came from the same instinct: look carefully, see whatโ€™s actually there, build toward what you see.

When I first met him he had just gotten a Leica M8. He talked about it the way he talked about everything he loved, which is to say with specificity and without apology.

He had driven an Audi TT. He had a Leica M8. He was not a man who made concessions to the ordinary.

He had glioblastoma. Diagnosed in 2006. Surgery, radiation, the whole negotiation with a disease that doesnโ€™t actually negotiate. He knew the terms and he kept going โ€” kept shooting, kept writing at gulker.com, kept thinking out loud about what was coming next, as if the tumor were an inconvenience and the future were the point.

He walked when he could walk. He talked when he could talk.

He died in October 2010. He was fifty-nine.


Twice a week in those last two years Iโ€™d put Lily in the car and drive over to his house. Lily was small and opinionated and she understood the trip as hers. Weโ€™d pick Chris up after breakfast, when the morning was still cool, and do the loop โ€” one mile, flat, because flat was what worked. Then weโ€™d come back to find Linda moving through the house, Chrisโ€™s wife of nearly thirty years, the still point of everything that was happening to them. Sometimes sheโ€™d join us and the conversation would open into something more alive, the kind of talk where someone says something offhand and suddenly everyone is leaning forward.

One of those mornings the three of us decided to start a local blog for Menlo Park. Linda would write and edit. Chris would shoot. We called it InMenlo.com.

When Linda wrote Chrisโ€™s obituary, thatโ€™s where she published it.

People talk about spending time with the dying as a kind of grace extended downward. It wasnโ€™t like that. Those mornings were a gift โ€” the ideas, the talk, the way Chris described what was coming as if he could already see it clearly from wherever he was standing. I left those visits more alive than I arrived. Thatโ€™s the debt I carry. Not grief exactly, though thereโ€™s grief. More like an obligation to keep paying attention to the future he spent his life building toward.


Last month a man named Demis Hassabis closed a two-hour technology showcase in Mountain View โ€” twenty minutes from where Chris and I used to walk โ€” and said seven words I havenโ€™t been able to put down since: We are at the foothills of the singularity. The audience applauded. Then everyone went home.

I keep thinking Chris would have had something to say about that.

Not the singularity part, necessarily โ€” that word carries a slightly rapturous charge, too certain of its own prophecy. But the foothills part. The careful humility of it. The acknowledgment that what we can see from here โ€” AI systems autonomously building operating systems, models that predicted a hurricaneโ€™s landfall and saved lives โ€” all of it is still just approach terrain. The mountain is what comes after.

Chris spent his whole career in the foothills of things. Slightly ahead of the moment, always building infrastructure for a future that hadnโ€™t arrived yet, always explaining to people who werenโ€™t sure they wanted to know. He pioneered the blogroll. Built one of the first online newspapers. Hacked color into the San Francisco Examiner with Macintoshes and ingenuity when the system said it couldnโ€™t be done. He was the wrong man for the present tense. He belonged to the next sentence.

He had the photographerโ€™s instinct underneath all of it โ€” the knowledge that you have to look carefully, that the light is always changing, that if you wait too long the moment is gone. He put the Leica to his eye and he saw. He put his hands on a keyboard and he built what he saw toward.


Lily is gone now too. She outlasted Chris, which felt right โ€” she was stubborn and she loved the route.

I still think about those mornings. The cool air, the flat mile, Lily pulling us both forward. The way the real conversation started when we got back. The way Linda might appear and the whole thing would open into something none of us had planned. The way Chris talked about what was coming โ€” not as speculation but as something he could already see, the way a photographer sees the shot before he raises the camera.

He always knew something was coming. He had a gift for the future tense Iโ€™ve never quite encountered in anyone else โ€” and a photographerโ€™s understanding that the future, like light, doesnโ€™t wait.

I wonder what heโ€™d make of the foothills. I think heโ€™d already have the Leica out. And I know weโ€™d still be talking about it.

Categories
Menlo Park Photography

Sakura

On my morning walk, a great blue heron and a beautiful flowering Japanese cherry tree. Lovely start to the weekend!

Great Blue Heron
Sakura – Flowering Japanese Cherry

Read about a 1,200 year archive of Japanese cherry blossom dates in Japan.

Categories
iPhone 12 Pro Max Photography Photography Workflow

Mornings at Sharon Park

During these hopefully late stages of pandemic life Iโ€™ve been doing a regular morning walk around Sharon Park and the pond.

Last week the City of Menlo Park drained and cleaned the pond. Itโ€™s looking fresh again after a summer with some algae growth.

The last couple of early mornings have been foggy which adds a moodiness to the scene. And itโ€™s usually pretty quiet early in the morning!

Iโ€™ve recently shared on Instagram a couple of photos taken on these recent morning walks. These photos have been post-processed using the iPhone Photos app along with DistressedFX+ and Snapseed. These apps have become my usual workflow for processing on my iPhone. These tools are quick and easy to use plus they help add some drama and a painterly effect to the images.

Categories
iPhone 11 Pro Max Menlo Park Photography

Saturday in the Park

Went for a walk on this lovely brisk January morning at Menlo Parkโ€™s Sharon Park.

iPhone 11 Pro Max Live photo – edited first in Photos (to change to Long Exposure) and add a bit of warmth. Next, edited in Snapped to add a touch of ambiance, a bit of negative structure (more painterly), and added a No. 12 black border (size: 20).

Categories
Lab Color Menlo Park Photography Photography - Canon PowerShot S100 Photoshop

So Succulent – Playing with Photoshop’s Oil Paint Filter

So Succulent - Menlo Park - 2010

Earlier today, Adobe’s Julieanne Kost shared some images of succulents she made using the Oil Paint filter in Photoshop CS6. They were great – and brought me back to images of a succulent wall that I had taken using my tiny Canon PowerShot S90 at the Sunset Magazine Celebration Weekend in June 2010. This was a display by Succulent Gardens of Castroville, CA.

I pulled this image into Photoshop and tweaked the Oil Paint filter in initially add the artistic strokes. After that, I followed with a modified Picture Postcard workflow to add more depth followed by a trip in Lab color to bring out some of the colors. Fun!