Neural Misfires

Graham Binns: Photographer, artist, writer, dreamer, geek.

[strongermonster]

perhaps some will disagree, but i think the world got worse when we changed the colour of the night

[strongermonster]

this is what i mean

[degenderates]

Via @bulbaderp

[itistimetodisappear]

To be clear, THIS is how nights of the future should be lit

This is bat friendly street lighting, which not only looks sick as fuck but allows bats to pass through without disturbance, as they cannot see red.

orange and especially white lights deter bats and prevent them from reaching feeding grounds at nighttime. Please if you can, write to your local council and encourage red street lights!!!!

[grahambinns]

In which one gets older, and changes direction twice

I'm now into my 39th trip around the sun. A bit balder, a bit greyer at the temples and in the beard (I'm starting to look as though I've eaten a badger and left the tail hanging out). A bit wiser? Maybe. One thing that I've definitely noticed over the last couple of years is that I'm less willing to tolerate having my time wasted (though I’m still perfectly happy to waste it myself).

At the start of the year I was embarking on a new endeavour: an MA in Photography with Falmouth University. I thought that it would help me find direction that I felt I’d lost, and maybe give me some of the drive to finish things that I’ve often struggled with.

Truth was, though, that the course wasn’t a good fit, and just last week I withdrew as a student. For one thing, it didn’t fit my learning style. I last studied for a postgraduate qualification back in 2002-3, and that was a very intensive course — one week of 9-5 studying, a week off for self-directed work, then another week of studying. This was a much more relaxed course and, being distance learning, I felt really disconnected from it.

I’m not the person that I was twenty years ago, and having studied photography on my own time and dime so much over the last ten years I figured that this whole distance learning approach would work for me. Turns out that I still need that intensity, taking in a lot in a short space of time, in order to be able to learn and want to learn more.

So, I’m the shortest-term MA dropout in the history of ever, possibly. I’m happy enough with that.

So what's next? April's already here, kids, there's no time to wait around.

In short, personal work, personal work, personal work. I've got notebooks full of the stuff that needs to get realised. Some of it is vastly different from what I've attempted before, some of it is projects that need to be finished lest they languish forever on a hard-drive somewhere.

All of that in between the regular work: headshots, fashion portraits, and music promo.

It's 2019, I'm Graham, and I still have no idea where the hell I'm going. Welcome to the ride.

[grahambinns]

Piazza di San Giovani, Firenze, Toscana, Italia http://bit.ly/2EZN9uM

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Firenze, Toscana, Italia http://bit.ly/2EWjCB9

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Piazza Santa Croce, Firenze, Toscana, Italia http://bit.ly/2SsRYQ8

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Ponte Vecchio, Firenze, Toscana, Italia http://bit.ly/2LGqIek

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Firenze, Toscana, Italia http://bit.ly/2AeW1IQ

[grahambinns]

For the first time in a little while, I’ve had time to shoot some #FifteenMinutePortraits again. Thoroughly enjoying playing with light again.

[onlinecounsellingcollege]

“Don’t compare yourself to others. That’s when you start to lose confidence in yourself.”

— Will Smith

[grahambinns]

Things one wishes one would actually learn, for a change.

[grahambinns]

Still relevant.

[onlinecounsellingcollege]

How to like yourself

1. Cultivate self-acceptance. That means you accept yourself for who you are right now. It means you don’t say things like “I would accept myself if … or … I’ll accept myself when.”

2. Stop going over all things you’ve done wrong, the mistakes you’ve made, and your (perceived) inadequacies.

3. Where there’s something in your past that you feel bad about say: “This is what I learned from that situation … And that was THEN and this is NOW.”

4. Don’t compare yourself to others . Instead notice the areas where you’ve grown and changed. Deliberately praise and focus on those positive changes.

5. Don’t fall into the trap of judging others – as that will often lead to being self-critical.

[grahambinns]

[Finally] announcing my Actors' Headshots service

I've shot quite a few actors' headshots over the years, along with bunch of more corporate headshot work, but I've never officially offered actors' headshots as as service; those shoots have always been on an ad-hoc basis.

But headshots are right in my wheelhouse: simple portraits that have to connect with their audience whilst allowing the subject to express a range of expressions. And I love working with actors: they're such versatile subjects, and are used to taking direction.

So I've decided it's finally time to offer a headshots service specifically targeted to actors. You can read the full service page to learn more about what I'm offering, but it breaks down to three packages:

Express session — 45 minutes, 1 final image, £74.99

Two-hour session — 3 final images, £179.99

Three-hour session — 5 final images, £249.99

I can't wait to start working more with local actors and actresses, and making amazing headshot portraits with them.

[aiweirdness]

Metal Band or My Little Pony?

Neural networks are algorithms that learn by example, rather than by following a programmer’s set of rules. Although on this blog I’ve mostly been using them to generate new examples of things (like paint colors, halloween costumes, or craft beers), neural networks can do a lot more.

One thing neural networks can do is classify things. Give them a bunch of examples of one kind of thing, and a bunch of examples of another kind of thing, and it will (hopefully) learn to tell the two apart. This is really useful - for identifying obstacles for self-driving cars, for telling diseased tissue from healthy tissue, and even (with mixed success) for identifying spam or troll comments. I wanted to test this kind of algorithm out, so I devised the simplest task I could think of: telling metal bands from My Little Ponies.

I’ve previously trained text-generating algorithms to generate metal bands and My Little Ponies, so I had datasets ready to go. IBM Watson has a very easy-to-use tool for training classifiers (there’s a classroom-friendly version at machinelearningforkids.co.uk). I loaded in all 1,300 of the My Little Pony names I had, and filled the rest of the tool’s memory with metal bands (about 18,700). 

Then I entered some new pony names - neural network-generated pony names so they weren’t in the original dataset - to see how it would classify them. The result:

The neural network labeled *everything* as metal. People who have worked with neural network classifiers before will have seen this coming: with a dataset that was 94% metal class and only 6% pony class, I had set myself up with a classic case of something called class imbalance. The neural network found it could achieve 94% accuracy on my training dataset by calling everything metal. Princess Pie? Metal band with 81% confidence. Sweetie Loo? 85% likely to be metal. Sparkle Cheer? 84% sure that’s a metal band. Flutter Buns? So, so metal. 97%. The only names it didn’t label as metal bands were ponies that were the original dataset. So, Twilight Sparkle? 100% pony. Twilight Sprinkle, though? 83% metal.

The fix was easy: I trained the classifier again, this time with equal numbers of ponies and metal bands. This time the results were a lot more believable. And, the classifier network mostly agreed with the generator neural network names. There were some surprises, though.

Generated metal bands:

Dragonred of Blood - 100% metal, 0% pony Deathhouse - 97% metal, 3% pony Vermit - 97% metal, 3% pony Sespessstion Sanicilevus - 97% metal, 3% pony Stormgarden - 97% metal, 3% pony Vomberdean - 96% metal, 4% pony Swiil - 96% metal, 4% pony Dragorhast - 96% metal, 4% pony Sun Damage Omen - 96% metal, 4% pony Squeen - 96% metal, 4% pony Inhuman Sand - 88% metal, 12% pony Snapersten - 3% metal, 97% pony Staggabash - 3% metal, 97% pony

Generated ponies:

Cherry Curls - 0% metal, 100% pony Starly Star - 1% metal, 99% pony Cheese Breeze - 0% metal, 100% pony Agar Swirl - 1% metal, 99% pony Sob Dancer - 1% metal, 99% pony Derdy Star - 1% metal, 99% pony Princess Sweat - 1% metal, 99% pony Raspberry Turd - 1% metal, 99% pony Arple Robbler - 3% metal, 97% pony Pocky Mire - 6% metal, 94% pony Cold Sting - 10% metal, 90% pony Pearlicket - 48% metal, 52% pony Blue Cuss - 79% metal, 21% pony Sunsrot - 84% metal, 16% pony Rade Slime - 84% metal, 16% pony Flustershovel Aoetel Pakeecuand - 96% metal, 4% pony

When I fed the classifier names that were generated by a neural network trained on BOTH metal bands and ponies, it was not as confused as I had expected. Instead, it classified them with high confidence as one or the other.

Pinky Doom - 99% metal, 1% pony Strike Berry - 0% metal, 100% pony Cryptic Mane - 1% metal, 99% pony Bloody Star - 4% metal, 96% pony Killy Power - 96% metal, 4% pony Spectral Apple - 1% metal, 99% pony

Of course, this classifier will also work on any text I give it.

Benedict Cumberbatch - 96% metal, 4% pony Jane Austen - 17% metal, 83% pony Dora the Explorer - 55% metal, 45% pony Aluminum - 17% metal, 83% pony Aluminium - 96% metal, 4% pony The Earth’s Core - 99% metal, 1% pony Lobsters - 18% metal, 82% pony Opossums - 97% metal, 3% pony Yogurt - 17% metal, 83% pony Kumquats - 96% metal, 4% pony

According to this neural network, we may need to rethink Star Wars canon. Leia Organa - 96% metal, 4% pony Luke Skywalker - 31% metal, 69% pony Darth Vader - 19% metal, 81% pony Kylo Ren - 18% metal, 82% pony

I had some fun with the metal/pony mashup algorithm and generated more names than will fit here. If you’d like them, enter your email here and I’ll send them to you.

Pony pictures created using General Zoi’s Pony Creator

[grahambinns]

And the Bees Still Buzz — One Year On 🐝

It's been a year. That went quickly. A year since people, just out for a good night at a gig were killed or maimed or mentally scarred by a coward. 22 dead, all of them far too young. More than 800 injured. And let's not beat about the bush here: for those that were there, the road to recovery, mental and physical, is a long one.

And this city is, as it always has been, resolute in the face of the bastards and the cowards, the monsters and the maladjusts who would try to bring it to it's knees. My adopted hometown is going to fall silent today to remember the lost, and think of the wounded. And there will be tears, and sorrow, and grief. Wounds like that heal, but they leave scars.

But this is Manchester. We do things differently here. This is a city that's faced down worse in its time, and stood with its head held high every single day.

I'm reminded of how Japanese Honey Bees defend their hives against hornets: they form a ball around the invader and, well, cook it. Mancunians — whose symbol has, for 175 years, bee a worker bee — gang together too, but with love and kindness and understanding and a togetherness that's a wonder to behold.

I love this town. I love its people. I love the staccato of the northern voices in the streets, the rumble of the derby days and the gentle susurrus in the city's squares on a sun-drenched* summer lunchtime. I'm proud to be one of its residents, one generation removed from being an actual Manc as I am.

Anniversaries like this are awful, and sad, and hopeful, and full of love. And there's no love like Northern love. Not anywhere in the world.

Peace to you all, Manchester. Chin up, r kid.

So come at us again, and again if you must, Time after time we'll rise from the dust, You'll never prevail, not against us, This is Manchester, our Manchester And the bees still buzz ~Ryan Williams

*Sometimes just drenched. It's Manchester, after all

[grahambinns]

Market Street, Manchester https://ift.tt/2vOKujJ

[grahambinns]

It's never too late to be what you might have been

I've been in a reflective mood of late. Questions like "what am I doing with my life?" and "will I ever be able to shift this last 10Kg?" and "who am I as a photographer, what is my voice?" keep popping into my mind, especially late at night, when fatigue overwhelms me. It's been a tiring month, April, for one reason or another.

It's very easy for me to fall into that way of thinking when I'm tired. The ease by which I convince myself of the futility of what I do sometimes staggers even me — it certainly staggers those around me.

So I've resolved that 2018 should be the year of relaxing and finding real joy in photography again. The simple joys: of a great composition, or great light, or the fortuitous happenstance that makes for Cartier Bresson's "Decisive Moment" (or as near as us mere mortals can reach, anyhow).

I want to shoot landscapes again. I'm not a landscape photographer, never will be, but there's something about the purity of a landscape photograph that attracts me. I have enjoyed in years gone by shooting landscapes which deliberately included people — because people are the core of my work — but just shooting landscapes for landscapes' sake would bring me great joy, too, I think.

I also want to conquer my fear of street portraiture. People around here are friendly for the most part, and more than willing to let you make a portrait of them. Who knows: if I were to carry an Instax printer with me I could even give them a copy. This is not an original idea, but it's one that I've been held back from trying for fear of failure.

So, more joy, less fear. That's the motto for the rest of the year. What's yours?

[grahambinns]

Piccadilly Gardens, Manchester https://ift.tt/2qXeoMU

[grahambinns]

Piccadilly Gardens, Manchester https://ift.tt/2vC5FoW