Monday, January 23, 2017

Getting to Yes! on the Bicycle

Photo processed thru Deepdream python script, with TensorFlow

Once I posted on this blog about a strategy of firmly yelling back at anyone who suggested that I and my bicycle don't belong on the road: "NO!" I suggested, yelled firmly and assertively back, would state the case clearly and effectively. It felt satisfying the few times I tried it out, but deep down it felt wrong. In its negativity, in its reactiveness, in its simple negation, it doesn't really work in the moment. Furthermore, it is unsatisfying in the moments and hours in the aftermath of such encounters, amounting to a visceral denial of something that actually occurred, in addition to a burial or repression of the feelings that go along with it.

While currently reading Tara Brach's "Radical Acceptance", it dawned on me that being open to the experience, recognizing it for what it is, then welcoming the feelings that accompany it with honest compassion, is potentially a more effective method for responding to road insults. If he's angry and I'm afraid, then that is what it is. It's about looking at reality openly, possibly differently, with less processing, rationalization, and denial, with more awareness, mindfulness, if you will. Too see what's there, be in the moment, and alive. Even when the moment itself has previously held strong negative potential. This, too, as Tara Brach might observe.

The photo above is an example of some recent forays I've taken into learning about Machine Learning (ML). So far, the math is about 80% over my head, but I'm chipping away at it through playful learning, which keeps me engaged. Instead of beating myself up for not really deeply understanding tensors or softmax so far, I play with the code and accept myself hack by hack and have fun with it. In my experience, the light usually goes on eventually, perhaps without me consciously willing it, or fighting against the setbacks, but just enjoying the act of creating trippy photos like the one above. Perhaps I will sneak up on understanding TensorFlow that way. Perhaps if part of our vision imagines sneaky weasel lurking beneath the share the road signs, we can get to "Yes" on our bicycles.

Why ML? I have this fantasy of programming and soldering something for bicycles, for cyclists. Raspberry Pi, Android, Python scripts, data overages, processing my commute through TensorFlow, I'm just saying yes, yes, yes, even if that's all crazy.


  1. I have no idea what you are talking about, but that picture is like things nightmares are made of, so I guess we are on the same page. Ride smile and repeat. =)

    1. John, once I get some cameras and sensors mounted on my bike, prepare to have your mind blown. Or, none of it will work, but there will be much bike riding involved, so every little thing will be all right.

  2. Great picture. More to come I hope, hope, hope...


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