Moore’s Law states that the number of transistors on an integrated circuit doubles every two years or so. That has produced an exponential increase in the number of transistors on microchips and continues to do so.
But if an observer today was to measure this rate of increase, it would be straightforward to extrapolate backwards and work out when the number of transistors on a chip was zero. In other words, the date when microchips were first developed in the 1960s.
What happens if you extrapolate backwards to the point of no complexity–the origin of life?
Sharov and Gordon say that the evidence by this measure is clear. “Linear regression of genetic complexity (on a log scale) extrapolated back to just one base pair suggests the time of the origin of life = 9.7 ± 2.5 billion years ago,” they say.
And since the Earth is only 4.5 billion years old, that raises a whole series of other questions. Not least of these is how and where did life begin.
It's really a deal if you think about it, just $46 per night, and you're even allowed in the house once per day to shower, with a vague opportunity to maybe eat inside. I'm totally not planning on doing this exact same thing in my back porch. Definitely not.
Me: "Thanks for the hug, kiddo."
Athena: "Your hand is so cuddly."
Me: "That's sweet."
Athena: "Because your knuckles are sooooo hairy."
Mostly we mess around with neural nets until they can hallucinate tentacles, dog heads, and other Lovecraftian horrors out of whatever images we throw at them.
Seriously, monkeying around with this system has eaten up a serious amount of workhours at Google lately. And some of the images coming out of it are truly inspired. Take a look at the whole gallery.
For the last few weeks, Googlers have been obsessed with an internal visualization tool that Alexander Mordvintsev in our Zurich office created to help us visually understand some of the things happening inside our deep neural networks for computer vision. The tool essentially starts with an image, runs the model forwards and backwards, and then makes adjustments to the starting image in weird and magnificent ways.
In the same way that when you are staring at clouds, and you can convince yourself that some part of the cloud looks like a head, maybe with some ears, and then your mind starts to reinforce that opinion, by seeing even more parts that fit that story ("wow, now I even see arms and a leg!"), the optimization process works in a similar manner, reinforcing what it thinks it is seeing. Since the model is very deep, we can tap into it at various levels and get all kinds of remarkable effects.
Alexander, , and Mike Tyka wrote up a very nice blog post describing how this works:
There's also a bigger album of more of these pictures linked from the blog post:
I just picked a few of my favorites here.
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Do you know how many words your child spoke today?