“PROCESS”
Bret victor emails
ASH - Ai Pocket Field Guide
A Pokedex, but real, helping a new generation of Ecology Guardians discover the magical world of nature with Ai and hands on play.
Science Banana on Twitter
School gets a bad rap but it’s actually really impressive - juvenile human brains are constantly learning from any available context and the fact that school manages to halt this process is an engineering feat on par with stopping a lava flow
いしわたり淳治 ＆ 砂原良徳 ＋ やくしまるえつこ「神様のいうとおり」（2010）
いしわたり淳治 ＆ 砂原良徳 ＋ やくしまるえつこ「神様のいうとおり」（2010）from the single “神様のいうとおり”directed by Masashi Kawamura & Takuya Hosogane“神様のいうとおり ”streaming https://kmu.lnk.to/827y... architectural drawings
scott on Twitter
absorbing. everything. whomwhomwhomwhomwhom pic.twitter.com/IJ3qHMj1oQ— scott (@jonsson_scott) March 18, 2023
Formality Considered Harmful
Evo-Devo (Despacito Biology Parody) | A Capella Science
This is how we go from single cells to people.
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EVO-DEVO
Huxley
B. Mac.
Oh Carroll, Carroll
Gould, Stephen Jay yeah
D-D-D-D-Davidson and Peter
See
One cell divide and decide on a thousand fates
Did you ever figure how they know?
B. Mac.
We
Are built of modules combined in a planned out way
Each new piece must be told where to go
Oh
Now there's a science helping us to understand
How our cells encode this architectural plan
Signalling each other with genetic tools oh
Oh yeah
Wow
Phenotype the interface for mouse and man
Genotype the files and the subprograms
What then are the switches, circuit boards and boot code?
Evo-Devo
Looking at the logic in the ways that we grow
Every gene directed by a signal key code
Proteins that can activate, enhance or veto
Evo-Devo
Signals are controlled by other genes that signal
Calculating in a network labyrinthal
Where the heart and liver and the hands and feet go
Signal mapping tells each region what it ought to be yo
With circuits so deeply built upon
They're older than the Paleo
The Paleozoic Era baby
In a crucial pathway changes tend to get torpedoed
Where they go calamity goes
As this cyclopic sheep knows..
See down they cascade like a domino
Like you and I drosophila
The path that makes us optical
Was laid a long long time ago
Back before we blew up the cambrian like a bomb bomb
Now my eye protein can make you see out of your bom bom
And Hedgehog and its relatives like Indian and Sonic
Set up set up in a gradient on segments embryonic
Split forebrains and asymmetric parts depend upon it
Flipping on genetic switches and logic
From devo to evo
Adult and embryo
Mostly don't evolve in the genes of the genome
Safer the mutation aimed at regulation
Keep the building blocks and swap their activation
From devo to evo
Parts have alter egos
Homologs evolved from repeats in the schema
Switch a couple bases in the proper places
You'll be watching flies grow legs out of their faces oh yeah
Evo-Devo
Stick around for Modern Synthesis the sequel
Only by combining can a new theory grow
Evolution and development amigos
Evo-Devo
Signals trigger patterns of complexity so
Switching up the switches of a signalling node
Gives a modular and simple way to evolve
Look at how our spinal segments generate a neat row
Built on a molecular clock
One cycle, one vertebra
One vertebra one vertebra baby
Speeding up its rate is snakes' developmental cheat code
That and where a lizard's feet grow
They turn off distal aminos
Evo-Devo
This is how we go from single cells to people
Every generation and in life primeval
Life in variations endless and beautiful
Badaboom
From devo to evo
Larva to mosquito
Patterns are resolved as the signals proceed yo
Map out a gene with a glow tag
Kill it with a morpholino
Short oligo morpholino baby
From devo to evo
Voyage of the Beagle
Body plans evolve when proteins steer the genome
In this manner life's beauty grows
Aesthetica in vivo
Evo-Devo
Learning representations of life
personal website
Pauling’s models were not merely a visualization tool to help him build intuitions for the molecular configurations of peptides. Rather, his models were precisely machined analog computers that allowed him to empirically evaluate hypotheses at high speed.
Over time, our questions began to veer into the realm of complex systems that are less amenable to analytical modeling, and molecular biology became more and more of an experimental science.
Machine learning tools are only now enabling us to regain the model-driven mode of inquiry we lost during that inflection of complexity.
In their first such proposal, Rosalind Franklin highlighted something akin to a software error – the modelers had failed to encode a chemical rule about the balance of charges along the sugar backbone of DNA and proposed an impossible structure as a result.
Only when they built the model and found that the resulting “bulges” were incompatible with chemical rules did Watson and Crick realize that heterotypic pairs – our well known friends A to T, C to G – not only worked structurally, but confirmed Edwin Chargaff’s experimental ratios4.
These essential foundations of molecular biology were laid by empirical exploration of evidence based models, but they’re rarely found in our modern practice. Rather, we largely develop individual hypotheses based on intuitions and heuristics, then test those hypotheses directly in cumbersome experimental systems.
The inductive bias guiding most experiments was that high-level biological phenomena – heredity, differentiation, development, cell division – could be explained by the action of a relatively small number of molecules.
John von Neumann […] asked, How does one state a theory of pattern vision? And he said, maybe the thing is that you can’t give a theory of pattern vision – but all you can do is to give a prescription for making a device that will see patterns!
In other words, where a science like physics works in terms of laws, or a science like molecular biology, to now, is stated in terms of mechanisms, maybe now what one has to begin to think of is algorithms. Recipes. Procedures. – Sydney Brenner9
By exploring these representations and model behaviors, we can extract insights similar to those gained from testing atomic configurations with a carefully machined structure.
One beautiful aspect of this approach is that the learned representations often reveal relationships between the observations that aren’t explicitly called for during training. For instance, our cell type classifier might naturally learn to group similar cell types near one another, revealing something akin to their lineage structure.
If we continue to explore the learned representation of our cell type classifier, we can use it to test hypotheses in much the same way Pauling, Crick, and countless others tested structural hypotheses with mechanical tools.
Regardless of how incorrect rules find their way into either type of model, the remedy is the same. Models are tools for hypothesis exploration and generation, and real-world experiments are still required for validation.
The main distinction is how those rules are encoded.
This distinction of how rules are derived is then rather small in the grand scheme.
Patrick McKenzie on Twitter
A high leverage pattern I see over and over again: designing processes/tools/etc such that they capture implicit knowledge and make it explicit, legible, and enduring.
Research as Understanding
Canvas Cycle: True 8-bit Color Cycling with HTML5
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Just a few months ago, we launched Trello, a super simple, web-based team coordination system. The feedback has been overwhelmingly positive and adoption has been very strong, even in its early, 1.…
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jsomers.net | I should have loved biology
In biology class, biology wasn't presented as a quest for the secrets of life. The textbooks wrung out the questing.
The Social Dynamics of Programming Together in Dynamicland
Pointing, eye contact, shared attention. A new form of computing that supports face-to-face conversation and learning.
Max Kreminski on Twitter
much HCI work assumes users come in w/ a fixed intent to accomplish a specific task & use tools to fulfill that intent as directly as possiblecreative tools challenge this view: creative intent is always formed *in conversation with* tools/materials https://t.co/5VbOu1EMv3— Max Kreminski (@maxkreminski) May 16, 2019