Bret Victor - Media for Thinking the Unthinkable

I look for different ways of representing the problem domain or the desired outcome. This will improve your vocabulary/toolbox for reasoning about and subdividing them into more approachable units. For some inspiration, I recommend watching this talk by Bret Victor ▶ this video (Media for Thinking the Unthinkable)

For the people asking who is Bret Victor: When "What are your favourite tech talks of all time?" is asked on HN, he gets more mentions than anyone else, usually. Some of his talks: Inventing on Principle link The Future of Programming link The Humane Representation of Thought link Media for Thinking the Unthinkable ▶ this video Seeing Spaces link Drawing Dynamic Visualizations link link

What a great article - and the way it's presented is insanely great. It reminds me of a talk given by Bret Victor ▶ this video (Media for Thinking the Unthinkable)

You make a great point about notations becoming more abstract for more advanced use cases, indeed this may be a "natural" process. However I'd like to add nuance with the observation that Bret may not be suggesting that we reverse the trend from abstract notation back to concrete visualizations. Actually I think he is calling for a rich variety of visual/concrete notations and symbolic/abstract notations that work in concert together to express our dynamic concepts, and extending those with the use of the dynamism of the computing medium to add interactive aspects to the notations (ala "Magic Ink"). His talk "Media for thinking the unthinkable" goes into detail on this, especially from minute 10 to minute 17: ▶ this video The essay Magic Ink: INFORMATION SOFTWARE AND THE GRAPHICAL INTERFACE also goes into detail about this topic link

There is the “tools for thought” space I came across where some work felt compelling for computer enabled teaching and learning. Bret Victor [0], Andy Matuschak [1], and Seymour Papert [2] to point to a few names. [0] ▶ this video [1] link [2] link

I cool a wafer scale with a liquid that boils at 43 C and immersing the wafer in this liquid. The bubbles (cavitation) of the boiling liquid should not damage the surface layers of the wafer, of course. This boiling liquid is further cooled by water and a sonic heat pump moving the heat into a water tank where the stored heat is used for showers or cooking [1]. Given 45 trillion transistors (45x10e12) times 3 femtojoule (3x10e-15) to switch each transistor at 1 Ghz (10e9) you get 1.000.000 joules/sec = 1 megawatt. These are ball-park numbers, back of the envelope calculations. In reality I make full physics simulations and electrical SPICE simulations of the entire wafer on a supercomputer aka on the wafer scale integration FPGA prototypes and the wafer supercomputer itself. The EDA (Electronic Design Automation) software tools we write ourselves in Smalltalk and Ometa, and these also need our own supercomputer to run on. Of course the feedback loops are Bret Victor style visualizers [3][2]. Apple Silicon or this small company demonstrate that only with custom EDA tools can you design ultra-low power transistors to prevent our wafer to melt. The FPGA prototype is a few thousand Cyclone 10 or Polarfire FPGA's with a few terabytes/sec memory bandwidth or a cluster of Mac Studio Ultra's networked together in a Slim Fly network that can double as a neighbourhood solar smart grid [5]. You need a dinosaur egg to design a dinosaur, or is is it the other way around? [6] A TDP (Therma

on one level, yes on another, they're down right scary and overwhelming, or at least dumbfounding and perplexing ..thinking specifically about The Humane Representation of Thought link I had to stop it half way through.. :-/ see also Seeing Spaces link and Media for Thinking the Unthinkable ▶ this video

This is really significant. At this moment in history, the growth of computer power has made a bunch of important signal-processing and statistical tasks just feasible, so we are seeing things like self-driving cars, superhuman image recognition, and so on. But it's been very difficult to take advantage of the available computational power, because it's in the form of GPUs and clusters. TensorFlow is a library designed to make it easy to do exactly these things, and to scale them with your available computing power, along with libraries of the latest tricks in neural networks, machine learning (which is pretty close to "statistics"). As a bonus, it has built-in automatic differentiation so that you can run gradient descent on any algorithm — which means that you can just write a program to evaluate the goodness of a solution and efficiently iterate it to a local maximum. If you do this enough times, hopefully you'll find a global maximum. There are a variety of other numerical optimization algorithms, but gradient descent is very simple and broadly applicable. And it runs in IPython (now Jupyter), which is a really amazingly powerful way to do exploratory software development. If you haven't tried Jupyter/IPython, google up a screencast and take a look. I'm just repeating the stuff that's on the home page in a different form, but this is a really big deal . Most of the most significant software of the next five or ten years is going to be built in TensorFlow or something like

In principle, yes, but in practice, no. Tools let us think previously unthinkable thoughts. See Bret Victor's Media for Thinking the Unthinkable [1], where he quotes al-Khwarizmi from the ninth century: What is the square which when taken with ten of its roots will give a sum of thirty nine? Now the roots in the problem before us are ten. Therefore take five, which multiplied by itself gives twenty five, and amount you add to thirty nine to give sixty four. Having taken the square root of this which is eight, subtract from this half the roots, five leaving three. The number three represents one root of this square, which itself, of course, is nine. Nine therefore gives the square. He's solving x^2+10x = 39 by completing the square. [1] ▶ this video