Core memory is the simplest kind of main memory you could build from really basic components (no chips!), and easy enough to understand well.
I always wanted to understand computers to their core (no pun intended), and to build one myself from the ground up. The following kit seems to make it quite fun to do that for working memory (RAM), while being physically large scale enough to allow for inspecting and measuring what is going on.
In the picture above, the core memory itself is just the wire net on the left with the small rings (that look like beads/dots). Enjoyably simple.
The chips on the board are there to easily write and read the hand-built memory and to provide an interface to an Arduino (or other microcontroller).
The kit comes with all the necessary components, including the PCB, magnetic cores, wires, etc., while requiring some soldering to assemble it. Head over to Jussi’s blog to read the complete documentation for the shield and how to get one.
Parsing the structured text files, provided by the Unicode consortium, at each startup is too inefficient, and merely storing the parsed text into a simple integer array wastes too much memory.
A more efficient storage uses a dictionary-like approach, to compress the needed data using a few layers of indirections, while still giving array-like performance with constant (and negligible) overhead.
In the following, I’ll briefly present the solution I found.
Before starting to learn Prolog I used various logic based systems, such as the SPIN model checker, or reasoners that work on ontologies encoded in OWL. The latter of them to have it reason about (visual) objects in RoboCup.
Prolog however seems to encode many problems in a more natural and fluent way, so I set out to make a few toy examples to test how well I could make it work and get a feel for its advantages and limitations.
Many concepts in AI are implicitly based on specific formulations / terminology as used for Prolog or its derivatives. Vague sounding words / expressions, often taken from general contexts, really mean something rather specific, and learning about Prolog sharpens the understanding of these wordings.
Analyzing these heatmaps can point out undesired correlations in the training data, between samples and labels. For example, an image classifiers for train track might rely on objects that are present in each picture (such as , while not being present in pictures of counter examples for horses. This artifact in the collected data set may be subtle, and not noticeable to a human, but would be visible on the heatmap that highlights the critical features in each image that drove the classification.
Ben Eater has created an excellent 8-bit computer that is true to the essential architecture of modern computers, yet is simple enough to fit on a few breadboards. It uses DIP-switches and push buttons as inputs, and LEDs and 7-segment displays as (debug) outputs. Even step-wise execution by stepping the computer clock is possible, such that every part of the computer can be observed as it functions and the internal state and memory can be modified by switches.
There are many explanations of pull-up or pull-down resistors that gloss a bit too much over the details, keeping you in doubt about how they really work, especially in conjunction with microcontrollers.
To improve our understanding, we will use a simplified schematic that models a microcontroller input-pin connected to a switch and a pull-up resistor. Then we calculate the voltages on the input-pin resulting from a closed or open switch.