Lotus: From Yoga to Software
The History of Computing
Nelumbo nucifera, or the sacred lotus, is a plant that grows in flood plains, rivers, and deltas. Their seeds can remain dormant for years and when floods come along, blossom into a colony of plants and flowers. Some of the oldest seeds can be found in China, where they’re known to represent longevity. No surprise, given their level of nitrition and connection to the waters that irrigated crops by then. They also grow in far away lands, all the way to India and out to Australia. The flower is sacred in Hinduism and Buddhism, and further back in ancient Egypt. Padmasana is a Sanskrit term...
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Section 230 and the Concept of Internet Exceptionalism
The History of Computing
We covered computer and internet copyright law in a previous episode. That type of law began with interpretations that tried to take the technology out of cases so they could be interpreted as though what was being protected was a printed work, or at least it did for a time. But when it came to the internet, laws, case law, and their knock-on effects, the body of jurisprudence work began to diverge. Safe Harbor mostly refers to the Online Copyright Infringement Liability Limitation Act, or OCILLA for short, was a law passed in the late 1980s that shields online portals and internet...
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Bluetooth: From Kings to Personal Area Networks
The History of Computing
Bluetooth The King Ragnar Lodbrok was a legendary Norse king, conquering parts of Denmark and Sweden. And if we’re to believe the songs, he led some of the best raids against the Franks and the the loose patchwork of nations Charlemagne put together called the Holy Roman Empire. We use the term legendary as the stories of Ragnar were passed down orally and don’t necessarily reconcile with other written events. In other words, it’s likely that the man in the songs sung by the bards of old are likely in fact a composite of deeds from many a different hero of the norse. Ragnar...
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One History Of 3D Printing
The History of Computing
One of the hardest parts of telling any history, is which innovations are significant enough to warrant mention. Too much, and the history is so vast that it can't be told. Too few, and it's incomplete. Arguably, no history is ever complete. Yet there's a critical path of innovation to get where we are today, and hundreds of smaller innovations that get missed along the way, or are out of scope for this exact story. Children have probably been placing sand into buckets to make sandcastles since the beginning of time. Bricks have survived from round 7500BC in modern-day Turkey where humans made...
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Adobe: From Pueblos to Fonts and Graphics to Marketing
The History of Computing
The Mogollon culture was an indigenous culture in the Western United States and Mexico that ranged from New Mexico and Arizona to Sonora, Mexico and out to Texas. They flourished from around 200 CE until the Spanish showed up and claimed their lands. The cultures that pre-existed them date back thousands more years, although archaeology has yet to pinpoint exactly how those evolved. Like many early cultures, they farmed and foraged. As they farmed more, their homes become more permanent and around 800 CE they began to create more durable homes that helped protect them from wild swings in the...
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The Evolution of Fonts on Computers
The History of Computing
Gutenburg shipped the first working printing press around 1450 and typeface was born. Before then most books were hand written, often in blackletter calligraphy. And they were expensive. The next few decades saw Nicolas Jensen develop the Roman typeface, Aldus Manutius and Francesco Griffo create the first italic typeface. This represented a period where people were experimenting with making type that would save space. The 1700s saw the start of a focus on readability. William Caslon created the Old Style typeface in 1734. John Baskerville developed Transitional typefaces in 1757....
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Flight Part II: From Balloons to Autopilot to Drones
The History of Computing
In our previous episode, we looked at the history of flight - from dinosaurs to the modern aircraft that carry people and things all over the world. Those helped to make the world smaller, but UAVs and drones have had a very different impact in how we lead our lives - and will have an even more substantial impact in the future. That might not have seemed so likely in the 1700s, though - when unmann Unmanned Aircraft Napoleon conquered Venice in 1797 and then ceded control to the Austrians the same year. He then took it as part of a treaty in 1805 and established the first Kingdom of Italy....
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Flight: From Dinosaurs to Space
The History of Computing
Humans have probably considered flight since they found birds. As far as 228 million years ago, the Pterosaurs used flight to reign down onto other animals from above and eat them. The first known bird-like dinosaur was the Archaeopteryx, which lived around 150 million years ago. It’s not considered an ancestor of modern birds - but other dinosaurs from the same era, the theropods, are. 25 million years later, in modern China, the Confuciusornis sanctus had feathers and could have flown. The first humans wouldn’t emerge from Africa until 23 million years later. By the 2300s BCE, the...
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SABRE and the Travel Global Distribution System
The History of Computing
Computing has totally changed how people buy and experience travel. That process seemed to start with sites that made it easy to book travel, but as with most things we experience in our modern lives, it actually began far sooner and moved down-market as generations of computing led to more consumer options for desktops, the internet, and the convergence of these technologies. Systems like SABRE did the original work to re-think travel - to take logic and rules out of the heads of booking and travel agents and put them into a digital medium. In so doing, they paved the way for future...
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The Story of Intel
The History of Computing
We’ve talked about the history of microchips, transistors, and other chip makers. Today we’re going to talk about Intel in a little more detail. Intel is short for Integrated Electronics. They were founded in 1968 by Robert Noyce and Gordon Moore. Noyce was an Iowa kid who went off to MIT to get a PhD in physics in 1953. He went off to join the Shockley Semiconductor Lab to join up with William Shockley who’d developed the transistor as a means of bringing a solid-state alternative to vacuum tubes in computers and amplifiers. Shockley became erratic after he won the Nobel Prize and...
info_outlineR is the 18th level of the Latin alphabet. It represents the rhotic consonant, or the r sound. It goes back to the Greek Rho, the Phoenician Resh before that and the Egyptian rêš, which is the same name the Egyptians had for head, before that. R appears in about 7 and a half percent of the words in the English dictionary.
And R is probably the best language out there for programming around various statistical and machine learning tasks. We may use tools like Tensorflow imported to languages like python to prototype but R is incredibly performant for all the maths. And so it has become an essential piece of software for data scientists.
The R programming language was created in 1993 by two statisticians Robert Gentleman, and Ross Ihaka at the University of Auckland, New Zealand. It has since been ported to practically every operating system and is available at r-project.org. Initially called "S," the name changed to "R" to avoid a trademark issue with a commercial software package that we’ll discuss in a bit. R was primarily written in C but used Fortran and since even R itself.
And there have been statistical packages since the very first computers were used for math.
IBM in fact packaged up BMDP when they first started working on the idea at UCLA Health Computing Facility. That was 1957. Then came SPSS out of the University of Chicago in 1968. And the same year, John Sall and others gave us SAS, or Statistical Analysis System) out of North Carolina State University. And those evolved from those early days through into the 80s with the advent of object oriented everything and thus got not only windowing interfaces but also extensibility, code sharing, and as we moved into the 90s, acquisition’s. BMDP was acquired by SPSS who was then acquired by IBM and the products were getting more expensive but not getting a ton of key updates for the same scientific and medical communities.
And so we saw the upstarts in the 80s, Data Desk and JMP and others. Tools built for windowing operating systems and in object oriented languages. We got the ability to interactively manipulate data, zoom in and spin three dimensional representations of data, and all kinds of pretty aspects. But they were not a programmers tool.
S was begun in the seventies at Bell Labs and was supposed to be a statistical MATLAB, a language specifically designed for number crunching. And the statistical techniques were far beyond where SPSS and SAS had stopped. And with the breakup of Ma Bell, parts of Bell became Lucent, which sold S to Insightful Corporation who released S-PLUS and would later get bought by TIBCO. Keep in mind, Bell was testing line quality and statistics and going back to World War II employed some of the top scientists in those fields, ones who would later create large chunks of the quality movement and implementations like Six Sigma. Once S went to a standalone software company basically, it became less about the statistics and more about porting to different computers to make more money.
Private equity and portfolio conglomerates are, by nature, after improving the multiples on a line of business. But sometimes more statisticians in various feels might feel left behind. And this is where R comes into the picture. R gained popularity among statisticians because it made it easier to write complicated statistical algorithms without learning an entire programming language. Its popularity has grown significantly since then. R has been described as a cross between MATLAB and SPSS, but much faster.
R was initially designed to be a language that could handle statistical analysis and other types of data mining, an offshoot of which we now call machine learning. R is also an open-source language and as with a number of other languages has plenty of packages available through a package repository - which they call CRAN (Comprehensive R Archive Network). This allows R to be used in fields outside of statistics and data science or to just get new methods to do math that doesn’t belong in the main language.
There are over 18,000 packages for R. One of the more popular is ggplot2, an open-source data visualization package. data.table is another that performs programmatic data manipulation operations. dplyr provides functions designed to enable data frame manipulation in an intuitive manner. tidyr helps create tidier data. Shiny generates interactive web apps. And there are plenty of packages to make R easier, faster, and more extensible.
By 2015, more than 10 million people used R every month and it’s now the 13th most popular language in use. And the needs have expanded. We can drop r scripts into other programs and tools for processing. And some of the workloads are huge. This led to the development of parallel computing, specifically using MPI (Message Passing Interface).
R programming is one of the most popular languages used for statistical analysis, statistical graphics generation, and data science projects. There are other languages or tools for specific uses but it’s even started being used in those.
The latest version, R 4.1.2, was released on 21/11/01. R development, as with most thriving open source solutions, is guided by a group of core developers supported by contributions from the broader community. It became popular because it provides all essential features for data mining and graphics needed for academic research and industry applications and because of the pluggable and robust and versatile nature.
And projects like tensorflow and numpy and sci-kit have evolved for other languages. And there are services from companies like Amazon that can host and process assets from both, both using unstructured databases like NoSQL or using Jupyter notebooks.
A Jupyter Notebook is a JSON document, following a versioned schema that contains an ordered list of input/output cells which can contain code, text (using Markdown), formulas, algorithms, plots and even media like audio or video. Project Jupyter was a spin-off of iPython but the goal was to create a language-agnostic tool where we could execute aspects in Ruby or Haskel or Python or even R. This gives us so many ways to get our data into the notebook, in batches or deep learning environments or whatever pipeline needs to be built based on an organization’s stack. Especially if the notebook has a frontend based on Amazon SageMaker Notebooks, Google's Colaboratory and Microsoft's Azure Notebook.
Think about this. 25% of the languages lack a rhotic consonant. Sometimes it seems like we’ve got languages that do everything or that we’ve built products that do everything. But I bet no matter the industry or focus or sub-specialty, there’s still 25% more automation or instigation into our own data to be done. Because there always will be.