The First Programming Languages: Crash Course Computer Science #11

This episode is brought to you by CuriosityStream. Hi, I’m Carrie Anne and welcome to CrashCourse
Computer Science! So far, for most of this series, we’ve focused
on hardware — the physical components of computing — things like: electricity and
circuits, registers and RAM, ALUs and CPUs. But programming at the hardware level is cumbersome
and inflexible, so programmers wanted a more versatile way to program computers – what
you might call a “softer” medium. That’s right, we’re going to talk about
Software! INTRO In episode 8, we walked through a simple program
for the CPU we designed. The very first instruction to be executed,
the one at memory address 0, was 0010 1110. As we discussed, the first four bits of an
instruction is the operation code, or OPCODE for short. On our hypothetical CPU, 0010 indicated a
LOAD_A instruction — which moves a value from memory into Register A. The second set of four bits defines the memory
location, in this case, 1110, which is 14 in decimal. So what these eight numbers really mean is
“LOAD Address 14 into Register A”. We’re just using two different languages. You can think of it like English and Morse
Code. “Hello” and “…. . .-.. .-.. —” mean
the same thing — hello! — they’re just encoded differently. English and Morse Code also have different
levels of complexity. English has 26 different letters in its alphabet
and way more possible sounds. Morse only has dots and dashes. But, they can convey the same information,
and computer languages are similar. As we’ve seen, computer hardware can only
handle raw, binary instructions. This is the “language” computer processors
natively speak. In fact, it’s the only language they’re
able to speak. It’s called Machine Language or Machine
Code. In the early days of computing, people had
to write entire programs in machine code. More specifically, they’d first write a
high-level version of a program on paper, in English, for example… “retrieve the next sale from memory, then
add this to the running total for the day, week and year, then calculate any tax to be
added” …and so on. An informal, high-level description of a program
like this is called Pseudo-Code. Then, when the program was all figured out
on paper, they’d painstakingly expand and translate it into binary machine code by hand,
using things like opcode tables. After the translation was complete, the program
could be fed into the computer and run. As you might imagine, people quickly got fed
up with this process. So, by the late 1940s and into the 50s, programmers
had developed slightly higher-level languages that were more human-readable. Opcodes were given simple names, called mnemonics,
which were followed by operands, to form instructions. So instead of having to write instructions
as a bunch of 1’s and 0’s, programmers could write something like “LOAD_A 14”. We used this mnemonic in Episode 8 because
it’s so much easier to understand! Of course, a CPU has no idea what “LOAD_A
14” is. It doesn’t understand text-based language,
only binary. And so programmers came up with a clever trick. They created reusable helper programs, in
binary, that read in text-based instructions, and assemble them into the corresponding binary
instructions automatically. This program is called — you guessed it — an
Assembler. It reads in a program written in an Assembly
Language and converts it to native machine code. “LOAD_A 14” is one example of an assembly
instruction. Over time, Assemblers gained new features
that made programming even easier. One nifty feature is automatically figuring
out JUMP addresses. This was an example program I used in episode
8:Notice how our JUMP NEGATIVE instruction jumps to address 5, and our regular JUMP goes
to address 2. The problem is, if we add more code to the
beginning of this program, all of the addresses would change. That’s a huge pain if you ever want to update
your program! And so an assembler does away with raw jump
addresses, and lets you insert little labels that can be jumped to. When this program is passed into the assembler,
it does the work of figuring out all of the jump addresses. Now the programmer can focus more on programming
and less on the underlying mechanics under the hood enabling more sophisticated things
to be built by hiding unnecessary complexity. As we’ve done many times in this series,
we’re once again moving up another level of abstraction. A NEW LEVEL OF ABSTRACTION! However, even with nifty assembler features
like auto-linking JUMPs to labels, Assembly Languages are still a thin veneer over machine
code. In general, each assembly language instruction
converts directly to a corresponding machine instruction – a one-to-one mapping – so
it’s inherently tied to the underlying hardware. And the assembler still forces programmers
to think about which registers and memory locations they will use. If you suddenly needed an extra value, you
might have to change a lot of code to fit it in. Let’s go to the Thought Bubble. This problem did not escape Dr. Grace Hopper. As a US naval officer, she was one of the
first programmers on the Harvard Mark 1 computer, which we talked about in Episode 2. This was a colossal, electro-mechanical beast completed in 1944 as part of the allied war effort. Programs were stored and fed into the computer
on punched paper tape. By the way, as you can see, they “patched”
some bugs in this program by literally putting patches of paper over the holes on the punch
tape. The Mark 1’s instruction set was so primitive,
there weren’t even JUMP instructions. To create code that repeated the same operation
multiple times, you’d tape the two ends of the punched tape together, creating a physical
loop. In other words, programming the Mark 1 was
kind of a nightmare! After the war, Hopper continued to work at
the forefront of computing. To unleash the potential of computers, she
designed a high-level programming language called “Arithmetic Language Version 0”,
or A-0 for short. Assembly languages have direct, one-to-one
mapping to machine instructions. But, a single line of a high-level programming
language might result in dozens of instructions being executed by the CPU. To perform this complex translation, Hopper
built the first compiler in 1952. This is a specialized program that transforms
“source” code written in a programming language into a low-level language, like assembly
or the binary “machine code” that the CPU can directly process. Thanks, Thought Bubble. So, despite the promise of easier programming,
many people were skeptical of Hopper’s idea. She once said, “I had a running compiler
and nobody would touch it. … they carefully told me, computers could
only do arithmetic; they could not do programs.” But the idea was a good one, and soon many
efforts were underway to craft new programming languages — today there are hundreds! Sadly, there are no surviving examples of
A-0 code, so we’ll use Python, a modern programming language, as an example. Let’s say we want to add two numbers and
save that value. Remember, in assembly code, we had to fetch
values from memory, deal with registers, and other low-level details. But this same program can be written in python
like so: Notice how there are no registers or memory
locations to deal with — the compiler takes care of that stuff, abstracting away a lot
of low-level and unnecessary complexity. The programmer just creates abstractions for
needed memory locations, known as variables, and gives them names. So now we can just take our two numbers, store
them in variables we give names to — in this case, I picked a and b but those variables
could be anything – and then add those together, saving the result in c, another variable I
created. It might be that the compiler assigns Register
A under the hood to store the value in a, but I don’t need to know about it! Out of sight, out of mind! It was an important historical milestone,
but A-0 and its later variants weren’t widely used. FORTRAN, derived from “Formula Translation”,
was released by IBM a few years later, in 1957, and came to dominate early computer
programming. John Backus, the FORTRAN project director,
said: “Much of my work has come from being lazy. I didn’t like writing programs, and so … I
started work on a programming system to make it easier to write programs.” You know, typical lazy person. They’re always creating their own programming
systems. Anyway, on average, programs written in FORTRAN
were 20 times shorter than equivalent handwritten assembly code. Then the FORTRAN Compiler would translate
and expand that into native machine code. The community was skeptical that the performance
would be as good as hand written code, but the fact that programmers could write more
code more quickly, made it an easy choice economically: trading a small increase in
computation time for a significant decrease in programmer time. Of course, IBM was in the business of selling
computers, and so initially, FORTRAN code could only be compiled and run on IBM computers. And most programing languages and compilers
of the 1950s could only run on a single type of computer. So, if you upgraded your computer, you’d
often have to re-write all the code too! In response, computer experts from industry,
academia and government formed a consortium in 1959 — the Committee on Data Systems Languages,
advised by our friend Grace Hopper — to guide the development of a common programming language
that could be used across different machines. The result was the high-level, easy to use,
Common Business-Oriented Language, or COBOL for short. To deal with different underlying hardware,
each computing architecture needed its own COBOL compiler. But critically, these compilers could all
accept the same COBOL source code, no matter what computer it was run on. This notion is called write once, run anywhere. It’s true of most programming languages
today, a benefit of moving away from assembly and machine code, which is still CPU specific. The biggest impact of all this was reducing
computing’s barrier to entry. Before high level programming languages existed,
it was a realm exclusive to computer experts and enthusiasts. And it was often their full time profession. But now, scientists, engineers, doctors, economists,
teachers, and many others could incorporate computation into their work . Thanks to these languages, computing went
from a cumbersome and esoteric discipline to a general purpose and accessible tool. At the same time, abstraction in programming
allowed those computer experts – now “professional programmers” – to create increasingly
sophisticated programs, which would have taken millions, tens of millions, or even more lines
of assembly code. Now, this history didn’t end in 1959. In fact, a golden era in programming language
design jump started, evolving in lockstep with dramatic advances in computer hardware. In the 1960s, we had languages like ALGOL,
LISP and BASIC. In the 70’s: Pascal, C and Smalltalk were
released. The 80s gave us C++, Objective-C, and Perl. And the 90’s: python, ruby, and Java. And the new millennium has seen the rise of
Swift, C#, and Go – not to be confused with Let it Go and Pokemon Go. Anyway, some of these might sound familiar
— many are still around today. It’s extremely likely that the web browser
you’re using right now was written in C++ or Objective-C. That list I just gave is the tip of the iceberg. And languages with fancy, new features are
proposed all the time. Each new language attempts to leverage new
and clever abstractions to make some aspect of programming easier or more powerful, or
take advantage of emerging technologies and platforms, so that more people can do more
amazing things, more quickly. Many consider the holy grail of programming
to be the use of “plain ol’ English”, where you can literally just speak what you
want the computer to do, it figures it out, and executes it. This kind of intelligent system is science
fiction… for now. And fans of 2001: A Space Odyssey may be okay
with that. Now that you know all about programming languages,
we’re going to deep dive for the next couple of episodes, and we’ll continue to build
your understanding of how programming languages, and the software they create, are used to
do cool and unbelievable things. See you next week. Hey guys, this week’s episode was brought
to you by CuriosityStream which is a streaming service full of documentaries and non­fiction
titles from some really great filmmakers, including exclusive originals. I just watched a great series called “Digits”
hosted by our friend Derek Muller. It’s all about the Internet – from its origins,
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like that John Green guy you keep mentioning in the comments. And Curiosity Stream offers unlimited access
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27 Replies to “The First Programming Languages: Crash Course Computer Science #11

  1. Back in the days: NO WAY we're using compilers. Too high level.

    Today: Compilers are like the lowest level thing ever… Python is the best!

    In the future: Interpreted languages are like programming on the metal! Just use plain english!

  2. Damn… This video is so good. Almost all my doubts about programming languages are cleared, thanks to this.

  3. the videos present some of the extraordinary details about computing in a simplistic way but i
    think i would appreciate if there is any way we can revise all that was taught from beginning. Tests or Assessments would be
    a great way to start with. I request you to provide short tests over the topics. Best Of Luck. Long live this channel.

  4. The painstaking effort the producers went to, in order to find so many "diverse" engineer pictures, is hilarious.

  5. One point is missed,how do i write 1s and 0s in the ram in voltages. Ram is loaded with predefined voltage instruction when i boot the computer. But how do i change the voltages to represent a different instruction?

  6. LiveCode would like a word with you regarding plain English being the realm of science fiction.

    It's been around since the 80s as well, under different names.

  7. …and now Fortran still remains a language of choice for heaviest numerical computations. Yes, nowaday codes do look much different. Not because they really have to, just for the sake of clarity, reusability etc.

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