Crash Course Computer Science Episode Rating Graph

So now that we’ve built and programmed our very own CPU, we’re going to take a step back and look at how CPU speeds have rapidly increased from just a few cycles per second to gigahertz! Some of that improvement, of course, has come from faster and more efficient transistors, but a number hardware designs have been implemented to boost performance. And you’ve probably heard or read about a lot of these - they’re the buzz words attached to just about every new CPU release - terms like instruction pipelining, cache, FLOPS, superscalar, branch prediction, multi-core processors, and even super computers! These designs are pretty complicated, but the fundamental concepts behind them are not. So bear with us as we introduce a lot of new terminology including what might just be the best computer science term of all time: the dirty bit. Let us explain.

Since Joseph Marie Jacquard’s textile loom in 1801, there has been a demonstrated need to give our machines instructions. In the last few episodes, our instructions were already in our computer’s memory, but we need to talk about how they got there - this is the heart of programming. Today, we’re going to look at the history of programming and the innovations that brought us from punch cards and punch paper tape to plugboards and consoles of switches. These technologies will bring us to the mid 1970s and the start of home computing, but they had limitations, and what was really needed was an easier and more accessible way to write programs - programming languages. Which we’ll get to next week.

So we ended last episode with programming at the hardware level with things like plugboards and huge panels of switches, but what was really needed was a more versatile way to program computers - software! For much of this series we’ve been talking about machine code, or the 1’s and 0’s our computers read to perform operations, but giving our computers instructions in 1’s and 0’s is incredibly inefficient, and a “higher-level” language was needed. This led to the development of assembly code and assemblers that allow us to use operands and mnemonics to more easily write programs, but assembly language is still tied to underlying hardware. So by 1952 Navy officer Grace Hopper had helped created the first high-level programming language A-0 and compiler to translate that code to our machines. This would eventually lead to IBM’s Fortran and then a golden age of computing languages over the coming decades. Most importantly, these new languages utilized new abstractions to make programming easier and more powerful giving more and more people the ability to create new and amazing things.

Today we’re going to take a step back from programming and discuss the person who formulated many of the theoretical concepts that underlie modern computation - the father of computer science himself: Alan Turing. Now normally we try to avoid “Great Man" history in Crash Course because truthfully all milestones in humanity are much more complex than just an individual or through a single lens - but for Turing we are going to make an exception. From his theoretical Turing Machine and work on the Bombe to break Nazi Enigma codes during World War II, to his contributions in the field of Artificial Intelligence (before it was even called that), Alan Turing helped inspire the first generation of computer scientists - despite a life tragically cut short.

Today, we’re going to talk about how HUGE programs with millions of lines of code like Microsoft Office are built. Programs like these are way too complicated for a single person, but instead require teams of programmers using the tools and best practices that form the discipline of Software Engineering. We'll talk about how large programs are typically broken up into into function units that are nested into objects known as Object Oriented Programming, as well as how programmers write and debug their code efficiently, document and share their code with others, and also how code repositories are used to allow programmers to make changes while mitigating risk.

So you may have heard of Moore's Law and while it isn't truly a law it has pretty closely estimated a trend we've seen in the advancement of computing technologies. Moore's Law states that we'll see approximately a 2x increase in transistors in the same space every two years, and while this may not be true for much longer, it has dictated the advancements we've seen since the introduction of transistors in the mid 1950s. So today we're going to talk about those improvements in hardware that made this possible - starting with the third generation of computing and integrated circuits (or ICs) and printed circuit boards (or PCBs). But as these technologies advanced a newer manufacturing process would bring us to the nanoscale manufacturing we have today - photolithography.

So last episode we talked about some basic file formats, but what we didn’t talk about is compression. Often files are way too large to be easily stored on hard drives or transferred over the Internet - the solution, unsurprisingly, is to make them smaller. Today, we’re going to talk about lossless compression, which will give you the exact same thing when reassembled, as well as lossy compression, which uses the limitations of human perception to remove less important data. From listening to music and sharing photos, to talking on the phone and even streaming this video right now the ways we use the Internet and our computing devices just wouldn’t be possible without the help of compression.

Today we begin our discussion of computer graphics. So we ended last episode with the proliferation of command line (or text) interfaces, which sometimes used screens, but typically electronic typewriters or teletypes onto paper. But by the early 1960s a number of technologies were introduced to make screens much more useful from cathode ray tubes and graphics cards to ASCII art and light pens. This era would mark a turning point in computing - computers were no longer just number crunching machines, but potential assistants interactively augmenting human tasks. This was the dawn of graphical user interfaces which we’ll cover more in a few episodes.

Today we're going to talk about the birth of personal computing. Up until the early 1970s components were just too expensive, or underpowered, for making a useful computer for an individual, but this would begin to change with the introduction of the Altair 8800 in 1975. In the years that follow, we'll see the founding of Microsoft and Apple and the creation of the 1977 Trinity: The Apple II, Tandy TRS-80, and Commodore PET 2001. These new consumer oriented computers would become a huge hit, but arguably the biggest success of the era came with the release of the IBM PC in 1981. IBM completely changed the industry as its "IBM compatible" open architecture consolidated most of the industry except for, notably, Apple. Apple chose a closed architecture forming the basis of the Mac Vs PC debate that rages today. But in 1984, when Apple was losing marketshare fast it looked for a way to offer a new user experience like none other - which we'll discuss next week.

Today, we're going to talk about how the Internet works. Specifically, how that stream of characters you punch into your browser's address bar, like "youtube.com", return this very website. Just to clarify we're talking in a broader sense about that massive network of networks connecting millions of computers together, not just the World Wide Web, which is a portion of the Internet, and our topic for next week. Today, we're going to focus on how data is passed back and forth - how a domain name is registered by the Domain Name System, and of course how the data requested or sent gets to the right person in little packets following standard Internet Protocol, or IP. We'll also discuss two different approaches to transferring this data: Transmission Control Protocol, or TCP, when we need to be certain no information is lost, and User Datagram Protocol, or UDP, for those time sensitive applications - because nobody wants an email with missing text, but they also don't want to get lag-fragged in their favorite first person shooter.

Today we’re going to discuss the World Wide Web - not to be confused with the Internet, which is the underlying plumbing for the web as well as other networks. The World Wide Web is built on the foundation of simply linking pages to other pages with hyperlinks, but it is this massive interconnectedness that makes it so powerful. But before the web could become a thing, Tim Berners-Lee would need to invent the web browser at CERN, and search engines would need to be created to navigate these massive directories of information. By the mid 1990’s we will see the rise of Yahoo and Google and monolithic websites like Ebay and Amazon, forming the web we know today. But before we end our unit on the Internet we want to take a moment to discuss the implications of Net Neutrality, and its potential to shape the Internet's future.

Today we're going to talk about hackers and their strategies for breaking into computer systems. Now, not all hackers are are malicious cybercriminals intent on stealing your data (these people are known as Black Hats). There are also White Hats who hunt for bugs, close security holes, and perform security evaluations for companies. And there are a lot of different motivations for hackers—sometimes just amusement or curiosity, sometimes for money, and sometimes to promote social or political goals. Regardless, we're not going to teach you how to become a hacker in this episode but we are going to walk you through some of the strategies hackers use to gain access to your devices, so you can be better prepared to keep your data safe.

Today we’re going to talk about how to keep information secret, and this isn’t a new goal. From as early as Julius Caesar’s Caesar cipher to Mary, Queen of Scots, encrypted messages to kill Queen Elizabeth in 1587, theres has long been a need to encrypt and decrypt private correspondence. This proved especially critical during World War II as Allan Turing and his team at Bletchley Park attempted to decrypt messages from Nazi Enigma machines, and this need has only grown as more and more information sensitive tasks are completed on our computers. So today, we’re going to walk you through some common encryption techniques such as the Advanced Encryption Standard (AES), Diffie-Hellman Key Exchange, and RSA which are employed to keep your information safe, private, and secure.

So we've talked a lot in this series about how computers fetch and display data, but how do they make decisions on this data? From spam filters and self-driving cars, to cutting edge medical diagnosis and real-time language translation, there has been an increasing need for our computers to learn from data and apply that knowledge to make predictions and decisions. This is the heart of machine learning which sits inside the more ambitious goal of artificial intelligence. We may be a long way from self-aware computers that think just like us, but with advancements in deep learning and artificial neural networks our computers are becoming more powerful than ever.

Today we’re going to talk about how computers understand speech and speak themselves. As computers play an increasing role in our daily lives there has been an growing demand for voice user interfaces, but speech is also terribly complicated. Vocabularies are diverse, sentence structures can often dictate the meaning of certain words, and computers also have to deal with accents, mispronunciations, and many common linguistic faux pas. The field of Natural Language Processing, or NLP, attempts to solve these problems, with a number of techniques we’ll discuss today. And even though our virtual assistants like Siri, Alexa, Google Home, Bixby, and Cortana have come a long way from the first speech processing and synthesis models, there is still much room for improvement.

In our SERIES FINALE of Crash Course Computer Science we take a look towards the future! In the past 70 years electronic computing has fundamentally changed how we live our lives, and we believe it’s just getting started. From ubiquitous computing, artificial intelligence, and self-driving cars to brain computer interfaces, wearable computers, and maybe even the singularity there is so much amazing potential on the horizon. Of course there is also room for peril with the rise of artificial intelligence and more immediate displacement of much of the workforce through automation. It’s tough to predict how it will all shake out, but it’s our hope that this series has inspired you to take part in shaping that future. Thank you so much for watching.