Crash Course Computer Science

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.

Today we’re going to go a little meta and talk about how computer science can support learning with educational technology. We here at Crash Course are big fans of interactive in-class learning and hands-on experiences, but we also believe in the additive power of educational technology inside and outside the classroom from the Internet itself.

So today, we’re going to discuss some psychological considerations in building computers like how to make them easier for humans to use, the uncanny valley problem when humanoid robots gets more and more humanlike, and strategies to make our devices work better with us by incorporating our emotions and even altering our gaze.

Robots are often thought as a technology of the future, but they're already here by the millions in the workplace, our homes, and pretty soon on the roads. We'll discuss the origins of robotics to its proliferation, and even look at some common control designs that were implemented to make them more useful in the workplace.

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.

We’ve long known that our digital cameras and smartphones can take incredibly detailed images, but taking pictures is not quite the same thing. For the past half-century, computer scientists have been working to help our computing devices understand the imagery they capture, leading to advancements everywhere, from tracking hands and whole bodies to biometrics to unlock our phones.

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’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.

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.

In today’s episode, we’re going to unpack these three goals and talk through some strategies we use like passwords, biometrics, and access privileges to keep our information as secure, but also as accessible as possible.

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.

Specifically, how that stream of characters you punch into your browser's address bar, like "youtube.com", return a website. Just to clarify, we're talking in a broader sense about that massive network of networks connecting millions of computers together.