{"id":109120,"date":"2023-06-29T09:22:54","date_gmt":"2023-06-29T16:22:54","guid":{"rendered":"https:\/\/www.backblaze.com\/blog\/?p=109120"},"modified":"2024-07-29T10:57:54","modified_gmt":"2024-07-29T17:57:54","slug":"ai-101-how-cognitive-science-and-computer-processors-create-artificial-intelligence","status":"publish","type":"post","link":"https:\/\/www.backblaze.com\/blog\/ai-101-how-cognitive-science-and-computer-processors-create-artificial-intelligence\/","title":{"rendered":"AI 101: How Cognitive Science and Computer Processors Create Artificial Intelligence"},"content":{"rendered":"\n
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Recently, artificial intelligence has been having a moment: It\u2019s gone from an abstract idea in a sci-fi movie, to an experiment in a lab, to a tool that is impacting our everyday lives. With headlines from Bing\u2019s AI confessing its love to a reporter<\/a> to the struggles over who\u2019s liable in an accident with a self-driving car<\/a>, the existential reality of what it means to live in an era of rapid technological change is playing out in the news. <\/p>\n\n\n\n

The headlines may seem fun, but it\u2019s important to consider what this kind of tech means. In some ways, you can draw a parallel to the birth of the internet, with all the innovation, ethical dilemmas, legal challenges, excitement, and chaos that brought with it. (We\u2019re totally happy to discuss in the comments section.)<\/p>\n\n\n\n

So, let\u2019s keep ourselves grounded in fact and do a quick rundown of some of the technical terms in the greater AI landscape. In this article, we\u2019ll talk about three basic terms to help you define the playing field: artificial intelligence (AI), machine learning (ML), and deep learning (DL).<\/p>\n\n\n\n

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Want to Know More About AI?<\/h4>\nThis article is part of an ongoing content arc about artificial intelligence (AI). The second article in the series is AI 101: GPU vs. TPU vs. NPU<\/a>. Stay tuned for the rest of the series, and feel free to suggest other articles you\u2019d like to see on this content in the comments.<\/div>\n\n\n\n

What Is Artificial Intelligence (AI)?<\/h2>\n\n\n\n

If you were to search \u201cartificial intelligence,\u201d you\u2019d see varying definitions. Here are a few from good sources. <\/p>\n\n\n\n

From Google<\/a>, and not Google as in the search engine, but Google in their thought leadership library:<\/p>\n\n\n\n

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Artificial intelligence is a broad field, which refers to the use of technologies to build machines and computers that have the ability to mimic cognitive functions associated with human intelligence, such as being able to see, understand, and respond to spoken or written language, analyze data, make recommendations, and more. <\/p>\n\n\n\n

Although artificial intelligence is often thought of as a system in itself, it is a set of technologies implemented in a system to enable it to reason, learn, and act to solve a complex problem.<\/p>\n<\/blockquote>\n\n\n\n

From IBM<\/a>, a company that has been pivotal in computer development since the early days:<\/p>\n\n\n\n

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At its simplest form, artificial intelligence is a field, which combines computer science and robust datasets, to enable problem-solving. It also encompasses sub-fields of machine learning and deep learning, which are frequently mentioned in conjunction with artificial intelligence. These disciplines are comprised of AI algorithms which seek to create expert systems which make predictions or classifications based on input data.<\/p>\n<\/blockquote>\n\n\n\n

From Wikipedia<\/a>, the crowdsourced and scholarly-sourced oversoul of us all:<\/p>\n\n\n\n

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Artificial intelligence is intelligence demonstrated by machines, as opposed to intelligence displayed by humans or by other animals. “Intelligence” encompasses the ability to learn and to reason, to generalize, and to infer meaning. Example tasks\u2026 include speech recognition, computer vision, translation between (natural) languages, as well as other mappings of inputs.<\/p>\n<\/blockquote>\n\n\n\n

Allow us to give you the Backblaze summary: Each of these sources are saying that artificial intelligence is what happens when computers start thinking (or appearing to think) for themselves. It’s the what<\/em>. You call a bot you\u2019re training \u201can AI;\u201d you also call the characteristic of a computer making decisions AI; you call the entire field of this type of problem solving and programming AI. <\/p>\n\n\n\n

However, using the term \u201cartificial intelligence\u201d does not define how <\/em>bots are solving problems. Terms like \u201cmachine learning\u201d and \u201cdeep learning\u201d are how<\/em> that appearance of intelligence is created\u2014the complexity of the algorithms and tasks to perform, whether the algorithm learns, what kind of theoretical math is used to make a decision, and so on. For the purposes of this article, you can think of artificial intelligence as the umbrella term for the processes of machine learning and deep learning. <\/p>\n\n\n\n

What Is Machine Learning (ML)?<\/h2>\n\n\n\n

Machine learning (ML) is the study and implementation of computer algorithms that improve automatically through experience. In contrast with AI and in keeping with our earlier terms, AI is when a computer appears intelligent, and ML is when a computer can solve a complex, but defined, task. An algorithm is a set of instructions (the requirements) of a task. <\/p>\n\n\n\n

We engage with algorithms all the time without realizing it\u2014for instance, when you visit a site using a URL starting with \u201chttps:\u201d your browser is using SSL (or, more accurately in 2023, TLS<\/a>), a symmetric encryption algorithm that secures communication between your web browser and the site. Basically, when you click \u201cplay\u201d on a cat video, your web browser and the site engage in a series of steps to ensure that the site is what it purports to be, and that a third-party can neither eavesdrop on nor modify any of the cuteness exchanged.<\/p>\n\n\n\n

Machine learning does not specify how much knowledge the bot you\u2019re training starts with\u2014any task can have more or fewer instructions. You could ask your friend to order dinner, or you could ask your friend to order you pasta from your favorite Italian place to be delivered at 7:30 p.m. <\/p>\n\n\n\n

Both of those tasks you just asked your friend to complete are algorithms. The first algorithm requires your friend to make more decisions to execute the task at hand to your satisfaction, and they\u2019ll do that by relying on their past experience of ordering dinner with you\u2014remembering your preferences about restaurants, dishes, cost, and so on. <\/p>\n\n\n\n

By setting up more parameters in the second question, you\u2019ve made your friend\u2019s chances of a satisfactory outcome more probable, but there are a ton of things they would still have to determine or decide in order to succeed\u2014finding the phone number of the restaurant, estimating how long food delivery takes, assuming your location for delivery, etc. <\/p>\n\n\n\n

I\u2019m framing this example as a discrete event, but you\u2019ll probably eat dinner with your friend again. Maybe your friend doesn\u2019t choose the best place this time, and you let them know you don\u2019t want to eat there in the future. Or, your friend realizes that the restaurant is closed on Mondays, so you can\u2019t eat there. Machine learning is analogous to the process through which your friend can incorporate feedback\u2014yours or the environment\u2019s\u2014and arrive at a satisfactory dinner plan.<\/p>\n\n\n\n

Machines Learning to Teach Machines<\/h3>\n\n\n\n

A real-world example that will help us tie this down is teaching robots to walk (and there are a ton of fun videos on the subject, if you want to lose yourself in YouTube). Many robotics AI experiments teach their robots to walk in simulated, virtual environments before the robot takes on the physical world.<\/p>\n\n\n\n

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