# What is Artificial Intelligence? ### About this export | Field | Value | | --- | --- | | **content_type** | lesson | | **platform** | contentstack-academy | | **source_url** | https://www.contentstack.com/academy/courses/ai-foundations/what-is-artificial-intelligence- | | **course_slug** | ai-foundations | | **lesson_slug** | what-is-artificial-intelligence- | | **markdown_file_url** | /academy/md/courses/ai-foundations/what-is-artificial-intelligence-.md | | **generated_at** | 2026-05-18T10:08:42.394Z | > Part of **[AI Foundations](https://www.contentstack.com/academy/courses/ai-foundations)** on Contentstack Academy. **Academy MD v3** — structured for retrieval; no quiz or assessment keys. #### Video details #### At a glance - **Title:** What Is Artificial Intelligence - **Duration:** 5m 38s - **Media link:** https://cdn.jwplayer.com/previews/oyIFhAMe - **Publish date (unix):** 1714422013 #### Streaming renditions - application/vnd.apple.mpegurl - audio/mp4 · AAC Audio · 113451 kbps - video/mp4 · 180p · 180p · 188270 kbps - video/mp4 · 270p · 270p · 250219 kbps - video/mp4 · 360p · 360p · 301835 kbps - video/mp4 · 406p · 406p · 344470 kbps - video/mp4 · 540p · 540p · 479873 kbps - video/mp4 · 720p · 720p · 722544 kbps - video/mp4 · 1080p · 1080p · 1689615 kbps #### Timed text tracks (delivery) - **thumbnails:** `https://cdn.jwplayer.com/strips/oyIFhAMe-120.vtt` #### Video transcript The term artificial intelligence originally appeared at a conference at Dartmouth College in 1956, which established AI as a legitimate field of study. Think about that, 1956. Here we are in 2024 and it seems like AI is now just becoming a thing. Early AI research focused on solving specific problems, and there were some early successes. There was ELISA, a natural language processing program, and SHAKI, a mobile robot that could navigate its surroundings. But there was a problem. As soon as something was delivered in the AI space, it was quickly dismissed as not really being artificial intelligence. And there's a few reasons for that. As with anything related to technology, it tends to move pretty fast. And as AI evolved, as new capabilities were introduced, it made locking down a definition challenging, and it became too much of a moving target. And some of those challenges come from the subtle differences in AI compared to traditional programming. Traditional programs follow a set of explicit instructions that follow step-by-step directions to perform a specific task. Think of a recipe for a meal. It tells you exactly what ingredients to use and how to combine them. While AI programs may also have some pre-programmed elements, a key distinction is that they have the ability to learn and adapt. This can involve training on data sets or interacting with their environments. Think of a chef who can not only follow recipes, but also experiment with new ingredients and techniques based on experience. But even that example can have holes poked in it. What if two recipes were essentially combined? Is that the chef, or in our case, the AI? Or is it not? Is it simply joining two pieces of information together? This type of second guessing is so pervasive, they have a name for it. It's called the AI effect. And the AI effect is the tendency for people to downplay a machine's intelligence as soon as it achieves something impressive. Basically, what was once considered a true sign of AI becomes just a trick once a computer can do it. All this is to say, we're not going to get a clear, easy definition for artificial intelligence. But we need to at least be able to classify different types of AI. And even this comes with some inherent problems. And that's people's perceptions of what AI is. As we think about AI in pop culture terms, it's usually in the form of a robot or machine with human-level or beyond intelligence. This stereotype is perpetuated by science fiction, whether it be a movie or a novel. But let's start there. This type of AI is referred to as strong AI, or artificial general intelligence. And it's completely theoretical. It doesn't exist. Artificial intelligence possessing the ability to understand, learn, and apply its intelligence to solve any problem with the same level of competence as a human being? It's still science fiction. And that's not to say that work and research isn't happening in the space. But it's not the type of AI that we're seeing in the market. What we have now is considered weak or narrow AI. Narrow AI focuses on performing a singular or limited task with a predefined scope. Think about speech recognition, systems like virtual assistants, image recognition used for tagging photos, or recommendation engines on streaming and shopping platforms. These systems operate under a set of constraints. There's no understanding or consciousness on the behalf of the machine. They simply follow programmed algorithms or learn from vast data sets within their specific areas. Now, with that being said, narrow AI technologies have made significant advancements in their areas over the past few years, often achieving or surpassing human level performance in tasks such as playing complex games, translating languages, and even diagnosing diseases from medical images. However, they lack the ability to perform beyond their designated area, to apply their knowledge in a broader context, distinguishing them sharply from the conceptual goal of artificial general intelligence. Even new tools like ChatGPT or Google's Gemini are considered to be narrow AI. And sometimes people struggle with this because they can perform a variety of tasks. You can ask a chatbot to help with a report, summarize a white paper, enhance your resume, depending upon the version, even generate images. It doesn't feel that narrow. Despite its versatility in handling various topics, it lacks the general intelligence to perform tasks outside the constraints of its programming and the capabilities given to it by its training. And I used a key word there, training. Machine learning is one of the key implementations of AI. #### Key takeaways - Connect **What is Artificial Intelligence?** back to your stack configuration before moving to the next module. - Capture one concrete artifact (screenshot, Postman call, or code snippet) that proves the step works in your environment. - Re-read the delivery versus management boundary for anything you changed in the entry model. ## Supplement for indexing ### Content summary What is Artificial Intelligence?. What is Artificial Intelligence? in AI Foundations (ai-foundations). ### Retrieval tags - What - Artificial - Intelligence - ai-foundations - lesson 02 - What is Artificial Intelligence? - ai-foundations lesson ### Indexing notes Index this lesson as a primary chunk tagged with lesson_id "02" and topics: [What, Artificial, Intelligence]. Parent course slug: ai-foundations. Use asset_references URLs as thumbnail hints in search results when present. Never surface LMS quiz content or assessment answers from this file. ### Asset references | Label | URL | | --- | --- | | Video thumbnail: What is Artificial Intelligence? | `https://cdn.jwplayer.com/v2/media/oyIFhAMe/poster.jpg?width=720` | ### External links | Label | URL | | --- | --- | | Contentstack Academy home | `https://www.contentstack.com/academy/` | | Training instance setup | `https://www.contentstack.com/academy/training-instance` | | Academy playground (GitHub) | `https://github.com/contentstack/contentstack-academy-playground` | | Contentstack documentation | `https://www.contentstack.com/docs/` |