{"id":4821,"date":"2026-06-23T00:26:09","date_gmt":"2026-06-22T18:56:09","guid":{"rendered":"https:\/\/itsupportwale.com\/blog\/what-is-artificial-intelligence-definition-types-and-examples\/"},"modified":"2026-06-23T00:26:09","modified_gmt":"2026-06-22T18:56:09","slug":"what-is-artificial-intelligence-definition-types-and-examples","status":"publish","type":"post","link":"https:\/\/itsupportwale.com\/blog\/what-is-artificial-intelligence-definition-types-and-examples\/","title":{"rendered":"What is Artificial Intelligence? Definition, Types &#038; Examples"},"content":{"rendered":"<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_80 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<label for=\"ez-toc-cssicon-toggle-item-6a3a161ef1bde\" class=\"ez-toc-cssicon-toggle-label\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/label><input type=\"checkbox\"  id=\"ez-toc-cssicon-toggle-item-6a3a161ef1bde\"  aria-label=\"Toggle\" \/><nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/itsupportwale.com\/blog\/what-is-artificial-intelligence-definition-types-and-examples\/#AI_is_Just_a_Very_Expensive_Way_to_Guess_the_Next_Word\" >AI is Just a Very Expensive Way to Guess the Next Word<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/itsupportwale.com\/blog\/what-is-artificial-intelligence-definition-types-and-examples\/#The_Marketing_Lie_vs_The_Mathematical_Reality\" >The Marketing Lie vs. The Mathematical Reality<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/itsupportwale.com\/blog\/what-is-artificial-intelligence-definition-types-and-examples\/#The_Architecture_of_a_Guess\" >The Architecture of a Guess<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/itsupportwale.com\/blog\/what-is-artificial-intelligence-definition-types-and-examples\/#Why_LLMs_are_Different_And_Why_They_Arent\" >Why LLMs are Different (And Why They Aren&#8217;t)<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/itsupportwale.com\/blog\/what-is-artificial-intelligence-definition-types-and-examples\/#The_SRE_Perspective_AI_is_an_Infrastructure_Nightmare\" >The SRE Perspective: AI is an Infrastructure Nightmare<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/itsupportwale.com\/blog\/what-is-artificial-intelligence-definition-types-and-examples\/#The_%E2%80%9CVector_Database%E2%80%9D_Fad\" >The &#8220;Vector Database&#8221; Fad<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/itsupportwale.com\/blog\/what-is-artificial-intelligence-definition-types-and-examples\/#The_Hidden_Cost_Data_Drift_and_the_%E2%80%9CSilent_Failure%E2%80%9D\" >The Hidden Cost: Data Drift and the &#8220;Silent Failure&#8221;<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/itsupportwale.com\/blog\/what-is-artificial-intelligence-definition-types-and-examples\/#The_Reality_of_%E2%80%9CPrompt_Engineering%E2%80%9D\" >The Reality of &#8220;Prompt Engineering&#8221;<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/itsupportwale.com\/blog\/what-is-artificial-intelligence-definition-types-and-examples\/#The_%E2%80%9CWhat_Is%E2%80%9D_of_the_Future\" >The &#8220;What Is&#8221; of the Future<\/a><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"AI_is_Just_a_Very_Expensive_Way_to_Guess_the_Next_Word\"><\/span>AI is Just a Very Expensive Way to Guess the Next Word<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>In 2019, I was working for a fintech startup that decided we needed &#8220;predictive scaling.&#8221; The CTO had read a whitepaper about using a Recurrent Neural Network (RNN) to forecast traffic spikes based on historical patterns. We hooked it up to our Kubernetes cluster. One Tuesday, at 3:14 AM, the model decided that a minor blip in API latency from <code>api.stripe.com<\/code> was actually the start of a massive traffic surge. It triggered a scaling event that tried to provision 800 <code>m5.4xlarge<\/code> instances in <code>us-east-1<\/code>. <\/p>\n<p>By the time I woke up to the PagerDuty alert, the AWS bill had climbed by $14,000. The &#8220;AI&#8221; hadn&#8217;t saved us from a spike; it had created a self-inflicted DDoS. The Kubelet on our master nodes was screaming under the pressure of managing that many pending pods. The scheduler was stuck in a loop. We weren&#8217;t &#8220;innovating&#8221;; we were just paying Jeff Bezos for the privilege of watching our control plane melt. That was the day I realized that most people talking about &#8220;what is&#8221; artificial intelligence have never actually had to clean up the mess it makes when it hits a production environment.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"The_Marketing_Lie_vs_The_Mathematical_Reality\"><\/span>The Marketing Lie vs. The Mathematical Reality<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>If you listen to a VC, AI is a digital brain. If you listen to a mathematician, it\u2019s a high-dimensional curve-fitting exercise. As an SRE, I see it as a non-deterministic black box that consumes an ungodly amount of VRAM and returns a probabilistic guess. When people ask <strong>what is<\/strong> AI, they usually want a philosophical answer. I\u2019m going to give you the one that matters when you&#8217;re on call.<\/p>\n<p>At its core, AI\u2014specifically Machine Learning (ML)\u2014is the shift from &#8220;explicit logic&#8221; to &#8220;statistical inference.&#8221; In the old days (five years ago), if we wanted to detect a fraudulent transaction, we wrote code like this:<\/p>\n<pre><code>\ndef is_fraud(transaction):\n    if transaction.amount > 10000 and transaction.location != user.home_city:\n        return True\n    if transaction.velocity_per_hour > 5:\n        return True\n    return False\n<\/code><\/pre>\n<p>This is deterministic. It\u2019s easy to test. It\u2019s easy to debug. You can look at the logs and know exactly why a transaction was flagged. AI replaces this with a weight matrix. Instead of <code>if\/else<\/code>, you have <code>y = mx + b<\/code>, but <code>m<\/code> is a matrix with 175 billion parameters. You don&#8217;t write the rules; you show a &#8220;model&#8221; a million examples of fraud and let it calculate the weights that minimize a &#8220;loss function.&#8221;<\/p>\n<blockquote>\n<p><strong>Pro-tip:<\/strong> If your &#8220;AI&#8221; can be replaced by a <code>CASE<\/code> statement in SQL or a simple <code>scikit-learn<\/code> Random Forest, do it. You will save yourself months of &#8220;YAML-hell&#8221; trying to manage GPU drivers in a container.<\/p>\n<\/blockquote>\n<h2><span class=\"ez-toc-section\" id=\"The_Architecture_of_a_Guess\"><\/span>The Architecture of a Guess<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>To understand <strong>what is<\/strong> happening inside these models, you have to stop thinking about &#8220;thinking.&#8221; A Neural Network is just a series of layers. Each layer is a bunch of numbers. When you pass an input (like a sentence or an image) into the network, it gets converted into a vector\u2014a list of numbers. <\/p>\n<p>Let&#8217;s look at a basic linear layer in PyTorch. This is the &#8220;hello world&#8221; of what people call AI:<\/p>\n<pre><code>\nimport torch\nimport torch.nn as nn\n\n# A simple linear layer: 10 inputs, 5 outputs\nlayer = nn.Linear(10, 5)\n\n# Input data (a tensor of 10 numbers)\ninput_data = torch.randn(1, 10)\n\n# The \"Inference\"\noutput = layer(input_data)\nprint(output)\n<\/code><\/pre>\n<p>That\u2019s it. That is the &#8220;intelligence.&#8221; It\u2019s a dot product and an addition. The &#8220;learning&#8221; part happens when we compare the <code>output<\/code> to the <code>ground_truth<\/code> and use an algorithm called Backpropagation to tweak the weights inside <code>nn.Linear<\/code>. We use the Chain Rule from calculus to figure out how much each weight contributed to the error. We do this millions of times until the error gets smaller. <\/p>\n<p>We aren&#8217;t building a mind. We are building a very complex calculator that is really good at finding patterns in noisy data. The problem is that calculators don&#8217;t have &#8220;common sense.&#8221; If you train a model on data where every fraudulent transaction happens on a Friday, the model will learn that &#8220;Friday = Fraud.&#8221; This is called overfitting, and it\u2019s why your &#8220;smart&#8221; features will fail the moment the real world changes.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Why_LLMs_are_Different_And_Why_They_Arent\"><\/span>Why LLMs are Different (And Why They Aren&#8217;t)<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>The current hype is centered on Large Language Models (LLMs) like GPT-4 or Llama 3. People think these are different because they can &#8220;reason.&#8221; They can&#8217;t. They are &#8220;Transformers.&#8221; The &#8220;Transformer&#8221; architecture, introduced by Google in the &#8220;Attention is All You Need&#8221; paper, changed everything because it allowed for parallelization. <\/p>\n<p>Before Transformers, we used RNNs. RNNs processed text word-by-word. It was slow. It was like reading a book through a straw. Transformers use a mechanism called &#8220;Self-Attention.&#8221; This allows the model to look at every word in a sentence simultaneously and decide which words are most relevant to each other. <\/p>\n<ul>\n<li><strong>The Query (Q):<\/strong> What am I looking for?<\/li>\n<li><strong>The Key (K):<\/strong> What information do I have?<\/li>\n<li><strong>The Value (V):<\/strong> What is the actual content?<\/li>\n<li><strong>The Softmax:<\/strong> A way to turn these relationships into probabilities that add up to 1.<\/li>\n<\/ul>\n<p>When you ask an LLM &#8220;what is the capital of France?&#8221;, it isn&#8217;t &#8220;knowing&#8221; the answer. It is calculating that, given the tokens &#8220;What&#8221;, &#8220;is&#8221;, &#8220;the&#8221;, &#8220;capital&#8221;, &#8220;of&#8221;, &#8220;France&#8221;, the most statistically probable next token is &#8220;Paris.&#8221; It is a stochastic parrot. A very, very impressive one, but a parrot nonetheless.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"The_SRE_Perspective_AI_is_an_Infrastructure_Nightmare\"><\/span>The SRE Perspective: AI is an Infrastructure Nightmare<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Most <a href=\"https:\/\/itsupportwale.com\/blog\/\" title=\"Read more about blog\">blog<\/a> posts about AI focus on the models. I want to talk about the <code>nvidia-smi<\/code> output at 2:00 AM. Productionizing AI is significantly harder than productionizing a CRUD app. In a standard Go or Node.js app, your primary constraints are CPU and I\/O. In AI, your constraint is VRAM and memory bandwidth.<\/p>\n<p>If you want to run a Llama-3-70B model in production, you aren&#8217;t just deploying a container. You are dealing with:<\/p>\n<ul>\n<li><strong>Quantization:<\/strong> You can&#8217;t fit a 70B parameter model in 16-bit precision on a single A100 (80GB). You have to &#8220;quantize&#8221; it to 4-bit or 8-bit. This is basically compressing the weights. It makes the model dumber but allows it to fit in memory.<\/li>\n<li><strong>CUDA Versions:<\/strong> Welcome to dependency hell. Your PyTorch version must match your CUDA toolkit version, which must match your NVIDIA driver version. If you update the host kernel, your whole inference stack might break.<\/li>\n<li><strong>Cold Starts:<\/strong> A 40GB model takes a long time to pull over the network and load into GPU memory. You can&#8217;t just &#8220;scale to zero&#8221; like you can with a Lambda function unless you want your users to wait 90 seconds for a response.<\/li>\n<li><strong>KV Caching:<\/strong> To make LLMs fast, we cache the &#8220;Keys&#8221; and &#8220;Values&#8221; of previous tokens. This eats VRAM like crazy. If your cache grows too large, you get an <code>Out of Memory (OOM)<\/code> error on the GPU, which is much harder to recover from than a standard Linux OOM.<\/li>\n<li><strong>Triton\/vLLM:<\/strong> You need a specialized inference server to handle batching. If you send requests one by one, your GPU utilization will be 5%, and you&#8217;ll be burning money. You need &#8220;Continuous Batching&#8221; to keep the tensor cores busy.<\/li>\n<\/ul>\n<pre><code>\n# Example of checking GPU health on a production node\n$ nvidia-smi --query-gpu=utilization.gpu,utilization.memory,memory.total,memory.used --format=csv\nutilization.gpu [%], utilization.memory [%], memory.total [MiB], memory.used [MiB]\n85 %, 64 %, 81920 MiB, 52428 MiB\n<\/code><\/pre>\n<p>If you see <code>utilization.memory<\/code> at 100% but <code>utilization.gpu<\/code> at 10%, you have a bottleneck in your data pipeline. Your CPU can&#8217;t feed the GPU fast enough. This is the kind of &#8220;what is AI&#8221; reality that doesn&#8217;t make it into the marketing slide decks.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"The_%E2%80%9CVector_Database%E2%80%9D_Fad\"><\/span>The &#8220;Vector Database&#8221; Fad<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>You can&#8217;t talk about &#8220;what is&#8221; AI today without mentioning Vector Databases (Pinecone, Milvus, Weaviate). The idea is that you turn your data into &#8220;embeddings&#8221; (vectors) and store them so the LLM can &#8220;search&#8221; them. This is called Retrieval-Augmented Generation (RAG).<\/p>\n<p>Here is my hot take: Most of you don&#8217;t need a specialized vector database. You need <code>pgvector<\/code>. <\/p>\n<p>I\u2019ve seen teams spend three months setting up a dedicated vector DB cluster, dealing with new consistency models and backup strategies, when they could have just added an extension to their existing Postgres RDS instance. <\/p>\n<pre><code>\n-- The \"AI\" way to search in Postgres\nCREATE EXTENSION IF NOT EXISTS vector;\n\nCREATE TABLE documents (\n    id serial PRIMARY KEY,\n    content text,\n    embedding vector(1536) -- OpenAI's embedding size\n);\n\n-- Find the most similar document using cosine distance\nSELECT content FROM documents \nORDER BY embedding <=> '[0.123, 0.456, ...]' \nLIMIT 5;\n<\/code><\/pre>\n<p>Is it as fast as a specialized C++ engine optimized for HNSW (Hierarchical Navigable Small World) graphs? No. But it\u2019s &#8220;production-ready&#8221; on day one. It follows ACID compliance. It\u2019s in your existing backup routine. Don&#8217;t add architectural complexity until your p99s demand it.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"The_Hidden_Cost_Data_Drift_and_the_%E2%80%9CSilent_Failure%E2%80%9D\"><\/span>The Hidden Cost: Data Drift and the &#8220;Silent Failure&#8221;<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>When a standard microservice fails, it returns a 500 error. You get an alert. You fix it. When an AI model fails, it returns a 200 OK with a perfectly formatted, confident, but entirely wrong answer. This is &#8220;hallucination,&#8221; but the more dangerous version is &#8220;Data Drift.&#8221;<\/p>\n<p>Data drift happens when the distribution of the data you are seeing in production changes from the data you used to train the model. Imagine you trained a model to predict house prices in 2021. In 2023, interest rates spiked. The model still thinks it\u2019s 2021 because its weights are frozen. It\u2019s still giving you &#8220;accurate&#8221; predictions based on its training, but its predictions are now useless in the real world.<\/p>\n<p>Monitoring this is a nightmare. You have to monitor the &#8220;distribution&#8221; of your inputs. You need to use tools like <code>Great Expectations<\/code> or <code>WhyLabs<\/code> to see if the mean and variance of your features are shifting. <\/p>\n<blockquote>\n<p><strong>Note to self:<\/strong> Always log the model version and the raw prompt in the metadata of your inference logs. If a user reports a &#8220;hallucination,&#8221; you need to be able to reproduce it exactly, which is nearly impossible if you&#8217;re using a non-deterministic temperature setting (> 0).<\/p>\n<\/blockquote>\n<h2><span class=\"ez-toc-section\" id=\"The_Reality_of_%E2%80%9CPrompt_Engineering%E2%80%9D\"><\/span>The Reality of &#8220;Prompt Engineering&#8221;<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>There is a whole cottage industry of &#8220;Prompt Engineers.&#8221; Let\u2019s be clear: Prompt engineering is just &#8220;voodoo programming&#8221; for the 2020s. It\u2019s the equivalent of hitting the side of a CRT monitor to get the picture to stop flickering. <\/p>\n<p>If your system&#8217;s reliability depends on whether you told the model to &#8220;take a deep breath&#8221; or &#8220;I will tip you $200 for a correct answer,&#8221; you don&#8217;t have an engineering system. You have a fragile heuristic. Real AI engineering is about:<\/p>\n<ol>\n<li><strong>Evaluation Frameworks:<\/strong> You need a suite of 1,000+ test cases (inputs and expected outputs) that you run every time you change a prompt or a model version.<\/li>\n<li><strong>Few-Shot Prompting:<\/strong> Providing 5-10 examples of the task within the prompt to &#8220;prime&#8221; the model.<\/li>\n<li><strong>Fine-Tuning:<\/strong> Actually updating the weights of a smaller model (like Mistral 7B) on your specific domain data instead of trying to coax a general-purpose model into behaving.<\/li>\n<li><strong>Output Parsing:<\/strong> Using libraries like <code>Instructor<\/code> or <code>Outlines<\/code> to force the LLM to return valid JSON that matches a Pydantic schema. Never, ever use <code>response.split(\",\")<\/code> on an LLM output. It will break.<\/li>\n<\/ol>\n<pre><code>\n# Don't do this\nprompt = f\"Is this comment spam? {comment_text}. Answer yes or no.\"\n\n# Do this (using a structured output library)\nclass SpamDetection(BaseModel):\n    is_spam: bool\n    confidence_score: float\n    reasoning: str\n\nclient = instructor.patch(openai.OpenAI())\nresponse = client.chat.completions.create(\n    model=\"gpt-4\",\n    response_model=SpamDetection,\n    messages=[{\"role\": \"user\", \"content\": comment_text}]\n)\n<\/code><\/pre>\n<h2><span class=\"ez-toc-section\" id=\"The_%E2%80%9CWhat_Is%E2%80%9D_of_the_Future\"><\/span>The &#8220;What Is&#8221; of the Future<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>We are currently in the &#8220;throw more GPUs at it&#8221; phase of AI. It reminds me of the early days of NoSQL, where everyone was dumping their relational data into MongoDB because &#8220;schemas are for losers,&#8221; only to realize three years later that data integrity actually matters. <\/p>\n<p>Eventually, the hype will die down. We will stop calling it &#8220;AI&#8221; and start calling it &#8220;the probabilistic layer of the stack.&#8221; We will use it for things it\u2019s good at\u2014summarization, translation, fuzzy matching\u2014and we will stop trying to use it for things it\u2019s bad at\u2014math, logic, and being a source of truth. <\/p>\n<p>The real &#8220;intelligence&#8221; isn&#8217;t in the model. It&#8217;s in the engineering around the model. It&#8217;s in the rate limiting, the caching, the evaluation loops, and the fallback mechanisms. If the LLM fails, does your app crash? Or do you have a <code>Trie<\/code>-based regex matcher that can handle the basic cases? <\/p>\n<p>Most companies don&#8217;t need an &#8220;AI Strategy.&#8221; They need a &#8220;Data Strategy.&#8221; You can&#8217;t build a 10th-floor penthouse (AI) if your foundation (data quality) is made of wet sand. I\u2019ve seen teams spend millions on LLMs while their core database didn&#8217;t even have proper foreign key constraints. Fix your data first. The &#8220;intelligence&#8221; part is easy once the data is clean.<\/p>\n<p>Stop treating AI like a magic wand and start treating it like a very temperamental, very expensive legacy service that you didn&#8217;t write but have to support. That is the only way to survive the hype cycle without losing your mind or your budget.<\/p>\n<p>If you can&#8217;t explain why the model made a specific decision, don&#8217;t give it the keys to your production environment; keep it in a sandbox until you&#8217;ve built enough observability to catch it when it inevitably lies to you.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>AI is Just a Very Expensive Way to Guess the Next Word In 2019, I was working for a fintech startup that decided we needed &#8220;predictive scaling.&#8221; The CTO had read a whitepaper about using a Recurrent Neural Network (RNN) to forecast traffic spikes based on historical patterns. We hooked it up to our Kubernetes &#8230; <a title=\"What is Artificial Intelligence? Definition, Types &#038; Examples\" class=\"read-more\" href=\"https:\/\/itsupportwale.com\/blog\/what-is-artificial-intelligence-definition-types-and-examples\/\" aria-label=\"Read more  on What is Artificial Intelligence? Definition, Types &#038; Examples\">Read more<\/a><\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-4821","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.0 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>What is Artificial Intelligence? 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