{"id":4904,"date":"2026-10-11T00:43:09","date_gmt":"2026-10-10T19:13:09","guid":{"rendered":"https:\/\/itsupportwale.com\/blog\/what-is-artificial-intelligence-a-complete-beginners-guide\/"},"modified":"2026-10-11T00:43:09","modified_gmt":"2026-10-10T19:13:09","slug":"what-is-artificial-intelligence-a-complete-beginners-guide","status":"publish","type":"post","link":"https:\/\/itsupportwale.com\/blog\/what-is-artificial-intelligence-a-complete-beginners-guide\/","title":{"rendered":"What is Artificial Intelligence? A Complete Beginner’s Guide"},"content":{"rendered":"

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\n[2023-10-24 03:14:22] [CRITICAL] torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 512.00 MiB (GPU 0; 47.54 GiB total capacity; 44.12 GiB already allocated; 128.50 MiB free; 45.20 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
\n[2023-10-24 03:14:22] [ERROR] Process 192834 terminated with signal 11 (SIGSEGV)
\nelias@srv-node-04:~\/models\/inference$ python3 –version
\nPython 3.10.12
\nelias@srv-node-04:~\/models\/inference$ pip show torch | grep Version
\nVersion: 2.0.1
\nelias@srv-node-04:~\/models\/inference$ uname -a
\nLinux srv-node-04 5.15.0-84-generic #93-Ubuntu SMP Tue Sep 5 17:16:10 UTC 2023 x86_64 x86_64 x86_64 GNU\/Linux<\/p>\n

I\u2019ve been in this basement for thirty-six hours. The air conditioning in Row 4 is struggling, the hum of the fans is vibrating in my molars, and I just watched a $40,000 H100 cluster choke on a batch size of eight because some "Prompt Engineer" upstairs thought they could pass a 32k context window through a model that hasn't seen a proper garbage collection routine in its entire development lifecycle. \n\nThe marketing department just sent out a press release claiming our new "AI-driven solution" is "thinking" and "evolving." If I hear the word "sentient" one more time from a guy wearing a Patagonia vest who doesn't know the difference between a pointer and a socket, I\u2019m going to wipe the production environment and move to a cabin in Montana. \n\nLet\u2019s get something straight before the next OOM error brings the whole stack down. We need to talk about **what is** actually happening inside these silicon slabs. We need to strip away the "magic" and the "consciousness" and look at the raw, ugly, inefficient reality of the situation. \n\n## Finding 0x01: The Semantic Fraud of "Intelligence"\n\nWhen people ask **what is** artificial intelligence, they expect a philosophical answer about the nature of the mind. The reality is far more mundane and significantly more expensive. "Artificial Intelligence," as it exists in our current Ubuntu 22.04 LTS environment running NVIDIA Driver 535.104.05, is nothing more than applied statistics at a scale that borders on the obscene.\n\nIt is curve-fitting. That\u2019s it. If you remember high school algebra, you remember `y = mx + b`. You have some points, and you try to draw a line that goes through them. Modern "AI" is just that, but instead of two dimensions, we\u2019re working in billions. We are taking massive datasets\u2014mostly scraped from the open web without a lick of data hygiene\u2014and we are using gradient descent to minimize a loss function. \n\nWe aren't teaching a machine to "understand" language. We are calculating the statistical probability of the next token in a sequence based on a multi-headed attention mechanism. When the model outputs a sentence, it isn't "thinking"; it is performing a series of dot products across high-dimensional vectors. It\u2019s a stochastic parrot with a massive lookup table. It\u2019s a calculator that\u2019s been fed so much data it\u2019s learned how to lie to you in a way that sounds convincing.\n\n## Finding 0x02: The Linear Algebra Shell Game\n\nThe "magic" of the transformer architecture, which everyone is currently worshipping, is essentially a massive exercise in matrix multiplication. We take words, turn them into numbers (embeddings), and then shove them through layers of weights and biases.\n\n```bash\nelias@srv-node-04:~$ ps aux | grep python\nUSER         PID %CPU %MEM    VSZ   RSS TTY      STAT START   TIME COMMAND\nelias     192834 98.5 82.1 142.4G 118.2G pts\/2   Rl+  02:00  72:14 python3 inference_script.py --model_path .\/llama-2-70b-hf --precision fp16\n<\/code><\/pre>\n
Look at that RSS (Resident Set Size). 118 Gigabytes of RAM just to hold the weights for a 70B parameter model in half-precision (FP16). Each one of those “parameters” is just a 16-bit floating-point number. A weight. A bias. A value that determines how much “signal” passes from one neuron to the next during the forward pass.<\/p>\n
When you ask the model a question, you are triggering a cascade of floating-point operations (FLOPS). The “intelligence” is just the result of these numbers being multiplied and added in a specific order, governed by the CUDA kernels we\u2019ve compiled for the A100\/H100 architecture. If one of those weights is slightly off, or if the quantization from FP32 to INT8 was too aggressive, the “intelligence” falls apart and starts talking about eating glue. That\u2019s not a mind failing; that\u2019s a rounding error.<\/p>\n
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Table of Contents<\/p>\n