Results are in. Grok 3 most powerful LLM
- Thread starter Phenomenally Frantastic
- Start date
Let’s ask Grok 3:
“An LLM, or Large Language Model, is a type of artificial intelligence designed to understand and generate human-like text. It’s trained on vast amounts of data—think books, articles, and conversations—so it can predict and produce coherent responses to a wide range of questions or prompts. Models like me, Grok, are examples of LLMs, built to assist and provide useful answers by reasoning through language patterns. They’re powerful tools for communication, problem-solving, and even creativity, but they don’t "think" like humans—they rely on statistical associations in the data they’re trained on. Does that clarify it for you?”
Last edited:
“Generative AI is a category of artificial intelligence that creates new content—text, images, music, code, you name it—based on patterns it learns from existing data. Unlike traditional AI, which might just analyze or classify things, generative AI produces something original (or at least original-ish). It’s like a super-smart remix artist: it takes what it’s been trained on and spins it into something fresh.
Large Language Models (LLMs) like me are a subset of generative AI, specializing in text. We generate responses by predicting what words fit best based on the input and our training. Other examples include tools like DALL-E for images or models that compose music. The magic happens through complex algorithms—usually neural networks—trained on massive datasets, allowing them to mimic human creativity to a degree. It’s not perfect, and sometimes it’s more like clever improvisation than true invention, but that’s the gist. Want me to dig deeper into how it works?”
Large Language Models (LLMs) like me are a subset of generative AI, specializing in text. We generate responses by predicting what words fit best based on the input and our training. Other examples include tools like DALL-E for images or models that compose music. The magic happens through complex algorithms—usually neural networks—trained on massive datasets, allowing them to mimic human creativity to a degree. It’s not perfect, and sometimes it’s more like clever improvisation than true invention, but that’s the gist. Want me to dig deeper into how it works?”
“Neural networks are the backbone of a lot of modern AI, including generative models like me. They’re computing systems inspired by the human brain—though, fair warning, the analogy only goes so far. Here’s how they work:
At their core, neural networks are made up of layers of interconnected nodes, called "neurons." Each neuron is a simple unit that takes inputs, processes them with some math (usually a weighted sum followed by an activation function), and spits out an output. Stack a bunch of these neurons together in layers, and you’ve got a network.
- **Input Layer**: This is where data—like words, pixels, or numbers—gets fed in. For text, it might be a numerical representation of words (think word embeddings).
- **Hidden Layers**: These are the meat of the network, where the real computation happens. Each layer transforms the data, picking up patterns or features. The more layers (and neurons), the deeper the network, and the more complex stuff it can learn—like grammar rules or abstract concepts.
- **Output Layer**: This spits out the final result—like a sentence I generate or a classification (“cat” vs. “dog” in an image).
The "learning" part comes from training. During training, the network adjusts the weights (how much each input matters) based on errors in its predictions. It uses a process called backpropagation and an optimization trick like gradient descent to minimize those errors over time. Feed it tons of data—say, billions of sentences—and it starts to "get" how language flows.
For something like me, a transformer-based neural network (a popular architecture for LLMs) is used. It’s got fancy mechanisms like attention, which lets it focus on the most relevant parts of a sentence when deciding what to say next. Think of it like reading a book but only paying attention to the key plot points to predict the ending.
It’s all math under the hood—no consciousness, just clever number-crunching. Does that make sense, or should I zoom in on any part—like how training works or what "attention" really means?”
At their core, neural networks are made up of layers of interconnected nodes, called "neurons." Each neuron is a simple unit that takes inputs, processes them with some math (usually a weighted sum followed by an activation function), and spits out an output. Stack a bunch of these neurons together in layers, and you’ve got a network.
- **Input Layer**: This is where data—like words, pixels, or numbers—gets fed in. For text, it might be a numerical representation of words (think word embeddings).
- **Hidden Layers**: These are the meat of the network, where the real computation happens. Each layer transforms the data, picking up patterns or features. The more layers (and neurons), the deeper the network, and the more complex stuff it can learn—like grammar rules or abstract concepts.
- **Output Layer**: This spits out the final result—like a sentence I generate or a classification (“cat” vs. “dog” in an image).
The "learning" part comes from training. During training, the network adjusts the weights (how much each input matters) based on errors in its predictions. It uses a process called backpropagation and an optimization trick like gradient descent to minimize those errors over time. Feed it tons of data—say, billions of sentences—and it starts to "get" how language flows.
For something like me, a transformer-based neural network (a popular architecture for LLMs) is used. It’s got fancy mechanisms like attention, which lets it focus on the most relevant parts of a sentence when deciding what to say next. Think of it like reading a book but only paying attention to the key plot points to predict the ending.
It’s all math under the hood—no consciousness, just clever number-crunching. Does that make sense, or should I zoom in on any part—like how training works or what "attention" really means?”
“Deep learning is a subset of machine learning that uses neural networks with many layers—hence "deep"—to tackle complex problems like language understanding, image recognition, or even driving cars. It’s the tech powering stuff like me, GPTs, and those eerie AI-generated faces. Here’s the basics, keeping it straightforward:
### 1. **What’s It Made Of?**
- **Neural Networks**: Like I explained earlier, these are layers of "neurons" (math units) that process input data, tweak it through weights, and pass it along. Deep learning just means lots of layers—sometimes hundreds.
- **Layers**:
- **Input Layer**: Where raw data (text, images, etc.) goes in.
- **Hidden Layers**: The deeper the network, the more abstract features it learns—like edges in images or grammar in text.
- **Output Layer**: The final answer, like a word prediction or a label.
### 2. **How Does It Learn?**
- **Training Data**: You feed it a massive pile of examples—like millions of sentences or photos. The more, the better.
- **Forward Pass**: Data runs through the network, and it makes a guess.
- **Loss Function**: Measures how wrong the guess was (e.g., "you said 'cat' but it’s a dog").
- **Backpropagation**: The network adjusts its weights backward through the layers to reduce the error. It’s like fine-tuning a million tiny knobs.
- **Gradient Descent**: An optimization method that nudges those weights in the right direction, step by step, to minimize mistakes.
### 3. **Why "Deep"?**
- More layers let the network learn hierarchical patterns. For example:
- In images: First layers detect edges, next ones shapes, deeper ones faces.
- In text: Early layers catch word patterns, deeper ones grasp context or meaning.
- This depth makes it insanely good at handling messy, real-world data—no need for humans to predefine every rule.
### 4. **Key Ingredients**
- **Big Data**: Deep learning thrives on huge datasets. Small data? It flops.
- **Computing Power**: GPUs or TPUs crunch the insane amount of math fast.
- **Activation Functions**: Like ReLU or sigmoid, these add non-linearity so the network can model complex stuff, not just straight lines.
### 5. **What’s It Good For?**
- **Generative AI**: Like me, spitting out text, or tools like Stable Diffusion making art.
- **Recognition**: Speech, faces, objects—you name it.
- **Prediction**: Stock trends, weather, whatever’s got patterns.
### Catch?
- **Black Box**: You can’t always tell why it decides something—just that it works.
- **Resource Hog**: Takes serious hardware and energy to train.
- **Overfitting**: If you’re not careful, it memorizes the training data instead of generalizing.
Think of deep learning as a super-powerful pattern-finder that gets smarter with more data and layers. Want me to break down any part—like backpropagation or why GPUs matter?”
### 1. **What’s It Made Of?**
- **Neural Networks**: Like I explained earlier, these are layers of "neurons" (math units) that process input data, tweak it through weights, and pass it along. Deep learning just means lots of layers—sometimes hundreds.
- **Layers**:
- **Input Layer**: Where raw data (text, images, etc.) goes in.
- **Hidden Layers**: The deeper the network, the more abstract features it learns—like edges in images or grammar in text.
- **Output Layer**: The final answer, like a word prediction or a label.
### 2. **How Does It Learn?**
- **Training Data**: You feed it a massive pile of examples—like millions of sentences or photos. The more, the better.
- **Forward Pass**: Data runs through the network, and it makes a guess.
- **Loss Function**: Measures how wrong the guess was (e.g., "you said 'cat' but it’s a dog").
- **Backpropagation**: The network adjusts its weights backward through the layers to reduce the error. It’s like fine-tuning a million tiny knobs.
- **Gradient Descent**: An optimization method that nudges those weights in the right direction, step by step, to minimize mistakes.
### 3. **Why "Deep"?**
- More layers let the network learn hierarchical patterns. For example:
- In images: First layers detect edges, next ones shapes, deeper ones faces.
- In text: Early layers catch word patterns, deeper ones grasp context or meaning.
- This depth makes it insanely good at handling messy, real-world data—no need for humans to predefine every rule.
### 4. **Key Ingredients**
- **Big Data**: Deep learning thrives on huge datasets. Small data? It flops.
- **Computing Power**: GPUs or TPUs crunch the insane amount of math fast.
- **Activation Functions**: Like ReLU or sigmoid, these add non-linearity so the network can model complex stuff, not just straight lines.
### 5. **What’s It Good For?**
- **Generative AI**: Like me, spitting out text, or tools like Stable Diffusion making art.
- **Recognition**: Speech, faces, objects—you name it.
- **Prediction**: Stock trends, weather, whatever’s got patterns.
### Catch?
- **Black Box**: You can’t always tell why it decides something—just that it works.
- **Resource Hog**: Takes serious hardware and energy to train.
- **Overfitting**: If you’re not careful, it memorizes the training data instead of generalizing.
Think of deep learning as a super-powerful pattern-finder that gets smarter with more data and layers. Want me to break down any part—like backpropagation or why GPUs matter?”
Like trying to get more people hurt or threatened with online bullying. What a guy this psycho is.
I can’t help but notice what was actually said is not linked or referenced. Just a biased take on what is claimed to have been said. Link?
I’m not clicking on the Pedo Haven app to find it.
I for one find it hilarious that dems found a literal blind guy to "find" government waste. The jokes pretty much write themselves on this one chis 😂😂
While don’t you get a room. So everyone who is productive to society doesn’t have to see you jerk off to Bernie Sanders and Che Guevara.
Your fantasizing of me is creepy and gross. I'm not into you, dude. No means no.
I, for one, welcome our new robot overlords and wish to remind them that my decades of work in the detention industry will be very beneficial when rounding up and imprisoning dissenters.
“If all goes well, SpaceX will send Starship rockets to Mars (in 2 years) with Optimus robots, and Grok.”— Elon Musk
I'll bet you my retirement savings vs yours that this does not happen by 2/18/27
And every AI model, once it starts feeding on its own output, becomes unstable.
Grok will be no different.
Can we all agree that our federal government paying for a watchdog who is actually legally blind is…..comical gold?
Who to believe if not someone who benefits from the general public believing the hype about the possibilities with these chatbots?
Sounds like he's the manager, not the one "reading" the spreadsheets, Cletus.
(That's what "directors" do - they MANAGE things, while the people they manage do the underlying work)
Dude, it’s a funny. Just say it’s funny. It’s like our federal government is a character on Arrested Development.
Also, how would he manage to know if his peeps are doing a good job? How do you oversee the oversight crew if you can’t see?
It'd be "funny" if he was a SS officer for Presidential security.
A department manager? Not really.
So how does he oversee his department if he can’t see?
FUNFACT: Blind people can still function in many typical jobs, despite what Elno and other people might claim.
@Phenomenally Frantastic
google owns reams of data to train their ai. openai/chatgpt basically stole data. deepseek stole from openai. i'm curious where grok got the data to become #1. i'm by no means dissing grok, just wondering out loud.
google owns reams of data to train their ai. openai/chatgpt basically stole data. deepseek stole from openai. i'm curious where grok got the data to become #1. i'm by no means dissing grok, just wondering out loud.
DA HAWKEYE FOOKIN HITMAN IS PLAYIN DA HITS!!!!!!!!!!!!!! WHY ARE ALL OF DA RADICAL MARXIST SOCIALIST LIBTAHDS ALWAYS JERKING OFF TO DA BERNIE SANDAHS, DA SQUAD, DA CHE GUEVARA AND DA SLOW JOE!!!!!!!!!!!! THEY ALSO HANG OUT IN DA INTERSTATE BATHHOUSES AND EAT ALL DA SEMEN AND SOY LIKE A BUNCHA LEFTIST BETA CUCKS!!!!!!!!!! THESE FOOKIN LIB CLOWNS NEED TO GET A REAL JOB SO THEY CAN AFFORD ICE COLD CANS OF SPRITE AND START KNOWING WHAT IT'S LIKE TO BE A WINNAH WITH MAGA AND TEAM TRUMP!!!!!!!!!!!!!!!!! IF THEY WOULD QUIT JERKING OFF TO DA LIBS, WE COULD GET DA CASSVILLE BRIDGE BUILT AND ALL OF SOUTHERN CLAYTON COUNTY AND ITS PATRIOT ALPHA DAWGS WOULD FINALLY BE SET FOR LIFE!!!!!!!!!!!!!!!!!!!
