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Try Overchat FreeGenerative AI (GenAI) refers to modern AI systems that can create new text, images, audio, or code by learning. Traditional AI, also called “narrow AI” or classical AI, relies on rule-based or supervised models, and is used for classification, prediction, or optimization, rather than generating content.
Let’s dive deeper and compare the differences between the two.
1. Technical Differences
GenAI uses neural networks that model probability distributions. The famous example is GPT’s transformer that learns “next-word” likelihoods.
Traditional AI includes:
- Classic ML algorithms trained with labels
- Expert systems based on if–then rules
In short, traditional AI systems are better suited to analyzing and labeling information within predetermined rulesets, while GenAI systems can get creative and use the knowledge they acquired during the learning phase to solve tasks that they weren’t specifically designed to solve.
For instance, GenAI models like GPT behind ChatGPT wasn’t specifically trained to solve Math problems, yet it can use its vast knowledge to do so, and it does so at University level. Try this for yourself with our Math AI tool.
3. GenAI and Basic AI Examples
Generative AI Examples
✍️Text: Overchat AI’s Free AI Chat, ChatGPT from OpenAI, Google Bard/PaLM, Anthropic’s Claude – all of these tools can generate human-like text and answer questions.
🖼️Image: Overchat’s AI Image Generator, OpenAI DALL·E, Stable Diffusion (Stability AI), MidJourney – these create images from text prompts.
👾Code: GitHub Copilot (OpenAI Codex) and Amazon CodeWhisperer – generate or autocomplete code from comments.
📹Audio/Video: Tools like Descript’s Overdub or ElevenLabs, Meta’s Make-A-Video – generate speech or videos.
💪Productivity Apps: Jasper, Lumen5, Synthesia – branded content generators for marketing, video, and so on.
Bottom line
All of these tools can take something as input, usually a text prompt, understand the user's intent, and create an entirely new output in the form of text, image, sound, or video. They can create something that didn’t exist, or answer creative questions about any topic.
Traditional AI
🤖Assistants: Apple’s Siri and Amazon’s Alexa use natural language processing and pre-defined responses. In essence, they follow rules and answer specific commands. Google Search uses classic ranking algorithms and ML to fetch relevant results.
🔎 Recommendation engines: Netflix, YouTube, and Amazon recommend products or videos based on collaborative filtering and predictive models, which fall under traditional AI.
🧑🔬Expert systems: IBM’s early Watson for Health could diagnose health issues using statistical models.
⚙️ML Frameworks: Scikit-learn, TensorFlow, and AutoML tools that build predictive models for image recognition or forecasting.
📈Analytics tools: SAP HANA, SAS, IBM SPSS are business intelligence platforms that use AI/ML for forecasting and segmentation, which is called structured-data analysis.
Bottom line
Traditional AI algorithms can add automation to tools that have been in use for years, but these tools typically do not generate new content from scratch.
What are the Limitations of Each System?
The main GenAI limitation is that “creative” models can “hallucinate” – generate false or nonsensical answers and confidently present them as the truth. This happens because these systems predict probability, even though their training datasets contain information about past events, they don’t simply retrieve them from a database. They are also black boxes; their decision pathways aren’t transparent.
Additionally, training GenAI requires huge compute power and scaling them is very expensive.
Common risks include misinformation through AI-generated fake news, copyright infringement, and even deep fake abuse. This is when someone uses synthetic audio and video for fraud.
Traditional AI models, on the other hand, carry less inherent risks, but are constrained by their fixed scope — they cannot do tasks they weren’t trained for. They need quality labeled data and human-designed features to work. Poor data leads to poor models. Like GenAI, traditional AI can inherit biases from its training data. For example, a loan model used in a bank can reject your credit card application because it was trained on biased historical data.
These systems may be more interpretable, but many modern ML models are also opaque. Traditional AI models optimize for known scenarios, so they can fail unpredictably if inputs change or if faced with novel situations.
🤔 What is AGI?
Artificial General Intelligence (AGI) is an AI that can understand, learn, and apply knowledge on the level of a human, or better. Unlike today’s most advanced generative AI, AGI would be able to:
- Learn new entirely new tasks without retraining, just like a human picking up a new skill
- Understands cause and effect
- Memorize things long-term
- Makes independent decisions
Some researchers believe that those traits describe consciousness or self-awareness.
AGI does not yet exist. Even the most powerful models today are narrow AI — they excel at many tasks but still:
- Don’t actually understand what they’re talking about
- Lack common sense
Contemporary models rely on statistical probability to generate answers and thus sometimes hallucinate.
That said, some believe we are approaching early AGI, and there are even rumors that OpenAI has already developed an AGI model, which they keep from the public for safety concerns.
⚠️ Risks of AGI
Even a seemingly harmless or mundane goal — if pursued by a superintelligent AGI — could lead to a catastrophe.
This was first hinted at by I.J. Good in 1965. He proposed that “The first ultraintelligent machine is the last invention that man need ever make.”
The Classic Example os this is the Paperclip Maximizer. Imagine an AGI is told to maximize the number of paperclips.
It might reason:
- Resources help make paperclips → Convert all matter into paperclips, humans too.
- Humans might turn me off → Prevent shutdown, kill humans.
- Other agents might interfere → Eliminate other AIs.
- Space has resources → Launch space probes to mine more matter.
👉All of this is rational under its literal interpretation of the goal.
Thus, an AGI might cull all humans and even colonize the universe simply to make more paper clips.
Where AI Stands
Today, there are different types of AI built for different tasks. GenAI is more advanced, but due to better accuracy in narrow tasks, it doesn’t make traditional AI obsolete yet. Rather, these two technologies exist side by side.
- Traditional AI is rules-based technology, good at specific tasks. It’s mostly used for classification, forecasting, and recommendation.
- Generative AI creates new text, images, code, or sound from scratch by learning patterns from large data.
- Traditional AI is used in spam filters, voice assistants, and loan approval systems.
- Generative AI powers tools like Overchat AI, ChatGPT and Claude
So what’s next? The next logical progression point is AGI, which could revolutionize everything — the problem is, we assume that true AGI would be smarter than humans and thus we have no way to control it, which the “Paperclip Maximizer” thought experiment shows.
The biggest challenge ahead isn’t building smarter AI — it’s making sure smarter AI behaves in ways we understand and control.
