TLDR;
This video compares generative AI and conventional AI, highlighting their fundamental differences, applications, and potential for future integration. Generative AI creates new content, while conventional AI analyses existing data for decision-making. The video also discusses the types of input and output each AI handles, their learning techniques, and when to use each type.
- Generative AI creates new, original content, while conventional AI analyses existing data to make decisions or predictions.
- Conventional AI uses structured data and produces decisions or classifications, while generative AI handles diverse inputs and generates new content.
- Combining both types of AI can lead to more powerful and versatile systems in the future.
Introduction to Generative AI and Conventional AI [0:00]
The video introduces a comparison between generative AI and conventional AI. Generative AI, a subset of machine learning, is capable of creating new, original content such as text, images, music, video, and 3D models. It works by ingesting vast amounts of data, recognising patterns, building a complex internal model, and then using that model to generate new content. Examples include chat GPT for text generation and d e for image creation. Conventional AI refers to AI systems that have been in use for decades, designed to analyse existing data, interpret it, and make decisions or predictions based on that analysis. It encompasses rule-based expert systems and modern machine learning algorithms, operating within predefined parameters to solve specific problems.
Examples of Conventional AI [1:44]
The video provides examples of conventional AI in daily life. Recommendation systems analyse past behaviour to suggest products or songs. Fraud detection systems monitor transactions to flag potentially fraudulent activity. Autonomous vehicles use sensors and AI algorithms to navigate roads and make driving decisions. Speech recognition systems interpret human speech for voice commands. In healthcare, AI systems analyse medical images and patient data to assist in diagnosing diseases.
Fundamental Differences in Primary Functions [3:10]
The primary difference between conventional and generative AI lies in their functions. Conventional AI focuses on analysis and decision-making, processing data to produce decisions, predictions, or classifications based on existing data. Generative AI focuses on creation, generating new, original content based on patterns learned from training data. For example, a conventional AI language model performs sentiment analysis on product reviews, while a generative AI language model like GPT-3 can write an entirely new product review.
Nature of Input and Output [4:37]
The nature of input and output differs significantly between conventional and generative AI. Conventional AI typically takes in structured data, such as numerical or categorical data, and outputs a decision, prediction, or classification. Generative AI can handle a wider variety of inputs, including text prompts, images, or music, and outputs new content in the same or a different medium. For instance, a text-to-image generative AI might take a text description as input and produce a new image as output.
Learning Techniques [5:41]
Conventional AI often uses supervised or unsupervised learning techniques. Supervised learning involves training the AI on labelled data, while unsupervised learning involves finding patterns in unlabelled data. Generative AI frequently employs more complex learning techniques, such as adversarial learning, where two AI models compete to generate realistic outputs. Transformer architectures are also key, enabling the AI to understand and generate content with a deep understanding of context. Generative AI requires larger data sets and more computational power than conventional AI.
Choosing Between Conventional and Generative AI [6:56]
Choosing between conventional and generative AI depends on the problem. Use conventional AI for well-defined problems with clear objectives, analysing structured data, or requiring high accuracy in prediction. Use generative AI to create new content, deal with creative tasks, explore solutions, generate synthetic data, or work with natural language requiring deep understanding.
Future Combination of Both Types [7:44]
The future holds the potential for combining conventional and generative AI to create more powerful systems. For example, a system could use conventional AI to analyse market trends and then use generative AI to create new product designs. In medicine, conventional AI could diagnose diseases, while generative AI creates personalised treatment plans. Advancements are likely to blur the lines between these two types of AI.
Recap of Key Differences [8:33]
The video recaps the key differences between conventional and generative AI. Conventional AI analyses and decides, while generative AI creates.
