Generative AI vs Traditional AI | Key Differences Explained Clearly

Key Takeaways

So in this video we are going to learn how generative AI differs from traditional AI. So let's start our tutorial with the concept introduction to AI and the need for comparison. So artificial intelligence or simply AI is a broad term used to describe missions or computer programs that are able to think, learn and solve problems just like humans. Over the years, AI has developed into many different types based on what it can do. Two main types that are often compared are traditional AI and generative AI. So even though both of them fall under the same big category of AI, they work in very different ways. Understanding the differences between them is important because it help us know when to use which type and how each one helps us in our daily life. So now let's see some of the goals and functions of traditional AI. So here traditional AI is mostly focused on solving specific problems using logic rules and predictions. Mostly machine learning may fall under traditional AI. So traditional AI is designed to take input analyze it using predefined instructions or learn patterns using some algorithms and it gives a specific output. For example, let's consider a spam filter in an email system which uses traditional algorithms like nase etc. So in that email spam classification system AI looks at the content of emails matches it with known patterns and decides if the email is spam or not. So another example is recommendation system like the one used on shopping websites which uses your past activity to suggest similar items. So traditionally I follows a very structured path and usually works well in task that have a clear goal and predictable outcomes. It needs a structured data. So now let's see some of the goals and functions of generative AI. So generative AI is focused on creating new content. Unlike traditional AI which mostly classifies, recommends or detect things, generative AI goes a step further. It produces original text, images, music or code based on what it has learned from the previous data. So it does not follow fixed rules. Instead, it learns the patterns from the large sets of information and uses that learning to generate something that looks real and meaningful. For example, it can write an article, it can create a picture or it can generate a new piece of music. So the main purpose of generative UI is not just to recognize or respond but to actually create something which is new and useful. Now let's see some of the data usage and learning approach in both traditional AI as well as in generative AI. So traditional AI learns by being trained or structured data which means the data that is clearly organized in the rows and columns or with fixed labels. It may uses supervised learning or unsupervised learning but supervised learning in most cases where the correct answers are already given and the model just learn to match input to those correct output using some functions. So in contrast generative AI often uses unstructured data such as long articles, images or recordings where there is no single right answer. It mostly uses unsupervised or self-supervised learning to understand the deep patterns and the relationships in the data. So this allows it to make creative output instead of only making predictions like in traditional AI. >> Now let's see output type and flexibility in both traditional AI and in generative AI. So another major difference is the type of output these systems will provide. So traditional AI is limited to giving specific concepts like yes or no, true or false or a set of defined chance defined choices or um estimating a continuous number. Mostly traditional AI is used for regression based problems, classification based problems or clusting based problems. For example, let's say our traditional AI model is trained to recognize whether the image is cat or dog. Now, if you give any image, it may say cat or dog because that model is trained on the images which contains only cat and dog. But generative A on the other hand is much more flexible. It can write a full paragraph, generate a photo of a cat that never existed or even created uh and also generate can create a conversation between you and the chatboard. So its output is often longer, more detailed and more humanlike. So this flexibility makes generi useful in areas like writing, designing, customer services, education and content creation. Now let's see some of the technologies used in both traditional AI as well as in Gen AI. So traditional AI system often uses some mathematical algorithms or some machine learning algorithms like decision support vector missions or rule based systems and some basic neural networks. So these models are powerful for task like classification, regression and logical decision making. So generative mostly uses advanced deep learning models especially transformer based models like GPT which stands for generative pre-trained transformer. So these models can understand context meaning and relationship in a language and use that understanding to generate realistic content. So the technology used behind generate is much more advanced and requires more computing power and training data than when compared to traditional AI. So now let's see some of the examples of use cases. So traditional AI is used in things like fraud detection, face recognition, root planning and weather forecasting. It is very good at automation and prediction in industries like banking, transportation and in manufacturing. But generative AI is used in creating social media content, designing websites, writing product descriptions, translating languages and even generating voice or video. It is most useful in creative industries, customer support and personalized experiences. While traditional AI focuses on making decisions or identifying patterns, but generate AI focuses on generating new material based on those patterns. Now let's see human involvement and control in both traditional AI and in generative AI. In traditional AI, human involvement is high during the designing phase or during the training the model. Experts often need to give clear rules, label the data and monitor the output. For example, when we are creating machine learning model, we need to take care from data collection to model deployment. We need to spend hours on data prep-processing, analyzing the data correctly and analyzing the problem statement, selecting the most relevant model and applying that model and finally evaluating that model. So human environment is high during the training the model in traditional AI. In generative AI once trained can operate more independently but creating a generative AI based model like model like in chart GPT takes more time or more computational power but once trained they can operate more independently. Those so human supervision is still necessary to ensure that the content it produces is appropriate and accurate because generative AI can produce incorrect or batched content. Human guidance is important to avoid those such type of problems. But the level of creativity generative offers allows humans to do more with less effort. Especially in the fields like marketing, writing and education. So creating a models in traditional AI are like very easy and it may take a few days but creating a model that comes under generative AI is very tough process like it may require more computational resources. It may require a lot of time it may require lots of data. Now let's see some of the summary and final thoughts that we discussed in this video. So both traditional AI and generative AI are important parts of modern technology. So they are designed for different task and solve different problems as well. So traditional AI is best when we need to fix uh like traditional AI is best when we need fixed clear and logical outcomes while generative AI is best when we want creativity variation and original content. As we move forward, those two forms of AI will continue to work together, helping humans in both structured decision making and imaginative creation. So, understanding the differences help us to each one more effectively and responsibly. Now, let's summarize what we learned in this particular video. So traditional AI solves problems like uh regression, classification, clustering but generative AI creates content. So traditional you you may use a structured or unstructured data but generative uses unstructured data. So traditionally AI gives exact answers like it can predict or it can estimate a continuous numbers. It can able to classify the things into a binary classification problem or a multi-classification problem or it may clusters the group of similar data. But generate AI gives flex flexible creative outputs like it can generate articles, images etc. So traditional AI uses supervised or unsupervised learning mostly in most cases traditional AI uses supervised learning. So generative AI uses unsupervised learning or self-supervis supervised learning. So traditional AI uses rule based models and some machine learning models. So generative AI uses transformers and deep learning models. So traditional AI is for prediction and classification and for clustering. But generative AI is for writing, designing and creating. So traditional AI needs more human rules. Generative AI needs more human review after the output given by an GI model like chart GPT. So in the next video we are going to learn how generative AI will produce us output that make the human creativity.