Table of Contents
Introduction
Artificial Intelligence, or AI, no longer remains a buzzword. It has become a part of our daily lives, from Alexa and Siri to Google Maps, Voice Assistant, ChatGPT, and more. Among many other emerging AI trends, generative AI, a subset of AI, has shown immense potential in recent times. Gen AI, coupled with other technologies, has led to significant technological advancements and operational efficiencies. But the differences between generative AI and traditional AI remain a key discussion point for AI developers and AI development companies.
Keep reading this blog, where we have discussed the crucial insights of Traditional AI vs Generative AI. From definition to key differences between Traditional AI and Generative AI for your understanding and clear decision making.
What is Traditional AI?
Traditional AI, popularly known as Narrow or Weak AI, is designed to be smart at one specific thing. It won’t create anything new, but it will do the specific job accurately for which it has been made. Think of it as a system built to respond to a particular set of instructions. These systems can learn from information and use that learning to make decisions or predictions.
For example, when you play computer chess, the computer knows all the rules. It can guess what you might do next and choose its own moves based on strategies it already knows. It’s not coming up with brand-new ways to play; it’s picking from the tactics it was programmed with. That’s traditional AI – a smart problem-solver within clear boundaries.
Other examples of traditional AI include Siri or Alexa (voice assistants. The suggestions Netflix or Amazon give you, or how Google finds search results. These AIs are trained to follow specific rules and do a particular job really well. But they don’t create original ideas or go beyond their programming.
What is Generative AI?
Generative AI, or GenAI, is a subset of artificial intelligence that can create new things. Unlike traditional AI, which mainly responds to specific instructions, generative AI can take the information you give it and produce something original.
For example, you ask the generative AI to write a blog post for you on any current topic. Let’s say “AI role in MVP development”! You just need to find the trending and most searched blog topic on this and write an 800+ word blog using the right keyword with all key points. Generative AI will twist this general piece of information to make the perfect blog with all key points and subpoints. It will attach real-world examples for you to modify and edit.
What’s interesting is that today’s generative AI isn’t just limited to text. It can also create images, music, and even computer code! These generative AI models learn from huge amounts of existing data and then figure out the patterns in that data. This allows them to generate new data that looks and feels similar to what they were trained on.
Key Differences Between Traditional AI and Generative AI
Applications
Traditional AI: Traditional AI, on the other hand, is helpful for everyday tasks like looking at data, finding fraud, and suggesting things you might like, such as on shopping websites.
Generative AI: Generative AI is being used in lots of interesting and creative ways. You’ll find it helps to make digital art, compose music, generate code, and create different kinds of content.
Adaptibility
Traditional AI: Traditional AI is less flexible. It usually sticks to the rules and focuses on improving at a specific task. It’s not really designed to learn and evolve in unexpected ways.
Generative AI: Generative AI is really good at adapting because it can learn and come up with new things in creative ways. As it learns from more and more information, it can create increasingly impressive content.
Practical Implication
Traditional AI: The power behind the chatbots we talk to online. The recommendation systems that suggest what to watch or buy, and predictive analytics that help forecast trends.
Generative AI: Generative AI opens up a lot of exciting new possibilities for being creative and coming up with fresh ideas. For example, in entertainment, it could help make new music, write movie scripts, or even create realistic-looking fake videos (called deepfakes). It could even help write news articles or reports.
Output
Traditional AI: Traditional AI takes existing information and uses it to give you insights or make predictions. So, the result is usually a label (like classifying an image), a suggestion (like a product recommendation), or a forecast based on what it has learned from past data.
Generative AI: Generative AI creates new things, giving every creation a futuristic touch. This version of AI can invent and bring fresh content.
Training Data Requirements
Traditional AI: Traditional AI, on the other hand, can often do a good job even with smaller, more focused sets of information. It’s more about spotting patterns and making predictions based on the specific data it has been trained on.
Generative AI: Generative AI usually needs a lot of different kinds of information to work well. The more varied and bigger the collection of data it learns from, the better it gets at creating unique things.
Performance Metrics
Traditional AI: With traditional AI, we usually measure its performance using more standard scores like accuracy, precision, and recall. These scores tell us how well the AI can correctly categorize information, predict what will happen, or spot patterns in the given data.
Generative AI: We often see how good generative AI is by looking at how creative and high-quality its creations are. This means checking what it makes, like images or text, looks real or new and interesting to us humans.
Cost
Traditional AI: Traditional AI is often free or doesn’t cost much. It can use existing technology, which makes it a practical option for many different uses. We actually use traditional AI all the time without even realizing it!
For example, Google Assistant is totally free on our phones, but it also provides us with a helpful AI service.
Generative AI: On the other hand, generative AI usually costs more. This is because it needs powerful computers and a lot of data to learn from. So, it can be a pricier option, especially for companies that want to try out the newest AI technologies.
For example, the premium version of ChatGPT (ChatGPT Plus costs $20 per month. ChatGPT Pro costs $200 monthly, which is quite expensive for most people. However, traditional AI often comes at no cost.
Learning Curve
Traditional AI: Traditional AI is generally easier to get into. The learning process is more gradual, especially if you already know the basics of how algorithms work and how to process data. This makes it more welcoming for beginners.
Generative AI: Gen AI can be a bit harder to learn at first. It often involves understanding complex ideas like deep learning and neural networks, which might be tough for people just starting out with AI.
Community & Support
Traditional AI: Traditional AI has been around longer, so it has a strong and well-established community. There are many resources available, like guides, lessons, and online forums. All these make it easier for people to find help and work together.
Generative AI: Generative AI is still pretty new, so its community and support are just starting to grow. You’ll see new online discussions and helpful materials popping up as the technology gets more advanced.
Future Trend
Traditional AI: Traditional AI is growing at a steady pace. It will likely focus on making current processes better, more accurate, and more efficient when it comes to making decisions based on data.
Generative AI: Generative AI is in a prime spot to bring about lots of new ideas. It has the potential to really change many industries. It opens up exciting ways to create content and interact with technology. Hire AI developers to get going if you have any innovative generative AI ideas!
Traditional AI vs. Generative AI: Final Takeaway
The root and expansion of AI are larger than our imagination. This game-changing technology is evolving rapidly. As we continue to explore its capabilities far and beyond, knowing the differences between traditional AI and generative AI is crucial for innovation. Both the AI have played and are playing an important role in shaping our future with unique possibilities. So, embracing these advanced technologies will be key for businesses looking to stay ahead of the evolving digital landscape curve.
If the world of AI excites you to innovate something new and unique, now is the time to get going. Contact Metizsoft, a leading AI development company, for a clear understanding of AI. Let’s create voice assistants or ChatGPT-like applications!
