AI is a bit of a puzzle for people. It has evolved a lot from just automated caller tune messages in the past to fully functional robots now. Almost every part of our life includes a lesser or higher form of artificial intelligence. 

However, there’s always a competitive shutdown between two subfields of AI. They are traditional AI and generative AI.  With the artificial intelligence wave, many business owners and marketers are perplexed about which technology to implement. 

Undoubtedly, traditional AI has been used vastly, and even now, many brands incorporate it into their strategy. But generative AI has successfully made its mark in a very short period. Some people think that traditional AI is a tale of the past. 

But is it true? Keep reading to find out.  According to statistics, around 91.5% of businesses invest in artificial intelligence technology. 

You might also be thinking about which AI subfield suits your business. You need to think quickly because, in this digital age, those who are quick and up-to-date about the latest technologies thrive. Meanwhile, those who stay behind only regret it. 

However, you don’t have to worry at all.  In this article, we’ll discuss two major AI subfields and which one you should implement in your business.  Let’s dive in.

What Is Artificial Intelligence?

AI stands for Artificial Intelligence. In simple terms, AI creates, trains, and develops machines that can simulate human intelligence. It is trained to think and operate just like human brains. It is designed to do a lot of tasks. 

These tasks can be as simple as voice recognition, a common feature in almost everyone’s smartphone. Or it can be as complex as making complex decisions and machinery. 

The first AI was introduced in 1955. The system was Logic Theorist, and you can tell what it’s about just by the name. 

It proved mathematical theorems using heuristic rules. This helped many mathematics get a deep insight into the field and opened doors for improvements. It was a huge milestone for modern AI research.

What Is Traditional AI?

Traditional AI, also known as classical AI or symbolic AI, refers to the early approach to artificial intelligence that emerged in the 1950s and dominated the field until the late 1980s. This approach uses symbolic rules and logic to model human cognition and problem-solving.

If you want to understand its working process, then you need to know about two things;

• Algorithms
• Pre-defined rules 

These are the two key factors on which the entire system of traditional AI operates. With its usage, you can easily achieve desired outcomes in business marketing. And that’s why many people have relied on it for years. 

For example, when you used to call customer service, your relative, or anyone, instead of the usual ringtone, you get a message. That message could be that the other person is busy, on another call, or the phone is switched off. That’s the magic of this technology. 

The information is already stored in the system by humans. And based on that data, you get a specific response. 

Pros & Cons Of Traditional AI 

Traditional AI is an amazing technology that can do wonders if it’s used correctly. Web search engines of big companies such as Netflix, Google Search, and Amazon even use traditional AI. However, it’s important to know the pros and cons of technology. It helps you in making the right decisions for your business. 

Pros

• Reliable and consistent due to pre-defined rules and algorithms.
• Transparent decision-making process with explicit programming.
• No data dependency, advantageous with limited data availability.
• Stable and predictable performance. It reduces the risk of unexpected behavior.

Cons

• Limited in solving complex problems. It lacks flexibility and adaptability.
• High maintenance overhead due to significant programming efforts for updates.
• Not suitable for ambiguity. It struggles with uncertain situations due to fixed rules.

What Is Generative AI?

Generative AI, also known as generative modeling, is a type of artificial intelligence that aims to create new data that resembles some existing data distribution. Unlike traditional AI, which focuses on solving specific tasks or problems based on predefined rules and data, generative AI is capable of generating entirely new data points based on patterns it has learned from the training data.

It won’t be wrong to say that generative AI is at least 10 steps ahead of traditional AI. Because it doesn’t have specific pre-defined rules or algorithms. It uses a unique approach based on two key aspects;

• Data 
• Patterns 

Based on that, it creates information that’s unique and easy to understand. It has overcome one of the biggest traditional AI drawbacks. Basically, you might have realized that the former subfield of AI doesn’t have a lot of creative freedom. 

In short, it’s not your dreamy “idea-generator.”

It can’t create engaging, fresh and different types of content without assistance. However, generative AI can do all of that without excessive coding. That’s why various industries already use this technology to its fullest potential. Some of the most popular use cases of generative AI are;

• Creating breathtaking art 
• Mesmerizing music compositions 
• Imaginative literary work 
• Editing videos 
• Persuasive ad copy 

And the list of its use cases goes on and on. As a marketer, you need to know how to use these technologies in your campaigns. Because it not only saves your time but also saves you from unnecessary expenses. 

Snapchat has already started working on it. My AI is an AI-powered chatbot by Snapchat, which is a huge hit. Over 150 million people have sent a whopping 10 billion messages. But what does it mean for their business?

It means more people are spending time on their apps. Their audience became their biggest marketer by spreading the word through social media posts and reels of My AI on social media sites such as Instagram, Twitter, and WhatsApp. It made more people curious and they downloaded the app just to use this chatbot. 

Pros & Cons Of Generative AI

Generative AI offers creative possibilities, adaptability, and realistic outputs. This field is progressing at a rapid pace, and that’s why it’s immensely important to stay up-to-date.  If you’re considering using generative AI for your business, you need to know some benefits and disadvantages. 

Pros

• Generates original and creative content for artistic purposes.
• Versatile and can adapt to new situations without additional programming.
• Requires less training data, making it suitable for limited data availability.
• It can quickly generate large amounts of data, saving time and resources in data creation for training other AI models or testing algorithms.
• Produces beautiful images and natural-sounding speech for various applications.

Cons 

• Unpredictable results, challenging to maintain full control over plain language content.
• Potential ethical concerns due to biased content or incomplete training data.
• A lack of diversity in generated outputs due to overfitting specific data.

Traditional AI vs. Generative AI

Now let’s discuss the main part of this article, which is the difference between Traditional AI vs. Generative AI.

	Traditional AI	Generative AI
Learning Approach	Follows pre-defined rules and algorithms. Best at recognizing patterns.	Creates new content based on complex patterns and data learned. Can create new patterns.
Adaptability	Limited adaptability to new situations without coding.	Highly adaptable, can generate content without coding.
Problem-Solving Capability	May struggle with complex, real-world scenarios.	Versatile problem solver who excels in creative tasks.
Content Creation	Cannot generate new content on its own.	Produces original and creative content. High-quality basic web content in vast quantities.
Data Dependency	Requires less data for training.	Requires more data for training.
Training Complexity	Less computationally intensive to train.	More computationally intensive to train.
Industry Applications	Commonly used in rule-based tasks and automation.	Used in creative industries, virtual worlds, and more.
Bias in Generated Content	Tends to be less biased in content generation.	Can be biased if trained on biased data.
Novelty	May lack novelty in generated outputs.	Produces fresh and innovative outputs.
Customization & Personalization	Less flexible in customizing outputs.	Offers more customization and personalization options.

Practical Applications Of Traditional AI

Traditional AI has been there for a while. You’ll be surprised to hear about the practical applications of traditional AI that you’ve been using.

Gmail:

Gmail has around 1.8 billion monthly active users, which keeps increasing. This platform heavily relies on traditional AI. It is basically incorporated into two major tasks;

Spam filtering:

These AI algorithms analyze all the content and the patterns of emails. Based on that, it differentiates legitimate emails from spam. Deep learning is constantly used to improve this technology to gain maximum accuracy rate.

Email Categorization:

Just imagine how difficult it would be if all your emails were in your inbox, such as your drafts, social media, and deleted emails. But thanks to AI, Gmail classifies emails into different categories such as drafts, sent, promotional, etc.

Virtual Assistants (Siri, Google Assistant)

Voice optimization is becoming the need of time. You can’t simply focus on content only and completely ignore voice searches. Even many famous voice assistants use this subfield of AI. But how is that possible?

Let me break it down for you in simple terms.

Speech Recognition:

These assistants use a very specific technique known as automatic speech recognition. It converts your words to text.

Spoken Words => Text

To ensure fewer mistakes, the machine is trained using loads of voice data. So it quickly and accurately transcribes the user’s commands.

Natural Language Understanding:

After transcription is completed, your spoken words reach the next stage. This stage is natural language processing. The algorithms understand your text to comprehend the intent and then extract information.

NLP => User Intent => Provides Data

With the latest updates and improvements, this system is becoming more advanced.

Google Maps

Google Maps are used to reach their destinations quickly. So even if you don’t know the route to a cafe in America, you can easily see the complete route to your destination in Google Maps. It employs traditional AI for its;

• Route optimization
• Traffic prediction features

Thus helping users find the most efficient routes and avoid congestion during navigation.

Practical Applications Of Generative AI

Generative AI applications have caused a sensation on the internet, captivating users and creators alike. In a short span, it has become the foundation for an endless array of innovative applications.

OpenAI’s GPT (Generative Pre-trained Transformer)

Do you remember spending countless hours to find a specific answer to your thesis paper? Or find a recipe that you once read?

This generative AI has solved the problem. GPT-3, developed by OpenAI, is a language model that uses Generative AI to generate human-like text based on given prompts. This advanced tool can perform tasks like;

• Language Translation
• Content creation
• Even answer complex questions

Companies utilize GPT-3 to create AI-powered writing assistants, chatbots, and language understanding systems.

DALL-E

Stock photos aren’t free, and even other stock websites demand thousands for subscriptions only. And even after paying, there’s no guarantee you find the perfect image.

DALL-E is an impressive Generative model from OpenAI. It generates creative and authentic images based on textual descriptions.

It’s a powerful tool to create illustrations of imaginary creatures, scenes, and objects that don’t exist in the real world.

Virtual Avatars and Influencers

Brand influencers can skyrocket any business’s success. But not everyone can afford that.

Generative AI came up with a unique solution for this problem. Some companies use this generative AI technology to create virtual avatars and influencers for marketing and entertainment purposes.

These avatars engage with users, generate a wide range of content, and interact on social media platforms. You can easily customize them and make them uniquely fit your brand values.

Using this popular technique Gucci is giving their customers a virtual tour of the Gucci Garden. There are various generative AI applications that even help in image recognition, making boring product ideas and product designs, persuasive and unique.

Luminoso

Luminoso Technologies specializes in Natural Language Understanding. They offer AI-driven text analytics solutions.

What does it mean for your business?

It’s a cloud-based platform. It helps organizations to extract valuable insights from unstructured data. The best part is you can get data in around 15 languages.

With advanced machine learning and sentiment analysis, Luminoso finds;

• Hidden patterns
• Sentiments
• Biases in customer feedback
• Employee responses
• Market research

The platform enables personalized recommendations, improves business intelligence, and fosters data-driven decision-making.

How Can You Use Traditional AI & Generative AI for Businesses?

Now you already know about AI and its two major types. However, your mind might be flooded with lots of questions.

• How can you implement it in your business?
• Will it help you gain any profit?
• Is traditional AI better, or is generative AI suitable for my business?

These are the questions that I asked myself. And after a lot of digging, I finally found the answer to all these questions.

Here’s a step-by-step guide on implementing AI in your business smoothly. It will give you a clear understanding of all the above answers.

Step 1: Define Crystal Clear Objectives

Before starting any marketing campaign or adopting new and powerful technologies for your business, you must do this one thing.

It’s Defining Objectives.

If you ace this step, the next steps will not be much difficult. If you don’t know how to define clear objectives, then ask yourself this;

• What do you want to achieve through AI implementation?
• In what areas can you add AI?
• How can you add value to your customer’s life through AI?

Remember to have a customer-centric approach. Because when you make customers happy, then money will fall easily.

Step 2: Conduct a Feasibility Study

I’ve seen marketers spend days and even months just doing a feasibility study. And I usually wondered why it’s so important.

But I eventually understood how important it is. You’ve to assess the readiness of your business to adopt AI. Maybe your business follows all the traditional approaches. How can you move it to generative? Or the best option might be transitioning from traditional AI to generative AI. Moreover, you evaluate;

• Data
• Resources
• Infrastructures

All the things which are required for successful implementation.

Step 3: Collecting Data & Preparing

You have to collect data which is followed by the feasibility study. Don’t rely on just one resource for data collection.

That can be extremely disastrous for your business, especially if you want to be profitable in the long run.

Now that you have collected all the data, you need to clean it.

How?

By processing the data and removing any information, which is;

• Misleading
• Duplicate
• Doesn’t align with your business mission or values

Step 4: Choose the Right AI Approach

You can do all the data research and cleaning, yet, nothing will work if you choose the wrong AI approach. A lot of time, energy, money, and data go in vain because of just one wrong step.

But how can you know which is the most suitable option? Here’s how.

• Traditional AI: If your business requires machine learning models, data mining, and NLP, then this is a suitable option.
• Generative AI: This is for you if creativity is the core of your business. Content generation and stimulation are required heavily, and GANs can be of immense help.

Step 5: Select AI Tools and Platforms

Make a list of all the tools and platforms your company can use. One good way to do this is by conducting a survey or sending a questionnaire to every department about the usage of AI models.

Ask them questions like;

• In which areas do they believe AI should be implemented?
• Which AI tools can help them increase efficiency?
• How much training will be required to implement these tools?

Based on answers to these questions, you can use respective tools from any subfields of AI. It can be TensorFlow to Google Bard and cloud services to any generative AI framework. In a nutshell, there is a wide range of applications you can use for business processes.

Step 6: Model Development and Training

If you’ve read this article so far, you might know that to get desired results, you must train these AI models. Here’s a little sneak peek into the process.

You need to choose any algorithm and then design and develop your system in it. Provide various datasets to the AI model to ensure it generates perfect results.

It’s generally applicable to both subfields of AI.

Step 7: Integration and Testing:

Now you’re ready to implement AI into your business. But you need to ensure that everything’s working perfectly. I highly recommend testing the system with the assistance of experts.

• Integrate system
• Test the processes or applications
• Ensure they work seamlessly with existing systems
• Check the accuracy in tasks and stability of the AI model thoroughly

Have regular discussions with your team members about how they are using it. Initially, you might get a mixed response. Some people understand AI, but some face difficulty executing even simple tasks.

What should you do if you ever encounter such a situation?

1. Provide training
2. Tell its benefits
3. Encourage the adoption of technology

Step 8: Continuous Improvement:

After implementing this system, you’re ready to leverage your business and take it to new heights. You’ve already achieved a milestone, but there’s still some work left for you.

• Regularly update AI models
• Monitor the AI system’s performance
• Gather feedback and insights for improvements

Remember that successful AI implementation requires an iterative and adaptive approach.

Nothing is perfect. You must keep polishing it and updating it according to need and time.

Future Of AI

The future of AI is bright, and it’ll grow even more to assist people. It promises exciting developments that will shape the landscape of artificial intelligence. A lot of;

• Hyper-personalized experiences
• Creating tailor-made original content
• Products
• Services for individual users.

This level of personalization will revolutionize customer engagement and brand loyalty.

Artists, writers, and designers will have AI co-creators, sparking new forms of art and storytelling. This collaboration will give rise to more opportunities.

However, there’ll be a lot of sophisticated, ethical considerations related to content creation and data privacy because of generative AI. Especially ensuring that AI-generated content is used responsibly and avoiding biased outputs will be challenging.

Conclusion

Traditional AI and generative AI are major subfields of AI. Both these branches hold immense potential to revolutionize a variety of industries, and their evolution in the coming years is eagerly anticipated.

At EchoFosh we help people skyrocket their business. Contact EchoFish today to make your business your audience’s top favorite.