Traditional AI Examples: Still Relevant in 2024?

Artificial intelligence (AI) is everywhere these days, from the moment you wake up to a smart alarm to the personalized recommendations on your favourite streaming service. But with the rise of new concepts like generative AI, you might wonder: is traditional AI still relevant?

In this blog, we'll tackle traditional AI, looking into specific examples you encounter daily. We'll see how these well-established techniques still power innovation and make our lives easier, even in the age of cutting-edge advancements. 

Let’s go and learn the modern value of traditional AI!

What is Traditional AI?

What is Traditional AI

Traditional AI, also known as symbolic AI or GOFAI (Good Old-Fashioned Artificial Intelligence), refers to the early approaches to artificial intelligence developed from the 1950s through the 1980s.

For example, an expert system is a traditional AI designed to act like a human expert. These systems are programmed with specific rules and knowledge, which they use to provide recommendations or make decisions. 

However, traditional AI systems have limitations. The key thing to understand about traditional AI is that it relies on pre-programmed rules and knowledge. The system doesn't learn or adapt on its own - it just follows the instructions it was given by the human experts who created it. 

This makes traditional AI systems good at specific, well-defined tasks but struggle with more open-ended, complex problems that require flexibility and learning.

Traditional AI Examples in Business

Traditional AI Examples in Business

Traditional AI, also known as Narrow AI, tackles specific tasks and excels at them. This is especially true in businesses. Here are some examples of Traditional AI and how they are used to propel businesses in 2024.

Automation and Process Optimization

Customer service is at the forefront of businesses, and traditional AI is just the right tool to keep customers happy. Here are examples of how it helps with customer service automation:

  • Robotic Process Automation (RPA): Using software robots to automate repetitive, rule-based tasks like data entry, invoice processing, or customer service inquiries.

  • Optimization Algorithms: Applying algorithms to optimize business processes, such as supply chain logistics, resource allocation, or scheduling.

Predictive Analytics and Decision Support

Businesses are increasingly using traditional AI to gain a competitive edge. For instance, retailers use predictive analytics to forecast demand and optimize inventory levels, ensuring they have the right products in stock when customers want them. 

Just like Amazon suggests new products you might enjoy based on what you've bought before, traditional AI helps businesses predict and personalize all sorts of things for their customers.

Natural Language Processing (NLP)

Businesses also use Natural Language Processing (NLP) to improve customer interactions and gain valuable insights. Examples include chatbots and virtual assistants to answer customer questions, troubleshoot problems, and even handle transactions, freeing up human representatives so they can handle more complex issues. 

NLP is also used for sentiment analysis, where businesses can analyze social media posts, customer reviews, and other text data to understand how customers feel about their brand, products, or services. 

Computer Vision

Computer vision is a field of artificial intelligence that enables computers and systems to derive meaningful information from digital images and videos. Two common computer vision applications include optical character recognition (OCR) and product inspection and quality control.

OCR involves extracting text from images or documents, which can be used to automate data entry or document processing tasks. 

Another application of computer vision is product inspection and quality control. Manufacturers can use computer vision systems to automate the inspection of their products, checking for defects or anomalies. This can help improve product quality, reduce errors, and increase production throughput compared to manual inspection processes.

Expert Systems

Expert systems are computer programs that act like expert consultants. They can be designed to diagnose problems (like in healthcare or IT support) by following a set of rules. In other cases, they use artificial intelligence to help managers make complex decisions, like what product to develop next.

Other Use Cases of Traditional AI

Use Cases of Traditional AI

Traditional AI can be used for many helpful things beyond business customer service.  

There is also AI in healthcare to help doctors detect diseases earlier. The AI can analyze medical scans like X-rays and MRIs and spot signs of problems that doctors might miss. Even AI chatbots can talk to patients and provide some initial guidance before they see a human doctor.

Governments are also using traditional AI. For example, they can use it to optimize traffic patterns and help police departments determine where to send officers to prevent crime. AI can spot patterns in the data that humans might not see.

In schools, AI is used to create "adaptive learning," where the computer program adjusts the lessons and pace based on each student's performance. This helps students learn better than a one-size-fits-all approach.

So, in all kinds of industries, traditional AI is being used to analyze data, make predictions, and customize experiences in ways that improve people's lives. 

How is Traditional AI Used in 2024?

Traditional AI, even in 2024, is still very much a part of our lives. Here are some ways we encounter traditional AI:

  • Smart searches: When you use a search engine and get results that seem to understand exactly what you're looking for, that's traditional AI at play. These algorithms sift through massive amounts of information to give you the most relevant answers.

  • Recommendations: Ever notice how streaming services or online stores suggest things you might like? Traditional AI analyzes your past choices and recommends similar items.

  • Virtual assistants: Those helpful chatbots you interact with on websites or phone apps? Traditional AI powers the ability of these AI tools and knowledge base systems to understand your questions and give you basic support.

  • Spam filtering: All that unwanted email? Traditional AI filters out spam messages before they ever reach your inbox.

  • Fraud detection: Used by banks and credit card companies, traditional AI is still used today to identify suspicious transactions and prevent fraud.

  • Manufacturing and quality control: In factories, traditional AI helps machines perform complex tasks and identify product defects.

While there are newer, flashier forms of AI emerging like Generative AI, Traditional AI remains essential for many of the technological conveniences we've come to rely on.

Which is Better in 2024, Traditional AI or Generative AI?

In 2024, both traditional AI and generative AI have their own strengths and are being used for different purposes. Here's a simple explanation of how they compare:

Traditional AI is better at tasks like data analysis, decision-making, and automating repetitive processes. Businesses use traditional AI to crunch numbers, spot patterns, and make predictions to help them run more efficiently. Doctors also rely on traditional AI to analyze medical scans and assist with diagnoses.

On the other hand, generative AI is better at creative and open-ended tasks. Generative AI models, like the ones that can write stories or generate images, excel at producing original content. This makes generative AI useful for marketing, entertainment, and education.

While traditional AI and generative AI have differences for different types of problems, traditional AI can be used as a powerhouse for optimization, analysis, and automation. Generative AI shines when you need fresh ideas, tailored content, or human-like communication.

The two types of AI also work well together. But if you want to use only one, the key is knowing which AI tool works best for the task at hand.

Want to know which of these tools is right for your business? We have resources for you to check out related to Generative AI in capturing leads!

© 2023 Frequentli. All Rights Reserved.

© 2023 Frequentli. All Rights Reserved.

© 2023 Frequentli. All Rights Reserved.