Traditional AI vs. Generative AI – do you know the difference?

‍In the evolving landscape of AI, two fundamental forms have emerged – traditional AI and generative AI. These represent two distinct paradigms with their own set of principles and methodologies. Each has unique characteristics and applications. Understanding the differences between them is essential to understanding the trajectory of AI and its impact on various industries

Artificial intelligence (AI) is valued for its potential to automate, improving business efficiency and overall quality of life. Harnessing the power of AI in conjunction with human-like competencies such as empathy, critical thinking, and creativity has benefits for many fields.

While traditional AI and generative AI are not mutually exclusive, it is crucial to understand how they differ and what to expect from tools based on each.

Traditional Artificial Intelligence (AI)

Traditional AI, also known as deterministic AI, relies on pre-programmed rules and algorithms. It is designed to excel at a single task or a limited set of functions based on a specific set of inputs.

💡 Deterministic Algorithms – algorithms that consistently produce the same output for the same input, operating based on defined rules, ensuring predictability and repeatability of results

Rule-based AI learns to identify patterns, using them to predict or generate outcomes. It is good at tasks that require logical reasoning, such as data analysis, decision-making, and problem-solving. Traditional AI excels at specific tasks and automating repetitive processes.

While it is less flexible than generative AI, traditional AI can still employ complex models, such as those in supervised learning. These models can adapt and improve with more data. However, traditional AI typically lacks the ability to generate truly innovative ideas.

To summarise, traditional AI:

• solves specific tasks according to predefined rules;
• uses supervised learning and discriminative models;
• follows specific rules to perform a given task but does not create anything new;
• excels at pattern recognition.

Traditional AI acts as a master strategist who can make decisions within a set of rules

Applications of traditional AI in businesses

Traditional AI is widely used in the finance, logistics, and manufacturing industries, where precise calculations and predictions are crucial. In project-based businesses, it can be used in various ways to improve efficiency, productivity, and decision-making.

1. Predictive analytics – analysis of historical data and patterns to provide accurate predictions of future project outcomes. Using predictive analytics, businesses can:

• identify potential risks;
• estimate resource requirements;
• optimise project schedules;
• make informed decisions and efficiently allocate resources, ultimately leading to better project outcomes.

2. Automation of repetitive tasks – streamlines processes such as data entry, report generation, and task assignment. It can:

• free up teams' time to focus on more critical activities;
• improve efficiency;
• reduce the risk of human error;
• ensure greater accuracy in project execution.

3. Virtual assistants – technology that effectively schedules, reminds and prioritises tasks. It can:

• provide real-time updates;
• improve team communication and collaboration;
• generate reports;
• answer questions, providing quick and efficient access to information.

4. Image and speech recognition – technology that streamlines processes and increases productivity by:

• using it to identify and track project progress through visual analysis and object recognition (e.g. construction site, production processes, etc.);
• voice updates of projects, allowing teams to provide real-time information without manually entering data.

Other forms of traditional AI applications

• voice assistants (e.g. Siri, Alexa);
• automatic speech recognition (ASR),
• analysis of financial transactions and fraud detection;
• personalising user experiences and increasing data security (e.g. in mobile devices);
• servicing machines and optimising production paths (e.g. in factories);
• analysing large amounts of data and improving diagnostics (e.g. in healthcare);
• recommendation engines (e.g. Netflix, Amazon, Spotify, etc.).

A recommendation engine works by searching for patterns in consumer behaviour in data so that users can find items and media that suit their tastes. There are three main types: collaborative filtering, content-based filtering, and a hybrid of the two.

Overall effects of traditional AI

• Increased productivity and efficiency in low-level, repetitive tasks.
• Elimination or localisation of human error due to high precision and accuracy, eliminating human error.
• Automation and 24/7 availability, improving customer service quality.

Generative Artificial Intelligence (GenAI)

Generative AI, while not a new concept, has recently gained sophistication and broader application with the release of ChatGPT (2022), marking a significant leap in AI evolution. This advancement inspires the future of AI and its potential to transform industries.

Like traditional AI, its models are fed and trained on large amounts of data. However, GenAI takes a unique approach to AI, moving towards more flexible and adaptive systems. It learns to identify patterns to generate new data and create new content.

Generative AI, in contrast, has the unique ability to predict future patterns and generate new textual, visual, and other content. It is not limited to existing sources but instead uses new data and feedback to continually improve its performance, which is reassuring of its adaptability.

In summary, GenAI:

• focuses on creating new content and data;
• answers complex questions and concepts;
• uses unsupervised learning and generative models;
• generates content and images that require high creativity;
• excels at creating patterns.

💡 Unsupervised Learning – a technique in which an algorithm analyses unlabeled data, discovering hidden patterns and structures without prior examples, useful for tasks like clustering

GenAI capabilities and applications

Over the past few years, GenAI has shown great potential to transform industries. The technology can create unique and original content, which benefits creators and companies looking to streamline their operations and increase their creativity. For example:

• enables artists to explore new horizons and push the boundaries of their imagination;
• helps pharmaceutical and healthcare manufacturers in drug discovery and medical image analysis;
• helps marketers create personalised campaigns, engaging high-quality written, visual, and audio content that overall revolutionises the customer experience;
• for journalists, it has become a powerful tool in content and information search, streamlining and speeding up the creation of articles and reports;
• in education, it adapts the level of learning to individual learning styles and preferences of students, improving education and knowledge discovery;
• it supports designers in creating countless prototypes in a matter of minutes, reducing the time needed for the idea-generation process;
• in the entertainment industry, it helps create new music, scripts, and even deepfakes.

Thanks to natural language processing, GenAI models such as GPT or DALL-E can create content, videos, and graphics from text commands, convert language to code, or summarise complex information.

Generative AI applications in companies:

1. Analysis and understanding of unstructured data such as project documentation, emails, and customer reviews.
2. Generative content in the form of social media posts or marketing materials: generative AI can create high-quality content tailored to the project's specific requirements.
3. Automation of code, its writing process, testing and debugging.
4. Generate ideas by analysing massive amounts of data and generating new concepts based on patterns and trends.
5. Support decision-making processes regarding project scheduling, resource allocation, and risk assessment.
6. Analyse customer preferences to generate personalised recommendations, offers, and experiences.

Overall effects of generative AI

GenAI's implications are wide-ranging. It provides new possibilities that have the potential to revolutionise any field where creativity and innovation are paramount.

• improved customer service and personalisation;
• increased productivity;
• significant savings in terms of time and operational costs;
• minimising errors.

Key differences between traditional AI and GenAI

Structured vs. unstructured data

One of the key differences between the two is their ability to handle data. Traditional AI is better suited for data and tasks that require precise and deterministic decision-making. GenAI excels at processing and understanding large amounts of unstructured data such as images, videos, and text. It can identify patterns and draw meaningful conclusions from this data, making it ideal for tasks such as image recognition, natural language processing and sentiment analysis.

Creating content and new data

Traditional AI lacks GenAI's ability to create new content. Conventional systems are primarily used to analyse data and make predictions, while GenAI goes a step further by creating new data similar to the training data. Traditional AI excels at pattern recognition, while GenAI excels at pattern creation. Traditional AI can analyse data and report what it sees, but GenAI can use the same data to create something entirely new.

Learning capabilities

Traditional AI relies on predefined rules and algorithms, which limits its learning capabilities. Updating and modifying rules and algorithms requires human intervention based on new information or changing scenarios. GenAI, on the other hand, can learn and improve over time in a process called deep learning. It can analyse large amounts of data, identify patterns, and make predictions based on that analysis. It makes generative AI highly adaptive and able to handle complex and dynamic scenarios.

💡 Deep Learning – a machine learning technique that uses multi-layered neural networks to identify complex patterns and relationships in large datasets, enabling machines to perform complex tasks like image recognition or natural language processing

Summary of differences:

1. Rule-based vs. learning-based:

• Traditional AI relies on explicit rules defined by human programmers.
• Generative AI learns from data and adapts its behaviour based on discovered patterns.

2. Flexibility and adaptability:

• Traditional AI systems are rigid and have difficulty adapting to new, unforeseen situations without manual intervention.
• Generative AI is more flexible and able to learn from large and diverse data sets and adapt to new scenarios.

3. Creativity and autonomy:

• Traditional AI lacks the creative capabilities and autonomy of generative AI systems.
• Generative AI can autonomously generate content, from images to text, demonstrating creativity unmatched by rule-based systems.

Commonalities between traditional AI and GenAI

Although traditional and generative AI have different functions, they are not mutually exclusive. They can support each other, providing even more robust solutions. For example, traditional AI can analyse user behaviour data, while generative AI can use this analysis to create personalised content.

The future of AI is finding ways to combine these approaches to create more efficient and versatile systems. Hybrid models are already emerging that leverage the strengths of both traditional and generative AI.

Challenges of AI

Traditional AI is limited to programmed rules, so it cannot adapt to new or unexpected situations and may not produce the desired results. It then requires adapting training data to handle new scenarios.

Generative AI, on the other hand, is prone to errors and incorrect responses through “hallucinations” or bias. There are potential threats of disseminating harmful content, risk of copyright infringement and data privacy.

AI is not always “right”, so it requires careful use, proper implementation and sensitivity to its potential falsifications

Summary

Both AI and GenAI have recently become very hot topics. There is a growing awareness of their possibilities and opportunities for the future, and many organisations are already using or planning to implement tools based on them. According to IBM research, 59% of company CEOs believe that competitive advantage hinges on having the most advanced generative AI, and 62% plan to revise their business strategies to adapt to a future dominated by GenAI.

If you want to choose and implement AI or GenAI in your company correctly and are looking for the best area of their application, we encourage you to contact us. Our experts will gladly present AI capabilities tailored to your company's unique needs.