• Yashica Chopra
• December 03, 2024
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Generative AI Services vs. Traditional AI – The Intelligent Choice?

Artificial intelligence comes in many types and forms. It has diverse uses in different industries and can support organizations to increase ROI and profits. Here, we’ll discuss traditional AI vs. generative AI services and how they help businesses in various ways. Artificial intelligence (AI) is the buzzword in today’s digital world. It is a part of our everyday lives in one way or another. With continuous research and development in the field, AI is becoming more powerful and useful on a large scale. For example, in the last couple of years, we went from using traditional AI to relying on generative AI.  All types of AI are being discussed and implemented in different industries. Traditional and genAI are both used in businesses for various purposes. The global AI market was valued at $454.12 billion in 2023 and is expected to touch $2500 billion by 2032 at a CAGR (compound annual growth rate) of 19%. According to McKinsey, generative AI will add $2.6 trillion to $4.4 trillion worth of value to the global economy.  But what exactly is traditional AI? How does generative AI differ from traditional AI? Should a business solely invest in generative AI services or should it stick to traditional AI? Which service is the right choice for a business?  Let’s find out in this blog.  What is Traditional AI? Traditional AI is a subset of the umbrella term artificial intelligence. It is also called narrow or weak AI and is predominantly used to perform tasks based on predefined parameters. The algorithms are trained to complete a set of actions for the given input. It can handle simple tasks efficiently and automate repetitive tasks as and when necessary. It also works well in domains where the rules don’t change often and follow a set pattern.  For example, online gaming, industrial automation, workflow automation, data analytics, medical diagnosis, spam filters, recommendation engines, virtual assistants, etc., are some traditional AI use cases across industries. It helps with decision-making and problem-solving at various levels in the enterprise. Since the rules are explicit, traditional AI is more transparent and the algorithms are easier to understand. The AI applications offer domain-specific services and are fairly reliable. However, its limited learning capabilities and strict rules don’t offer a chance for the models to become more powerful.   Many companies offer traditional AI consulting services for businesses to streamline their processes, shorten production cycles, and understand customer data. Existing models can be customized or new models can be developed from scratch to help organizations achieve their goals. Starting with traditional AI adoption usually helps as it allows employees to get used to new technology before dealing with advanced versions. sights. What is GenAI? Generative AI is a new take on artificial intelligence to provide more adaptive, flexible, and sophisticated algorithms. Unlike traditional AI, generative AI can create new content (text, images, audio, and videos) by analyzing large datasets to identify patterns. Instead of relying on strict rules or parameters, it learns by analyzing the input and datasets to provide a creative and unique result to the end user. For example, a generative AI application can process the input text and generate an image based on the prompt. It goes beyond what narrow AI can achieve and pushes the boundaries farther.  Naturally, there are questions like – is GenAI related to LLM, or is ChatGPT a generative AI? The answer is yes to both questions. GenAI is a broader concept dealing with different types of models that generate content. LLM (large language model) is a specific form of generative AI and acts as a foundation model to run a wide range of NLP (natural language processing) tasks. ChatGPT by OpenAI is a form of generative AI that can converse with users like another human and provide a relevant answer/ result to their input.  Generative AI also uses machine learning, deep learning, and neural networks to analyze the datasets and produce new content. While content creation, personalized recommendations, and virtual assistants are some uses of GenAI, it is not without some flaws. There is ambiguity in how the algorithms ‘create’ content and the use of public data for training the models can violate copyright and IP rights. Additionally, the generated content may not be 100% accurate or reliable as genAI is still in the development stage.  Nevertheless, businesses can vastly benefit from generative AI services if they have a clear idea of what they want and how to use the applications to increase performance and reduce risk. Some services can be offered through traditional and generative AI. For example, AI chatbot solutions can be built on narrow AI and genAI models. What the chatbots can achieve depends on the type of model used. Naturally, generative AI-based chatbots are more conversational and can deliver better results, especially when trained on high-quality data.  How is Generative AI Different from Other AI Approaches? Generative AI differs from other artificial intelligence approaches that focus on data analysis or making predictions. While both types analyze data and identify patterns, generative AI uses this to generate content and create something new, which other AI cannot do.  Here, we’ll compare generative AI with other models to understand the difference. AI vs. Generative AI vs. Machine Learning We have already discussed the difference between AI (traditional) and generative AI. Machine learning is a subset of artificial intelligence that combines concepts like statistics and computer programming to identify hidden patterns and trends in diverse datasets. It uses data and algorithms to enable AI models to mimic how humans learn and can improve their accuracy through the feedback loop. Machine learning models are classified into three types – supervised, unsupervised, and reinforcement learning.  The primary difference between generative AI and machine learning lies in how and when they are deployed. ML is a part of genAI applications and is used for prediction and optimization based on insights derived from data analysis. Generative AI analyzes data to create similar structures or samples exhibiting the required characteristics. Additionally, machine learning