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.
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.
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.
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.
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 works better with labeled data while generative AI doesn’t require labeled data. However, both need large datasets to train the algorithms.
While machine learning works best for anomaly detection (hence used in fraud prevention and disease diagnosis), generative AI services are useful to create new data formats (designing new products and realistic simulations). Businesses have to choose the right model based on their requirements. ML and GenAI are both important and have different uses.
Predictive AI is another type of artificial intelligence and is different from generative AI. While GenAI creates content by analyzing large datasets, predictive AI focuses on predictions and forecasting. It analyzes the data (historical and present) to trace patterns and trends and uses this information to predict future outcomes or the possible occurrence of certain events. It is essentially a combination of statistical analysis and big data that help business in making inventory, shipping, marketing, and sales related decisions.
For example, organizations can predict which products will generate more sales and need to be produced/ stocked more, which customers are likely to show interest in the product, and so on. Similarly, it can be used in the healthcare industry to anticipate the recurrence of an infection or disease, to predict how quickly a patient can heal, and to plan post-treatment care. However, like generative AI, predictive AI is also not 100% reliable or accurate.
AI consulting services providers offer a range of solutions to handle the various use cases and requirements of businesses. They choose the best type of AI service depending on the challenges faced by the organization or the objectives to achieve. Here are a few AI vs. generative AI examples commonly found across different industries.
The simplicity of traditional AI makes it a perfect choice for data analysis, identification, pattern detection, etc. It can easily handle repetitive tasks when the rules are straightforward.
Fraud detection is where the historical and current data is analyzed to determine the potential cases of fraudulent transactions. This is helpful in retail, eCommerce, insurance, banking, fintech, and healthcare industries. Wrong claims, fake cashback complaints, defaulter accounts, etc., can be identified in advance to prevent losses. For example, insurance companies and banks can determine the risk factor of offering a policy or a loan to a customer by analyzing their past records.
Traditional AI is also used in the medical field. By combining machine learning and computer vision, doctors and lab technicians can analyze X-rays, scans, etc., to detect the potential signs of diseases before they affect the patients. This early detection helps doctors provide appropriate treatment at the right time and prevent the diseases from harming the person. Tumors, cancer cells, etc., can be caught in the early stages to increase the success rate of the treatment and save the patient’s life.
We have been using voice assistants for a while now. Alexa, Siri, etc., use narrow AI and NLP to understand human voice input and provide an output in the same audio format. Employees use voice assistants at work to manage their tasks effectively. From scheduling alerts/ emails to getting reminders and translating content from one language to another, voice assistants have many uses in our professional and personal lives.
Platforms like Amazon, Netflix, Spotify, etc., have recommendation engines to suggest products and services to users based on their search/ view/ listening history. The recommendations are not always accurate since the algorithms are based on traditional AI that analyzes the customer’s data. It doesn’t consider the intricacies like whether or not the user liked what they found and hence can sometimes provide recommendations for things they don’t prefer.
Artificial intelligence has enhanced the gaming industry in many ways. From role-playing to AR and VR (augmented reality and virtual reality), search patterns, alertness and communications, etc., AI-based games feel more realistic and offer an immersive experience to players. Computer vision, NLP, and machine learning are used to create entertaining games.
Like traditional AI, generative AI also has a crucial role in the healthcare and pharma industry. It can analyze a large volume of patient data to classify them based on various criteria. For example, it can predict how patients with certain traits will respond to a drug with a specific composition. It can help accelerate clinical trials and facilitate new drugs to be released into the market quickly.
One of the best uses of generative AI is its ability to create new content. While there are still some ethical concerns about plagiarism and copyrights, users can use genAI tools to write code, create short pieces of text, brainstorm new ideas, and so on. Developers find it easier to generate code using these tools as it saves time and energy.
Similarly, generative AI is also useful for generating voice notes, audio, and music. Businesses can use these tools to create instructional videos, and voiceovers, convert text into multimedia, etc. Though genAI cannot replace real artists, it can save resources for a business by quickly converting the input prompt into a desired output format. It also helps musicians seamlessly synthesize tunes and create new music.
Image generation is among the most popular generative AI use cases around the world. Platforms like ChatGPT, Bing, Meta AI, NightCafe, etc., are AI image generators that convert input data into a picture file. Users can create paintings, illustrations, realistic images, etc., using these online tools. Despite copyright concerns, these images can be useful in creating storyboards, planning marketing strategies, generating new product designs, 3D imaging, and so on.
Reading several pages of data can be time-consuming and is not always a feasible task for employees. Generative AI can read it on their behalf and provide short summaries, highlight the key points, or create reports that can be easily read and understood. The legal teams, customer care representatives, marketers, etc., can benefit from this.
While generative AI is data-driven learning and has the flexibility that traditional AI doesn’t, it is not likely to replace anything. The increasing competition in the global market has made it necessary for all businesses to adopt AI in some form. A startup requires artificial intelligence as much as a multinational company.
Traditional AI and generative AI have their advantages and disadvantages. Both are useful in different ways but also have their limitations. Using genAI will not make everything possible.
In short, generative AI will not replace traditional AI. Instead, they complement each other and together provide the necessary services to businesses.
Generative AI is only a part of the vast spectrum of services offered by artificial intelligence. An AI services company will understand the business’s current situation, the challenges, the needs, industry standards, market trends, etc., to create a comprehensive AI adoption strategy. There are some instances where traditional AI delivers better results and using genAI can complicate matters, resulting in losses. Generally speaking, most organizations need traditional and generative AI services to achieve their goals and future-proof the business.
Be it traditional AI or generative AI, there is no one-size-fits-all solution. Artificial intelligence cannot solve everything. It is a powerful tool that empowers human talent and enhances their abilities to achieve success.
Enterprises should focus on building robust, reliable, flexible, and scalable AI systems to streamline their processes and increase business value. For this, they need to partner with AI service providers and invest in different types of artificial intelligence solutions.
Generative AI can be provided as an AIaaS (AI as a service) solution for businesses to strengthen their IT infrastructure and adopt the latest tools and technologies. The key to success is to identify the right reasons for investing in genAI and using the relevant tools. An experienced service provider can help make the right decisions.
Head over to the below links for more information about generative AI services.
Fact checked by –
Akansha Rani ~ Content Creator & Copy Writer
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