While there are multiple types of AI, based on which applications can be created, all types of AI are functions based. The function of normal AI is to provide some form of output based on an input through various intelligence extraction and presentation. In the case of generative AI, the generative response to a query is based on training data and a large language model.
There is also predictive AI, which is when the AI solution is used to make predictions about future events based on past data and other factors. All types of AI can be used in various forms to generate output that is necessary based on the input query.
By understanding the core differences between them, enterprises can use them in different ways to create new solutions based on refined insights and analyses.
What's the difference between AI, Predictive AI, and Generative AI?
The main difference, apart from the architecture, is the major goal of the technologies. These technologies were developed with specific goals in mind, which can help them become more applicable in various systems. Data augmentation, historical data analysis, foundation models, and other components are used for each type of AI.
Technology
The underlying architecture of general AI is different from that of predictive AI and generative AI. In the case of general AI, there is a framework and a design for specific tasks that the tool is aiming to accomplish. For generative AI, there is an integration of neural networks, large language models, and deep learning to create meaningful information based on query inputs.
For predictive AI, there is a statistical and machine learning model base which can help it predict trends and forecasts. Enterprises can understand generative AI vs predictive AI, to know more about how artificial intelligence can enable their solutions empowerment.
Function
Generative AI is used to create new types of information, in the form of videos, images, or outputted data, based on an input query. General AI can be used to perform a range of analytical tasks based on training data. Predictive AI can be used to form future insights about potential events.
The function of these AI solutions largely dictate how they're used on the real world applications. AI can be used to create analytical results based on data that it is trained on. This can be supercharged with generative architectures, which can help it develop new information through extraction.
Use-cases
The use cases for each of the technologies also differs, which is why it is vital to know more about these critical technologies. The use cases for general AI can be around manufacturing defects detection, chat bots, customer service interactions, etc. Use cases for predictive AI can be around financial trends mapping, customer trends identification, etc.
Some of the use cases of generative AI can be around patient data intelligence, legal research, training employees, etc. The generative aspect of Gen AI can help enterprises adopt new solutions based on capturing information from external and protected internal sources as well.
Limitations
Understanding the limitations of these AI systems is vital as well. The key differences in their limitations can help enterprises know what technology can be applied in what scenarios. Traditional AI excels at using machine learning algorithms to create analyses that is limited to the training data. Predictive AI can be limited to historical bias, non-updated data sets, and incorrect assessments for more complex data predictions.
Generative AI focuses on generating new information, which can include new media and output. However, there may be certain biases attached to them based on the external sources, protected information, etc. A generative AI model is limited by its design and access to new information.
Advantages of predictive AI and generative AI
By understanding the advantages of predictive and generative AI, enterprises can differentiate their use cases and know more about their applications. This can help firms review their current AI infrastructure and add generative or predictive to various business systems.
Process optimization
A key role of predictive AI is in process optimization. In fact, even general AI can be used to detect assets malfunctions, accurate maintenance cycles, automotive functioning optimization, and many more processes. Predictive AI can also be used for optimizing financial portfolio management, investing, agricultural management, etc.
Deep research and insights
Healthcare, legal, technology, and other industries can use generative AI to improve their drug research, legal research, development processes, etc. Generative AI can be used to extract knowledge from internal and external sources, to help improve insight generation for better research. Firms can also use generative AI and predictive AI together to help improve their research outcomes.
Indicating trends
Through the use of predictive AI, enterprises can detect new growth opportunities, find new trends, and forecast demand for a range of products. Firms can find new avenues of exploration, by analysing market reports, news, alerts, economic factors, changes in policies, etc. Banking, financial, healthcare, technology, and manufacturing firms can create new solutions and iterate on what consumers prefer by using predictive and generative AI.
Creating new information
The main focus of generative AI, unlike traditional AI, is to create new information that previously didn't exist. This could include customer service interactions, research domains, technical infrastructure development, banking assistance, etc. While traditional AI focuses on specific task and output expression, generative AI models focus on a wider range of outputs in different mediums for multiple functions.
Conclusion
General AI can be used for a limited number of tasks, whereas generative AI can be used to generate output that is based on extracting external and internal information. Firms can create new applications and solutions based on the roles and functions of predictive, general and generative AI.
By understanding market trends and core differences between these technologies, it can make it easier to integrate these solutions into systems. Automotive firms can introduce generative AI for new designs, and predictive AI for analysing colour preferences. Healthcare firms can use both predictive and GenAI to check patient treatment effectiveness and doctor protocols.