Generative AI vs Predictive AI: What is the Difference?
AI has drastically altered several industries by allowing machines to execute challenging jobs while making intelligent choices. Predictive AI and Generative AI are the best-known and used AI sisters. Although both have a powerful ability to adapt, their potential uses and distinctive benefits vary depending on what exactly is being accomplished with them.
But how do you choose the one that best suits the needs of your business goals?
While predictive AI uses previous data to make predictions, generative AI generates new data. To determine the best technology for the marketing departments of your organization, look into this comparison: generative AI vs. predictive AI.
Table of contents
- What is Generative AI?
- Generative AI Models
- Performance Measurement of Generative AI
- Pros and Cons of Generative AI
- Application of Generative AI
- What is Predictive AI?
- Pros and Cons of Predictive AI
- Applications of Predictive AI
- Generative AI vs Predictive AI
- Frequently Asked Questions
What is Generative AI?
Generative AI, an improved form of machine learning, works by developing software models to generate predictions based on data without specific programming limitations. They use probability distributions to understand the fundamental trends in the data and then create results that demonstrate similar patterns whenever asked for.
Generative AI encompasses unsupervised and semi-supervised machine learning techniques. It enables computing devices to use pre-existing data, including text, audio and video files, images, and codes, to create fresh forms of content. The primary goal is to produce 100% authentic results that closely resemble the real thing.
In particular, AI models are provided with massive amounts of recent data to train models to generate novel content. Such models leverage neural networks to discover patterns and frameworks in existing data to create innovative and distinctive output.
The essential elements of generative AI models are Latent space, training data, and generative architectures.
- Data is represented compressedly in latent space, which incorporates the critical characteristics of the data.
- The basis for learning is training data, which helps models comprehend the fundamental patterns.
- The structural components that make generative modeling achievable are generative architectures.
Generative AI models promise to understand and replicate human inventiveness by gaining insight from vast data and delivering unique results.
Generative AI Models
The most used generative AI models are:
GANs (Generative Adversarial Networks)
GANs happen to be used effectively in many different fields. They comprise two networks: the generator network and the discriminator network. While the discriminator aims to discriminate between created and original samples, the generator trains how to generate data that mimics accurate data. GANs continuously advance through a continuous procedure, resulting in the generation of highly accurate results.
GANs are more suitable for developing data associated with a specific domain since they can swiftly generate results with high-quality samples and less pattern diversity.
Variation Encoders (VAEs)
VAEs are generative models that harness the strength of autoencoders and probabilistic modeling for learning a simple input representation. By selecting endpoints from the acquired distribution, VAEs can create fresh observations by encoding the data provided into a low-dimensional latent space.
They function by developing two networks: a decoder network that recreates the provided data from the latent space and an encoder network that translates the raw data to the latent space.
They can create new and unique data sets that mimic the training data by taking observations from each point in the latent space where the newly acquired pattern is present. VAEs’ potential to create new data and move across the latent area enables imaginative research and the creation of further data.
Diffusion models, or probabilistic diffusion models, have parameterized Markov chains constructed via variational inference to generate samples that fit the data set after a certain period.
In simple terms, diffusion models can produce data that resembles the training data. The training procedure involves introducing Gaussian noise into the data while discovering how to retrieve data via denoising. The model makes findings throughout the inference phase using randomly generated noise.
The diffusion model could be broken into three main components: a trained text encoder, a UNet noise predictor, and a variational autoencoder-decoder model. To create a finished high-resolution image, the decoder has an upsampler network.
Performance Measurement of Generative AI
Performance indicators like BLEU, ROUGE, METEOR scores, and prediction embeddings make keeping an eye on text-based generative models simple.
- BLEU is a precision-focused statistic that evaluates the n-gram overlapping between the original text and the produced text.
- ROUGE is an indicator that assesses the recall overlaps between the source text and the newly created text. There are three kinds of rouge: rouge-n, rouge-l, and rouge-s.
- METEOR is a measurement that rates the effectiveness of generated text by measuring how well it aligns with the reference text.
- BERTScore is based on similarities of reference and created text implementing contextual embeddings.
Pros and Cons of Generative AI
Generative AI has specific pros and cons. One should be aware of them before using them for their benefit.
The pros of using generative AI are as follows:
- It generates new and creative data.
- It can easily interpret large amounts of data.
- It reduces time and effort and minimizes human error.
- It has many innovative applications like content, image, video, and music generation.
- It can help in creating personalized content.
The cons of using generative AI are as follows:
- It lacks the human eye for error correction in codes.
- Images generated through these models can violate privacy, copyright, or personal data infringement.
- Due to the lack of recent data, the results caused a lack of accuracy and quality.
- The data sources generated by the AI model might not be accurate and trustworthy.
Application of Generative AI
The popularly known applications of generative AI include the following:
It would be best if you came up with creative ideas to grab customers’ interest. ChatGPT and other generative AI models enhance ideation periods by providing innovative solutions and different viewpoints. Businesses can use these AI-generated concepts as a starting place for creative thinking, leading to fresh content strategy.
From creating social media and blog posts to creating promotional emails, AI models can generate draft material that human managers can modify and tailor. Generative AI offers hyper-personalization through assessing large amounts of data and adapting information to specific tastes and actions.
Code Generative and Interpretation
Generative AI can be implemented in software development to construct and enhance source code, thereby improving the quality and effectiveness of software. With maximizing efficiency, the results of generative technologies can be customized to operate better and at a lower price, resulting in the best financial outcomes for businesses.
Developers can utilize AI-based tools to get ideas for the code that they create during the time they are working on it. These tools review the code using natural language processing, provide applicable code suggestions or keywords, and finish incomplete code using complete, precise syntactic language.
What is Predictive AI?
Predictive AI, often known as “Narrow AI,” is a system that forecasts events based on data trends and inputs. It uses past data to make accurate choices or predictions about upcoming occurrences. Such an AI system is particularly efficient at operations like stock market projections, weather forecasting, and recommendation engines that require algorithm estimating trends.
Predictive AI systems function within established parameters and do not produce new data. Instead, they study details already in existence to offer insight into possible developments in the future.
It demands access to a vast quantity of data. The accuracy of the forecasts increases with data volume. Machine learning is being applied to massive amounts of data. It can analyze many variables to find patterns related to future recurrences of similar events.
Pros and Cons of Predictive AI
There are specific pros and cons of predictive AI when it comes to generative AI vs. predictive AI.
The pros of using predictive AI models are as follows:
- It can help you stay ahead of the competition.
- It can enhance your sales.
- It can help in saving time and human resources.
- It enhances the supply chain management systems.
The cons of predictive AI models are as follows:
- It lacks accurate human behavior predictions.
- The data needs to be updated regularly.
- Due to the lack of current data, the outcomes can be inaccurate.
- It can be overwhelming for non-technical people to understand.
Applications of Predictive AI
The applications of predictive AI models in different industries are:
Using several meteorological data sets, AI may be taught to interpret the data and produce more precise forecast charts. This is due to the distinct machine-learning functions of predictive AI.
By analyzing endless data, models can find out more about patterns and predict them more accurately than an average person could. The algorithms’ ability to recognize patterns and provide greater insight into the likelihood of future weather will increase with the quality of the data we collect.
Because of recent advancements in the speed and precision of predictive AI, financial institutions may widen the limits of how they use it. Furthermore, integrate the technology more effectively across their organization’s tactical and strategic parts.
Predictive AI lets you correctly plan your financial investments and spending and estimate cash flows. It helps reduce financial risks and enhances the quality of client relationships.
Generative AI vs Predictive AI
Using past data and current occurrences, predictive AI functions as a magic 8-ball that could help you identify trends and decide the next step. On the contrary, generative AI is an artistic assistant, assisting you in ideation, creation, and content expansion. Additionally, it’s a fantastic resource for businesses to collect, access, and pass on data.
Generative AI creates new content with characteristics related to the training data it initially provided using advanced algorithms and deep learning methods. Predictive AI uses machine learning and statistical algorithms to analyze data and predict future occurrences.
Generative AI is widely used in creative fields like music, art, and fashion. At the same time, Predictive AI is commonly used in domains like healthcare, finance, and marketing.
Artificial intelligence is a new level of technology with tremendous developments and opportunities. Generative AI and predictive AI are two of the most widely used AI models for different domains. When we discuss generative AI vs. predictive AI, both are pretty effective and versatile. While Generative AI flourishes at generating utterly new content, Predictive AI is adept at forecasting based on trends. With a proper understanding of these AI models, you can use them to enhance your organization’s operations and use them to their full potential. Analytics Vidhya offers Generative AI courses for individuals looking to enhance their knowledge regarding the most upcoming technology in the world.
Frequently Asked Questions
A. Predictive AI uses artificial intelligence and machine learning techniques to analyze existing data and predict future events or trends. It relies on statistical algorithms and historical data patterns to make informed predictions, often used in various fields like finance, healthcare, and marketing.
A. Generative AI encompasses applications that create new content, such as text, images, and music. Examples include:
-Text generation models like GPT-3 that produce human-like text.
-StyleGAN for generating realistic images.
-DeepDream, which creates unique visual patterns.
-MuseNet, an AI that generates music compositions.
A. Generative AI is a subset of artificial intelligence (AI). AI is a broad term encompassing all forms of machine intelligence, while generative AI specifically focuses on creating new content. In contrast, other AI types may involve classification, regression, or data analysis tasks.
A. Generative AI primarily focuses on content creation and lacks the core functionality for predictive analytics. While it can learn patterns from data, its primary purpose is to generate new content rather than making predictions based on historical data.
A. Generative AI, or Gen AI, is not typically used for prediction tasks. It excels in creative content generation but lacks the specialized algorithms and techniques for accurate predictive analytics. Predictive tasks are better suited for traditional predictive AI models.