Generative AI vs Predictive AI: What's the Difference?

Before our chatbot Ante, there was chatbot Eliza, so you could say that giving names to chatbots has been usual business since the 1960s.

What would the world be without generative and predictive AI? Consider how much they influence your daily interactions, like the custom recommendations on Netflix or the enhanced security measures that help reduce fraud. Without these AI technologies, our digital experiences would be less personalized and potentially more risky.

In this blog post, let's see what the difference is between generative AI vs predictive AI and how they helped and shaped the industries over the years.

Definition of generative AI vs predictive AI

Generative AI and predictive AI are two primary subfields of AI. Generative AI focuses on creating novel content, such as text, images, or audio, from input data.

Predictive AI, on the other hand, leverages historical data to forecast future outcomes. This provides valuable insights for decision-making in various sectors.

Generative AI

History of generative AI

Generative AI is based on machine learning and deep learning algorithms. We talked about Arthur Samuel in a blog on how to become an ai developer and how he was the one that came up with the phrase "machine learning".

Generative AI was introduced in the 1960s in the form of chatbots. In the 1960s and 1970s, Joseph Weizenbaum created the chatbot ELIZA (although we like our chatbot Ante's name more), demonstrating how AI could simulate natural language conversations.

This era also saw advancements in computer vision, with researchers developing facial recognition techniques using 21 specific markers to identify individuals. The 1970s also introduced the backpropagation technique, which helped train deep neural networks. In the 1980s, another significant milestone was the Cognitron, a multilayered neural network, and the Neocognitron, which pioneered deep learning models.

The 1990s saw the rise of technologies like LSTM networks, improving speech recognition and deep learning. The 2000s brought significant improvements to facial recognition, and in 2014, Ian Goodfellow introduced generative adversarial networks (GANs).

More recently, we have all seen the rise of smarter chatbots like ChatGPT, which combine generative AI with large language models to produce comprehensive responses.

What are examples of generative AI in the software industry?

Generative AI has numerous applications in the software industry. We already talked about large language models like GPT, which generate text and assist in tasks like content creation and software code generation.

GANs help create realistic images, supporting computer vision tasks. To explain in more detail how GANs function, GANs (Generative Adversarial Networks) use two models: a generator and a discriminator. The generator creates new data, like images or text, and the discriminator tries to distinguish between real data and the generator's data. The models compete against each other until the discriminator can't reliably tell the difference.

Generative AI also finds use in developing synthetic data for data augmentation, training other AI models, and enhancing machine learning algorithms.

Some other examples are Bing Chat, Midjourney, GitHub Copilat (that our developers at Devōt use), Dall-E 2, etc.

How generative AI helped various industries

• Content creation (generative AI generates text, media, and other content)

• Data augmentation (it creates synthetic datasets, enhancing training data quality for machine learning models)

• Software development (supporting tasks like automated code completion in tech companies)

For example, in finance, it helps balance datasets for fraud detection models by generating synthetic data, ensuring accurate training.

What are the limitations of generative AI?

• High costs: Training generative AI models can be expensive and time-consuming due to the need for powerful hardware and extensive training time.

• Data quality: Generative AI relies heavily on high-quality input data. Poor quality or biased training data can lead to inaccurate or low-quality outputs (which affects the performance of the AI system).

• Bias and ethics: Generative AI models can unintentionally create biased content, reflecting any biases present in the training data. This can result in ethically questionable outputs.

• Limited creativity: While generative models create new content, their creativity is bounded by their training data. They may struggle to produce genuinely novel content, especially in complex areas.

• Generalization: Generative AI models might have difficulty generalizing to new or unseen data, leading to overfitting or performance issues when applied to real-world scenarios.

Predictive AI

History of predictive AI

Predictive AI has a foundation in statistical algorithms and early machine learning models from the 1970s and 1980s. In the 1970s, the backpropagation technique was developed, allowing errors to be propagated backward (or sent back through the model), refining machine learning models over time.

The 80s were ahead of the time, not just in fashion but also in algorithms that analyzed historical data, leading to the development of models for predicting future trends.

For example, ARIMA (AutoRegressive Integrated Moving Average) was developed by George E.P. Box and Gwilym Jenkins. They introduced the model in their 1970 book "Time Series Analysis: Forecasting and Control," which laid the foundation for many time series forecasting methods used today.

Examples of predictive AI in the software industry

• Recommendation systems: Predictive models power recommendation systems for platforms like Netflix and Spotify. They analyze users' past behavior to suggest new content. Fun fact: Netflix's recommendation system accounts for over 80% of content viewed by users.

• Fraud detection: Predictive models analyze patterns in customer data, helping financial institutions identify and prevent fraud. For example, these models monitor transaction history and unusual spending patterns to flag suspicious activity. Predictive analytics models help in fraud detection and fraud prevention. So, who knows, maybe predictive AI helped you from credit card fraud. Also, predictive AI applications assist in risk assessment.

• Supply chain operations: Predictive analytics forecast demand, manage resources, and minimize risks in supply chains. For example, they help companies like Amazon predict inventory needs based on historical sales data, reducing stockouts and improving customer satisfaction.

Benefits of predictive AI

• Market Trends

• Customer satisfaction

• Risk assessment

• Operational efficiency

• Diagnosing medical conditions

We already talked about how AI has a significant impact on healthcare, particularly in diagnosing medical conditions. For instance, the company IBM Watson uses predictive algorithms to analyze patient data and provide doctors with treatment options.

IBM Watson was able to diagnose a rare form of leukemia in a Japanese woman in 2016 by cross-referencing her symptoms and genetic data against a vast medical database.

What are the limitations of predictive AI?

• Data quality: The accuracy of predictive models depends heavily on the quality of historical data used for training. Incomplete, biased, or outdated data can lead to inaccurate predictions, limiting the model's effectiveness.

• Bias and ethics: Predictive AI models can exhibit biases, reflecting biases in their training data. This can lead to inaccurate results, particularly in sensitive areas such as hiring or criminal justice, where biased predictions can have significant ethical implications.

• Model maintenance: Predictive AI models require frequent retraining to stay current with changes in input data or market trends. This need for maintenance can make predictive models costly and resource-intensive (particularly in rapidly changing industries such as finance or retail).

• Generalization: Predictive AI models might struggle to generalize to new or unseen data, leading to performance issues in real-world scenarios. For instance, a model trained on past sales data might not accurately predict future sales if market conditions shift unexpectedly.

• Lack of interpretability: Predictive models, particularly those based on complex algorithms or neural networks, can be challenging to interpret, making it difficult for users to understand how predictions are made. This lack of transparency can hinder trust in the model's outputs and limit its adoption in sensitive fields. Interpretability is important; it helps understand the reasoning behind predictions, making it easier to assess risks and benefits.

Comparing generative vs predictive AI

Generative AI and predictive AI are two key areas of artificial intelligence, each with its unique functions. Generative AI and Predictive AI serve distinct functions. Generative AI creates new content or data based on input data, such as generating text, images, or audio. Predictive AI, on the other hand, analyzes existing data to forecast future outcomes, providing insights into trends and behaviors.

The key differences lie in their objectives: generative AI aims to generate novel content, while predictive AI seeks to provide insights based on existing data. This distinction makes generative AI valuable for content creation and creative tasks, while predictive AI is essential for analytics, decision-making, and forecasting.

How do generative AI vs predictive AI work together?

The terms "generative" and "predictive" AI represent distinct approaches to artificial intelligence, but in comparison, predictive AI is often considered more traditional.

Generative AI and predictive AI complement each other and can work well in combination to achieve various goals. For example, in natural language processing, generative models like GPT (Generative Pre-trained Transformer) can generate additional text data, which can then be used to train predictive models for tasks like sentiment analysis or text classification.