Artificial Intelligence is becoming integrated with every aspect of our lives. From predicting financial trends to forecasting weather or creating brand-new texts and images — AI can do it all.
Previously, AIs largely involved predicting outcomes from historical data analysis. These prediction-based models were aptly named Predictive AI. But recent developments in machine learning now allow AIs to synthesize new data too. These models are known as Generative AIs.
Generative models are absolutely unavoidable now. Tools like ChatGPT, DALL-E, Bing AI, or Gemini are everywhere. And the companies behind them, such as OpenAI, Google, or Anthropic are at the center of a booming industry.
All this raises the question of how these two AIs work, how they’re trained, and what their applications may be. And that’s what we’ll be focusing on today.
This piece will dive deep with a detailed comparison between Generative AI vs Predictive AI, their training approaches, and possible applications.
The Fundamentals of Machine Learning
Before exploring the mechanisms and differences of the two AIs, let's explore Machine Learning, which is the building block for training AI models.
ML refers to any models or algorithms through which a system can learn and perform tasks without requiring explicit instructions. For instance, when an Artificial Intelligence finds patterns from a large data set without being told how to do it — that's ML. This can involve various approaches.
1. Supervised Machine Learning
Supervised Machine Learning pre-determines the correct type of inputs and outputs before training.
2. Unsupervised Machine Learning
This machine learning method doesn't determine the right outputs. Instead, it lets the model learn to find its own patterns to generate outputs.
3. Reinforcement-Based Learning
This approach takes the outputs from the model and tells it if they're right or wrong. The trial and error process gradually optimizes the outputs and inference capabilities of the model.
There are other subsets of ML, such as deep learning, which we'll dive into later.
Understanding Generative AI
Think of an AI chatbot like Google's Gemini or Anthropic's Claude AI. These models have billions of parameters (in short, more parameters generally mean improved capabilities).
You could ask these models to write you a new blog or a poem, and they would come up with something new!
The key aspect of generative AI is creating new content by analyzing existing data. These models sort through a massive amount of training data to generate similar probable patterns. These patterns are then used to generate new AI content for that domain (texts, images, or otherwise).
Although generative AI generates 'new' content, whether they're truly original or derivative often results in debates. We'll leave that out for another day.
How Generative AI Models Are Trained
While training methods vary, there's one thing in common for generative AIs — they go through a ton of training data. For instance, OpenAI's Large Language Model (LLM) GPT-4 had around 10 trillion words.
Deep Learning
For training, Generative AIs (especially generative adversarial networks) rely on Deep Learning. This is a type of machine learning that can find out complex patterns from huge amounts of data and make accurate inferences.
Furthermore, this training method finds patterns from unlabeled data without structures (unsupervised learning). This requires reduced human interventions and improves the number of parameters. Hence, generative AIs can create much more complex and sophisticated outputs.
Neural Networks
While deep learning is a type of machine learning, neural networks are a type of deep learning. This technique is popular for generative AIs. Otherwise known as Deep Neural Networks (DNNs), they emulate the neurons (referred to as nodes) of a human brain. Neural networks allow models to learn with minimal human assistance and even with inadequate training data.
There are currently many types of generative tools that utilize deep learning. Natural Language Processing or NLP, image generation tools, and text generators are such examples.
Types of Generative AI
Generative AIs have expanded over to multiple domains, meaning they aren't limited to one type of data (texts, images, etc).
Multimodal models like GPT-4 and Gemini can process and create multiple types of data (both image and text, in this case).
With that said, different types of generative AIs include:
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Text to Image generators such as Stable Diffusion or
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Natural Language Generation (NLG) models with different Large Language Models (LLMs).
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Speech Synthesis tools like WaveNet or music generators like Mubert.
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Video generation models like OpenAI's Sora.
Advantages and Drawbacks of Generative AI
Here's what sets generative AIs apart in advantages and drawbacks.
Advantages
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Contextualizing massive, arbitrary data sets: Generative AIs can analyze and infer from huge, unlabeled data sets. This reduces the need for human intervention compared to earlier predictive models.
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Synthesizing new data or content: Other models were often limited to predicting patterns based on previous data. Generative AIs can synthesize new data (texts, images, videos, etc.) after learning from the training data.
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Scalability: Generative models can keep improving as they keep training on more data. For instance, NLPs using transformers can recognize broader textual contexts and scale exceptionally quickly.
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Continuous Learning and Optimized Outputs: The outputs of generative models improve as they receive more training data. This means they can incorporate new additions to their output even after the primary training.
Related Read: Key Benefits of Artificial Intelligence With Examples
Disadvantages
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Compute Costs: The most significant hurdle with generative models is their compute cost. Training on big data involving billions of parameters requires access to added computational resources. Hence, most large generative models use large cloud servers with powerful GPUs.
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Increased Unpredictability: Generative AIs are good at contextualizing vast, unlabeled data sets and synthesizing new data. However, that can also make them unpredictable. Outputs from generative models can be unexpected and sometimes even irrelevant. This is problematic where consistency is vital.
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Hallucinations and Biases: Generative models are often known to create facts that don't even exist in reality. This is known as hallucination.
Additionally, if the training data includes particular biases, the generative model is likely to be biased too. Such issues can involve racial biases and stereotypes being included in the generated outputs.
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Decreased Transparency: Large generative models like LLMs are difficult to figure out. Hence, there's no way of knowing the exact process that created an output. This increases risks of misuse, potentially harmful results, or privacy violations against individuals and organizations.
Related Read: Limitations of Artificial Intelligence
Uses of Generative AI
Idea Generation and Content Creation
Generative AIs have revolutionized the pace at which humans can generate ideas. For example, one can create ideas for blogs, marketing campaigns, or even start-ups with a generative model within seconds.
These ideas can be given life through these AIs, too. Models like Sora can create videos, Dorik can create websites from simple prompts, Claude can generate texts, Alpha3D can create 3D models, and so forth.
This quickens and optimizes content generation, even if the outputs often require some human modification.
Interactive Chats and Search Engines
Thanks to the prowess of Natural Language Processing (NLP), generative AIs can create chatbots with human-like language. Different enterprises and businesses already incorporate such chatbots, which engage customers with natural conversations.
This increases customer retention, reduces response time and costs, while improving customer experience. Search engines are also integrating GAIs to enhance their search results and provide relevant responses. Google's Gemini (previously known as Bard) and Bing AI are such examples.
Research Fields
GAI is used as a research tool for numerous fields. Taking large data sets, finding patterns in them, and using them for data summarization or research automation has become common.
The healthcare industry is using it for enhanced diagnosis and personalized plans of action. This is especially beneficial if there's limited understanding and samples in the field. It has also cut down on the time needed for research.
Processing and Contextualizing Big Data
Since generative AI can make sense of unstructured data sets in multiple domains, it can help process and utilize big data. Notably, analyzing billions of lines of codes, sensor data, or classifying large texts is now efficient and easy.
Consequently, GAI can analyze and classify such data and generate new, relevant ones independently — with much less time.
Understanding Predictive AI
Let's imagine an enterprise scenario. Say, a brand determines how much inventory it should maintain by analyzing historical sales data through machine learning. That's essentially Predictive Artificial Intelligence.
Predictive AI looks at previous data and relies on statistical analysis to determine trends and patterns and make future predictions. Its ability to utilize machine learning for improved foresight and predictions separates it from typical data analysis.
How Predictive AIs Are Trained
Predictive AIs benefit from large data sets, often called Big Data. Firstly, this training data has to be collected, formatted, and pre-processed for the training phase.
Once that's done, the AI begins training on the data based on a relevant predictive model for the data type. Examples of such models would be neural networks or linear regression.
As the model keeps training, it requires further evaluation and tweaking to optimize its accuracy, precision, and predictive performance.
Advantages and Drawbacks of Predictive AI
Advantages
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Increased Control and Predictability: The scope for a predictive AI's predictions is more controlled, and so is the training data. Predictive AI tends to optimize and achieve expected outputs more easily than generative models. This reduces risks like hallucinations as well.
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Lower Computing Costs: Compared to generative models, predictive models are less resource-intensive. This resource efficiency is even higher during inference compared to generative models in action. This makes them available to a larger collection of users and enterprises.
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Easier Interpretation and Utilization: Most typical predictive models (e.g., linear regression) are much easier to interpret and utilize than highly intricate neural networks. Hence, inference from predictive models is easier to interpret, control, and predict.
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Enhanced Automation and Scalability: Predictive models are data-driven, meaning they're good at scaling as they're fed more data. After the training phase, predictive models require little human intervention to perform as expected. This makes them easier to automate, saving cost and effort.
Drawbacks
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Reliance on Feature Engineering: Feature engineering helps a predictive model identify the right features and variables for that specific data set. While this can improve prediction accuracy, predictive models' reliance on this can be time and resource-consuming.
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Tendency to Overgeneralize: Unlike generative models, predictive models aren't as great at making sense of new, unseen data points. Consequently, it tries to generalize and fit such unseen data into existing features and variables.
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Dependence on Training Data: The quality of a predictive model is dependent on the training data. While this is also true for generative models, predictive models are more limited in this aspect and are less likely to get out of that limitation.
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Narrower Scope: A predictive AI will likely have narrower scopes than a multimodal Large Language Model (LLM). As a result, it's never as flexible or effective in different domains. This affects these models' usability in many cases.
Uses of Predictive AI
Improving Customer Experience and Retention
In enterprise scenarios, predictive AI can provide businesses with valuable insights. For example, it could show which portion of the user experience results in higher cart abandonments or reduced engagements.
Brands can then target these points to optimize the user experience, increasing customer retention and conversions.
Moreover, this can be pinpointed to the preferences of individuals. Through this understanding, predictive AI can help design a custom, personalized user experience.
Eliminating Cyber Threats
Threat recognition algorithms are now a vital part of cyber security, driven by predictive AIs. These models analyze plenty of data to understand behavior patterns and attack strategies of ill actors.
Consequently, threats like zero-day attacks are less likely to occur, and the overall response time against any threats is much quicker.
Financial Forecasts
Making the right strategic choices in the financial market is vital. Modern predictive models can now accommodate a large amount of statistical data to make informed decisions.
When given enough variables and data points, a predictive AI model can provide accurate and automated forecasts for rapid decision-making.
Inventory and Supply Chain Management
Many businesses rely on predictive AI to determine the demand curve at any particular time. This allows for efficient inventory management while boosting revenues.
This is also relevant to supply chain management. Since these chains are often complex and variable, a predictive model can analyze historical data to find the ideal routes and supply schedules.
Environmental Forecasts
Environmental forecasting has been revolutionized with predictive models. These can train on decades of weather and climate patterns to forecast potential disasters or changes. Such AIs are also playing a vital role in gauging and preparing for the impact of climate change.
Generative AI vs Predictive AI — Summing Up
| Elements | Generative AI | Predictive AI |
| Training time | Takes longer to train, especially with unlabeled data sets | Comparatively less time-consuming, although dependent on training data |
| Resource requirements | Requires more computational prowess | Doesn't require as much computational power |
| Purposes | Creating new content and ideas | Predicting trends and patterns from historical data |
| Optimization requirements | Might require occasional evaluation, especially as new data points are introduced | Doesn't require as much maintenance |
| Accuracy | Accuracy often varies, along with issues like hallucination | Much more accurate due to the limited and controlled scope |
| Control and flexibility | Difficult to control outputs, but more flexible in contextualizing large, unfamiliar datasets | Easier to control and design, but with reduced flexibility in outputs |
Wrapping Up
Artificial Intelligence has brought in a paradigm shift. Recent machine learning and computational power improvements have made that shift even more significant.
Both generative and predictive models have their quirks and advantages. Hence, focusing on the right usage is paramount instead of a plain Generative AI vs Predictive AI discussion.
