Although not exactly new, both generative AI and machine learning (ML) have evolved relatively quickly and gained massive traction in recent years. Generative AI refers to systems that produce images, text, videos, sounds, and other outputs based on patterns learned from existing data. Machine learning focuses on helping computers adapt and improve by analyzing data and making predictions, judgments, or decisions based on the results.
Both genAI and ML play increasingly diverse and impactful roles across sectors, from increasing developer productivity to personalization and predictive analysis. According to McKinsey, up to 25% of C-suite executives admit to using genAI tools for work and actively advocate for their integration into their boards' agendas. These leaders also seek to leverage ML algorithms to streamline operations and enhance user experience.
Understanding the primary differences between machine learning vs. generative AI can lead to new opportunities and gaining a competitive edge. Including them in tech budgets and strategies, however, is associated with several issues to keep in mind.
What Is Generative AI?
Not to be confused with generative machine learning (which models data distribution to create artificial samples that mirror a training set), this branch of AI is able to generate new content in real time based on its training data. It traces its roots back to the 1960s, but it has certainly come a long way since the introduction of the first chatbots and virtual assistants. Today, generative AI relies on artificial neural networks with multiple layers, enabling it to decode intricate patterns from vast amounts of data. Unlike traditional AI methods, genAI is not rule-based, so it doesn’t depend on algorithms to process data.
This cutting-edge technology can generalize patterns from large datasets and produce novel content that looks and feels like human intelligence. As a result, it’s become a valuable resource in various creative and real-world practical applications. A recent study at MIT found companies are aggressively pursuing generative AI, with plans to more than double its use in strategic analysis and product innovation. Two specific types of deep learning models that have gained prominence in advancing this AI technology type are:
- Generative Adversarial Networks (GANs): These consist of a generator network that creates new data samples and a discriminator network that evaluates their authenticity. Through iterative training, the generator network learns to produce increasingly realistic data to pass the discriminator network assessments. Paired with computer vision, GANs are particularly effective in applications like image generation and data augmentation.
- Variational Autoencoders (VAEs): These learn to encode input data into a lower-dimensional latent space and then decode it back into the original data space. In other words, they shrink data to a smaller, more manageable form and recreate it. This process makes it possible to generate new data via samples from the learned latent space. VAEs are great for tasks involving data compression and generative modeling.
One great example of genAI—and perhaps the most popular current AI-powered tool —is Open AI’s ChatGPT. This AI application uses natural language processing (NLP) to draft essays, compose poems, tell jokes, engage in human-like conversations, and much more by following user prompts. However, ChatGPT is just the tip of the iceberg. Other famous generative AI systems that can create all types of original content include Midjourney, Gemini (formerly known as Bard), Jasper, Duet AI, and Bing.
What Is Machine Learning?
Machine learning is a subset of artificial intelligence that enhances several aspects of the data science workflow and other fields like automation and robotics. It develops algorithms and models capable of learning from data and making predictions or decisions without being explicitly programmed. This technology fuels recommendation systems, fraud detection, and natural language processing.
There are three main types of machine learning models:
- Supervised learning: This approach trains the algorithm on a dataset, including input features and corresponding target labels. Its goal is to learn a mapping from inputs to outputs so that the algorithm can accurately predict the labels of new data points. For example, the algorithm can be trained on a dataset of labeled images to eventually classify new ones based on the features it has learned from the training data.
- Unsupervised learning: This ML model requires training the algorithm on a dataset that doesn't include explicit target labels. Instead, it discovers hidden patterns or structures within the data. To help a business optimize customer segmentation, for example, unsupervised learning algorithms can automatically identify similar purchasing behaviors or demographics to classify buyers into distinct segments.
- Reinforcement learning: This machine learning technique entails training AI models through trial and error to make optimal decisions in a specific environment. The algorithm receives positive reinforcement for good actions and penalties for incorrect or inadequate ones, allowing it to learn and improve its forecasting and decision-making skills. For example, to teach an app to create more realistic images through reinforcement learning, the app is given higher rewards for portraits with realistic skin tones and proper eye placement. This feedback loop would help the generative AI learn and improve its portrait creation over time.
Applications for Generative AI and Machine Learning
Generative AI complements human creativity and allows businesses to design solutions that expand the boundaries of what’s possible. Large language models (LLMs) have helped automate the basics of social media content creation to help teams focus on strategy, for example. However, as mentioned earlier, generative AI models have several other use cases that benefit various fields, such as software development and data analysis.
Thanks to its many capabilities, this technology can assist in conceptualizing and designing software architectures. It can also generate high-level requirements from user input and even autonomously write AI-generated code for specific functionalities. Meanwhile, ML can simplify tasks such as optimization and testing, enabling software developers to write cleaner and more efficient code.
Regression, classification, clustering, and other machine-learning techniques for predictive modeling and pattern recognition are incredibly useful in data analysis. Similarly, genAI can enhance data analysis by providing interpretative capabilities, allowing for deeper insights into software development KPIs.
In complex projects, ML and genAI can work synergistically. For example, in autonomous vehicles, ML algorithms can process sensor data to interpret road conditions, enabling real-time driving decisions. GenAI can then enhance this process so that vehicles can adapt to unforeseen circumstances, such as sudden changes in weather conditions or unexpected road obstructions.
Many expect the development of more sophisticated AI systems capable of learning from fewer data and reasoning across multiple domains. Soon, education, law, marketing, and especially software engineers will likely be able to automate the most tedious and mundane parts of their jobs, making room for added developer productivity. In addition, we will likely witness models that excel at collaborating with humans in more natural and intuitive ways rather than competing with them or "pushing them out of their industries," as many fear.
7 Key Differences Between Machine Learning vs. Generative AI
The most notable difference between Machine Learning and AI is that the former is a specific technique within the latter. In other words, and as we’ve stated earlier, ML is a subset of AI that revolves around learning from data. Although related, both technologies have different AI components and represent distinct approaches within a broader field that help computers mimic human intelligence.
In a strictly foundational approach, ML algorithms are designed to learn patterns and relationships from data for prediction and optimization. Conversely, generative AI algorithms focus on capturing the data's underlying structure and creating new, realistic samples that exhibit similar characteristics, as mentioned above. Several key differences between generative AI and machine learning often dictate when and how to deploy them:
1. Data Requirements
Machine learning algorithms typically require large amounts of labeled data for training. This means that each data point must have a corresponding label or classification. For example, training a machine learning model for image recognition or speech recognition requires a dataset of images or audio clips labeled as what you’re aiming for the algorithm to recognize (e.g., "cat" or "not cat").
Generative AI models can also benefit from large datasets. However, the data doesn't necessarily need a label. Models can learn the patterns from all types of unstructured data. It’s still important to remember that the quality and quantity of the data used play a significant role in what the generated outputs will look like.
2. Processing Capabilities
ML algorithms are primarily focused on analyzing and interpreting existing data models. Unlike genAI, they don't aim for broader intelligence resembling human cognition and adaptability. As a result, they typically excel at tasks like classification and anomaly detection.
Generative AI algorithms, on the contrary, are better for creating new and original data formats. One of their primary purposes is to imitate what a person would do, learning abilities similar to humans. That’s why they are typically used for tasks that include:
- Generating product designs
- Creating realistic simulations
- Composing novel music pieces
- Editing complex images
- Crafting text content from scratch
3. Desired Outcomes
ML is primarily outcome-oriented. It seeks to optimize a specific task, such as minimizing error or maximizing accuracy. ML models are trained to make predictions or decisions based on input data to achieve predefined performance metrics.
Generative AI has one primary purpose: to create something similar but not identical to the data on which it’s been trained. The success of generative AI models is often measured by the quality and diversity of the generated samples rather than their performance on specific tasks.
4. Application Scope
ML algorithms are useful in various realms, including data classification, which is great for spam filtering and object recognition. Regression is another strong suit of ML, and we can use it to solve a wide range of prediction and decision-making tasks. These application cases make machine learning a highly valuable resource in fields such as:
- Healthcare
- Finance
- Marketing
- Autonomous systems
Generative AI is a better fit for duties that involve image synthesis, text generation, music composition, and other similar tasks. As a result, generative AI has diverse applications in creative industries and data augmentation.
5. Training Paradigm
As previously discussed, ML models typically follow supervised or unsupervised learning paradigms, depending on what we're going for. Either way, clear data examples with answers or feedback are needed to learn the relationship between input and output. The ML training process involves adjusting model parameters to minimize a predefined loss function—which measures the disparity between its predictions and the actual outcomes.
Generative AI models often rely on unsupervised or self-supervised learning approaches. They may also use adversarial training techniques, such as GANs, where two neural networks compete against each other to improve the quality of generated samples.
6. Handling Uncertainty
ML algorithms often provide point estimates or probabilistic predictions based on the input data. These algorithms aim to minimize prediction errors, reduce AI bias, and maximize predictive accuracy within the given uncertainty bounds. It's a more data-centered approach that requires more structure to deliver the expected results.
Generative AI embraces uncertainty as an inherent part of the creative process. Consequently, generative AI models can produce diverse and spontaneous outputs with varying degrees of novelty. This allows for exploration and creativity in the generated samples and prevents them from looking the exact same every time.
7. Interpretability and Explainability
ML models are often designed so that users can understand and describe how predictions are made and which features influence the model's decisions. This feature is crucial, especially when transparency and regulatory compliance are essential.
Generative AI models, however, may sacrifice interpretability for the sake of creativity and complexity. As these models progress, making them understandable and trustworthy for users has become increasingly important. This helps guarantee that people can relate to and rely on the content and AI applications they produce.
Considerations When Using Machine Learning and Generative AI
Fully grasping the difference between generative AI and machine learning is not the only step when deciding which technology to use. Ultimately, the final choices will depend on the nature of the problem that's trying to be solved and the desired outcome.
Data quality plays a crucial role in the effectiveness of both ML and generative AI. Without sufficient and relevant data, ML models may struggle to generalize well to new data instances. Similarly, poor data quality when building an AI may lead to biased or inaccurate results.
On a separate note, proper data anonymization, encryption, and access controls will always be essential to prevent sensitive information from falling into the wrong hands. Furthermore, responsible use can help us avoid the most common ethical concerns associated with generative AI.
Lastly, integrating ML and generative AI technologies into business operations requires ongoing training and upskilling programs. These actions will help teams keep up with emerging and disruptive technology trends and ensure everyone has what it takes to leverage both technologies effectively and maximize their value.
Hire Skilled Software Engineers to Build AI With Revelo
Generative AI and machine learning seem inescapable. Both technologies are here to stay and offer significant advantages for tech companies that want to build a strong engineering culture.
However, no AI beats the power of a skilled team of developers ready to tackle your business's everyday operations. Revelo matches tech companies with rigorously vetted development professionals. After hiring, we’ll continue to contribute and handle the intricacies of payroll, benefits administration, taxes, and local compliance so that you can focus on core business requirements.
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