Generative AI vs Machine Learning — What’s Best?
The PoorGPU guy Newsletter 2024 week 5 - Choosing the Right Artificial Intelligence Tech for Business Use Cases
Artificial intelligence is transforming industries across the board, from healthcare to finance. But with so many options available, it can be difficult to determine which type of AI will best serve your business needs and goals.
Do we really need a Language Model for every Business application?
The two leading technologies in this space are Generative AI and Machine Learning. Both have their advantages but also some key differences that should factor into any decision about adopting AI for the workplace.
Machine Learning (ML) and Generative AI are two distinct subfields of Artificial Intelligence (AI). Both involve training algorithms using vast amounts of data to make predictions or generate new content, but they differ significantly in their approach and capabilities.
Machine Learning
Machine learning algorithms are trained to recognize patterns within large datasets without generating new content.
Machine Learning involves feeding data into an algorithm to enable it to identify patterns and relationships within the input. These algorithms then are used to extract insights for informed decisions or predictions about future events based on the observed trends.
For instance, ML algorithms can classify images, recognize speech, predict stock prices, and recommend products to users. They typically operate by optimizing a loss function, which measures how well the model performs relative to the desired output. This process allows the algorithm to adjust its parameters until it achieves satisfactory accuracy.
Generative AI
Generative AI systems like ChatGPT, are Generative Adversarial Networks (GANs). We can think of them as “creators” — they take input in the form of text prompts and generate human-like responses based on patterns learned from training data.
Generative AI (like Large Language Models) goes beyond merely identifying patterns; it seeks to generate entirely new content that resembles the original dataset.
In contrast to Machine Learning, which identifies patterns in existing data to make predictions or to guide decisions, Generative AI creates entirely new content that looks similar to the original dataset.
To achieve this, Generative Adversarial Networks (GANs) employ two neural networks: a generator and a discriminator. The generator generates artificial samples, while the discriminator checks whether these samples are genuine or fabricated by comparing them to real examples from the dataset. By engaging in a repeated cycle, the generator enhances its capacity to produce lifelike outputs, while the discriminator develops greater proficiency in discerning between generated and authentic data.
The complex result of this interaction leads to the production of incredibly precise and believable counterfeit data, including images, videos, audio, and even text.
So what is best?
They are both cool! But because of their specifications it is important to pick the right solution that fit your use case.
So what use cases might lend themselves better to generative AI versus machine learning? As a rule of thumb here the 2 main areas:
Generating unique, human-written text is a strength of systems like ChatGPT — it can be used for things like customer service chatbots or automated marketing campaigns.
Machine learning excels at analyzing large datasets and detecting patterns in complex information like images, audio files, or video footage. It’s ideal for tasks like fraud detection, predictive analytics, and natural language processing.
Let’s give them few more thoughts.
Machine Learning use cases
Predictive maintenance using time series data from sensors in machinery to identify potential issues before they lead to downtime or failures, enabling preventative maintenance.
Fraud detection by analyzing large datasets of transaction data and identifying anomalies or patterns associated with fraudulent behavior using supervised ML models like random forest classifiers.
Medical diagnosis: Machine learning can be used to diagnose a variety of medical conditions, such as cancer, heart disease, and diabetes. Machine learning algorithms can be trained on data that includes patient records, medical images, and other relevant information, and then used to identify patterns that are indicative of a particular disease.
Recommendation systems: Machine learning can be used to develop recommendation systems that suggest products, movies, music, and other items to users based on their past behavior and preferences. Recommendation systems are used by a variety of companies, such as Amazon, Netflix, and Spotify.
Large Language Models use cases
Content creation, including text summarization, writing etc. LLMs like GPT-3 are trained to generate human-like text from prompts, making them useful for content generation tasks where creativity is important.
Text classification to categorize documents, emails or social media posts into predefined categories based on their content by fine-tuning an LLM on the desired category labels during training. This allows for fast and accurate text classification tasks.
Art and music generation: Generative models can be used to create new works of art and music. Generative models can be trained on data that includes examples of art and music, and then used to generate new works in a similar style.
Data augmentation: Generative models can be used to augment existing datasets by generating new data points. Data augmentation can be used to improve the performance of machine learning models, as it provides them with more data to train on.
Conclusions
LLMs and ML are both powerful AI technologies with the potential to solve a wide range of problems. However, they are not equally suited for all applications. LLMs are best suited for tasks that require natural language understanding, such as chatbots and machine translation. ML is best suited for tasks that require pattern recognition, such as image classification and fraud detection.
When choosing between LLMs and ML, it is important to consider the following factors:
The nature of the task
The available data
The desired performance
If you are not sure which technology to use, it is always a good idea to consult with an AI expert: you can contact many of the expert here on Medium. You will not regret it!
In addition to the factors discussed above, there are a few other things to keep in mind when choosing between LLMs and ML.
LLMs are still under development, and their capabilities are constantly improving. ML is a more mature technology, but it is also more complex.
LLMs are more expensive to train and operate than ML models.
ML models are more transparent than LLMs, which means that it is easier to understand how they work.
This is only the start!
Hope you will find all of this useful. Feel free to contact me on Medium.
I am using Substack only for the newsletter. Here every week I am giving free links to my paid articles on Medium.
Follow me and Read my latest articles https://medium.com/@fabio.matricardi
Here few articles I wrote on Medium about them. They are free for you reading this Newsletter!