The AI revolution is steered by three core technologies, namely, Generative AI, Machine Learning, and Deep Learning. However, as these terms are usually used interchangeably, there is a misconception that these three are the same. But that’s not the case; rather, there are stark differences between them.
This blog sheds light on the key features and differences of
Generative AI vs Machine Learning vs Deep Learning, With these technologies redefining how we live and work, staying informed about their development is no longer optional; it’s essential.
Let’s break this down in this informative guide.
Starting with GenAI, it is a subset of Artificial Intelligence that generates new data from existing models and patterns. It works through 3 main steps: training the algorithm, generating content, and evaluating the output.
This process involves the working of neural networks and two key components, encoders and decoders. ChatGPT, Google’s Bard, and Dall-E are among the latest
Gen AI examples.
These Gen AI models are widely used to create content (written, audio &video), images, generate code, and personalise customer service. GenAI caters to diverse sectors such as healthcare & pharmaceuticals, advertising & marketing, manufacturing, finance, media & entertainment are among the other crucial sectors.
ML or Machine Learning is another AI subset that empowers machines to learn from a vast amount of data and makes error-free predictions. ML algorithms can be classified into 3 broad categories- supervised learning, unsupervised learning, and reinforcement learning.
Machine Learning operations follow 7 major steps: collecting data, preprocessing, model selection, model training, model evaluation, fine-tuning, and ultimately, prediction.
Some of the potential applications of
Machine Learning in AI are
Amazon Marketplace is a classic example of how it applies machine learning to identify consumers’ shopping patterns and dynamic behaviour. Based on that, the platform recommends personalised products and services.
A subset of ML, DL deploys neural networks (artificial, with various layers ) to analyse a large volume of data. Based on that analysis, the DL technology detects patterns, forecasts predictions, as well as automates tasks. However, its ability to deliver accurate and informed decisions depends on the quality and quantity of input data.
From powering voice assistants and self-driving cars to spotting diseases in medical images, DL applications are endless.
Rooted in the same technological ecosystem, these three innovations hold some striking similarities, such as:
Considering their rapid development and diverse applications, it is important to understand the
concept of Generative AI vs Machine Learning, vs Deep Learning. The power trio is revolutionising the technological and business landscape to create experiences that were fictional even a few years ago. In that light, theer is a staggering demand for professionals skilled in Gen AI, DL, and ML. Enroll in our expert-curated
Machine Learning Course to scale up your career in the AI domain.
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