Computer Vision and NLP in IBM's AI Fundamentals
Having established a solid base in the fundamentals of AI, I recently ventured into the second module of IBM’s AI Fundamentals course, which focused on the worlds of Computer Vision and Natural Language Processing (NLP). These aren't just abstract concepts; they're the keys that unlock AI's ability to perceive and understand the world around us, and this module really brought that to life.
Natural Language Processing: Making Sense of the Messiness of Language
The module kicked off by tackling the inherent complexity of human language. It's not neat and tidy like structured data; it's messy, ambiguous, and full of nuances. That's where NLP comes into play. It's the magic that allows machines to decipher the meaning behind our words. The course clearly laid out the step-by-step process:
First, it's about segmenting sentences, breaking down large chunks of text into manageable units.
Next, we delve into tokenization, which is like taking a sentence apart piece by piece into its smallest, meaningful components – often words or even parts of words.
Then, these tokens are categorized and organized based on what they mean, which is the point where AI starts to understand text on a deeper level.
The module also zoomed in on some key concepts in NLP:
Entities are the concrete nouns: people, places, and things. They're the 'who' and 'what' in a sentence.
Relationships are the connections between entities, painting the larger picture of how things are related within the text.
Concepts are the unspoken ideas, the things implied but not explicitly stated. AI needs to infer these to get the full picture.
Beyond these core elements:
Emotion Detection, which focuses on identifying human emotions explicitly stated in the text, like joy, sadness or anger.
Sentiment Analysis, which gauges the overall feeling of the text, classifying it as positive, negative, or neutral.
Chatbots: NLP in Action
The module then brought NLP to life by exploring how chatbots work. It's not just a matter of programming canned responses; they rely on a deep understanding of what we're saying. In this, the module highlighted that a Chatbot has a frontend that reads user text or voice prompts, and a backend with the logic to correctly interpret the prompt. The backend is the part that performs the heavy lifting in the following manner:
It seeks to identify the intent of the user's message—the purpose behind their words, what action they're hoping to take.
It then seeks to identify the entities within the text—the nouns that provide the context needed to understand the intent.
Based on the entities and the intent, the system uses the preprogrammed dialog, or conversation flow, to formulate its response.
Computer Vision: Giving AI the Power to See
Switching gears, the module then jumped into the realm of Computer Vision, and it's here where we explore how computers can "see" and interpret images. The module focused on Convolutional Neural Networks (CNNs). The main functions of CNN's are:
To analyze images, the system begins by comparing overlapping groups of pixels in an image.
By making these thousands of pixel comparison, it can start to recognize key features in the image.
Finally, the extracted components are compared to the model's corpus to arrive at an overall identification of what is in the image.
The module also showed how Generative Adversarial Networks (GANs) can be used to create new drawings and images, which was a testament to the creative potential of AI.
NLP and Computer Vision: Working Together
It became very clear that both NLP and computer vision are about empowering AI, making it capable of automating tasks that once required a human's perception and understanding. Whether it is identifying a face from a photo, or helping people make better informed decisions, both have a powerful impact.
Top comments (0)