Artificial Intelligence (AI) is changing industries worldwide, affecting sectors like healthcare, finance, entertainment and education. For those starting with AI, knowing basic ideas offers a solid base for examining its various uses. This blog presents four main AI ideas: machine learning, neural networks, deep learning and natural language processing. These topics are crucial to current AI systems and important for anyone wanting to learn about or engage with AI.
Role of Machine Learning
AI is a general term that includes machine learning and robotic systems, amongst other things, but machine learning is a subset of AI, which means it is only a portion of AI. In simple terms, it’s about training models on data so they recognise a pattern, predict the outcome, and ultimately get better every time. Machine learning can be thought of as a process where machines ingest examples and derive some knowledge from those to be used over new data.
Types of Machine Learning:
Supervised Learning: This approach learns with models from labelled data. Let’s use an example: Say you want a model to classify images of animals, and then to train the model, you would initiate it with a data set that will label each of the images.
Unsupervised Learning: This type of learning uses unlabelled data and learns patterns without the use of independent discovery. However, it’s often used to cluster customers based on their purchase behaviour.
Reinforcement Learning: In this case, models are trained with error feedback on their actions. This approach is applied to certain applications, such as game AI or robotics.
Neural Networks in AI
Neural networks are a set of algorithms that process data in a comparatively simplistic form inspired by the structure of the human brain. On top of that, they are the basis of most modern AI, helping AI systems identify patterns that traditional algorithms aren’t able to, like voice and image recognition.
Structure of Neural Networks:
Layers: Neural networks are composed of layers: input layer (data enters here), hidden layers (process data here) and output layer (where the data result is).
Nodes (or Neurons): It takes input and passes to the next layer by each node. The weight of these nodes is then adjusted by the “learning” of a neural network.
Types of Neural Networks:
Convolutional Neural Networks (CNNs): CNNs use visual data to find features like edges, shapes, and textures, which are used, for the most part, for image recognition.
Recurrent Neural Networks (RNNs): RNNs are the type of networks most suited for sequential data — i.e. text and speech.
Deep Learning in Artificial Intelligence
Machine learning is a further evolved subset of deep learning that relies on deep neural networks — networks having multiple layers of nodes. For large datasets, problems that are difficult for someone to work on, such as image and speech recognition, deep learning models are particularly powerful: you don’t really need much human intervention.
Importance of Deep Learning:
Models using deep learning have the capability to perform more complex tasks because it’s possible to have them find subtle patterns in complex data. However, these models are resource-hungry, with large datasets and lots of computing power needed, but they are working very well for many applications.
Examples of Deep Learning Applications:
Autonomous Vehicles: Self-driving cars use deep learning to detect obstacles, pedestrians and road signs.
Medical Diagnosis: Deep learning models are used to examine medical images in healthcare and identify or predict diseases such as cancer at an early stage.
Natural Language Processing (NLP)
Machines can understand, interpret, and create human language with Natural Language Processing (NLP). Behind chatbots, translation software and voice-activated devices is NLP. The approach is a combination of computational linguistics, machine learning, and deep learning that processes data in language.
Core Components of NLP:
Tokenisation: Discretising sentences into individual words or tokens.
Sentiment Analysis: Find out if the text is positive or negative, i.e., sentiment or emotion.
Named Entity Recognition (NER): Finding nouns, adjective phrases and locations in a sentence.
Challenges in NLP:
Language is nuanced — let’s just state the unsure and let the nuances be wrong in possible ways. To know the context and also generate appropriate responses, there would be these intricacies that the NLP model has to deal with.
Concepts Working Together in AI Systems
In AI app implementations, these concepts are combined. For instance, an NLP-based voice-activated virtual assistant uses NLP to interpret spoken language, deep learning for voice recognition and machine learning to use past user interactions to improve response further.
They work together as one cohesive AI ecosystem that can understand, learn and respond to human needs, making AI applications smarter, more intelligent and more responsive to human needs.
Conclusion
It’s certainly an impossible thing, but diving deep into the fundamentals of Machine learning, neural networks, deep learning, and NLP is a great place to start for anybody who wants to learn about Artificial intelligence (AI). These technologies enable AI to be used, enabling machines to identify patterns, use language interpretations, and use data-based reasoning. Working with AI is an emerging and very interesting field, and there are many opportunities in every industry, as well as many new opportunities. The London School of Emerging Technology (LSET) understands AI’s value and has developed an Artificial Intelligence course where you can learn its core concepts and flow. You will also get hands-on experience with your learning, which will help you become industry-ready.