Top 20 AI Buzzwords Explained
Artificial Intelligence has become a part of our everyday lives. No day goes without new AI products being released. Almost every company either has plans to incorporate AI, is actively using it, or is rebranding its old rule-based engines as AI-enabled technologies.
Curious to learn more about that growing world of AI, I’m starting by decoding its buzzwords and sharing my findings with you!
Top 20 AI buzzwords in 2024
1 — Artificial Intelligence (AI):
A broad field of computer science focused on creating systems that can perform tasks that typically require human intelligence, including problem-solving, decision-making, and language understanding.
The term originally coined by emeritus Stanford Professor John McCarthy serves as an umbrella term for various approaches and methodologies such as Machine Learning, Deep Learning.
2 — Machine Learning (ML):
Machine learning is a subset of Artificial Intelligence (AI) that focuses on the development of algorithms and statistical models that enable computer systems to improve their performance on a specific task through experience without being explicitly programmed.
This process typically involves analyzing datasets to detect patterns and construct models and algorithms that you can build, host, and query.
The ultimate goal of Machine Learning is to enable the ML system to learn and make decisions independently without human intervention or assistance.
3 — Supervised Machine Learning.
Supervised Learning uses labeled data to train an algorithm/model to predict unknown data accurately. It involves inputting data associated with output labels and then training the algorithm on this data to recognize complex patterns in it.
Example: image recognition is a system trained to differentiate between images of cats and dogs. The training process involves providing the algorithm with a large dataset of labeled images, where each image is tagged as either “cat” or “dog.” The algorithm learns to recognize patterns and features specific to each category, such as the shape of ears, the size of the animal, or the texture of the fur. After sufficient training, the system can accurately classify new, unlabeled images as either a cat or a dog by analyzing the visual features and comparing them with the learned patterns.
4 — Unsupervised Machine Learning.
Unsupervised Learning focuses on finding patterns and structures in data without relying on labeled data. It’s an approach to discovering hidden connections and valuable insights from large datasets.
Example: music recommendation system that organizes songs into different genres without prior labeling. The system analyzes the features of each song, such as beat, tempo, and melody, and groups similar songs together. This process, known as clustering, allows the system to discover patterns and relationships in the data, enabling it to recommend songs from a specific genre to users who have shown a preference for that genre, even though the songs were never explicitly labeled by genre during the training phase.
5 — Reinforcement Machine Learning.
Reinforcement Learning uses decision-making algorithms to learn from repeated interactions with an environment. It’s an area that focuses on agent development that can interact to take action to achieve specific goals. But hold on, the practical applications of the subfields can go beyond what I just mentioned; look after concepts like Deep Reinforcement Learning.
Example: Training a chess bot where the agent (the AI player) interacts with the environment (the chessboard). The action is making a chess move, and the reward system is structured such that the agent receives a positive reward for capturing an opponent’s piece and a negative reward for losing its piece. Over time, by playing multiple games and experiencing the outcomes of its moves, the AI learns and refines its strategy to maximize positive rewards, progressively improving its chess-playing skills.
6 — Deep Learning:
Deep Learning is a subset of machine learning, focusing on neural networks. These are computational models inspired by the human brain, consisting of interconnected nodes (neurons) with associated weights. Information is processed through multiple layers in these networks, hence the term “deep.” This structure enables the model to learn complex patterns in data
Deep learning is highly effective for tasks such as image recognition (identifying objects in photos) and natural language processing (like translating between languages).
7 — CNN (Convolutional Neural Network):
Convolutional Neural Networks are a type of neural network specifically designed for processing grid-like data, such as images and videos. They are designed to recognize patterns and shapes in pictures, making them great for tasks like spotting objects or faces.
Here’s how they work, simplified:
- Convolution: Think of this as a flashlight shining over an image. The CNN moves this flashlight across the whole picture, focusing on small parts one at a time. Each small part is analyzed to find patterns (like edges, textures, or colors).
- Pooling: After finding patterns, CNN wants to make a simpler version of the image. It does this by pooling, which means taking small areas and summarizing them with the most important piece of information from that area (like the strongest pattern).
- ReLU (Rectified Linear Unit): This step helps decide if the pattern found is strong or weak. It keeps the strong ones and gets rid of the weak ones, making the model more efficient and focused.
After doing all these steps, CNN has a good idea of what’s in the picture and can tell if it’s a cat, a dog, a car, etc.
CNNs are feedforward neural networks that use filters and pooling layers
Example: Used in image recognition tasks like identifying objects in photos, facial recognition, and self-driving cars for detecting road signs.
8— RNN (Recurrent Neural Network):
Recurrent Neural Network (RNN) is a type of neural network designed for data that comes in sequences, like sentences or time series. Unlike regular neural networks, RNNs have a memory that captures information about what has been calculated so far. This memory helps the network understand the context and sequence of the data.
Unlike CNNs which are feedforward neural networks, RNNs feed results back into the network. Thus they name Recurrent Neural Networks.
Example: RNNs are useful in understanding and generating language. They are used in natural language processing (NLP) tasks such as writing predictive text (like what you see when texting), converting speech to text (like in voice assistants), and translating languages in real time.
9 — MLP (Multilayer Perceptron)
MLP is a deep, feedforward, artificial neural network. It consists of at least three layers: an input layer, one or more hidden layers, and an output layer. Each neuron in one layer is connected to all neurons in the next layer, making the network fully connected. MLP utilizes a technique called backpropagation for training, where the model learns from the errors it makes by adjusting the weights of the connections.
Example: MLPs are widely used in classification problems where the input data is labeled and the output is a discrete class label. For instance, in a handwriting recognition task, the input could be pixel data from images of handwritten digits, and the output would be which digit the image represents (0 through 9). The MLP would learn to recognize patterns in the pixel data corresponding to each digit and accurately classify the images.
10— GAN (Generative Adversarial Network):
A Generative Adversarial Network (GAN) is a type of machine learning framework composed of two neural networks: a generator and a discriminator. These two networks are trained simultaneously in a sort of competition. The generator tries to create data that looks real, while the discriminator tries to distinguish between real and generated data. Over time, the generator gets better at producing realistic data, and the discriminator gets better at telling real from fake.
Examples: Used to create photorealistic images from sketches, generate realistic human faces or objects, produce deepfake videos where one person’s face is convincingly swapped with another, and enhance the resolution of images in a process known as super-resolution.
11— Transformers:
Transformers are a type of deep learning architecture that has significantly advanced the field of natural language processing (NLP). Unlike previous models that processed input data sequentially, transformers process entire sequences of data all at once, allowing for parallelization and handling long-range dependencies more efficiently. This is largely due to their use of attention mechanisms, which enable the model to weigh the importance of different parts of the input data differently, focusing more on the relevant parts for a given task.
Example: Transformers serve as the foundational architecture for state-of-the-art NLP models like BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pre-trained Transformer). These models have set new benchmarks in a wide range of NLP tasks, including text classification, translation, summarization, and question-answering, by effectively capturing the context and nuances of language.
12 — GPT (Generative Pre-trained Transformer):
GPT (Generative Pre-trained Transformer) is a type of transformer-based model that excels in generating text that closely resembles human writing. It’s pre-trained on a vast corpus of text data, enabling it to understand and generate language with remarkable coherence and context relevance. GPT models are particularly known for their ability to perform a wide range of natural language understanding and generation tasks without needing task-specific training data.
Example: GPT 4 developed by OpenAI, has garnered attention for its ability to produce text that can be indistinguishable from that written by humans. Its applications range from creating content and conversing in chatbots to more complex tasks like coding and providing sophisticated text-based services in various industries.
13— GenAI (Generative Artificial Intelligence):
GenAI (Generative Artificial Intelligence) encompasses AI systems that can autonomously create content, such as text, images, videos, or music. These systems leverage advanced techniques like Generative Adversarial Networks (GANs) or Large Language Models (LLMs) to produce new, original outputs that can be remarkably similar to human-created content. GenAI is particularly notable for its creative potential and its capacity to mimic various styles and formats.
Example: Applications of GenAI include chatbots that generate conversational responses indistinguishable from humans, AI-driven platforms that compose music or write stories and systems that create realistic visual artwork or deepfake videos. These systems are increasingly used in entertainment, media, and customer service, showcasing the versatility and creative potential of generative AI technologies.
14— NLP (Natural Language Processing):
NLP (Natural Language Processing) is a branch of artificial intelligence that deals with the interaction between computers and human (natural) language. It involves enabling machines to understand, interpret, and generate human language in a way that is both meaningful and useful. NLP combines computational linguistics with machine learning and deep learning models to process and analyze large amounts of natural language data.
Example: Applications of NLP are widespread and include chatbots that can engage in conversation with human-like responses, sentiment analysis systems that determine the mood or opinion expressed in text, and language translation services like Google Translate, which can accurately convert text or speech from one language to another.
15 — LLM (Large Language Model):
LLM (Large Language Model) refers to an AI model that has been trained on vast datasets of text to understand and generate language that closely resembles human writing. These models leverage deep learning techniques to process and produce text, excelling in grasping the nuances, context, and complexities of language. LLMs are known for their ability to perform a wide range of language-related tasks with remarkable proficiency.
Example: Notable instances of LLMs include GPT-4 by OpenAI and BERT by Google. These models are utilized in a variety of natural language processing applications, ranging from generating coherent and contextually relevant text to enhancing language understanding in tasks such as text summarization, sentiment analysis, and conversational AI systems.
16 — TTS (Text-to-Speech):
Text-to-speech is a form of assistive technology that reads digital text aloud. It’s a significant advancement in the field of human-computer interaction, enabling computers, smartphones, and other digital devices to convert any type of text into spoken voice output. This technology involves the synthesis of human-like speech from written text, allowing for a wide range of applications where verbal communication is preferable or necessary.
Examples: Text-to-speech is widely used in voice assistant applications such as Siri, Alexa, and Google Assistant. It’s also employed in audiobook narrations, offering an alternative to reading traditional books, and in GPS navigation systems. Additionally, TTS technology offers significant benefits in accessibility, assisting individuals who have difficulties with reading by providing a spoken version of the text.
17— RAG (Retrieve and Generate):
RAG, or retrieval augmented generation, is a method introduced by Meta AI researchers that combines retrieval-based models and generation-based models to address knowledge-intensive tasks. The retrieval component fetches relevant information or documents from a large external dataset, while the generation component uses this information to create coherent, contextually appropriate responses or summaries.
Example: RAG is employed in sophisticated chatbots and question-answering systems, enabling them to provide accurate and contextually enriched answers. These systems first retrieve the most relevant information from a comprehensive knowledge base or database in response to a query. Then, they use the generative model to craft a response that not only answers the query but also integrates the retrieved information smoothly, ensuring the output is both informative and naturally phrased.
18— XAI (Explainable Artificial Intelligence):
XAI (Explainable Artificial Intelligence) refers to AI systems that are explicitly designed to make their operations, decisions, and actions understandable to human users. The focus of XAI is to ensure transparency and interpretability, allowing users to comprehend how the AI arrived at a particular decision or conclusion. This understanding fosters trust and facilitates more effective collaboration between humans and AI systems, especially in critical applications where the reasoning behind decisions is as important as the decisions themselves.
Example: In the medical field, XAI plays a crucial role. For instance, AI systems used for diagnosing diseases or recommending treatments are more valuable when they can explain their reasoning. Doctors and healthcare professionals can review and understand the AI’s diagnostic process or treatment suggestions, ensuring that the AI’s recommendations align with medical standards and practices, and providing an additional layer of validation before making critical decisions.
19 — AGI (Artificial General Intelligence):
AGI (Artificial General Intelligence) refers to a level of artificial intelligence that can understand, learn, and apply knowledge in a way that is indistinguishable from human intelligence. Unlike specialized AI that excels in specific tasks, AGI would have the ability to perform any intellectual task that a human can, including reasoning, problem-solving, and creativity across a wide range of domains, all while understanding context and subtleties.
As of now, AGI remains a theoretical concept and a goal for future AI research. No current AI systems have achieved the broad, adaptable, and comprehensive cognitive abilities that would qualify as AGI, making it more of a vision for the future of AI rather than a present reality.
20 — ASI (Artificial Superintelligence):
ASI (Artificial Superintelligence) represents a form of AI that not only matches but significantly surpasses human intelligence across all fields, including creativity, general wisdom, and problem-solving. It embodies an AI that is superior to human intelligence in every aspect, potentially possessing the ability to improve itself autonomously, leading to exponential growth in its capabilities.
ASI remains a theoretical and speculative concept, far beyond the current capabilities of AI technology. No examples of ASI exist today, and its potential realization is a subject of much debate and contemplation in the fields of AI ethics, future studies, and technology forecasting.
Conclusion
Artificial Intelligence isn’t just a trend. It’s now a crucial driver of business innovation. It automates tasks, gives insights through data analysis, and boosts customer engagement. AI is changing how businesses operate and opening new possibilities across different areas.
Stackademic 🎓
Thank you for reading until the end. Before you go:
- Please consider clapping and following the writer! 👏
- Follow us X | LinkedIn | YouTube | Discord
- Visit our other platforms: In Plain English | CoFeed | Venture | Cubed
- More content at Stackademic.com