AI (Artificial Intelligence):
– AI refers to the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using rules to reach approximate or definite conclusions), and self-correction.

AGI (Artificial General Intelligence):
– AGI refers to a form of artificial intelligence that possesses the ability to understand, learn, and apply knowledge in a manner similar to human intelligence. Unlike narrow AI, which is designed for specific tasks, AGI would exhibit intelligence across a wide range of tasks and domains.

Embodied AI
– Embodied artificial intelligence, is a growing research area that combines machine learning, computer vision, and robotics. It allows AI  agents to interact with the environment through a physical embodiment, such as a robot. Through trial and error, the AI agent develops a “world view”, which is an abstract representation of the spatial or temporal dimensions of the world.

LLMs (Large Language Models):
– LLMs are a type of artificial intelligence model that is capable of understanding and generating human-like text at scale. They are trained on vast amounts of text data and can be fine-tuned for various natural language processing tasks such as text generation, translation, summarization, and question answering.

NLP (Natural Language Processing):
– NLP involves the interaction between computers and humans using natural language. It focuses on the interaction between computers and humans, understanding and processing human language, and enabling computers to understand, interpret, and generate human language in a valuable way.

Transformers:
– Transformers are a type of deep learning model architecture introduced in the paper “Attention is All You Need”. They have revolutionized various natural language processing tasks by enabling parallel computation across sequences, making them highly efficient for processing large amounts of text data.

ANN (Artificial Neural Networks od Neural Nets):
– Neural networks are a computational model inspired by the structure and function of the human brain. They consist of interconnected nodes, called neurons, organized in layers. Neural networks are capable of learning complex patterns in data and are widely used in various machine learning tasks.

Reinforcement Learning:
– Reinforcement learning is a type of machine learning paradigm where an agent learns to make decisions by interacting with an environment. The agent receives feedback in the form of rewards or penalties based on its actions, allowing it to learn optimal strategies for achieving its goals over time.

Deep Learning:
– Deep learning is a subset of machine learning where artificial neural networks, inspired by the structure and function of the human brain, learn from large amounts of data. Deep learning algorithms are capable of automatically learning representations of data through multiple layers of abstraction.

Machine Learning:
– Machine learning is a subset of artificial intelligence that focuses on the development of algorithms that can learn from and make predictions or decisions based on data. Machine learning algorithms enable computers to learn and improve performance on a task without being explicitly programmed.

Supervised Learning:
– Supervised learning is a type of machine learning where the algorithm learns from labeled data, consisting of input-output pairs. The algorithm learns a mapping from input to output by generalizing from the labeled examples provided during training.

Unsupervised Learning:
– Unsupervised learning is a type of machine learning where the algorithm learns patterns from unlabeled data. The algorithm discovers the inherent structure in the data without explicit guidance, such as identifying clusters or associations among the data points.

Generative AI:
– Generative AI refers to a class of artificial intelligence algorithms that can generate new data samples similar to those in the training data. These algorithms include generative adversarial networks (GANs), variational autoencoders (VAEs), and other techniques capable of generating images, text, audio, and more.

LaMDA (Language Model for Dialogue Applications)
It’s the newest variant of Google’s BERT (Bidirectional Encoder Representations from Transformers) AI model, specifically tailored for dialogue applications. LaMDA aims to improve the naturalness and coherence of conversational AI systems by focusing on understanding and generating dialogue-based interactions.

AI Chatbot:
– A chatbot is a conversational AI system designed to interact with users through text or speech interfaces. Chatbots can be rule-based or AI-powered, and they are used for various purposes such as customer service, information retrieval, and entertainment.

AI Image Processing:
– Image processing refers to the manipulation of digital images using algorithms and techniques to enhance or extract information from the images. This field encompasses tasks such as image filtering, segmentation, feature extraction, and object recognition.

AI Computer Vision:
– Computer vision is a field of artificial intelligence that enables computers to interpret and understand the visual world. It involves tasks such as image recognition, object detection, image segmentation, and scene understanding.

AI Object Detection:
– Object detection is a computer vision task that involves locating and classifying objects within an image or video frame. Object detection algorithms identify and draw bounding boxes around objects of interest, enabling applications such as surveillance, autonomous driving, and image understanding.

AI Video Processing:
– Video processing involves the analysis and manipulation of digital video data. It includes tasks such as video compression, object tracking, action recognition, and video summarization.

AI Audio Processing:
– Audio processing refers to the manipulation of digital audio signals using algorithms and techniques. This field encompasses tasks such as speech recognition, sound classification, audio synthesis, and music analysis.

AI Speech Recognition:
– Speech recognition, also known as automatic speech recognition (ASR), is a technology that enables computers to transcribe spoken language into text. Speech recognition systems convert audio signals into text representations, enabling applications such as virtual assistants and voice-controlled devices.

AI Emotion Recognition:
– Emotion recognition is a field of AI that focuses on identifying and analyzing human emotions from facial expressions, vocal intonations, and physiological signals. Emotion recognition systems are used in applications such as sentiment analysis, human-computer interaction, and affective computing.

AI Chapot Assistants
The use and diversity of chatbots is constantly evolving, with various platforms and applications utilizing chatbots for different purposes. Here are some of the most widely used chatbots:

Google Assistant:
– Google Assistant is a virtual assistant developed by Google. It is available on smartphones, smart speakers, smart displays, and other devices. Google Assistant can perform tasks such as answering questions, setting reminders, controlling smart home devices, and more.

Amazon Alexa:
– Alexa is Amazon’s virtual assistant, powering devices like the Echo smart speaker. Alexa can perform various tasks through voice commands, including playing music, providing weather updates, ordering products from Amazon, and controlling smart home devices.

Apple Siri:
– Siri is Apple’s virtual assistant, available on iPhones, iPads, Macs, and other Apple devices. Siri can perform tasks such as sending messages, making calls, setting reminders, searching the web, and interacting with other apps on the device.

Microsoft Cortana:
– Cortana is Microsoft’s virtual assistant, integrated into Windows 10, Microsoft Edge, and other Microsoft products. Cortana can assist users with tasks such as scheduling appointments, sending emails, providing recommendations, and accessing information from Microsoft services.

Facebook Messenger Chatbots:
– Facebook Messenger supports chatbots that businesses and developers can build to interact with users on the Messenger platform. These chatbots can provide customer support, deliver news updates, facilitate transactions, and offer various services within the Messenger app.

WhatsApp Business Chatbots:
– WhatsApp Business allows businesses to create chatbots to automate customer interactions on the platform. These chatbots can provide customer support, send automated messages, process orders, and engage with users in conversation.

Slack Bots:
– Slack, a popular team collaboration platform, supports bots that can automate tasks, provide information, and integrate with other services within Slack channels. Slack bots can help teams streamline workflows, manage tasks, and enhance communication.

Chatbots for Customer Support:
– Many companies across industries deploy chatbots for customer support on their websites, mobile apps, and messaging platforms. These chatbots handle common inquiries, provide assistance, and escalate complex issues to human agents when needed.

Chatbots for E-commerce:
– E-commerce businesses often use chatbots to assist customers with product recommendations, order tracking, and customer service inquiries. Chatbots can help improve the shopping experience, increase engagement, and drive sales.

Healthcare Chatbots:
– Healthcare providers and organizations deploy chatbots to assist patients with scheduling appointments, accessing medical information, receiving symptom assessments, and answering health-related questions.