Foundations to Advanced AI: A Comprehensive Guide

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Artificial intelligence (AI) and Natural language processing (NLP) are making a big difference in our daily lives. A virtual assistant chatting like a real person or As we finish typing, the search AI engine recognises our needs and returns all of the relevant stuff. These technologies are changing how we interact with machines. Take, for example, AI chatbots like ChatGPT, which can provide information on any topic with just a few clicks. Similarly, an AI agent like YourGPT Chatbot can help businesses perform dynamic actions to execute tasks other than just answering knowledge base queries to improve customer interaction and enhance overall efficiency. Another example is Perplexity, which searches the web and finds the most relevant answer to your query by analysing various web pages.

Doesn’t it make you curious to learn more about AI?

In this blog post, we will cover everything from Fundamental to Advanced Concepts. We will start with understanding the basics of AI, gradually understand more advanced topics. Our aim is to provide you with a better understanding of the inner workings of these technologies and their applications in our daily lives.

Section 1: Understanding AI Fundamentals

1. Artificial Intelligence (AI)

Artificial Intelligence Image

AI stands for artificial intelligence, which refers to the ability of machines to perform tasks that would normally require human intelligence, such as learning, problem solving, and decision making. AI systems can be trained on large amounts of data and use algorithms to make predictions or decisions based on that data. AI is used for a wide range of applications, including speech recognition, image generation, analysis, natural language processing and so much more.

History of artificial intelligence (AI)

1. Early Beginnings (1950s-1960s)

  • The term “Artificial Intelligence” was coined by John McCarthy in 1956.
  • Early AI research focused on problem-solving and symbolic reasoning.
  • Important developments include the Logic Theorist (1956), the General Problem Solver (1959), and the first expert system, DENDRAL (1965).

2. The AI Winter (1970s-1980s)

  • AI research faced criticism and funding cuts due to overpromising and unrealistic expectations.
  • However, important developments continued, such as the Stanford Cart (1973) and the development of expert systems for specific domains.

3. AI Resurgence (1980s-1990s)

  • Expert systems and knowledge-based systems found practical applications in various industries.
  • The introduction of neural networks and machine learning techniques, including backpropagation (1986), revived interest in AI.

4. The Era of Big Data and Deep Learning (2000s-present)

  • The availability of large datasets and increased computational power enabled the development of deep learning techniques.
  • Breakthroughs in image recognition, natural language processing, and reinforcement learning were achieved.
  • AI systems like IBM’s Watson (2011), Apple’s Siri (2011), and Google’s AlphaGo (2016) demonstrated the power of AI in various domains.

5. AI Today and the Future

  • AI is being applied in a wide range of fields, including healthcare, finance, transportation, entertainment, business, and governance.
  • Advancements in areas like generative AI (e.g., ChatGPT).
  • The future of AI is expected to bring even more transformative changes, with the potential for artificial general intelligence (AGI) and the continued integration of AI into various aspects of our lives.

Now Let us explore a subset of AI known as machine learning (ML)

2. Machine Learning (ML): 

Machine Learning

Machine learning is a subfield of artificial intelligence that involves training computers to automatically improve their performance on a specific task through experience, rather than being explicitly programmed. Simply we can say that machine learning algorithms can learn from data and make predictions or decisions without being explicitly programmed to perform those tasks.

There are different types of machine learning, including:

1. Supervised learning

Supervised learning is a type of machine learning in which an algorithm is trained on a labelled dataset, where the desired outputs are already known. The algorithm uses this labelled data to learn patterns and relationships between the input features and the output labels, so that it can make accurate predictions on new, unseen data.

There are two types of supervised learning:

Supervised Learning Techniques


Predicting a continuous output, such as predicting house prices based on features such as size, location, and age.


Predicting a categorical output, such as predicting whether an email is spam or not based on features such as the sender and subject line.

2. Unsupervised learning

Unsupervised learning is a type of machine learning in which an algorithm is trained on an unlabelled dataset, where the desired outputs are not known in advance. The goal of unsupervised learning is to find patterns or structures in the data, such as clustering similar data points together or reducing the dimensionality of the data.

Some common unsupervised learning algorithms include:

Data Science Techniques


Grouping similar data points together, such as clustering customers based on their purchasing behaviour.

Dimensionality Reduction

Reducing the number of features in a dataset, such as reducing the number of pixels in an image.

Association Rule Mining

Discovering relationships between variables in a dataset, such as discovering which products are often purchased together.

3. Reinforcement learning

Reinforcement learning is a type of machine learning in which an algorithm learns by interacting with an environment and receiving rewards or penalties for its actions. The goal of reinforcement learning is to find the optimal sequence of actions that maximises the total reward over time.

Reinforcement learning is often used in situations where the optimal behaviour is not known in advance, such as playing a game or controlling a robot. The algorithm learns by trial and error, receiving rewards or penalties for its actions and adjusting its behaviour accordingly.

Some common reinforcement learning algorithms are:

Reinforcement Learning Techniques


Learning the optimal policy by updating a Q-table based on the rewards received for each action.

Deep Q-networks

Using a deep neural network to approximate the Q-function in Q-learning, enabling the algorithm to handle high-dimensional state spaces.

Actor-critic methods

Using separate networks for the policy (actor) and value function (critic), enabling the algorithm to learn both the optimal policy and the value function at the same time.

4. Deep learning

Deep learning is a subset of machine learning that uses artificial neural networks with many layers (hence the term “deep”) to model complex, nonlinear relationships between inputs and outputs.

In a deep learning model, the layers are organised into a hierarchy, with each layer building on the features learned by the previous layer. The lowest layer learns the simplest features, while the highest layer learns the most complex features.

Deep learning models can be trained on large amounts of data and have shown impressive performance on tasks such as image classification, speech recognition, and natural language processing.

Some common types of deep learning models include:

Neural Network Techniques

Convolutional Neural Networks (CNNs)

Used primarily for image classification and object detection tasks, CNNs consist of convolutional layers that learn to detect features in the input images.

Recurrent Neural Networks (RNNs)

Used primarily for sequence data such as time series or natural language, RNNs have a “memory” that allows them to remember previous inputs and use that information to make predictions.

Generative Adversarial Networks (GANs)

Used for generating realistic images, GANs consist of two neural networks — a generator that creates images and a discriminator that tries to distinguish between real and generated images.


Used for dimensionality reduction and feature extraction, autoencoders consist of an encoder that compresses the input data into a lower-dimensional representation and a decoder that reconstructs the original data from the compressed representation.

Machine learning has many applications, including image recognition, speech recognition, fraud detection, and recommendation systems, to name just a few.

3. Artificial General Intelligence (AGI)

Artificial General Intelligence

AGI stands for Artificial General Intelligence. It refers to a type of artificial intelligence that will have the ability to understand, learn, and perform any intellectual task that a human being can. This includes abstract thought, reasoning, problem-solving, and learning from past experiences. An AGI system would be capable of handling complex, unfamiliar situations with human-like intelligence.

Currently, artificial intelligence systems are typically designed for specific tasks, referred to as narrow AI or weak AI.

Narrow AI means an AI that performs a specific task or a set of tasks.

AGI, also known as strong AI, would have the potential to transform not just humanity but this earth.

Section 2: Language AI

1. Natural Language Processing (NLP)

Natural Language Processing
  • Natural language processing (NLP) is a subfield of artificial intelligence that focuses on the interaction between humans and computers using natural language. The goal of NLP is to enable computers to understand, interpret, and generate human language, allowing humans to communicate with computers in a more natural way.
Some common NLP tasks include:
  • Language translation: translating text from one language to another.
  • Sentiment analysis: determining the sentiment or emotion expressed in a piece of text, such as positive, negative, or neutral.
  • Speech recognition: converting spoken language into written text.
  • Text classification: categorising text into different topics or categories, such as spam vs. non-spam emails.
  • Named entity recognition: identifying and categorising named entities in text, such as people, organisations, and locations.
  • Text summarization: condensing a long piece of text into a shorter summary while preserving the most important information.
  • Question answering: answering questions posed in natural language by extracting information from a given context.

NLP uses linguistic, computer science, and machine learning techniques to achieve these tasks, including rule-based systems, statistical models, and deep learning models such as recurrent neural networks and transformer networks.

2. Natural Language Understanding (NLU): 

Natural Language Understanding (NLU) is a subtopic of Natural Language Processing (NLP) that focuses on machine reading comprehension. It is concerned with the interpretation of human language input and the extraction of meaning and intent from the text or speech.

NLU enables AI to understand the meaning of human language, such as context, semantics, and pragmatics, in order to accurately respond to or act upon the input. This includes tasks such as:

  • Intent classification: determining the user’s intention or goal behind a text or speech input, such as whether it’s a question, a request, or a command.
  • Entity recognition: identifying and categorising relevant information in the input, such as people, locations, organisations, dates, and other entities.
  • Semantic parsing: understanding the meaning of the input and mapping it to a structured representation that can be used by a machine.
  • Contextual understanding: inferring meaning based on the context in which the input was given, including previous utterances, user preferences, and other relevant information.
  • Pragmatic analysis: interpreting the input based on social and cultural factors, such as sarcasm, humour, or implied meaning.

NLU is an essential component of conversational AI systems, chatbots, and virtual assistants, enabling them to engage in more natural and meaningful interactions with humans.

3. Natural Language Generation (NLG): 

Natural Language Generation

Natural Language Generation (NLG) is a subtopic of Natural Language Processing (NLP) that focuses on generating human-like text from structured data or unstructured information. The goal of NLG is to produce coherent, fluent, and contextually appropriate text that is indistinguishable from text written by a human.

NLG systems use various techniques and algorithms to accomplish this, including:

  • Rule-based systems: using pre-defined templates and rules to generate text based on the input data.
  • Statistical methods: using machine learning algorithms to learn patterns and generate text based on the input data.
  • Neural network models: using deep learning techniques, such as sequence-to-sequence models and generative transformer models, to generate text based on the input data.

NLG has a wide range of applications, including:

  • Text summarization: generating a summary of a longer text while preserving the most important information.
  • Machine translation: generating text in a target language from text in a source language.
  • Dialogue systems: generating responses in a conversation with a human user.
  • Content creation: generating articles, reports, or other types of written content based on structured data or input parameters.
  • Data-to-text: transforming structured data into human-readable text, such as generating weather reports from weather data.

Section 3: Key AI Concepts

1. Large Language Model (LLM):

Large Language Model

A Large Language Model (LLM) is a type of deep learning model that is designed to process and generate natural language text. LLMs are typically based on transformer architecture and are trained on massive amounts of text data to learn the patterns and relationships between words and sentences.

LLMs have become increasingly popular in recent years due to their impressive performance on a wide range of NLP tasks, such as language translation, text summarization, question answering, and language generation.

Some well-known LLMs include:

  • GPT (Generative Pre-training Transformer): developed by OpenAI, GPT is a family of LLMs that have shown impressive performance on various language tasks, including text generation and question answering.

Common Terminology in context with LLM

1. Top-p:

A parameter that controls the randomness in the model’s predictions by defining the probability mass that must be assigned to the most probable tokens.

  • A higher Top-p value results in more predictable and less diverse outputs.
  • A lower value leads to more randomness and a greater variety of potential outputs.

A parameter that determines the number of potential tokens the model considers when generating the next token.

  • Top-k filtering restricts the number of tokens to a smaller subset (k), allowing the model to focus on the most probable options.
  • This can help optimise performance and reduce computational overhead.
3. Temperature:

A scaling factor is used to modify the output probabilities generated by the model.

  • Temperature affects the randomness and diversity of the generated text.
  • A temperature value of 1 leaves the probabilities unchanged.
  • Lower values result in more conservative, predictable text.
  • Higher values lead to more random and diverse output.
4. Context Size:

In the context of Large Language Models (LLMs) and natural language processing (NLP), context size is a parameter that defines the maximum length of the input sequence a model can process when making predictions.

  • Input Sequence Length: Context size determines how much information from the input text the model can consider when generating output.
  • Larger Context Sizes: Larger context sizes allow models to capture more context and dependencies within the input text, which can improve the quality and relevance of the generated output.

For example, If a model has a context size of 1024 tokens, it can process up to 1024 tokens (words or characters) from the input text when generating output. If the input text is longer than the context size, the model might process it in chunks or use techniques like sliding windows or hierarchical processing to handle longer inputs.

Let’s understand it with a more simplified example:

Imagine you are reading a book. The context size is like how many pages you can look at while trying to understand a particular sentence. A larger context size means you can look at more pages (more text) to better understand the meaning of that sentence based on the surrounding information.

Context size plays an important role in the performance and capabilities of an LLM, as it affects the model’s ability to handle longer and more complex texts, as well as its memory usage and computational requirements.

2. Generative AI: 

Generative AI

Generative AI refers to a type of artificial intelligence that focuses on generating new content, rather than simply analyzing or classifying existing data. Generative AI models use various machine learning techniques to learn the patterns and relationships in a dataset and then generate new data that is similar in style or content to the training data.

Some commonly implemented applications of generative AI include:

  • Text generation: generating human-like text, such as stories, poems, or articles.
  • Image generation: generating new images based on a given style or content.
  • Audio generation: generating new audio, such as music or speech.
  • Video generation: generating new videos or animations.
  • Game level generation: generating new levels or environments for video games.

Generative AI models can be used for various purposes, including creative expression, content creation, data augmentation, and automation of repetitive tasks.

Some popular generative AI models include Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformer-based models like GPT.

3. Conversational AI: 

Conversational AI

Conversational AI refers to the use of artificial intelligence technologies to create systems that can engage in human-like conversations with users. These systems are designed to understand, interpret, and respond to user queries and requests in a natural and conversational manner.

Conversational AI systems typically involve the integration of various components and technologies, including:

  • Natural Language Processing (NLP): to analyze and interpret user input, extract relevant information, and identify the user’s intent and entities.
  • Natural Language Understanding (NLU): to comprehend the meaning and context of the user’s input beyond just the words used.
  • Natural Language Generation (NLG): to generate appropriate and coherent responses to the user.
  • Dialogue management: to maintain the flow and coherence of the conversation, track context, and handle user interactions.
  • Machine learning: to continuously improve the system’s performance, accuracy, and understanding of user input based on data and feedback.

Conversational AI is commonly used in various applications, such as:

  • Virtual assistants and smart speakers (e.g., Siri, Alexa, and Google Assistant, YourGPT AI Agent).
  • Customer service chatbots on websites and messaging platforms.
  • Voice-enabled devices and applications.
  • Virtual agents in call centers.
  • Healthcare and therapy chatbots.

The goal of conversational AI is to provide users with an efficient, personalised, and human-like interaction that can help them accomplish tasks, access information, or simply engage in a pleasant conversation.

4. Chatbot:

AI Chatbot

A chatbot, or chatter, is a specific type of bot designed for conversational interactions with users. Chatbots are often integrated into messaging platforms, websites, or mobile apps to provide real-time support, answer questions, or facilitate transactions.

Chatbots use various techniques, including Natural Language Processing (NLP), Machine Learning (ML), and Artificial Intelligence (AI), to understand and respond to user input. Some chatbots are rule-based, following pre-defined scripts and patterns to generate responses, while others use more advanced AI techniques to learn from user interactions and adapt their responses over time.

Chatbots have become increasingly popular in recent years, with applications ranging from customer service and e-commerce to entertainment and social interaction. They offer several benefits, such as 24/7 availability, instant responses, and cost-effectiveness for businesses.

5. AI Agent: 

AI Agent

An AI agent, also known as an intelligent agent or a virtual agent, refers to a computer program or system that uses Artificial Intelligence (AI) technologies to perform tasks or interact with users on behalf of another entity, such as a human user or an organisation.

AI agents are designed to operate autonomously, using machine learning, natural language processing (NLP), and other AI techniques to understand and respond to user input, make decisions, and execute actions. They can be used in a wide range of applications, including:

  • Customer service: AI agents can handle customer inquiries, resolve issues, and provide personalised responses.
  • Personal assistants: AI agents can manage calendars, make appointments, and handle other administrative tasks for individuals or more.
  • Healthcare: AI agents can monitor patient data, provide health advice, and even diagnose minor conditions.
  • Education: AI agents can offer personalised learning experiences, answer student questions, and provide feedback on assignments.
  • Finance: AI agents can analyse financial data and execute tasks.

AI agents can be integrated into various platforms, such as websites, mobile apps, or messaging channels, to provide a seamless and intuitive user experience.

6. IVR (Interactive Voice Response): 

Interactive Voice Response (IVR)

IVR stands for Interactive Voice Response, which is an automated telephony system that interacts with callers through voice commands and touch-tone keypad selections. IVR systems are commonly used by businesses and organisations to provide 24/7 customer support, automate repetitive tasks, and reduce call center workloads.

IVR works by presenting callers with a pre-recorded voice menu or series of prompts, which guide them through a set of options and actions. Depending on the caller’s selections, the IVR system can provide information, execute transactions, or connect the caller to a live agent for further assistance.

Some common use cases for IVR include:

  • Service: Callers can perform tasks such as checking account balances, paying bills, or making reservations without the need for human intervention.
  • Call routing: IVR can direct callers to the appropriate department or agent based on their selections.
  • Customer surveys: IVR can be used to gather customer feedback or collect data for market research.
  • Appointment scheduling: Callers can use IVR to schedule appointments or manage their calendars.

Section 3: Advanced Techniques

1. RAG (Retrieval-Augmented Generation): 


Retrieval-Augmented Generation (RAG) is a technique used to optimise the output of large language models, such as GPT, by incorporating knowledge from external sources. In other words, RAG helps LLMs generate more accurate and relevant responses by referencing information from outside their original training data.

The purpose of RAG is to improve the performance of LLMs in specific domains or contexts, without the need for re-training the entire model. This makes RAG a cost-effective approach for enhancing LLM output, as it enables the model to stay up-to-date and useful in various situations.

RAG is particularly important in applications such as intelligent chatbots, where the goal is to create conversational AI systems that can provide accurate and helpful responses to user queries. By leveraging RAG, these systems can access new information from external sources, ensuring their responses remain relevant and informed.

2. Embeddings

Embeddings Image

Embeddings are a key concept in machine learning, particularly in natural language processing (NLP) and computer vision, which help represent data in a more meaningful and efficient manner. They involve mapping high-dimensional data, such as text or images, into a lower-dimensional space called an “embedding space” while preserving the important relationships and structure of the original data.

In NLP, embeddings are used to represent words or sentences as dense vectors in a multi-dimensional space. This allows models to understand the relationships between words and capture semantic and syntactic similarities. By representing words as dense vectors, machine learning algorithms can work with textual data more effectively, enabling tasks such as sentiment analysis, document classification, and translation.

Some of the popular embedding techniques in industry are Word2Vec, GloVe, BERT, and ELMo.

3. Semantic Search

Semantic search refers to a search methodology that uses natural language processing (NLP) and machine learning (ML) techniques to understand the meaning and context of user queries. By using embeddings, which represent words and sentences as vectors in a high-dimensional space, semantic search captures the semantic relationships between terms. This approach enables search engines and other information retrieval systems to provide more relevant and accurate results for complex, conversational queries, outperforming traditional keyword-based search methods.

In semantic search, the system analyses the user’s query to identify the intent and entities involved, such as people, places, or things. It then uses this understanding to find and rank results that best match the user’s search intent, even if the exact keywords are not present in the query or the results.

Some benefits of semantic search include:

  • Improved relevance: Semantic search can better understand the user’s intent, resulting in more relevant and useful search results.
  • Contextual understanding: Semantic search can handle ambiguous or multi-faceted queries by considering the context and relationships between words and concepts.
  • Enhanced user experience: Semantic search allows users to express their queries more naturally, using conversational language and complex queries.

4. Turing Test: 

The Turing test is a measure of a machine’s ability to exhibit intelligent behaviour that is indistinguishable from that of a human. Proposed by British mathematician and computer scientist Alan Turing in 1950, the test involves a human evaluator engaging in natural language conversations with a machine and a human, without knowing which is which.

Key points about the Turing Test include:

  • Test Setup: The evaluator exchanges messages with both the machine and the human, asking questions and making comments.
  • Objectives: The machine’s goal is to convince the evaluator that it is human, while the human’s goal is to help the evaluator make a correct identification.
  • Passing the Test: If the evaluator cannot reliably distinguish between the machine and the human, the machine is said to have passed the Turing Test, indicating its conversational abilities match human intelligence.
  • Criticism and Significance: The test has been influential in shaping the field of AI but has faced criticism for focusing on deception and imitation. Despite this, it remains an important benchmark for evaluating conversational AI systems.

Some similar tests are the Multimodal Turing Test, The AI-Box Experiment and The Chinese Room Argument.

5. Evals:

Evals is an abbreviation for “Evaluation Metrics.” Evaluation metrics are a set of quantitative measures used to assess the performance and capabilities of language models for specific tasks. These metrics help researchers and developers compare different models, identify their strengths and weaknesses, and drive improvements in model design and training.

Some common evaluation metrics are:

  1. Accuracy: Measures the proportion of correct predictions made by the model. 
  2. Precision: Calculates the proportion of positive predictions that are actually correct (true positives) out of all positive predictions (true positives + false positives).
  3. Recall: Calculates the proportion of positive predictions that are actually correct (true positives) out of all actual positives (true positives + false negatives).
  4. F1-Score: Combines precision and recall into a single metric, providing a balanced measure of model performance.

Section 4: Voice AI Concepts

1. Automatic Speech Recognition (ASR)

Automatic Speech Recognition

ASR is a technology that enables the recognition and conversion of spoken words into text by computers. It is a subfield of natural language processing (NLP) that focuses on capturing and transcribing human speech, allowing users to interact with systems and devices using their voices.

ASR systems work by analysing audio input and applying various algorithms and models to identify phonemes, words, and phrases in the speech signal. Some common approaches used in ASR include:

  • Acoustic modelling: analysing audio features, such as frequency and energy, to identify phonemes, the basic units of speech.
  • Language modelling: Predicting the probability of word sequences to help the system choose among similar-sounding words or phrases.
  • Pronunciation modelling: Incorporating rules and patterns of pronunciation to improve recognition accuracy.

2. Text To Speech:

Text to speech

Text-to-Speech (TTS) is a technology that converts written text into spoken words or audio output. TTS systems take input in the form of text and use various algorithms, linguistic rules, and recorded speech segments to generate synthesised speech that resembles natural human speech.

TTS is an essential part of speech synthesis, which aims to create artificial speech that mimics the nuances and prosody of human speech. TTS systems use two main approaches to generate speech:

  1. Concatenative synthesis: This approach relies on a large database of pre-recorded speech segments, such as phonemes, words, or entire sentences. The system selects and concatenates these segments to produce the desired spoken output.
  2. Parametric synthesis: This method uses statistical or machine learning algorithms to model the parameters of human speech, such as pitch, volume, and duration. The system generates speech by modifying these parameters according to the input text and the desired speech characteristics.

3. Speech-to-Speech (STS) 

Speech to speech

Speech-to-Speech (STS) have two main applications: Speech-to-Speech Translation (S2ST) and Speech-to-Speech Synthesis (STSS).

  • Speech-to-Speech Translation (S2ST): This technology converts speech in one language into speech in another language in real-time, without intermediate written representation. It combines Automatic Speech Recognition (ASR), Machine Translation (MT), and Text-to-Speech (TTS) technologies.
  • Speech-to-Speech Synthesis (STSS): Also known as Voice Conversion or Voice Morphing, this technology transforms the voice characteristics of one speaker to match those of another while preserving the content. It involves speech analysis, transformation, and synthesis to achieve the desired effect.

Frequently asked questions (FAQ)

Q: What is the difference between AI, NLP, and Machine Learning?

  • AI, NLP, and Machine Learning are related but distinct fields:
    • AI: Artificial intelligence focuses on making machines perform tasks that typically require human intelligence, such as problem-solving and learning.
    • NLP: Natural Language Processing is a subfield of AI that helps machines understand, interpret, and manipulate human language.
    • Machine Learning: Machine learning is a technique within AI where algorithms that enable machines to improve performance on tasks through experience and data analysis without explicit programming.

Q: Can you explain the difference between supervised and unsupervised learning?

  • Supervised learning involves training an algorithm on labelled data, where it learns to make predictions based on input-output pairs. Unsupervised learning, on the other hand, works with unlabelled data, aiming to find patterns or structures within the data without predefined output labels.

Q: What is the significance of context size in Large Language Models (LLMs)?

  • Context size in LLMs determines the maximum length of input text the model considers when generating output. A larger context size allows the model to capture more context and dependencies, potentially improving the quality and relevance of the generated text.

Q: Which components of language processing do Large Language Models (LLMs) use for generating responses?

  • LLMs use NLP to process text structure, NLU to interpret meaning and context, and NLG to generate coherent, contextually relevant responses. These components work together to enable human-like conversations.

Q: How do embeddings work?

  • Embeddings map high-dimensional data (like words or sentences) into a low-dimensional space, enabling AI models to understand relationships between words. For example, in a 2D space, similar words like “cat” and “dog” would have similar vector representations, such as (0.8, 0.1) and (0.9, 0.2), due to their shared characteristics like being animals and pets. By capturing similarities, embeddings improve language understanding and processing for AI models.

Q: What is the Turing Test and its significance in AI?

  • The Turing Test evaluates a machine’s ability to exhibit intelligent behaviour indistinguishable from a human’s. It is significant as a benchmark for assessing conversational AI systems’ capabilities to mimic human-like interactions.

Q: What are some common applications of Generative AI?

  • Generative AI is used for text, image, audio, code, video, level generation, content creation, data augmentation, and automation of repetitive tasks.

Q: How can I make an AI chatbot for my business?

  • To create an AI chatbot for your business, you can use platforms like YourGPT AI Chatbot. YourGPT Chatbot offers no-code tools to build, train, and deploy chatbots personalised to your business needs without extensive coding knowledge. Additionally, you can explore hiring AI developers or leveraging pre-built chatbot frameworks for custom solutions.

Q: What is this Neural Network?

  • A neural network is a machine learning model that mimics the human brain’s neural connections to process data and learn patterns for tasks like image recognition and language processing. It consists of interconnected nodes (artificial neurons) organised in layers that transmit signals and adjust connection weights during training to improve performance.

Q: Can you explain the difference between ASR and TTS?

  • Automatic Speech Recognition (ASR) converts spoken words into text, while Text-to-Speech (TTS) synthesises written text into spoken words or audio output.

Q: What are the main components of Conversational AI systems?

  • Conversational AI systems combine NLP, NLU, NLG, dialogue management, and machine learning, enabling them to engage in human-like conversations and continuously improve their performance.

Q: How do the capabilities of Large Language Models (LLMs) in handling human language data differ from traditional Natural Language Processing (NLP) tasks?

  • Large Language Models (LLMs), unlike traditional Natural Language Processing (NLP) models, train on massive datasets, allowing them to use a greater number of parameters. This makes them more complex and closer to human language.

Q: What are the different parameters in LLM?

  • In Large Language Models (LLMs), a parameter refers to a setting or value that influences the behaviour or performance of the model, such as the Top-p, Top-k, or Temperature parameters, which control aspects like randomness and diversity in generated text.

Q: How does Reinforcement Learning different from other types of machine learning?

  • Reinforcement learning involves learning by interacting with an environment and receiving rewards or penalties for actions taken. It differs from supervised learning, where algorithms learn from labelled data, and unsupervised learning, where algorithms find patterns in unlabelled data.

Suggested Reading


AI has transformed how we communicate daily, enabling virtual assistants to engage in human-like conversations and provide answers to our inquiries. Additionally, search engines can now understand our queries and deliver the most relevant responses. This transformation isn’t limited to communication alone; AI has significantly impacted various other areas as well.

In this blog post, we break down concepts like AI, ML, and NLP, and dig into more complex stuff like RAG and speech AI. Artificial intelligence is moving fast, and there’s huge potential for amazing new things like conversational AI and artificial general intelligence (AGI). These advancements will help technology make our lives even better.

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Rohit Joshi
April 29, 2024

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