Artificial Intelligence (AI), a term coined by the distinguished Stanford Professor John McCarthy in 1955, was initially defined as "the science and engineering of creating intelligent machines." Early AI research focused on programming machines to exhibit specific behaviors, such as playing games. Today, however, the emphasis has shifted to developing machines capable of learning, mimicking certain aspects of human learning processes.
Source: Andresen, S. L. (2002). John McCarthy: father of AI. IEEE Intelligent Systems, 17(5), 84-85.
Artificial intelligence (AI) is often associated with machine learning (ML), though they are distinct concepts. Likewise, Large Language Models (LLMs) are frequently labeled as AI and fall within the broader category of AI alongside ML, but neither truly exhibits genuine intelligence.
Tools like Tableau, Power BI, or Google AI Studio create visual representations of your data including interactive charts, graphs, or even 3D models that make your research data easier to understand complex relationships in your research.
Pattern Recognition in Complex Datasets: to help you identify trends, outliers, and relationships that may not be immediately obvious.
Example: Tableau, Power BI, or Google AI Studio
Deep Learning for Complex Data in genomics or neuroscience : Images, text, or even videos
Deep Learning is a more advanced type of Machine Learning.
Deep learning models are great for processing large, unstructured data, such as images, text, or even videos.
This is particularly useful in fields like genomics or neuroscience, where researchers deal with vast amounts of complex data.
Example:TensorFlow or PyTorch-TensorFlow and PyTorch are two widely-used deep learning frameworks that allow researchers to build complex neural networks.
Machine Learning for Research
Machine Learning is a type of AI that can analyze large datasets and improve over time. It’s perfect for research where you need to:
Data Analysis & Pattern Recognition (Survey results or patient records): Analyze complex datasets: For example, in social sciences or public health, ML can spot patterns in large sets of data (like survey results or patient records).
Predictive Modeling & Forecasting (Stock market behavior or disease outbreaks): Predict outcomes: ML models can forecast trends, like predicting stock market behavior or disease outbreaks based on historical data.
Classification of Research Data on specific themes or topics: Classify data: It can be used to automatically categorize research papers or experimental data based on specific themes or topics.
Example: Scikit-learn is a popular Python library that provides simple and efficient tools for data mining and machine learning. It’s used for tasks like classification, regression, clustering, and dimensionality reduction. Researchers use Scikit-learn to build predictive models using algorithms like decision trees, support vector machines, and k-nearest neighbors.
Natural Language Processing (NLP) for Literature Reviews and Text Analysis
Literature Review Automation: NLP tools like Google Scholar or Semantic Scholar can scan and recommend relevant papers based on your research interests, saving hours of manual searching.
Text Mining & Keyword Extraction: It helps you extract key terms, ideas, and themes from a large corpus of academic texts, making it easier to analyze trends and identify important topics in your field.
Summarizing Research Papers:Tools like SMMRY, ChatGPT can summarize long academic papers, allowing you to quickly grasp the main points without reading every single detail.
Example: SGPT (like ChatGPT)-Generative Pretrained Transformers (GPT) are advanced NLP models developed by OpenAI that excel at understanding and generating human-like text.
Conversational AI encompasses technologies like chatbots and virtual assistants, enabling human interaction with computers through natural language. These systems utilize natural language processing (NLP) to understand user intent and generate human-like responses. The generative AIs mentioned below are considered part of Conversational AI, as they are designed to interact in various ways, such as answering questions, suggesting code, assisting with searches, or offering personalized advice. Although they can be further categorized into specific types, such as developer or search assistants, "Conversational AI" remains the most accurate and comprehensive term for these systems.
Explore conversational AI tools by clicking on each tab.
Note: This guide primarily introduces AI tools with basic features. After exploring each tool, you can choose to upgrade to a premium plan.
ChatGPT, developed by OpenAI, is an advanced conversational AI designed to generate human-like text based on user input. It is widely used for tasks like writing assistance, customer support, and casual conversation.
Category: Conversational AI, Virtual Assistant, Natural Language Processing (NLP)
Description: ChatGPT is an AI chatbot that specializes in generating human-like text based on prompts. It's used for natural language processing tasks, such as answering questions, creative writing, code generation, and more.
Copilot is an AI-powered code assistant that helps developers write code faster by providing real-time code suggestions and autocompletion. It is powered by OpenAI’s Codex model and integrates seamlessly into code editors like Visual Studio Code.
Category: Code Generation AI, Developer Assistant
Description: GitHub Copilot is an AI tool designed to assist developers by generating code suggestions, completing code snippets, and providing explanations of code. It uses conversational interactions to support coding workflows. While its core function is to help with coding, it's a conversational tool in the sense that developers "converse" with the system via coding queries.
Felo.ai is an AI-driven search engine developed by Felo Inc., a Tokyo-based startup.
It is designed to facilitate conversational search, allowing users to ask questions in natural language and receive comprehensive answers.
Key features include:
Natural language processing (NLP) for understanding user queries.
Multilingual support to break down language barriers.
Capabilities for academic research, including document translation.
Providing answers with citations.
Felo.ai does provide a conversational style of interaction when the user is searching for information.
It also has features that build upon the conversational aspect by allowing the user to continue to ask follow up questions, and to organize information that is found in a conversational manner.
Gemini is an AI platform developed by Google DeepMind, designed to provide powerful language models for a wide range of tasks such as search, summarization, and creative writing. It focuses on pushing the boundaries of AI capabilities with advanced conversational abilities.
Category: Conversational AI, Generative AI
Description: Gemini is an advanced AI model for conversational and generative tasks. It integrates cutting-edge machine learning techniques to engage in deep, context-aware conversations and handle a range of tasks, from answering questions to generating content. It competes with other top-tier conversational models and aims to offer human-like interactions.
Perplexity AI is a search and conversational platform that allows users to ask questions and receive accurate, relevant answers in real-time. It integrates large language models to offer insights on a wide range of topics, enhancing the user’s search experience.
Category: Search Assistant AI, Conversational Search
Description: Perplexity is an AI-powered search tool that engages users in a conversational manner to provide answers based on web searches and information extraction. It can be categorized as a search assistant, blending conversational AI with real-time information retrieval.
Explore the free tiers or trials of these tools to see which ones best fit your specific needs and workflows.
AI-powered Large Language Models (LLMs) such as ChatGPT, Gemini, Copilot, and Perplexity can also be utilized for brainstorming, topic development, and initial source discovery during the early stages of research.
Conversational AI tools such as ChatGPT, Gemini, Claude AI, Copilot, Perplexity AI, and Llama offer a significantly more interactive and dynamic approach to literature review. These tools can be leveraged for topic exploration, clarifying terminology, identifying key works, brainstorming and idea generation, source analysis and summarization, synthesis and comparison, and structuring and writing assistance.
Generative AI tools are a form of artificial intelligence that leverage machine learning to generate new content, such as text, images, audio, or video, based on user inputs or requests, rather than just fetching or organizing pre-existing information. -Gemini AI Overview
Select a tab below to generate new charts, images, audios, videos. presentations, or quizzes.
Note: The generative AI tools listed below can be used for free or with limitations.
Note: While the tools listed above offer free options, they often come with limitations such as watermarks, shorter video lengths, restricted features, or a limited number of video generations. Paid subscriptions usually unlock the full potential of these AI video creation platforms.
Artificial intelligence is quickly reshaping higher education, as educators adopt AI tools to enhance teaching and improve student outcomes. These tools help streamline administrative work, personalize learning, and make educational content more engaging—benefits now more accessible thanks to free or low-cost options.. The tabs below provide an overview of how AI is currently being used in higher education, highlighting different types of tools, practical applications, key benefits and challenges, ethical considerations, and future developments.
Source: Gemini report on The Use of AI Tools by Educators in Higher Education
https://docs.google.com/document/d/1At87Sb4-GynpZF-E0xnnw1hM_WehYYMBetGAjesWNkI/edit?usp=sharing