Swinburne University of Technology

Nakul Nambiar

Australian National University

Aishwarya Nambissan

A novel approach to improving the efficiency of text search in graph databases utilizing Neo4j, OpenAI, and Typesense. Authors  Nakul Nambiar (ORCID: 0009–0009–9720–9233) Aishwarya Nambissan (ORCID: 0009–0003–3823–6609)  The ability to use cutting-edge tools and frameworks is essential for staying ahead in the ever-changing field of technology.

Typesense and Neo4j in a hybrid information retrieval solution

Qingqin Fang

Exploring the OpenAlex Data Structure and Visualization Author: Qingqin Fang (

 ORCID:

0009–0003–5348–4264)

 Introduction to OpenAlex

In today’s world, the realm of research papers is brimming with countless hot topics, and the sheer volume of publications can be overwhelming.

Unveiling Research Trends through OpenAlex Visualization

Wenyi Pi

Neo4j APOC Library Use Case

 Author

Wenyi Pi (

 ORCID:

0009–0002–2884–277)

 Introduction

In the realm of Neo4j, the APOC (Awesome Procedures on Cypher) library stands as a powerful tool. Previously, We have talked about the importance of APOC in optimising Cypher queries and improving query efficiency in our article Exploring Methods of Cypher Query Optimisations.

Introduction to APOC: Enhancing Neo4j Capabilities

Vaibhav Khobragade

Unlocking the Future of AI: The Transformative Journey of Large Language Models Author · Vaibhav Khobragade (

 ORCID:

0009–0009–8807–5982)

 Introduction

Human language development is innate and evolves throughout life. Machines lack this ability to evolve without advanced AI algorithms.

The Journey of Large Language Models: Evolution, Application, and Limitations

How to efficiently retrieve information for different applications

Author Wenyi Pi (ORCID: 0009-0002-2884-2771) This article aims to explore various ways in which Retrieval-Augmented Generation (RAG) can be utilised to retrieve information and generate responses effectively within the dialogue system. The rationale behind utilising RAG as well as potential ways in which it can be employed effectively will be covered.

Efficient Information Retrieval and Response Generation with Retrieval-Augmented Generation (RAG)

Zhuochen Wu

Authors:  Nakul Nambiar (ORCID: 0009-0009-9720-9233) Zhuochen Wu (ORCID: 0009-0000-5642-5348)  Research Graph is a structured representation of research objects that captures information about entities and the relationships between Researcher, Organisation, Publication, Grant and Research Data.

How to use GPT API to export a research graph from PDF publications

Amanda Kau

Unlocking the Power of Questions — A deep dive into Question Answering Systems

Author:

 Amanda Kau (ORCID:


 0009–0004–4949–9284


 )

Virtual assistants have popped up on numerous websites over the years.

Harnessing The Power of Knowledge Graphs in Question Answering

Amir Aryani

Authors  Nakul Nambiar (ORCID: 0009-0009-9720-9233) Amir Aryani (ORCID: 0000-0002-4259-9774)  Knowledge graphs, which offer a structured representation of data and its relationships, are revolutionising how we organise and access information.

Mini RAG using Neo4j

Author  Amir Aryani (ORCID: 0000-0002-4259-9774) Introduction   In this article we look at Research Graph as an information model , and an approach to connect and capture the connections between research outputs, researchers and research activities. We explore the metadata model, and we discuss how to capture this graph in a Neo4j Graph Database.

Research Graph 101

Aland Astudillo

How to use GROBID to extract text from PDF  Author  Aland Astudillo (ORCID: 0009-0008-8672-3168)  GROBID is a powerful and useful tool based on machine learning that can extract text information from PDF files and other files to a structured format. One of the key challenges in knowledge mining from academic articles is reading the content of PDF files.

How to use GROBID

Dhruv Gupta

Unlocking the power of language models: A deep dive into BERT


 Author:


 Dhruv Gupta (ORCID:


 0009–0004–7109–5403


 )

Clive Humby, in 2006 rightly said, “Data is the new oil”. With data being present everywhere, it has never been more valuable.

Language Models: Deep Dive into BERT

Xuzeng He

An introduction to tools supporting PubMed Author: Xuzeng He (

 ORCID:

0009–0005–7317–7426) PubMed is a free Web literature search service developed and maintained by the National Center for Biotechnology Information (

 NCBI

), and it is also a part of NCBI’s Entrez retrieval system that provides access to a diverse set of 38 databases.

Enhancing PubMed: from a Medical Database to beyond

Exploring the potentials and limitations of Vision Language Models

Author:

 Amanda Kau (ORCID:


 0009–0004–4949–9284


 )

The human brain is more extraordinary than any machine we could build. From an early age, many of us gain the ability to comprehend what our eyes tell us and articulate it. Furthermore, we combine evidence from all our senses to reason.

How Much Can Vision Language Models Really “See”?

Addressing Misinformation, Bias, and Privacy Concerns

Author:

 Wenyi Pi (ORCID:


 0009–0002–2884–2771


 )

Recently, the spread of false or misinformation has become an important concern for people all over the world, especially in the era of big data and multimedia, where people are faced with a flood of information that makes it difficult to determine its authenticity.

The Ethical and Social Impact of Artificial Intelligence

Science in the age of large language models

Generative AI entails a credit–blame asymmetry

Exploring AI’s Ethical Terrain: Addressing Bias, Security, and Beyond

Author: Vaibhav Khobragade (

 ORCID:

0009–0009–8807–5982) Large language models (LLMs) like OpenAI’s GPT-4, Meta’s LLaMA, and Google Gemini (previously called Bard) have showcased their vast capabilities, from passing bar exams and crafting articles to generating images and website code.

Ethics and AI: Confronting the Challenges Ahead

The AI Helper Turning Mountains of Data into Bite-Sized Instructions

Author Aland Astudillo (ORCID: 0009-0008-8672-3168) LLMs have been changing the way the entire world deals with problems and day-by-day tasks. To make them better for specific applications, they need huge amounts of data and complex and expensive approaches to training them.

What is LLMLingua?

Tools and Platform for Integration of Knowledge Graph with RAG pipelines.

Authors Aland Astudillo (ORCID: 0009-0008-8672-3168) Aishwarya Nambissan (ORCID: 0009-0003-3823-6609) Many users of chatbots such as ChatGPT, have encountered the problem of receiving inappropriate or incompatible responses. There are several reasons why this might happen.

Unveiling the Synergy: Retrieval Augmented Generation (RAG) Meets Knowledge Graphs

Author: Vaibhav Khobragade (

 ORCID:

0009–0009–8807–5982) Introduction Large language models (LLMs) are becoming increasingly popular in natural language processing for their superior competence in various applications.

Enhancing Language Models: The Role of Knowledge Graph Augmentation in Overcoming LLM Challenges

Amir Aryani

A brief overview of different types of clustering techniques and their algorithms. Authors Aishwarya Nambissan (ORCID: 0009-0003-3823-6609) Amir Aryani (ORCID: 0000-0002-4259-9774)

 Background

Clustering is a fascinating technique used in machine learning, where patterns or data points are grouped based on their similarities. It’s like finding hidden connections among different data points without predefined labels.

19 Clustering Techniques

Unlocking the power of knowledge graphs in research catalogues: A deep dive into OpenAlex


 Author:


 Dhruv Gupta (ORCID:


 0009–0004–7109–5403


 )

Clive Humby, in 2006 rightly said, “Data is the new oil”. With data being present everywhere, it has never been more valuable.

Knowledge Graphs: Deep Dive into OpenAlex

An Introduction to RA-CM3, MuRAG and RACE Author  Xuzeng He (ORCID: 0009-0005-7317-7426)  Generative Artificial Intelligence (GAI) has demonstrated impressive performances in tasks such as text generation and text-to-image generation.

Recent Advances in using Retrieving Multimodal Information for Augmented Generation

Improving the performance and application of Large Language Models

Author Amanda Kau (ORCID: 0009-0004-4949-9284) Large language models (LLMs) like GPT-4, the engine of products like ChatGPT, have taken centre stage in recent years due to their astonishing capabilities. Yet, they are far from perfect.

Understanding Retrieval Pitfalls: Challenges Faced by Retrieval Augmented Generation (RAG) models

Yunzhong Zhang

Efficient creation of a stoplight report with data dashboard images

Author: Yunzhong Zhang (ORCID: 0009–0002–8177–419X) Comparing data dashboards is crucial for understanding trends and performance differences. Traditionally, this task required manual effort, which was slow and sometimes inaccurate. Now, thanks to OpenAI’s GPT-4 with Vision (GPT-4V), we are able to automate and improve this process.

How to use GPT-4V For Stoplight Report

Author Dhruv Gupta (

 ORCID

: 0009–0004–7109–5403) Introduction Large Language Models (LLMs) have become the new face of Natural language processing (NLP). With their generative power and ability to comprehend human language, the human reliance on these models is increasing every day. However, the LLMs have been known to hallucinate and thus produce wrong outputs.

Prompt engineering: A Way to Smartly Use AI

Recent Advances in Using Machine Learning with Graphs — Part 2 Latest findings in multiple research directions for handling graph construction and network security issues Author · Xuzeng He (

 ORCID:

0009–0005–7317–7426) Introduction A graph, in short, is a description of items linked by relations, where the items of a graph are called nodes (or vertices) and their relations are called edges (or links). Examples of

Recent Advances in Using Machine Learning with Graphs — Part 2

Bridging Human Perception and AI’s Future: The Convergence of Visual Understanding and Semantic Networks Author · Vaibhav Khobragade (

 ORCID:

0009–0009–8807–5982)

 Introduction

The fusion of Vision-Language Models (

 VLMs

), Generative Models, and Knowledge Graphs (

 KGs

) is reshaping how artificial intelligence (AI) understands and interacts with the world.

Tuning Vision-Language Models and Generative Models with Knowledge Graph

Using intelligence to use artificial Intelligence: A deep dive into Prompt Engineering


 Author


 Dhruv Gupta (ORCID:


 0009–0004–7109–5403


 )

Introduction Large Language Models (LLMs) have become the new normal in the field of Natural Language Processing (NLP). With their improved performance and generative power, people around the world are relying on it for

Prompt Engineering

An Overview of Constructing a Knowledge Graph Author · Qingqin Fang (

 ORCID:

0009–0003–5348–4264) 1. Introduction 1.1 What is a Knowledge Graph Knowledge Graphs are structured semantic knowledge bases used to rapidly describe concepts and their relationships in the physical world.

Unlocking Intelligence: The Journey from Data to Knowledge Graph

Understanding Knowledge Networks From A Graph Perspective


 Author:


 Amanda Kau (ORCID:


 0009–0004–4949–9284


 )

Since 2020, over ten million scholarly articles have been published annually. To put that into perspective, say all ten million articles were released on the first day of the year.

Uncovering The Secrets Behind The Most Influential Scholarly Publications With AceMap

Boosting Performance for Knowledge Graphs with Neo4j APOC Library

 Author

Wenyi Pi (ORCID: 0009–0002–2884–2771) Introduction A

 knowledge graph

(graph database) captures information about main entities in a domain and the relationships between them. It was an augmented feature store for connected data which gave access to compute, access and operationalise structure features.

Exploring Methods of Cypher Query Optimisations

Aditya Iyengar

The Most Recent Cutting-Edge Models


 Author

Aditya Iyengar (ORCID: 0009–0005–1959–9724)

 Introduction

The past 6 months marked significant technological leaps in the field of artificial intelligence (AI), especially in the realm of image generation. In this six-month window, multiple innovations redefined the capabilities of text-to-image, video generation, and multimodal AI systems.

The New Era of AI Image Generation

Mingrui Gao

Working Examples


 Author

Mingrui Gao (

 ORCID

: 0009–0005–7271–2677)

 Introduction

Machine learning has revolutionised countless industries, but deploying ML models remains a significant challenge for many data scientists and developers.

What is Cog?

Wendi Fan

Let’s explore MLflow together. Author Wendi Fan (

 ORCID: 0000–0003–0284–9166

) Introduction MLflow is an open-source platform designed to manage the entire lifecycle of machine learning projects. It helps developers and data scientists streamline their workflows by tracking experiments, managing models, and deploying them efficiently.

MLflow Beginner’s Guide: How to Get Started with MLflow

Tarun Krishnan

Unleashing FLUX1.1 with the BFL API


 Author

Tarun Krishnan (

 ORCID

: 0009–0006–6647–127X)

 Introduction

With the growing demand for

 image generation models

in various applications, the need for

 APIs

that seamlessly integrate these models into developers’ workflows has skyrocketed.

The Fast Lane of Image Generation

Tohfa Siddika

The Godfather of AI Author Tohfa Siddika Barbhuiya (

 ORCID

: 0009–0007–2976–4601) In a monumental moment for the world of Artificial Intelligence (AI), Geoffrey Hinton, known as the “Godfather of AI,” has been awarded the Nobel Prize for his pioneering work in neural networks and deep learning. He has been awarded the 2024 Nobel Prize in physics by the Royal Swedish Academy of Sciences.

Geoffrey Hinton Wins Nobel Prize

Evaluating the Role of Self-Reflection in AI: Innovation or Overcomplication?

Author Tohfa Siddika Barbhuiya (

 ORCID

: 0009–0007–2976–4601) Introduction Artificial Intelligence (AI) has made remarkable strides over the past decade, with new advancements pushing the boundaries of what machines can achieve. One of the latest developments in the field is the introduction of “reflection” within AI models.

The Idea of Reflection Models

Zijian Yang

OpenAI Canvas and DevDay: Advancing AI Collaboration with Voice Realtime API, Model Distillation, and More Explore OpenAI’s latest tools and features — Canvas, Realtime API, Prompt Cache, Visual Fine-Tuning Author Zijian Yang (

 ORCID:

0009–0006–8301–7634) Introduction OpenAI recently introduced a new feature called Canvas, which is designed to provide ChatGPT users with a more powerful writing and coding collaboration

OpenAI Canvas and DevDay: Advancing AI Collaboration with Voice Realtime API, Model Distillation…

Working Examples Author Mingrui Gao (

 ORCID

: 0009–0005–7271–2677) Introduction In the rapidly evolving landscape of artificial intelligence, Large Language Models (LLMs) have emerged as powerful tools capable of understanding and generating human-like text. While cloud-based services offer convenient access to these models, there’s a growing demand for local, hands-on experimentation.

What is LM Studio?

A novel compression technique ensuring comparable performance with 70% less parameters

Author Amanda Kau (

 ORCID

: 0009–0004–4949–9284) Introduction The sizes of large language models (LLMs) have been steadily increasing over the last few years.

CompactifAI: Large Language Models Don’t Actually Have To Be Large

Let’s explore ChatGPT’s Advanced Voice Mode Author Wendi Fan (

 ORCID: 0000–0003–0284–9166

) Introduction In today’s fast-paced world, where convenience and efficiency are paramount, OpenAI’s latest addition to ChatGPT, the

 Advanced Voice Mode

, sets a new standard for human-computer interaction.

Unlocking the Power of ChatGPT’s Advanced Voice Mode

Automated Knowledge Graph Construction with Large Language Models — Part 2

 Harvesting the Power and Knowledge of Large Language Models

Author Amanda Kau (

 ORCID

:

 0009–0004–4949–9284

) Introduction Knowledge graphs (KGs) are a structured representation of data in a graphical format, in which entities are represented by nodes and are connected by edges representing relationships

Automated Knowledge Graph Construction with Large Language Models — Part 2

Understanding the Evolutionary Journey of LLMs Author Wenyi Pi (

 ORCID

: 0009–0002–2884–2771) Introduction When we talk about large language models (LLMs), we are actually referring to a type of advanced software that can communicate in a human-like manner. These models have the amazing ability to understand complex contexts and generate content that is coherent and has a human feel.

Brief Introduction to the History of Large Language Models (LLMs)

An improvement architecture superior to the Transformer, proposed by Meta Author · Qingqin Fang (

 ORCID:

0009–0003–5348–4264) Introduction Recently, researchers from Meta and the University of Southern California have introduced a model called Megalodon. They claim that this model can expand the context window of language models to handle millions of tokens without overwhelming your memory.

The longer the context, the better? Unlimited Context Length in Megalodon

Attention mechanism not getting enough attention Author Dhruv Gupta (

 ORCID

: 0009–0004–7109–5403) Introduction As discussed in this article, RNNs were incapable of learning long-term dependencies. To solve this issue both LSTMs and GRUs were introduced. However, even though LSTMs and GRUs did a fairly decent job for textual data they did not perform well.

Transformers Models in NLP

The Three Oldest Pillars of NLP

Author Dhruv Gupta (

 ORCID

: 0009–0004–7109–5403) Introduction Natural Language Processing (NLP) has almost become synonymous with Large Language Models (LLMs), Generative AI, and fancy chatbots. With the ever-increasing amount of textual data and exponential growth in computational knowledge, these models are improving every day.

RNNs vs GRUs vs LSTMs

Large Language Models for Fake News Generation and Detection Author Amanda Kau (

 ORCID

: 0009–0004–4949–9284) Introduction In recent years, fake news has become an increasing concern for many, and for good reason. Newspapers, which we once trusted to deliver credible news through accountable journalists, are vanishing en masse along with their writers.

Are Large Language Models Our Allies or Enemies in the Fight Against Fake News?

A Unified and Collaborative Framework for LLM Author · Qingqin Fang (

 ORCID:

0009–0003–5348–4264) Introduction In today’s rapidly evolving field of artificial intelligence, large language models (LLMs) are demonstrating unprecedented potential. Particularly, the Retrieval-Augmented Generation (RAG) architecture has become a hot topic in AI technology due to its unique technical capabilities.

RAG 2.0 is Coming?

Exploring the Potential of Temporal Feature-Logic Embedding (TFLEX) in Complex Query Resolution

Author · Vaibhav Khobragade (

 ORCID:

0009–0009–8807–5982)

 Introduction

Artificial intelligence (AI) and knowledge representation in the field of temporal knowledge graphs are rapidly gaining interest.

Harnessing Temporal Dynamics: Advanced Reasoning using Temporal Knowledge Graphs

Integrating temporal data into static knowledge graphs

 Author

Amanda Kau

 (ORCID:


 0009–0004–4949–9284


 )

Introduction Knowledge graphs (KGs) have proven to be an effective method of data representation that is increasingly popular. In KGs, entities and concepts are represented as nodes, while the relationships between nodes are depicted as edges.

Dynamic Knowledge Graphs: A Next Step For Data Representation?

Understanding the Balance between Internal Knowledge and External Sources

Author Qingqin Fang (

 ORCID:

0009–0003–5348–4264) Introduction Previous research often emphasized the limitations of LLM’s information acquisition pathways, focusing on enhancing its capabilities in this regard.

When RAG and LLM Conflict: Who Will AI Listen to?

Exploring the Boundaries of Creativity and Responsibility in the Age of AI-Driven Media

 Author

Vaibhav Khobragade (

 ORCID:

0009–0009–8807–5982)

 Introduction

In 2024, the discipline of Generative AI takes a big step forward with the launch of revolutionary models that convert text into dynamic films, altering the landscape of digital content creation.

Generative AI’s Leap into Video in 2024 and its Ethical Horizon

Author  Amir Aryani: (ORCID: 0000-0002-4259-9774) Definition   A research collaboration network is a group of researchers, and practitioners, or both, working together on joint research activities. These networks often span across disciplines, geographic boundaries, and sectors, enabling participants to share resources, expertise, and data to address common research goals more effectively than they could individually.

Research Collaboration Network

Understanding Sequential Data Modelling with Keras for Time Series Prediction

 Author

Wenyi Pi (

 ORCID

: 0009–0002–2884–2771) Introduction Recurrent Neural Networks (RNNs) are a special type of neural networks that are suitable for learning representations of sequential data like text in Natural Language Processing (NLP). We will walk through a complete example of using RNNs for time series prediction, covering

Beginner’s Guide to Recurrent Neural Networks (RNNs) with Keras

Incorporating Knowledge Graphs to explain reasoning processes


 Author

Amanda Kau (

 ORCID:

0009–0004–4949–9284) Introduction Large language models (LLMs) like GPT-4 possess remarkable language abilities, allowing them to function as chatbots, translators, and much more.

Combining Knowledge Graphs With Language Models for Interpretability

Latest findings in multiple research directions for handling graph prediction and optimization Author · Xuzeng He (

 ORCID:

0009–0005–7317–7426) A graph, in short, is a description of items linked by relations, where the items of a graph are called

 nodes

(or vertices) and their relations are called

 edges

(or links). Examples of graphs can include social networks (e.g. Instagram) or knowledge

Recent Advances in using Machine Learning with Graphs

Prompt Engineering — Part 2


 Using intelligence to use artificial Intelligence: A deep dive into Prompt Engineering

Author Dhruv Gupta

 (ORCID:

0009–0004–7109–5403

 )

Introduction In the previous article we discussed what prompt engineering and some of the techniques used for prompt engineering.

Prompt Engineering — Part 2

Using Mistral for Data tagging Author · Xuzeng He (

 ORCID:

0009–0005–7317–7426) Introduction Data tagging, in simple terms, is the process of assigning labels or tags to your data so that they are easier to retrieve or analyse.

How to use Large Language Models to tag your data: A complete tutorial

Ministral — The World’s Best Edge Language Model
 


 Revolutionising On-Device AI: High-Performance, Low-Resource Language Models for Edge Computing


 Author

Tarun Krishnan (

 ORCID

: 0009–0006–6647–127X)

 Introduction

In recent years, there’s been a notable shift in the AI landscape, with many companies moving away from developing massive,

Ministral — The World’s Best Edge Language Model

Transformative Advances in Language Models through External Knowledge Integration

Author: Qingqin Fang (

 ORCID:

0009–0003–5348–4264)

 Introduction

In the dynamic field of natural language processing, the integration of external knowledge has emerged as a pivotal strategy for enhancing the performance of language models.

Deeper understanding of In-Context Retrieval-Augmented Language Models

Enhancing Data Interactivity with LLMs and Neo4j Knowledge Graphs Author Wenyi Pi (

 ORCID

: 0009–0002–2884–2771) Introduction Since OpenAI launched ChatGPT, a

 large language model (LLM)

based chatbot, in 2023, it has set off a technological wave.

The integration of large language models (LLMs) with Neo4j-based knowledge graphs

Latest findings in multiple research directions for tackling reasoning and common sense challenges Author: Xuzeng He (

 ORCID:

0009–0005–7317–7426) Knowledge Graphs, such as Wikidata, contain rich relational information between entities and have been widely used as a structured format for storing and representing relational information.

Large Language Models and Knowledge Graphs: Ways to combine them

From Naive to Modular: Tracing the Evolution of Retrieval-Augmented Generation

Author · Vaibhav Khobragade (

 ORCID:

0009–0009–8807–5982) Introduction Large Language Models (LLMs) have achieved remarkable success.

Three Paradigms of RAG

Latest effort in assessing the security of the code generated by large language models Author · Xuzeng He (

 ORCID:

0009–0005–7317–7426) Introduction With the surge of Large Language Models (LLMs) nowadays, there is a rising trend among developers to use Large Language Models to assist their daily code writing. Famous products include GitHub Copilot or simply ChatGPT.

Large Language Models for Code Writing: Security Assessment

Exploring innovative Strategies in Combating Misinformation with Enhanced Multimodal Understanding Author Wenyi Pi (

 ORCID

: 0009–0002–2884–2771) Introduction Misinformation refers to false or inaccurate information that is often given to someone in a deliberate attempt to make them believe something that is not true. This has a significantly negative impact on public health, political stability and social trust and harmony.

Multimodal Large Language Models for Misinformation Detection and Reasoning

Techniques to integrate Knowledge Graphs into Language Models

 Author

Amanda Kau

 (ORCID:


 0009–0004–4949–9284


 )

Introduction Both knowledge graphs (KGs) and pre-trained language models (PLMs) have gained popularity due to their ability to comprehend world knowledge and their broad applicability.

A Deep Dive Into Knowledge Graph Enhanced Pre-trained Language Models

Understanding the Power and Applications of Natural Language Processing

 Author

Dhruv Gupta (

 ORCID:

0009–0004–7109–5403) Introduction We are living in the era of generative AI. In an era where you can ask AI models almost anything, they will most certainly have an answer to the query. With the increased computational power and the amount of textual data, these models are bound to improve their performance.

NLU vs. NLG: Unveiling the Two Sides of Natural Language Processing

Understanding how RNNs work and its applications


 Author

Wenyi Pi (

 ORCID

: 0009–0002–2884–2771) Introduction In the ever-evolving landscape of artificial intelligence (AI), bridging the gap between humans and machines has seen remarkable progress. Researchers and enthusiasts alike have tirelessly worked across numerous aspects of this field, bringing about amazing advancements.

An Introduction to Recurrent Neural Networks (RNNs)

Harvesting the Power and Knowledge of Large Language Models


 Author

Amanda Kau

 (ORCID:

0009–0004–4949–9284

 )

Introduction Knowledge Graphs are networks that represent data in a graphical format. The beauty of Knowledge Graphs lies in their representation of concepts, events and entities as nodes, and the relationships between them as edges.

Automated Knowledge Graph Construction with Large Language Models

Enhancing Open-Domain Conversational Question Answering with Knowledge-Enhanced Models and Knowledge Graphs


 How knowledge-enhanced language models and knowledge graphs are advancing open-domain conversational question answering

Author:

 Wenyi Pi (ORCID:


 0009-0002-2884-2771


 )

When searching for information on the website, it is common to come across a flood of

Enhancing Open-Domain Conversational Question Answering with Knowledge-Enhanced Models and…

Refining AI Vision: How Retrieval-Augmented Generation Transforms Image Captioning in Large Language Models   Leveraging External Knowledge to Enhance the Descriptive Capabilities of AI Systems Author  Vaibhav Khobragade (ORCID: 0009–0009–8807–5982) Introduction   Large Language Models (LLMs) are artificial intelligence models that are trained on massive amounts of text data in order to generate human-like language and produce coherent

Refining AI Vision: How Retrieval-Augmented Generation Transforms Image Captioning in Large…

An Introduction to Retrieval Augmented Generation (RAG) and Knowledge Graph

Author Qingqin Fang (ORCID: 0009–0003–5348–4264)

 Introduction

Large Language Models (LLMs) have transformed the landscape of natural language processing, demonstrating exceptional proficiency in generating text that closely resembles human language.

Hallucination in Large Language Models and Two Effective Alleviation Pathways

Dhruv Gupta

Improving the performance of Large Language Models Author  Dhruv Gupta (ORCID: 0009-0004-7109-5403)  ChatGPT, which first came out in late 2022, took the world by storm. Since then, various LLM models and LLM based products such as Meta’s Llama and Google’s Gemini have emerged, demonstrating the power of LLMs.

RAG: The next big thing after LLMs?

Latest findings for the use of Knowledge Graph in the field of QA in multiple research directions Author: Xuzeng He (

 ORCID:

0009–0005–7317–7426) Question Answering (QA), the ability to interact with data using natural language questions and obtaining accurate results, has been a long-standing challenge in computer science dating back to the 1960s.

Question Answering enhanced by Knowledge Graph

Latest findings in pre-training graphs and using them for link recommendation Author · Xuzeng He (

 ORCID:

0009–0005–7317–7426) Introduction A graph, in short, is a description of items linked by relations, where the items of a graph are called nodes (or vertices) and their relations are called edges (or links). Examples of graphs can include social networks (e.g. Instagram) or knowledge graphs (e.g. Wikipedia). In Instagram

Working with Graphs: Pre-training and Application

Anthropic’s Groundbreaking AI, Claude 3.5, Uses Computers Like a Human and Becomes a Game-Changer in Automation


 Author

Zijian Yang (

 ORCID:

0009–0006–8301–7634)

 Introduction

Claude 3.5 Receives a Major Upgrade Overnight! As anticipated, Anthropic AI made a significant move this week with the launch of Claude 3.5 Haiku.

New Claude 3.5 Can Control Computer: Outsmarts o1 in Coding and Redefines Agent Capabilities

Solutions to Enhance LLM Performance in Long Contexts Author · Qingqin Fang (

 ORCID:

0009–0003–5348–4264) Introduction In the era of AI breakthroughs, large language models (LLMs) are not just advancements; they are revolutions, transforming how we interact with technology, from casual conversations with chatbots to the intricate mechanisms behind sophisticated data analysis tools.

Navigating the Long Context Conundrum: Challenges in Language Models’ Information Processing

Supervised Fine-tuning, Reinforcement Learning from Human Feedback and the latest SteerLM Author · Xuzeng He (

 ORCID:

0009–0005–7317–7426) Introduction Large Language Models (LLMs), usually trained with extensive text data, can demonstrate remarkable capabilities in handling various tasks with state-of-the-art performance. However, people nowadays typically want something more personalised instead of a general solution.

Fine-tuning Large Language Models: A Brief Introduction

Let’s explore GR-2 together.


 Author

Wendi Fan (

 ORCID: 0000–0003–0284–9166

)

 Introduction

GR-2 is a cutting-edge generative model designed for versatile and generalisable robot manipulation, developed by the Robotics Research Team at ByteDance Research.

GR-2: A Generative Video-Language-Action Model for Robot Manipulation

How Apple’s Research Exposes AI’s Inability to Reason and OpenAI Highlights ChatGPT’s Name-Based Biases


 Author

Zijian Yang (

 ORCID:

0009–0006–8301–7634)

 Introduction

As artificial intelligence continues to evolve, the capabilities and limitations of large language models (LLMs) are under increasing scrutiny.

ChatGPT’s Name Bias and Apple’s Findings on AI’s Lack of Reasoning: Major Flaws Revealed

How AI is Transforming Medicine and Improving Lives
 
 AI in the Healthcare Domain
 


 Author

Aditya Iyengar (ORCID: 0009–0005–1959–9724)

 Introduction

The rapid integration of Artificial Intelligence (AI) into healthcare is no longer just a futuristic vision — it is happening now and at an extraordinary pace.

The Future of Healthcare

Working Examples


 Author

Mingrui Gao (

 ORCID

: 0009–0005–7271–2677)

 Introduction

In the fast-paced world of artificial intelligence, a new platform is gaining attention by making AI-powered content creation more accessible.

What is Glif?

Stability AI’s latest release offering New Models and Features


 Author

Tohfa Siddika Barbhuiya (

 ORCID

: 0009–0007–2976–4601)

 Stable Diffusion 3.5

by

 Stability AI

marks a major step forward in AI-driven image generation.

Stable Diffusion 3.5

Research Graph

Google’s Groundbreaking AI Model Explained

Introduction In the rapidly evolving landscape of artificial intelligence, Google continues to stand at the forefront, consistently pushing the boundaries of what these technologies can achieve. One of their latest advancements, GEMMA2, represents a significant leap in AI capabilities. Let’s delve into the details of this new AI model by Google.

What is Gemma2?

Stories by Research Graph on Medium

#### Working Examples

<figure>
<img src="https://cdn-images-1.medium.com/max/768/0*DVi_ZdufKNfS1wgj" />
<figcaption>Cog Logo</figcaption>
</figure>

### Author

- Mingrui Gao (**ORCID**: 0009--0005--7271--2677)

### Introduction

Machine learning has revolutionised countless industries, but deploying
ML models remains a significant challenge for many data scientists and
developers. The process often involves wrestling with complex
dependencies, managing environment configurations, and navigating the
intricacies of containerisation. Enter Cog, an open-source tool designed
to simplify the packaging and deployment of machine learning models. By
automating much of the containerisation process and providing a
standardised interface for model interaction, Cog aims to bridge the gap
between model development and production deployment.

In this article, we'll explore the capabilities of Cog through
practical, working examples. We'll dive into three key functionalities
that make Cog a game-changer in the ML deployment landscape: running
predictions on a model, building Docker images, and deploying models to
cloud platforms for API-based inference. By the end of this guide,
you'll have a clear understanding of how Cog can streamline your ML
workflow, from local development to production-ready deployment. Whether
you're a seasoned ML engineer or just starting your journey in machine
learning, Cog offers a powerful toolkit to enhance your model deployment
process.

### What is Cog

Cog is an open-source tool that simplifies the process of packaging and
deploying machine learning models. At its core, Cog provides a
standardised way to define the environment, dependencies, and interface
for any machine learning model. It achieves this by automating the
creation of Docker containers tailored specifically for ML workloads,
eliminating much of the complexity traditionally associated with
containerisation.

One of Cog's key strengths lies in its simplicity and flexibility. With
just two main configuration files --- cog.yaml for defining the
environment and dependencies, and predict.py for specifying the model's
interface --- developers can quickly containerise their models. Cog
handles the intricate details of setting up the correct CUDA versions,
managing Python dependencies, and ensuring compatibility between various
ML libraries. This approach not only saves time but also reduces the
potential for errors in the deployment process.

Furthermore, Cog seamlessly integrates with existing ML workflows and
cloud platforms. It generates a standard HTTP API for model inference,
facilitates easy scaling through container orchestration platforms, and
supports various input and output types common in ML tasks. Whether
you're working with image classification, natural language processing,
or any other ML domain, Cog provides a consistent and efficient way to
package and deploy your models. This standardisation makes it easier to
version, share, and collaborate on ML projects, bridging the gap between
model development and production deployment.

### Getting Started with Cog

Cog simplifies the process of containerising and deploying machine
learning models. This section will guide you through the initial steps
of setting up Cog and understanding its core components.

1. **Install Cog**: There are two main ways to install Cog, depending
 on your operating system --- For macOS users:



 brew install cog

For Linux users (and as an alternative for macOS):

 sudo curl -o /usr/local/bin/cog -L https://github.com/replicate/cog/releases/latest/download/cog_`uname -s`_`uname -m` sudo chmod +x /usr/local/bin/cog

Ensure you have **Docker** installed and running on your machine, as Cog
relies on it for containerisation.

**2. Project Setup**: Let's create a new directory for our Cog project
and initialise it:

 mkdir cog-quickstart cd cog-quickstart cog init

The cog init command generates two crucial files: cog.yaml and
predict.py. These files form the backbone of your Cog project, defining
its environment and prediction interface.

<figure>
<img
src="https://cdn-images-1.medium.com/max/1024/0*W8kR1ztRUdFwEPSQ" />
<figcaption>Files created by Cog initialisation</figcaption>
</figure>

**3. Understanding cog.yaml and predict.py**:

*cog.yaml*: This file is the configuration center of your Cog project.
It defines the build environment, dependencies, and other settings for
your model. Here's a brief overview:

 build:
 python_version: "3.8"
 python_packages:
 - "torch==1.9.0"
 - "transformers==4.11.3"
 predict: "predict.py:Predictor"

The build section specifies the Python version and packages required for
your model. The predict line points to the file and class that define
your model's prediction interface.

*predict.py*: This file contains the Python code that defines how
predictions are run on your model. It typically includes a Predictor
class with two main methods:

 from cog import BasePredictor, Input, Path

 class Predictor(BasePredictor):
 def setup(self):
 # Load model here
 pass
 def predict(self, input: Input) -> str:
 # Run prediction here
 pass

The setup() method is used for loading your model, while the predict()
method runs predictions. These files work together to create a
standardised, reproducible environment for your machine learning model.
The cog.yaml file ensures that all necessary dependencies are installed
in the container, while predict.py provides a consistent interface for
running predictions.

### Working Example 1: Running Predictions on a Model

In this example, we'll walk through the process of running predictions
on a pre-trained model using Cog. We'll use a ResNet50 image classifier,
which is a popular convolutional neural network for image classification
tasks. Let's break down the process into steps:

1. Obtain pre-trained model weights: First, we need to download the
 pre-trained weights for our ResNet50 model:



 WEIGHTS_URL=https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels.h5 curl -O $WEIGHTS_URL

2\. This command downloads the pre-trained weights and saves them in
your current directory.

3\. Configure the model in predict.py: Next, we'll modify the predict.py
file that was generated earlier. We'll update its contents with the
following code:

 from typing import Any
 from cog import BasePredictor, Input, Path
 from tensorflow.keras.applications.resnet50 import ResNet50
 from tensorflow.keras.preprocessing import image as keras_image
 from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions
 import numpy as np

 class Predictor(BasePredictor):
 def setup(self):
 """Load the model into memory to make running multiple predictions efficient"""
 self.model = ResNet50(weights='resnet50_weights_tf_dim_ordering_tf_kernels.h5')

 def predict(self, image: Path = Input(description="Image to classify")) -> Any:
 """Run a single prediction on the model"""
 # Preprocess the image
 img = keras_image.load_img(image, target_size=(224, 224))
 x = keras_image.img_to_array(img)
 x = np.expand_dims(x, axis=0)
 x = preprocess_input(x)

 # Run the prediction
 preds = self.model.predict(x)

 # Return the top 3 predictions
 return decode_predictions(preds, top=3)[0]

This script defines a Predictor class that loads the ResNet50 model in
the setup method and processes images for prediction in the
predict method.

4\. Set up dependencies in cog.yaml: Update your \`cog.yaml\` file to
include the necessary dependencies:

 build:
 python_version: "3.11"
 python_packages:
 - pillow==9.5.0
 - tensorflow==2.12.0
 predict: "predict.py:Predictor"

5\. Run a local prediction using cog predict: Now that we have our model
set up, let's run a prediction on a sample image:

 IMAGE_URL=https://gist.githubusercontent.com/bfirsh/3c2115692682ae260932a67d93fd94a8/raw/56b1024853305c9f6d5d17f90840c4f967b16716/cat.jpg curl $IMAGE_URL > input.jpg cog predict -i image=@input.jpg

6\. When you execute 'cog predict' for the first time, it initiates a
build process to create a Docker container capable of running your
model. For subsequent 'cog predict' commands, this pre-built container
will be utilised, eliminating the need for rebuilding.

<figure>
<img
src="https://cdn-images-1.medium.com/max/1024/0*6pysF3ULbZcH7cqg" />
<figcaption>Docker container generation upon executing
cog predict</figcaption>
</figure>

7\. Once the Docker container has been successfully built, you can
expect to see output similar to the following:

<figure>
<img
src="https://cdn-images-1.medium.com/max/1024/0*YGjauOWSI2PP1OjV" />
<figcaption>Top Predicted Feline Classifications Using the Deployed
Container</figcaption>
</figure>

<figure>
<img src="https://cdn-images-1.medium.com/max/575/0*cRSsLnaFeq1iBsgt" />
<figcaption>Cat Image Provided as Input for the Classification
Model</figcaption>
</figure>

This output shows the top three predictions for the input cat image,
along with their confidence scores. By following these steps, we've
successfully set up a pre-trained ResNet50 model using Cog and run a
local prediction. With just a few configuration files and commands,
we've containerised a complex machine learning model and made it ready
for predictions.

### Working Example 2: Building a Docker Image

In this example, we'll explore how to build a Docker image of our model
using Cog. This process packages your model's code, trained weights, and
the entire Docker environment into a single image. The resulting image
can serve predictions via an HTTP server and can be deployed anywhere
Docker runs, enabling real-time predictions.

1. Build the Docker image: First, let's build the Docker image using
 the cog build command:



 cog build -t resnet

This command builds a Docker image and tags it with the name "resnet".
You should see output similar to:

<figure>
<img
src="https://cdn-images-1.medium.com/max/1024/0*zD_-OOn-TsYzv6hf" />
<figcaption>Creating Docker Images using the cog
build command</figcaption>
</figure>

2\. Run predictions using the built image: Now that we have our Docker
image, we can use it to run predictions in two ways:

a\. Using Cog:

 cog predict resnet -i image=@input.jpg

This command runs a prediction using the "resnet" image we just built,
with "input.jpg" as the input image.

b\. Using Docker directly:

 docker run -d --rm -p 5000:5000 resnet

This command initiates the Docker image, making an HTTP server
accessible on port 5000. Subsequently, we can observe the container's
logs using Docker's "docker logs" command, as demonstrated below.

<figure>
<img
src="https://cdn-images-1.medium.com/max/1024/0*wRRpZDiZAKb9_eo6" />
<figcaption>Observing Docker Container Logs via Docker CLI</figcaption>
</figure>

3\. Test the HTTP server: With the Docker container running, we can test
the HTTP server using python:

 import requests
 import json

 # URL of the prediction endpoint
 url = "http://localhost:5000/predictions"

 # Headers
 headers = {
 'Content-Type': 'application/json'
 }

 # Data payload
 data = {
 "input": {
 "image": "https://gist.githubusercontent.com/bfirsh/3c2115692682ae260932a67d93fd94a8/raw/56b19f53f7643bb6c0b822c410c366c3a6244de2/mystery.jpg"
 }
 }

 # Make the POST request
 response = requests.post(url, headers=headers, data=json.dumps(data))

 # Check if the request was successful
 if response.status_code == 200:
 # Print the response JSON
 print(json.dumps(response.json(), indent=2))
 else:
 print(f"Error: {response.status_code}")
 print(response.text)

<figure>
<img
src="https://cdn-images-1.medium.com/max/1024/0*ADhHa_Ht5vtFABLs" />
<figcaption>Output from invoking the HTTP server’s prediction
API</figcaption>
</figure>

4\. Share your image via a registry (Optional): To make your image
available to others, consider uploading it to a Docker registry such as
Docker Hub or AWS Elastic Container Registry. Once uploaded, the image
can be deployed and run on any system that supports Docker

### Working Example 3: Integrating with Replicate

This example demonstrates the process of integrating a Diffusers model
with Replicate, a platform designed for deploying and scaling machine
learning models as APIs. Replicate offers a streamlined approach to
hosting your models, version management, and API accessibility. By
leveraging Replicate for your model deployment, you can take advantage
of benefits like scalable infrastructure, robust versioning
capabilities, and seamless integration across various applications.

1. Initiate a new model on Replicate: Begin by navigating to
 'replicate.com/create'. Here, you'll need to provide essential
 details for your model setup. This includes specifying the model's
 name, determining its visibility settings, and selecting the
 appropriate hardware configuration for your model's execution. For
 instance, when working with a diffuser model, it's common to utilise
 an A40 GPU for optimal performance.

<figure>
<img src="https://cdn-images-1.medium.com/max/845/0*EpfN-FkoVD9H5klR" />
</figure>

2\. Obtain the Stable Diffusion codebase: Replicate offers a
foundational repository specifically for Stable Diffusion models. This
repository comes equipped with the essential cog.yaml and predict.py
files, providing a convenient starting point for your project.

 git clone https://github.com/replicate/cog-stable-diffusion
 cd cog-stable-diffusion

3\. Tailor your model configuration: Navigate to the predict.py file and
adjust the MODEL_ID parameter to incorporate an alternative pre-trained
model. You have the flexibility to select any model from Hugging Face
that is compatible with the diffusers library.

 MODEL_ID = "prompthero/openjourney-v2"

4\. Acquire model weights: Execute the following command to fetch the
model weights: This operation might require several minutes to complete,
but it's a one-time process.

 cog run script/download-weights

<figure>
<img
src="https://cdn-images-1.medium.com/max/1024/0*vZPFhW0LcAEYeEkV" />
<figcaption>Retrieving Model Weights</figcaption>
</figure>

5\. Upload your model to Replicate: Begin by authenticating your
Replicate account:

 cog login

Next, push your model to Replicate by executing the following command,
making sure to substitute \<your-username\> and \<your-model-name\> with
your actual Replicate username and chosen model name:

 cog push r8.im/<your-username>/<your-model-name>

<figure>
<img
src="https://cdn-images-1.medium.com/max/1024/0*0MP7EG-F6hFGCDpZ" />
<figcaption>Uploading the Model to Replicate’s
Cloud Platform</figcaption>
</figure>

6\. Use your model on Replicate: Once pushed, your model is live on
Replicate! You can now:

- Run the model directly from the Replicate website using the
 "Playground" tab.

<figure>
<img
src="https://cdn-images-1.medium.com/max/1024/0*ovwsfgyLV5fvTydA" />
<figcaption>Uploading the Model to Replicate’s
Cloud Platform</figcaption>
</figure>

- Use the API to integrate the model into your own applications.



 import replicate
 import requests
 from PIL import Image
 from io import BytesIO

 MODEL_ID = "<your-user-name>/test-push-sd:c8e6c8fd8b6aaf9f27a86ce57a5865ffb6848adb4d3c6245578c83524a6d676c"
 OUTPUT_FILENAME = "astronaut_on_mars.png"

 def generate_and_save_image():
 output = replicate.run(
 MODEL_ID,
 input={
 "width": 512,
 "height": 512,
 "prompt": "a photo of an astronaut riding a horse on mars",
 "scheduler": "DPMSolverMultistep",
 "num_outputs": 1,
 "guidance_scale": 7.5,
 "num_inference_steps": 20
 }
 )

 image_url = output[0].url
 response = requests.get(image_url)
 image = Image.open(BytesIO(response.content))
 image.save(OUTPUT_FILENAME)
 print(f"Image saved as {OUTPUT_FILENAME}")

 if __name__ == "__main__":
 generate_and_save_image()

By integrating your custom Diffusers model with Replicate, you've
created a scalable API for your image generation model. This allows you
to easily share your model with others, integrate it into various
applications, and leverage Replicate's infrastructure for handling
prediction requests. The platform takes care of scaling, versioning, and
providing a consistent API, allowing you to focus on improving your
model and building applications around it.

### Conclusion

In conclusion, Cog streamlines the process of packaging and deploying
machine learning models, from local development to scalable APIs. By
simplifying containerisation, prediction running, and integration with
platforms like Replicate, Cog allows developers to focus on model
creation rather than infrastructure. This approach accelerates the
journey from development to production, enhancing collaboration and
making ML models more accessible for real-world applications.

### References

\[1\] Replicate. Push a Diffusers model.
[View Docs.](https://replicate.com/docs/guides/push-a-diffusers-model)

\[2\] Cog. Getting Started.
[View Docs.](https://cog.run/getting-started/)

\[3\] GitHub. Replicate/Cog Repository. [View
Repository.](https://github.com/replicate/cog/tree/main)

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