Dr. Benjamin DELSOL (PhD, LL.M)’s Post

Generative AI continues to fascinate me. Even though several doubts are being raised about its over-hyped relevance and accuracy, I believe we are still in the early days and the future belongs to organizations that have the perseverance to use generative AI on its own corporate data. Databases with capabilities to store and index vector embeddings and perform vector searches have become the lynchpin in the success of generative #ai applications. The most popular method today is the retrieval augmented generation (RAG). My newest research lays out definitions of various terms used in #generativeai, building blocks of RAG and an evaluation criteria for selecting the optimal #vectordatabase. I hope you will find this research useful -

Well worth a read if you are planning on using Generative AI use cases with your own corporate data. Cuts through the jargon and offers some practical advice.

To all data scientist colleagues, you might find the following article on Retrieval-Augmented Generation interesting within the context of Large Language Models (LLMs) and Generative AI. In particular note the vector storage and comparisons. https://lnkd.in/eKskbQhf #largelanguagemodel #LLM #generativeai #RAG #datascience #vectorstore #langchain

AI Canon by Derrick Harris, Matt Bornstein, and Guido Appenzeller Andreesen Horowitz Research in artificial intelligence is increasing at an exponential rate. It’s difficult for AI experts to keep up with everything new being published, and even harder for beginners to know where to start. In this post, the authors share a curated list of resources to get smarter about modern AI. The list is called “AI Canon” because these papers, blog posts, courses, and guides have had an outsized impact on the field over the past several years. The list starts with an introduction to transformer and latent diffusion models, which are fueling the current AI wave. Next, there is a deep dive on technical learning resources; practical guides to building with large language models (LLMs); and analysis of the AI market. Finally, there is a reference list of landmark research results, starting with “Attention is All You Need” — the 2017 paper by Google that introduced the world to transformer models and ushered in the age of generative AI.

Check out this article that features five open-source AI frameworks you should become familiar with. These frameworks are becoming increasingly in demand, making them essential for anyone interested in AI and machine learning. Stay ahead of the curve and learn more about them now. #artificialintelligence #techtrends #development #deeplearning Link: https://lnkd.in/g4-VHUYM

Advanced RAG Techniques | Cazton This article delves into the revolutionary technologies of Retrieval-Augmented Generation (RAG) and vector databases, showcasing their pivotal role in advancing Artificial Intelligence (AI). https://lnkd.in/eDksjJT8

https://lnkd.in/e_zZfX6T Welcome to "Exploring #ArtificialIntelligence (#AI) Essentials: A Comprehensive #Course." As you embark on this journey through the dynamic realm of artificial intelligence, you are venturing into one of the most #transformative and rapidly evolving fields of #moderntechnology. Whether you are a curious novice seeking to unravel the mysteries of AI or an #experiencedprofessional aiming to deepen your understanding, this course is designed to equip you with the #knowledge, #skills, and #ethical insights necessary to navigate the complexities of AI with confidence and integrity. In recent years, the proliferation of AI technologies has revolutionized nearly every aspect of our lives, from how we #communicate and #conductbusiness to how we approach #healthcare and #transportation. Behind the scenes of this technological revolution lies a diverse array of algorithms, models, and methodologies collectively known as artificial intelligence. At its core, AI seeks to imbue machines with the ability to perceive, reason, and act autonomously, mimicking or surpassing #humanintelligence in various tasks. The journey of understanding AI begins with a fundamental question: What is AI? In our first module, we will embark on an exploration of AI's origins, definitions, and fundamental principles. From the early pioneers of AI research to the groundbreaking advancements of the present day, we will trace the evolution of AI and uncover its myriad applications across different industries and domains. As we delve deeper into the inner workings of AI, we encounter three foundational concepts: machine learning, deep learning, and neural networks. These concepts form the backbone of modern AI systems, enabling machines to learn from data, recognize patterns, and make informed decisions. In Module 2, we will unravel the intricacies of these concepts, demystifying complex algorithms and architectures through interactive lessons and hands-on exercises. However, AI is not merely a theoretical construct confined to research labs and academic papers—it is a powerful tool with real-world applications that are reshaping the way we live, work, and interact. In Module 3, we will explore the diverse landscape of AI applications and use cases, ranging from #predictiveanalytics and #NLP #naturallanguageprocessing to #autonomousvehicles and #personalizedmedicine. Through compelling #casestudies and industry insights, we will witness firsthand the transformative impact of AI across various sectors. #Bitcoin #BTCUSD #Blockchain #Ethereum #ETHUSD #Education

Knowledge Graphs: The Game-Changer in AI and Data Science: Introduction Knowledge graphs have emerged as a powerful and versatile approach in AI and Data Science for recording structured information to promote successful data retrieval, reasoning, and inference. This article examines state-of-the-art knowledge graphs, including construction, representation, querying, embeddings, reasoning, alignment, and fusion. We also discuss the many applications of knowledge graphs, such as recommendation […] The post Knowledge Graphs: The Game-Changer in AI and Data Science appeared first on Analytics Vidhya. #DataAnalytics #DataScience #DataDriven #BigDataAnalytics #DeepLearning #AI #CIO #BusinessIntelligence

I just watched an amazing presentation by Adam Craven, Director at Y-Align, at AI & Big Data Expo Global. He shared his insights on the practical applications of AI and the pitfalls often overlooked in the hype surrounding it. 🙌 He explained how neural networks, embeddings, and transformers are the key components of AI, especially large language models (LLMs). He showed how LLMs can be powerful summarising engines for research, but also cautioned against relying solely on them for complex tasks. 🧠 He also gave some practical use cases for organisations, such as documentation tools, high-level decision-making, code review tools, and multimodal decision-makers. He advised a thoughtful evaluation of when LLMs are useful, ensuring they align with organisational values and principles. 💯 I learned a lot from his presentation and I highly recommend you to check it out. You can find the link to the event and the slides in the comments below. 👇 What are your thoughts on AI and its impact on your industry? Let me know in the comments. I would love to hear from you. 🙏 #AI #BigData #LLMs #AIExpo #YAlign

🌟 Exploring the Frontiers of AI with Kolmogorov-Arnold Networks: A Game Changer in Machine Learning I am sure for past few weeks Kolmogorov-Arnold Network (KAN) has had lot of buzz, and its quite easy for it to get lost in the 'LLM' space, but KAN is beyond that.. 🤓 lets try and understand 𝙆𝙤𝙡𝙢𝙤𝙜𝙤𝙧𝙤𝙫-𝘼𝙧𝙣𝙤𝙡𝙙 𝙉𝙚𝙩𝙬𝙤𝙧𝙠 (𝙆𝘼𝙉), which could revolutionize how we approach machine learning models, particularly in the realm of forecasting. 🔍 𝚆𝚑𝚊𝚝 𝚊𝚛𝚎 𝙺𝚘𝚕𝚖𝚘𝚐𝚘𝚛𝚘𝚟-𝙰𝚛𝚗𝚘𝚕𝚍 𝙽𝚎𝚝𝚠𝚘𝚛𝚔𝚜?🤷♂️ Kolmogorov-Arnold Networks are based on the Kolmogorov-Arnold representation theorem, which posits that any multivariate continuous function can be represented as a superposition of continuous functions of two variables. KANs leverage this theorem to construct neural network architectures that are fundamentally different from traditional networks. Instead of layers of neurons, KANs use a series of functions that specifically aim to decompose complex, high-dimensional data into simpler, manageable components. 🚀𝚆𝚑𝚢 𝙰𝚛𝚎 𝚃𝚑𝚎𝚢 𝙶𝚊𝚖𝚎 𝙲𝚑𝚊𝚗𝚐𝚎𝚛𝚜? 1. Efficiency in Complexity: KANs promise more efficient processing of complex data patterns than traditional neural networks, potentially reducing the computational load and improving the speed of learning.    2. Reduction in Data Needs: By their nature, KANs could require fewer data to achieve similar or superior performance compared to deeper, more complex networks. This is a significant advantage in fields where large labeled datasets are scarce or expensive to obtain. 3. Enhanced Generalization: Initial studies suggest that KANs might be better at generalizing from limited samples, making them particularly valuable for tasks like few-shot learning, where the goal is to understand new concepts from very few examples. 4. Potential in Non-Linear Function Mapping: Their unique structure allows them to excel in tasks involving the mapping of non-linear functions, which are common in real-world scenarios, from natural language processing to complex systems modeling. 5. Advanced Forecasting Abilities: KANs show immense potential in forecasting applications due to their ability to model and interpret complex temporal and spatial data patterns. This makes them ideal for use in financial markets, weather prediction, and supply chain logistics, where accurate forecasting can provide a substantial competitive edge. 🔗 𝘐𝘮𝘱𝘭𝘪𝘤𝘢𝘵𝘪𝘰𝘯𝘴 𝘧𝘰𝘳 𝘈𝘐 𝘋𝘦𝘷𝘦𝘭𝘰𝘱𝘮𝘦𝘯𝘵 The potential of Kolmogorov-Arnold Networks to streamline and enhance machine learning processes makes them a significant area of research. As we continue to explore and understand these networks, they could lead to more robust, efficient, and accessible AI systems, particularly in domains where data is complex and multifaceted. #ArtificialIntelligence #MachineLearning #DataScience #InnovationInAI #KolmogorovArnoldNetworks #KAN #Forecasting