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Did you know that AI technologies are evolving faster than you can say "machine learning"? In fact, some experts suggest that AI development doubles in capability every 18 months. That's faster than your morning coffee brews! ☕ Speaking of morning coffee: If you're looking for exciting reading to accompany your coffee, check out our website! Our blog "AI to Go" features insights from top AI experts who demystify the tech transforming our world. Whether you're a seasoned pro or just getting started, "AI to Go" serves up knowledge with a side of wit. Ready to keep up with the fast-paced world of AI? Click the link and get inspired: https://lnkd.in/dThvGEDY #ai #digitaltransformation #data

Last week I wrote an article on a crucial #AI topic: AI Drift. As AI continues to transform industries, keeping our models accurate and relevant over time is very important. So, what's AI Drift? In simple terms it's when the performance of AI models declines due to changes in the data they were trained on. This can happen because of : ➡ Data Drift: Changes in the input data's statistical properties. ➡ Concept Drift: Shifts in the relationship between input data and the target variable. ➡ Model Degradation: Models becoming stale or new data introducing biases. In the article, I wrote in detail about : 👉 Types of Concept Drift: Sudden, Gradual, Incremental, and Recurring. 👉 How to Evaluate AI Drift: Monitoring performance metrics, data distribution analysis, and specialised detection algorithms. 👉 Corrective Measures: Regular retraining, incremental learning, ensemble methods, data augmentation, and human-in-the-loop validation. Understanding and addressing AI drift is crucial for maintaining the accuracy and reliability of AI systems. Check out the full article in the comments below and I kindly request you to share your thoughts. They will definitely help me research better and come up with more important topics like these. Thank you. #AI #DataScience #MachineLearning #AIDrift #TechInsights

📊 𝗤𝘂𝗮𝗻𝘁𝗶𝘇𝗮𝘁𝗶𝗼𝗻: 𝗧𝗵𝗲 𝗞𝗲𝘆 𝘁𝗼 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁 𝗔𝗜 📊 Quantization is becoming a crucial strategy in the race to develop more powerful AI. We can achieve faster, more efficient systems by reducing the size of AI models without compromising accuracy. GPT-4o is a prime example of this shift towards smarter, leaner AI. 🧩 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝘁𝗵𝗲 𝗖𝗼𝗻𝗰𝗲𝗽𝘁 Quantization allows for smaller, faster AI models that maintain their effectiveness. For instance, it’s like streamlining a complex lab process to use fewer resources while still delivering the same high-quality results. 🚨 𝗪𝗵𝘆 𝗬𝗼𝘂 𝗦𝗵𝗼𝘂𝗹𝗱 𝗖𝗮𝗿𝗲 In quality management and life sciences, where decisions are often data-driven, the ability to run powerful AI models on less resource-intensive systems can lead to more agile and responsive operations. This could be particularly beneficial in real-time monitoring, predictive maintenance, and ensuring compliance. ************************************************** 📢 For more, follow me here: https://lnkd.in/efHgEZQA 🔔 Ring the bell to ensure you get notified of new posts. ************************************************** 🔗 How will this trend affect AI development in the coming years? Let’s connect and share insights. #AI #MachineLearning #TechInnovation

It was my privilege to present the opening keynote at Digital Transformation Summit, exploring one of my more provocative topics: "𝐀𝐈 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧: 𝐀 𝐂𝐥𝐚𝐬𝐡 𝐰𝐢𝐭𝐡 𝐇𝐮𝐦𝐚𝐧 𝐄𝐱𝐩𝐞𝐫𝐭𝐢𝐬𝐞." This presentation navigated the audience through the evolving roles of humans (both laypersons and AI experts) in the progression from traditional, rule/logic/knowledge-based AI (GOFAI) to data-driven machine learning. It covered the journey from supervised to unsupervised learning/deep learning and onto the seismic shifts brought about by foundation models, illustrating how human expertise is systematically removed, commoditised, uplifted, transformed, and revolutionised. Understanding this progression and its future trajectory is crucial for fully grasping and harnessing the true nature of AI transformation and formulating effective AI strategies. My litmus test for any AI transformation strategy is simple: if you can substitute 'AI' with any other technology term in your strategy and it still makes sense, then what you have isn't a transformative AI strategy but rather a generic technology strategy for managing any new technologies. Below are selected slides from the presentation. book: https://lnkd.in/g9BCu6nn research: https://lnkd.in/gyzjE4-i CSIRO's Data61 #aitransformations #responsibleai

#AI_For_Non_AIs This series of posts will be dedicated to non technical AI Users in simple yet organized flow. Let’s deep dive! As you use the generative AI, A little bit of understanding how AI behaves will help you to better utilize the AI capabilities and coup with AI fast paced advancements, Thus being able to develop use cases that make you on the top of the technology not only using it. Why 𝗥𝗔𝗚 𝗠𝗼𝗱𝗲𝗹𝘀 are so important today? and will they be that important after the tremendous shift in systems to reasoning and thinking? In an era where the volume of digital data is growing at an unprecedented rate, artificial intelligence (AI) must become more efficient, context-aware, and capable of handling vast amounts of information. However with such large volumes of data, AI hallucinations becomes more obvious, leading to the probability of false/inaccurate information as it relies only on pre-trained data that might be old. 𝗥𝗔𝗚 𝗺𝗼𝗱𝗲𝗹𝘀 are transforming the way AI systems handle large-scale information, improving the accuracy of generated responses, and optimizing resource use. By combining retrieval and generation in a seamless process, RAG allows AI systems to remain flexible, up-to-date, and highly effective in a wide range of applications. Whether you're dealing with high-volume customer service, niche expert-level queries, or dynamic fields requiring constant updates, RAG models provide a powerful solution for today's AI challenges. How RAG works, what type of embedding models used and what are the challenges faced during the retrieval process, this is what we will discuss in the next post ISA #AI #RAG #ArtificalIntellegence #GenAI #AIModels #AI_For_Non_AI

Day 15 of 100 Days of AI Discovery: Unfolding Data Augmentation Techniques Data Augmentation 🎨 is a strategy that enables us to significantly increase the diversity of data available for training models, without actually collecting new data. Here's why it's beneficial: > Performance: It can improve model performance, especially in cases of limited data. > Variety: It creates varied and more generalizable models by providing more diverse examples. > Overfitting: It can help reduce overfitting by increasing the amount of training data. Here's a simple breakdown of some popular Data Augmentation Techniques: 1. Image Data: Techniques include rotation, scaling, flipping, cropping, and brightness adjustment. 2. Text Data: Techniques include synonym replacement, random insertion, random swap, and random deletion. 3. Audio Data: Techniques include noise injection, shifting time, changing pitch, and speed. Data Augmentation is a powerful tool that improves model robustness and helps the model generalize better. #100DaysOfAIDiscovery #AI #MachineLearning #DataAugmentation #DataScience

AI tools and services are a game-changer for research that requires rapid analysis of vast amounts of information. They can automate tasks that were previously done manually, which allows researchers to focus on more creative and strategic aspects of their work. AI tools can also be used to develop new insights from existing data, which can lead to new discoveries and innovations.

What's next for Knowledge Graphs? 🤔 Anthony Alcaraz's article on 'The Value of Multi-modal Knowledge Graphs in Enhancing AI Capabilities' sheds light on the key values of MMKGs: 🔹 Comprehensive Knowledge Representation: MMKGs unify diverse data types, capture fine-grained semantics, and empower sophisticated AI reasoning. 🔹 Enhanced Reasoning Capabilities: leveraging cross-modal information fills knowledge gaps and boosts AI's question-answering abilities. 🔹 Robustness and Adaptability: MMKG-based AI excels in generalization, continuous learning, and scalability. The future implications for AI, emphasizing human-centric interactions, are profound amongst other areas. MMKGs are revolutionizing RAG systems. 🚀 Read more: [The Value of Multi-modal Knowledge Graphs in Enhancing AI Capabilities](https://lnkd.in/e9urBmZj) #KnowledgeGraphs #AI #ArtificialIntelligence #MMKGs #FutureTech