Martin Ciupa’s Post

Artificial Intelligence Coaching should not be equated to Human Conscious Coaching, specifically because AIs are not 'exposed as embodied life forms to visceral unmodeled reality'. Yes, I concur with Martin Ciupa, again and again. 'Evolutionary Algorithms running on a Digital Computer platform, learning from modeled data (in particular given text/language evolved in humans 20-30k years ago - a sliver of time in evolutionary history) is not the same as the actual Biological Evolution of trillions of life forms over billions of years, exposed as embodied life forms to visceral unmodeled reality.'

#AI #ML #Tech Now You See Me (CME): Concept-based Model Extraction: A label-efficient approach to Concept-based Models From the AIMLAI workshop paper presented at the CIKM conference: “Now You See Me (CME): Concept-based Model Extraction” (GitHub)Visual abstract. Image by the��author. TL;DR Problem — Deep Neural Network models are black boxes, which cannot be interpreted directly. As a result — it is difficult to build trust in such models. Existing methods, such as Concept Bottleneck Models, make such models more interpretable, but require a high annotation cost for annotating underlying concepts Key Innovation — A method for generating Concept-based Models in a weakly-supervised fashion, requiring vastly fewer annotations as a result Solution — Our Concept-based Model Extraction (CME) framework, capable of extracting Concept-based Models from pre-trained vanilla Convolutional Neural Networks (CNNs) in a semi-supervised fashion, whilst preserving end-task performanceVanilla CNN End-to-End input processing. Image by the author.Two-stage Concept-based Model processing. Image by the author. Concept Bottleneck Models (CBMs) In recent years, the realm of Explainable Artificial Intelligence (XAI) [1] has witnessed a surging interest in Concept Bottleneck Model (CBM) approaches [2]. These methods introduce an innovative model architecture, in which input images are processed in two distinct phases: concept encoding and concept processing. During concept encoding, concept information is extracted from the high-dimensional input data. Subsequently, in the concept processing phase, this extracted concept information is used to generate the desired output task label. A salient feature of CBMs is their reliance on a semantically-meaningful concept representation, serving as an intermediate, interpretable representation for downstream task predictions, as shown below:Concept Bottleneck Model Processing. Image by the author. As shown above, CBM models are trained with a combination of task loss for ensuring accurate task label prediction, as well as concept loss, ensuring accurate intermediate concept prediction. Importantly, CBMs enhance model transparency, since the underlying concept representation provides a way to explain and better-understand underlying model behaviour.Concept Bottleneck Models offer a novel type of CNNs interpretable-by-design, allowing users to encode existing domain knowledge into models via concepts. Overall, CBMs serve as an important innovation, bringing us closer to more transparent and trustworthy models. Challenge: CBMs have a high concept annotation cost Unfortunately, CBMs require a high amount of concept annotations during training. At present, CBM approaches require all training samples to be annotated explicitly with both end-task, and concept annotations. Hence, for a dataset with N samples and C… #MachineLearning #ArtificialIntelligence #DataScience

#100DaysOfML Day 49✅ 🤖 Today, I dived into advanced image generation techniques - stable diffusion, encoders, and autoencoders! These methods are key in crafting images from text, much like DALL-E, known for generating diverse images from natural language prompts. 🔍 Stable diffusion boosts model stability by maintaining distances between data points in shifting environments. It's a vital part of representation learning, creating compact and meaningful data representations used in classification and visualization. 🧠 Encoders and autoencoders compress data into lower-dimensional spaces, decoding them back without labels. Variational autoencoders (VAEs) produce diverse samples by mapping data to a probability distribution. 🎨 I explored denoising - eliminating image noise using neural networks like 'unet.' Latents, representing data in a lower-dimensional space, are crucial for generating new data. The CLIP text encoder and multimodal text/image encoders extract relationships between text and images, aiding tasks like image captioning. ⚙️ Optimization, solving differential equations, and managing time steps are pivotal in this learning journey. #machinelearning #datascience #imagegeneration #autoencoders #AI #representationlearning #neuralnetworks #optimization #DL #FastAI Check out my Blog Post: https://lnkd.in/gbhmxsQc

The Challenge: Condense Boolean formulas only from input-output samples. "A research team from Apple and EPFL introduces the Boolformer model that offers a ground-breaking approach to problems in symbolic logic. It’s the first machine-learning method to infer condensed Boolean formulas only from input-output samples. " #machinelearning #boolean "To emphasize, Boolformer is demonstrated to generalize consistently to functions and data that are more sophisticated than those encountered during training. This defining feature of advanced comprehension has so far eluded other state-of-the-art models." Challenge Met: The Boolformer Model It's the first #transformer trained to perform end-to-end symbolic regression of Boolean functions. This will be a real game changer and a major step in "making machine learning more accessible, logical, and scientific." #innovation #ai https://lnkd.in/g7MGk7KS

🌟 For 𝐆𝐞𝐧𝐀𝐈 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐬: Key Points to Keep in Mind 🧠 🌟 #Day2: 𝐓𝐲𝐩𝐞𝐬 𝐨𝐟 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐌𝐨𝐝𝐞𝐥𝐬 - 𝐆𝐀𝐍𝐬, 𝐕𝐀𝐄𝐬, 𝐚𝐧𝐝 𝐀𝐮𝐭𝐨𝐫𝐞𝐠𝐫𝐞𝐬𝐬𝐢𝐯𝐞 𝐌𝐨𝐝𝐞𝐥𝐬 Welcome back to our #GenerativeAI exploration series! Yesterday, we covered what 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐈 is and its exciting applications. 𝐓𝐨𝐝𝐚𝐲, let's delve into the different types of 𝐠𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐦𝐨𝐝𝐞𝐥𝐬 that power this amazing technology. Ready for some interactive learning? Let's go! 🚀 🤔 𝐖𝐡𝐚𝐭 𝐚𝐫𝐞 𝐭𝐡𝐞 𝐓𝐲𝐩𝐞𝐬 𝐨𝐟 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐌𝐨𝐝𝐞𝐥𝐬? Q1: What are the main types of generative models? 📌 The main types of generative models are Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Autoregressive Models. Each has a unique way of generating new data. 🌟 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐝𝐯𝐞𝐫𝐬𝐚𝐫𝐢𝐚𝐥 𝐍𝐞𝐭𝐰𝐨𝐫𝐤𝐬 (𝐆𝐀𝐍𝐬) Q2: What is a GAN? 📌 A GAN consists of two neural networks, a generator and a discriminator, that compete against each other. The generator creates new data, while the discriminator evaluates it. Over time, this competition helps the generator produce highly realistic data. Q3: How do GANs work? 📌 Imagine an art forger (generator) trying to create fake paintings and an art expert (discriminator) trying to spot the fakes. As the forger improves their skills to deceive the expert, the expert also gets better at identifying fakes. This back-and-forth leads to the creation of highly realistic paintings. Q4: Where are GANs used? 📌 GANs are used in various fields such as generating realistic images, creating art, and enhancing images (e.g., upscaling low-resolution images). 🌟 𝐕𝐚𝐫𝐢𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐀𝐮𝐭𝐨𝐞𝐧𝐜𝐨𝐝𝐞𝐫𝐬 (𝐕𝐀𝐄𝐬) Q5: What is a VAE? 📌 A VAE is a type of neural network that learns to encode data into a lower-dimensional space (latent space) and then decode it back to the original data. This helps in generating new data that is similar to the original dataset. Q6: Where are VAEs used? 📌 VAEs are commonly used for generating new images, data compression, and anomaly detection in data. 🌟 𝐀𝐮𝐭𝐨𝐫𝐞𝐠𝐫𝐞𝐬𝐬𝐢𝐯𝐞 𝐌𝐨𝐝𝐞𝐥𝐬 Q7: What are autoregressive models? 📌 Autoregressive models generate data one step at a time, predicting each new data point based on the previous ones. This makes them very powerful for tasks where data has a sequential nature, like text and music generation. Q8: Where are autoregressive models used? 📌 These models are widely used in text generation (like GPT-3), music composition, and time-series forecasting. 🗣️ 𝐉𝐨𝐢𝐧 𝐭𝐡𝐞 𝐂𝐨𝐧𝐯𝐞𝐫𝐬𝐚𝐭𝐢𝐨𝐧! -> Which type of generative model interests you the most and why? 📌 Share your thoughts in the comments! 📅 𝐒𝐭𝐚𝐲 𝐓𝐮𝐧𝐞𝐝 Tomorrow, we will dive into the practical applications of these models and how they are transforming industries. Thank You! 🙏 #PythonDev #LLM

🤖 𝗟𝗲𝘁’𝘀 𝗚𝗲𝘁 𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹! Here are some RAG-related methods for improving LLM performance I’ve been following: 👉 𝗚𝗿𝗮𝗽𝗵𝗥𝗔𝗚 (Graphs + Retrieval Augmented Generation) from Microsoft is "a technique for richly understanding text datasets by combining text extraction, network analysis, and LLM prompting and summarization into a single end-to-end system.” https://lnkd.in/ex_Y9EPf 👉 𝗚-𝗥𝗔𝗚 from UCLA and Google is “a reranker based on graph neural networks (GNNs) between the retriever and reader in RAG. [Their] method combines both connections between documents and semantic information (via Abstract Meaning Representation graphs) to provide a context-informed ranker for RAG.” https://lnkd.in/et2BxR7y 👉 𝗚𝗡𝗡-𝗥𝗔𝗚 (Graph Neural Networks RAG) from the University of Minnesota is “a novel method for combining language understanding abilities of LLMs with the reasoning abilities of GNNs in a retrieval-augmented generation (RAG) style.” https://lnkd.in/ecJuj3g5 👉 𝗖𝗥𝗔𝗚 (Corrective RAG) from the University of Science and Technology of China, UCLA, and Google is “a lightweight retrieval evaluator…designed to assess the overall quality of retrieved documents for a query, returning a confidence degree based on which different knowledge retrieval actions can be triggered.” https://lnkd.in/eRxmex4z 👉 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗥𝗔𝗚 (Agent-Based RAG) with LlamaIndex “explores how agents can be incorporated into existing RAG pipelines for enhanced, conversational search and retrieval.” https://lnkd.in/eyFEjVqz 𝗧𝗵𝗲 𝗕𝗼𝘁𝘁𝗼𝗺 𝗟𝗶𝗻𝗲 🚀 These innovative approaches are shaping the future of LLM performance in exciting new ways. 🙏 Your feedback on this work and suggestions for further reading are greatly appreciated! ----------------------- Please note that these technical papers are emerging works and have not yet undergone peer review at the time of this post.

🔍𝐄𝐱𝐩𝐥𝐨𝐫𝐢𝐧𝐠 𝐭𝐡𝐞 𝐁𝐚𝐭𝐭𝐥𝐞 𝐨𝐟 𝐑𝐞𝐜𝐮𝐫𝐫𝐞𝐧𝐭 𝐍𝐞𝐮𝐫𝐚𝐥 𝐍𝐞𝐭𝐰𝐨𝐫𝐤𝐬: 𝐋𝐒𝐓𝐌 𝐯𝐬. 𝐆𝐑𝐔💥 In the world of sequential data processing, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) stand out as formidable contenders. Let's delve into their strengths and applications: 🔹 𝑳𝒐𝒏𝒈 𝑺𝒉𝒐𝒓𝒕-𝑻𝒆𝒓𝒎 𝑴𝒆𝒎𝒐𝒓𝒚 (𝑳𝑺𝑻𝑴): LSTM, with its intricate memory cell design, excels in capturing long-term dependencies within sequential data. Key features include: 🧠 Memory Cells: Ability to retain information over extended sequences. ⏳ Forget, Input, and Output Gates: Fine-grained control over information flow. 📈 Applications: Language modeling, speech recognition, and sentiment analysis. 🔹 𝑮𝒂𝒕𝒆𝒅 𝑹𝒆𝒄𝒖𝒓𝒓𝒆𝒏𝒕 𝑼𝒏𝒊𝒕 (𝑮𝑹𝑼): GRU, a more streamlined variant, offers comparable performance with fewer parameters, making it computationally efficient. Highlights include: 🚀 Simplicity: Fewer gates and memory units, leading to faster training times. 💡 Update and Reset Gates: Effective mechanisms for managing information flow. 📊 Applications: Machine translation, video analysis, and anomaly detection. 🔹 𝑻𝒉𝒆 𝑩𝒂𝒕𝒕𝒍𝒆: 𝑳𝑺𝑻𝑴 𝒗𝒔. 𝑮𝑹𝑼: While both architectures excel in capturing temporal dependencies, the choice between LSTM and GRU often boils down to the specific task requirements: 🏆 LSTM's elaborate memory cells make it ideal for tasks demanding precise long-term memory retention. 💡 GRU's simplicity and efficiency make it a preferred choice for applications where computational resources are limited or training time is critical. In the arena of recurrent neural networks, LSTM and GRU continue to shape the landscape of sequential data processing. Whether it's tackling natural language understanding or analyzing time series data, understanding the nuances of LSTM and GRU empowers AI practitioners to craft optimal solutions. 💻🔑 #LSTMvsGRU #DeepLearning #AIInnovation

I just published "Q* Algorithm is NOT Q-Learning" In this blog, I present an in-depth analysis of the Q* algorithm which is completely different from Q-learning. I have focused on the Q* algorithm’s roles development in neural open information extraction and reinforcement learning, which is speculative and only my take on it. I aim to clarify their complex structures and significant roles possible within artificial intelligence. #artificialintelligence #openai #ai #llm #gpt #agi https://lnkd.in/gECNm77k

Machine Learning & Artificial Intelligence: Crash Course Computer Science #34 The Videos are copyrighted to YouTube and the author. The videos are reproduced here for the easy access by the viewers. So we've talked a lot in this series about how computers fetch and display data, but how do they make decisions on this data? From spam filters and self-driving cars, to cutting edge medical diagnosis and real-time language translation, there has been an increasing need for our computers to learn from data and apply that knowledge to make predictions and decisions. This is the heart of machine learning which sits inside the more ambitious goal of artificial intelligence. We may be a long way from self-aware computers that think just like us, but with advancements in deep learning and artificial neural networks our computers are becoming more powerful than ever. Produced in collaboration with PBS Digital Studios: https://lnkd.in/dBmUKnRk Want to know more about Carrie Anne? https://lnkd.in/d-4gccgf The Latest from PBS Digital Studios: https://lnkd.in/dRHv6mQy Want to find Crash Course elsewhere on the internet? Facebook - https://lnkd.in/dhSR7wM3... Twitter - https://lnkd.in/dVdwERxM Tumblr - https://lnkd.in/dJtjkHuG Support Crash Course on Patreon: https://lnkd.in/d8GJE5XG CC Kids: https://lnkd.in/djcb3VzX source https://lnkd.in/dJ-6AY_h

--- 🌟Binary vs. Multiclass Classification in Machine Learning🌟 Understanding the nuances between binary and multiclass classification is essential in machine learning for effectively categorizing data and making informed predictions. 🔍 Binary Classification: Binary classification involves distinguishing between two classes or categories. Examples include spam detection in emails (spam or not spam) or medical diagnosis (positive or negative). Key points include: - Objective: Classify data into one of two mutually exclusive categories. - Algorithms: Logistic regression, support vector machines, and decision trees are commonly used for binary classification tasks. - Applications: Used in scenarios where decisions need to be made based on a yes/no or true/false outcome. 🔍 Multiclass Classification: Multiclass classification extends the concept to more than two classes, where each instance is assigned to one of several classes. Examples include facial recognition (identifying multiple individuals) or document classification (categorizing into multiple topics). Key points include: - Objective: Classify data into three or more classes. - Algorithms: Algorithms like multinomial logistic regression, naive Bayes, and neural networks are used, often requiring adaptations of binary classification techniques. - Applications: Widely applicable in fields such as natural language processing, image recognition, and medical diagnostics. 📊 Practical Insights: Mastering both binary and multiclass classification techniques empowers data scientists to build robust models capable of handling a wide range of classification tasks. Understanding the differences helps in selecting appropriate algorithms, preprocessing data effectively, and interpreting model outputs accurately. By leveraging these classification methods, organizations can make data-driven decisions that enhance efficiency, accuracy, and predictive power across various domains. 🔍 #MachineLearning #DataScience #BinaryClassification #MulticlassClassification #AI ---