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In this article, we will delve into the concept of semi-supervised learning, exploring its definition, and its differences from supervised and unsupervised learning. By the end, you will have a comprehensive understanding of how semi-supervised learning is shaping the future of AI and its potential to drive innovation across various industries. Read more at: https://lnkd.in/gA-mBEyY #AI #machinelearning #EGS #eastgatesoftware #supervisedlearning

Exploring the Spectrum of Machine Learning: A Deep Dive into Its Three Main Types https://buff.ly/3GLrSmb #artificialintelligence #ai #machinelearning #technology #datascience

Self-supervised learning : The Future of Artificial Intelligence Self-supervised learning (SSL), a transformative subset of machine learning, liberates models from the need for manual tagging. Unlike traditional learning that relies on labeled datasets, SSL leverages the inherent structure and patterns within the data to create pseudo labels. This innovative approach significantly reduces the dependence on costly and time-consuming curation of labeled data, making it a game-changer in AI. Read more 👇 #artificialintelligence #selfsupervisedlearning #ai #genai #technology #selfsupervised

🌟 Exploring Few-Shot Learning in Machine Learning 🚀📚 Today, I stumbled upon an incredibly intriguing topic while diving into some AI/ML resources: Few-Shot Learning (FSL). What is Few-Shot Learning? Few-Shot Learning is a fascinating approach where models learn to generalize to new tasks with very few training examples. Unlike traditional machine learning, which relies on vast amounts of labeled data, FSL aims to achieve remarkable performance with minimal data. Why Few-Shot Learning Matters - Data Efficiency: It drastically cuts down the need for large datasets, making it perfect for scenarios where collecting data is tough or expensive. - Quick Adaptation: Models can swiftly adapt to new tasks, enhancing their flexibility and usability. - Cost Reduction: Fewer data requirements mean lower costs for data labeling and storage. Key Applications 1. Medical Imaging: Diagnosing rare diseases with limited case data. 2. Natural Language Processing: Translating low-resource languages with minimal examples. 3. Robotics: Teaching robots new tasks with just a few demonstrations. 4. Image Recognition: Classifying new object categories with very few labeled images. How Few-Shot Learning Works Few-Shot Learning often involves techniques such as: 1. Meta-Learning: Also known as "learning to learn," where models are trained on various tasks to quickly adapt to new ones. 2. Transfer Learning: Using knowledge from pre-trained models on large datasets to perform new tasks with limited data. 3. Siamese Networks: Employing a pair of identical neural networks to find similarities between examples, facilitating classification with minimal examples. Challenges and Solutions - Overfitting: With limited data, models might overfit to the few examples they have. Regularization techniques and data augmentation can help combat this. - Generalization: Ensuring the model generalizes well to new tasks is tricky. Meta-learning and cross-validation are key in addressing this. - Scalability: Scaling FSL techniques to more complex tasks requires advanced model architectures and training paradigms. Few-Shot Learning in Action Few-Shot Learning is transforming industries. In healthcare, it’s enabling quicker diagnoses of rare conditions. In NLP, it’s breaking down barriers for low-resource languages. In robotics, it’s allowing machines to learn new tasks with minimal instruction. Let’s discuss and explore the potential of Few-Shot Learning together! See you soon for more deep dives into AI/ML! 🚀 #FewShotLearning #AI #MachineLearning #TechJourney #Innovation #FutureOfAI #DataScience #MetaLearning #TransferLearning

We read a lot about the problems with AI but this article doesn’t just discuss the problems but shares a study that leads us to a way of harnessing AI as a tutor. https://lnkd.in/gV8T2Jmw

It's interesting to see this paper being framed as generative AI harming learning, when the data seem to suggest that the effective use of tools such as GPT-4 improves performance in non-exam conditions (i.e., in realistic settings?) when used correctly as a supplement to learning, rather than a crutch. We must resist the urge to throw the baby out with the bathwater when it comes to generative AI in higher education. Teach students to use it appropriately, ethically, and in more sophisticated ways - don't demonise it as an evil to be eradicated.

DeepLearning.AI Coursera Generative AI For Everyone Breif summary: -Supervised learning is a fundamental machine learning technique that involves training an AI model on labeled data. - Large language models (LLMs) are trained using supervised learning. Unlocking the Potential of Generative AI - Writing: LLMs can craft compelling articles, generate creative copywriting, and assist in brainstorming innovative ideas. - Reading: LLMs excel at summarizing complex texts, proofreading content, and providing accurate translations. - Chatting: Generative chatbots can engage in natural conversations, provide customer support, and personalize user experiences. Addressing the Limitations of Generative AI Despite its remarkable capabilities, generative AI faces certain limitations: - Frozen Knowledge: LLMs' knowledge is limited to the data they were trained on, potentially hindering their ability to adapt to new information. - Hallucinations: LLMs may occasionally generate inaccurate or nonsensical content, requiring careful evaluation. - Contextual Limitations: LLMs are constrained by the length of input and output sequences, limiting the scope of their responses. - Harnessing the Power of Prompts - Specificity: Provide detailed and specific prompts to steer the LLM towards the intended outcome. - Guidance: Guide the LLM's thought process by framing the prompt with clear instructions and context. - Iteration: Continuously experiment and refine prompts to achieve optimal results. - Image generation models often rely on diffusion models. - Navigating the Lifecycle of Generative AI Projects - Scope and Define: Clearly define the project's scope, objectives, and target outcomes. - Build and Refine: Develop and iterate on the generative AI system, incorporating feedback and improvements. - Internal Evaluation: Rigorously evaluate the system's performance through internal testing and validation. - Deployment and Monitoring: Deploy the system in a production environment and continuously monitor its performance and impact. -Enhancing Generative AI Models: - Prompting: Experiment with different prompting techniques to optimize LLM performance. - RAG (Retrieval-Augmented Generation): Utilize RAG to incorporate external knowledge sources, enhancing LLM accuracy. - Fine-tuning: Fine-tune the LLM's parameters on specific tasks to improve its performance. - Pre-training: Train the LLM from scratch on a broader dataset to enhance its general knowledge. - RHLF is a technique that fine-tunes LLMs by incorporating human input. -Tools for Enhancing LLM Capabilities - Agents: Agents serve as intermediaries between LLMs and the environment, enabling them to execute tasks in real-time. -Retrieval Systems: Retrieval systems provide LLMs with access to external knowledge sources, enhancing their contextual understanding. Link To The Summary: https://lnkd.in/dvxxJQTB #learningandgrowing #generativeai #ai https://lnkd.in/d6XuYkCK

🚀 Zero-Shot Learning (ZSL) 🚀 In the ever-evolving landscape of generative AI, Zero-Shot Learning (ZSL) stands out as a groundbreaking concept. Imagine a machine learning model that can recognize and generate data for classes or concepts it has never encountered before—this is the essence of ZSL. 🔍 What is Zero-Shot Learning? Zero-Shot Learning enables models to make accurate predictions on new, unseen classes without prior training on those specific classes. Unlike traditional machine learning, which requires explicit training data for every class, ZSL leverages knowledge transfer and shared representations to bridge the gap between seen and unseen classes. 🧠 Core Principles of Zero-Shot Learning: 1. Knowledge Transfer: The cornerstone of ZSL, this involves transferring knowledge from known classes to unknown ones through shared representations. 2. Auxiliary Information: - Attributes: Descriptive characteristics that link seen and unseen categories. - Semantic Embeddings: High-dimensional class representations from external sources like word embeddings (e.g., Word2Vec, GloVe) or textual descriptions. - Ontology or Taxonomy: Hierarchical class representations that encode relationships and similarities. 3. Generative Models: Models such as Generative Adversarial Networks (GANs) or Variational Autoencoders (VAEs) generate samples for unseen classes using shared representations. 🛠️ Methodologies in Zero-Shot Learning: 1. Embedding-Based Methods: - Semantic Embeddings: Projecting both seen and unseen classes into a common semantic space. - Attribute-Based Methods: Learning to predict class-specific attributes from input data. 2. Generative Methods: - Conditional GANs (cGANs): Generating synthetic examples of unseen classes. - VAEs: Generating data for unseen classes from a latent space. 3. Hybrid Models: Combining discriminative and generative approaches for robust ZSL systems. 🌐 Applications of Zero-Shot Learning: 1. Image Classification: Recognizing novel objects in fields like wildlife monitoring. 2. Natural Language Processing (NLP): Understanding and generating text for new concepts. 3. Robotics: Adapting to new tasks or objects in dynamic environments. 4. Healthcare: Identifying rare diseases by leveraging similarities to known diseases. ⚡ Challenges and Future Directions: 1. Quality of Auxiliary Information: Ensuring accurate and comprehensive auxiliary information. 2. Scalability: Maintaining efficiency and accuracy with large, diverse datasets. 3. Model Generalization: Improving robustness to novel classes. 4. Evaluation Metrics: Developing standardized metrics for assessing ZSL models. #ai #machinelearning #generativeai

Objective-Driven AI: Yann LeCun's Blueprint for Human-Level Intelligence ... In the quest for artificial general intelligence (AGI), we've made remarkable progress with machine learning techniques like supervised learning, reinforcement learning, and self-supervised learning. However, as Yann LeCun highlighted at AAAI 2024, our AI systems still lag far behind humans and animals in rapidly learning new tasks, understanding the world, reasoning, planning, and exercising innate common sense. 👉 The Inevitability of AI Assistants Soon, AI assistants will mediate our interactions with the digital world through smart glasses, voice interfaces, and more. To realize this future, we need machines with human-level intelligence that deeply understand the world and can remember, reason, and plan - essentially brilliantly capable "digital people" working tirelessly for us. 👉 The Paradox of Simplicity Yet despite their prowess in some areas, our current AI remains remarkably inept at many deceptively simple tasks humans and animals excel at, like a child learning to clear a table from one observation. We continue to face Moravec's paradox where the easy for humans is hard for AI. 👉 Limitations of Large Language Models The fluent abilities of large language models like GPT have generated excitement, but LeCun argues they are fundamentally flawed and cannot reach human-level intelligence. No matter the training data size, they cannot reliably provide truthful, consistent, and non-toxic outputs. They lack true reasoning, world understanding, and planning capabilities. Mathematically, their coherence probability decreases exponentially with output length. 👉 Bridging the Gap to AGI To bridge the gap to AGI, LeCun believes we need systems that can: - Learn generative world models from raw sensory inputs beyond just text - Build large-scale multi-modal associative memory - Perform multi-step reasoning and hierarchical planning to achieve objectives - Remain inherently safe and controllable by design 👉 An Objective-Driven Architecture LeCun proposed a modular cognitive architecture driven by objectives, not just pattern recognition. It has modules for perceiving the current world state, learning to predict future states with generative world models, defining task objectives to minimize alongside inviolable safety constraints, and an actor to plan the optimal action sequence to achieve objectives while respecting constraints. This supports reasoning over uncertainties in how the world evolves and hierarchical planning where higher levels provide subgoals to lower levels over different time horizons. The key challenge is developing self-supervised learning methods to build multi-modal generative world models that capture intuitive physics, actions, and high-level causal representations of the reality behind observations. As we push towards AGI, these insights light the path to a new era where AI assistants become our capable, intelligent companions.