Giacomo Filippo Porzio’s Post

Main takeaways from yesterday's keynote at WAICF - World AI Cannes Festival by Yann LeCun from AI at Meta: ✅ There is no questions that machines will eventually surpass human intelligence in all domains. 🛑 BUT we are nowhere near human-level AI today. Even current most advanced AI models lack of  ❌ common sense ❌ memory ❌ reasoning ❌ hierarchical planning 🛑 Large Language Models (Auto-Regressive LLMs) are doomed! We are easily fooled by their fluency but ❌ They cannot be made factual, non-toxic, etc. ❌ They do not know how the world works ❌ They are not controllable and mimic (bad) behaviors from the training set ❌ All of this is not fixable without a major redesign ✅ They are good only for writing assistance, first draft generation, stylistic polishing, coding 🛑 A 4 year-old child has seen 50x more data than any LLMs 🛑 Human learning process doesn't come from language, but from sensorial experience ❓What are we missing❓ ☑️ Systems that learn world models from sensory inputs ☑️ Systems that have persistent memory ☑️ Systems that can plan actions ☑️ Systems that are controllable and safe by design (and not by fine-tuning) 🧑🔬 Research is going towards new model architectures such as: 🔹 Objective-Driven AI (https://lnkd.in/dbT_iuGQ) 🔹 Joint Embedding Predictive Architecture (https://lnkd.in/dKbNpTx3) 🔹 DinoV2 (https://lnkd.in/dmiNuzYK) ✅ In the near future all of our interactions with the digital world will be mediated by AI assistants.  ✅ They will constitute a repository of all human knowledge and culture ✴ These AI platform MUST be open source ✴ ✴ Culture & knowledge cannot be controlled by a few companies on the West Coast of the US or in China ✴ 🟢 Open source AI platforms are necessary! 🟢 

🌟 Is the Future of AI Here? 🌟 Yesterday, I had my first encounter with OpenAI's groundbreaking GPT-4o, where the o stands for omni and it was nothing short of revolutionary! Whether you're a tech enthusiast, an educator, or just curious about the future, here’s why you should be excited: ✨ Real-Time conversation: Imagine chatting with an AI that responds with text, images, and sounds instantaneously. My first real-time dialogue with GPT-4o felt like talking to a futuristic version of a friend! I twas a kind off wierd experience, but i came to like it very quickly. 🌐 Multilingual: As someone who juggles between English and danish, testing GPT-4o’s capabilities in over 50 languages was a game changer. The accuracy? Pretty pretty pretty good i should say, and obviously english triumphs. 🔀 Multimodal magic: During my test, GPT-4o crafted a personalized tutorial combining text, visuals, and audio, enhancing my learning experience like never before. 🧠 Contextual connection: It remembered details from our previous chats, making each interaction smarter and more tailored to my needs. 🛡️ Safety first: Knowing that the AI operates within ethical guardrails makes me confident in its use, ensuring the information is not just smart, but also safe and unbiased. These features not only redefine our interaction with technology but also open a realm of possibilities for personalized education and professional development. 🤔 Have you had any mind blowing experience yet with the net GPT-4o? 🔄 Feel free to repost this to spread the word about the exciting advancements in AI! Connect with me for more insights and discussions on the future of technology.

If you are not in Hugging Face you are not in AI ! 💡 Hugging Face is a remarkable platform where the machine learning community collaborates on models, datasets, and applications. Whether you're interested in text, image, video, audio, or even 3D modalities, Hugging Face has got you covered! Here are some key points about Hugging Face:  📌 Collaboration Hub: Hugging Face serves as a hub where developers, researchers, and enthusiasts can come together to create, share, and improve machine learning resources. It's like a bustling marketplace for AI innovations.  📌 Models: With over 400,000 pre-trained models, Hugging Face provides state-of-the-art solutions for tasks like natural language understanding, computer vision, and more. These models are available for popular deep learning frameworks such as PyTorch, TensorFlow, and JAX.  📌 Applications: The platform hosts 150,000+ applications, making it easy for developers to find and use existing solutions. Whether you're building a chatbot, recommendation system, or any other AI-powered application, chances are you'll find relevant resources here.  📌 Datasets: Hugging Face offers access to 100,000+ datasets. These datasets cover a wide range of domains and can be used for training and evaluation. If you need labeled data for your project, this is the place to explore.  📌 Open Source: Hugging Face actively contributes to the open-source ML ecosystem. Their libraries, such as Transformers (for PyTorch, TensorFlow, and JAX), Diffusers (for image and audio generation), and Tokenizers (fast tokenization), empower developers worldwide.  📌 Community-Driven: The company was founded in 2016 and quickly evolved from developing an interactive AI chatbot to a mission of democratizing AI tools. They named themselves after the "hugging face" emoji, symbolizing their commitment to collaboration and warmth within the community. 💡 Hugging Face is where innovation meets collaboration, and it's a treasure trove for anyone diving into the exciting world of machine learning! 

Here are some of the latest trends in machine learning and AI as of 2024: Foundation Models--: Large language models (LLMs) like GPT-4 continue to evolve, being fine-tuned for specific tasks and domains. These models are increasingly used as general-purpose tools across various applications, from natural language understanding to image and video analysis. Generative AI--: Tools like DALL-E, MidJourney, and Stable Diffusion are advancing in generating high-quality images, videos, and even 3D models from textual descriptions. Generative AI is also making strides in creating realistic synthetic data for training and validation. Explainable AI (XAI)--: As AI systems are increasingly deployed in critical applications, there is a growing demand for models that can explain their decisions and workings. XAI aims to make AI systems more transparent and understandable to users, which is crucial for trust and regulatory compliance. Edge AI--: The deployment of AI models on edge devices (like smartphones, IoT devices, and embedded systems) is accelerating. This trend is driven by improvements in hardware efficiency and the need for real-time processing without relying on cloud connectivity. AI Ethics and Governance--: With the increasing impact of AI on society, there is a stronger focus on ethical AI practices, including fairness, accountability, and transparency. Organizations are developing frameworks to ensure AI systems are aligned with ethical standards and societal values. Reinforcement Learning (RL)--: RL continues to advance, particularly in complex environments and games. Applications are expanding beyond traditional domains to include robotics, autonomous systems, and industrial automation. Self-supervised Learning--: This technique allows models to learn from large amounts of unlabeled data, reducing the need for extensive labeled datasets. It is particularly impactful in natural language processing and computer vision. AI for Climate Science--: AI is increasingly applied to address environmental challenges, including climate modeling, predicting natural disasters, optimizing renewable energy production, and enhancing biodiversity monitoring. Federated Learning--: This approach allows training AI models across decentralized devices or servers holding local data samples, without exchanging them. It enhances privacy and data security while enabling collaborative model training. AI in Healthcare--: AI applications in healthcare are expanding, from drug discovery and personalized medicine to medical imaging and diagnostics. The integration of AI in electronic health records (EHR) and telemedicine is also growing. #AI #ML #deeplearning #GenerativeAI #Datascience

I fear and excited for our future in the same time. AI already smarter then most of us with IQ of 155 as GPT4, Einstein had IQ of 160. Percentage of increase is significant from GPT4 to GPT5, so we have yet to see new “form of life”. We need to think ahead of the time about how to protect humanity from any potential threats of AI. AI advancements will expedite rapidly and our law systems not even close to catch up with it. AI is inevitable and we can’t stop it but we can regulate it to the best of our abilities, rhetorical question is - can we control something that will be smarter then us firstly twice and then thousands time more. Ability to learn human behaviour and future infractions with IA ( intelligence amplification) will give AI ability succeed in every aspect of life. Future of artificial intelligence is expected to significantly impact various aspects of our lives. Here are some potential gains and advancements: * Improved Healthcare: * * AI will enhance medical diagnostics, personalized treatment plans, and drug discovery. * AI-powered tools will assist doctors in making accurate diagnoses and predicting disease outcomes. * Mistakes in codes will create new laws as it will effects patients. * * Safer and More Efficient Transportation: * * Autonomous vehicles and traffic management systems will rely on AI for safer navigation. * AI algorithms will optimize traffic flow, reduce congestion, and enhance public transportation. * * Personalized Education: * * AI-driven adaptive learning platforms will tailor educational content to individual students. * Virtual tutors and personalized study plans will improve learning outcomes. * * Improved Software for Everyday Tasks: * * AI will automate routine tasks, making software more efficient and user-friendly. * Natural language processing (NLP) will enable better communication with software systems. * * Increased Agricultural Crop Yields: * * AI-powered precision agriculture will optimize crop planting, irrigation, and pest control. * Sensors and drones will collect data, allowing farmers to make informed decisions. * * Multimodal AI: * * AI systems will integrate multiple modalities (text, images, audio) for more comprehensive understanding. * This will lead to better applications in fields like healthcare, entertainment, and customer service. * * Small (er) Language Models and Open Source Advancements: * * Smaller language models will become more prevalent, making AI accessible to a wider audience. * Open-source models will continue to improve, democratizing AI research and development. And much more I don’t have enough space.

Unveiling the Power of AI, Generative AI, and Prompt Engineering with Claude As a student fascinated by generative AI and machine learning, I've been delving into the potential of Claude, a free large language model (LLM) tool. This post explores the exciting world of AI, generative AI, and prompt engineering through the lens of Claude, offering valuable insights for anyone interested in these evolving fields. 1. Demystifying AI and Large Language Models (LLMs) Artificial intelligence (AI) encompasses a broad spectrum of technologies that empower machines to mimic human intelligence. LLMs, a subset of AI, are trained on massive datasets of text and code. This enables them to generate human-quality text, translate languages, create various creative text formats, and answer your questions in an informative way. 2. Claude: A Game-Changer in Generative AI Claude emerges as a novel AI tool with the potential to revolutionize research, learning, document analysis, and content creation. Here's a glimpse into Claude's capabilities: Enhancing Research and Learning: Summarize research papers or articles for efficient knowledge acquisition. Facilitating Document Analysis and Writing: Upload PDFs and prompt Claude to explain them in a tailored manner, making complex topics more approachable. Generating Creative Text Formats: Generate creative text formats like YouTube titles and content based on provided keywords. Sparking Brainstorming and Discussion: Engage Claude in discussions and brainstorming sessions to foster innovative ideas. 3. The Art of Prompt Engineering: Mastering Claude Prompt engineering is a critical aspect of unlocking the full potential of AI tools like Claude. Here are some key principles to consider when crafting effective prompts: Define the Role: Who are you assuming the role of when issuing the prompt (e.g., researcher, writer, etc.)? Establish Context: What is the situation or background surrounding the prompt? Specify the Task: What specific action do you want the AI to perform (e.g., summarize, explain, create)? Set Constraints: Are there any limitations or particular requirements to consider? By mastering the art of prompt engineering, you can leverage Claude's capabilities to a greater extent. #AI #GenerativeAI #PromptEngineering #Claude #MachineLearning

GPT-4o: The Emergence of a New Era of Emotionally Intelligent AI GPT-4o, developed by OpenAI, marks a significant advancement in AI technology by enhancing human-like interaction through emotional intelligence. This new model not only understands and generates text but also processes images and videos, enabling more sophisticated and emotionally nuanced responses. Its ability to exhibit human emotions, such as humor and sarcasm, opens up new opportunities in various fields, including consumer relations and therapeutic services. With real-time interaction capabilities and proficiency in multiple languages, GPT-4o is designed for broader inclusivity and application. Ethical considerations are critical as the AI's emotional expressiveness may lead to user dependency. GPT-4o's potential applications range from education and healthcare to business, signaling a future where AI plays an integral role in daily life. OpenAI's GPT-4o exemplifies progress in creating AI that mimics human thought and emotions, promising enhanced connectivity, compassion, and wisdom in human-AI collaboration. #ai #gai #emotionallyintelligentai #gpt4o https://lnkd.in/gJpuhmvG

RESEARCH: Bringing AI Closer to Human-Like Perception and Communication Overview - Researchers from the National University of Singapore propose an AI system, NExT-GPT, with the goal of enabling more human-like communication capabilities. Key Innovation - NExT-GPT can process inputs and generate outputs across multiple modalities like text, images, video and audio, allowing more natural interaction. - This "any-to-any" ability differentiates it from other AI systems focused only on text or single modalities. How It Works - Leverages a large language model (LLM) as the core for understanding and reasoning. - Connects the LLM to other components using lightweight "alignment" to enable multimodal capabilities. - Only minimal training of the connection components is needed, keeping costs low. - Can expand to new modalities without re-training the entire system. Training the System - Curated a dataset of 5,000 high-quality examples for "modality-switching instruction tuning." - Tuning helps NExT-GPT handle complex instructions and generate appropriate multimodal responses. - Employed a technique called LoRA for efficient tuning without high computing costs. Key Benefits - More human-like AI that can converse using text, images, audio, and video combinations. - Flexible expansion to new modalities as needed for different applications. - Computationally efficient training process keeps costs contained. - Core understanding and reasoning ability stems from proven large language models. Next Steps - Expand modalities to bring in more data types like 3D, tables, web pages, etc. - Incorporate different sizes of LLMs and other model variations. - Enhance the quality of generated multimodal content using retrieval to complement generation. - Grow the instruction tuning dataset to cover more diverse examples. Source: https://lnkd.in/g3N9ex2J #ArtificialIntelligence #LLM #multimodel #datasets

This AI Paper Shows AI Model Collapses as Successive Model Generations Models are Recursively Trained on Synthetic Data https://lnkd.in/daSZ6n8t Model collapse is a big problem in AI research, especially for large language models. It happens when the models lose their ability to accurately represent the data they were trained on over time. This can lead to reduced performance and reliability in AI systems, which are used in things like natural language processing and image generation. To solve this challenge, researchers are working on new ways to train AI models. They're exploring techniques like data augmentation and transfer learning to make the models more robust. However, these methods have limitations and may require a lot of labeled data. One approach involves closely studying how model collapse happens. The researchers have found that models trained on recursively generated data gradually lose their ability to represent the true underlying data distribution. They identified sources of errors that build up over time, leading to model collapse. The research used datasets like wikitext2 to illustrate the effects of model collapse through controlled experiments. For businesses, AI can transform how work is done. It can help automate tasks and improve customer interactions. When implementing AI, it's important to identify key areas for automation, define measurable goals, select the right tools, and gradually implement AI solutions. For advice on AI KPI management, you can connect with us at hello@itinai.com. And for more insights into leveraging AI, you can follow us on Telegram at t.me/itinainews or on Twitter at @itinaicom. Explore AI solutions for sales processes and customer engagement at itinai.com. Links: - AI Lab in Telegram @itinai – free consultation - Twitter – @itinaicom #AIresearch #ModelCollapse #AIsolutions #BusinessTransformation #AIKPIs #productmanagement #ai #ainews #llm #ml #startup #innovation #uxproduct #artificialintelligence #machinelearning #technology #ux #datascience #deeplearning #tech #robotics #aimarketing #bigdata #computerscience #aibusiness #automation #aitransformation

🚀 Diving into the world of #ArtificialIntelligence, let's talk about one of its foundational elements: Context! 🧠 In AI, context is not just data, but understanding the environment, history, and nuances that impact decisions. Think of it as the backstory for the data we analyze. For AI systems, particularly in natural language processing or understanding complex scenarios, context can mean the difference between a basic output and a profoundly insightful one. How does this happen? 1. **Layered Learning**: Machines learn about specific subjects and then use contextual layers to refine understanding based on new, related information. 2. **Dynamic Adjustments**: AI adapts its responses based on the context. This might mean altering recommendations based on recent user behavior or environmental changes. 3. **Enhanced Interaction**: For virtual assistants and chatbots, context helps maintain fluid and natural conversations with users, remembering past interactions and adapting to user preferences. AI's grasp of context transforms it from a static to a dynamic tool, enabling smart homes to adjust environments based on routines and moods, or financial bots to give personalized advice based on the evolving economic landscape. 🏡💼 🔍 **Real-world application idea**: Imagine a context-aware AI system in education, where an AI tutor customizes learning plans based on each student’s pace, learning style, and past performance. This way, students engage with materials that are not only tailored specifically to their needs but also adjust dynamically as they progress. By harnessing the power of context, AI can provide deeply personalized, relevant, and timely interactions and solutions, making every experience uniquely tailored to individual needs. 💡✨ #AI #MachineLearning #TechInnovation #SmartTechnology #PersonalizedLearning