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🚀 Meet UMMO: Revolutionizing Customer Support with AI-Powered Communication 🤖📞 UMMO is making waves in the customer service industry with its cutting-edge AI communication platform! Designed to handle complex customer interactions seamlessly, UMMO is helping businesses deliver faster, more personalized support experiences. Here’s why UMMO stands out: 1️⃣ Smart Conversation Management 💬     UMMO’s AI understands context and intent, allowing it to resolve customer queries quickly and accurately, improving satisfaction and reducing response time. 2️⃣ 24/7 Availability ⏰     With UMMO, companies can offer round-the-clock support, ensuring customers are never left waiting and enhancing brand loyalty. 3️⃣ Scalable Solutions 📈     UMMO’s platform is scalable, making it a perfect fit for businesses of all sizes looking to enhance their customer service without overextending resources. UMMO is shaping the future of customer support by merging technology with communication to deliver impactful service experiences. #UMMO #CustomerSupport #AI #FutureOfCustomerService #DigitalTransformation #CustomerExperience #TechInnovation

What types of #AI solutions should your organisation create or specialize in? You need to create #artificialminds as the world/universe/reality modeling and simulation, learning and inference, computation and interaction intelligence platform, making Real Machine Intelligence and Learning World Models. Universal man-machine ontology (UMMO) provides a comprehensive and exhaustive classification and listing, describing and understanding of the world of realities in terms of entities (things that exist), relationships (how things are interrelated) and properties (the states, quantities or qualities or processes that entities possess or undergo), at various levels of scales and scopes, depth and granularity. The UMMO of reality provides a #generalintelligence, learning and inference framework for all world's knowledge, including common sense knowledge, scientific knowledge or cultural knowledge. It is reified as general computational systems of causal undirected multigraph hypergraph networks, embracing all mathematical structures, algorithms and data types, sciences, theories and laws. You should design and develop transdisciplinary artificial intelligence integrating Machine Intelligence and Learning with all possible AI models, rules-based AI, predictive AI, machine learning algorithms, generative AI and large language models, #AGI models with human intelligence. Today's AI systems, however impressive, are like narrow non-AI computing, as calculators or computers, compared to the human mind/brain.They excel at some narrow, specific tasks but lack the broad understanding of the world, as well as agency, autonomy and adaptability that defines real intelligence, human or machine. Unlike AI/ML or LLMs such as OpenAI GPT-4, UMMO would not depend on a huge amounts of training data, to reliably know and reason about the world and its physical, mental, social, digital or virtual realities. There is nothing common between AI its deep NNs and human intelligence or human brain, having multitrillion connections, In the human brain, some 100 billion neurons form 100 trillion connections to each other. The connection matrix of the human brain is complete causally-connected multigraph hypergraph networks with billions of nodes and trillions of edges, with weights, loops and self-loops. Even if connectomics aiming to comprehensively map connections between neurons will succeed, there is the great enigma of the mind behind it all. Due to lack of the internal mental world models, AI models “trained” on existing data contain errors, biases, disparities and inconsistencies. As an analogy with the human mind, Machine Intelligence and Learning could be designated as machine/digital/artificial psyche, mentality, soul, spirit, cognition, intellect, intelligence, consciousness or brains. it makes an independent entity that constitutes the intelligent essence of AI beings, governed by universal computing ontology of world’s data/information/knowledge/intelligence.

Exactly, to make an AI APP is different from to make a trustworthy AI. #ai4esg #ai #iso42001 #trustworthyai

Imagine a future where AI models can not only understand and interpret the world around us but also reason, plan, and make physics/causality-based decisions. This is the promise of #worldmodels, an emerging concept in its nascent stages that will revolutionize the AI ecosystem and transform enterprise applications when fully realized. At their core, world models enable AI systems to represent and understand their environment by perceiving the world, reasoning about it, and planning their actions accordingly. These models consist of several key components: perception (interpreting sensory data), representation (ability to intuit an accurate model of the environment), reasoning (process of drawing logical inferences), and planning (generating and evaluating potential courses of action). In the past year, #LLMs like Claude Opus have achieved remarkable success in NLP tasks, such as translation and summarization. However, as impressive as these models are, they have significant limitations when it comes to understanding and reasoning about the world. LLMs are essentially pattern-matching machines that learn to predict the next token in a sequence based on statistical regularities in the training data. They lack a true understanding of the meaning behind the words and the concepts they represent. In contrast, world models aim to capture the underlying intuition and the physical/causal nature of the world. OpenAI's #Sora represents a significant step towards this future. As an early iteration of the capabilities of world models, Sora demonstrates the ability to generate hyper-realistic, full-length videos from simple text prompts. These videos showcase realistic movements of objects and organisms and physics-based representations that largely capture accurate cause-and-effect relationships. One of the most exciting aspects of world models is their potential to enable more capable AI #agents and agentic systems. In the future, world models could enable AI systems to tackle complex problems that require deep understanding, reasoning, and planning. These world models would be critical to the realization and enablement of agents in an AI system that successfully executes function calls, tool use, and retrievals/searches in diverse digital environments. This comprehensive capability is crucial in environments where adaptive decision-making and operational flexibility are paramount. For example, in a cloud computing context, an AI agent equipped with a robust world model could manage resources dynamically, optimizing for cost, efficiency, and workload demands in real-time. Similarly, in industrial applications, such agents could oversee manufacturing processes, predicting maintenance issues before they lead to downtime. As world models continue to mature, they will undoubtedly reshape the AI landscape, ushering in a new era of #autonomous systems that can understand, adapt, and interact with the world in ways that were previously impossible.

My mind is blown by the staggering growth of the AI market which is projected to hit $1.05 trillion by 2031! I can't even imagine how the world will look by then! But I know that with the advances in machine learning and big data, AI is already transforming industries at every touchpoint. However, we must also start to prepare for challenges like establishing ethical guidelines and enhancing education to harness AI effectively to solve problems. Exciting times ahead to lean into - because we can't lose out! 🤖 https://lnkd.in/dw8etW4x).

🤖 Understanding the Differences: Machine Learning vs. Generative AI 🔍 In the rapidly evolving field of artificial intelligence, it's crucial to differentiate between various concepts and technologies. Machine Learning (ML) and Generative AI are often used interchangeably, but they serve distinct roles within the AI ecosystem. Here’s a breakdown of their key differences: Purpose and Functionality: Machine Learning: Refers to the broader category of AI where models learn from data to make predictions or decisions. ML encompasses various algorithms like regression, classification, and clustering to identify patterns and improve over time with exposure to more data. Generative AI: A subset of ML focused on generating new content, such as images, text, or music. Models like GPT (Generative Pre-trained Transformer) and GANs (Generative Adversarial Networks) create data that mimics the input they were trained on, enabling creative applications such as content creation and design. Outputs: Machine Learning: Typically aims at delivering predictions or classifications based on learned patterns. For example, predicting stock prices, classifying emails, and recognizing objects in images. Generative AI: Produces new, synthetic instances of data. These could be a never-before-seen artwork or a continuation of a text prompt, showcasing AI's ability to simulate human-like creativity. Applications: Machine Learning: Extensively used in data-driven fields like healthcare (patient diagnosis), finance (fraud detection), and marketing (customer segmentation). Generative AI: Applied in creative industries for generating music, art, and even writing code. It’s also used in simulations, enhancing virtual environments, and creating realistic training datasets. Model Complexity: Machine Learning: Covers a range of model complexities from simple linear regressions to complex neural networks. The choice of model depends on the task complexity and data structure. Generative AI: Often involves complex models like deep neural networks to create high-quality, realistic outputs. These models require significant computational power for training. Understanding the nuances between machine learning and generative AI not only enhances our grasp of AI's capabilities but also helps us harness its potential in innovative ways. As these technologies continue to evolve, staying informed is key to leveraging their benefits effectively. What are some generative AI applications that have caught your attention lately? Share your thoughts in the comments! 💬 #MachineLearning #GenerativeAI #ArtificialIntelligence #Innovation #TechInsights

Hybrid AI Refers to the combination of different artificial intelligence approaches and techniques to create more versatile, adaptable, and capable systems. It typically blends symbolic AI (also known as rule-based systems) with machine learning (ML), particularly deep learning models. The goal is to combine the strengths of both approaches to overcome their individual limitations and create more powerful AI systems. Key Concepts of Hybrid AI: 1. Symbolic AI (Rules-based AI):   - Relies on explicit logic, rules, and knowledge representations.   - Well-suited for tasks requiring reasoning, structured knowledge, and explainability.   - Examples: Expert systems, rule-based decision systems, and knowledge graphs. 2. Machine Learning (ML)/Neural Networks:   - Uses data-driven methods to learn patterns and make predictions.   - Powerful in handling large, unstructured data (like images, text, and speech).   - Examples: Deep learning models, transformers, and reinforcement learning systems. Why Hybrid AI? - Explainability: While machine learning models (e.g., deep learning) are often considered "black boxes" due to their complexity, symbolic AI provides transparency and explainability. Combining both allows for systems that can perform complex tasks while offering more understandable outputs. - Generalization: Symbolic AI excels in handling tasks with predefined rules, but it may struggle in unstructured environments. Machine learning models, on the other hand, can generalize better by learning from vast amounts of data. Hybrid AI uses symbolic reasoning for structure and ML for learning from data, improving flexibility. - Improved Performance: In many tasks, like language understanding or robotics, combining deep learning with rule-based approaches results in more accurate, adaptable, and reliable performance. Hybrid AI represents the next phase in AI evolution, bringing together the interpretability of symbolic reasoning and the flexibility of data-driven learning for more advanced, reliable, and explainable AI systems. #PromptEngineering #AI #MachineLearning #LLM #DataScience #AItools #TechInnovation #GenAI #ML #DL

In today's tech-driven landscape, organizations are increasingly turning to cutting-edge solutions that harness the potential of advanced technology to gain valuable insights from data in real time. One such transformative tool is artificial intelligence (AI), a field of computer science that simulates human intelligence processes using algorithms and data. By leveraging AI capabilities, businesses can unlock a world of possibilities in real-time data analysis, enabling them to make informed decisions swiftly and stay ahead in an ever-evolving market. #ai #artificialintelligence #aisolutions #tech #technology #futuretechnology #markettrends #innovations #machinelearning #deeplearning

🌟 In the vanguard of artificial intelligence, a new class of technologies is spurring unprecedented creativity - welcome to the world of diffusion models in machine learning. Our latest comprehensive guide peels back the layers on: 🔄 The step-by-step methodology of diffusion models and their sweeping applications, transcending traditional boundaries to include artistic creation 🎨 and scientific research 🔬. 📈 The anticipated impact these models bear on the evolution of AI. 🔮 How diffusion models harness randomness to yield structured, meaningful results, embodying the convergence of technology 💻 and human ingenuity 🧠. Explore the benchmark models setting industry standards, and learn about the precision of Stable Diffusion models tailored for image generation, known for their detailed outputs that closely align with user prompts. What sets this journey apart is not just the ingenuity of the algorithms; it's the potential that diffusion models have to transform industries, streamline decision-making processes, and ignite novel solutions to complex challenges. 👇 Dive into our article for a panoramic view of diffusion models and their place in the AI landscape. How do you envision deploying these models within your organization, and what ethical frameworks will you implement? #AI #DiffusionModels #MachineLearning #Innovation #BusinessStrategy #EthicalAI #DataScience https://lnkd.in/gkraYNyF

Yesterday during the #AIforGood summit, Dr. Rumman Chowdhury pointed out a very good point : people often discussed together Artificial Intelligence (AI) models and Machine Learning (ML) models but they do have distinct characteristics. Let´s get back to it : ➡ One of the key differences is that AI models are generally 𝗽𝗿𝗼𝗯𝗮𝗯𝗶𝗹𝗶𝘀𝘁𝗶𝗰, while traditional ML models can sometimes be 𝗱𝗲𝘁𝗲𝗿𝗺𝗶𝗻𝗶𝘀𝘁𝗶𝗰 : 𝗔𝗜 𝗠𝗼𝗱𝗲𝗹𝘀: AI models are designed to mimic human intelligence and often involve complex decision-making processes. They are typically probabilistic, meaning they provide outputs based on probabilities. This is because many AI tasks, such as natural language understanding, image recognition, and decision-making, inherently involve uncertainty and variability. 𝗠𝗟 𝗠𝗼𝗱𝗲𝗹𝘀: Machine learning is a subset of AI that focuses on training models using data. ML models can be either probabilistic or deterministic. A probabilistic ML model, like a Bayesian network, provides a range of possible outcomes with associated probabilities. A deterministic ML model, such as a decision tree, always produces the same output for a given input, without any randomness. Really? 📈 𝗗𝗲𝘁𝗲𝗿𝗺𝗶𝗻𝗶𝘀𝘁𝗶𝗰 𝗠𝗼𝗱𝗲𝗹𝘀: These models provide a specific output for a given input. There is no randomness in their process, and they will always yield the same result for the same input. Examples include traditional algorithms like sorting algorithms, or even certain ML algorithms like decision trees (without randomization). 📉 𝗣𝗿𝗼𝗯𝗮𝗯𝗶𝗹𝗶𝘀𝘁𝗶𝗰 𝗠𝗼𝗱𝗲𝗹𝘀: These models incorporate randomness and provide outputs that have associated probabilities. They acknowledge and manage uncertainty, which is more aligned with real-world scenarios where inputs can have various possible outcomes. Examples include Bayesian networks, Markov models, and many neural networks. An example of this model in decision making process is autonomous driving 🚘 : in dynamic environments, AI models must consider various possible scenarios and their probabilities to make safe and effective decisions. So, now you know... no more confusion. AI models are different from traditional ML models. They often are probabilistic rather than deterministic. This probabilistic nature allows AI models to handle the inherent uncertainty and variability found in real-world applications, making them more robust and adaptable to complex tasks. While ML models can be both deterministic and probabilistic, the trend in advanced AI applications leans towards probabilistic approaches to better mimic human-like decision-making and understanding.