Lee James’ Post

Ask the AI Expert: “How Should I Be Leveraging Machine Learning Operations (ML Ops) to Put AI in Production?”  The more mainstream AI becomes in business, the more important it becomes for developers and engineers to adopt best practices – and for technology buyers and IT managers to understand those practices.   https://lnkd.in/eN5xCvNW #ai #ki #ml

Since generative AI went mainstream in late 2022, many have become familiar with terms like “prompts” and “machine learning.” As AI evolves, so does its vocabulary. Here’s a quick guide to help you keep up. 🦾 1 - Reasoning/Planning AI can now solve problems and create plans by learning from historical data. For example, organizing a theme park visit with specific goals can be broken down into a logical, step-by-step itinerary by an AI. 2 - Training/Inference AI training is like its education, where it learns from datasets. Inference is when it uses what it learned to make predictions or decisions on new data, such as predicting home prices based on previous sales. 3 - SLM/Small Language Model Small language models (SLMs) are efficient versions of large language models (LLMs). SLMs, like Phi-3, use less computational power and can work offline, making them ideal for mobile apps. 4 - Grounding Grounding connects AI models to real-world data to improve accuracy and reduce errors. This process helps AI give more reliable and contextually relevant responses. 5 - Retrieval Augmented Generation (RAG) RAG allows AI to access external data sources for more accurate responses without retraining. It’s like consulting an expert for additional information to fill in gaps. 6 - Orchestration The orchestration layer manages the sequence of tasks in AI to provide coherent responses. It ensures context is maintained in conversations and searches for up-to-date information as needed. 7 - Memory AI doesn’t have memory like humans, but it can temporarily store and use previous interactions to maintain context in ongoing tasks. Developers are working on improving this aspect. 8 - Transformer Models and Diffusion Models Transformers, like those in ChatGPT, excel at generating coherent text by understanding context. Diffusion models, used for creating images, refine random pixels into clear pictures based on prompts. 9 - Frontier Models Frontier models are advanced AI systems that push the limits of what AI can do. These models are at the cutting edge of AI research and development. 10 - GPU Graphics Processing Units (GPUs) are essential for AI because they can handle massive calculations efficiently. They are crucial for training and running advanced AI models. Learn more about AI here: https://lnkd.in/didtJ-2e #MicrosoftWE #AI #MicrosoftAdvocate

Since generative AI went mainstream in late 2022, many have become familiar with terms like “prompts” and “machine learning.” As AI evolves, so does its vocabulary. Here’s a quick guide to help you keep up. 🦾 1 - Reasoning/Planning AI can now solve problems and create plans by learning from historical data. For example, organizing a theme park visit with specific goals can be broken down into a logical, step-by-step itinerary by an AI. 2 - Training/Inference AI training is like its education, where it learns from datasets. Inference is when it uses what it learned to make predictions or decisions on new data, such as predicting home prices based on previous sales. 3 - SLM/Small Language Model Small language models (SLMs) are efficient versions of large language models (LLMs). SLMs, like Phi-3, use less computational power and can work offline, making them ideal for mobile apps. 4 - Grounding Grounding connects AI models to real-world data to improve accuracy and reduce errors. This process helps AI give more reliable and contextually relevant responses. 5 - Retrieval Augmented Generation (RAG) RAG allows AI to access external data sources for more accurate responses without retraining. It’s like consulting an expert for additional information to fill in gaps. 6 - Orchestration The orchestration layer manages the sequence of tasks in AI to provide coherent responses. It ensures context is maintained in conversations and searches for up-to-date information as needed. 7 - Memory AI doesn’t have memory like humans, but it can temporarily store and use previous interactions to maintain context in ongoing tasks. Developers are working on improving this aspect. 8 - Transformer Models and Diffusion Models Transformers, like those in ChatGPT, excel at generating coherent text by understanding context. Diffusion models, used for creating images, refine random pixels into clear pictures based on prompts. 9 - Frontier Models Frontier models are advanced AI systems that push the limits of what AI can do. These models are at the cutting edge of AI research and development. 10 - GPU Graphics Processing Units (GPUs) are essential for AI because they can handle massive calculations efficiently. They are crucial for training and running advanced AI models. Learn more about AI here: https://lnkd.in/didtJ-2e #Microsoft #AI #MicrosoftAdvocate

Completed this course a few months back that I highly recommend for anyone interested in foundational courses in designing for A.I. https://lnkd.in/eAFc98zd Topics include: Dealing with Uncertainty and Error Learn how to prepare for and address uncertainty and error in AI. Examine how an interactive system should model uncertainty and address it in a user interface. AI-Driven Design Artificial Intelligence can drive design and innovation within an organization. Learn how design mining and visual preferences can enhance AI-driven design. Interactive Machine Learning Machine learning models can be difficult to predict, design, and debug. Examine intelligible machine learning and how the power of AI-enabled machine learning can help interpret, simplify, and influence an organization.

Artificial intelligence (AI) is a field of computer science that focuses on creating systems or machines capable of performing tasks that would typically require human intelligence. It involves developing algorithms and models that enable machines to learn from data, adapt to new inputs, and perform tasks with a level of autonomy. There are various types of AI: 1. **Narrow/Weak AI:** This type of AI is designed for a specific task, such as voice recognition, image recognition, or playing chess. It operates within a defined set of parameters and cannot perform tasks outside its intended scope. 2. **General AI:** Also known as strong AI, this aims to create machines with human-like intelligence capable of understanding, reasoning, and solving problems across various domains. General AI remains a theoretical concept and is yet to be achieved. 3. **Machine Learning:** It's a subset of AI that focuses on enabling machines to learn from data without explicit programming. It includes techniques like supervised learning, unsupervised learning, and reinforcement learning. 4. **Deep Learning:** A specialized form of machine learning inspired by the structure and function of the human brain's neural networks. Deep learning uses artificial neural networks to learn and make decisions from vast amounts of data. AI has numerous applications across industries such as healthcare, finance, transportation, entertainment, and more. It's used for predictive analytics, natural language processing, autonomous vehicles, recommendation systems, and even in creative fields like art and music generation. Ethical concerns around AI include issues related to bias in algorithms, privacy concerns with the use of personal data, job displacement due to automation, and the potential for AI to outpace human control. As technology advances, the capabilities of AI continue to grow, leading to both excitement about its potential benefits and caution regarding its ethical and societal implications. .. . #snsct #snsdesignthinkers #designthinking #snsinstitutions

"🚀 Exciting news from NVIDIA! Learn about the groundbreaking Diffusion Vision Transformers (DiffiT) model, which is revolutionizing generative learning with its state-of-the-art performance and unique time-dependent self-attention layer. #AI #ML #GenerativeLearning #DiffusionVisionTransformers"

Technique enables AI on edge devices to keep learning over time #AI #PockEngine #sensordata #Deeplearning #LLMs #MachineLearning #NeuralNetworks #SmartSystems

Hiii connections!!!! I would like to share about workshop about AI and ML which was delivered by the team of MindFulAI for SNSCT..... #happyatmindfulai MINDFULAI #snsinstitutions #snsdesignthinkers #designthinking What is AI? Artificial Intelligence, or AI, refers to the simulation of human intelligence in machines that are programmed to think and learn like humans. It encompasses a wide range of technologies and techniques that enable machines to perform tasks that typically require human intelligence. Types of AI: 1. Narrow AI (Weak AI): Designed and trained for a particular task, e.g., virtual personal assistants. 2. General AI (Strong AI): Possesses the ability to understand, learn, and apply knowledge across diverse tasks, similar to human intelligence (currently theoretical). Machine Learning (ML): A subset of AI, ML involves the development of algorithms that enable machines to learn patterns and make decisions based on data. Key types of ML include: 1. Supervised Learning: Training the model on labeled data. 2. Unsupervised Learning: Allowing the model to find patterns in unlabeled data. 3. Reinforcement Learning: Training models through reward-based systems. Deep Learning (DL): DL is a subfield of ML that involves neural networks with multiple layers (deep neural networks). It excels at tasks such as image and speech recognition. Generative AI: Generative models, like Generative Adversarial Networks (GANs) or Variational Autoencoders (VAEs), create new data instances that resemble existing data. GANs, for instance, are widely used in generating realistic images. Applications of AI: AI is applied across various industries, including: - Healthcare (diagnosis and treatment planning). - Finance (fraud detection and algorithmic trading). - Autonomous vehicles. - Natural Language Processing (chatbots, language translation). - Robotics. Challenges and Ethical Considerations: AI presents challenges like bias in algorithms, job displacement, and ethical concerns. Ensuring fairness, transparency, and ethical AI development are critical considerations. AI is a dynamic field with continuous advancements, impacting many aspects of our daily lives and industries.

MACHINE LEARNING (ML) Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy. Machine learning involves showing a large volume of data to a machine so that it can learn and make predictions, find patterns, or classify data. The three machine learning types are supervised, unsupervised, and reinforcement learning. Machine Learning methods: Supervised machine learning relies on patterns to predict values on unlabeled data. It is most often used in automation, over large amounts of data records or in cases where there are too many data inputs for humans to process effectively. Machine learning is a subfield of artificial intelligence that uses algorithms trained on data sets to create models that enable machines to perform tasks that would otherwise only be possible for humans, such as categorizing images, analyzing data, or predicting price fluctuations. Machine learning is an application of AI that enables systems to learn and improve from experience without being explicitly programmed. Machine learning focuses on developing computer programs that can access data and use it to learn for themselves. #snsinstitutions #snsdesignthinking #designthinkers

Technique enables AI on edge devices to keep learning over time