𝐓𝐞𝐧𝐬𝐨𝐫 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 𝐔𝐧𝐢𝐭 (𝐓𝐏𝐔) Google Cloud’s TPUs are custom-developed application-specific integrated circuits (ASICs) designed to accelerate machine learning workloads, particularly those built on TensorFlow. Here’s a closer look at what makes TPUs a powerhouse for ML and AI applications: ◈ 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 𝐚𝐧𝐝 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 🔹 𝙈𝙖𝙩𝙧𝙞𝙭 𝙈𝙪𝙡𝙩𝙞𝙥𝙡𝙞𝙘𝙖𝙩𝙞𝙤𝙣 :High-throughput, low-latency matrix computations. 🔹𝐕𝐞𝐜𝐭𝐨𝐫 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 :Efficient neural network operations with hardware accelerators. ◈ 𝐒𝐜𝐚𝐥𝐚𝐛𝐢𝐥𝐢𝐭𝐲 🔹𝐓𝐏𝐔 𝐏𝐨𝐝𝐬: Distributed system, petaflops of compute power, large-scale model training like GPT-3 and BERT. 🔹𝐓𝐏𝐔 𝐒𝐥𝐢𝐜𝐞𝐬 :For less demanding tasks, TPU slices offer a cost-effective solution by partitioning TPU resources to match workload requirements. ◈ 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧 𝐰𝐢𝐭𝐡 𝐓𝐞𝐧𝐬𝐨𝐫𝐅𝐥𝐨𝐰 🔹 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐞𝐝 𝐟𝐨𝐫 𝐓𝐞𝐧𝐬𝐨𝐫𝐅𝐥𝐨𝐰 : TPUs are tightly integrated with TF, supporting high-level APIs and delivering significant speedups. 🔹 𝐃𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐞𝐝 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 : TensorFlow’s distributed training capabilities leverage TPU pods for data parallelism, reducing training times for large datasets. ◈ 𝐌𝐞𝐦𝐨𝐫𝐲 𝐚𝐧𝐝 𝐃𝐚𝐭𝐚 𝐇𝐚𝐧𝐝𝐥𝐢𝐧𝐠 : 🔹 𝐇𝐢𝐠𝐡 𝐁𝐚𝐧𝐝𝐰𝐢𝐝𝐭𝐡 𝐌𝐞𝐦𝐨𝐫𝐲 (𝐇𝐁𝐌): HBM providing high memory bandwidth crucial for feeding data into the processors quickly & Continously. 🔹 𝐄𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐭 𝐃𝐚𝐭𝐚 𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐢𝐧𝐠:Advanced data pipelining techniques minimize data transfer overhead, optimizing the Data flow. ◈ 𝐔𝐬𝐞 𝐂𝐚𝐬𝐞𝐬 𝐚𝐧𝐝 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 🔹 𝐍𝐚𝐭𝐮𝐫𝐚𝐥 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 (𝐍𝐋𝐏) : TPUs power state-of-the-art NLP models like BERT and T5, enabling rapid advancements in language understanding and generation. 🔹 𝐂𝐨𝐦𝐩𝐮𝐭𝐞𝐫 𝐕𝐢𝐬𝐢𝐨𝐧 :High-resolution image processing and complex convolutional neural networks (CNNs) benefit from the parallel processing capabilities of TPUs. 🔹𝐑𝐞𝐢𝐧𝐟𝐨𝐫𝐜𝐞𝐦𝐞𝐧𝐭 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 :TPUs accelerate the training of reinforcement learning models by efficiently handling the computational demands of deep Q-networks (DQN) and policy gradients. ◈ 𝐆𝐨𝐨𝐠𝐥𝐞 𝐂𝐥𝐨𝐮𝐝 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧 : 🔹 𝐕𝐞𝐫𝐭𝐞𝐱 𝐀𝐈 : Seamlessly integrate TPUs with Vertex AI for end-to-end machine learning lifecycle management, from data preparation to model deployment. 🔹 𝐁𝐢𝐠𝐐𝐮𝐞𝐫𝐲 𝐌𝐋 : Utilize TPUs for scalable, high-performance machine learning within BigQuery, enabling analytics and ML on massive datasets.🔹Scale automatically with traffic. ◈ 𝐓𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 𝐒𝐩𝐞𝐜𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 : 🔹 𝐓𝐏𝐔 𝐯𝟒 :Delivers up to 275 teraflops per chip, with a TPU pod comprising 4096 TPU v4 chips, providing over 1 exaflop of compute power. 🔹 𝐌𝐞𝐦𝐨𝐫𝐲 : Each TPU v4 chip includes 16 GB of HBM with a memory bandwidth of 600 GB/s. #GoogleCloud #TPU #TensorFlow #MachineLearning #DeepLearning #AI
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𝐓𝐞𝐧𝐬𝐨𝐫 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 𝐔𝐧𝐢𝐭 (𝐓𝐏𝐔) Google Cloud’s TPUs are custom-developed application-specific integrated circuits (ASICs) designed to accelerate machine learning workloads, particularly those built on TensorFlow. Here’s a closer look at what makes TPUs a powerhouse for ML and AI applications: ◈ 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 𝐚𝐧𝐝 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 🔹 𝙈𝙖𝙩𝙧𝙞𝙭 𝙈𝙪𝙡𝙩𝙞𝙥𝙡𝙞𝙘𝙖𝙩𝙞𝙤𝙣 :High-throughput, low-latency matrix computations. 🔹𝐕𝐞𝐜𝐭𝐨𝐫 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 :Efficient neural network operations with hardware accelerators. ◈ 𝐒𝐜𝐚𝐥𝐚𝐛𝐢𝐥𝐢𝐭𝐲 🔹𝐓𝐏𝐔 𝐏𝐨𝐝𝐬: Distributed system, petaflops of compute power, large-scale model training like GPT-3 and BERT. 🔹𝐓𝐏𝐔 𝐒𝐥𝐢𝐜𝐞𝐬 :For less demanding tasks, TPU slices offer a cost-effective solution by partitioning TPU resources to match workload requirements. ◈ 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧 𝐰𝐢𝐭𝐡 𝐓𝐞𝐧𝐬𝐨𝐫𝐅𝐥𝐨𝐰 🔹 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐞𝐝 𝐟𝐨𝐫 𝐓𝐞𝐧𝐬𝐨𝐫𝐅𝐥𝐨𝐰 : TPUs are tightly integrated with TF, supporting high-level APIs and delivering significant speedups. 🔹 𝐃𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐞𝐝 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 : TensorFlow’s distributed training capabilities leverage TPU pods for data parallelism, reducing training times for large datasets. ◈ 𝐌𝐞𝐦𝐨𝐫𝐲 𝐚𝐧𝐝 𝐃𝐚𝐭𝐚 𝐇𝐚𝐧𝐝𝐥𝐢𝐧𝐠 : 🔹 𝐇𝐢𝐠𝐡 𝐁𝐚𝐧𝐝𝐰𝐢𝐝𝐭𝐡 𝐌𝐞𝐦𝐨𝐫𝐲 (𝐇𝐁𝐌): HBM providing high memory bandwidth crucial for feeding data into the processors quickly & Continously. 🔹 𝐄𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐭 𝐃𝐚𝐭𝐚 𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐢𝐧𝐠:Advanced data pipelining techniques minimize data transfer overhead, optimizing the Data flow. ◈ 𝐔𝐬𝐞 𝐂𝐚𝐬𝐞𝐬 𝐚𝐧𝐝 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 🔹 𝐍𝐚𝐭𝐮𝐫𝐚𝐥 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 (𝐍𝐋𝐏) : TPUs power state-of-the-art NLP models like BERT and T5, enabling rapid advancements in language understanding and generation. 🔹 𝐂𝐨𝐦𝐩𝐮𝐭𝐞𝐫 𝐕𝐢𝐬𝐢𝐨𝐧 :High-resolution image processing and complex convolutional neural networks (CNNs) benefit from the parallel processing capabilities of TPUs. 🔹𝐑𝐞𝐢𝐧𝐟𝐨𝐫𝐜𝐞𝐦𝐞𝐧𝐭 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 :TPUs accelerate the training of reinforcement learning models by efficiently handling the computational demands of deep Q-networks (DQN) and policy gradients. ◈ 𝐆𝐨𝐨𝐠𝐥𝐞 𝐂𝐥𝐨𝐮𝐝 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧 : 🔹 𝐕𝐞𝐫𝐭𝐞𝐱 𝐀𝐈 : Seamlessly integrate TPUs with Vertex AI for end-to-end machine learning lifecycle management, from data preparation to model deployment. 🔹 𝐁𝐢𝐠𝐐𝐮𝐞𝐫𝐲 𝐌𝐋 : Utilize TPUs for scalable, high-performance machine learning within BigQuery, enabling analytics and ML on massive datasets.🔹Scale automatically with traffic. ◈ 𝐓𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 𝐒𝐩𝐞𝐜𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 : 🔹 𝐓𝐏𝐔 𝐯𝟒 :Delivers up to 275 teraflops per chip, with a TPU pod comprising 4096 TPU v4 chips, providing over 1 exaflop of compute power. 🔹 𝐌𝐞𝐦𝐨𝐫𝐲 : Each TPU v4 chip includes 16 GB of HBM with a memory bandwidth of 600 GB/s. #GoogleCloud #TPU #TensorFlow #MachineLearning #DeepLearning #AI
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AI vs Machine Learning - Difference Between Artificial Intelligence and ML - AWS Understanding the Distinctions Between AI and ML: A Comprehensive OverviewArtificial Intelligence (AI) and Machine Learning (ML) are pivotal technologies that drive innovation across industries, yet they hold distinct characteristics, objectives, and methodologies. AI aims to emulate complex human tasks efficiently, including learning, problem-solving, and pattern recognition. This is achieved through a wide range of methods like genetic algorithms, neural networks, and notably, machine learning itself among others. In contrast, ML focuses on analyzing vast datasets to identify patterns and predict outcomes with a certain degree of confidence, employing supervised and unsupervised learning methods. When it comes to implementations, building ML models involves selecting a relevant dataset and strategy, such as linear regression or decision trees, to train the model. Through continuous refinement and quality data, the accuracy of ML models is enhanced. AI development, however, often leverages prebuilt solutions for integration into products and services, simplifying the creation of AI-driven applications. Regarding infrastructure, ML requires a modest setup starting from a few hundred data points and manageable computational resources. AI's infrastructure needs can vary greatly, from minimal for simple tasks to extensive systems for high-computing demands. In summary, while AI encompasses a broad set of technologies aiming to mimic human intelligence, ML is a focused subset of AI dedicated to learning from data to make predictions. Both fields offer prebuilt solutions for easy integration, yet their applications, methodologies, and requirements distinctly differ, marking the importance of understanding each technology's nuances for effective implementation. Reference Link https://lnkd.in/d6aE5Xfu
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AI vs Machine Learning - Difference Between Artificial Intelligence and ML - AWS Understanding the Distinctions Between AI and ML: A Comprehensive OverviewArtificial Intelligence (AI) and Machine Learning (ML) are pivotal technologies that drive innovation across industries, yet they hold distinct characteristics, objectives, and methodologies. AI aims to emulate complex human tasks efficiently, including learning, problem-solving, and pattern recognition. This is achieved through a wide range of methods like genetic algorithms, neural networks, and notably, machine learning itself among others. In contrast, ML focuses on analyzing vast datasets to identify patterns and predict outcomes with a certain degree of confidence, employing supervised and unsupervised learning methods. When it comes to implementations, building ML models involves selecting a relevant dataset and strategy, such as linear regression or decision trees, to train the model. Through continuous refinement and quality data, the accuracy of ML models is enhanced. AI development, however, often leverages prebuilt solutions for integration into products and services, simplifying the creation of AI-driven applications. Regarding infrastructure, ML requires a modest setup starting from a few hundred data points and manageable computational resources. AI's infrastructure needs can vary greatly, from minimal for simple tasks to extensive systems for high-computing demands. In summary, while AI encompasses a broad set of technologies aiming to mimic human intelligence, ML is a focused subset of AI dedicated to learning from data to make predictions. Both fields offer prebuilt solutions for easy integration, yet their applications, methodologies, and requirements distinctly differ, marking the importance of understanding each technology's nuances for effective implementation. Reference Link https://lnkd.in/d6aE5Xfu
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AI vs Machine Learning - Difference Between Artificial Intelligence and ML - AWS Understanding the Distinctions Between AI and ML: A Comprehensive OverviewArtificial Intelligence (AI) and Machine Learning (ML) are pivotal technologies that drive innovation across industries, yet they hold distinct characteristics, objectives, and methodologies. AI aims to emulate complex human tasks efficiently, including learning, problem-solving, and pattern recognition. This is achieved through a wide range of methods like genetic algorithms, neural networks, and notably, machine learning itself among others. In contrast, ML focuses on analyzing vast datasets to identify patterns and predict outcomes with a certain degree of confidence, employing supervised and unsupervised learning methods. When it comes to implementations, building ML models involves selecting a relevant dataset and strategy, such as linear regression or decision trees, to train the model. Through continuous refinement and quality data, the accuracy of ML models is enhanced. AI development, however, often leverages prebuilt solutions for integration into products and services, simplifying the creation of AI-driven applications. Regarding infrastructure, ML requires a modest setup starting from a few hundred data points and manageable computational resources. AI's infrastructure needs can vary greatly, from minimal for simple tasks to extensive systems for high-computing demands. In summary, while AI encompasses a broad set of technologies aiming to mimic human intelligence, ML is a focused subset of AI dedicated to learning from data to make predictions. Both fields offer prebuilt solutions for easy integration, yet their applications, methodologies, and requirements distinctly differ, marking the importance of understanding each technology's nuances for effective implementation. Reference Link https://lnkd.in/dnzY-Xgs
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AI vs Machine Learning - Difference Between Artificial Intelligence and ML - AWS Understanding the Distinctions Between AI and ML: A Comprehensive OverviewArtificial Intelligence (AI) and Machine Learning (ML) are pivotal technologies that drive innovation across industries, yet they hold distinct characteristics, objectives, and methodologies. AI aims to emulate complex human tasks efficiently, including learning, problem-solving, and pattern recognition. This is achieved through a wide range of methods like genetic algorithms, neural networks, and notably, machine learning itself among others. In contrast, ML focuses on analyzing vast datasets to identify patterns and predict outcomes with a certain degree of confidence, employing supervised and unsupervised learning methods. When it comes to implementations, building ML models involves selecting a relevant dataset and strategy, such as linear regression or decision trees, to train the model. Through continuous refinement and quality data, the accuracy of ML models is enhanced. AI development, however, often leverages prebuilt solutions for integration into products and services, simplifying the creation of AI-driven applications. Regarding infrastructure, ML requires a modest setup starting from a few hundred data points and manageable computational resources. AI's infrastructure needs can vary greatly, from minimal for simple tasks to extensive systems for high-computing demands. In summary, while AI encompasses a broad set of technologies aiming to mimic human intelligence, ML is a focused subset of AI dedicated to learning from data to make predictions. Both fields offer prebuilt solutions for easy integration, yet their applications, methodologies, and requirements distinctly differ, marking the importance of understanding each technology's nuances for effective implementation. Reference Link https://lnkd.in/d6aE5Xfu
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AI vs Machine Learning - Difference Between Artificial Intelligence and ML - AWS Understanding the Distinctions Between AI and ML: A Comprehensive OverviewArtificial Intelligence (AI) and Machine Learning (ML) are pivotal technologies that drive innovation across industries, yet they hold distinct characteristics, objectives, and methodologies. AI aims to emulate complex human tasks efficiently, including learning, problem-solving, and pattern recognition. This is achieved through a wide range of methods like genetic algorithms, neural networks, and notably, machine learning itself among others. In contrast, ML focuses on analyzing vast datasets to identify patterns and predict outcomes with a certain degree of confidence, employing supervised and unsupervised learning methods. When it comes to implementations, building ML models involves selecting a relevant dataset and strategy, such as linear regression or decision trees, to train the model. Through continuous refinement and quality data, the accuracy of ML models is enhanced. AI development, however, often leverages prebuilt solutions for integration into products and services, simplifying the creation of AI-driven applications. Regarding infrastructure, ML requires a modest setup starting from a few hundred data points and manageable computational resources. AI's infrastructure needs can vary greatly, from minimal for simple tasks to extensive systems for high-computing demands. In summary, while AI encompasses a broad set of technologies aiming to mimic human intelligence, ML is a focused subset of AI dedicated to learning from data to make predictions. Both fields offer prebuilt solutions for easy integration, yet their applications, methodologies, and requirements distinctly differ, marking the importance of understanding each technology's nuances for effective implementation. Reference Link https://lnkd.in/d6aE5Xfu
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AI vs Machine Learning - Difference Between Artificial Intelligence and ML - AWS Understanding the Distinctions Between AI and ML: A Comprehensive OverviewArtificial Intelligence (AI) and Machine Learning (ML) are pivotal technologies that drive innovation across industries, yet they hold distinct characteristics, objectives, and methodologies. AI aims to emulate complex human tasks efficiently, including learning, problem-solving, and pattern recognition. This is achieved through a wide range of methods like genetic algorithms, neural networks, and notably, machine learning itself among others. In contrast, ML focuses on analyzing vast datasets to identify patterns and predict outcomes with a certain degree of confidence, employing supervised and unsupervised learning methods. When it comes to implementations, building ML models involves selecting a relevant dataset and strategy, such as linear regression or decision trees, to train the model. Through continuous refinement and quality data, the accuracy of ML models is enhanced. AI development, however, often leverages prebuilt solutions for integration into products and services, simplifying the creation of AI-driven applications. Regarding infrastructure, ML requires a modest setup starting from a few hundred data points and manageable computational resources. AI's infrastructure needs can vary greatly, from minimal for simple tasks to extensive systems for high-computing demands. In summary, while AI encompasses a broad set of technologies aiming to mimic human intelligence, ML is a focused subset of AI dedicated to learning from data to make predictions. Both fields offer prebuilt solutions for easy integration, yet their applications, methodologies, and requirements distinctly differ, marking the importance of understanding each technology's nuances for effective implementation. Reference Link https://lnkd.in/dnzY-Xgs
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VP & Chief AI Scientist | 20+ Years in AI | AI Agents Training (LLM + Vision) | Data Science Training (ML + DL) | Newsletter AI Horizon Watchers (24k+)
𝐓𝐡𝐞 𝐄𝐧𝐝 𝐨𝐟 𝐏𝐫𝐨𝐦𝐩𝐭 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐚𝐬 𝐖𝐞 𝐊𝐧𝐨𝐰 𝐈𝐭? 𝐑𝐢𝐬𝐞 𝐨𝐟 𝐆𝐫𝐚𝐩𝐡-𝐁𝐚𝐬𝐞𝐝 𝐏𝐫𝐨𝐦𝐩𝐭 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 According to one research team, no human should manually optimize prompts ever again. This has cast doubt on prompt engineering’s future—and increased suspicions that a fair portion of prompt-engineering jobs may be a passing fad, at least as the field is currently imagined. Let's dive into the Research. ✺ Challenge with Prompt Engineering Existing LM pipelines are typically implemented using hard-coded "prompt templates", i.e. lengthy strings discovered via trial and error. What’s best for any given model, dataset, and prompting strategy is likely to be specific to the particular combination at hand. ✺ DSPY uses Graphs as Solution DSPy, a programming model that abstracts LM pipelines as text transformation graphs, i.e. imperative computational graphs where LMs are invoked through declarative modules. DSPy modules are parameterized, meaning they can learn (by creating and collecting demonstrations) how to apply compositions of prompting, finetuning, augmentation, and reasoning techniques. DSPy programs can express and optimize sophisticated LM pipelines that reason about math word problems, tackle multi-hop retrieval, answer complex questions, and control agent loops. ✺ Test Results Within minutes of compiling, a few lines of DSPy allow GPT-3.5 and llama2-13b-chat to self-bootstrap pipelines that outperform standard few-shot prompting (generally by over 25% and 65%, respectively) and pipelines with expert-created demonstrations (by up to 5-46% and 16-40%, respectively). On top of that, DSPy programs compiled to open and relatively small LMs like 770M-parameter T5 and llama2-13b-chat are competitive with approaches that rely on expert-written prompt chains for proprietary GPT-3.5. DSPy is available at this https URL ✺ A Dive into DSPY Github Let's Use Neural Network as Analogy. When we build neural networks, we don't write manual for-loops over lists of hand-tuned floats. Instead, you might use a framework like PyTorch to compose declarative layers (e.g., Convolution or Dropout) and then use optimizers (e.g., SGD or Adam) to learn the parameters of the network. Same applies to DSPy for optimizing LM prompts. DSPy gives you the right general-purpose modules (e.g., ChainOfThought, ReAct, etc.), which replace string-based prompting tricks. To replace prompt hacking and one-off synthetic data generators, DSPy also gives you general optimizers (BootstrapFewShotWithRandomSearch or BayesianSignatureOptimizer), which are algorithms that update parameters in your program. With DSPy's code available for exploration we can all try it. Papers and Githubs in the Comments ⤋ ⭆ 𝐅𝐨𝐫 𝐀𝐩𝐩𝐥𝐢𝐞𝐝 𝐀𝐈, 𝐋𝐋𝐌𝐬 𝐚𝐧𝐝 𝐏𝐲𝐭𝐡𝐨𝐧 𝐋𝐢𝐛𝐫𝐚𝐫𝐢𝐞𝐬 𝐣𝐨𝐢𝐧 𝐦𝐲 𝐍𝐞𝐰𝐬𝐥𝐞𝐭𝐭𝐞𝐫: https://lnkd.in/eJM6an-t
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That's for English language ONLY. There are six official UN languages, and many more other non-UN human languages. Also, written words must be very accurate, especially for legal and safety issues. AI can't meet this requirement. Let's try an accurate fact-check question: "Is there any AI data products you know, that can interpret and answer the following humble but realistic Chinese-English multilingual questions?" With our IP, a copyrighted multilingual metadata, we can provide real time answers for policy/decision making as evidence. "Who, in the Ontario province of Canada, has new US patents granted on the nearest Tuesday, when the USPTO releases the newly granted US patents on a weekly basis?" "Who, in the "江蘇‘’ province of China, has new US patents granted on the nearest Tuesday, when the USPTO releases the newly granted US patents on a weekly basis?" Metadata is an enabler to let us find the data we want. Without metadata, NO data can be found/retrieved, even by the most advanced technologies, like AI, NVIDIA chips, supercomputers, etc. https://lnkd.in/g-aJFnXR Our IP can also make your information service UNIQUE in the world.
VP & Chief AI Scientist | 20+ Years in AI | AI Agents Training (LLM + Vision) | Data Science Training (ML + DL) | Newsletter AI Horizon Watchers (24k+)
𝐓𝐡𝐞 𝐄𝐧𝐝 𝐨𝐟 𝐏𝐫𝐨𝐦𝐩𝐭 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐚𝐬 𝐖𝐞 𝐊𝐧𝐨𝐰 𝐈𝐭? 𝐑𝐢𝐬𝐞 𝐨𝐟 𝐆𝐫𝐚𝐩𝐡-𝐁𝐚𝐬𝐞𝐝 𝐏𝐫𝐨𝐦𝐩𝐭 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 According to one research team, no human should manually optimize prompts ever again. This has cast doubt on prompt engineering’s future—and increased suspicions that a fair portion of prompt-engineering jobs may be a passing fad, at least as the field is currently imagined. Let's dive into the Research. ✺ Challenge with Prompt Engineering Existing LM pipelines are typically implemented using hard-coded "prompt templates", i.e. lengthy strings discovered via trial and error. What’s best for any given model, dataset, and prompting strategy is likely to be specific to the particular combination at hand. ✺ DSPY uses Graphs as Solution DSPy, a programming model that abstracts LM pipelines as text transformation graphs, i.e. imperative computational graphs where LMs are invoked through declarative modules. DSPy modules are parameterized, meaning they can learn (by creating and collecting demonstrations) how to apply compositions of prompting, finetuning, augmentation, and reasoning techniques. DSPy programs can express and optimize sophisticated LM pipelines that reason about math word problems, tackle multi-hop retrieval, answer complex questions, and control agent loops. ✺ Test Results Within minutes of compiling, a few lines of DSPy allow GPT-3.5 and llama2-13b-chat to self-bootstrap pipelines that outperform standard few-shot prompting (generally by over 25% and 65%, respectively) and pipelines with expert-created demonstrations (by up to 5-46% and 16-40%, respectively). On top of that, DSPy programs compiled to open and relatively small LMs like 770M-parameter T5 and llama2-13b-chat are competitive with approaches that rely on expert-written prompt chains for proprietary GPT-3.5. DSPy is available at this https URL ✺ A Dive into DSPY Github Let's Use Neural Network as Analogy. When we build neural networks, we don't write manual for-loops over lists of hand-tuned floats. Instead, you might use a framework like PyTorch to compose declarative layers (e.g., Convolution or Dropout) and then use optimizers (e.g., SGD or Adam) to learn the parameters of the network. Same applies to DSPy for optimizing LM prompts. DSPy gives you the right general-purpose modules (e.g., ChainOfThought, ReAct, etc.), which replace string-based prompting tricks. To replace prompt hacking and one-off synthetic data generators, DSPy also gives you general optimizers (BootstrapFewShotWithRandomSearch or BayesianSignatureOptimizer), which are algorithms that update parameters in your program. With DSPy's code available for exploration we can all try it. Papers and Githubs in the Comments ⤋ #ai #deeplearning #datascience #ml #llms ⭆ 𝐅𝐨𝐫 𝐀𝐩𝐩𝐥𝐢𝐞𝐝 𝐀𝐈, 𝐋𝐋𝐌𝐬 𝐚𝐧𝐝 𝐏𝐲𝐭𝐡𝐨𝐧 𝐋𝐢𝐛𝐫𝐚𝐫𝐢𝐞𝐬 𝐣𝐨𝐢𝐧 𝐦𝐲 𝐍𝐞𝐰𝐬𝐥𝐞𝐭𝐭𝐞𝐫: https://lnkd.in/eJM6an-t
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AI vs Machine Learning - Difference Between Artificial Intelligence and ML - AWS Understanding the Differences: AI vs. Machine Learning Artificial Intelligence (AI) and Machine Learning (ML) are two pivotal technologies shaping the future, yet they have distinct objectives, methodologies, implementations, and requirements. At its core, AI aims to enable machines to perform complex human tasks such as learning, problem-solving, and pattern recognition. AI employs a broad spectrum of methods, including genetic algorithms, neural networks, deep learning, and more, to achieve this goal. Machine Learning, a subset of AI, focuses on analyzing large datasets. Using statistical models, ML identifies patterns within the data to make predictions or decisions, each accompanied by a probability of accuracy. The methodology in ML is categorized mainly into supervised and unsupervised learning, targeting the processing of labeled and unlabeled data, respectively. In contrast, AI spans a wider range of problem-solving methods. Implementing ML involves selecting and preparing a dataset and choosing an appropriate ML model like linear regression or decision trees. This process demands consistent refinement and error checking to enhance model accuracy. AI, however, can entail a more intricate development process, often leading users to adopt prebuilt AI solutions through APIs for ease of integration. In terms of infrastructure, ML solutions might require a handful of servers, depending on the complexity of the task. AI, due to its broad applicability and sophisticated analysis, may necessitate a much larger computational effort, potentially involving thousands of machines for high-end tasks. Despite these requirements, both AI and ML functionalities are increasingly accessible via APIs, allowing for seamless integration into applications without the need for extensive resources. In summary, while both AI and ML revolutionize technology with their capabilities, they serve different purposes and operate within specific scopes. Understanding their differences is crucial for leveraging their full potential in application and development. Reference Link https://lnkd.in/duQB8p7D #devs #production #testing
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