Gopinath Parasurama’s Post

I had started with the Fundamentals of Generative AI Course under Microsoft AI Fundamentals . I have learnt about the transformer architecture which is used in various language models . The vast sources from internet are being used to train the language models . When a prompt is fed as an input , 1. It is tokenized using tokenizer , usually separating into meaningful words and id is assigned to each of the words . Id for each word is available globally from the trained words . 2. The tokens are then fed into encoder , where it is converted to vectors which is a list of numeric values . Vectors(Embeddings) are generated based on it's context and it's relationship with other tokens . The elements of the tokens in the embeddings represent semantic attribute of the token . 3. The vectors are then fed to the decoder , which has a attention module . The attention layer assigns a numeric weight to each token in the sequence and it is done by the positional encoding layer . This layer considers which tokens are most influential for guessing the next token . 4. A neural network is then used to evaluate all possible tokens to determine the most probable token with which to continue the sequence. The process continues iteratively for each token in the sequence, essentially building the output one token at a time . In practice implementation of Architecture Vary . Bidirectional Encoder Representations from Transformers (BERT) model developed by Google uses only the encoder block, while the Generative Pretrained Transformer (GPT) model developed by OpenAI uses only the decoder block.

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

📍 𝗪𝗲𝗲𝗸 𝟵: 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗱𝘃𝗲𝗿𝘀𝗮𝗿𝗶𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝘀 (𝗚𝗔𝗡𝘀) 𝗮𝗻𝗱 𝗩𝗮𝗿𝗶𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗔𝘂𝘁𝗼𝗲𝗻𝗰𝗼𝗱𝗲𝗿𝘀 (𝗩𝗔𝗘𝘀) ☀ 𝗗𝗮𝘆 𝟭: 𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝘁𝗼 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗠𝗼𝗱𝗲𝗹𝘀 𝗶𝗻 𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 Generative models are a captivating area of deep learning focused on creating new data samples that resemble existing ones. Unlike models that classify data into categories (discriminative models), generative models learn to mimic the data's structure, creating new, similar data points. This week, we'll explore these models, starting with an introduction today. What are Generative Models? Generative models learn the underlying patterns in data and generate new samples that resemble the original data. Key Concepts Generative vs. Discriminative Models: Discriminative Models: Think of a security guard who decides whether a person should be allowed in based on their ID. Examples include logistic regression and neural networks. Generative Models: Imagine a novelist who writes new stories based on different themes and styles they’ve read. Examples include Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). Applications of Generative Models: Image Generation: Like a photographer who creates new images from different elements. Data Augmentation: Expanding your photo album by adding new pictures that match the existing ones. Anomaly Detection: Identifying unusual activities by knowing what’s normal. Style Transfer: Applying the painting style of Van Gogh to a modern photograph. Text Generation: Writing new paragraphs that mimic the style of Shakespeare. Types of Generative Models Generative Adversarial Networks (GANs): Analogy: Think of a forger and a detective. The forger creates fake paintings, and the detective tries to identify the fakes. Over time, both improve at their tasks, resulting in very realistic forgeries. Mechanism: Two networks, the generator (forger) and the discriminator (detective), compete, enhancing each other’s performance. Variational Autoencoders (VAEs): Analogy: Imagine compressing a high-quality photo into a small file and then decompressing it back to the original. VAEs learn to compress data into a "latent space" and then reconstruct it. Mechanism: Encode data into a compressed form and then decode it, ensuring the compressed form follows a specific distribution. Autoregressive Models: Analogy: Writing a story one word at a time, using previous words to decide the next one. Mechanism: Model data points based on previous points, like PixelCNN and WaveNet. This week, we will delve deeper into GANs and VAEs, exploring their mechanisms and applications. Stay tuned for an exciting journey into the world of generative models! 𝗡𝗼𝘁𝗲𝘄𝗼𝗿𝘁𝗵𝘆 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀: https://lnkd.in/gwJNkjBz https://lnkd.in/g2swfpi7 https://lnkd.in/g2swfpi7 #DeepLearning #AI #MachineLearning #NeuralNetworks #TechLearning #LearnWithMe #12WeeksOfDeepLearning

🚀💡 From AutoML simplifying data science processes to the wonders of Transformer models in NLP, the realms of Data Science, Machine Learning, and AI are experiencing a revolutionary phase! Here are some highlights: 🔍 **Data Science Trends**: Featured advances include AutoML platforms like Google's AutoML, the emerging concept of Data Fabric for seamless data handling, and enriched Data Visualization tools such as Tableau and Power BI. Not to forget the evolution of Big Data Processing with Apache Spark 3.0! 🤖 **AI Innovations**: We're seeing breakthroughs from Google's BERT in Natural Language Processing to Soft Robotics for more adaptive machines, and a growing focus on Ethical AI to ensure fairness and transparency in AI applications. 📊 **Machine Learning Innovations**: The spotlight is on Transformer models for NLP tasks, the versatility of Autoencoders in data representation, and the critical importance of Explainable AI (XAI) for understanding complex model decisions. 🌐 **Impact Across Industries**: From healthcare leveraging AI for better diagnostics to finance integrating machine learning for fraud detection, and the automotive sector's drive towards self-driving technology - AI, and ML are truly reshaping industries. 🔬 **Research and Development**: Key focus areas include deep learning, reinforcement learning, and transfer learning, with exciting conferences like NeurIPS on the horizon. 📈 **Rapid Market Growth**: Expectations set for dramatic growth in the data science services, machine learning, and AI markets signal a transformative period ahead. What's the most exciting innovation in data science, AI, or ML you've seen lately? Share your thoughts and let's explore the future of technology together! 💬🌟

Character Detection Matching (CDM): A Novel Evaluation Metric for Formula Recognition Mathematical formula recognition has progressed significantly, driven by deep learning techniques and the Transformer architecture. Traditional OCR methods prove insufficient due to the complex structures of mathematical expressions, requiring models to understand spatial and structural relationships. The field faces challenges in representational diversity, as formulas can have multiple valid representations. Recent advancements, including commercial tools like Mathpix and models such as UniMERNet, demonstrate the potential of deep learning in real-world applications. Despite these advancements, current evaluation metrics for formula recognition exhibit significant limitations. Commonly used metrics like BLEU and Edit Distance focus primarily on text-based character matching, failing to accurately reflect recognition quality due to diverse formula representations. This leads to low reliability, unfair model comparisons, and a lack of intuitive scoring. The need for improved evaluation methods that account for the unique challenges of formula recognition has become evident, prompting the development of new approaches, such as the Character Detection Matching (CDM) metric proposed. Mathematical formula recognition faces unique challenges due to complex structures and varied notations. Despite advancements in recognition models, existing evaluation metrics like BLEU and Edit Distance exhibit limitations in handling diverse formula representations. This paper introduces CDM, a novel evaluation metric that treats formula recognition as an image-based object detection task. CDM renders predicted and ground-truth LaTeX formulas into images, employing visual feature extraction and localization for precise character-level matching. This spatially-aware approach offers more accurate and equitable evaluation, aligning closely with human standards and providing fairer model comparisons. CDM addresses the need for improved evaluation methods in formula recognition, enhancing objectivity and reliability in assessment. Researchers from Shanghai AI Laboratory and Shanghai Jiao Tong University developed a comprehensive methodology for evaluating formula recognition. Their approach begins with converting PDF pages to images for model input, followed by formula extraction using tailored regular expressions. The process compiles recognized formulas into text files for each PDF, facilitating subsequent matching. The methodology employs extraction algorithms to identify displayed formulas from model outputs, which are then matched against ground truth formulas. This systematic approach enables the computation of evaluation metrics, including BLEU and the newly introduced CDM metric. Extensive experiments were conducted to validate the effectiveness of the CDM metric. Results from the Tiny-Doc-Math evaluation demonstrated CDM’s reliability in 96% of cases, with the...

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

Machine Learning and Artificial Intelligence Artificial Intelligence and its subcategory, Machine Learning, are very hot topics these days. It seems like everyone is associating themselves with AI or machine learning to build a business case for increased profit, current skills and keeping up with the latest in technology. Maybe I’m doing the same. But in my case I’ve been intrigued by the utilization of using computers to enhance our lives for decades. When I was about 19 years old and had finished my first programming class at Princeton, taking Pascal, I attempted to create a C program that would mimic human responses while interning at Bell Laboratories. Next I decided to build a robot that would navigate its environment autonomously. When I graduated, I proposed the idea of having computers aid doctors in the diagnosis of illnesses to a prospective future wife who was studying to be a medical doctor. And then a few years later, I developed a simple two neuron based speech detector that would discriminate noise from speech. It was nearly brilliant with the exception that it couldn’t handle continuous tones. These are primitive examples of AI now that we have models with billions of parameters and their associated weights and biases. Now years later, I embarked on a journey to master this advanced machine learning to see how this area had matured. From my perspective, machine learning and its plethora of algorithms draw from Digital Signal Processing, Probability theory and Statistics. Matrix computations. 2D and 3D convolutions performed by Graphical Processing Units and their plethora of computational cores (10,000+ in some cases). And the analysis of data allowing for the prediction of outcomes based on previous observations. So by analyzing, say a million to billion examples of an issue, you can create models that allow you to calculate the probability of an outcome based on previous outcomes. Add in large language models and attention, and you have Generative Pre-Trained Transformers (GPT). From my perspective, we have a ways to go to mimic human intelligence and create Artificial General Intelligence (AGI), but the research and progress that’s been made in this area is astounding. And the utilization of what is possible now is incredible. Take a look at what SORA can do!

💸 Revenue Team Wants AI Costs, But Your MVP's Still Loading... 🤯 A Founding MLE Guide to Pre-MVP Cost Estimation 🔮 Initial Cost Estimation 1. Select Representative Models 📊:    Choose models at various scales. For NLP, consider BERT, GPT-J, (FLAN-)T5-XXL, and Falcon-40B. 2. Match Models to Hardware 🖥️:   Pair each model with appropriate GPU hardware. Example: GPT-J on A10G, Falcon-40B on A100 40GB. 3. Estimate Request Completion Time ⏱️:   Approximate time for each model to complete a request. Example:   - GPT-J: 1 second on A10G (made up number!)   - Falcon-40B: 10 seconds on A100 40GB (made up number!) 4. Calculate Hourly Costs 💸:   Research current GPU pricing. Example:   - A10G: ~$2 per hour (Modal Labs pricing)   - A100 40GB: ~$5 per hour (Modal Labs pricing) 5. Compute Cost per 1000 Requests 🧮:   Use the formula: (Seconds per request * Cost per hour) / (3600 seconds) * 1000   Examples:   - GPT-J: (1 * $2) / 3600 * 1000 ≈ $0.60 to serve 1000 requests   - Falcon-40B: (10 * $5) / 3600 * 1000 ≈ $3.00 to serve 1000 requests 6. Provide Order of Magnitude (OOM) Estimates 📏:   Present a range of costs based on different models. In this case, $0.60 to $3.00 per 1000 requests. 7. Factor in SLAs and Latency Requirements ⚡:   SLAs affect costs and can help constriant the solution space. For example, achieving a p99 latency of Xms might be 10x more expensive due to keeping a machine warm 🔧 Ongoing Cost Optimization (Thanks for the beautiful post Outerbounds) - Analyze Top-line Costs 📈:    Regularly review cloud bills to focus optimization efforts. - Identify Cost-driving Instances and Workloads 🔍:    Use tools to pinpoint expensive instances and tasks. - Monitor Resource Utilization 📊:    Avoid over-provisioning; pay attention to actual usage. - Optimize Workloads 🎛️:    Right-size resource requests based on real usage patterns. - Choose Optimal Execution Environments 🌐:    Leverage multi-cloud strategies for cost advantages. - Refine Based on Specific Needs 🎯:   Narrow estimates by understanding customer problems and required model scales. - Explore Serverless Options ☁️: - Stay Informed on Pricing 📚: Valuable resources: https://lnkd.in/gwragJBw https://lnkd.in/gVkGenhi https://lnkd.in/gq8pi4qd This framework is inspired by countless interactions with the ML/AI community #MachineLearning #CostEstimation #DataScience #AI #CloudOptimization

Al software is a category of computer software that makes it possible for any Artificial intelligence (AI) company to process massive amounts of data in order to do tasks that would otherwise require human intelligence. These include NLP, text recognition, voice recognition, image recognition, and video analytics. It used to be that artificial intelligence was viewed with suspicion or even trepidation, and scary cinematic representations like Terminator haven't helped change that perception. The article enlists 10 AI software platform that you should know in 2023. Google Cloud Learning Machine Anyone looking to advance their machine learning (ML) projects will find the google cloud learning machine to be of great use. You may easily and affordably create and develop your own machine learning apps thanks to this program's integrated toolchain. Because this programme is Google-based, once you deploy your application, you will have access to all of Google's cutting-edge AI technologies, such as Tensorflow, TPUs, or TFX tools. IBM Watson IBM created Watson, a highly acclaimed artificial intelligence programme. The pre-built applications and tools included in this package let you to create, execute, and administer your AI while watching and recording your data to forecast and influence probable outcomes. By integrating this tool into your workflow, you can concentrate on producing more imaginative, high-quality work without being distracted by the tediousness of data entry. Data scientists have benefited from IBM Watson's assistance in understanding and developing AI. You may access your AI at scale through any cloud thanks to the simple user interface and open, extensive model operation of Watson Machine Learning. NVIDIA Deep Learning AI Software It's not surprising to see NVIDIA on this list considering how popular it has become because to their promising computer hardware and software. Machine learning-focused artificial intelligence solutions include NIVIDA Deep Learning Ai. This AI software is delivered wherever you need it and depends on GPU acceleration. In order to truly access your projects from anywhere, NVIDIA Deep Learning AI is also available on the majority of cloud platforms like Amazon or Google. This tool promises to create the greatest predictive analytics for your project, enabling you to continually improve your job.

Q-Sparse: A New Artificial Intelligence AI Approach to Enable Full Sparsity of Activations in LLMs LLMs excel in natural language processing tasks but face deployment challenges due to high computational and memory demands during inference. Recent research [MWM+24, WMD+23, SXZ+24, XGZC23, LKM23] aims to enhance LLM efficiency through quantization, pruning, distillation, and improved decoding. Sparsity, a key approach, reduces computation by omitting zero elements and lessens I/O transfer between memory and computation units. While weight sparsity saves computation, it struggles with GPU parallelization and accuracy loss. Activation sparsity, achieved via techniques like the mixture-of-experts (MoE) mechanism, also needs full efficiency and requires further study on scaling laws compared to dense models. Researchers from Microsoft and the University of Chinese Academy of Sciences have developed Q-Sparse, an efficient approach for training sparsely-activated LLMs. Q-Sparse enables full activation sparsity by applying top-K sparsification to activations and using a straight-through estimator during training, significantly enhancing inference efficiency. Key findings include achieving baseline LLM performance with lower inference costs, establishing an optimal scaling law for sparsely-activated LLMs, and demonstrating effectiveness in various training settings. Q-Sparse works with full-precision and 1-bit models, offering a path to more efficient, cost-effective, and energy-saving LLMs. Q-Sparse enhances the Transformer architecture by enabling full sparsity in activations through top-K sparsification and the straight-through estimator (STE). This approach applies a top-K function to the activations during matrix multiplication, reducing computational costs and memory footprint. It supports full-precision and quantized models, including 1-bit models like BitNet b1.58. Additionally, Q-Sparse uses squared ReLU for feed-forward layers to improve activation sparsity. For training, it overcomes gradient vanishing by using STE. Q-Sparse is effective for training from scratch, continue-training, and fine-tuning, maintaining efficiency and performance across various settings. Recent studies show that LLM performance scales with model size and training data follow a power law. The researchers explore this for sparsely-activated LLMs, finding their performance also follows a power law with model size and an exponential statute with sparsity ratio. Experiments reveal that, with a fixed sparsity ratio, sparsely-activated models’ performance scales are similar to those of dense models. The performance gap between sparse and dense models diminishes with increasing model size. An inference-optimal scaling law indicates that sparse models can efficiently match or outperform dense models with proper sparsity, with optimal sparsity ratios of 45.58% for full precision and 61.25% for 1.58-bit models. The researchers evaluated Q-Sparse LLMs in various set...