Brilworks Software

🍽️ Brilworks Software Bringing the team together for a scrumptious lunch goes beyond just enjoying great food; it’s about strengthening connections and making cherished memories! 🥗👩🍳 Let’s relish the camaraderie and joy as we recharge for success! 💪🥪 #TeamBonding #LunchtimeFun #WorkFamily Brilworks Software #Brilworks 🍕🥗👫

OpenAI has just launched GPT-4o mini, 𝐻𝑒𝑟𝑒'𝑠 𝑤ℎ𝑦 𝐺𝑃𝑇-4𝑜 𝑚𝑖𝑛𝑖 𝑖𝑠 𝑎 𝑔𝑎𝑚𝑒-𝑐ℎ𝑎𝑛𝑔𝑒𝑟: - 𝐒𝐮𝐩𝐞𝐫𝐢𝐨𝐫 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞: Scoring 𝟖𝟐% 𝐨𝐧 𝐌𝐌𝐋𝐔, GPT-4o mini surpasses previous models like GPT-3.5 Turbo and even 𝐨𝐮𝐭𝐩𝐞𝐫𝐟𝐨𝐫𝐦𝐬 𝐆𝐏𝐓-𝟒 𝐢𝐧 𝐜𝐡𝐚𝐭 𝐩𝐫𝐞𝐟𝐞𝐫𝐞𝐧𝐜𝐞𝐬 on the LMSYS leaderboard. - 𝐂𝐨𝐬𝐭-𝐄𝐟𝐟𝐞𝐜𝐭𝐢𝐯𝐞: Priced at just 𝟏𝟓 𝐜𝐞𝐧𝐭𝐬 𝐩𝐞𝐫 𝐦𝐢𝐥𝐥𝐢𝐨𝐧 input tokens and 𝟔𝟎 𝐜𝐞𝐧𝐭𝐬 𝐩𝐞𝐫 𝐦𝐢𝐥𝐥𝐢𝐨𝐧 𝐨𝐮𝐭𝐩𝐮𝐭 𝐭𝐨𝐤𝐞𝐧𝐬, it's an order of magnitude more affordable than earlier frontier models, making AI more accessible than ever. - 𝐁𝐫𝐨𝐚𝐝 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬: Ideal for 𝐭𝐚𝐬𝐤𝐬 that require chaining or parallelizing multiple model calls, 𝐡𝐚𝐧𝐝𝐥𝐢𝐧𝐠 𝐥𝐚𝐫𝐠𝐞 𝐯𝐨𝐥𝐮𝐦𝐞𝐬 𝐨𝐟 𝐜𝐨𝐧𝐭𝐞𝐱𝐭, or providing fast, 𝐫𝐞𝐚𝐥-𝐭𝐢𝐦𝐞 𝐭𝐞𝐱𝐭 𝐫𝐞𝐬𝐩𝐨𝐧𝐬𝐞𝐬—𝐭𝐡𝐢𝐧𝐤 𝐜𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐬𝐮𝐩𝐩𝐨𝐫𝐭 𝐜𝐡𝐚𝐭𝐛𝐨𝐭s and more! - 𝐌𝐮𝐥𝐭𝐢𝐦𝐨𝐝𝐚𝐥 𝐂𝐚𝐩𝐚𝐛𝐢𝐥𝐢𝐭𝐢𝐞𝐬: Supporting text and vision with 𝐟𝐮𝐭𝐮𝐫𝐞 𝐩𝐥𝐚𝐧𝐬 to include 𝐯𝐢𝐝𝐞𝐨 𝐚𝐧𝐝 𝐚𝐮𝐝𝐢𝐨 inputs and outputs, GPT-4o mini excels in both textual intelligence and multimodal reasoning. - 𝐄𝐧𝐡𝐚𝐧𝐜𝐞𝐝 𝐒𝐚𝐟𝐞𝐭𝐲: Built-in safety measures and advanced techniques like OpenAI's instruction hierarchy method ensure reliable and secure model responses, making it safer to use at scale. GPT-4o mini is available in the Assistants API, Chat Completions API, and Batch API. 𝐹𝑜𝑙𝑙𝑜𝑤 me 𝑓𝑜𝑟 𝑚𝑜𝑟𝑒 𝑢𝑝𝑑𝑎𝑡𝑒𝑠 𝑜𝑛 𝐴𝐼 & 𝐴𝑊𝑆 #chatgpt #openai #chatbot #virtualassistance Image source : OpenAI website

🚀 𝐁𝐮𝐢𝐥𝐝 𝐘𝐨𝐮𝐫 𝐀𝐈-𝐄𝐧𝐚𝐛𝐥𝐞𝐝 𝐕𝐢𝐫𝐭𝐮𝐚𝐥 𝐅𝐢𝐞𝐥𝐝 𝐀𝐬𝐬𝐢𝐬𝐭𝐚𝐧𝐭 𝐰𝐢𝐭𝐡 𝐀𝐖𝐒! 🚀 In the Energy, Resources, and Industrials sectors, engineers often face challenges while working in remote or demanding environments. Imagine having an on-demand, AI-enabled virtual field assistant that provides instant access to engineering knowledge, helping you solve problems, improve efficiency, and make informed decisions. With AWS, this vision can become a reality. 🌟 𝐇𝐨𝐰 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐈 𝐂𝐚𝐧 𝐇𝐞𝐥𝐩: 𝐄𝐚𝐬𝐢𝐥𝐲 𝐀𝐜𝐜𝐞𝐬𝐬𝐢𝐛𝐥𝐞 𝐓𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 𝐈𝐧𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧: A Generative AI-enabled virtual field assistant serves as a reference tool, offering quick access to a vast amount of technical information. It helps by responding to questions about specific engineering concepts, principles, or calculations. 𝐓𝐫𝐨𝐮𝐛𝐥𝐞𝐬𝐡𝐨𝐨𝐭𝐢𝐧𝐠 𝐚𝐧𝐝 𝐃𝐢𝐚𝐠𝐧𝐨𝐬𝐭𝐢𝐜𝐬: Describe the problem to the virtual assistant, and it will guide you through identifying the cause and offer step-by-step solutions for resolution. 🔧 𝐀𝐖𝐒 𝐒𝐞𝐫𝐯𝐢𝐜𝐞𝐬 𝐭𝐨 𝐁𝐮𝐢𝐥𝐝 𝐘𝐨𝐮𝐫 𝐕𝐢𝐫𝐭𝐮𝐚𝐥 𝐅𝐢𝐞𝐥𝐝 𝐀𝐬𝐬𝐢𝐬𝐭𝐚𝐧𝐭: 𝐀𝐦𝐚𝐳𝐨𝐧 𝐁𝐞𝐝𝐫𝐨𝐜𝐤: Simplify building and scaling Generative AI applications with foundation models, allowing you to focus on innovation rather than infrastructure management. 𝐀𝐦𝐚𝐳𝐨𝐧 𝐂𝐨𝐦𝐩𝐫𝐞𝐡𝐞𝐧𝐝: Extract insights and relationships from unstructured data. This can help in understanding and categorizing technical documents and manuals. 𝐀𝐦𝐚𝐳𝐨𝐧 𝐋𝐞𝐱: Build conversational interfaces into any application using voice and text. Lex can be the conversational engine that interacts with engineers in the field. 𝐀𝐦𝐚𝐳𝐨𝐧 𝐏𝐨𝐥𝐥𝐲: Convert text into lifelike speech. Polly can give a voice to your virtual assistant, making interactions more natural and engaging. 𝐀𝐖𝐒 𝐋𝐚𝐦𝐛𝐝𝐚: Run code in response to events and automatically manage the underlying compute resources. Use Lambda to process requests and manage backend operations seamlessly. 𝐀𝐦𝐚𝐳𝐨𝐧 𝐒3: Store and retrieve any amount of data at any time. Use S3 to store technical documents, manuals, and other resources that the virtual assistant might need to access. Harness the power of AWS services to support engineers in the field, improve productivity, and ensure safety. Explore the possibilities of building a virtual field assistant with AWS today! 🌐 #GenerativeAI #Engineering #AI #VirtualAssistant #AWS #MachineLearning #TechInnovation

In today's data-driven world, efficiently 𝐞𝐱𝐭𝐫𝐚𝐜𝐭𝐢𝐧𝐠 𝐢𝐧𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧 𝐟𝐫𝐨𝐦 𝐝𝐨𝐜𝐮𝐦𝐞𝐧𝐭𝐬 is vital. Traditional methods are slow and error-prone. Our solution uses AWS Bedrock and the Anthropic Claude 3 Haiku model to revolutionize this process. 𝐊𝐞𝐲 𝐁𝐞𝐧𝐞𝐟𝐢𝐭𝐬: 🔹 Prompt-Based Extraction: Seamlessly define and extract specific data from PDFs and images. 🔹 Scalable Processing: Handle large and multipage documents with ease using AWS services. 🔹 Accuracy with Human Review: Incorporate Amazon A2I for high-quality results through customizable human review loops. 𝐒𝐭𝐞𝐩𝐬: 1. 𝐒𝐞𝐫𝐯𝐞𝐫𝐥𝐞𝐬𝐬 𝐖𝐨𝐫𝐤𝐟𝐥𝐨𝐰: Implement AWS Step Functions to manage the entire workflow. 𝟐. 𝐏𝐚𝐠𝐞 𝐄𝐱𝐭𝐫𝐚𝐜𝐭𝐢𝐨𝐧: Extract individual pages from multipage PDFs. 𝟑. 𝐂𝐨𝐧𝐜𝐮𝐫𝐫𝐞𝐧𝐭 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠: Use the Map state to process multiple pages concurrently with the Amazon Bedrock API. 𝟒. 𝐕𝐚𝐥𝐢𝐝𝐚𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐑𝐞𝐯𝐢𝐞𝐰: Validate extracted data against business rules and use Amazon A2I for human review if needed. 𝟓. 𝐃𝐚𝐭𝐚 𝐒𝐭𝐨𝐫𝐚𝐠𝐞: Store the final output in an Amazon DynamoDB table. 𝑅𝑒𝑎𝑑𝑦 𝑡𝑜 𝑒𝑛ℎ𝑎𝑛𝑐𝑒 𝑦𝑜𝑢𝑟 𝑑𝑜𝑐𝑢𝑚𝑒𝑛𝑡 𝑝𝑟𝑜𝑐𝑒𝑠𝑠𝑖𝑛𝑔? 𝐿𝑒𝑎𝑟𝑛 ℎ𝑜𝑤 𝐴𝑊𝑆 𝐵𝑒𝑑𝑟𝑜𝑐𝑘 𝑐𝑎𝑛 𝑡𝑟𝑎𝑛𝑠𝑓𝑜𝑟𝑚 𝑦𝑜𝑢𝑟 𝑤𝑜𝑟𝑘𝑓𝑙𝑜𝑤𝑠! #AWS #DocumentProcessing #AI #Automation #DataExtraction #CloudComputing Img-ref:aws

🚀 𝐌𝐚𝐬𝐭𝐞𝐫 𝐓𝐢𝐦𝐞 𝐒𝐞𝐫𝐢𝐞𝐬 𝐅𝐨𝐫𝐞𝐜𝐚𝐬𝐭𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐀𝐖𝐒 𝐃𝐞𝐞𝐩𝐀𝐑! 🚀 In today’s data-driven world, accurate forecasting is essential for making informed decisions. AWS DeepAR is a powerful time series forecasting algorithm that leverages Recurrent Neural Networks (RNNs) to provide precise and reliable predictions. Here’s what DeepAR is and how it can be used: 🌟 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐃𝐞𝐞𝐩𝐀𝐑? DeepAR is a supervised time series forecasting algorithm built on Recurrent Neural Networks (RNNs). Unlike traditional forecasting methods that fit a single model to each time series, DeepAR can train on multiple related time series simultaneously, resulting in more accurate forecasts. 📊 𝐔𝐬𝐞 𝐂𝐚𝐬𝐞𝐬 𝐟𝐨𝐫 𝐃𝐞𝐞𝐩𝐀𝐑: 𝐏𝐫𝐨𝐝𝐮𝐜𝐭 𝐃𝐞𝐦𝐚𝐧𝐝 𝐅𝐨𝐫𝐞𝐜𝐚𝐬𝐭𝐢𝐧𝐠: Predict future product demand to optimize inventory and reduce stockouts or overstock situations. Passenger Traffic Prediction: Forecast passenger numbers for airlines, public transportation, or ride-sharing services to improve scheduling and resource allocation. 𝐖𝐞𝐚𝐭𝐡𝐞𝐫 𝐓𝐫𝐞𝐧𝐝 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬: Analyze and predict weather patterns to assist in planning and preparedness activities. 𝐄𝐧𝐞𝐫𝐠𝐲 𝐂𝐨𝐧𝐬𝐮𝐦𝐩𝐭𝐢𝐨𝐧 𝐅𝐨𝐫𝐞𝐜𝐚𝐬𝐭𝐢𝐧𝐠: Estimate future energy usage to manage supply and demand efficiently. 𝐑𝐞𝐭𝐚𝐢𝐥 𝐒𝐚𝐥𝐞𝐬 𝐅𝐨𝐫𝐞𝐜𝐚𝐬𝐭𝐢𝐧𝐠: Predict future sales trends to manage inventory, plan marketing campaigns, and improve supply chain efficiency. 𝐇𝐞𝐚𝐥𝐭𝐡𝐜𝐚𝐫𝐞 𝐀𝐩𝐩𝐨𝐢𝐧𝐭𝐦𝐞𝐧𝐭𝐬: Forecast patient appointment trends to optimize scheduling and resource allocation in healthcare facilities. 𝐅𝐢𝐧𝐚𝐧𝐜𝐢𝐚𝐥 𝐌𝐚𝐫𝐤𝐞𝐭 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧: Analyze historical financial data to forecast stock prices, currency exchange rates, or economic indicators. AWS SageMaker makes implementing DeepAR straightforward and scalable. With support for various data formats like JSON Lines and Parquet, you can easily train and deploy your forecasting models using SageMaker. Would you be ready to take your forecasting to the next level? Explore DeepAR with AWS SageMaker today and unlock the potential of accurate time series predictions! 🌐 #MachineLearning #DeepAR #AWS #AI #DataScience #Forecasting #Tech

𝐁𝐞𝐡𝐢𝐧𝐝 𝐭𝐡𝐞 𝐒𝐜𝐞𝐧𝐞𝐬: 𝐈𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐢𝐧𝐠 𝐀𝐖𝐒 𝐑𝐞𝐚𝐥-𝐓𝐢𝐦𝐞 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 At Brilworks Software, we’ve been leveraging AWS to unlock real-time data analytics for our clients in healthcare, fintech & IOT. Here’s a quick look at our process! 𝐓𝐡𝐞 𝐏𝐫𝐨𝐜𝐞𝐬𝐬:  - 𝐈𝐧𝐠𝐞𝐬𝐭𝐢𝐨𝐧: AWS Kinesis for seamless data intake.  - 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠: AWS Lambda and Kinesis Data Analytics for real-time data transformations.  - 𝐒𝐭𝐨𝐫𝐚𝐠𝐞: Amazon S3 and DynamoDB for scalable storage.  - 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Amazon QuickSight for interactive, real-time dashboards. 𝐖𝐡𝐲 𝐀𝐖𝐒 𝐑𝐞𝐚𝐥-𝐓𝐢𝐦𝐞 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬?  - 𝐒𝐜𝐚𝐥𝐚𝐛𝐢𝐥𝐢𝐭𝐲: Handle large data volumes effortlessly.  - 𝐒𝐩𝐞𝐞𝐝: Make proactive, real-time decisions.  - 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧: Works seamlessly with other AWS services.  - 𝐂𝐨𝐬𝐭-𝐄𝐟𝐟𝐞𝐜𝐭𝐢𝐯𝐞: Pay only for what you use. 𝐖𝐡𝐞𝐫𝐞 𝐲𝐨𝐮 𝐜𝐚𝐧 𝐮𝐬𝐞:  - 𝐅𝐫𝐚𝐮𝐝 𝐃𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧: Identify fraudulent transactions in real-time, minimizing financial losses.  - 𝐇𝐞𝐚𝐥𝐭𝐡𝐜𝐚𝐫𝐞 𝐌𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠: Continuously monitor patient vitals and trigger alerts for immediate medical attention.  - 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬: Analyze customer behavior in real-time to personalize marketing and improve user experience.  - 𝐈𝐨𝐓 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬: Process data from connected devices for real-time insights and proactive maintenance.  - 𝐒𝐮𝐩𝐩𝐥𝐲 𝐂𝐡𝐚𝐢𝐧 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Track and optimize inventory levels and logistics in real-time, reducing costs and improving efficiency. For our clients, this means smarter decisions, improved efficiency, and a competitive edge. 𝐶𝑢𝑟𝑖𝑜𝑢𝑠 𝑎𝑏𝑜𝑢𝑡 ℎ𝑜𝑤 𝐴𝑊𝑆 𝑟𝑒𝑎𝑙-𝑡𝑖𝑚𝑒 𝑑𝑎𝑡𝑎 𝑎𝑛𝑎𝑙𝑦𝑡𝑖𝑐𝑠 𝑐𝑎𝑛 𝑡𝑟𝑎𝑛𝑠𝑓𝑜𝑟𝑚 𝑦𝑜𝑢𝑟 𝑏𝑢𝑠𝑖𝑛𝑒𝑠𝑠? 𝐿𝑒𝑡’𝑠 𝑐𝑜𝑛𝑛𝑒𝑐𝑡! #AWS #RealTimeAnalytics #DataScience #TechInnovation #BrilworksSoftware #Healthcare #Fintech #DataDriven

🚀 𝐃𝐞𝐭𝐞𝐜𝐭 𝐀𝐧𝐨𝐦𝐚𝐥𝐢𝐞𝐬 𝐰𝐢𝐭𝐡 𝐀𝐖𝐒 𝐑𝐚𝐧𝐝𝐨𝐦 𝐂𝐮𝐭 𝐅𝐨𝐫𝐞𝐬𝐭! 🚀 In the complex world of data, identifying anomalies is crucial for maintaining system integrity and security. AWS Random Cut Forest (RCF) is a powerful unsupervised algorithm designed to detect outliers and anomalous data points effectively. Here’s what RCF is, when to use it, and some real-world applications: 🌟 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐑𝐚𝐧𝐝𝐨𝐦 𝐂𝐮𝐭 𝐅𝐨𝐫𝐞𝐬𝐭? Random Cut Forest is an unsupervised, tree-based ensemble method used to detect anomalies in your data. It assigns an anomaly score to each data point, making it easier to identify outliers. This algorithm is highly efficient, even with very large datasets, thanks to its linear complexity for computing model parameters. 📊 𝐖𝐡𝐞𝐧 𝐚𝐧𝐝 𝐖𝐡𝐞𝐫𝐞 𝐭𝐨 𝐔𝐬𝐞 𝐑𝐚𝐧𝐝𝐨𝐦 𝐂𝐮𝐭 𝐅𝐨𝐫𝐞𝐬𝐭: 𝐓𝐫𝐚𝐟𝐟𝐢𝐜 𝐒𝐩𝐢𝐤𝐞 𝐃𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧: Identify sudden spikes in network traffic due to rush hours or accidents. 𝐃𝐃𝐨𝐒 𝐀𝐭𝐭𝐚𝐜𝐤 𝐃𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧: Detect Distributed Denial of Service attacks by monitoring unusual traffic patterns. 𝐔𝐧𝐚𝐮𝐭𝐡𝐨𝐫𝐢𝐳𝐞𝐝 𝐃𝐚𝐭𝐚 𝐓𝐫𝐚𝐧𝐬𝐟𝐞𝐫 𝐃𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧: Spot suspicious data transfer activities that could indicate security breaches. 𝐓𝐢𝐦𝐞 𝐒𝐞𝐫𝐢����𝐬 𝐀𝐧𝐨𝐦𝐚𝐥𝐲 𝐃𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧: Analyze time series data to find irregular patterns, such as unexpected drops or surges in sales or usage. 𝐅𝐢𝐧𝐚𝐧𝐜𝐢𝐚𝐥 𝐓𝐫𝐚𝐧𝐬𝐚𝐜𝐭𝐢𝐨𝐧𝐬: Detect fraudulent transactions in real-time by monitoring and analyzing transaction patterns for anomalies. 𝐌𝐚𝐧𝐮𝐟𝐚𝐜𝐭𝐮𝐫𝐢𝐧𝐠 𝐐𝐮𝐚𝐥𝐢𝐭𝐲 𝐂𝐨𝐧𝐭𝐫𝐨𝐥: Identify defects in the manufacturing process by detecting deviations from the normal pattern of production metrics. 𝐒𝐞𝐫𝐯𝐞𝐫 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐌𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠: Monitor server performance to detect anomalies that could indicate potential hardware failures or cyber-attacks. AWS SageMaker simplifies the implementation of Random Cut Forest, supporting various data formats (CSV, JSON, RecordIO) for training and inference. With SageMaker, you can quickly build, train, and deploy your RCF models at scale. Would you be ready to secure your systems and uncover hidden insights? Explore Random Cut Forest with AWS SageMaker today! 🌐 #MachineLearning #RandomCutForest #AWS #AI #DataScience #AnomalyDetection #Tech #Security

𝐀𝐫𝐞 𝐲𝐨𝐮 𝐥𝐨𝐨𝐤𝐢𝐧𝐠 𝐭𝐨 𝐫𝐞𝐯𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐢𝐳𝐞 𝐡𝐞𝐚𝐥𝐭𝐡care? Here’s a quick guide to some powerful 𝐀𝐖𝐒 𝐭𝐨𝐨𝐥𝐬 𝐭ℎ𝐚𝐭 𝐜𝐚𝐧 𝐞𝐥𝐞𝐯𝐚𝐭𝐞 𝐲𝐨𝐮𝐫 ℎ𝐞𝐚𝐥𝐭ℎ𝐜𝐚𝐫𝐞 𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐬: 1. 𝐀𝐦𝐚𝐳𝐨𝐧 𝐂𝐨𝐦𝐩𝐫𝐞𝐡𝐞𝐧𝐝 𝐌𝐞𝐝𝐢𝐜𝐚𝐥: Extract insights from clinical notes. 2. 𝐀𝐦𝐚𝐳𝐨𝐧 𝐓𝐫𝐚𝐧𝐬𝐜𝐫𝐢𝐛𝐞 𝐌𝐞𝐝𝐢𝐜𝐚𝐥: Automate doctor-patient interaction documentation. 3. 𝐀𝐦𝐚𝐳𝐨𝐧 𝐑𝐞𝐤𝐨𝐠𝐧𝐢𝐭𝐢𝐨𝐧: Analyze medical images for early diagnosis. 4. 𝐀𝐦𝐚𝐳𝐨𝐧 𝐒𝐚𝐠𝐞𝐌𝐚𝐤𝐞𝐫: Develop models for personalized patient care. 5. 𝐀𝐦𝐚𝐳𝐨𝐧 𝐊𝐞𝐧𝐝𝐫𝐚: Intelligent search across medical records. 6. 𝐀𝐦𝐚𝐳𝐨𝐧 𝐀𝐭𝐡𝐞𝐧𝐚: Analyze large patient datasets. 7. 𝐀𝐦𝐚𝐳𝐨𝐧 𝐐𝐮𝐢𝐜𝐤𝐒𝐢𝐠𝐡𝐭: Data visualization for healthcare KPIs. 𝟖. 𝐀𝐦𝐚𝐳𝐨𝐧 𝐊𝐢𝐧𝐞𝐬𝐢𝐬: Real-time data streaming from medical devices. 𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒 𝑡ℎ𝑒𝑠𝑒 𝑡𝑜𝑜𝑙𝑠 𝑡𝑜 𝑒𝑛ℎ𝑎𝑛𝑐𝑒 𝑒𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑐𝑦, 𝑖𝑚𝑝𝑟𝑜𝑣𝑒 𝑝𝑎𝑡𝑖𝑒𝑛𝑡 𝑜𝑢𝑡𝑐𝑜𝑚𝑒𝑠, 𝑎𝑛𝑑 𝑑𝑟𝑖𝑣𝑒 𝑖𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛 𝑖𝑛 ℎ𝑒𝑎𝑙𝑡ℎ𝑐𝑎𝑟𝑒. 𝐿𝑒𝑡’𝑠 𝑚𝑎𝑘𝑒 𝑎 𝑑𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑐𝑒 𝑡𝑜𝑔𝑒𝑡ℎ𝑒𝑟! 💡 Do you know any other tools for health tech? 𝑃𝑙𝑒𝑎𝑠𝑒 𝑐𝑜𝑚𝑚𝑒𝑛𝑡 𝑏𝑒𝑙𝑜𝑤! ⬇️ Want to learn more? Please refer to the first comment link. #healthtech #digitalhealth #healthcareinnovation #medtech #healthIT #telemedicine #patientcare #healthstartup #healthdata #healthtechsolutions

🚀 𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐢𝐧𝐠 𝐌𝐨𝐬𝐡𝐢 𝐀𝐈 𝐛𝐲 𝐊𝐲𝐮𝐭𝐚𝐢: 𝐓𝐡𝐞 𝐍𝐞𝐱𝐭-𝐆𝐞𝐧 𝐕𝐨𝐢𝐜𝐞 𝐀𝐈 𝐓𝐨𝐨𝐥! 🚀 In the ever-evolving landscape of artificial intelligence, staying ahead of the curve is crucial. Meet Moshi AI by Kyutai, a powerful voice AI tool that's setting new standards in the industry. 🌟 I have personally tried Moshi AI, and I can confidently say that it is super fast in delivering natural voice conversations. Its advanced capabilities ensure seamless interactions, catering to a wide array of applications from customer service to personal assistants. 📞💬 𝐖𝐡𝐲 𝐢𝐬 𝐌𝐨𝐬𝐡𝐢 𝐀𝐈 𝐦𝐚𝐤𝐢𝐧𝐠 𝐰𝐚𝐯𝐞𝐬? 🌊 🔹 𝐒𝐩𝐞𝐞𝐝 𝐚𝐧𝐝 𝐄𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲: Moshi AI offers unparalleled speed in processing and generating voice responses, ensuring a smooth user experience. 🔹 𝐇𝐢𝐠𝐡-𝐐𝐮𝐚𝐥𝐢𝐭𝐲 𝐂𝐨𝐧𝐯𝐞𝐫𝐬𝐚𝐭𝐢𝐨𝐧𝐬: Leveraging state-of-the-art AI technology, Moshi AI provides natural, human-like interactions that can rival the best in the industry. 🔹 𝐕𝐞𝐫𝐬𝐚𝐭𝐢𝐥𝐢𝐭𝐲: From e-commerce to healthcare, Moshi AI is adaptable to various sectors, enhancing productivity and customer engagement. Stay ahead of the curve with Moshi AI! #VoiceAI #ArtificialIntelligence #AI #ChatGPT #MoshiAI #Kyutai

🚀 Unlock Personalized Recommendations with AWS Factorization Machines!🚀 In the age of data, personalization is key to engaging users and enhancing their experience. AWS Factorization Machines (FM) is a powerful algorithm designed to handle high-dimensional, sparse datasets, making it ideal for personalized recommendations. Here’s what you need to know about Factorization Machines and when to use them: 🌟 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐚 𝐅𝐚𝐜𝐭𝐨𝐫𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐌𝐚𝐜𝐡𝐢𝐧𝐞? Factorization Machines are a versatile algorithm that models interactions between features using factorized parameters. They excel at estimating interactions even with very sparse datasets and can handle both regression and classification tasks with linear computational complexity. This makes them suitable for large-scale data and a variety of applications. 📊 𝐖𝐡𝐞𝐧 𝐭𝐨 𝐔𝐬𝐞 𝐅𝐚𝐜𝐭𝐨𝐫𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐌𝐚𝐜𝐡𝐢𝐧𝐞𝐬? 𝐏𝐞𝐫𝐬𝐨𝐧𝐚𝐥𝐢𝐳𝐞𝐝 𝐑𝐞𝐜𝐨𝐦𝐦𝐞𝐧𝐝𝐚𝐭𝐢𝐨𝐧𝐬: Predict user preferences and recommend products, movies, or content tailored to individual tastes. 𝐂𝐥𝐢𝐜𝐤 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧 𝐟𝐨𝐫 𝐀𝐝 𝐏𝐥𝐚𝐜𝐞𝐦𝐞𝐧𝐭: Improve ad targeting by predicting the likelihood of user clicks. 𝐂𝐨𝐧𝐭𝐞𝐧𝐭 𝐏𝐞𝐫𝐬𝐨𝐧𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Customize news or social media feeds to match user interests. 𝐇𝐢𝐠𝐡-𝐃𝐢𝐦𝐞𝐧𝐬𝐢𝐨𝐧𝐚𝐥 𝐒𝐩𝐚𝐫𝐬𝐞 𝐃𝐚𝐭𝐚: When dealing with datasets that have many features with few non-zero values, Factorization Machines can effectively capture the interactions between these sparse features. 𝐄𝐱𝐚𝐦𝐩𝐥𝐞 𝐔𝐬𝐞 𝐂𝐚𝐬𝐞𝐬: 𝐌𝐨𝐯𝐢𝐞 𝐑𝐞𝐜𝐨𝐦𝐦𝐞𝐧𝐝𝐚𝐭𝐢𝐨𝐧𝐬: Predict how a user would rate a movie and recommend movies based on user ratings and similarities. 𝐏𝐫𝐨𝐝𝐮𝐜𝐭 𝐑𝐞𝐜𝐨𝐦𝐦𝐞𝐧𝐝𝐚𝐭𝐢𝐨𝐧𝐬: Suggest products that a user is likely to purchase based on their past interactions and preferences. 𝐀𝐝 𝐏𝐥𝐚𝐜𝐞𝐦𝐞𝐧𝐭: Enhance ad performance by predicting the probability of user engagement with ads. AWS SageMaker makes it easy to implement Factorization Machines, offering support for both recordio-protobuf and JSON data formats for training and inference. With SageMaker, you can quickly build, train, and deploy your FM models at scale. Ready to deliver personalized experiences and drive user engagement? Explore Factorization Machines with AWS SageMaker today! 🌐 #MachineLearning #FactorizationMachines #AWS #AI #DataScience #Personalization #Tech