Brainy Neurals is an AI development company that provides a range of AI-related services including product engineering, custom AI solutions, AI consulting, and AI training. They focus on building customized AI technologies for various industries such as healthcare, finance, and more.

Brainy Neurals

NVIDIA Corporation provides graphics, and compute and networking solutions in the United States, Taiwan, China, and internationally. The company's Graphics segment offers GeForce GPUs for gaming and PCs, the GeForce NOW game streaming service and related infrastructure, and solutions for gaming platforms; Quadro/NVIDIA RTX GPUs for enterprise workstation graphics; vGPU software for cloud-based visual and virtual computing; automotive platforms for infotainment systems; and Omniverse software for building 3D designs and virtual worlds. Its Compute & Networking segment provides Data Center platforms and systems for AI, HPC, and accelerated computing; Mellanox networking and interconnect solutions; automotive AI Cockpit, autonomous driving development agreements, and autonomous vehicle solutions; cryptocurrency mining processors; Jetson for robotics and other embedded platforms; and NVIDIA AI Enterprise and other software. The company's products are used in gaming, professional visualization, datacenter, and automotive markets. NVIDIA Corporation sells its products to original equipment manufacturers, original device manufacturers, system builders, add-in board manufacturers, retailers/distributors, independent software vendors, Internet and cloud service providers, automotive manufacturers and tier-1 automotive suppliers, mapping companies, start-ups, and other ecosystem participants. It has a strategic collaboration with Kroger Co. NVIDIA Corporation was incorporated in 1993 and is headquartered in Santa Clara, California.

Jensen Huang

NVIDIA Corporation

Brainy Neurals - AI Development Company | 1,842 followers on LinkedIn. Intelligence Redefined | Brainy Neurals is a leading artificial intelligence (AI) company based in Ahmedabad, India. With a strong focus on AI innovation, we have been transforming the way businesses operate since our inception in 2023. Our founder brings over 8 years of experience in AI, making us one of the most experienced AI-focused companies in the industry.

Building AI solutions | Computer Vision, Large Language Models, Generative AI, Edge AI, IoT, AI Product Development

Mitesh P.

Experienced WordPress Developer | Crafting Custom Solutions for Web Success 🚀 | Full Stack Proficiency | Let's Build Your Digital Presence Together!

Ritu patel

Senior Business Development Manager @ BrainyNeurals | MBA

Hariom Patel

Pursuing Master's @IIIT Vadodara | AI, ML, Python | Django Developer

Rushabh Shah

Brainy Neurals - AI Development Company

🌟 𝐀𝐫𝐞 𝐖𝐞 𝐑𝐞𝐚𝐥𝐥𝐲 𝐂𝐥𝐨𝐬𝐞 𝐭𝐨 𝐀𝐜𝐡𝐢𝐞𝐯𝐢𝐧𝐠 𝐀𝐫𝐭𝐢𝐟𝐢𝐜𝐢𝐚𝐥 𝐆𝐞𝐧𝐞𝐫𝐚𝐥 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 (𝐀𝐆𝐈)? ⁣ ⁣ There are two main types of AI: 𝐍𝐚𝐫𝐫𝐨𝐰 𝐀𝐈 and 𝐀𝐫𝐭𝐢𝐟𝐢𝐜𝐢𝐚𝐥 𝐆𝐞𝐧𝐞𝐫𝐚𝐥 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 (𝐀𝐆𝐈).⁣ ⁣ 𝐍𝐚𝐫𝐫𝐨𝐰 𝐀𝐈 excels at specific tasks like facial recognition or voice assistants, but its abilities are limited to its programmed domain. 𝐀𝐆𝐈, on the other hand, is the concept of an AI that can understand, learn, and adapt across a wide range of tasks, much like a human brain.⁣ ⁣ As AI evolves, the question arises: "𝐇𝐚𝐯𝐞 𝐰𝐞 𝐫𝐞𝐚𝐜𝐡𝐞𝐝 𝐀𝐆𝐈 𝐲𝐞𝐭?" The short answer is 𝐧𝐨, but let’s break it down!⁣ ⁣ Today's AIs are advanced in specific areas like language translation, defect detection, product recommendations, and many more. However, these systems are still narrow AI, meaning they are limited to the tasks they’ve been trained for and can’t adapt to new areas. Whereas AGI would be able to think, learn, and adapt across various tasks, solving problems it hasn’t been trained for, like a human brain.⁣ ⁣ 𝐒𝐨, 𝐰𝐡𝐞𝐫𝐞 𝐚𝐫𝐞 𝐰𝐞 𝐭𝐨𝐝𝐚𝐲?⁣ While Narrow AI is making impressive progress—from medical diagnostics to self-driving cars—𝐀𝐆𝐈 𝐫𝐞𝐦𝐚𝐢𝐧𝐬 𝐚𝐧 𝐚𝐦𝐛𝐢𝐭𝐢𝐨𝐮𝐬 𝐠𝐨𝐚𝐥. We’re still years away from developing an AI that can reason and adapt like a human brain.⁣ ⁣ 𝐖𝐡𝐚𝐭 𝐚𝐫𝐞 𝐲𝐨𝐮𝐫 𝐭𝐡𝐨𝐮𝐠𝐡𝐭𝐬? 𝐃𝐨 𝐲𝐨𝐮 𝐭𝐡𝐢𝐧𝐤 𝐀𝐆𝐈 𝐢𝐬 𝐜𝐥𝐨𝐬𝐞𝐫?⁣ ⁣ #BrainyNeurals #ArtificialIntelligence #AGI #MachineLearning #TechTrends #AIResearch #FutureOfAI

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𝐑𝐞𝐯𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐢𝐳𝐢𝐧𝐠 𝐁𝐌𝐑 𝐕𝐚𝐥𝐢𝐝𝐚𝐭𝐢𝐨𝐧 𝐰𝐢𝐭𝐡 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧 🔬⁣ ⁣ In the pharmaceutical industry, every step matters. Precision, and safety are non-negotiable—but traditional methods of 𝐁𝐚𝐭𝐜𝐡 𝐌𝐚𝐧𝐮𝐟𝐚𝐜𝐭𝐮𝐫𝐢𝐧𝐠 𝐑𝐞𝐜𝐨𝐫𝐝 (𝐁𝐌𝐑) validation often fall short due to inefficiencies, human errors, and inconsistencies.⁣ ⁣ 𝐈𝐧 𝐭𝐡𝐢𝐬 𝐚𝐫𝐭𝐢𝐜𝐥𝐞, 𝐰𝐞 𝐞𝐱𝐩𝐥𝐨𝐫𝐞:⁣ ✔️ The critical role of BMRs in pharma⁣ ✔️ Challenges of traditional validation methods⁣ ✔️ How automation drives efficiency and ensures compliance⁣ ⁣ At 𝐁𝐫𝐚𝐢𝐧𝐲 𝐍𝐞𝐮𝐫𝐚𝐥𝐬, we believe automation is the future. With OCR technology, we can make BMR validation faster, more accurate, and fully aligned with standards.⁣ ⁣ #BrainyNeurals #PharmaAutomation #BMRValidation #OCRTechnology #AIinPharma #PharmaInnovation #AutomationSolutions #TechForPharma ⁣

🌟 𝐀𝐈 𝐨𝐧 𝐄𝐝𝐠𝐞 𝐃𝐞𝐯𝐢𝐜𝐞𝐬: 𝐓𝐡𝐞 𝐅𝐮𝐭𝐮𝐫𝐞 𝐨𝐟 𝐒𝐦𝐚𝐫𝐭𝐞𝐫, 𝐅𝐚𝐬𝐭𝐞𝐫 𝐂𝐨𝐦𝐩𝐮𝐭𝐢𝐧𝐠 🌟⁣ As the demand for real-time data processing grows, edge computing is becoming more crucial than ever. By deploying AI models on edge devices like those powered by Qualcomm, we're enabling devices to process data locally, reducing latency and enhancing performance.⁣ ⁣ 💡 𝐊𝐞𝐲 𝐢𝐧𝐬𝐢𝐠𝐡𝐭𝐬:⁣ 💻 What exactly are edge devices?⁣ 🚀 The step-by-step process of deploying AI models on Qualcomm devices.⁣ ⁣ At Brainy Neurals, we’re passionate about harnessing these cutting-edge technologies to drive innovation. Let’s connect to explore how edge computing and AI can empower your business and unlock new opportunities!⁣ ⁣ #BrainyNeurals #AI #EdgeComputing #Qualcomm #EdgeAI #MachineLearning #Innovation #TechTransformation

𝐖𝐡𝐚𝐭 𝐢𝐬 𝐄𝐝𝐠𝐞 𝐀𝐈? 🌟⁣ ⁣ 𝐄𝐝𝐠𝐞 refers to the physical layer where data is generated—such as sensors, devices, or machines at the "𝐞𝐝𝐠𝐞" of a network. Unlike centralized systems that rely on cloud servers , edge computing brings computation and data storage closer to these devices.⁣ ⁣ Edge AI takes it a step further by embedding AI models directly into edge devices. Instead of constantly sending data to the cloud, these devices process and analyze it locally, enabling real-time decision-making.⁣ ⁣ 𝐊𝐞𝐲 𝐀𝐝𝐯𝐚𝐧𝐭𝐚𝐠𝐞𝐬 𝐨𝐟 𝐄𝐝𝐠𝐞 𝐀𝐈:⁣ - 𝐑𝐞𝐝𝐮𝐜𝐞𝐝 𝐋𝐚𝐭𝐞𝐧𝐜𝐲: Real-time decisions with local processing.⁣ - 𝐈𝐦𝐩𝐫𝐨𝐯𝐞𝐝 𝐏𝐫𝐢𝐯𝐚𝐜𝐲 & 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲: Data stays on the device.⁣ - 𝐋𝐨𝐰𝐞𝐫 𝐁𝐚𝐧𝐝𝐰𝐢𝐝𝐭𝐡 𝐔𝐬𝐚𝐠𝐞: Saves cloud data transmission costs.⁣ - 𝐑𝐞𝐥𝐢𝐚𝐛𝐢𝐥𝐢𝐭𝐲: Operates independently from cloud connectivity.⁣ ⁣⁣ 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 𝐨𝐟 𝐄𝐝𝐠𝐞 𝐃𝐞𝐯𝐢𝐜𝐞𝐬:⁣⁣ 📸 𝐒𝐦𝐚𝐫𝐭 𝐂𝐚𝐦𝐞𝐫𝐚𝐬: Real-time facial recognition, security surveillance, and anomaly detection.⁣⁣ 🏭 𝐈𝐧𝐝𝐮𝐬𝐭𝐫𝐢𝐚𝐥 𝐈𝐨𝐓: Predictive maintenance, machinery monitoring, and operational optimization.⁣⁣ 🛍️ 𝐑𝐞𝐭𝐚𝐢𝐥 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬: Real-time customer behavior insights, inventory tracking, and shopping pattern analysis.⁣⁣ ⁣⁣ 🌐 AI is reshaping how we engage with the world in real time. ⁣⁣ #BrainyNeurals #EdgeAI #AI #ArtificialIntelligence #RealTimeProcessing #SmartDevices

𝐔𝐧𝐥𝐨𝐜𝐤𝐢𝐧𝐠 𝐀𝐈 𝐰𝐢𝐭𝐡 𝐒𝐲𝐧𝐭𝐡𝐞𝐭𝐢𝐜 𝐃𝐚𝐭𝐚⁣ 🌐 ⁣ High-quality data is essential for building accurate AI models in today's data-driven world. But real-world data isn’t always readily available, especially when privacy concerns, limited access, or high costs are involved. This is where synthetic data steps in.⁣ ⁣ 𝐒𝐲𝐧𝐭𝐡𝐞𝐭𝐢𝐜 𝐝𝐚𝐭𝐚 is artificially generated to accurately mirror the patterns, characteristics, and diversity of real-world data. It enables AI systems to learn and improve by providing abundant, realistic data—without relying on actual data. Synthetic data recreates real-world scenarios in a controlled, privacy-safe environment, accelerating model training and enabling innovation without the limitations of data availability, privacy concerns, or lengthy data collection processes.⁣ ⁣ 𝐖𝐡𝐲 𝐝𝐨 𝐰𝐞 𝐧𝐞𝐞𝐝 𝐬𝐲𝐧𝐭𝐡𝐞𝐭𝐢𝐜 𝐝𝐚𝐭𝐚?⁣ ⁣ In AI diverse and high-quality data is key to building accurate models, yet real-world data often has its limitations. Here’s why synthetic data has become an essential solution:⁣ ⁣ - 𝐇𝐚𝐧𝐝𝐥𝐢𝐧𝐠 𝐋𝐢𝐦𝐢𝐭𝐞𝐝 𝐑𝐞𝐚𝐥-𝐖𝐨𝐫𝐥𝐝 𝐃𝐚𝐭𝐚: Real-life data doesn’t always cover every possible scenario, especially rare or unusual ones. For example, training an AI system to recognize rare medical conditions or manufacturing defects requires a vast amount of data that’s hard to come by. Synthetic data can be customized to include these unique cases.⁣ - 𝐀𝐝𝐝𝐫𝐞𝐬𝐬𝐢𝐧𝐠 𝐏𝐫𝐢𝐯𝐚𝐜𝐲 𝐂𝐨𝐧𝐜𝐞𝐫𝐧𝐬: Working with real-world data can raise privacy issues, particularly in sensitive areas like healthcare and finance. By using synthetic data, which replicates real data patterns without including any personal information.⁣ - 𝐒𝐚𝐯𝐢𝐧𝐠 𝐓𝐢𝐦𝐞 𝐚𝐧𝐝 𝐑𝐞𝐝𝐮𝐜𝐢𝐧𝐠 𝐂𝐨𝐬𝐭𝐬: Collecting, cleaning, and labeling large datasets is often time-consuming and expensive. Synthetic data can be generated quickly and on a large scale.⁣ ⁣ Synthetic data doesn’t just fill gaps—it opens new doors for AI development, helping to create robust, privacy-safe, and efficient systems ready for real-world applications.⁣ ⁣ Brainy Neurals - AI Development Company Mitesh P. #BrainyNeurals #SyntheticData #AI #AIDevelopment #Innovation #ModelTraining ⁣

💡 𝐀𝐈 𝐇𝐚𝐥𝐥𝐮𝐜𝐢𝐧𝐚𝐭𝐢𝐨𝐧: 𝐖𝐡𝐚𝐭 𝐀𝐫𝐞 𝐓𝐡𝐞𝐲, 𝐚𝐧𝐝 𝐇𝐨𝐰 𝐂𝐚𝐧 𝐖𝐞 𝐒𝐨𝐥𝐯𝐞 𝐓𝐡𝐞𝐦? 🔍⁣⁣⁣ ⁣⁣⁣ 𝐍𝐨𝐭𝐡𝐢𝐧𝐠 𝐢𝐬 𝐩𝐞𝐫𝐟𝐞𝐜𝐭. 𝐈𝐭 𝐭𝐚𝐤𝐞𝐬 𝐭𝐢𝐦𝐞 𝐚𝐧𝐝 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐭𝐨 𝐚𝐜𝐡𝐢𝐞𝐯𝐞 𝐩𝐞𝐫𝐟𝐞𝐜𝐭𝐢𝐨𝐧—𝐞𝐯𝐞𝐧 𝐢𝐟 𝐢𝐭’𝐬 𝐀𝐈⁣⁣⁣ ⁣⁣⁣ In AI, 𝐡𝐚𝐥𝐥𝐮𝐜𝐢𝐧𝐚𝐭𝐢𝐨𝐧 refers to a situation where an AI system generates information that appears correct but is actually inaccurate, misleading, or entirely incorrect. While AI is designed to provide logical and helpful answers, it doesn't have true understanding like a human does. Instead, it relies on patterns in data to predict responses.⁣⁣⁣ ⁣⁣⁣ 𝐖𝐡𝐲 𝐃𝐨𝐞𝐬 𝐓𝐡𝐢𝐬 𝐇𝐚𝐩𝐩𝐞𝐧? ⁣⁣⁣ Hallucinations occur because AI models are trained on vast amounts of data, and when faced with new, unclear, or uncertain information, they try to generate the most likely answer based on patterns they’ve learned. However, sometimes due to limitations in training data, lack of real-world understanding, or insufficient context on specific data, AI may generate responses that seem logical but are factually incorrect.⁣⁣⁣ ⁣⁣⁣ 𝐇𝐨𝐰 𝐂𝐚𝐧 𝐖𝐞 𝐒𝐨𝐥𝐯𝐞 𝐀𝐈 𝐇𝐚𝐥𝐥𝐮𝐜𝐢𝐧𝐚𝐭𝐢𝐨𝐧𝐬? ⁣⁣⁣ ⁣⁣⁣ - 𝐅𝐢𝐧𝐞-𝐓𝐮𝐧𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐒𝐩𝐞𝐜𝐢𝐚𝐥𝐢𝐳𝐞𝐝 𝐃𝐚𝐭𝐚: Providing focused, up-to-date datasets improves predictions and reduces hallucinations by enhancing data accuracy.⁣⁣⁣ - 𝐑𝐞𝐢𝐧𝐟𝐨𝐫𝐜𝐞𝐦𝐞𝐧𝐭 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐇𝐮𝐦𝐚𝐧 𝐅𝐞𝐞𝐝𝐛𝐚𝐜𝐤 (𝐑𝐋𝐇𝐅): Human feedback helps AI learn correct responses and adapt over time.⁣⁣⁣ - 𝐂𝐨𝐧𝐟𝐢𝐝𝐞𝐧𝐜𝐞 𝐄𝐬𝐭𝐢𝐦𝐚𝐭𝐢𝐨𝐧: AI models include a feature where they indicate how confident they are in their answers. If the model is uncertain, it marks the response, prompting users to verify its accuracy.⁣⁣⁣ - 𝐂𝐨𝐧𝐭𝐢𝐧𝐮𝐨𝐮𝐬 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠: Regular retraining with new data helps AI stay accurate and reduce hallucinations.⁣⁣⁣ - 𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥-𝐀𝐮𝐠𝐦𝐞𝐧𝐭𝐞𝐝 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧 (𝐑𝐀𝐆): RAG uses external sources or a large database to fetch relevant, up-to-date information, enhancing accuracy and reducing hallucinations.⁣⁣⁣ ⁣⁣⁣ 𝐀𝐈 𝐆𝐞𝐭𝐬 𝐁𝐞𝐭𝐭𝐞𝐫 𝐎𝐯𝐞𝐫 𝐓𝐢𝐦𝐞, as AI models continue to learn from better data and human input, they get more accurate and reliable. ⁣ ⁣⁣⁣ At 𝐁𝐫𝐚𝐢𝐧𝐲 𝐍𝐞𝐮𝐫𝐚𝐥𝐬, we focus on creating advanced AI solutions. Our expertise ensures that AI systems are more accurate, reliable, and impactful for your business needs.⁣⁣⁣ ⁣⁣⁣ #BrainyNeurals #AI #AIHallucination #MachineLearning #AIAccuracy #AIChallenges #ReinforcementLearning #HumanFeedback #AIModels ⁣⁣⁣

𝐀𝐈 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬 𝐟𝐨𝐫 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐜 𝐒𝐭𝐨𝐜𝐤 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 𝐢𝐧 𝐑𝐞𝐭𝐚𝐢𝐥 📦⁣⁣⁣ ⁣⁣⁣ Here’s a demonstration of how 𝐀𝐈-𝐝𝐫𝐢𝐯𝐞𝐧 𝐬𝐭𝐨𝐜𝐤 𝐦𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 can transform inventory monitoring in grocery environments. Using 𝐨𝐛𝐣𝐞𝐜𝐭 𝐝𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧, 𝐨𝐛𝐣𝐞𝐜𝐭 𝐜𝐨𝐮𝐧𝐭𝐢𝐧𝐠, 𝐚𝐧𝐝 𝐬𝐭𝐨𝐜𝐤 𝐥𝐞𝐯𝐞𝐥 𝐚𝐧𝐚𝐥𝐲𝐬𝐢𝐬, AI can help ensure optimal stock at all times.⁣⁣⁣ ⁣⁣⁣ The system concept automatically tracks inventory levels with a color-coded alert system:⁣⁣⁣ ⁣⁣⁣ 𝐑𝐞𝐝: Stock is below 40% and needs immediate restocking.⁣⁣⁣ 𝐎𝐫𝐚𝐧𝐠𝐞: Stock is partially consumed but still sufficient.⁣⁣⁣ 𝐆𝐫𝐞𝐞𝐧: Stock is fully available.⁣⁣⁣ ⁣⁣⁣ This AI-based approach can help keep shelves stocked efficiently and reduce the risk of stockouts.⁣⁣⁣ ⁣⁣⁣ 𝘕𝘰𝘵𝘦: 𝘛𝘩𝘦 𝘪𝘮𝘢𝘨𝘦 𝘪𝘴 𝘶𝘴𝘦𝘥 𝘧𝘰𝘳 𝘳𝘦𝘧𝘦𝘳𝘦𝘯𝘤𝘦 𝘱𝘶𝘳𝘱𝘰𝘴𝘦𝘴 𝘰𝘯𝘭𝘺.⁣⁣⁣ ⁣

𝐑𝐀𝐆 (𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥 𝐀𝐮𝐠𝐦𝐞𝐧𝐭𝐞𝐝 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧) 🔎: 𝐑𝐞𝐚𝐥-𝐓𝐢𝐦𝐞, 𝐑𝐞𝐥𝐞𝐯𝐚𝐧𝐭 𝐀𝐈 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬⁣⁣⁣⁣ ⁣⁣⁣⁣ In today’s AI-driven world, how we train and utilize models can make all the difference. While pre-trained LLMs provide answers based on fixed datasets, they lack the ability to adapt or access real-time information. This is where 𝐑𝐀𝐆 steps in—combining the power of retrieval and generation to bring in dynamic, up-to-date responses.⁣⁣⁣⁣ ⁣⁣⁣⁣ 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝 𝐑𝐀𝐆, 𝐈𝐭𝐬 𝐁𝐚𝐬𝐢𝐜 𝐖𝐨𝐫𝐤𝐟𝐥𝐨𝐰, 𝐚𝐧𝐝 𝐢𝐭𝐬 𝐀𝐝𝐯𝐚𝐧𝐭𝐚𝐠𝐞𝐬.⁣⁣⁣⁣ ⁣⁣⁣⁣ #BrainyNeurals #RAG #AI #MachineLearning #LLM #AIInnovation #ArtificialIntelligence ⁣⁣⁣⁣ ⁣⁣⁣⁣

𝐄𝐯𝐞𝐫𝐲𝐨𝐧𝐞 𝐤𝐧𝐨𝐰𝐬 𝐂𝐡𝐚𝐭𝐆𝐏𝐓, 𝐛𝐮𝐭 𝐰𝐡𝐚𝐭 𝐞𝐱𝐚𝐜𝐭𝐥𝐲 𝐢𝐬 𝐢𝐭?⁣ ⁣ We’ve all heard of and used ChatGPT in our daily lives, but have you ever wondered about the technology that makes it possible?⁣ ⁣ Let’s talk about 𝐋𝐚𝐫𝐠𝐞 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐌𝐨𝐝𝐞𝐥𝐬 (𝐋𝐋𝐌𝐬)—the foundation behind ChatGPT and many other AI-driven tools.⁣ ⁣ 𝐋𝐋𝐌𝐬 are complex AI models trained on massive datasets with huge computational power. By learning from this extensive data, they can understand, generate, and respond to human language in a meaningful way.⁣ ⁣ 𝐇𝐨𝐰 𝐝𝐨 𝐋𝐋𝐌𝐬 𝐰𝐨𝐫𝐤?⁣ Imagine reading thousands of books—LLMs do this on a much larger scale. They learn not only words and sentences but also the relationships between them. When you ask a question, an LLM uses these relationships to generate meaningful responses by predicting the most likely sequence of words based on context.⁣ ⁣ “𝐂𝐡𝐚𝐭𝐆𝐏𝐓 𝐢𝐬 𝐧𝐨𝐭 𝐚 𝐭𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲; 𝐢𝐭 𝐢𝐬 𝐚 𝐩𝐫𝐨𝐝𝐮𝐜𝐭 𝐛𝐚𝐬𝐞𝐝 𝐨𝐧 𝐋𝐋𝐌𝐬.”⁣ ⁣ 𝐍𝐋𝐏 𝐯𝐬. 𝐋𝐋𝐌𝐬⁣ 𝐍𝐚𝐭𝐮𝐫𝐚𝐥 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 (𝐍𝐋𝐏) helps computers understand, interpret, and work with human language. It includes tasks like speech-to-text, text-to-speech, and translation.⁣ 𝐋𝐚𝐫𝐠𝐞 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐌𝐨𝐝𝐞𝐥𝐬 (𝐋𝐋𝐌𝐬), on the other hand, are designed to understand and generate language in more sophisticated ways. They handle tasks like engaging in conversations, generating creative writing, and answering complex questions.⁣ ⁣ LLMs are just the beginning—there’s so much more that AI can do. While many people think of AI as just ChatGPT, AI is a vast field that includes everything from recognizing images and analyzing data to making predictions and even powering robots.⁣ ⁣ At 𝐁𝐫𝐚𝐢𝐧𝐲 𝐍𝐞𝐮𝐫𝐚𝐥𝐬, we provide AI development services and are excited to contribute to a smarter, more connected world through innovative AI solutions.⁣ ⁣ #BrainyNeurals #LLMs #ChatGPT #NLPs #AI

Metric
Company Name	Brainy Neurals	NVIDIA Corporation
Symbol		NVDA
MSH ID	COM:BRAINYNEURALS	COM:NVIDIA
Market	PRIVATE	STOCKS
Sector	Technology	Technology
Industry	AI and Software Development	Semiconductors
Country	IN	US
Stage
Employee Count		29.60K
Website	brainyneurals.com	nvidia.com
LinkedIn	brainy-neurals-ai-company	nvidia
Founders

Metric
Market Cap		3.32T
Enterprise Value
Monthly Web Traffic		65.44M
Web Traffic Growth		0.04
Valuation
Raised Capital		5M

Metric
Revenue		60.92B
Revenue (LTM)
Revenue (NTM)
Gross Profit		44.30B
EBITDA		34.48B
Operating Income		32.97B
Net Income		29.76B
EPS		12.05
Diluted EPS		11.93
Revenue 2022
Revenue 2023
Revenue 2024
Revenue 2025
Revenue 2026

Metric
Revenue Growth (LTM)
Revenue Growth (NTM)
Gross Profit Growth (LTM)
EBITDA Growth (LTM)
Net Income Growth (LTM)
14-Day Growth Score
28-Day Growth Score
90-Day Growth Score
365-Day Growth Score

Metric
Gross Profit Margin		0.76
Operating Profit Margin		0.62
EBITDA Margin		0.57
Net Profit Margin		0.55
Return on Equity		1.16
Return on Assets		0.62
Return on Capital Employed		0.84

BRAI

COM:BRAINYNEURALS

Brainy Neurals

Company Overview

Market Metrics

Financial Performance

Growth Metrics

Profitability Ratios

Valuation Multiples

Operational Metrics

Cash Flow Metrics

Balance Sheet Metrics

Efficiency Metrics

Metric
P/E Ratio		90.14
P/B Ratio		31.44
Revenue Multiple (LTM)
Revenue Multiple (NTM)
EBITDA Multiple (LTM)
EBITDA Multiple (NTM)
Price to Sales Ratio		41.48
EV Multiple		69.02

Metric
Days Sales Outstanding		67.59
Days Payables Outstanding		64.22
Days Inventory Outstanding		115.99
Operating Cycle		175.90
Cash Conversion Cycle		116.63
Asset Turnover		1.13

Metric
Operating Cash Flow		28.09B
Free Cash Flow		27.02B
Cash Flow to Debt		5.59
Operating Cash Flow/Sales		0.51
Free Cash Flow Yield		0.01

Metric
Cash & Equivalents		7.28B
Accounts Receivable		-6.17B
Inventory		5.28B
Goodwill		4.43B
Debt to Capitalization		0.19
Debt to Assets		0.20
Current Ratio		3.59
Quick Ratio		3.79

Metric
Rule of 40 (LTM)
Rule of 40 (NTM)
Stock Comp to Revenue		0.07
R&D to Revenue		0.18
SG&A to Revenue