Hancy Khalid Generative AI Engineer’s Post

Imagine an AI that can read "War and Peace" 14 times over in a single sitting. That's the power of Mistral NeMo, the game-changing 12B parameter model just released by Mistral AI and NVIDIA. Here's why it's set to disrupt the AI landscape: 1️⃣ Collaborative Power:   • Joint effort between Mistral AI and NVIDIA   • Leverages Mistral's data expertise and NVIDIA's hardware prowess 2️⃣ Mind-Blowing Specs:   • 12 billion parameters (that's billion with a 'B'!)   • 128K token context window (equivalent to ~100,000 words or 200+ pages)   • Trained on 3,072 NVIDIA H100 GPUs (talk about computational muscle!) 3️⃣ Standout Features:   • Masters text generation, summarization, translation, and sentiment analysis   • Introduces Tekken: A tokenizer that crunches code and languages 30% more efficiently   • Enterprise-ready with FP8 inference capabilities 4️⃣ Performance That Turns Heads:   • Outsmarts larger models like Gemma 2 (9B) and Llama 3 (8B)   • Wallet-friendly at just $0.3 per 1M tokens   • Punches above its weight against GPT-4 (32k) and Mixtral 8x22B 5️⃣ Why It Matters:   • Sets new benchmarks in AI efficiency and multilingual prowess   • Could revolutionize how we approach global product development and marketing As someone who leverages AI daily to augment my work and lifestyle, I'm thrilled about Mistral NeMo's potential. I've been using AI to boost my learning, productivity, and both visual and text content creation. With Mistral NeMo's enhanced efficiency and multilingual capabilities, I anticipate even more powerful tools for cross-border business consulting, content creation, and product development. How are you integrating AI into your work and life? Are you using ChatGPT or have you moved to Claude like the top AI adopters? #AIRevolution #MistralNeMo #AIProductivity #GlobalInnovation #ContentCreation

🔎 Get the scoop on the latest in AI developments! This week's AI highlights at a glance 👇 1️⃣ AI in Healthcare: Optimise ➤ The University of Leeds uses AI to revolutionize heart health. ➤ Optimise scanned 2 million health records, identifying over 400,000 at high risk. ➤ AI’s role in healthcare improves early detection and prevention. 2️⃣ Elon Musk’s xAI Launches Colossus Supercomputer ➤ Colossus, the world’s most powerful AI supercomputer, features 100,000 Nvidia H100 processors. ➤ Built in just 122 days, it’s designed to train xAI’s advanced large language model, Grok-3. ➤ Colossus represents a leap in AI training capabilities. 3️⃣ NEO Beta by 1X: Cutting-edge Humanoid Robotics ➤ NEO’s AI continuously learns and evolves through physical interaction and sensors. ➤ It handles tasks like cleaning, cooking prep, and organizing. ➤ NEO also offers companionship and mobility assistance for home use. 4️⃣ ChatGPT’s Milestone ➤ 200 million users are now engaging with ChatGPT weekly! ➤ 92% of Fortune 500 companies rely on ChatGPT ➤ The cost-efficient GPT-4o mini model has fueled enterprise adoption and doubled API usage. 5️⃣ Google’s Gemini Integration in Chrome ➤ Google’s Gemini chatbot is now embedded into Chrome’s address bar. ➤ It provides real-time assistance during everyday browsing. ➤ Enhanced AI-driven image generation boosts user experience. 🎬 That’s a wrap for this week! Catch you next time with more AI breakthroughs! #GenerativeAI #AINews #AITools #Google #OpenAI #Grok-3 #1XAI

Exciting Times Ahead for AI: $100 Billion Models Incoming! AI training costs are skyrocketing! Today’s models like ChatGPT-4 cost $100 million to train, but future models could reach $100 billion, says Anthropic CEO Dario Amodei. ⚡ This rapid growth is spurred by advances in AI tech and a massive demand for powerful hardware. Last year alone, 3.8 million GPUs were delivered to data centres, each gulping up energy comparable to residential electricity needs.🏘️ The goal? Creating AI that surpasses human capabilities. Amodei believes this could be achieved between 2025 and 2027, providing significant benefits but also raising critical questions about our future. 🤖 How do you see AI impacting society? Share your thoughts! Link to Article https://lnkd.in/ezxC8JyK #AI #TechInnovation #ArtificialIntelligence #MachineLearning #FutureTech #Innovation #TechNews #Nvidia #DataCenters #GPT4 #Anthropic #AIModels #DigitalFuture

Exciting Times Ahead for AI: $100 Billion Models Incoming! AI training costs are skyrocketing! Today’s models like ChatGPT-4 cost $100 million to train, but future models could reach $100 billion, says Anthropic CEO Dario Amodei. ⚡ This rapid growth is spurred by advances in AI tech and a massive demand for powerful hardware. Last year alone, 3.8 million GPUs were delivered to data centres, each gulping up energy comparable to residential electricity needs.🏘️ The goal? Creating AI that surpasses human capabilities. Amodei believes this could be achieved between 2025 and 2027, providing significant benefits but also raising critical questions about our future. 🤖 How do you see AI impacting society? Share your thoughts! Link to Article https://lnkd.in/emUZkmFz #AI #TechInnovation #ArtificialIntelligence #MachineLearning #FutureTech #Innovation #TechNews #Nvidia #DataCenters #GPT4 #Anthropic #AIModels #DigitalFuture

Ever wondered why there’s so much buzz around 𝐌𝐞𝐭𝐚'𝐬 𝐋𝐥𝐚𝐦𝐚 3.1 among developers? 🤔 This isn't just the latest tech trend—it's a revolution in AI development, thanks to its open-source nature that clearly contrasts with proprietary models like 𝐎𝐩𝐞𝐧𝐀𝐈'𝐬 𝐂𝐡𝐚𝐭𝐆𝐏𝐓 𝐚𝐧𝐝 𝐀𝐧𝐭𝐡𝐫𝐨𝐩𝐢𝐜'𝐬 𝐂𝐥𝐚𝐮𝐝𝐞. Here’s how Llama 3.1 is reshaping the world of AI by offering unparalleled customization and freedom. * 𝐋𝐥𝐚𝐦𝐚 3.1 is a hit among developers because it’s open-source, offering freedom to innovate without the restrictions of proprietary models like OpenAI's ChatGPT and Anthropic's Claude. * Developers enjoy the ability to deeply customize and control Llama 3.1, tailoring its architecture and training processes to their specific needs. * The 𝐋𝐥𝐚𝐦𝐚 3.1 405𝐁 model is powered by 16,000 Nvidia H100 GPUs and boasts 405 𝐛𝐢𝐥𝐥𝐢𝐨𝐧 𝐩𝐚𝐫𝐚𝐦𝐞𝐭𝐞𝐫𝐬, allowing it to process complex data and perform a wide range of tasks. * Its capabilities extend to advanced AI functions like image recognition, setting it apart in versatility and utility. * Being open-source, Llama 3.1 has been 𝐝𝐨𝐰𝐧𝐥𝐨𝐚𝐝𝐞𝐝 𝐧𝐞𝐚𝐫𝐥𝐲 400 𝐦𝐢𝐥𝐥𝐢𝐨𝐧 𝐭𝐢𝐦𝐞𝐬 𝐟𝐫𝐨𝐦 𝐇𝐮𝐠𝐠𝐢𝐧𝐠 𝐅𝐚𝐜𝐞, demonstrating its popularity and the trust developers place in it. * Meta benefits strategically from Llama 3.1 by fostering a collaborative ecosystem of developers, enhancing innovation and maintaining its leadership in AI technology. * This model is a powerful tool for any developer interested in cutting-edge AI, thanks to its scalability and the community-driven enhancements it supports. If you’re exploring AI solutions, Llama 3.1 is definitely worth a look! https://lnkd.in/dGC64wXD https://lnkd.in/d-6C2pJy #Llama3_1 #MetaAI #ArtificialIntelligence Views are personal.

🚀 𝐄𝐱𝐜𝐢𝐭𝐢𝐧𝐠 𝐍𝐞𝐰𝐬 𝐟𝐫𝐨𝐦 𝐆𝐨𝐨𝐠𝐥𝐞 𝐀𝐈! Google has just unveiled its latest AI model, Gemma 2 2B, and it's making waves in the AI community. 🌊 🔍 𝐊𝐞𝐲 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬: - 𝐆𝐞𝐦𝐦𝐚 2 2𝐁: Despite its compact size with only 2 billion parameters, this model outpaces larger competitors like ChatGPT 3.5 in both speed and flexibility. It excels in tasks such as text generation, translation, and Q&A. - 𝐒𝐡𝐢𝐞𝐥𝐝𝐆𝐞𝐦𝐦𝐚: A set of safety classifiers designed to filter out harmful content like hate speech, harassment, and explicit materials. This tool enhances AI safety and promotes transparency. - 𝐆𝐞𝐦𝐦𝐚 𝐒𝐜𝐨𝐩𝐞: A powerful tool that uses sparse autoencoders to provide insights into how AI models make decisions. It helps researchers understand and develop more reliable AI systems. 💡 𝐖𝐡𝐲 𝐓𝐡𝐢𝐬 𝐌𝐚𝐭𝐭𝐞𝐫𝐬: - 𝐄𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲: Gemma 2 2B runs efficiently across a wide range of hardware, from edge devices to cloud platforms, making it versatile and accessible for various applications. - 𝐂𝐨𝐦𝐩𝐚𝐭𝐢𝐛𝐢𝐥𝐢𝐭𝐲: Optimized for NVIDIA GPUs and compatible with popular AI frameworks like Keras, JAX, and Hugging Face, simplifying integration into existing projects. - 𝐎𝐩𝐞𝐧 𝐀𝐜𝐜𝐞𝐬𝐬: All these tools are freely available! You can download the Gemma 2 2B model weights from platforms like Kaggle, Hugging Face, and Vertex AI Model Garden. Test it out on Google AI Studio and Google Colab. 𝐆𝐨𝐨𝐠𝐥𝐞 𝐜𝐨𝐧𝐭𝐢𝐧𝐮𝐞𝐬 𝐭𝐨 𝐩𝐮𝐬𝐡 𝐭𝐡𝐞 𝐛𝐨𝐮𝐧𝐝𝐚𝐫𝐢𝐞𝐬 𝐨𝐟 𝐀𝐈 𝐰𝐢𝐭𝐡 𝐢𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐨𝐧𝐬 𝐭𝐡𝐚𝐭 𝐩𝐫𝐢𝐨𝐫𝐢𝐭𝐢𝐳𝐞 𝐛𝐨𝐭𝐡 𝐩𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐚𝐧𝐝 𝐬𝐚𝐟𝐞𝐭𝐲. 🌐💡 #GoogleAI #Gemma2 #AI #MachineLearning #AIsafety #Innovation #AVA #Follow

🚀 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐈 is entering an exciting new phase with real-time capabilities! 𝐎𝐩𝐞𝐧𝐀𝐈, powered by 𝐍𝐕𝐈𝐃𝐈𝐀’s powerful new GPUs, has announced 𝐆𝐏𝐓-𝟒𝐎—the new flagship model that can reason across audio, vision, and text in real time. The GPT-4O (Omni Model) matches the intelligence of GPT-4 Turbo but is faster and more affordable, costing only half the price of GPT-4 Turbo. 🔮 🕒 The real-time demos are mind-blowing. You’ll be amazed by the speed, human-like responses, and the incredible quality of its voice. GPT-4O will also be available to free users, though with limited model availability. 🌍 Another groundbreaking feature: GPT-4O understands 50 languages, covering 97% of humanity. 💻 With the release of the ChatGPT desktop app, you now have an entire team at your fingertips. The app can see your screen, hear your voice, answer your questions, provide suggestions, and execute actions. 🔥 This is a game-changer! For more details and real-time demos, check out the announcement here: https://lnkd.in/gWk8XuUk. What are your thoughts? How do you envision leveraging GPT-4O in your field? 🤔 #AI #GPT4O #NVIDIA #EmergingTech #Innovation #RealTimeAI #MultimodalAI #GenerativeAI #GenAI #OpenAI

#WallStreetAI https://lnkd.in/gNBJH_Xw #GenAI is a powerful tool - but very expensive. It is estimated OpenAI spent 4B in just Nvidia chips to power the ChatGPT4 model While enterprises, at least initially, will be leveraging commercially available GPT models to power their AI applications which (I presume) will be priced reasonably in a SaaS model - there are other costs that add up quickly. Besides license cost, there is tech development cost to build applications on top of models, model training costs, risk/compliance cost, run-maintain cost - and cost of upgrades and enhancement. Standard enterprise IT lifecycle cost - only this time one of talking of talent that is very expensive and very hard to get. The obvious question is - are there sufficient business cases that will/can justify the cost/investment? At DTCC, we are exploring this. We have a few promising use cases that could justify the investments we are making this year if the models perform as expected, delivering unprecedented productivity gains and in some use cases delivering what was tried multiple times before but simply cant be done without this new tool. It has the promise to be truly transformative - but we need to test and prove it out this year. I did however use ML at my previous firm (BNYM) - not the GenAI, but plain ML - and they delivered on their business cases in spades! We were trying to read and enrich payment instructions - more than 200K instructions a month with a cumulative value of more than $1T - where these payments instructions literally came in all types. Including - and I kid you not - email with 500+ pages bond indenture attachment saying 'please find payment instructions therein'! To say the least - there was no obvious logic to program/automate. We turned to ML. The ML model wrote its own models - more than 600 of them, as it crunched thru thousands of examples, updating its neural net. it picked up slowly, going from 70% STP all to way to 98% thru supervised learning. We built one of the largest ML powered enterprise payments platform, performing a Critical Business Service! But did it meet the business case? Handsomely! The Payback period was only 18 months! #GenAI, #ml #fintech #digital

Do You know what #Generative means in #GenerativeAI ? For example #ChatGPT stands for Chat Generative Pretrained Transformer. Essentially we take a #training dataset, in case of chatbots its language data set. And then you #train the #neuralnetwork (#Transformer architecture) and once the #error is minimized i.e. training is completed. The #weights (think as function F in an equation Y=F(X) ) can be used to run again and again to generate new outputs (Y) given new inputs (X). These #weights when used again and again is called #inference to generate new outputs. Now as any simulation method we have to be careful to check: 1). Garbage In is Garbage Out: If you are working on GenAI products Ask questions around what data was used to train the model? Where was the data retrieved from and the most critical piece Who cleaned the data and how was the data cleaned? 2). How do we break the model? Aka #edge cases, this sounds easy in writing but hard in practice since its an infinite premutation and combination of the input datasets when inferred. Industry term is #Hallucination. Requires a lot of creativity and smarts. 3). All models are wrong but some are useful. Try to gather how the outputs will be used and what guardrails do we need to protect the outputs from going out of bounds of the expected output range. It becomes easier for scientific applications where physics, chemistry and engineering bounds can be applied. #Nvidia released their #AIFoundry (#NeMo platform) with custom #Llama 3.1 GenAI models. This is great for the open source community since everybody has the same power to develop new GenAI apps and GenAI is being #democratized at global scale. Its already jumped to no. 1 custom classifier on #Huggingface Reward Leaderboard. It has 340 Billion weights and can be used as a #foundation model to build #domainspecific #supermodels. We can use not only #domain datasets but #synthetic datasets to train these supermodels #accelerating model development. https://lnkd.in/gYjsmDU5

AI Benchmarks Get Real: Speeding Up Responses & Images Big news for AI developers and designers! The MLCommons group just launched new benchmarks focusing on how quickly AI applications can churn out responses and visuals. This is a game-changer for tools like ChatGPT, potentially making them lightning fast. ⚡️ Here's the scoop: These benchmarks measure how quickly AI models can generate text responses and create images. Think faster chatbots, more efficient design tools, and a boost for the entire AI development field. The benchmarks also consider power consumption, because powerful AI can be a real energy hog! This is exciting news for anyone working in AI, especially: Software Developers: Building next-gen chatbots and intelligent applications. Graphic Designers: Exploring AI-powered design tools and creative workflows. Want to learn more? Check out the full article! ➡️ :https://lnkd.in/dMpPirw2 #artificialintelligence #AI #machinelearning #softwaredevelopment #graphicdesign #technology #innovation #MLCommons #ChatGPT