Praneesh Jayaraj

Mastering the art of impactful communication is key to making a difference, especially when leading a team that is in the driving seat to understand and guide others through a phase of unprecedented buzz and hype around data and AI. I just did my part and am glad that I have something to show for it. There is already so much to catch up on, but taking a step back and reflecting on what is truly required made me realize this. This is a course that I recommend to everyone who wants to understand the right framework of communication or validate the assumptions that have helped them shape their communication style. Thanks to INSEAD and Ian Woodward for an incredibly well-structured program. #Leadership #Communication #ContinuousLearning #INSEAD #ProfessionalGrowth

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New article up in Towards Data Science… “Ongoing challenges with digital tracking has led to a recent resurgence in marketing mix modelling (MMM). At the recent Causal AI conference, Judea Pearl suggested that marketing may be the first industry to adopt Causal AI. So I decided it was time start writing about my learnings from the last 7 years in terms of how MMM, Causal AI and experimentation intersect.” Let us know what you think! https://lnkd.in/gQZk943x

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👵 Did you know, Lenskart.com's AI & ML can forecast a store’s revenue with 95% accuracy prior to launching? 😲😲😲 Read my latest article about how AI is helping the retail industry ✒️ - https://lnkd.in/gehr53RA #artificalintelligence #machinelearning #retail #lenskart #consumer #supplychain #technology #transformation

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If you are still thinking that AI is just a hype, you should listen the conversation between Mukesh Ambani and NVIDIA CEO Jensen Huang. Why ? Mukesh Ambani bullishly talks about Generative AI models, GPUs, need of open source models etc. He takes up the challenge to surprise world with AI capabilities in coming years. He is not someone who has been leading IT/Software companies day in day out like Narayan Murthy/ Shiv Nadar/Bill Gates etc. What it means? AI has already arrived and every big firm, irrespective of their traditional business, wants to build business and create impact using this. AI is not just another cool tech anymore. It is the industrial revolution changing every facet of our life. Embrace it or get ready for classic "Kodak Case study" in your career and business. Watch here: https://lnkd.in/ejvnCMHZ

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On AI Appreciation day, I shared my views with The Economic Times on how Artificial Intelligence, grounded in contextually-relevant information, is reshaping business dynamics. AI's ability to uncover real-time insights and democratize complex data is revolutionizing decision-making across all levels of organizations. However, to harness AI's full potential responsibly, businesses must invest in robust data integration and governance frameworks. Read more here: https://lnkd.in/gFAUbtaN

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Loved the course about the utilization of LLM in constructing an AI-powered game. The course built by #together.ai and #deeplearning.ai highlighted creating multiple cities, towns, heroes at real-time; the content developed was mind boggling... creativity is the key. Incorporating Meta-Llama/LlamaGuard ensured the appropriateness of user inputs and LLM outputs. Noteworthy was the insight gained on preserving player States and using gradio. Exploring the conversion of these games into comprehensive experiences with video and audio elements would be a promising direction. #keeplearning #llms DeepLearning.AI Together AI Niki Birkner Nick Walton

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#videopodcastseries - weekend watching and listening into the deep dive of #episode84 of XTraw AI conversations with the CPMO, Rahul Singhal from Innodata Inc. on harnessing the power of AI to transform businesses. We delved into key strategies that are shaping the future such as enhancing efficiency, driving innovation and more in the context of AI and data engineering. 👉Don’t forget to subscribe if you haven't yet! #AI #GenAI #Data #Analytics #Innovation #DataEngineering https://lnkd.in/giyaRfj5

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Last month I had the privilege of participating in a panel discussion on Generative AI at Enterprise, alongside Sayan Maity, PhD and Arpita V., hosted by Roger M. & Data Science Salon at Google HQ in San Francisco.  We tackled critical topics, such as why rapid prototyping is commonplace across companies, yet few successfully transition AI projects into production. Arpita pointed out that for recommendation and ranking problems, inference speed is crucial. Companies often utilize LLMs for offline evaluations while relying on high-performing, traditional models for online consumer interactions to mitigate the risk of unpredictable outputs. It's essential to recognize that many problems can be effectively addressed using smaller models like ROBERTA or BERT, without overhauling existing machine learning pipelines with #LLMs. Our discussion also covered #copilots , the #agentic framework, model size, #inference time, #cost , and #scalability . Ultimately, the pivotal question remains: Is the ROI substantial enough? Does deploying generative AI into production pay off? Furthermore, can we quantify the productivity gains for individuals using copilots? For insights and answers to these discussions, I invite you to listen to our conversation. #engineeringexcellence, #AI, #evaluations , #bestpractices, #GenAI, #DSSSF https://lnkd.in/gefHHDhb

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🚀 Day 18 of My 25-Day Generative AI and LLM Challenge 🤖🚀 🌟 How Variational Autoencoders (VAEs) Work: Encoding & Decoding 🌟with my friends HARSH JAVIA and Maanan Jagani! Variational Autoencoders (VAEs) are a type of generative model that aim to learn the underlying structure of data. Here's a simplified breakdown of how they work, using a bit of professional flair and emojis to keep it engaging! 🔍 1. Understanding the Basics🔍 At a high level, VAEs consist of two main components: 1. Encoder 🧠: - Takes input data (e.g., images, text) and compresses it into a lower-dimensional space known as the **latent space**. - Think of this as summarizing the essence of the input data into a compact representation. 2. Decoder 💡: - Takes the encoded representation from the latent space and tries to reconstruct the original input data. - This process helps the model learn to generate new data that is similar to the input data. 🚀 2. Step-by-Step Process 🚀 Step 1: Encoding 📥 - The encoder network receives the input data and transforms it into a mean (μ) and a standard deviation (σ). - From these, a latent vector \( z \) is sampled using the reparameterization trick: \( z = \mu + \sigma \cdot \epsilon \), where \( \epsilon \) is a random variable from a standard normal distribution. Step 2: Latent Space 🌌 - The latent space is a lower-dimensional representation that captures the essential features of the input data. - This space allows the model to explore various possible data representations. Step 3: Decoding 📤 - The decoder network takes the latent vector \( z \) and attempts to reconstruct the original input data. - The quality of the reconstruction indicates how well the model has learned the data's underlying structure. 🌟 3. Why VAEs are Powerful🌟 - Data Generation 🎨: VAEs can generate new, unseen data that is similar to the training data, useful in creative applications like art and music. - Dimensionality Reduction 📉: They reduce the complexity of data, making it easier to visualize and analyze. - Anomaly Detection 🔍: VAEs can identify anomalies by recognizing data that doesn’t fit the learned patterns. 🏆 4. Professional Takeaway 🏆 Variational Autoencoders are a powerful tool in the machine learning toolbox, enabling efficient data compression, reconstruction, and generation. Their ability to model complex data distributions makes them invaluable for various applications, from image generation to anomaly detection. #MachineLearning #AI #DataScience #DeepLearning #GenerativeModels #VariationalAutoencoders #VAEs #NeuralNetworks #DataInnovation #TechTrends #ArtificialIntelligence #MLApplications #DataGeneration #TechInspiration #Innovation #AIResearch #DataAnalytics #DeepLearningModels #TechCommunity

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For this iteration, I decided to test out a new model trained on drum breaks, courtesy of fellow creative coder, Abhinay Khoparzi. The signal chain mirrors that of Fractured Neurons #1, but the training dataset posed an interesting challenge due to its periodicity. When I fed periodic material from the glitch machine into the trained model, the latency from the parallel processed signal became quite noticeable when layered with the dry glitch machine signal. Initially, I attempted to implement a dynamic logic to address the latency in real-time, but the results were uninspiring. In the end, Occam's razor held true. Applying a static delay to the dry signal turned out to be the most effective solution. I think this iteration has a unique sonic signature. Some of the sounds evoke timbres that feel both familiar and alien, and there’s something about the overall rhythmic structure that subtly hints at a glitchy tabla progression—but that might just be my perception and biases at play. Excited to keep exploring the sonic possibilities that arise as this project evolves! #neuralnetwork #machinelearning #proceduralaudio #generativemusic #creativecoding #maxmsp #flucoma #ircam

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Please join me on June 25th San Francisco, CA, USA for a panel titled "A BLUEPRINT FOR SCALABLE & RELIABLE ENTERPRISE AI/ML SYSTEMS" w/ moderation by Hira Dangol from Bank of America and my fellow panelists Rama Akkiraju from Nvidia and Nitin Aggarwal from Google. Recent breakthroughs in AI technology have captured the world's attention, and now it's time to address the challenge of creating truly robust and reliable AI systems. AIQCon's mission is to harness the power of our community to develop new GOLD STANDARDS for AI quality. The global movement is bringing together renowned leaders and professionals from across the industry to establish reliable AI quality solutions and standards. If you're working on AI systems, you won't want to miss this one! Use the discount code "Community" for a 15% discount on registration. Register at https://lnkd.in/gqRE48fC. #AI #EnterpriseAI #AIQuality #AIQCon https://lnkd.in/gqRE48fC

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Ai : Hunanity's greatest creation for Good and Bad. To secure ourself #zerotrustai In the journey, MS brings up VASA-1 will AI model which essentially spins a selfie image and turn it into a talking clip of you. All you have to do is upload a photo along with a voice note and let the AI model do the talking for you.

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My recent blog post explored whether AI *CAN* model human behavior. The overwhelming response led to a follow-up question: *SHOULD* AI model human behavior? In the follow-up blog, I explore this complex question, looking at historical examples which blur the lines between machine and human. Read my latest blog to explore the possibilities and pitfalls of emotionally intelligent AI: ✍️: https://lnkd.in/gwJp2K2p Let's discuss!

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#MAJOR #AI 💡 break-through #SURFACED THIS WEEK 💬 While majority of the folks (and candidates I am having for job interviews) dream about riding the #LLM's horses of #GPT-type, in the background there are new trends really emerging. I have been watching closely work of Sepp Hochreiter and his team in giving #GenAI new hope by "resurrecting" LSTM. 🔭 For those of you, who don't follow the AI tech deeply, Long-Short-Term-Memory (or #LSTM) models have been the pre-cursors of the GPT technology. Most of the translators and other language bots being on market before launch of ChatGPT have been actually some form of LSTM application. When OpenAI broke through with Generative Pretrained Transformer (hence GPT) line of models, they looked like good-bye kiss to LSTM. GPT could work with so many more tokens, achieved better precision in tasks and was also faster in inference. 🪜 However, Sepp and his team recognized also one strong weakness of GPT family of models. Their learning is #quadratic, which makes push for more and more parameters prohibitive expensive. That also means that GPT's already got close to their (reasonable) ceiling of how big single model can be. (This is reason why they have been geared into round-of-experts model platforms, but that will be subject for another post of mine later). 💥Not succumbing to GPT dominance, Sepp Hochreiter worked hard on upgrading the LSTM by adding #exponential #gating, #matrix #memory and parallel training. This week they published paper on #xLSTM brand new generation of LSTMs that actually competes well with GPT-technology and are much cheaper to train. And that is after much of the hype a real technological advancement. Large Language Models are not peek of AI, they are just transition to another phase. And this whitepaper is major step on that pathway. Read full detail here :

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It was time well spent discussing the various use cases of Neo4J today with Bhaskar Sampathkumar, Praveen Athmanath, Thomas Smoker at the Neo4J Graph summit. Generative AI was one of the focus areas and we discussed how Graph RAG can significantly reduce the surface area of search and help identify the needle in the haystack more efficiently. However the key is to know what to put in the Graph. On that, we discussed about the importance of knowledge modelling to extract meta information from unstructured data which requires domain knowledge and human involvement. We discussed how we can leverage schema based extraction of the meta information using LLM to aid in that modelling exercise. LLM memory is one more thing that requires a Graph, much like human memory. I continue to reiterate that memory will be an important component of Gen AI applications. It is much like our brain which is a memory engine and not a compute engine. That is why our brain is so efficient and can work with only 12-20 watts of power. Ajay Bolina Manoj Suvarna

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Looking forward to this panel where Divakar Tantravahi, Meduri Ravi Kumar, and Cosimo Spera will talk about the latest AI technologies that are changing product and customer support, along with their real world examples and practical strategies. If you have not registered, use this link - https://lnkd.in/gdTMgryA #ai #businessoutcomes #product #customer

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