Suhas Pai

The 2024 Nobel Prize in Physics has been awarded to John J. Hopfield of Princeton University and Geoffrey E. Hinton of the University of Toronto, whose pioneering work laid the foundation for the machine learning algorithms that power today’s artificial intelligence revolution. By merging principles of physics with computational models, their breakthroughs in artificial neural networks have enabled computers to detect patterns in vast datasets, transforming industries from healthcare to finance. Hopfield and Hinton's research has made technologies such as facial recognition and language translation feasible, positioning their discoveries at the heart of AI systems that influence modern life. Their work in training artificial neural networks, inspired by the biological processes of the brain, has not only deepened the scientific understanding of cognition but also driven advancements in automation and decision-making. At a time when AI is reshaping economies and global governance, this recognition underscores the profound impact of fundamental research in physics on the future of technology. https://lnkd.in/ed5TDuPB

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To Label or to Pseudo Label? Active Learning vs Semi-Supervised Learning: I'm thrilled to share that as H3M Analytics, our Mitacs collaboration with the University of Ottawa has resulted in our first paper being published in this project at the The 37th Canadian Conference on Artificial Intelligence! The paper, co-authored by Bahar Afshar, Paula Branco, Utku Gorkem Ketenci, and Hikmet Mazmanoglu, explores the effectiveness of Active Learning and Semi-Supervised Learning methods for Windows malware detection. Using the Microsoft malware prediction dataset, we found that both methodologies enhance detection accuracy, especially in scenarios with limited labeled data, with Semi-Supervised Learning showing superior outcomes in certain settings. For more details, check out the paper here: https://lnkd.in/eK2gTE7d #Research #Innovation #AI #Compliance

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Our baby is finally heading out to the world on its own!! 🎊 Multimodal AI on RayBan launched to 100% of US/Canadian users yesterday. If you're already a proud owner of a pair of RayBan smart-glasses, it's time to take them out for a joyride. Go ahead, ask them anything that pops into your head. Too lazy to pull out your phone to find the answer? No problem! Your stylish specs are here to save the day. Don't have a pair yet? Well, there's no time like the present! Swing by your nearest RayBan store and ask for a test drive 😎 Personal anecdote time: Those of you who know me personally are very well aware that this has been the most rewarding product of my career. Countless sleepless nights babysitting your models, fretting over every detail and a team who is the perfect balance between fun/work.😛 And there is still a long road to go where we can take this product!! Here is a paper by our team if anyone is interested in the nitty gritty details: https://lnkd.in/g5vqxMen More details about the feature: go.fb.me/ejkqvs

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🚀 Advanced AI-RAG Chatbot using Langchain for the Landlord and Tenant Board of Ontario is now LIVE! 🚀 👨‍⚖️ Navigating legal waters in landlord-tenant disputes just got easier. I am delighted to present my project on an advanced AI-powered chatbot designed to democratize legal advice for Ontarians. Leveraging advanced technologies such as NLP, ML, and RAG, the chatbot delivers real-time, accurate legal assistance - making it a game-changer in legal tech! 📈 With FAISS-powered retrieval and sophisticated reranking algorithms, this platform is not just fast - it’s razor-sharp in delivering contextually relevant advice. 🔗 Dive into my project presentation to see the details of the project: https://lnkd.in/dU32f7ze 💡 This isn't just a project; it's a project with a purpose. My goal? To ensure that everyone has access to the legal information they need, when they need it, without any barriers. ✨ A special shoutout to the incredible team: Atharva Pandkar, Tarun Reddy who worked with me from time to time and our Professor Dr. Uzair Ahmad, without whose guidance this would not have been possible. We're just getting started on our mission to transform access to legal services. 👇 Check out my project, join the mission, and let's drive legal tech forward together! GitHub Repo: https://lnkd.in/dBDpjFp8 #LegalTech #Innovation #AI #LegalAidChatbot #AccessToJustice #OntarioLaw #TechForGood #ArtificialIntelligence #Disruption

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#Day3 of #MonthOfGraphs LQ)547.Number of provinces Today I tackled the "Number of Provinces" problem on LeetCode, a classic problem in graph theory. While many opt for Breadth-First Search (BFS), I decided to take a different approach and implemented a Depth-First Search (DFS) solution. 🧠💻 While both DFS and BFS can solve this problem efficiently, I found DFS to be more intuitive for recursively exploring all connected nodes🌟. It was a great exercise to reinforce my understanding of graph traversal algorithms✨️. Whether using DFS or BFS, the key is to understand the underlying structure of the problem and choose the approach that best fits the scenario✨️. Stay tuned for more updates, insights, and learnings as we navigate through this journey together! Let's conquer graphs as a team! 🌟 If you have any suggestions or feedback, please feel free to share. Let's continue pushing ourselves to grow and learn from each other😇. Karthik Paila,Pradeep Kumar Puvvala,Praveen Kumar Inti,Sahu Akshaya #LeetCode #GraphTheory #DFS #Coding #Algorithms #Programming #Tech #ProblemSolving #Day3

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As AI tutors like Khanmigo are set to expand to reach 1M+ students in the next year, new research from Stanford and OpenAI shows disturbing patterns in how AI responds to and depicts diverse students. The magnitude of bias is shocking - for example, one AI model portrayed Latine male students as over 1,300 times more likely to be shown as "struggling" than as "star" students. We saw this bias play out last Friday in our first set of workshops in Mexico City. When we asked ChatGPT to create images of a typical Mexican classroom and staff room, the images were so stereotypical that they bordered on being offensive. In the Stanford study, GenAI models were asked to create stories about learners, they found that the models consistently: ERASED multiple groups: - Indigenous/Native American, Native Hawaiian/Pacific Islander students are rarely depicted - Non-binary and queer students were severely underrepresented - When minority groups are shown they are often done so in limited stereotypical ways REINFORCED harmful stereotypes • Asian students were portrayed as "model minority" STEM experts, erasing individual differences • "White savior" narratives were common where minority students lack agency • Latina students (e.g. "Maria") were almost never shown excelling in STEM These AI biases can deeply impact students': • Sense of belonging • Academic confidence • Identity development • Educational outcomes With AI tools rapidly entering classrooms and increasingly being used by young people in and out of school, we need: • Culturally responsive AI development that takes into account diverse teacher and student perspectives • Additional research and analysis of these impacts • Protection for vulnerable students built into student facing GenAI tools • Critical examination of AI tools before and after deployment You can check out the links to the studies and interview with some of the authors in the links. AI for Education #genai #bias #responsibleai

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Month of AI - Day 6 After some of the complex math of some of the previous days reading, you will be pleased to hear that today we go to the university of Toronto for a paper titled "Keeping Neural Networks Simple". Yes I know, it has a lot of math to, but that I'm afraid is the nature of the beast. Geoffrey Hinton and Drew van Camp propose a method to improve the generalization of supervised neural networks by minimizing the description length of the weights. This involves adding Gaussian noise to the weights and adapting the noise level during learning to balance the expected squared error and the information content in the weights. The technique focuses on efficiently computing derivatives of the expected squared error and weight information, leading to effective regularization and preventing overfitting. Keeping NN Simple by Minimizing the Description Length of the Weights (toronto.edu)

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Just a few days out from my talk at Toronto Machine Learning Society (TMLS)‘ Summit. I will be speaking next Monday July 15 at 11:30 in Room 1. My talk, “GenAI: A New Renaissance in Product Development” is about actionable tips for how to incorporate GenAI in your day to day workflows. I cover three major topics on how to: 1. Get the most from work with global teams 2. Expedite user research (while still talking to real humans!) 3. Find the best tools to leverage during the product development process Along the way I’ll be sharing caveats and pitfalls to be on the lookout for. I’ve got some fun visuals to backdrop the talk. Plus, I will be launching my playbook for how to use GenAI in the product development process. It covers a whole lot more and the audience will have first access! So come on out from 11:30 - 12 in Room 1! And it’s my first trip to Toronto so please send tips!

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I just got my own copy of “Building LLMs for Production” by Louis-François Bouchard and Louie Peters. As I am getting more into applications of GenAI I expect this book to become one of my go-to resources in the upcoming months. https://amzn.to/3Yv5FmL #AI #GenAI #LLMs

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HOW TAKING A BREAK SPARKED THE COOLEST IDEA IN DEEP LEARNING In 2014, Ian Goodfellow, pursuing his PhD at the University of Montreal, was at a bar with friends celebrating a lab-mate’s new job at the company named DeepMind. As the drinks flowed and the conversation turned to AI, a debate arose about how to create a machine capable of generating photorealistic images (Remember this was 2014, DALL-E, Midjourney didnt exist publicly). Goodfellow’s colleagues proposed various complex solutions, but he, fueled by craft beer, suggested a radically different approach: a neural network that learned from another neural network. Here's how he thought: - One network would generate an image and attempt to fool the second network into believing it was real. - The second network would identify flaws in the generated image, providing feedback to the first. - This adversarial process would continue until the first network created images indistinguishable from real photographs. Goodfellow’s friends, convinced this approach was overly complicated, dismissed the idea. A sober Goodfellow might have agreed. However, driven by his beer-inspired conviction, he went home and immediately started coding. That night, he proved his friends wrong, creating the first Generative Adversarial Network (GAN). GAN became notoriously famous as underlying technology behind DeepFakes. But it had many positive applications too- for image enhancement, generating realistic art, restoring old photos, medical imaging, augmenting datasets for AI training etc. In 2016, Yann LeCun called GANs “the coolest idea in deep learning in the last twenty years. Goodfellow's story emphasizes a valuable lesson: deliberate time away from work, allowing the mind to relax and explore unconventional ideas, can lead to breakthroughs that ultimately benefit our work. In my book- "What’s Your Human Edge? : 53 Timeless Ideas for Thriving in the Age", I make a mention of the following (inspired from David Perell)- Creatives oscillate between two primary work modes – Beer mode and Coffee mode. (Included a sketchnote from my book) Beer mode embodies a relaxed, unfocused state of play—a sandbox where novel ideas germinate and flourish.  (that's Goodfellow at pub where the inspiration striked him) Coffee mode represents a concentrated phase, characterized by deliberate efforts to achieve specific results (that's Goodfellow at night going deep and trying the idea he got at pub) This balance between allowing the mind to wander and harnessing its focus is crucial for fostering innovation and realizing visionary ideas. What do you think? #DeepLearning #WhatsYourHumanEdge

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Control Theory… that classical subject, arguably under-appreciated, and yet applicable to such a diverse range of systems: continuous (linear / non linear), digital, discrete event, etc. And since literally EVERYTHING in the world (physical stuff and digital stuff) is one of these systems, maybe it’s not surprising that Control Theory can be applied to rigorously study LLMs? #genai #llm #controltheory

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If you are in Vancouver, there is an Intel AI Summit event on Dec 12. I'll be there, so feel free to drop by and say hi if you make it #vancouver #meetup #bigdata #genai

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Deep reinforcement learning (DRL) is an artificial intelligence approach that pertains to developing programs that can solve problems that require intelligence. Intelligence is something that we are obsessed with replicating outside our brains. In fact we associate the term artificial intelligence with this capability, and strive to attain Artificial General Intelligence (AGI) at some point. While DRL might still not be what we can call true AGI on its own, but it does mimic some form of intelligence. Specifically the decision making intelligence used by many species. The purpose of this article series is to demystify deep reinforcement learning, one of the most powerful approaches in artificial intelligence that leverages deep learning, and combined with other approaches, can help humans plan businesses, specifically operational and tactical planning, with zero defects. #data #analytics #ai #ml #datascience #designedanalytics #deeplearning #reinforcementlearning #deepreinfrocementlearning https://lnkd.in/gs66sSCD

While this may surprise some, it shouldn’t. The bias is found in the search terms because the LLM (Large Language Model) is just a collection of possibilities prioritized by the descriptive language (stored in probability weighted Vector data types. If #Google was trying to shift your search, they’d use some form of prompt engineering to subtilly redirect user questions (search terms) but they don’t appear to do so. Unfortunately, generative AI can reinforce the perspective based on how you articulate your questions. #ai #itnews #technews #media #news

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No amount of scaling (throwing billion$ for computational resources & trillions of words) would help LLMs achieve the two key cognitive capabilities that make human intelligence so special: 1. Learn - Autonomously, Adaptively, Conceptually, Contextually & Incrementally. 2. Reason - Conceptually, Contextually & Metacognitively We discussed the criticality of this in greater detail here: https://lnkd.in/ggZm_W-w It is for this reason, LLMs can never get us to AGI: https://lnkd.in/d3vEPJ2P So, what will get us to AGI: https://lnkd.in/ggFTxuUz It's no wonder Demis Hassabis, CEO of Google DeepMind said this about LLMs Intelligence on July 9, 2024 at the Future of Britain Conference hosted by Tony Blair: “We’re still not even at cat intelligence yet, as a general system,” https://lnkd.in/gtyQNZyc It is great to see Yann LeCun arriving at the same conclusions about LLMs. https://lnkd.in/gm9Nfe4P

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The basics on Lie groups and algebra (tangent space) that are critical to extract and understand data manifolds.

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