Jawad Amin

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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For the last 11 years, I've had the privilege of teaching product development, design, and entrepreneurship to talented computer science students at the University of Toronto. While having access to the latest educational resources is crucial, what I’ve found truly transformative are the skills of critical thinking, creativity, and vision. These are the tools that drive innovation and change. I couldn’t agree more that staying up to date is important, but applying that knowledge in a unique, forward-thinking way truly revolutionizes the future of work and industry. What further skills should people develop/learn to complement their AI education? #AIeducation #futureofwork #careerdevelopment #backtoschool https://lnkd.in/g6TQEwqF

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Are you at NeurIPS 2024? Check out Google Research & Google DeepMind booths to see the latest AI developments. The coolest demos and results will be shown!

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MIT 👨‍⚕️ 👩‍⚕️ SciAgents Imagine a world where scientific discovery isn't just the domain of researchers in labs but is also powered by intelligent systems that can think outside the box. SciAgents aims to do just that. It’s a system designed to automate scientific discovery using a multi-agent intelligent graph reasoning framework. - Alireza Ghafarollahi and Markus J. Buehler, [SciAgents: Automating scientific discovery through multi-agent intelligent graph reasoning](https://lnkd.in/gZ65PxA3), 9 Sep 2024. SciAgents represents a shift in how we approach scientific discovery. Combining large-scale knowledge graphs, powerful language models, and collaborative agents opens up new avenues for research that may have been overlooked. First, we have Large-Scale Ontological Knowledge Graphs. These are not just any knowledge graphs; they integrate vast amounts of scientific data from diverse domains. This integration allows the system to map relationships between concepts that may escape human researchers. Think of it as a vast web of knowledge where every node connects to others in ways we might not initially see. Next, there are LLMs. These models are like the brains of the operation. They process and retrieve information from the knowledge graphs, helping to generate research hypotheses based on the patterns and connections identified. Imagine having a research assistant who’s read every relevant paper and can synthesize that knowledge in a flash. That’s the power of LLMs in this framework. Finally, we have Multi-Agent Systems with In-Situ Learning. This is where the magic happens. Multiple agents, each with a specific role—whether it’s knowledge extraction, hypothesis generation, or evaluation—collaborate to automate the entire process. It’s reminiscent of a well-oiled research team, where each member plays a crucial role in advancing the project. The potential for serendipitous discoveries is immense, and as this technology evolves, we might find ourselves standing on the brink of breakthroughs we never thought possible. #ScientificDiscovery #MultiAgents #LLM #Ontology #KnowledgeGraph

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I'm excited that this week at the CAIAC - Canadian Artificial Intelligence Association conference George Wang will be presenting a paper Adaptive Learning Rates for Gradient Boosting Trees, based on work he did as an intern at Kinaxis last fall, in collaboration with Christopher Wang, Yunfei O., Behrouz Soleimani, PhD. There are 3 reasons this is both impressive and cool: 1. They combine heuristics, machine learning, and optimization to speed up the training for an important algorithm, Gradient Boosted Machines, widely used in both academia and industry. Faster training improves model performance. 2. This paper is just one of the many sparks of innovation in #artificialintelligence coming out of Kinaxis, where > 50% of our patents filed in the last several years have been in this space, because we continue to push the boundaries by inventing new solutions like this one to solve problems our customers face. We've been delivering AI to the market since 2018 and were the first to provide solutions powered by AI to address both demand and supply. We are rooted in Canada, which brought the world out of the last AI winter in 2012 when a team from the University of Toronto won the famous Imagenet competition by blowing the lid off previous results. Their submission was layers of neural networks called deep learning, which later became the foundation for the #generativeAI revolution we are seeing today. 3. We have an outstanding intern program in #machinelearning at Kinaxis, where interns work on real problems, guided by senior staff. As a testament to letting the students shine, when their paper was accepted they decided to have George present it. So check out the paper if you're at the conference, but if you miss the event you can't miss the fact that we are actively speaking and sparking on AI. In fact, just last week Chantal Bisson-Krol spoke in Edmonton at the #upperbound conference on AI-Powered Strategies for Resilient Supply Chains and I spoke at the Reuters Supply Chain USA event on Embedding AI in supply chain to deliver end-to-end value.

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Discover the Phi-3 mini models, AI creations with immense potential. The short context model, Phi-3-mini-4k-instruct-onnx, handles prompts up to 4k words, while the long context version deals with much lengthier inputs and outputs. Lee Stott shares how to leverage these models for text generation using NLP techniques. From setting up Python to utilizing the generate() API, Lee provides step-by-step instructions and code examples. Let's unleash the capabilities of Phi-3 mini models! https://lnkd.in/gp8c_2T7 #phi3 #onnx #msftadvocate

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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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Canada has been shaping the modern machine learning and AI revolution from the start. Many of the deep learning breakthroughs that have defined the past decade were born in the research labs of Canadian universities, where visionary ideas were explored long before they hit the global stage. I hope this new investment in building local infrastructure ignites fresh momentum and secures Canada’s position as a driving force in AI innovation for the next decade.

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Short video: brief summaries of several key issues in ML history and modern ML.

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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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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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The Rise of Agentic-Architectures in Generative AI: Old wine in a new bottle?      Tldr; As generative AI evolves, developers are increasingly recognizing the need for sophisticated orchestration of Large Language Models, Retrieval Augmented Generation, and multi-agent architectures to handle complex queries, prompting us to better leverage established multi-agent systems and AI planning techniques in the field of Computer Science to build more capable and efficient agents.     ——— As developers gain a deeper understanding of the challenges in building and deploying generative AI applications such as chatbots and copilots with Large Language Models (LLMs), Retrieval Augmented Generation (RAGs), and agents, they are recognizing the limitations of solely relying on prompt engineering and basic agent architectures. To address complex, real-world challenges, sophisticated orchestration of prompts, LLMs, and specific task execution tools is required.      For example, a generative AI solution with simple and single agent architecture even with a powerful LLM is unlikely to address complex multi-part queries such as ‘compare the revenue of company XYZ from Q1 through Q4 of FY2024 and provide an analytical commentary on the key contributing factors that led to the changes in revenues during this time’.  These kinds of use cases are driving increasing interest towards multi-agent architectures capable of complex task decomposition and more.        So, naturally, these days, we are increasingly noticing discussions, and papers about agentic architectures for generative AI solutions. Interestingly, many of the design patterns that are gaining traction in agent architectures are reminiscent of earlier research in Multi-agent Systems (MAS) and AI planning in the field of Computer science. Old wine in a new bottle? Well, time will tell. In any case, some noteworthy agent patterns from previous works include:     * Layered Architectures: Enable task decomposition through hierarchical agent structuring.   * Blackboard Architectures: Facilitate independent agent work with a communal 'blackboard' for sharing results, ideal for diverse expertise collaboration.   * Belief-Desire-Intention (BDI) Models: Support planning and decision-making with agents driven by their beliefs, desires, and intentions.   * Contract Net Protocol (CNP): Uses a decentralized protocol for task allocation where agents bid on tasks based on their capabilities.   * Market-Based Architectures: Inspired by economic models, these architectures use auctions and currency systems for efficient resource allocation.   * Hybrid Architectures: Combine various frameworks to leverage the strengths of different systems, creating robust and flexible solutions.     As we dig deeper into the world of agentic architectures for generative AI, the wisdom from decades of research in MAS, and AI planning domains seem increasingly relevant. So, let's build on prior work and not reinvent the wheel.  #genAI

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Meta’s artificial intelligence chief, Yann LeCun, has said that large language models (LLM) such as #ChatGPT would never achieve the ability to reason and plan like humans. He also said that depending on LLMs to reach human-level intelligence is not right as these models need the right training data to answer human prompts correctly, and hence they are ‘intrinsically unsafe’. LeCun shared his thoughts with The Financial Times and said these LLMs, “do not understand the physical world, do not have persistent memory, cannot reason in any reasonable definition of the term and cannot plan hierarchically.” #artificialintelligence #largelanguagemodel

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📰 Tech News Snippet #841 Phil Panaro, a respected former senior advisor at Boston Consulting Group and ex-CEO of a BCG subsidiary, has made a compelling forecast regarding NVIDIA's future. He suggests that NVIDIA's stock might surge by over 500% by the end of this decade. This prediction isn't just a wild guess; it's rooted in NVIDIA's strategic positioning within a rapidly evolving tech landscape. NVIDIA has consistently demonstrated innovation in AI, gaming, and data centers — sectors with substantial growth trajectories. The integration of artificial intelligence across industries can act as a significant tailwind for NVIDIA, further embedding the company's significance in the market. As NVIDIA expands its ecosystem, strategic partnerships and technological advancements will likely bolster its stock performance. This forecast reflects not only the potential of NVIDIA's ongoing projects but also the trust in its ability to adapt and lead in emerging tech markets. Investors and tech enthusiasts alike should keep a close eye on NVIDIA, as the company might just redefine industry standards and shareholder expectations. #NVIDIA #TechNews #Investing #StockMarket #AI

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🚀 Exciting news in the AI world! Microsoft has just unveiled a new class of small language models that pack a powerful punch in a tiny package. The Phi-3 family, including the remarkable Phi-3-mini, is set to revolutionize how we use AI, making it more accessible and efficient. 📱These models are not only smaller and trained on less data, but they also outperform their larger counterparts in various benchmarks. This means organizations with limited resources can now leverage AI more effectively, and applications can run directly on devices, ensuring quick responses and enhanced privacy. 🔮The future is here, and it's all about choice. Whether you need a model for simple tasks or complex reasoning, there's now an AI model tailored to your needs. Check out Microsoft's latest innovation and explore the possibilities for small language models. #AI #MachineLearning #Innovation #MicrosoftAI https://lnkd.in/etN9n-NB

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LLM's embedded in operating systems will be a game changer! This week, Microsoft announced the new SnapDragon X Elite PCs, e.g., the CoPillot+ PCs from themselves, Dell, Samsung, etc. Among the many exciting advances, one that really stood out to me is Microsoft's embedding of an LLM locally in Windows. The new Phi-Silica LLM is what will be embedded - see: https://lnkd.in/gRUSXZqn The implications of an embedded LLM are exciting. Windows app developers now have access to a free, high-performance, and private local LLM that they can leverage to enhance their apps. Features like text summarization, reasoning over text, and LLM-driven text generation can be incorporated into apps, enhancing their functionality. I see developers taking advantage of this to bring innovative new features to their apps. Google has already done this with Android; Gemini Nano is embedded on the latest Pixel and Samsung phones, and I expect Apple to announce something similar at next month's WDC. Having a local LLM will open up exciting capabilities for any app, and these capabilities are FREE since the LLM is running locally. One of my 2024 predictions was the rise of Edge LLMs (SLMs = small language models), which is coming to fruition. And yes, these SLMs will be limited, but they will only get better and better. SLMs are getting more and more capable. Look at the advances Llama and the Microsoft Phi models have rapidly made, #GenAI #LLM #AI #EdgeLLM #SLM #ConversationalAI

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Generative AI is not just a Silicon Valley game. The core computer science was developed at the University of Toronto (Geoffrey Hinton), University of Montreal (Yoshua Benigo) and Yann LeCun (Bell Labs and NYU). DeepMind was based in London. And Chinese companies have been hyperactive in the space. It is important to keep an open mind on where the innovations will come from. https://lnkd.in/gCTVNFJ3 #AI #generativeAI #innovation

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🚀 Jensen Huang NVIDIA Call to Action: CEOs Must Embrace AI or Risk Obsolescence 🌍 Speaking at Gartner’s IT Symposium, Nvidia CEO Jensen Huang warned business leaders that companies must adopt AI or face becoming obsolete. Nvidia itself has embraced this future, using AI to revolutionize its internal processes, creating what Huang calls an “AI brain” for their operations. Technical Deep Dive: For engineers, Huang’s message about adopting AI highlights a growing shift in data-centric computing. The ability to embed AI into the core infrastructure of companies is key, not just for operational efficiency, but for scaling decision-making processes. Nvidia is pushing forward with using AI to automate chip design and software development, focusing on creating AI-powered tools that learn from data and continuously optimize performance. Engineers need to focus on integrating AI systems into existing processes, whether it’s for data flow optimization, automating repetitive tasks, or even leveraging AI agents that Huang mentions will be a key component in future workplaces. AI Leadership Take: This is a wake-up call for AI leaders—those who don’t act now will fall behind. In my experience, scaling AI systems isn’t just about adding more models or computing power; it’s about creating an AI-first mindset across the organization. Whether you’re optimizing data pipelines or building multimodal systems, the key is to have AI baked into every part of your infrastructure. Huang’s emphasis on AI-driven processes, like chip design, shows that no industry is immune to AI transformation. Nvidia is already collaborating with industry giants like SAP and ServiceNow to build AI agents that can autonomously manage large-scale tasks in private data centers. Leaders should pay attention—these AI agents represent the future of intelligent automation. Google Meta Anthropic OpenAI are already competing AI - super hard.

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LLMs are powerful, but they need structure to avoid getting lost in a sea of data. Enter Knowledge Graphs — the key to organizing and connecting information in a way that AI can truly understand. This is an incredibly important topic as we move into the future of agentic AI. Great post Rohan Rao Benika Hall, PhD Sunil Patel Christopher Brissette (Ph.D) Gordana Neskovic https://lnkd.in/eAAQZNmn #generativeAI #knowledgegraph #LLM #aiagents

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