Baiju Devani

𝗖𝗼𝗺𝗽𝘂𝘁𝗲 𝗶𝘀 𝗮 𝗺𝗮𝗷𝗼𝗿 𝗽𝗶𝗹𝗹𝗮𝗿 𝗳𝗼𝗿 𝗔𝗜.  Should countries breed their own champions or yield to US dominance? Earlier this summer, the Canadian Government launched a consultation to inform their strategy to invest $2B in compute for AI. Leah Elizabeth Morris and David Katz shared a great summary of Radical Ventures' position below ⬇ Key message that I fully agree with: "Subsidies should prioritize Canadian headquartered AI startups and scale-ups building and commercializing AI technologies rather than the suppliers (chipmakers, hyperscalers, data centre operators), enabling free market forces to further strengthen the Canadian AI ecosystem." See here: https://lnkd.in/gVizfQGx Having said that, I do believe there is an opportunity to support specialized Canadian hardware providers. Eric Flaningam recently shared the graphic below. Currently the hardware space is totally dominated by the US (80%+), but that space is still emerging and we do have potential champions here as well ... Found this valuable? 🔖 Save for quick reference ♻️ Repost to share the knowledge 🔔 Follow Patrick Tammer for insights on AI for business leaders Untether AI D-Wave Tenstorrent WEKA #AI #Compute #Infra

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Excited to be back at Tech Thursdays next week, co-hosted with CFA Society Calgary and YYC Data Society, for a discussion on the impact of #AI & #Cloud in investment management! In this session we will be diving into questions like: 👉 What's the impact of AI on Macro Investment decisions? 👉 Will AI revolutionize the game, or will changes be gradual? 👉 Are traditional portfolio managers still pivotal, or is the future dominated by quantitative methods? 👉 How do we think about proprietary data sets through AI and how they are being monetized? 👉 And most importantly.... Where are the limits of AI? And where does AI not work? I'll be sharing the stage with some of the top professionals in their fields Julian Klymochko, Wish Bakshi, Benjamin Reeves and Chad Langager!

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I just released the April edition of my AI & the Web newsletter. Nathaniel Simard, founder & CEO of Tracel Technologies and inventor of the burn ML framework, spoke with me about how he got into ML, burn, Tracel and the future of AI. WebGPU, the successor of WebGL, will soon also be supported in Firefox and Safari. Small, powerful LLMs running right in the browser is not a distant future, but are the next big thing in cost-effective and privacy-friendly AI. Google is working hard on better WASM support in the browser that makes ML models running fast even on low-end devices without GPU. With Llama 3 by AI at Meta and Phi 3 by Microsoft AI we got some exciting new, small modells. Llama 3 was already ported to run in the browser and we'll see Phi 3 soon as well. MusicGen has been ported to the browser in transformers.js by Hugging Face engineers and developed an online Jukebox running right in the browser. #webgpu #webml #ai #ml #llama3 #wasm https://lnkd.in/drJCY7Ew

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#AI , #Ethics and #Sustainability have never been as interconnected as today! Looking forward to this discussion tomorrow with amazing Dr. Sasha Luccioni and moderated by Monish Gandhi Come say hello if you are around Toronto Machine Learning Society (TMLS) #TMLS2024

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The Scientific Research and Experimental Development (SR&ED) tax incentive is one of Canada's most powerful tools for fostering tech innovation - enabling businesses to recoup up to 65% of their R&D salary costs. Here's the troubling reality though: While this program distributes ~$4B annually to 20,000 businesses, an unnecessary "SR&ED consulting industry" has emerged that extracts $400M-800M from these funds. Why? These consultants capitalize on companies' fears about complex paperwork, despite the fact that 94% of SR&ED applications are approved. They charge 10-20% of the tax credit simply to complete forms. Let that sink in: Nearly a billion dollars meant for Canadian innovation is instead diverted to middlemen who add minimal value. At Publicus, we're passionate about streamlining government processes through automation. While SR&ED reform isn't our current focus, we believe in raising awareness about the systemic, government-related inefficiencies that hold back Canadian tech companies. #CanadaTech #GovTech #Grants #Innovation

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AI / MBA / CFO question: How can a small business reasonably be “supported” to implement AI? Other than training employees to use LLMs, I haven’t seen any Cost-to-Benefit Ratio that works. Have you? Assumptions: 70-75% of people in Canada/US work in a small business. Average sales in the US for small business is 300k/year, about the same in Canada Net profit available for reinvestment approx 10% / 30k

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Last week Asian founders did... 🚀 Aditya Rajagopal PhD launched nCompass Technologies Inc.'s first service, an API that supports any open-source, fine-tuned, or custom LLM with no rate limits while maintaining a high QoS. 🚀 Danielle J. Yoesep and Shuying Zhang launched Verse Therapy (YC W24), a healthcare platform to connect patients with speech therapists, and empower speech therapists to start their own practices. 🚀 Vidy Thatte launched Webclips, a no-code platform to build mobile apps on your phone. 🚀 Stephen Chau and Mike Chu launched Cove, a new type of collaborative AI that directly edits content with you, learns from your content, proactively suggests next steps, and more. 💸 Ankur Goyal raised a $36M Series A for Braintrust, an end-to-end platform for building AI applications which focuses on making LLM software development robust and iterative. 💸 Jeff Chang raised a $50M Series B for Rad AI, an AI workflow platform enabling radiologists to save time, reduce burnout, and improve patient care. 💸 Hamzah Chaudhary raised an $11M Series A for Lightdash, a platform which turns business data into a self-serve full-stack BI platform. 💸 Annie Liao 🇦🇺 raised a $1.75M Pre-Seed round for Build Club, an AI e-learning platform empowering builders through hands-on, gamified AI education. 🎙️ Jason Patel, founder of OpenForge, shares his journey building a an AI-powered email sales tool on AI Innovaters with Rob May 🎙️ Arvind Jain, co-founder of Rubrik and Glean, talks about Glean and the future of AI-work on the Venture with Grace podcast Links to the podcasts in the comments ⬇️

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Collision Conf is one of the biggest tech+AI events in North America and I am looking forward to learning more in this exploding space. If anyone is heading that way, feel free to ping and we can connect! #AI #generativeAI #digitaltransformation

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Canada's AI paradox is costing us billions: Canada leads the G7 in AI talent, yet 86% of our businesses aren't even considering AI adoption... Working in AI and data, I see this disconnect daily. Yes, we have $2.4B in government support, but money alone won't solve our adoption problem. The real roadblocks I'm seeing: • Setup costs are too high for many businesses (e.g. CAPEX versus OPEX) • Companies worry about keeping data safe (e.g. over-classification of documents due to "better safe than sorry" culture) • Not enough trained workers • Business leaders aren't sure they can trust AI Canadian banks are showing how it's done - they're using AI better than most banks worldwide. But other industries need to catch up if Canada wants to stay competitive. Waiting comes at a cost!!! AI could make Canadian businesses 1-6% more productive in the next 10 years. That's a lot of missed opportunities. Share your firsthand experiences: What's stopping your organization from embracing AI? Let's identify concrete solutions. https://lnkd.in/eAdB5evJ https://lnkd.in/ehvtjRHf

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Cohere CEO Aidan Gomez says that #1 blocker for Enterprise GenAI use cases is data privacy and #2 blocker is the cost (implies ROI). He also explained what Canadian AI startup, Cohere does and how they make money. A must watch for anyone who is interested in Enterprise GenAI use cases. He also said that GenAI fear mongering is cheap ways to get attention (I am paraphrasing, he didn’t use the word cheap). Quite a bit commentary on Google Cloud AI developments as well. Other mentions Oracle and Massachusetts Institute of Technology. Aidan comes across as a mature tech leader for his young age. He sounds more realistic and less me too, in GenAI hyped era. Aidan was talking to CNBC’s Steve Kovach. https://lnkd.in/g36SFYfn ____________________ Last month, Cohere has raised $450 million in funding from returning investors such as Nvidia and Salesforce Ventures, as well as new investors including Cisco and Canadian pension fund PSP Investments cc David Vellante David Linthicum John Furrier Ron Miller Shelly DeMotte Kramer Robert Hof

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Recently I interviewed Nathaniel Simard, founder & CEO of Tracel Technologies and inventor of the burn ML framework: Jan: Nathaniel, we're excited to have you as our guest today! To kick off our conversation, could you please introduce yourself and share a bit about your background and what got you into ML? Nathaniel: Sure, I'm the creator of Burn, a deep learning framework written in Rust, and the founder of Tracel AI. I started coding in the first year of university, where I was studying mechanical engineering, but I quickly switched to software engineering after, since I instantly fell in love with programming. Then I explored different facets of the field from backend to frontend development, and I decided to start my career as a consultant focused on software quality. After some time, I wanted to go deeper into AI, since I was always interested in the process of learning, so I enrolled for a master's degree at MILA. J: According to Github you started to work on burn in summer 2022. Since then has already earned over 7000 stars on GH. What was your initial motivation to develop a new ML framework? N: I always had a side project going on, for fun mostly and to learn new things. I wanted to explore asynchronous and sparse neural network architectures, where each sub-network can learn and interact with other sub-networks asynchronously and independently. I wasn't able to actually create something useful because I needed fine control over the gradients and the concurrency primitives, which is not easily done with Python and PyTorch. At the same time, I was working on machine translation models at my current job, and it was quite painful to put models into production. I decided to switch my side project to a new deep learning framework, with more flexibility regarding gradients and concurrency primitives as well as being more reliable and easier to deploy on any system. J: Amazing to see that this was born out of a side project! I feel the struggle with concurrency in Python and this is something where Rust really shines. What are some of the other key features that makes burn special? N: I think there are two things that really set Burn apart. First, almost all neural network structures are generic over the backend. The goal is that you can ship your model with almost no dependency, and anybody can run it on their hardware with the most appropriate backend, even embedded devices without an operating system. Second, Burn really tries to push the boundaries of what is possible in terms of performance and flexibility. It offers a fully eager API, but also operation fusion and other optimizations that are normally only found in static graph frameworks. The objective is that you don't have to choose between portability, flexibility, and performance; you can have it all! To read the rest of the interview with Nathaniel's plans for burn & trends in AI here: https://lnkd.in/dKv9ztMm #ml #ai

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I had the opportunity to attend the Toronto Machine Learning Society (TMLS) Summit. Sharing my learnings and observations: ➡ RAG RAG RAG ... and did I say RAG? Contextual Semantic Search is how Enterprises are unlocking the value of GenAI ➡ Evaluating the accuracy and reliability of LLMs is becoming increasingly important. Frameworks such as RAGAS.io and TruEra are there to help! 🏁 Hamza Farooq hosted an incredibly interactive session covering the LLM ecosystem and what to take into account for GenAI applications. 🆕 Royal Sequeira Aslesha Pokhrel Christopher Tee delivered a hands-on workshop covering DSPy to optimize your prompts like a software engineer. First interaction with this framework and I really liked how modular it is and felt like Object Oriented Programming for LLMs. 🔗 Yizhi Yin covered Graph RAG and helped us build a personalized product recommendation engine. Pretty cool. 🧠 Ehsan Amjadian talked about lessons learned from building a RAG solution for the financial industry. Very insightful. I need to look into beyond just my chunking strategy 🤓 🙏 Thank you to David Scharbach and the entire organization committee for a great event. The sessions were incredibly useful, relevant and dynamic. I took so many notes and learned directly from AI/ML leaders building in the real world. Looking forward to learning more from this amazing community and contributing to future events! #ML #GenAI #AI #Events #Toronto #summit

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Aidan Gomez, CEO of Cohere, reveals how they're tackling AI hallucinations and improving reasoning abilities. He also explains why Cohere doesn't use any output from GPT-4 for training their models. Aidan shares his personal insights into the world of AI and LLMs and Cohere's unique approach to solving real-world business problems, and how their models are set apart from the competition. Aidan reveals how they are making major strides in AI technology, discussing everything from last mile customer engineering to the robustness of prompts and future architectures. He also touches on the broader implications of AI for society, including potential risks and the role of regulation. He discusses Cohere's guiding principles and the health the of startup scene. With a particular focus on enterprise applications. Aidan provides a rare look into the internal workings of Cohere and their vision for driving productivity and innovation. https://lnkd.in/eB3HAtDS

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Despite Canada’s world-class institutions and talent producing remarkable AI research and products, commercialization remains a significant challenge. Canadian startups struggle to find customers, hindering their growth. The ecosystem excels in innovation but lacks the market support to transition from research to commercial success. Addressing this gap is crucial for leveraging Canada's AI potential fully. Read more about this issue here(https://lnkd.in/gjjWU4GZ).

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I spent 30 hours working on this presentation! Last week, I had the opportunity to join Shaw Talebi on The Data Entrepreneurs' show First off - this was really really fun for me This was the first "webinar" I've ever done! When Shaw and I first got connected, he suggested it may be fun and a great opportunity to share my experience actually developing and publishing custom AI solutions for the last year and a half. I spent a while thinking about the best way to present our learnings and this is ultimately what I delivered! I wanted to create something that would last and could help business owners understand what AI is and how to use it to make MONEY I tried to distill as many of our learnings as I could. I broke it into 3 main sections: 1. What the heck even is AI? 2. Trends, Observations, and Learnings 3. AI Roadmap to Generate PROFIT Was also joined by some TDE members for a Q&A at the end, where they dug into some of the questions I'm sure you've all been wondering. Go check it out! Let me know what you think and if you have any questions, definitely let me know in the comments below, happy to answer anything :) https://lnkd.in/evDXJFX4

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Use it. Build it. Break it. 🛠️ I sat down with Luka Lamaj and the Microsoft Reactor to talk about the AI landscape, Canadian startups and how larger organizations are navigating their own AI transformations. Hang on to the end to hear what quote guides my work daily 💡 https://lnkd.in/gspwd_NW #AI #CanadianStartups #Founders #Innovation #TechOdyssey

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📢 I'm thrilled to announce the publication of "The Investor's Guide to Profiting from the AI Boom Part I: Explore the AI Landscape and Invest with Confidence"! 🧠💼 In this guide, I delve into the AI revolution, offering insights on navigating the new tech boom while avoiding the pitfalls that plagued the dot-com era. Learn essential strategies to thrive in AI investments and position yourself for long-term success. Gain valuable knowledge and make informed investment decisions. 🚀📈 #AIInvesting #TechBoom #InvestmentGuide #AIRevolution #FinancialGrowth Read the full guide https://lnkd.in/gYEM-bbP Stay tuned for Part II, where I'll share my top investment picks and criteria for identifying the AI winners of tomorrow!

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Don't Be Driven to Complacency With a POC: Enterprise GenAI Business Transformation is a Different Beast Building a cool LLM application to demo is exciting! But scaling it for real-world enterprise use and getting tangible business value? That's a whole new challenge, regardless of whether you use a vendor LLM or a custom on-prem LLM . Here's why: POC vs. Production-Ready: Sure, your LLM can demonstrate promising results on a small clean, sanitized sandbox. But can your end-to-end systems sustain performance on real-world scenarios, and with messy enterprise data with the rigor, accuracy, robustness and consistency that is necessary for enterprise use ? LLMs are a small part of a much larger equation: Many of us are enthralled by the shiny LLMs, but enterprises are about many factors including data, platforms, heterogeneous systems with complex processes, and human stakeholders (back-office, middle-office functions, and clients).  Are your data assets consumable and integrated with the vendor or custom GenAI solutions? Security, Governance & Safety: Bias, fairness, and responsible AI practices take center stage. Are you ready to monitor outputs and mitigate potential risks? Can it handle sensitive data, integrate with existing enterprise systems, and meet rigorous security standards? Extensible: Are your e2e platforms modular (vs monolithic) to accommodate the rapid tech advances to ensure your products and solutions dont become “stale” in 3-6 months?! Continuous Improvement: Can your AI platforms leverage the virtuous cycle of continuous improvement by learning from client usage and get better over time? This is overlooked in many AI initiatives and I cannot emphasize enough the business benefits that can be derived by data-driven AI customization and tuning Transitioning from POC to production requires careful strategy, planning, execution, and e2e enterprise-ready platforms, and change management. This takes skills, experience and a cultural mindset shift. The blog from Jerry Liu at Llamaindex expressed this very well:  https://lnkd.in/eUM4H6Af Quote: An issue I see in enterprise LLM app development is that the “build a chatbot in 5 minutes” appeal of LLMs becomes a hindrance when you try to productionize anything. Setting up a demo is easy. But over half a developer’s time is spent on tuning and iterating rather than building. #enterpriseAI #LLMs #AIresponsibility

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