Basit Ayantunde

Let's Discuss: Is Embedded Rust the future of the Robotics industry...? I've noticed an ever growing demand in my network and amongst our clients for embedded rust over the last few months, and it certainly seems to be where the future is set to be in terms of Embedded Software, Ground/Flight Software and Robotics. With a 37% growth in Embedded Engineers using Rust in the USA over the last year, I think this is just the start... Most interesting to me, is the 114% increase in individuals using this technology at Stealth Stage Start-ups, showing us Rust certainly seems to be the choice of much of the booming Silicon Valley tech scene. I’ll be posting more updates on this in the near future.

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A new blog post is now live on my website, exploring the use of Hexadecimal Numbers in Computer Science. Thanks for looking! #joshlehman #blog #cs #hexadecimal

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Have you ever wanted to know how fast one dimensional reversible cellular automata can be rendered using SIMD? No? Well you should, here's an article I wrote a while ago: https://lnkd.in/eAVpGQq

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What's up with AI and the 2024 Physics Nobel Prize? Reference Finshots : https://lnkd.in/gA6gEr-n Back in 2009, Nvidia turned down Hinton to get a free GPU for his AI experiments. In 2012, he and his students used their GPUs anyway & developed an artificial neural network that could teach itself to recognise images, thanks to CUDA platform (Nvidia’s software tools to optimise AI applications for their GPUs.) Before this, CUDA was mainly used for high-performance computing such as CT scans, financial modelling, and animation. Hinton proved that GPUs could dramatically accelerate AI training, that GPUs had massive potential beyond gaming. John Hopfield, developed the Hopfield Network, which uses principles of physics to model how the neural networks can learn from incomplete data. For instance, if a picture of a cat is blurry, his model helped the computer guess what it should look like. It was great at recognising patterns but wasn’t enough to build AI systems that could predict or generate new information. Hinton took Hopfield’s ideas to the next level by introducing something called the Boltzmann Machine. He added hidden layers to neural networks, allowing machines to analyse data in more sophisticated ways. These hidden layers act like a “subconscious,” helping computers not just recognise things but also make predictions. For example, instead of just identifying how the cat would look like in a photo, a computer could now guess what the cat might look like in a completely different scene. Neural networks are based on 3 principles from physics : 🧠 Biophysics : Models how the brain works using math. 🤖 Statistical physics : Helps AI process huge amounts of data and find patterns. 💻 computational physics : How computers solve complex problems (AI models). So, without the work of Hopfield and Hinton, AI wouldn’t be what it is today. AI is also solving problems in fields like astronomy, particle physics, and climate science, working with data at a speed and scale we never thought possible. John J. Hopfield - https://lnkd.in/g7gXrYe9 Geoffrey E. Hinton - https://lnkd.in/g3iDb-sp The Nobel Prize #NobelPrize2024

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🚀 Google’s AI just broke the chip design game wide open! 🤖 No I don’t mean 🍟 🙄 The most interesting thing in AI Recruitment… Google DeepMind's AlphaChip is designing computer chips in HOURS, not months. This isn't just faster - it's superhuman. Key points: 👉🏻 Used in Google's last 3 TPU generations 👉🏻 Adopted by MediaTek for smartphone chips 👉🏻 Pre-trained model released for further research 🔄 The kicker? Better AI designs better chips, enabling even more advanced AI, designing even better chips... You see where this is going? 🤔 How do you think this AI-driven acceleration will reshape the tech landscape? Share your thoughts below! Thanks for everything and see you at work! ❤️ Chris Digitalent - AI & Machine Learning Recruitment #AIRevolution #ChipDesign #TechInnovation #FutureOfAI

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📚Exciting News Alert!📚 A big shoutout to Sohini Ghosh and Packt for sending me a copy of "Beginning C++ Game Programming" by John Horton for early review. This book, published by Packt Publishing UK, is a fantastic guide for anyone interested in learning C++ game development. It explains complex ideas in a clear and easy-to-understand way, making it perfect for beginners and experienced developers alike. If you're keen on diving into game development or want to enhance your skills, this book is a must-have! Stay tuned for its release and grab your copy. It's a game-changer! #CPlusPlus #GameDevelopment #Programming #TechBooks #LearningResources #PacktPublishing

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New partnership between Stack Overflow and Open AI. I will be super interested in seeing how this impacts the short and long term accuracy of LLMs in the future. In this video Theo shares a very thought-provoking picture of what this could mean for LLMs in the future. https://lnkd.in/gph_wGPs

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for all those of you leet-coding out there... those graph algorithms will serve you well! At Spur (YC S24) this week we went back to college, using graph algos for our latest feature enabling test modules and prioritizations. Cant wait to ship this by the end of the weekend to our customers! 🚀🚀 #YC #AI #TestAutomation

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Have you ever dreamt of an improbable books crossover, like Frodo from 𝘓𝘰𝘳𝘥 𝘰𝘧 𝘵𝘩𝘦 𝘙𝘪𝘯𝘨𝘴 becoming the main character of the 𝘖𝘥𝘺𝘴𝘴𝘦𝘺 or Emma Bovary from 𝘔𝘢𝘥𝘢𝘮𝘦 𝘉𝘰𝘷𝘢𝘳𝘺 acting as a modern-days Shakespearean Juliet? Well, all of this is now possible! I'm thrilled to introduce my latest #opensource product for #storytelling: 𝐛𝐨𝐨𝐤𝐬-𝐦𝐢𝐱𝐞𝐫-𝐚𝐢 𝐯𝟎.𝟎.𝟎: built with #ReactJS and shipped through Vercel, this #webapp combines the power of two #AI tools: - #gpt4omini by OpenAI, which takes care of cooking new and intriguing plots starting from the user's instructions, the titles and the summaries of the two books to mix (summaries are scraped through Wikipedia, the Free Encyclopedia API) - #text2img realtime API by ModelsLab, which provides a stable diffusion pipeline to create a thumbnail for your newly-generated story Everything is provided under a simple and intuitive #UI, which uses #chatscope's React template kit. Curious of trying? The app is already live at: https://lnkd.in/dsS6jDsh And you can also have a tour of the GitHub repo (and leave a little ⭐ while you're there): https://lnkd.in/djHQ8gkg The documentation is still under construction, but will become available soon😊 Have fun!📚📚

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Fixing a bug in the KDTree implementation of my raytracer that causes some triangles to disappear at certain angles. To detect such issues automatically in the future, I've automated the process of comparing renders with known good outputs and generating image diffs. Now, I can run the ray tracer overnight to check for regressions. https://lnkd.in/gV7azHyA

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(Gift link) - Nice oped from Martin Casado and Katherine Boyle at a16z VC on the blatant bias for Big Tech in D.C. on all things AI. I've never seen such government favoritism for incumbent tech companies in my 40+ year career. It's frankly obscene, self-destructive behavior. It may be some part exploitation of ignorance on the part of Big Techs (you've been duped), it may be some part faulty ideology, but I don't think there is any question it's also political corruption. It's one of the most extreme examples of government capture we've ever seen. To clarify my position -- I don't think it was necessary to even mention open source in this op-ed as it muddies the water with two issues, even if related (I support some open source, though in LLMs it does have security considerations -- all LLMs do). I'm also not against all regulation in AI, but I am fiercely opposed to regulation that favors dominant monopoly incumbents and/or new ventures with multiple billion dollar valuations that don't even have a product. As I repeat often, the super majority of problems and risks that need regulating in AI are caused by LLM firms and their Big Tech partners. That's where the focus should be, and since they are conflicted, the government shouldn't be relying on them for advice like they are, or internal AI advisers who came from them who are on the payroll of non-profits funded by tech billionaires with a myriad of conflicts. It's ugly. Bottom line: While the U.S. has a serious problem with the Big Tech oligopoly and their oligarchs, we are not quite yet an oligarchy, but we're getting very close to meeting that definition. We better fix it before it's too late. ~~~~~ Quote: Although the public-facing argument for AI regulation is to promote safety, we believe the true purpose is to suppress open-source innovation and deter competitive startups. Many emerging technology companies are well-equipped to help solve national-security challenges, but of the 22 members on the board, none represent startups, or what we call “little tech.” Only two are private companies, and the smallest organization on the board hovers around $1 billion in value. The AI companies selected for the board either are among the world’s largest companies or have received significant funding from those companies, and all are public advocates for stronger regulations on AI models.

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The Google DeepMind's AGI Safety & Alignment team have been producing some fantastic work. They are actively working on technical approaches to mitigate existential risks from AI systems. The team, which has grown over the past year, is led by Anca Dragan, Rohin Shah, Allan Dafoe, and Dave Orr, with Shane Legg as executive sponsor. They are part of the broader AI Safety and Alignment organisation at Google. Their main focus areas for the past 1.5 years have been: 1. Amplified oversight: Developing techniques to provide appropriate learning signals for aligning models to avoid catastrophic risks. 2. Frontier safety: Analysing whether models are capable of posing catastrophic risks. 3. Mechanistic interpretability: Enabling both frontier safety and alignment goals. Other research streams include Causal Alignment, exploring how understanding causal incentives can contribute to designing safe AI systems, and various emerging topics such as unsupervised LLM knowledge discovery and explaining grokking phenomena. The team has also engaged in numerous collaborations, both within Google and with external researchers, covering topics like ethics of advanced assistants, detecting hallucinations in large language models, and contributing to benchmarks for evaluating potentially unsafe AI behaviour. P.S. What do you think about the work the Google DeepMind AGI Safety & Alignment team are doing?

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New blog post on category theory concepts for data engineering. Some rough thoughts on how we as a discipline might benefit from using a more generalised, algebraic, abstract approach to systems design. #dataengineering #math #softwareengineering https://lnkd.in/ezkB6ETQ

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"On-device generative-AI processing promises to make the technology faster, more personal and more private. And new AI accelerators - called neural processing units, or NPUs - are cropping up to handle the load with negligible impact on battery life.". Second on that, and its just beginning, with more to come. https://lnkd.in/daTK3Nvh

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Thanks for sharing Brent Hoberman … wonderful (but not surprising) how personal networks and amazing mentors have helped the remarkable Alex Kendall and Wayve team become #GlobalChampions plus .. “The company also benefited from five aspects of being in the UK: 🎓Academic thought leadership, with great Professors such as Zoubin Ghahramani and Roberto Cipolla 👩‍💻Talent density, thanks to multiple leading global companies w large UK AI centres 🏛️Regulatory regime & supportive government, with testing allowed on UK roads & autonomous vehicle bill going through parliament 🇬🇧Leading corporates with UK HQs willing to partner 🌎Skilled immigration regime allowing immigrant founders to move to the UK I hope that Wayve acts as a beacon to other international founders, to show how a once contrarian approach can attract the world’s best brains and capital, and how the UK can focus on technology opportunities and encourage broader entrepreneurship across the country to drive more growth. Wayve announces Europe's largest AI raise - $1bn+ led by SoftBank, Microsoft and NVIDIA. This should be celebrated as a pivotal deal for the UK to stake its claim to be a global leader in the platform shift for embodied AI.”  #ScaleUp #AI #7Cs #Leadership #EveryoneDeservesACoachMentor

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I'm excited to finally get my hands on this treasure - "Build a Large Language Model (From Scratch)" by Sebastian Raschka, PhD, a renowned author and AI expert who I greatly admire 🙌🏻. For those following my journey, you're aware of my appreciation for Sebastian. I first discovered his work through Lightning AI ⚡️ (previously PyTorch Lightning), and later through his insightful book "Machine Learning with PyTorch and Scikit-Learn," as well as his lecture series on Machine Learning and Deep Learning. I also make it a point to follow his blog posts and recommend them to others. What I truly appreciate about Sebastian is his unique ability to teach me something new, regardless of my familiarity with the topic. His gentle, insightful, elegant, and thorough teaching style never fails to captivate me. Initially, I planned to explore his new book on Large Language Models (LLMs) in early January. However, my priorities shifted towards diving into MLOps and learning the Korean language, causing a delay. Nevertheless, I realized that postponing it further would only hinder my progress in understanding #LLMs, a topic I'm eager to explore. This also enables me to support one of my favorite figures in this field which makes me feel happy. I wanted to support him through his blog but his blog is completely free (like what 🤯). With that in mind, my current plan is to: 🚀 Continue working on the model registry for my organization. 🎨 Progress with Chip Huyen's book "Designing Machine Learning Systems." 🧩 Begin delving into Sebastian Raschka, PhD's "Build a Large Language Model" and his accompanying blog posts. I'm committed to continuous growth and looking forward to the exciting journey ahead. Let's keep learning and growing together 🙏🏻! Link to the blog: https://lnkd.in/gP9NY4-W Book link: https://lnkd.in/g_QJwADj #languagemodeling #deeplearning #nlp #transformers #llms

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UCL are using no code to generate additional revenue 🎓 Here's how they built a SaaS tool on Bubble👇🏽 UCL are one of the top universities in the world. The Medical School were fed up of their dated Microsoft Access system. Things were moving too slow, and they needed a change. They explored traditional software development but it was too expensive. They came across Bubble and got building. #1 They built a personal tutoring app The idea was to assign students a tutor. Students could see the notes, and the tutor could respond. #2 They built an app to manage student records #3 They build a curriculum map app - which now has 12,000+ users It guides students on their learning and helps them prepare for exams. The tool is so good that other departments and universities started reaching out. So good that UCL have started reselling this as a SaaS tool with customisations... Goodspeed Bubble Taylor Bennie

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