Page 40 – Advance Aii

Build an Animal/Object Tracking Camera App...

Introducing PetCam: a non-invasive machine-learning-powered pet tracker that runs on an old smartphone. This project is a collaboration between me and Jason Mayes, who came up with the idea. Also, funny story, uh… my colleague Markku Lepistö built (almost) THE EXACT SAME PROJECT at the same time on his own YouTube show, Level Up, which you can see here. We use old smartphones. He uses a Coral development board. Choose your own adventure. When I was young and lived at home in New Jersey, my parents were really strict with me about remembering to close the garage at night. Because if I didn’t close the garage, something like this would happen: Then the next morning, we’d walk out the front door, get hit with a strong whiff of dirty diapers, and see a trash bag torn up and emptied all over our driveway. Clearly someone had a wild night. Bears love eating trash. Raccoons love eating trash. Even foxes will have a go at trash at it if you make it easy for them. All considered, I probably should have gotten better about remembering to close the garage. But it also would’ve been nice to have hacked a little machine...

I have been asked a lot of questions lately about Stanford’s online course offerings and why somebody would choose them over myriad of options online. This is my attempt to bundle them together to help a broader audience. 1. How do you choose classes?It depends on your goals and interests. Here are some questions to ask. Goal: What do you want to achieve out of a particular course? Are you learning for fun or do you want to apply the knowledge to build something? Do you want to extend/switch careers to become a Deep Learning practitioner? Do you think these tools will help you solve a real-life problem? Interests: Read through the course page, find out the topics they cover and search for applications/projects related to it. Do these topics interest you? Here is my review on some of the classes to help you get started. 2. Reference Materials for classes? Most classes at Stanford are self-contained but you are welcomed to read and research resources, there is tons of literature on most topics as it’s an active field of research. Teaching staff usually provides references for each topic which help you consolidate concepts. 3. Are there any forums to...

Introduction to Grounding in Artificial Intelligence In the fast-changing landscape of artificial intelligence, Large Language Models (LLMs) have become powerful tools that generate human-like text. However, these outputs are not always accurate or contextually appropriate. That’s where grounding AI comes in—anchoring models to real-world data to improve factuality and relevance. Ungrounded models might sound coherent but can be misleading or flat-out wrong. In high-stakes sectors like healthcare, finance, and legal services, grounding is vital for ensuring trust and reducing harmful outcomes. The Importance of Grounding in Language Models Without grounding, AI models often “hallucinate“—producing content not based on actual data. This can lead to dangerous misinformation, like flawed medical or legal advice. Real-world examples show the risks: an AI chatbot once shared inaccurate legal information, creating confusion and eroding trust. Grounding is essential to keep models both logical and reliable. Techniques for Grounding AI Here are key methods that help AI stay tethered to real-world truth: External Database Integration Connects AI to structured, vetted databases for accurate outputs. These techniques enhance both the accuracy and context-awareness of AI systems. Advantages of Grounded AI Systems Grounded AI models offer significant benefits: Enhanced Accuracy Reduced risk of spreading false information. Increased User...

October 10, 2021 Vasilis Vryniotis . No comments The last couple of weeks were super busy in “PyTorch Land” as we are frantically preparing the release of PyTorch v1.10 and TorchVision v0.11. In this 2nd instalment of the series, I’ll cover some of the upcoming features that are currently included in the release branch of TorchVision. Disclaimer: Though the upcoming release is packed with numerous enhancements and bug/test/documentation improvements, here I’m highlighting new “user-facing” features on domains I’m personally interested. After writing the blog post, I also noticed a bias towards features I reviewed, wrote or followed closely their development. Covering (or not covering) a feature says nothing about its importance. Opinions expressed are solely my own. New Models The new release is packed with new models: Kai Zhang has added an implementation of the RegNet architecture along with pre-trained weights for 14 variants which closely reproduce the original paper. I’ve recently added an implementation of the EfficientNet architecture along with pre-trained weights for variants B0-B7 provided by Luke Melas-Kyriazi and Ross Wightman. New Data Augmentations A few new Data Augmentation techniques have been added to the latest version: Samuel Gabriel has contributed TrivialAugment, a new simple but highly effective...

Most conversations between decision makers and data professionals begin back-to-front If you’re a decision maker who wants to make the best use of data and other information resources, such as artificial intelligence (AI), to support your decisions, chances are that conversations between you and your data analysts is happening back-to-front. Data analysts should encourage us to explain, “Here’s what I need you to do for me…” Instead, so often they begin with: “Here’s what we have for you…”. The solutions that the analysts produce offer insights that might be relevant. But they do not actually give the decision maker the information they need to make their decision with a level of justification they can be confident in. And there is no audit trail to show how the facts support the conclusion that taking certain actions will lead to the desired outcomes. And the whole process is likely to take too long and require a lot of unnecessary work. There is a better way; it begins with starting the conversation in the right direction. Consider a closely analogous situation. When an IT organization creates software to solve a problem, step one is understanding the customer’s requirements. The interaction between data science...

Every month, financial teams race against time – reconciliations, approvals, and reports! The month-end close can often feel like Groundhog Day, with teams working overtime to process hundreds of invoices, match countless transactions, and catch any unusual entries before they become problems. Today’s businesses can’t afford to operate in monthly cycles anymore. That’s why Sage Intacct, one of the leading cloud financial management systems, has been incorporating AI features into their platform. With additions like Sage Copilot and GL Outlier Assistant, the company hopes to help finance teams break free from the monthly close trap and move toward continuous, real-time financial management. In this guide, I will take you through Sage’s different AI features and how to expand Sage’s AI capabilities. Sage Intacct AI: Native features explained The future of accounting isn’t about faster monthly closes. The focus is gradually shifting to eliminating closing cycles. At least this has been the driving force behind Sage’s recent updates. The aim is to have AI constantly monitor your financial data, catching issues before they become problems and automating routine tasks in real-time. More of continuous accounting rather than periodic sprints. Let me walk you through Sage’s native AI...

Previously, I logged the history of insect-machine hybrid robots in “A Brief History of Cyborg Insects (and Spiders)”. I gave a quick update a year ago in “Cyborg Insects Update: The Amazing Cockroach, Biomimetic Robots and Cyborg Bugs in Recent Years.” So far in 2025, I’ve noticed two new stories about cyborg insects. Both of these projects come from Japan. Stick Insects Tech Briefs recently reported on biohybrid robot research using stick insects at Tohoku University. The research was actually done back in 2023. © 2023, Owaki et al. CC BY 4.0 They didn’t actually let the stick insect cyborg loose to wander around, they had it attached to a piece of wood. However, their experiments showed a more precise way to control the torque of the insect’s joints when electrically stimulating its muscles. More Cockroaches According to a press release: Cyborg insects have a lot of advantages over traditional robots. Power consumption is less of an issue, so it’s easier to miniaturize them, and they are even ‘pre-built’ in a sense. However, research on cyborg insects has been limited to simple environments, like flat surfaces supplemented with external devices to aid navigation. The research team wanted to see if...

This web scraper runs entirely in your browser and is perfect for creating training data for AI models. It works by reading the website’s sitemap.xml file, making it particularly well-suited for modern platforms like Squarespace and Shopify that automatically generate sitemaps. The scraper preserves the structure of your content, including headings, paragraphs, lists, and tables, while removing unnecessary elements like navigation menus and footers. It also captures metadata, images, and PDF documents.

🔘 Laboratory page: github.com/pinballsurgeon/deluxo_adjacency/blob/main/auto_circuits_humongous.ipynb Summary «AutoQML, self-assembling circuits, hyper-parameterized Quantum ML platform, using cirq, tensorflow and tfq. Trillions of possible qubit registries, gate combinations and moment sequences, ready to be adapted into your ML flow. Here I demonstrate climatechange, jameswebbspacetelescope and microbiology vision applications… [Thus far, a circuit with 16-Qubits and a gate sequence of [ YY ] – [ XX ] – [CNOT] has performed the best, per my blend of metrics…]». Dan Ehlers. [linkedin.com/posts/dan-ehlers-32953444_cirq-tensorflow-tfq-activity-6960956732453924864-OM8m?utm_source=linkedin_share&utm_medium=member_desktop_web] Process – Choose vision dataset (James Webb, Bacteria Gram Stains, Wild Fires, or MNIST). Define qubit grid range (ig. 1-5 for free tier colab, 36 total qubits). Define number of experiements you want auto designed and ran. Define range of gate combinations (ig. a range of [3-5] would produce random combination of 3, 4 or 5 gates defined in the next step ). Define types of possible gate (ig. XX, YY, CNOT, ISWAP ect.). Define Tensorflow epoch, batch size, learnign rate, optimzier. loss and metrics ect. Enjoy and test you quantum ciruit, one which may yet to have ever existed. Author Dan Ehlers | github.com/pinballsurgeon | Liked this post? Follow this blog to get more.

This blog post presents an overview of classifier-free guidance (CFG) and recent advancements in CFG based on noise-dependent sampling schedules. The follow-up blog post will focus on new approaches that replace the unconditional model. As a small recap bonus, the appendix briefly introduces the role of attention and self-attention on Unets in the context of generative models. Visit our previous articles on self-attention and diffusion models for more introductory content on diffusion models and self-attention. Introduction Classifier-free guidance has received increasing attention lately, as it synthesizes images with highly sophisticated semantics that adhere closely to a condition, like a text prompt. Today, we are taking a deep dive down the rabbit hole of diffusion guidance. It all began when , in 2021, were looking for a way to trade off diversity for fidelity with diffusion models, a feature missing from the literature thus far. GANs had a straightforward way to accomplish this tradeoff, the so-called truncation trick, where the latent vector is sampled from a truncated normal distribution, yielding only higher likelihood samples in inference. The same trick does not work for diffusion models as they rely on the noise to be Gaussian during training and inference. In search of...