This 2018 paper demonstrates how common spreadsheet software can be used to simulate neural networks, offering a readily accessible and interactive educational tool. It details the implementation of a multilayer perceptron (MLP) within a spreadsheet, using built-in functions to perform calculations for forward propagation, backpropagation, and gradient descent. The authors argue that this approach allows for a deeper understanding of neural network mechanics due to its transparent and step-by-step nature, which can be particularly beneficial for teaching purposes. They provide examples of classification and regression tasks, showcasing the spreadsheet's capability to handle different activation functions and datasets. The paper concludes that spreadsheet-based simulations, while not suitable for large-scale applications, offer a valuable pedagogical alternative for introducing and exploring fundamental neural network concepts.
Posh, a YC W22 startup, is hiring an Energy Analysis & Modeling Engineer. This role will involve building and maintaining energy models to optimize battery performance and efficiency within their virtual power plant (VPP) software platform. The ideal candidate has experience in energy systems modeling, optimization algorithms, and data analysis, preferably with a background in electrical engineering, mechanical engineering, or a related field. They are looking for someone proficient in Python and comfortable working in a fast-paced startup environment.
The Hacker News comments express skepticism and concern about Posh's business model and the specific job posting. Several commenters question the viability of Posh's approach to automating customer service for banks, citing the complexity of financial transactions and the potential for errors. Others express concerns about the low salary offered for the required skillset, particularly given the location (Boston). Some speculate about the high turnover hinted at by the constant hiring and question the long-term prospects of the company. The general sentiment seems to be one of caution and doubt about Posh's potential for success.
John Salvatier's blog post argues that reality is far more detailed than we typically assume or perceive. We create simplified mental models to navigate the world, filtering out the vast majority of information. This isn't a flaw, but a necessary function of our limited cognitive resources. However, these simplified models can lead us astray when dealing with complex systems, causing us to miss crucial details and make inaccurate predictions. The post encourages cultivating an appreciation for the richness of reality and actively seeking out the nuances we tend to ignore, suggesting this can lead to better understanding and decision-making.
Hacker News users discussed the implications of Salvatier's post, with several agreeing on the surprising richness of reality and our limited capacity to perceive it. Some commenters explored the idea that our simplified models, while useful, inherently miss a vast amount of detail. Others highlighted the computational cost of simulating reality, arguing that even with advanced technology, perfect replication remains far off. A few pointed out the relevance to AI and machine learning, suggesting that understanding this complexity is crucial for developing truly intelligent systems. One compelling comment connected the idea to "bandwidth," arguing that our senses and cognitive abilities limit the amount of reality we can process, similar to a limited internet connection. Another interesting observation was that our understanding of reality is constantly evolving, and what we consider "detailed" today might seem simplistic in the future.
The blog post "Common mistakes in architecture diagrams (2020)" identifies several pitfalls that make diagrams ineffective. These include using inconsistent notation and terminology, lacking clarity on the intended audience and purpose, including excessive detail that obscures the key message, neglecting important elements, and poor visual layout. The post emphasizes the importance of using the right level of abstraction for the intended audience, focusing on the key message the diagram needs to convey, and employing clear, consistent visuals. It advocates for treating diagrams as living documents that evolve with the architecture, and suggests focusing on the "why" behind architectural decisions to create more insightful and valuable diagrams.
HN commenters largely agreed with the author's points on diagram clarity, with several sharing their own experiences and preferences. Some emphasized the importance of context and audience when choosing a diagram style, noting that highly detailed diagrams can be overwhelming for non-technical stakeholders. Others pointed out the value of iterative diagramming and feedback, suggesting sketching on a whiteboard first to get early input. A few commenters offered additional tips like using consistent notation, avoiding unnecessary jargon, and ensuring diagrams are easily searchable and accessible. There was some discussion on specific tools, with Excalidraw and PlantUML mentioned as popular choices. Finally, several people highlighted the importance of diagrams not just for communication, but also for facilitating thinking and problem-solving.
Researchers have identified spontaneous, synchronized oscillations in the movement of dense human crowds, similar to those observed in flocks of birds or schools of fish. By analyzing high-resolution trajectory data from high-density crowd events, they discovered distinct collective oscillatory modes where individuals unconsciously coordinate their movements, swaying side-to-side or back-and-forth. These oscillations emerge at certain critical densities and appear to be driven by local interactions between individuals, enhancing crowd fluidity and facilitating navigation. This discovery sheds light on the fundamental principles governing human collective behavior and could contribute to safer and more efficient crowd management strategies.
Hacker News users discussed the study on crowd oscillations with a mix of skepticism and interest. Some questioned the novelty of the findings, pointing out that synchronized swaying in crowds is a well-known phenomenon, especially at concerts. Others expressed concern about the methodology, particularly the reliance on overhead video and potential inaccuracies in tracking individual movements. Several commenters suggested alternative explanations for the observed oscillations, such as subconscious mimicking of neighbors or reactions to external stimuli like music or announcements. There was also a thread discussing the potential applications of the research, including crowd management and understanding collective behavior in other contexts. A few users appreciated the visualization and analysis of the phenomenon, even if they weren't surprised by the underlying behavior.
This post explores the inherent explainability of linear programs (LPs). It argues that the optimal solution of an LP and its sensitivity to changes in constraints or objective function are readily understandable through the dual program. The dual provides shadow prices, representing the marginal value of resources, and reduced costs, indicating the improvement needed for a variable to become part of the optimal solution. These values offer direct insights into the LP's behavior. Furthermore, the post highlights the connection between the simplex algorithm and sensitivity analysis, explaining how pivoting reveals the impact of constraint adjustments on the optimal solution. Therefore, LPs are inherently explainable due to the rich information provided by duality and the simplex method's step-by-step process.
Hacker News users discussed the practicality and limitations of explainable linear programs (XLPs) as presented in the linked article. Several commenters questioned the real-world applicability of XLPs, pointing out that the constraints requiring explanations to be short and easily understandable might severely restrict the solution space and potentially lead to suboptimal or unrealistic solutions. Others debated the definition and usefulness of "explainability" itself, with some suggesting that forcing simple explanations might obscure the true complexity of a problem. The value of XLPs in specific domains like regulation and policy was also considered, with commenters noting the potential for biased or manipulated explanations. Overall, there was a degree of skepticism about the broad applicability of XLPs while acknowledging the potential value in niche applications where transparent and easily digestible explanations are paramount.
The blog post explores using linear programming to optimize League of Legends character builds. It frames the problem of selecting items to maximize specific stats (like attack damage or ability power) as a linear program, where item choices are variables and stat targets are constraints. The author details the process of gathering item data, formulating the linear program, and solving it using Python libraries. They showcase examples demonstrating how this approach can find optimal builds based on desired stats, including handling gold constraints and complex item interactions like Ornn upgrades. While acknowledging limitations like the exclusion of active item effects and dynamic gameplay factors, the author suggests the technique offers a powerful starting point for theorycrafting and understanding item efficiency in League of Legends.
HN users generally praised the approach of using linear programming for League of Legends item optimization, finding it clever and interesting. Some expressed skepticism about its practical application, citing the dynamic nature of the game and the difficulty of accurately modeling all variables, like player skill and enemy team composition. A few pointed out existing tools that already offer similar functionality, like Championify and Probuilds, though the author clarified their focus on exploring the optimization technique itself rather than creating a fully realized tool. The most compelling comments revolved around the limitations of translating theoretical optimization into in-game success, highlighting the gap between mathematical models and the complex reality of gameplay. Discussion also touched upon the potential for incorporating more dynamic factors into the model, like build paths and counter-building, and the ethical considerations of using such tools.
Physics-Informed Neural Networks (PINNs) offer a novel approach to solving complex scientific problems by incorporating physical laws directly into the neural network's training process. Instead of relying solely on data, PINNs use automatic differentiation to embed governing equations (like PDEs) into the loss function. This allows the network to learn solutions that are not only accurate but also physically consistent, even with limited or noisy data. By minimizing the residual of these equations alongside data mismatch, PINNs can solve forward, inverse, and data assimilation problems across various scientific domains, offering a potentially more efficient and robust alternative to traditional numerical methods.
Hacker News users discussed the potential and limitations of Physics-Informed Neural Networks (PINNs). Some expressed excitement about PINNs' ability to solve complex differential equations, particularly in fluid dynamics, and their potential to bypass traditional meshing challenges. However, others raised concerns about PINNs' computational cost for high-dimensional problems and questioned their generalizability. The discussion also touched upon the "black box" nature of neural networks and the need for careful consideration of boundary conditions and loss function selection. Several commenters shared resources and alternative approaches, including traditional numerical methods and other machine learning techniques. Overall, the comments reflected both optimism and cautious pragmatism regarding the application of PINNs in computational science.
Summary of Comments ( 1 )
https://news.ycombinator.com/item?id=43155881
HN users discuss the practicality and educational value of simulating neural networks in spreadsheets. Some find it a clever way to visualize and understand the underlying mechanics, especially for beginners, while others argue its limitations make it unsuitable for real-world applications. Several commenters point out the computational constraints of spreadsheets, making them inefficient for larger networks or datasets. The discussion also touches on alternative tools for learning and experimenting with neural networks, like Python libraries, which offer greater flexibility and power. A compelling point raised is the potential for oversimplification, potentially leading to misconceptions about the complexities of real-world neural network implementations.
The Hacker News post titled "Computer Simulation of Neural Networks Using Spreadsheets (2018)" linking to the arXiv paper "Reliable Training and Initialization of Deep Residual Networks" has several comments discussing the practicality and educational value of implementing neural networks in spreadsheets.
Several commenters are skeptical of the usefulness of this approach for anything beyond very simple networks or educational purposes. One commenter points out the computational limitations of spreadsheets, especially when dealing with large datasets or complex architectures. They argue that specialized tools and libraries are far more efficient and practical for serious neural network development. Another commenter echoes this sentiment, suggesting that while conceptually interesting, the performance limitations would make this approach unsuitable for real-world applications.
Others see value in the spreadsheet approach for educational purposes. One commenter suggests it could be a good way to visualize and understand the underlying mechanics of neural networks in a more accessible way than abstract code. They emphasize the benefit of seeing the calculations unfold step-by-step, which can aid in grasping the concepts of forward and backward propagation. Another agrees, adding that the readily available nature of spreadsheets makes them a low barrier to entry for beginners interested in experimenting with neural networks.
A recurring theme in the comments is the limitations of spreadsheets in handling the scale and complexity of modern deep learning. One comment highlights the difficulty of implementing more advanced techniques like convolutional or recurrent layers within a spreadsheet environment. Another points out that even for simpler networks, training time would be significantly longer compared to dedicated deep learning frameworks.
Some commenters discuss alternative tools for educational purposes, such as interactive Python notebooks, arguing that they offer a better balance between accessibility and functionality. While acknowledging the simplicity of spreadsheets, they emphasize the importance of transitioning to more powerful tools as learning progresses.
A few comments also touch upon the potential use of spreadsheet implementations for very specific, limited applications where computational resources are extremely constrained or where a simple model is sufficient. However, these are presented as niche scenarios rather than a general recommendation.
Overall, the comments express a mix of skepticism and cautious optimism regarding the use of spreadsheets for neural network simulation. While recognizing the potential educational value for beginners, they overwhelmingly agree that spreadsheets are not a viable alternative to dedicated tools for serious deep learning work. The limitations in performance, scalability, and implementation of complex architectures are seen as major drawbacks that outweigh the perceived simplicity of the spreadsheet approach.