The hottest Algorithms Substack posts right now

And their main takeaways
Category
Top Technology Topics

Must Learn Quantum Security Preface: The Power of Quantum Computing in Advancing Artificial Intelligence

Rod’s Blog • 79 implied HN points • 15 Sep 23
🕹 Technology Quantum Computing Artificial Intelligence Machine Learning Algorithms Security
  1. Quantum computing has the potential to significantly enhance computational power and speed in AI tasks, offering faster and more accurate predictions.
  2. Quantum computing enables the development of more sophisticated machine learning techniques by processing and analyzing large amounts of data more efficiently.
  3. Quantum-inspired algorithms can be leveraged to improve classical AI algorithms, showcasing the benefits of quantum computing even without fully-fledged quantum computers.

What CEOs, leaders, and investors need to know about the different meanings of AI

Mike Talks AI • 78 implied HN points • 27 Jul 23
🕹 Technology AI Analytics Deep Learning Machine Learning Algorithms
  1. The term AI can mean different things and understanding those meanings is crucial for clear communication, better decisions, and addressing concerns.
  2. Different definitions of AI include AGI or artificial general intelligence, deep learning for solving complex problems, and tools like ChatGPT for tasks like writing and summarizing.
  3. CEOs, leaders, and investors should explore opportunities in AGI, deep learning, ChatGPT, and practical AI to stay relevant and make informed decisions.
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Fun with Serial and Parallel Sum Reductions

CPU fun • 121 implied HN points • 22 Feb 24
🔬 Science Mathematics Computer Science Algorithms
  1. Floating point arithmetic can be more complex than expected, especially due to limited mantissa bits, affecting the accuracy of calculations.
  2. Complaining about OpenMP reductions giving 'the wrong answer' is misguided; the issue likely existed in the serial code and is now being exposed.
  3. Changing the type of the accumulator to 'double' can help resolve issues with floating point arithmetic and accuracy during sum reductions.

Grief for Twitter

Kyle Chayka Industries • 175 implied HN points • 11 Oct 23
🎭️ Culture Social media Internet Community Algorithms
  1. Twitter used to be a vibrant platform for diverse discussions and connections, but has now deteriorated due to algorithms and glitches.
  2. Finding spaces for meaningful human interaction on the internet is becoming increasingly challenging as platforms focus more on algorithms and less on authentic conversations.
  3. Despite the challenges, platforms like Substack are emerging as potential spaces for cultivating genuine communities and conversations.

105 trillion digits of pi

A Piece of the Pi: mathematics explained • 60 implied HN points • 15 Mar 24
🔬 Science Mathematics Algorithms
  1. The number pi has now been calculated to 105 trillion decimal places using the Chudnovsky algorithm over 75 days.
  2. Ramanujan's formula for pi has been expanded and improved upon over the years, with the Chudnovsky brothers developing a formula that computes pi to 13 decimal places.
  3. Bellard's formula and the BBP formula provide ways to compute specific digits of pi without having to calculate all earlier digits, making validations faster and more efficient.

Building a Working Recommendation Engine from Scratch

Building a Recommendation Engine • 3 HN points • 04 Aug 24
🕹 Technology Algorithms Programming APIs Data Management Machine Learning
  1. A recommendation engine can work without complex machine learning. Instead, it can be built using straightforward connections between content to suggest things users might like.
  2. Using an API from a platform like Are.na allows easy access to user content and helps find connections between different channels, making recommendations more relevant.
  3. It's important to filter out content that users already know or follow to give them fresh and exciting recommendations. Regular updates to the recommendations can also help keep things interesting.

Judging myself by likes

Kyle Chayka Industries • 195 implied HN points • 22 Jul 23
🎭️ Culture Technology Social media Digital Culture Algorithms Influence
  1. Likes can impact how we judge our online success, but their significance varies across platforms.
  2. Algorithms on social media have changed how engagement is measured, making it harder to interpret likes as a true reflection of content quality.
  3. The age of likes is evolving, with platforms like TikTok shifting focus away from visible likes but still using them to influence content.

Using the Rabin Karp Algorithm to speed up your code[Technique Tuesdays]

Technology Made Simple • 59 implied HN points • 19 Apr 23
🕹 Technology Algorithms Programming AI Tech industry
  1. The Rabin Karp algorithm is a string-searching technique that uses hashing to efficiently find patterns in texts.
  2. It is useful for tasks like detecting plagiarism, finding keywords, or searching for DNA sequences in large texts.
  3. The algorithm works by calculating hash values at each position of the text, making it faster than naive string-matching algorithms.

HackerNews Ranking Algorithm: How would you have done it?

Vigneshwarar’s Newsletter • 181 HN points • 09 Apr 23
🕹 Technology Algorithms Ranking Discussion Search Engine Manipulation
  1. The current HackerNews ranking algorithm is based on a simple formula involving points, age, and a constant factor.
  2. Proposing a new approach called HackerRank that incorporates PageRank-like scoring for user profiles based on upvotes and takes flagging into account.
  3. Additional ideas for improving the ranking algorithm include considering user submission upvotes, reading time, and website reputation.

k-Core Decomposition

Graphs For Science • 52 implied HN points • 24 Feb 24
🔬 Science Graph Theory Algorithms Data Analysis
  1. k-Core Decomposition is a way to explore the structure of networks by identifying the largest subgraph where every node has a specified minimum degree.
  2. The k-Core Decomposition algorithm involves recursively removing nodes with degrees lower than a specified threshold to reveal the k-core and k-shell structure of a graph.
  3. The degree of a node in a k-core doesn't have an upper limit, providing unique insights into network connectivity beyond traditional degree-based analysis.

[Solution]Problem 76: Combination Sum [Google]

Technology Made Simple • 59 implied HN points • 24 Feb 23
🕹 Technology Programming Algorithms Graphs Recursion Backtracking
  1. The problem involves backtracking, recursion, and graph modeling to find unique combinations that sum to a target.
  2. Modeling the problem as a graph with states and transitions helps in traversal mechanics using DFS.
  3. Implementing a simple graph traversal algorithm, backtracking, and a global variable to track combinations can efficiently solve the problem.

The Atomized Empire

Based Meditations • 39 implied HN points • 25 Nov 23
🕹 Technology Internet Algorithms Social Interaction Tech Addiction Digital Culture
  1. The Atomized Empire metaphorically represents how technology has enslaved us, influencing behavior through digital means.
  2. Technology, like a modern Trojan Horse, has stealthily infiltrated our lives, controlling us through addictive algorithms and impacting human culture.
  3. Our increasing addiction and reliance on technology is leading to loneliness, social disconnection, and a detachment from the real world, hindering deep human connections and meaningful interactions.

📝 Guest Post: An introduction to Similarity Search*

TheSequence • 182 implied HN points • 03 Apr 23
🕹 Technology Embeddings Models Algorithms
  1. Vector similarity search is essential for recommendation systems, image search, and natural language processing.
  2. Vector search involves finding similar vectors to a query vector using distance metrics like L1, L2, and cosine similarity.
  3. Common vector search strategies include linear search, space partitioning, quantization, and hierarchical navigable small worlds.

On Social Media, Demand Slopes Upwards

Never Met a Science • 66 implied HN points • 15 Nov 23
🕹 Technology Social media Economics Algorithms Producers Consumers
  1. In the attention economy of social media, demand can increase as supply increases, leading to an unsustainable positive feedback loop.
  2. The attention economy operates differently from traditional market economies, with attention being a key commodity rather than money.
  3. Consumers, producers, and algorithms play unique roles in driving the positive feedback loop of the attention economy, which can have far-reaching implications.

Specifying objectives in RLHF

Democratizing Automation • 90 implied HN points • 02 Aug 23
🕹 Technology Machine Learning Artificial Intelligence Research Optimization Algorithms
  1. Reinforcement learning from human feedback involves using proxy objectives, but over-optimizing these proxies can negatively impact the final model performance.
  2. Optimizing reward functions for chatbots with RLHF can be challenging due to the disconnect between objective functions and actual user preferences.
  3. A new paper highlights fundamental problems and limitations in RLHF, emphasizing the need for a multi-stakeholder approach and careful consideration of current technical setups.

The Authority of the Algorithms

The Digital Anthropologist • 19 implied HN points • 12 Feb 24
🕹 Technology Algorithms Artificial Intelligence Ethics Data Society
  1. Algorithms are deeply integrated into our daily lives, impacting everything from music to job applications, showing both benefits and risks.
  2. Algorithms, designed by humans, are gaining authority in society, prompting questions about ethical guidelines and accountability for their creators.
  3. Concerns about algorithms creating a bland, uniform world are present, but societal values and human creativity may prevent dystopian outcomes.

We've Entered the Era of Hyper-Personalization

Embracing Enigmas • 39 implied HN points • 31 May 23
🕹 Technology Personalization AI Data Collection Algorithms Content creation
  1. We are entering an era of hyper-personalization where content is tailored to specific individuals beyond just what they might like.
  2. The progression of personalization stages includes one-size-fits-all, segmentation, behavioral personalization, predictive personalization, and now hyper-personalization.
  3. The main components needed for hyper-personalization are data about the individual, algorithms for content selection, content creation, and a trust layer for quality control.

Twitter open-sourced their recommendation algorithm

MLOps Newsletter • 39 implied HN points • 09 Apr 23
🕹 Technology Algorithms Machine Learning Open Source Neural Networks Data science
  1. Twitter has open-sourced their recommendation algorithm for both training and serving layers.
  2. The algorithm involves candidate generation for in-network and out-network tweets, ranking models, and filtering based on different metrics.
  3. Twitter's recommendation algorithm is user-centric, focusing on user-to-user relationships before recommending tweets.

XGBoost is the Secret of ML Energy

Sector 6 | The Newsletter of AIM • 39 implied HN points • 06 Sep 23
🕹 Technology Artificial Intelligence Machine Learning Data science Algorithms Computing
  1. XGBoost, or Extreme Gradient Boosting, helps improve the performance and speed of machine learning models that deal with tabular data. It's known for being really good at finding patterns and making predictions.
  2. This algorithm works best for supervised learning when you have lots of training examples, especially when you have both categorical and numeric data. It can handle a mix of different data types well.
  3. If you're working with a dataset that has many features, XGBoost is a strong choice to enhance the capabilities of your machine learning model. It makes it easier to get accurate results.

Implementing Softmax using RISC-V Vector

Fprox’s Substack • 41 implied HN points • 12 Feb 24
🕹 Technology Programming Algorithms Computing Simulation Hardware
  1. Softmax is a non-linear normalization layer commonly used in neural networks to compute probabilities of multiple classes.
  2. When implementing Softmax, numerical stability is crucial due to exponential function's rapid growth, requiring clever techniques to prevent overflow.
  3. RISC-V Vector (RVV) can be used to efficiently implement complex functions like Softmax, with stable and accurate results compared to naive implementations.

Algorithms you should know before taking System Design Interviews [System Design Sundays]

Technology Made Simple • 79 implied HN points • 11 Sep 22
🕹 Technology Algorithms
  1. Focus on understanding a few key algorithms that frequently show up in System Design interviews. Knowing these well can lead to great results.
  2. Prioritize learning the 'Five-star' algorithms, understand why they exist and the problems they solve. These are very important for interviews.
  3. Remember the 80/20 rule - most results come from basics that are commonly tested. Invest time in mastering those before moving on to advanced topics.

GPT-4 invalidates the Turing test

Gray Mirror • 110 implied HN points • 13 Apr 23
🕹 Technology AI Computing Intelligence Models Algorithms
  1. Large language models like GPT-4 are not AI, but they are powerful tools that connect patterns and rely on intuition.
  2. The Turing test is not a valid test for AGI, as machines like LLMs can invalidate it by excelling in certain tasks while lacking in others.
  3. Understanding the difference between general and special intelligence is key to not overestimating the capabilities of tools like GPT-4.

Claiming LLMs are merely "next token predictors" is a fundamental misunderstanding

The Future of Life • 19 implied HN points • 18 Jan 24
🕹 Technology AI Machine Learning Neural Networks Algorithms Computational Theory
  1. LLMs are more than just next-token predictors. They use complex internal algorithms that let them understand and create language beyond simple predictions.
  2. The process that powers LLMs, like token prediction, is just a tool that leads to their true capabilities. These systems can evolve and learn in many sophisticated ways.
  3. Understanding LLMs isn't easy because their full potential is still a mystery. What limits them could be anything from their training methods to the data they learn from.

[Solution]Problem 65:Find the Fixed Point in a sorted array[Apple]

Technology Made Simple • 59 implied HN points • 18 Nov 22
🕹 Technology Coding Algorithms Software Development Tech Solutions
  1. A fixed point in a sorted array is an element whose value matches its index. Binary search can be used to efficiently find a fixed point if the array is sorted.
  2. When optimizing algorithms, focus on improving the major components like loop traversal to enhance the overall performance.
  3. In sorted arrays, utilizing comparators and the inherent comparison order can simplify the coding process and boost efficiency.

[Solution]Problem 52:Search 2D Matrix[Microsoft]

Technology Made Simple • 79 implied HN points • 18 Aug 22
🕹 Technology Coding Algorithms Software Development
  1. Understanding the problem clearly is crucial in finding efficient solutions. This particular problem doesn't require special knowledge, just logic and basic algebra.
  2. Recognizing patterns and properties of the data can significantly enhance the algorithm. In this case, the unique rules about the sorted matrix rows were key to optimizing the search process.
  3. Optimizing code by leveraging insights about the data structures can simplify solutions and reduce unnecessary complexity. It's important to make the most of the given information to write efficient algorithms.

Best YouTube Channels to learn about Graph Theory for Software Engineering for free[Math Mondays]

Technology Made Simple • 59 implied HN points • 10 Oct 22
🚌 Education Math Coding Computer Science Theory Algorithms
  1. Focus on using a mix of channels to become an expert in Graph Theory for Software Engineering. Channels vary in their emphasis on math, coding, and computer science.
  2. Utilize the recommended channels like Wrath of Math, David Amos, Trev Tutor, and FreeCodeCamp to sharpen your understanding of Graph Theory.
  3. Engage with the content from different channels to build strong theoretical foundations and improve your performance in coding interviews.

[Solution]Problem 72: Permutation in String [Amazon]

Technology Made Simple • 39 implied HN points • 27 Jan 23
🕹 Technology Algorithms Binary Trees Graphs Coding Data Structures
  1. The problem discussed is about validating a binary search tree, ensuring the left subtree contains smaller values, the right subtree contains greater values, and both are valid binary search trees.
  2. Examples are provided to illustrate the concept, showing a valid and an invalid binary search tree.
  3. Constraints include the number of nodes and the value ranges in the tree.

Problem 72: Permutation in String [Amazon]

Technology Made Simple • 39 implied HN points • 25 Jan 23
🕹 Technology Coding Binary Trees Algorithms Online Learning
  1. The problem discusses validating a binary search tree by checking if the left subtree contains keys less than the node's key and the right subtree contains keys greater than the node's key.
  2. It's important to ensure that both the left and right subtrees of a node are also binary search trees, following specific rules for structure and key values.
  3. Validating a binary search tree involves evaluating constraints like the number of nodes in the tree and the range of node values it can contain.