The hottest Algorithms Substack posts right now

And their main takeaways
Category
Top Technology Topics

I left AI Research to build an AI Startup. Here is what I learned.

Oleksii Sidorov • 10 HN points • 14 Feb 23
🕹 Technology AI Startups Engineering Algorithms Models
  1. In real life, business cares more about whether your AI solution solves a problem than about complex models or theories.
  2. Simplicity often wins in AI solutions - using what you understand well and can deploy quickly can be more effective than complex algorithms.
  3. Understanding the problem domain deeply and focusing on impact rather than endless research is crucial for successful AI projects.

How to improve social media - Part II

Optimally Irrational • 3 implied HN points • 31 Jan 24
🕹 Technology Algorithms Social media Networks Incentives
  1. Content creation on social media platforms can be improved by incentivizing users to produce better quality content that appeals broadly across partisan lines.
  2. Implementing systems like upvoting and downvoting mechanisms can help reduce extreme and polarizing content on social media platforms.
  3. Platforms can consider algorithms that prioritize consensual content and users' contributions from diverse perspectives to enhance the quality of information presented.

Siêu trí tuệ: tìm số nguyên tố hay là tăng minh bạch cho ngành đánh đề

Thái | Hacker | Kỹ sư tin tặc • 39 implied HN points • 27 Dec 19
🔬 Science Mathematics Algorithms Randomness Probability Cryptography
  1. When faced with challenges involving prime numbers, clever algorithms can help quickly eliminate composite numbers and pinpoint the secret numbers.
  2. The difficulty of a problem depends on the randomness of number selection within a matrix and the position of prime numbers.
  3. Designing a fair random number generation system is crucial for ensuring transparency, not only in intellectual competitions but also in traditional gambling industries.
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Bloom Filters and Beyond: An Illustrated Introduction and Implementation

Confessions of a Code Addict • 5 HN points • 05 Sep 23
🕹 Technology Data Structures Programming Algorithms Python Applications
  1. Bloom filters are efficient data structures for quick searches in large datasets and minimize memory usage, with a probabilistic approach to determining membership
  2. Bloom filters use hash functions and bit vectors to store data item membership information while conserving memory by not storing actual items
  3. Counting Bloom Filters are an extension that allow item deletion but come with weaknesses such as handling hash collisions and counter overflow, providing an advanced data handling tool

Data Science Weekly - Issue 393

Data Science Weekly Newsletter • 19 implied HN points • 03 Jun 21
🕹 Technology Data science Machine Learning Algorithms Visualization Artificial Intelligence
  1. Generating coherent noise using Fourier transforms can create impressive 3D terrain effects. It's interesting to see how a complex math concept can produce realistic visuals.
  2. Deepfake technology can alter maps, which raises concerns about misinformation. It's a reminder to be cautious about what we see online.
  3. Learning data science should start with foundational knowledge, not just jumping into deep learning. Understanding basic concepts is key to building effective models.

Step By Step Parsing of Mathematical Expressions From Scratch

Luminotes • 7 implied HN points • 21 Mar 23
🕹 Technology Mathematics Programming Algorithms Evaluation Implementation
  1. Balancing parenthesis in mathematical expressions can be achieved by using an array or stack.
  2. Identifying the correct operators, like handling "**" for power of, is crucial for parsing expressions accurately.
  3. Converting mathematical expressions to postfix notation simplifies the evaluation process, removing the need for brackets.

The Computer Scientist's Guide to Graph Theory, ep. 02

The Palindrome • 3 implied HN points • 13 Dec 23
🔬 Science Graph Theory Algorithms Mathematics
  1. Matching problems can be modeled using bipartite graphs where no edges go between vertices of the same type.
  2. In graph theory, a full matching of one partition of a bipartite graph implies that every vertex in that partition has at least as many neighbors in the other partition.
  3. Hall's theorem provides a necessary and sufficient condition for determining the existence of a full matching in a bipartite graph.

Data Science Weekly - Issue 379

Data Science Weekly Newsletter • 19 implied HN points • 25 Feb 21
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Algorithms
  1. Writing a book on data science can be a fun way to inspire others to use data in their lives. The process can feel challenging but is ultimately rewarding.
  2. Learning about Python concurrency can be tricky but understanding it is important for data scientists moving into software engineering roles. Engaging with live coding talks can clarify complex concepts.
  3. Feature stores are becoming essential for managing machine learning data and making it easier to deploy models. They help data scientists collaborate and quickly get their work into production.

System Design Fundamentals: What is a Load Balancer ?

Engineering At Scale • 2 HN points • 15 Jan 24
🕹 Technology System Design Algorithms Hardware Software
  1. Load Balancers distribute client requests to different servers, improving system reliability and scalability.
  2. Load Balancers handle growing internet usage by evenly distributing workloads, preventing servers from being overwhelmed.
  3. Different types of Load Balancers include Hardware, Software, and Cloud Load Balancers, each with unique benefits for system optimization.

Data Science Weekly - Issue 352

Data Science Weekly Newsletter • 19 implied HN points • 20 Aug 20
🕹 Technology Data science Machine Learning Artificial Intelligence Algorithms Computing
  1. minGPT is a smaller version of the GPT model that aims to be simple and easy to understand. It’s only about 300 lines of code, which makes it a good resource for learning.
  2. Biased training data, like the CoNLL-2003 dataset, can lead AI models to perform poorly on diverse names and future data. This can cause ongoing issues with how these models recognize different groups.
  3. Reinforcement learning has challenges in real-world applications due to assumptions that often don't hold up. Researchers need to address these challenges to make RL more practical and effective.

Data Science Weekly - Issue 343

Data Science Weekly Newsletter • 19 implied HN points • 18 Jun 20
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Algorithms
  1. AI models can now generate images just like they generate text, thanks to advanced training methods. This shows how powerful these technologies have become in creating complex visuals.
  2. MLOps is key for data scientists as it helps them work together better by automating tasks like testing and versioning. This makes their processes smoother and more efficient.
  3. Regulating algorithms is important because they influence many aspects of our lives without any oversight. A new system is needed to ensure they are used fairly and responsibly.

Password Hashing Explainer

ciamweekly • 1 HN point • 18 Mar 24
🕹 Technology Security Passwords Algorithms Guidelines
  1. Passwords are still widely used due to being supported by many applications, being cost-effective, and familiar to users.
  2. Hashing passwords adds a crucial layer of security by making it harder for attackers to retrieve passwords in the event of a breach.
  3. When it comes to password hashing algorithms, it's important to stay updated on recommendations, such as NIST guidelines, and to choose wisely based on current security best practices.

Data Science Weekly - Issue 307

Data Science Weekly Newsletter • 19 implied HN points • 10 Oct 19
🕹 Technology Artificial Intelligence Machine Learning Data science Computer Vision Algorithms
  1. Deep learning is great at spotting patterns but struggles to explain the reasons behind those patterns. This is something experts want to improve.
  2. Some scientists are using their skills in machine learning for everyday tasks like fashion recommendations instead of just space research.
  3. Tiny AI models can make phone features like autocorrect and voice assistants work much better and faster.

AI-assisted Ant Colony Optimization with GPT-4 for Solving the Traveling Salesman Problem

Rustic Penn • 2 HN points • 28 Apr 23
🕹 Technology AI Optimization Artificial Intelligence Algorithms Data science
  1. The article explores the synergy between GPT-4 and Ant Colony Optimization for solving the Traveling Salesman Problem.
  2. GPT-4 showcases its potential in guiding and assisting the implementation of the Ant Colony Optimization algorithm.
  3. The combination of AI like GPT-4 with nature-inspired algorithms can lead to innovative and efficient problem-solving solutions.

Data Science Weekly - Issue 285

Data Science Weekly Newsletter • 19 implied HN points • 09 May 19
🕹 Technology AI Machine Learning Data science Robotics Algorithms Data Analysis
  1. Machine learning is good at finding patterns in data, but understanding why those patterns exist is still a challenge. This breakthrough could help us understand complex systems better.
  2. Robots can avoid obstacles more effectively with a special type of camera that reduces perception delays. This can help improve how robots navigate through tricky environments.
  3. Stitch Fix uses a game called 'Style Shuffle' to quickly learn about customer preferences. This fun method helps them suggest clothes that people are more likely to buy.

Data Science Weekly - Issue 280

Data Science Weekly Newsletter • 19 implied HN points • 04 Apr 19
🕹 Technology AI Machine Learning Data science Algorithms Analytics
  1. AI is being developed by companies like DeepMind to create powerful technology, raising questions about who controls it. It's an important topic as AI continues to evolve.
  2. Tools like Warby Parker's virtual try-on algorithm show how technology can improve shopping experiences by using real-life simulations, making it easier for customers to make choices.
  3. Innovations in AI, like personalized travel recommendations from TripAdvisor and enhanced speech recognition for Alexa, demonstrate how machine learning can enhance user experiences in daily life.

Data Science Weekly - Issue 279

Data Science Weekly Newsletter • 19 implied HN points • 28 Mar 19
🕹 Technology Artificial Intelligence Machine Learning Data science Algorithms Robotics
  1. Three scientists won the Turing Award for their groundbreaking work on neural networks. This award is like the Nobel Prize for computing and comes with a $1 million prize.
  2. Adversarial machine learning could pose security risks by allowing enemies to reverse-engineer AI systems. Experts urge caution as this threat could impact important technologies.
  3. The fast-food giant McDonald's is investing heavily in machine learning by acquiring a startup. This shows how businesses are increasingly using data and AI to improve operations.

Data Science Weekly - Issue 271

Data Science Weekly Newsletter • 19 implied HN points • 31 Jan 19
🕹 Technology Data science Machine Learning AI Algorithms Statistics
  1. Machine learning projects can be tricky to manage because teams often struggle with setting clear goals and expectations.
  2. Data science can help predict startup valuations, revealing interesting properties and trends in how these valuations are distributed.
  3. New research in AI is making strides in speech reconstruction and facial recognition fairness, but these technologies also raise ethical concerns.

Data Science Weekly - Issue 266

Data Science Weekly Newsletter • 19 implied HN points • 27 Dec 18
🕹 Technology Data science Machine Learning Artificial Intelligence Algorithms Big Data
  1. Netflix's data team often clashes with the content team, highlighting the importance of balancing data insights with creative decisions.
  2. Teaching AI to write generates funny results, showcasing the difficulties of making machines understand human language.
  3. Data is not just raw information; it is influenced by human judgment and context, making it essential to analyze it carefully.

Introduction to Hash Tables

Photon-Lines Substack • 2 HN points • 09 Jul 23
🕹 Technology Data Structures Algorithms Hashing Computing Programming
  1. Hash tables allow for efficient storage and retrieval of key-value pairs using hash functions.
  2. Real-world applications of hash tables include dictionaries, caching systems, database indexing, and symbol tables in compilers.
  3. Good hash functions must be efficient, deterministic, and ensure a uniform distribution of generated keys to avoid collisions.

Data Science Weekly - Issue 245

Data Science Weekly Newsletter • 19 implied HN points • 02 Aug 18
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Algorithms
  1. Hiring the right people is crucial for data science teams. Companies should look for candidates who can work independently and fit well with the team culture.
  2. Understanding uncertainty in models is important. This helps in interpreting results and debugging any issues that arise in data science projects.
  3. Learning resources are abundant in data science. There are many tools and tutorials available to help beginners and advanced users improve their skills.

Data Science Weekly - Issue 230

Data Science Weekly Newsletter • 19 implied HN points • 19 Apr 18
🕹 Technology Data science Machine Learning Artificial Intelligence Programming Algorithms
  1. You can learn how to become a data scientist with specific guides focused on gaps in knowledge, portfolio building, and resume writing.
  2. There are fun projects in AI, like training models to recognize dogs or create cartoons, showing how diverse applications of data science can be.
  3. Bias in machine learning models is a big issue, and it's important to understand how these biases can affect results in various tasks.

Data Science Weekly - Issue 223

Data Science Weekly Newsletter • 19 implied HN points • 01 Mar 18
🕹 Technology Data science AI Machine Learning Algorithms Programming
  1. AI still struggles with creativity and emotional understanding in music, meaning it can't fully replace human DJs and playlist makers.
  2. Female characters are underrepresented in superhero comics, and their portrayal is important to analyze as well.
  3. Containerization is a complex topic for data scientists, and balancing their autonomy with the need for engineering support is essential for success.

Twitter Algorithm Review - part 5 - looking at the code

PashaNomics • 2 HN points • 04 Apr 23
🕹 Technology Algorithms Social media Coding Machine Learning Data Analysis
  1. Twitter made a good move by removing potentially biased algorithm features like 'author_is_elon'.
  2. There are potential issues in Twitter's code related to page rank algorithm, training data pollution, and tweet scoring.
  3. Twitter's algorithm may have inconsistencies and issues, like boosting trends and a flawed approach to fighting spam.

Data Science Weekly - Issue 209

Data Science Weekly Newsletter • 19 implied HN points • 24 Nov 17
🕹 Technology AI Machine Learning Data science Computer Vision Algorithms
  1. Flies have a unique way of recognizing and categorizing odors, which inspired a new computer algorithm for searching similar images online.
  2. AI can now identify art forgeries just by analyzing brushstrokes, making the detection process easier and less expensive.
  3. Apple is still catching up in the AI field, despite previous promises to collaborate more with researchers and improve their technology.

GPU 2D Drawing: Rectangls. Masking. Rotations.

Hasen Judi • 2 HN points • 04 Jul 23
🕹 Technology Graphics Programming Design Algorithms Transformation
  1. Rectangles can be defined in different ways for SDF purposes, but simplifying it to (center, half_size) helps in calculations such as distance to edges or corner points.
  2. Simple transformations like growing, bordering, and inversion, along with masking and rotations, can create interesting visual effects and shapes.
  3. Combining these shape manipulation techniques can lead to the creation of more complex shapes and effects in GPU-rendered graphics.