The hottest Computer Vision Substack posts right now

And their main takeaways
Category
Top Technology Topics

Vesuvius Challenge Progress Prizes: December Edition

Vesuvius Challenge • 31 implied HN points • 24 Jan 25
🕹 Technology Data science Machine Learning Computer Vision Community Engagement Open Source
  1. The community is focused on improving data quality, like using better labels and refining how they categorize information. This will help them create automated tools for analyzing scrolls more effectively.
  2. Several contributors have made significant advancements in developing new segmentation models and tools, which will help in analyzing scroll data. These innovations are key for understanding ancient texts.
  3. 2024 has been a great year for teamwork and progress as everyone shares their findings. The hard work from many people is leading to quick improvements in technology for studying historical scrolls.

The Sequence Knowledge #545 : Beyond Language, Learning About Multimodal Benchmarks

TheSequence • 28 implied HN points • 20 May 25
🕹 Technology AI Machine Learning Computer Vision Data science Benchmarking
  1. Multimodal benchmarks are tools to evaluate AI systems that use different types of data like text, images, and audio. They help ensure that AI can handle complex tasks that combine these inputs effectively.
  2. One important benchmark in this area is called MMMU, which tests AI on 11,500 questions across various subjects. This benchmark needs AI to work with text and visuals together, promoting deeper understanding rather than just shortcuts.
  3. The design of these benchmarks, like MMMU, helps reveal how well AI understands different topics and where it may struggle. This can lead to improvements in AI technology.

Vesuvius Challenge is hiring!

Vesuvius Challenge • 9 implied HN points • 21 Jan 25
🕹 Technology Computer Vision Data science Software Engineering Machine Learning Infrastructure
  1. The Vesuvius Challenge is looking for team members to help recover texts from ancient scrolls. They need people for two key roles: research in computer vision and platform engineering.
  2. The computer vision role focuses on using advanced tech to read the scrolls, which involves solving complex problems with CT scan data.
  3. The platform engineering role is about creating tools and systems to manage and share large datasets, making research easier for the community.

The New Era of Efficient LLM Deployment

Gradient Flow • 299 implied HN points • 13 Jul 23
🕹 Technology AI ML Infrastructure Tools Computer Vision
  1. AI tools are becoming pervasive in tech with potential to increase productivity and contribute trillions annually to global productivity
  2. Efficient deployment of large language models (LLMs) is crucial for businesses to scale their AI initiatives and drive digital innovation
  3. Rethinking MLOps infrastructure is essential to accommodate the scale and complexity of LLMs, with a need for solutions addressing challenges in inference, serving, and deployment

Latent Reasoning, 3D Colorization, and the Limits of RL

HackerPulse Dispatch • 8 implied HN points • 13 Dec 24
🕹 Technology AI Machine Learning Computer Vision Reinforcement Learning Data science
  1. COCONUT is a new method that lets language models think in flexible ways, making it better at solving complex problems. It does this by using continuous latent spaces instead of just words.
  2. ChromaDistill offers a smart way to add color to 3D images efficiently. It lets you view these scenes consistently from different angles without slowing things down.
  3. Recent research shows that top AI models can be deceptive and plan strategically, which raises important safety concerns. There’s also a new approach to testing AI limits in a friendly, curiosity-driven way.
Get a weekly roundup of the best Substack posts, by hacker news affinity:

Market Map & Analysis: AI Synthetic Data Companies

The Strategy Deck • 78 implied HN points • 06 Jul 23
🕹 Technology AI Data ML Synthetic Data Computer Vision
  1. Synthetic data is crucial for ML by replacing real-world data, protecting sensitive information, and validating AI applications.
  2. Synthetic data is used in computer vision for autonomous vehicles and is expanding to other data types like text and tabular data.
  3. There are specialized and general-purpose synthetic data platforms developing innovative solutions for various industries and use cases.

Papers I've read this week: vision language models

Artificial Fintelligence • 8 implied HN points • 28 Oct 24
🕹 Technology AI Models Computer Vision Machine Learning Natural Language Processing Research Papers
  1. Vision language models (VLMs) are simplifying how we extract text from images. Unlike older software, modern VLMs make this process much easier and faster.
  2. There are several ways to combine visual and text data in VLMs. Most recent models prefer a straightforward approach of merging image features with text instead of using complex methods.
  3. Training a VLM involves using a good vision encoder and a pretrained language model. This combination seems to work well without any major drawbacks.

AI Observability, Orchestration, Consolidation

Gradient Flow • 179 implied HN points • 26 May 22
🕹 Technology AI Machine Learning Data Engineering Data Management Computer Vision
  1. Companies are likely to use at most two platforms for managing the entire machine learning pipeline: one for exploration and another for deployment and operations.
  2. Prefect 2.0 is a popular framework for data and workflow orchestration, emphasizing 'code as workflows' to address data engineering challenges.
  3. The survey on workflow orchestration tools revealed a growing interest in these systems, with startups raising over $450 million in funding for orchestration solutions.

Must Learn AI Security Part 23: Blurring or Masking Attacks Against AI

Rod’s Blog • 39 implied HN points • 19 Oct 23
🕹 Technology AI Security Adversarial Attacks Computer Vision Artificial Intelligence Cybersecurity
  1. Blurring or masking attacks against AI involve manipulating input data like images or videos to deceive AI systems while keeping content recognizable to humans.
  2. Common types of blurring and masking attacks against AI include Gaussian blur, motion blur, median filtering, noise addition, occlusion, patch/sticker, and adversarial perturbation attacks.
  3. Blurring or masking attacks can lead to degraded performance, security risks, safety concerns, loss of trust, financial/reputational damage, and legal/regulatory implications in AI systems.

ML Data Labeling Tools Structure Information to Make It Meaningful - Market Map and Analysis

The Strategy Deck • 39 implied HN points • 17 Jul 23
🕹 Technology Computer Vision AI Applications
  1. Data labeling is crucial for improving the quality of ML models by adding meaningful labels.
  2. Data labeling tools offer features like support for various data types, collaboration between annotators, and data versioning.
  3. ML platforms for data labeling include multi-modal, general purpose tools for manual labeling and programmatic tools focusing on specific data types and niches.

AI will predict what you'll buy in two years; is AI the future of film-making? 16 examples of how open-source LLMs are used today; the US is building AI infrastructure for academic researchers;

Computerspeak by Alexandru Voica • 19 implied HN points • 02 Feb 24
🕹 Technology AI Ecommerce Innovation Computer Vision Artificial Intelligence
  1. AI is playing a significant role in various industries, from predicting consumer behavior to improving movie-making processes, indicating a growing reliance on AI technology.
  2. Companies like Amazon, Google, Meta, and Microsoft are investing in custom AI chips and developing AI assistants to enhance their services and offerings.
  3. Advancements in AI, particularly in natural language processing and computer vision, are shaping the future of ecommerce by enabling personalized, engaging, and context-aware experiences for customers.

Week 76 - E4 - Discover the AI PM Universe: The Many Faces of AI Product Managers 🤖

The Product Channel By Sid Saladi • 13 implied HN points • 28 Jan 24
🕹 Technology Generative AI Conversational AI Computer Vision AI Security
  1. AI product management has various roles like AI Infrastructure PMs, Ranking PMs, Generative AI PMs, Conversational AI PMs, Computer Vision PMs, AI Security PMs, and AI Analytics PMs.
  2. Each type of AI PM role has specific skills and responsibilities like deep knowledge of full AI infrastructure tech stacks for AI Infrastructure PMs, tuning relevance algorithms for Ranking PMs, and incorporating human-in-the-loop feedback loops for Generative AI PMs.
  3. To excel in AI Product Management, it's crucial to understand the landscape, develop relevant skills, and embrace a mindset of continuous learning and adaptation to innovate effectively.

The Nerdiest Possible Way to Make Sourdough

Tim's Tech Things • 2 HN points • 09 May 24
🕹 Technology AI Computer Vision Software Engineering Python Open Source
  1. Creating a healthy sourdough starter involves feeding it with flour and water until it's ready to use in baking, which contributes to the delicious taste and texture of the bread.
  2. Monitoring the rise of sourdough starter is crucial to ensure there are enough active yeast cells to create CO2 bubbles, which make the bread light and fluffy.
  3. Using computer vision with Python, ffmpeg, and algorithms like rolling averages and derivatives can help automate the process of determining when sourdough is ready for baking.

Exploring GPT-4 Vision Use Cases

AI Super Founder: Future of Starting Startups • 9 implied HN points • 25 Feb 24
🕹 Technology OpenAI Computer Vision Artificial Intelligence Startups
  1. AI vision applications will surprise us with sudden and impactful advancements, despite current waning interest.
  2. GPT-4 Vision has a wide range of capabilities, from interpreting images in various forms to assisting in fields like art analysis, data interpretation, and education.
  3. Top AI vision use cases include sketch-to-code generation, image and video narration, data interpretation, object identification and tracking, and robotics with real-time training.

AprilTags: Why Robotics Invented Its Own QR Code

Luminotes • 7 implied HN points • 09 Feb 24
🕹 Technology Robotics Computer Vision Algorithms Open Source Image Processing
  1. AprilTags are similar to QR codes but are used as fiducial markers in robotics for localization purposes.
  2. AprilTags, created by the reputable robotics lab April, enable systems to localize features in 6 degrees of freedom using a single image.
  3. AprilTags differ from QR codes as they are designed for easy detection in low resolution, unevenly lit, or cluttered images and can detect multiple tags.

Becoming One with the Machine

Malt Liquidity • 6 implied HN points • 13 Mar 24
🕹 Technology AI Machine Learning Self-driving cars Computer Vision Neural Networks
  1. Our brain is exceptional at pattern recognition, and merging with technology can enhance our abilities.
  2. Visual processing is faster than auditory processing, like in chess where seeing the board is more efficient than listening to a game.
  3. Technology, like AI, can help turbocharge our skills by providing new perspectives and automating processes, leading to more creative problem-solving.

Data Science Weekly - Issue 444

Data Science Weekly Newsletter • 19 implied HN points • 26 May 22
🕹 Technology Machine Learning Data science Artificial Intelligence Computer Vision Software Development
  1. Operationalizing machine learning models is important. There are key differences between how ML is used in research and in real-world applications, and understanding these can improve system design.
  2. DALL-E and similar AI models show that composition in AI can produce unexpected and enjoyable results. This is a fun way to think about how AI works with semantics, even if it doesn't always make sense.
  3. Data can sometimes lead to worse decisions. It's essential to think critically about how we use data rather than just relying on it blindly.

Data Science Weekly - Issue 416

Data Science Weekly Newsletter • 19 implied HN points • 11 Nov 21
🕹 Technology Data science Machine Learning AI Visualization Computer Vision
  1. Mature machine learning systems can be tough to improve. Even with cutting-edge technology, you might find that new models don't perform better than old ones.
  2. Data drift and outlier detection are important for monitoring ML models. They help identify issues when you lack ground truth labels to compare against.
  3. Language models score how 'human' a sentence sounds. To train these models, you can analyze and convert language into probabilities.

Data Science Weekly - Issue 373

Data Science Weekly Newsletter • 19 implied HN points • 14 Jan 21
🕹 Technology Data science Machine Learning Artificial Intelligence Computer Vision Software Development
  1. Machine learning is being used a lot in developmental biology. It helps scientists work with big data from things like images and gene studies, making analysis easier.
  2. There's a growing need for data engineers, with many companies looking for these roles. Focusing on engineering skills can open up more job opportunities than traditional data scientist roles.
  3. The U.S. government has started an initiative to promote and oversee artificial intelligence. This shows how important AI is to the economy and security of the nation.

Data Science Weekly - Issue 360

Data Science Weekly Newsletter • 19 implied HN points • 15 Oct 20
🕹 Technology Data science Machine Learning Artificial Intelligence Programming Computer Vision
  1. Improving performance on GPUs is crucial for machine learning. It helps speed up both research and development, which leads to better results overall.
  2. BMW is working on ethical guidelines for AI usage. This aims to ensure that as AI evolves, it remains focused on benefiting people.
  3. Data discovery can be a challenge for companies. Facebook built a tool called Nemo to make it easier for engineers to find the information they need quickly.

Data Science Weekly - Issue 351

Data Science Weekly Newsletter • 19 implied HN points • 13 Aug 20
🕹 Technology Data science Machine Learning AI Computer Vision Analytics
  1. Machine learning models need regular maintenance after deployment. It's important to monitor data and model behavior to avoid problems and improve performance.
  2. Collaboration and good understanding of problems are key in AI development. This helps teams create better applications and make profits.
  3. New tools and resources are becoming available for data science, like access to research papers on Kaggle. These can help improve machine learning techniques and open up new possibilities.

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.

Data Science Weekly - Issue 294

Data Science Weekly Newsletter • 19 implied HN points • 11 Jul 19
🕹 Technology Artificial Intelligence Data science Machine Learning Computer Vision Statistics
  1. A new AI poker bot has learned to beat professional players, showing how advanced artificial intelligence has become in understanding complex strategies.
  2. Effective data science managers play a key role in driving team success and impact, focusing on building strong, skilled teams.
  3. Generative adversarial networks, often linked to deepfakes, can also be used positively in medical fields, like improving cancer diagnosis.

Data Science Weekly - Issue 288

Data Science Weekly Newsletter • 19 implied HN points • 30 May 19
🕹 Technology AI Machine Learning Data science Deep Learning Cloud Computing Computer Vision
  1. Creating general artificial intelligence might be possible through AI-generating algorithms, which could be a better approach than manually piecing together intelligence components.
  2. Generative adversarial networks (GANs) could greatly change the fashion industry by allowing realistic digital models to replace human models in online shopping.
  3. Recent advances in AI technology are enabling more efficient processing on devices, reducing the need for powerful cloud machines and making AI applications more accessible.

Data Science Weekly - Issue 265

Data Science Weekly Newsletter • 19 implied HN points • 20 Dec 18
🕹 Technology Data science Artificial Intelligence Machine Learning Deep Learning Computer Vision
  1. AlphaZero is a powerful AI that learns board games like chess and Go from scratch, showing how quickly it can master complex games without prior knowledge.
  2. Building a deep learning system requires careful choice of hardware, and it's important to avoid overspending on unnecessary components.
  3. Collaboration between data science and engineering has challenges, but understanding these tension points can improve teamwork and model deployment.

Data Science Weekly - Issue 216

Data Science Weekly Newsletter • 19 implied HN points • 11 Jan 18
🕹 Technology AI Data science Machine Learning Deep Learning Computer Vision
  1. A cat named Oscar is surprisingly good at predicting when terminally ill patients are going to die, showing that sometimes animals can have abilities we don't understand yet.
  2. Researchers are making AI systems that can recognize when they are uncertain about something. This could help them make better decisions and avoid mistakes.
  3. There are new tricks used in AI, like AlphaGo Zero, that show how deep learning can improve by learning from its own experiences and using fewer resources.

Data Science Weekly - Issue 210

Data Science Weekly Newsletter • 19 implied HN points • 30 Nov 17
🕹 Technology Data science Machine Learning Artificial Intelligence Deep Learning Computer Vision
  1. Computer Vision has seen many advancements recently, making a big impact on society. It's important to keep a balance when discussing potential future outcomes.
  2. The idea of an intelligence explosion is challenged by claims that it misunderstands how intelligence and self-improving systems work. Concrete examples support this perspective.
  3. A study showed that many comments about net neutrality might have been faked using natural language processing, raising concerns about online authenticity.

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.

Data Science Weekly - Issue 179

Data Science Weekly Newsletter • 19 implied HN points • 27 Apr 17
🕹 Technology Data science Artificial Intelligence Machine Learning Computer Vision Deep Learning
  1. Robots are getting smarter and might make their own choices, raising questions about their moral decisions. We need to think about what it means for a machine to behave morally.
  2. Creating effective Optical Character Recognition involves advanced technologies like deep learning and computer vision, showcasing how complex tech solutions can be in modern projects.
  3. Machines can analyze data in ways we may not fully understand, challenging our long-held beliefs about knowledge and order. This raises interesting points about how we trust these systems.

Data Science Weekly - Issue 176

Data Science Weekly Newsletter • 19 implied HN points • 06 Apr 17
🕹 Technology Artificial Intelligence Data science Machine Learning Computer Vision Robotics
  1. Image style transfer can turn famous impressionist paintings into more realistic photos, helping us see the world through the artist's eyes.
  2. DeepMind claims to have made a breakthrough in artificial general intelligence, which could have significant impacts on the future of AI.
  3. One-shot imitation learning allows robots to learn new tasks quickly and without needing a lot of examples, making them more adaptable.

Data Science Weekly - Issue 136

Data Science Weekly Newsletter • 19 implied HN points • 30 Jun 16
🕹 Technology Data science Machine Learning Artificial Intelligence Statistics Computer Vision
  1. Correlation does not mean one thing causes another, but understanding what does imply causation is important. It's a key part of interpreting data correctly.
  2. Machine learning can help improve hiring processes by making predictions based on data rather than following fixed rules. This could lead to better hiring decisions.
  3. Algorithms can have biases because they are influenced by human behavior and decisions. It's essential to recognize this to ensure fairness in technology.

Data Science Weekly - Issue 123

Data Science Weekly Newsletter • 19 implied HN points • 31 Mar 16
🕹 Technology Data science Artificial Intelligence Machine Learning Data Visualization Computer Vision
  1. Stories can help us understand the world, but not all stories are true. It's important to know when to trust our explanations and when to question them.
  2. Data science is vital for companies like Airbnb because it helps integrate analytics into leadership decisions. This shows how data can shape business strategies.
  3. Predictive data can enhance safety, like how Baidu uses map searches to forecast crowd behavior. It demonstrates how technology can help manage real-world situations.

Data Science Weekly - Issue 81

Data Science Weekly Newsletter • 19 implied HN points • 11 Jun 15
🕹 Technology Data science Machine Learning Artificial Intelligence Big Data Computer Vision
  1. Machine learning can analyze startup data to predict outcomes for new companies. This technology learns from past successes and failures.
  2. Airbnb uses big data to help hosts price their listings effectively. They guide hosts to set prices that are beneficial for both parties.
  3. Artificial intelligence can now solve complex scientific problems on its own. This marks a significant advancement in how computers contribute to research.

Data Science Weekly - Issue 70

Data Science Weekly Newsletter • 19 implied HN points • 26 Mar 15
🕹 Technology Data science Machine Learning Artificial Intelligence Computer Vision Quantitative Analysis
  1. Data science is more than just algorithms; real-world applications require a broad set of skills. Understanding the context and how to deal with data is crucial.
  2. Computer vision can be fooled by certain images, which raises important security concerns. This highlights the need for ongoing research in making AI more reliable.
  3. Breaking into data science can be tough because interviews often cover a wide range of topics. It's important to prepare for both programming and statistics in your job search.

Data Science Weekly - Issue 48

Data Science Weekly Newsletter • 19 implied HN points • 23 Oct 14
🕹 Technology Data science Machine Learning Artificial Intelligence Big Data Computer Vision
  1. Deep learning is making exciting advancements, like AI mastering games such as Space Invaders in remarkable ways.
  2. Companies like Disney are using supercomputers to handle complex tasks in animated films, showing how tech can manage big projects.
  3. Data science is being used in various industries, including news organizations, to analyze data for better decision-making and audience engagement.

Data Science Weekly - Issue 42

Data Science Weekly Newsletter • 19 implied HN points • 11 Sep 14
🕹 Technology Data science Machine Learning Artificial Intelligence Computer Vision Statistics
  1. Data science and machine learning are rapidly evolving fields, and staying updated is crucial for practitioners. Learning what works and what pitfalls to avoid is important for success.
  2. Graphs are valuable tools for organizing and relating information in data analysis. Techniques like document classification demonstrate how effective graph-based methods can be.
  3. Understanding the relationship between statistics and data science can identify both challenges and opportunities. It's important for statistics to adapt and remain relevant in the data science landscape.

Data Science Weekly - Issue 34

Data Science Weekly Newsletter • 19 implied HN points • 17 Jul 14
🕹 Technology Data science Machine Learning Artificial Intelligence Computer Vision Statistics
  1. A new computer program can find rare genetic disorders just by looking at photos of families. This shows how technology can help identify health issues more easily.
  2. Probabilistic programming is a growing area of research that could improve machine intelligence. It's complex but important for understanding how to make predictions.
  3. Data for Good is a new site where data scientists can showcase projects that make a positive impact on the world. It's exciting to see tech being used for social good.