The hottest Computer Vision Substack posts right now

And their main takeaways
Category
Top Technology Topics

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.

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.

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.

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.

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.
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Market Map & Analysis: AI Synthetic Data Companies

The Strategy Deck • 78 implied HN points • 06 Jul 23
🕹 Technology AI Data ML 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.

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.

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.

Coming soon

Jinay's Substack • 0 implied HN points • 04 Apr 23
🕹 Technology Blogging Software Machine Learning Computer Vision
  1. The author has moved their blog to Substack for its ease of use and wide adoption in the software field.
  2. Older blog posts can still be accessed at the previous blog domain.
  3. Some of the topics covered in the blog include programmatic blogging, backpropagation in machine learning, turning a toy project into a viral challenge, and using computer vision to tell time.

Google AI and Princeton discover this about Deep Learning

Technology Made Simple • 0 implied HN points • 25 Dec 21
🕹 Technology Machine Learning Deep Learning AI Computer Vision NLP
  1. The speed at which a machine learning model 'learns' is influenced by the learning rate, which can make or break the model.
  2. Choosing the correct step size is crucial in machine learning behavior, as highlighted by a study that compared the importance of step size versus direction.
  3. Step size, or the learning rate, seems to be a dominating factor in model learning behavior, showcasing the potential for optimizing performance by combining different optimizer techniques.