The hottest Computing Substack posts right now

And their main takeaways
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Top Technology Topics

From Punch Cards to AI Agents: The Evolution of Computing Interfaces

The Future of Life • 1 HN point • 14 Aug 24
🕹 Technology AI Interfaces Human-Computer Computing Innovation
  1. AI personal agents will soon replace screens and keyboards, using voice and video to interact with us. They will be more like assistants who help manage our tasks while we focus on the bigger picture.
  2. These agents will understand our preferences and handle transactions for us, much like a personal librarian suggesting books. We can still browse if we want, but the agent will personalize the experience.
  3. AI agents will help us create content as well, handling everything from gathering information to visualizing data. This will make it easier for us to express ideas without getting bogged down in technical details.

Geometric Constraints on Computing

Bzogramming • 30 implied HN points • 29 Jan 24
🕹 Technology Computing Physics Architecture Optimization Future Trends
  1. The physical constraints of computing, such as distance and volume, significantly impact performance and efficiency.
  2. Parallelism at different scales within a program can affect latency and performance, offering opportunities for optimization.
  3. Considerations like curvature of computation, square-cube law, and heat generation play a crucial role in the design and limitations of computer chips.

Non-Newtonian Computing?

Bzogramming • 30 implied HN points • 07 Jan 24
🕹 Technology Computing Physics Cryptography Algorithms AI
  1. Physics has alternative framings like Lagrangian and Hamiltonian mechanics, which could inspire new ways of viewing computation.
  2. Reversible computing, preserving information by having bijective gates, is crucial for energy efficiency and future computing technologies.
  3. Studying constraint solvers and NP-complete problems can lead to insights for accelerating search algorithms and developing new computing approaches.

Log Transformations for efficient multiplication between lots of positive numbers [Technique Tuesdays]

Technology Made Simple • 39 implied HN points • 02 Nov 22
🔬 Science Mathematics Computing Big Data Machine Learning Algorithms
  1. Log transformations can be used for efficient multiplication between large numbers by converting the problem into addition of logs, making it more manageable.
  2. Logs have interesting properties that make them useful for handling computations with very large or very small numbers.
  3. Using log transformations is a clever math technique that is commonly used in fields like AI, Big Data, and Machine Learning to handle large computations.

Oil Money is All You Need

Sector 6 | The Newsletter of AIM • 19 implied HN points • 07 Sep 23
🕹 Technology AI Funding Startups Computing Development
  1. To develop large language models (LLMs), companies need substantial amounts of money, around $100 billion, to scale their operations effectively.
  2. Sam Altman mentioned that OpenAI might seek significant funding in the future to improve its models and work towards artificial general intelligence (AGI).
  3. Currently, OpenAI's total funding is about $11.3 billion, which shows there's still a long way to go in terms of financial support for ambitious AI projects.
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Shh… CUDA at Work!

Sector 6 | The Newsletter of AIM • 19 implied HN points • 09 Aug 23
🕹 Technology AI Software Hardware Computing Innovation
  1. NVIDIA's GPUs are essential for running AI smoothly, much like how our brains work while we sleep. They help process and manage lots of data quickly.
  2. CUDA, NVIDIA's special software, plays a crucial role in enhancing AI performance. It's a powerful tool that often doesn't get the spotlight it deserves.
  3. NVIDIA's combination of powerful hardware and effective software supports the ongoing AI revolution, making it a key player in this technology shift.

The Mouse and the Cat

The Intersection • 19 implied HN points • 09 Jun 23
🕹 Technology VR Computing Augmented reality Innovation Spatial Computing
  1. In order to succeed, it's more important to be smart than fast. The tale of the mouse outsmarting the ox in the Zodiac race exemplifies this.
  2. Apple's Vision Pro launch marks their entry into spatial computing, but they are not the pioneers in personal or mobile computing.
  3. Apple aims to dominate individual interface access, while Meta focuses on connections and monetizing data. Both have different business focuses and target markets.

What's wrong with JUST generating text?

John Ball inside AI • 1 HN point • 31 Jul 24
🕹 Technology AI Software Development Engineering Computing
  1. Text generation alone isn't enough; it needs to convey real meaning. Without meaning, responses can be confusing or untrustworthy.
  2. Future digital assistants should focus on Natural Language Understanding to provide clearer, more useful answers. This will help developers create better, more reliable bots.
  3. Many generative AI models struggle with context and can produce incorrect information. Solutions involving deeper comprehension of language are needed to address these issues.

Christopher Strachey and the Dawn of Interactive Text

50 Years of Text Games • 49 HN points • 16 May 23
🕹 Technology Computing Programming History
  1. Computers evolved quickly in their early years, with innovations being made and lost before becoming standardized.
  2. Computer games with text came before those with graphics, highlighting the initial challenge of dealing with language.
  3. Christopher Strachey, an early computer programmer, paved the way for text-based computer games and made significant contributions to the field of computer science.

Great, now the machines start to understand me

Klement on Investing • 1 implied HN point • 06 Dec 24
🕹 Technology AI Machine Learning Natural Language Computing Research
  1. Generative AI has made big strides in understanding language, but it still struggles with things like irony and context. These are important parts of how people communicate every day.
  2. Recent studies show that chatGPT-4 is getting much better at understanding complex human interactions, sometimes even matching or surpassing human understanding. This shows how AI is evolving.
  3. AI still has weaknesses; for example, it can struggle with recognizing social mistakes people make in conversations. Unlike chatGPT, another model called LLaMA2 did better at this specific task.

Can AGI destroy us without trial & error?

First principles trivia • 39 implied HN points • 13 Jun 22
🕹 Technology Artificial Intelligence Robotics Simulation Engineering Computing
  1. AGI development faces challenges in translating from a computer-based system to independently-operating physical entities, requiring decades of complex R&D
  2. Historical examples show that novel engineering, especially without a basis of previous work, takes significant time, even for AGI with higher intellect
  3. Human scientific progress evidences challenges and limitations in advancing technology efficiently, potentially slowing AGI's ability to advance rapidly

NVIDIA and the battle for the future of Generative AI

Sector 6 | The Newsletter of AIM • 39 implied HN points • 07 Nov 22
🕹 Technology AI Deep Learning Software Computing Innovation
  1. NVIDIA released a new AI model called eDiffi that creates better images than existing tools like DALL.E 2 and Stable Diffusion. This shows they are making strides in generative AI technology.
  2. In 2022, there was a prediction about NVIDIA launching text-to-image models, and eDiffi is finally their answer to that anticipation. It signifies a new chapter for creative AI tools.
  3. NVIDIA's previous tool, GauGAN, allowed sketches to become realistic landscapes, and now they are advancing to text-based inputs with eDiffi. This represents a move toward more versatile and user-friendly AI innovations.

Cooling Servers Through Code

Sector 6 | The Newsletter of AIM • 19 implied HN points • 02 Jun 23
🕹 Technology AI Environment Computing Data Sustainability
  1. Generative AI can have a big environmental impact. For example, GPT-3 used a lot of energy, like driving 123 cars for a year.
  2. There is concern that generative AI may not just affect the environment but could also pose other risks in the future.
  3. Researchers are exploring ways to cool servers more efficiently through coding techniques to reduce their environmental footprint.

Infrastructure and General-Purpose Tools

Bzogramming • 22 implied HN points • 05 Oct 23
🕹 Technology Infrastructure Tools Computing Interactions Personalization
  1. Ubiquitous Computing envisioned computers fading into the background to be convenient means to solve problems.
  2. Simplicity has limits due to the finite number of interactions and outcomes possible with tools.
  3. Personalization and infrastructure are crucial for making general-purpose tools convenient and efficient for individual users.

What do Alpacas have to do with AI's future?

do clouds feel vertigo? • 19 implied HN points • 20 Mar 23
🕹 Technology AI Computing Innovation Business Models Data science
  1. AI training costs are dropping significantly, which makes it easier for more people to create their own AI models.
  2. AI models can become more common and even borrowed from others, which leads to questions about ownership and competition.
  3. Companies now face a choice between buying AI capabilities or building their own, affecting how they manage privacy and efficiency.

The Iceberg that sank the Snowflake?

Brick by Brick • 9 implied HN points • 01 Mar 24
🕹 Technology Data Warehousing Open Source Computing
  1. Snowflake's stock dropped significantly after the announcement of CEO Frank Slootman's retirement, with a key concern being the impact of Apache Iceberg on moving data out of Snowflake.
  2. Apache Iceberg is a powerful technology that allows for the efficient migration of data out of Snowflake to other systems for processing, causing revenue loss in both storage and compute for Snowflake.
  3. The paradigm shift towards technologies like Iceberg takes time in enterprise settings but can have a significant impact, highlighting the importance of capturing the compute dollars in data processing.

Intermediate Superintelligence

Am I Stronger Yet? • 15 implied HN points • 12 Sep 23
🕹 Technology AI Intelligence Productivity Research Computing
  1. Intermediate superintelligence is not expected to happen overnight, but gradually surpass human capabilities on various tasks.
  2. Intelligence significantly impacts productivity in tasks; talented individuals can find more efficient solutions and execute them quickly.
  3. AI advancements go beyond intelligence, offering unique advantages like relentless focus, lack of fatigue, and enhanced communication abilities.

#23

The Nibble • 12 implied HN points • 02 Sep 23
🕹 Technology AI Internet Software Computing Security
  1. Microsoft plans to bring AI capabilities to Paint and Photos app on Windows 11.
  2. Reliance showcased JioFiberAir, providing high-speed internet without wires for high-paying households.
  3. Domains, like Anguilla's .ai, are becoming valuable assets in the digital world.

Huang sees the Matrix 🔮

Sector 6 | The Newsletter of AIM • 19 implied HN points • 27 Mar 22
🕹 Technology AI Computing Innovation Industry News Leadership
  1. NVIDIA is focused on changing the game with its technology. They are making significant advancements in the AI field.
  2. Jensen Huang, the head of NVIDIA, is a well-known figure and has been recognized for his influence in the tech industry.
  3. The recent GTC 2022 event showcased major innovations and ideas in AI, making headlines and capturing attention globally.

LLMs and Generative AI don't deal with concepts

Top Carbon Chauvinist • 1 HN point • 13 Apr 24
🕹 Technology AI Machine Learning Generative models Computing Patterns
  1. LLMs and generative AI focus on patterns, not real concepts. They generate outputs based on learned data but don’t actually understand what those outputs mean.
  2. When asked to create an image, like an ouroboros, generative AI often misses the mark. It replicates the look without truly grasping the idea behind it.
  3. To get the desired result, people often have to give very detailed prompts, which means the AI is more about matching shapes than understanding or creating an actual concept.

The war for large language models

Sector 6 | The Newsletter of AIM • 19 implied HN points • 19 Dec 21
🕹 Technology AI Machine Learning Computing Data science Innovation
  1. DeepMind has released a new language model called Gopher with 280 billion parameters. This shows how competitive the field of AI is getting.
  2. Google followed with its own model called GLaM, which is even larger at 1.2 trillion parameters. These advancements highlight the rapid progress in AI technology.
  3. Both companies are pushing the boundaries of what large language models can do, using innovative techniques to improve performance and efficiency. It's exciting to see how these developments will shape the future of AI.

AI Is First New UI Paradigm in 60 Years

Jakob Nielsen on UX • 7 implied HN points • 22 Jun 23
🕹 Technology AI UI Computing Generative AI Artificial Intelligence
  1. AI is introducing the third user-interface paradigm in computing history, shifting from command-based interaction to intent-based outcome specification.
  2. The first UI paradigm was batch processing, where users submitted complete workflows and got results much later, usually with issues in usability.
  3. Command-based interaction, the second UI paradigm, allowed users to assess and modify commands one at a time, with GUIs dominating for about 40 years; AI's intent-based paradigm reverses user control, representing a new era in UI design.

Data in Rust

Cybernetic Forests • 19 implied HN points • 11 Apr 21
🕹 Technology Data Storage Art Music Programming Computing
  1. Tape was the first data storage medium, made of iron oxide with data inscribed by magnets, and tape art and music have explored its possibilities.
  2. Music on tape has influenced data on tape, with notable examples like Brian Eno and Delia Darbyshire using tape as a creative tool.
  3. Art, like music experimentation, serves as a space for safe exploration and where things can break, contributing to science and knowledge without being driven solely by profit or power.

Data Science Weekly - Issue 407

Data Science Weekly Newsletter • 19 implied HN points • 09 Sep 21
🕹 Technology Machine Learning Data science Artificial Intelligence Computing Software Development
  1. Machine learning compilers help improve the efficiency of ML models, especially for edge computing, by addressing compatibility and performance issues.
  2. Scikit-learn, a popular machine learning library, has reached a significant version milestone at 1.0.0, showcasing its growth and community support since it started back in 2007.
  3. Synthetic data is becoming more important in computer vision, and using 3D content from the gaming and film industries can greatly enhance the process of creating such data.

Data Science Weekly - Issue 406

Data Science Weekly Newsletter • 19 implied HN points • 02 Sep 21
🕹 Technology AI Machine Learning Data science Computing Ethics
  1. MIT has developed a smart carpet that can estimate human poses without using cameras, which might be useful for healthcare and smart home technologies.
  2. Google has introduced amazing AI technology that can enhance photos, making them look much more realistic than before.
  3. The financial machine learning space has a high failure rate, with many managers making critical mistakes; learning from these can lead to better success.

The cost of AI reasoning over time.

sémaphore • 2 implied HN points • 29 Mar 24
🕹 Technology AI Machine Learning Computing Data science Innovation
  1. AI models are getting better at reasoning while the costs to run them are getting lower. This means we can expect more affordable and capable AI in the future.
  2. There are different types of customers based on their needs: some care more about low prices, others want a balance of cost and performance, and some prioritize performance above all else.
  3. As AI continues to improve, we might see exciting new developments, like specialized models for various industries and new ways to measure their effectiveness.

The Relentless Pursuit of Moore's Law

Bits and Bytes • 5 HN points • 16 Jul 23
🕹 Technology Computing Innovation Abstraction Complexity Chip Design
  1. Moore's Law has driven progress in computing for decades by doubling transistor counts every 2 years.
  2. The management of complexity in computing has been achieved through abstraction and refactoring across multiple disciplines.
  3. Future advances in computing will likely involve raising the level of abstraction and introducing new tools to handle increasing transistor counts.

VOL 65: Unsung Hero: Exploring Gary Kildall, the Man Who Almost Became Bill Gates

MAP's Tech Newsletter. • 4 implied HN points • 16 Jun 23
🕹 Technology History Software Entrepreneurship Computing Innovation
  1. Gary Kildall was a key figure in computer history, creating CP/M and Digital Research, making personal computers accessible.
  2. IBM approached Kildall for an operating system, but a missed opportunity led to Microsoft purchasing a similar system instead.
  3. Kildall's failure to secure a deal with IBM and legal battles with Microsoft had a significant impact on his career and personal life.

Data Science Weekly - Issue 355

Data Science Weekly Newsletter • 19 implied HN points • 10 Sep 20
🕹 Technology Data science Machine Learning Artificial Intelligence Big Data Computing
  1. DeepMind and Google Maps are using advanced Graph Neural Networks to improve the accuracy of travel time predictions, making them even more reliable in cities around the world.
  2. AI is now being used to detect deepfake videos by identifying unique signals from the videos, which can help spot how they were made.
  3. There are resources available to help people get started in data science, build their portfolios, and improve their resumes to land jobs in this field.

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 346

Data Science Weekly Newsletter • 19 implied HN points • 09 Jul 20
🕹 Technology Artificial Intelligence Data science Machine Learning Neural Networks Computing
  1. AI training costs are dropping much faster than usual, which means AI technology is becoming easier and cheaper to develop. This could lead to more companies using AI over the next decade.
  2. Training Generative Adversarial Networks (GANs) can be tough, but there are new algorithms that help make it more stable and efficient. This is important for many applications in science and engineering.
  3. Moving from traditional statistics to machine learning involves a different way of thinking. Understanding this shift can help those with a stats background adapt and excel in machine learning.

Data Science Weekly - Issue 345

Data Science Weekly Newsletter • 19 implied HN points • 02 Jul 20
🕹 Technology AI Machine Learning Data science Software Development Computing
  1. Making machine learning useful in real life is a key focus for companies like startups, especially when they provide machine learning as a service.
  2. Documentation is important in machine learning to explain how models work and to clarify their intended use, which helps avoid misuse.
  3. There are ongoing discussions about improving the machine learning community, addressing issues like toxicity, fairness, and the peer-review process.

Data Science Weekly - Issue 341

Data Science Weekly Newsletter • 19 implied HN points • 04 Jun 20
🕹 Technology Data science Machine Learning Artificial Intelligence Computing Training
  1. Mathematics often requires new methods to solve problems, showing how innovation is crucial in research.
  2. GPT-3 is a massive language model that significantly improves deep learning and natural language processing capabilities.
  3. Many people find data science jobs disappointing, and it's important to manage your expectations in any job field.