The hottest Artificial Intelligence Substack posts right now

And their main takeaways
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Top Technology Topics

Technology to the Rescue!

Life Since the Baby Boom • 1844 implied HN points • 28 Oct 24
🕹 Technology Artificial Intelligence Social media Communication Consumption Human behavior
  1. People have always believed that technology will solve human problems, from the telephone to AI. But no matter the advancements, our fundamental human nature remains the same.
  2. Many technologists share a faith in technology similar to religious beliefs, seeing it as a way to achieve progress and even redemption for humanity.
  3. Connecting people through technology, like social media, often leads to conflicts instead of harmony, reminding us that simply being connected doesn't guarantee understanding or peace.

Ocean Whoosh

New World Same Humans • 47 implied HN points • 09 Feb 25
🕹 Technology Artificial Intelligence Virtual reality Human-computer interaction Digital Culture
  1. We are entering a new era with advanced technology, like superintelligent machines, which will challenge what it means to be human. This could lead to a stronger connection with our real world and each other.
  2. Nature, especially the sound of the ocean, can remind us of a simpler, more authentic way of being. It's like a song from the past that connects us to who we really are.
  3. As we face a future filled with technology, it's important to hold onto our human values and create spaces where we can truly be ourselves. We need to nurture what makes us unique and human.

Data Science Weekly - Issue 564

Data Science Weekly Newsletter • 119 implied HN points • 12 Sep 24
🕹 Technology Data science Machine Learning Artificial Intelligence Data Visualization Engineering
  1. Understanding AI interpretability is important for building resilient systems. We need to focus on why interpretability matters and how it relates to AI's resilience.
  2. Testing machine learning systems can be challenging, but starting with basic best practices like CI pipelines and E2E testing can help. This ensures the models work well in real-world scenarios.
  3. Visualizing machine learning models is crucial for better understanding and analysis. Tools like Mycelium can help create clear visual representations of complex data structures.

Background Work

Software Design: Tidy First? • 1568 implied HN points • 28 Oct 24
🕹 Technology Software Design Programming Engineering Data Analysis Artificial Intelligence
  1. Background work is doing extra research or tasks beyond what's necessary. It's a way to learn and grow your skills.
  2. Successful programmers often engage in background work, which helps them become more knowledgeable and credible.
  3. While background work can sometimes feel like extra effort, it usually pays off quickly and can save time in the long run.

✨🎄 Some AGI optimism: an early Xmas present

Faster, Please! • 639 implied HN points • 23 Dec 24
🕹 Technology Artificial Intelligence Machine Learning Data science Software Development Tech Innovation
  1. OpenAI has released a new AI model called o3, which is designed to improve skills in math, science, and programming. This could help advance research in various scientific fields.
  2. The o3 model performs much better than the previous model, o1, and other AI systems on important tests. This shows significant progress in AI performance.
  3. There's a feeling of optimism about AGI technology as these advancements might bring us closer to achieving more intelligent and capable AI systems.
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$142 million AI drone warfare contract from 'Project Maven' was silently erased from public record

All-Source Intelligence Fusion • 1485 implied HN points • 26 Oct 24
🇺🇸 U.S. Politics Government Contracts National Security Artificial Intelligence Military technology
  1. The U.S. government removed records of a $142 million contract for AI drone warfare called 'Project Maven.' This deletion happened without any public announcement.
  2. Interestingly, another related contract worth $52 million was also deleted from public records. These actions raise concerns about transparency in government spending.
  3. The defense spokesperson stated that the deletions were justified for national security reasons. This suggests that some information might be kept secret for safety.

Where Was RDNA4 at AMD’s Keynote?

More Than Moore • 326 implied HN points • 06 Jan 25
🕹 Technology Hardware Software Gaming Electronics Artificial Intelligence
  1. AMD didn't announce RDNA4 at the CES keynote because they felt a short presentation wouldn't do it justice. They want to provide detailed information rather than leave people with questions.
  2. AMD plans to share more about RDNA4 through partners at CES, but a dedicated event will follow for an in-depth reveal. They are close to launch but wanted to wait for the right time.
  3. The naming scheme for new graphics cards will be clearer to help users make better comparisons. AMD aims to improve performance in key gaming areas and ensure good value for consumers.

Sam Altman Wants $7 Trillion

Astral Codex Ten • 16656 implied HN points • 13 Feb 24
🕹 Technology AI Machine Learning Artificial Intelligence Big Data Computing
  1. Sam Altman aims for $7 trillion for AI development, highlighting the drastic increase in costs and resources needed for each new generation of AI models.
  2. The cost of AI models like GPT-6 could potentially be a hindrance to their creation, but the promise of significant innovation and industry revolution may justify the investments.
  3. The approach to funding and scaling AI development can impact the pace of progress and the safety considerations surrounding the advancement of artificial intelligence.

Data Science Weekly - Issue 563

Data Science Weekly Newsletter • 139 implied HN points • 05 Sep 24
🕹 Technology Data science Artificial Intelligence Machine Learning Data Engineering Data Visualization
  1. AI prompt engineering is becoming more important, and experts share helpful tips on how to improve your skill in this area.
  2. Researchers in AI should focus on making an impact through their work by creating open-source resources and better benchmarks.
  3. Data quality is a common concern in many organizations, yet many leaders struggle to prioritize it properly and invest in solutions.

Empty Laughter

Where's Your Ed At • 16914 implied HN points • 16 Jan 24
🎭️ Culture Comedy Artificial Intelligence Plagiarism Entertainment Creativity
  1. Art should be unique and come from personal experiences, not generated by AI or copied from others.
  2. Creativity is limited by the individual, and the magic of art comes from the context and experiences of the artist.
  3. Plagiarism and reliance on generative AI for art creation show a lack of curiosity, entitlement, and a desire to imitate rather than create.

As UnitedHealth and Cigna are Sued for AI-Based Claims Denials, Documents Suggest Major AI Expansion

HEALTH CARE un-covered • 739 implied HN points • 11 Jul 24
🏥 Health Politics Healthcare policy Artificial Intelligence Legal issues Insurance Practices Patient rights
  1. UnitedHealth and Cigna are facing lawsuits for denying medical claims using a flawed AI system, which many believe does not work correctly. This has led to patients not receiving the care they need or having to pay high costs for care.
  2. Despite the lawsuits and public criticism, these companies plan to expand their use of AI in health care decision-making. They are investing more in technology, aiming for efficiency even at the risk of more denied claims.
  3. Experts warn that using AI in health care can leave patients feeling helpless and confused when their claims are denied. They believe that patients under AI-driven systems may struggle to advocate for their own health needs effectively.

Data Science Weekly - Issue 562

Data Science Weekly Newsletter • 179 implied HN points • 29 Aug 24
🕹 Technology Data science Artificial Intelligence Machine Learning Data Engineering Statistics
  1. Distributed systems are changing a lot. This affects how we operate and program these systems, making them more secure and easier to manage.
  2. Statistics are really important in everyday life, even if we don't see it. Talks this year aim to inspire students to understand and appreciate statistics better.
  3. Understanding how AI models work internally is a growing field. Many AI systems are complex, and researchers want to learn how they make decisions and produce outputs.

✨ 📈 The AI Paradox: When will artificial intelligence boost US economic growth?

Faster, Please! • 913 implied HN points • 21 Nov 24
💼 Business Economics Artificial Intelligence Productivity Innovation Growth
  1. Alan Greenspan raised questions about why technological advances in the 1990s didn't seem to improve productivity statistics. He suggested that it might take time for new technologies to show their full effects.
  2. Greenspan believed that traditional methods of measuring productivity might not capture the real progress happening, especially with services. This mismeasurement could lead to bad decisions on economic policies.
  3. The role of artificial intelligence in boosting productivity is still uncertain. There's hope that AI can help workers produce more, but it's unclear when we will see these benefits reflected in economic growth.

You Are Not The Man In The Arena

Where's Your Ed At • 24184 implied HN points • 30 Aug 23
🕹 Technology Startup Culture Tech industry Venture Capital Cryptocurrency Artificial Intelligence
  1. The man in the arena speech by Theodore Roosevelt emphasizes the importance of taking action over criticism.
  2. Chamath Palihapitiya symbolizes a detrimental mindset in Silicon Valley of valuing image over actual value creation.
  3. The tech industry's obsession with funding specific kinds of founders and companies has created a harmful monoculture that prioritizes profit over societal impact.

Weekly Top Picks #95

The Algorithmic Bridge • 191 implied HN points • 20 Jan 25
🕹 Technology Artificial Intelligence Open Source Deep Learning Public Perception
  1. DeepSeek-R1 shows that open-source AI models can compete with OpenAI's offerings, proving that smaller and cheaper options are just as effective.
  2. OpenAI's partnership with EpochAI raises questions about fairness, as they had exclusive access to important tools like FrontierMath.
  3. Writers are starting to recognize AI's writing abilities, a change they need to accept, even if it feels challenging at first.

Substack Reads: Beatrix Potter’s notes, and the dark side to high-growth business

read • 8294 implied HN points • 15 Apr 23
📚 Literature Memoir Poetry Artificial Intelligence
  1. Beatrix Potter's fascination with mushrooms led to groundbreaking scientific discoveries.
  2. The relationship between European countries and their food reputation is complex and tied to historical influences.
  3. Poetry can be deeply inspired by personal stories and historical events, leading to powerful expressions of emotions and experiences.

OpenAI's Reinforcement Finetuning and RL for the masses

Democratizing Automation • 427 implied HN points • 11 Dec 24
🕹 Technology Artificial Intelligence Machine Learning Deep Learning Data science API Development
  1. Reinforcement Finetuning (RFT) allows developers to fine-tune AI models using their own data, improving performance with just a few training samples. This can help the models learn to give correct answers more effectively.
  2. RFT aims to solve the stability issues that have limited the use of reinforcement learning in AI. With a reliable API, users can now train models without the fear of them crashing or behaving unpredictively.
  3. This new method could change how AI models are trained, making it easier for anyone to use reinforcement learning techniques, not just experts. This means more engineers will need to become familiar with these concepts in their work.

The Bitter Religion: AI’s Holy War Over Scaling Laws

The Generalist • 920 implied HN points • 14 Nov 24
🕹 Technology Artificial Intelligence Computing Innovation Research Engineering
  1. The AI community is divided over whether achieving higher levels of computation will lead to better artificial intelligence or if there are limits to this approach. Some think more resources will keep helping AI grow, while others fear we might hit a ceiling.
  2. There’s a growing debate about the importance of scaling laws and whether they should continue to guide AI development. People are starting to question if sticking to these beliefs is the best path forward.
  3. If doubt begins to spread about scaling laws, it could impact investment and innovation in AI and related fields, causing changes in how companies approach building new technologies.

Prediction Technologies and Innovation

New Things Under the Sun • 160 implied HN points • 08 Jan 25
🕹 Technology Innovation Artificial Intelligence Research Methods
  1. Prediction technologies help scientists make better guesses about what to explore next, like using AI to identify promising research areas. However, they can also lead people to focus too much on certain topics, missing out on other important areas.
  2. Research tools can change what scientists choose to study. For example, a tool might encourage research on proteins we already know about instead of new, less understood ones, which could slow down innovation.
  3. Different prediction technologies have different effects. Some can help researchers discover more unique solutions, while others may cause everyone to chase the same problems, limiting overall progress.

16 Cybersecurity Startups Selected for Google Growth Academy

The Security Industry • 11 implied HN points • 16 Feb 25
🕹 Technology Cybersecurity Artificial Intelligence Startups Data Management Cloud Computing
  1. IT-Harvest is part of Google's Growth Academy for 2025, focusing on supporting cybersecurity startups. This helps them connect with experts and gain valuable resources.
  2. The platform has evolved to meet the needs of security teams, showing strong interest in their data tools and features. Users can now map their security tools to important frameworks like NIST CSF.
  3. They are using AI to streamline data collection and analysis, which makes understanding cybersecurity products faster and easier. This change has made their tools more appealing to companies and consultants alike.

A Visual Guide to Mamba and State Space Models

Exploring Language Models • 3942 implied HN points • 19 Feb 24
🕹 Technology Artificial Intelligence Machine Learning Data science Software Development Algorithm Design
  1. Mamba is a new modeling technique that aims to improve language processing by using state space models instead of the traditional transformer approach. It focuses on keeping essential information while being efficient in handling sequences.
  2. Unlike transformers, Mamba allows for selective attention, meaning it can choose which parts of the input to focus on. This makes it potentially better at understanding context and relevant information.
  3. The architecture of Mamba is designed to be hardware-friendly, helping it to perform well without excessive resource use. It uses techniques like kernel fusion and recomputation to optimize speed and memory use.

Deepseek: From Hedge Fund to Frontier Model Maker

ChinaTalk • 400 implied HN points • 09 Dec 24
🕹 Technology Artificial Intelligence Innovation Research Startups Business Models
  1. High-Flyer, a hedge fund, is making big moves by venturing into AI research through a new company called DeepSeek. They want to create human-level AI instead of just copying existing models.
  2. Their success in the AI field comes from a unique hiring process that focuses on curious and passionate individuals rather than experience. This helps foster innovation within the company.
  3. Despite the high costs of running AI research, High-Flyer believes in funding their projects through a mix of their own resources and philanthropy. They prioritize long-term research over quick financial returns.

Quick Essay: A Short History of OpenAI

Chamath Palihapitiya • 5758 implied HN points • 20 Nov 23
🕹 Technology Artificial Intelligence Corporate Structure History
  1. OpenAI transitioned from a non-profit to a 'capped-profit' model in 2019, allowing for capital raises while serving its mission
  2. OpenAI made significant advancements in AI research, developing projects like 'OpenAI Five' and models like ChatGPT and GPT-3
  3. Conflict within OpenAI's leadership led to the removal of co-founder Sam Altman as CEO due to concerns over commercialization conflicting with the company's primary goal of developing AGI safely

LLMs Fight With Both Hands Tied Behind Their Back

Am I Stronger Yet? • 313 implied HN points • 27 Dec 24
🕹 Technology Artificial Intelligence Machine Learning Programming Data science Software Development
  1. Large Language Models (LLMs) like o3 are becoming better at solving complex math and coding problems, showing impressive performance compared to human competitors. They can tackle hard tasks with many attempts, which is different from how humans might solve them.
  2. Despite their advances, LLMs struggle with tasks that require visual reasoning or creativity. They often fail to understand spatial relationships in images because they process information in a linear way, making it hard to work with visual puzzles.
  3. LLMs rely heavily on knowledge in their 'heads' and do not have access to real-world knowledge. When they gain access to more external tools, their performance could improve significantly, potentially changing how they solve various problems.

Welcome to the afterlife of ordinary thigns

Soaring Twenties • 139 implied HN points • 20 Jan 25
🕹 Technology Artificial Intelligence Memory Digital Culture Social media Human-computer interaction
  1. Our digital memories are endless because machines keep everything we've posted or photographed. They don't know which moments are really important.
  2. AI creates new 'memories' by analyzing our past, sometimes making connections between events that never actually mattered to us but seem significant to a computer.
  3. The way we remember things is changing as technology evolves. We're not just recalling past experiences; we're also feeling emotions for moments that never truly happened.

Multi-robot collaboration,Grok 3 , smallest video language model, Generative AI Model for Gameplay, AI co-scientist, Mistral Saba, Fiverr Go, Step-Video-T2V and Step-Audio, Pikaswaps & more

AI Brews • 15 implied HN points • 21 Feb 25
🕹 Technology Artificial Intelligence Robotics Machine Learning Software Development Open Source
  1. Grok 3 is a powerful reasoning model that can handle a massive amount of information at once, making it one of the best tools for chatbots right now.
  2. New advancements in AI, like the Vision-Language-Action model Helix and the generative AI model Muse, are making robots smarter and more capable in their tasks.
  3. AI tools are getting more user-friendly, such as Pikaswaps, which allows you to easily replace parts of videos with your own images, making editing simpler for everyone.

BLT: Byte Latent Transformer

Gonzo ML • 315 implied HN points • 23 Dec 24
🕹 Technology Artificial Intelligence Machine Learning Data processing Software Development Innovation
  1. The Byte Latent Transformer (BLT) uses patches instead of tokens, allowing it to adapt based on the complexity of the input. This means it can process simpler inputs more efficiently and allocate more resources to complex ones.
  2. BLT can accurately encode text at a byte level, overcoming issues with traditional tokenization that often lead to mistakes in understanding languages and simple tasks like counting letters.
  3. BLT architecture has shown better performance than older models, handling tasks like translation and sequence manipulation more effectively. This advancement could improve the application of language models across different languages and reduce errors.

GenAI Isn’t Replacing Devs (And It Shouldn’t)

HackerPulse Dispatch • 8 implied HN points • 18 Feb 25
🕹 Technology Software Development Artificial Intelligence Programming Languages Engineering Tech industry
  1. Firing programmers to replace them with AI can backfire. Companies might end up facing big problems like untrained workers and high costs to hire good developers back.
  2. Experience and human intuition are important in software development. AI can't solve every problem, and skilled developers are still needed for complex tasks.
  3. The new Python 3.14 interpreter will make code run faster without needing any changes. This is great for developers because it saves time and effort.

Report: OpenAI Spends Millions a Year Miscounting the R's in 'Strawberry'

The Algorithmic Bridge • 573 implied HN points • 22 Nov 24
🕹 Technology Artificial Intelligence Machine Learning Tech Ethics Data science Software Development
  1. OpenAI has spent a lot of money trying to fix an issue with counting the letter R in the word 'strawberry.' This problem has caused a lot of confusion among users.
  2. The CEO of OpenAI thinks the problem is silly but feels it's important to address because users are concerned. They are also looking into redesigning how their models handle letter counting.
  3. Some employees joked about extreme solutions like eliminating red fruits to avoid the R issue. They are also thinking of patches to improve letter counting, but it's clear they have more work to do.

"AI Won't Take Your Job, a Person Using AI Will"—Yes, You Using AI Will Replace You Not Using It

The Algorithmic Bridge • 392 implied HN points • 11 Dec 24
🕹 Technology Artificial Intelligence Automation Job Market Human-computer interaction Digital Tools
  1. Embracing AI tools is essential. If you don't use them, someone who does will likely take your place.
  2. Technology is becoming a part of our lives whether we like it or not. You might not notice it, but AI is already in everyday tools that can help you do better.
  3. It's common to resist new tech because we feel comfortable, but eventually, we adapt. Just like we moved from pencils to keyboards, we will embrace AI too.

Toward the eradication of medical diagnostic errors

Ground Truths • 6255 implied HN points • 28 Jan 24
🏥 Health & Wellness Artificial Intelligence
  1. Diagnostic errors in medicine are a serious problem, leading to harm or disability for many patients.
  2. Artificial intelligence (AI) shows promise in improving diagnostic accuracy by supporting clinicians and reducing workload.
  3. Using advanced AI models like GPT-4 can enhance diagnostic accuracy and provide valuable second opinions in medical practice.

Mission Control Center - brains of the Space System. How does it work?

Space Ambition • 319 implied HN points • 26 Jul 24
🕹 Technology Space Tech Engineering Innovation Data processing Artificial Intelligence
  1. The Mission Control Center (MCC) is crucial for managing spacecraft. It collects data, controls systems, and predicts emergencies.
  2. Different specialists work in the MCC, each focusing on specific parts of the spacecraft. The center’s size varies based on the mission's complexity, from small setups to large control rooms.
  3. New technology, including AI, is changing how MCCs operate. AI helps with monitoring systems and predicting spacecraft movement, making the process more efficient.

LCM: Large Concept Model

Gonzo ML • 189 implied HN points • 04 Jan 25
🕹 Technology Artificial Intelligence Machine Learning Natural Language Processing Data science Computational Models
  1. The Large Concept Model (LCM) aims to improve how we understand and process language by focusing on concepts instead of just individual words. This means thinking at a higher level about what ideas and meanings are being conveyed.
  2. LCM uses a system called SONAR to convert sentences into a stable representation that can be processed and then translated back into different languages or forms without losing the original meaning. This creates flexibility in how we communicate.
  3. This approach can handle long documents more efficiently because it represents ideas as concepts, making processing easier. This could improve applications like summarization and translation, making them more effective.

🤘ACDC (not that one)

Gonzo ML • 63 implied HN points • 29 Jan 25
🕹 Technology Artificial Intelligence Machine Learning Neural Networks Data Analysis Automation
  1. The paper introduces a method called ACDC that automates the process of finding important circuits in neural networks. This can help us better understand how these networks work.
  2. Researchers follow a three-step workflow to study model behavior, and ACDC fully automates the last step which helps identify connections that matter for a specific task.
  3. While ACDC shows promise, it isn't perfect. It may miss some important connections and needs adjustments for different tasks to improve its accuracy.

The ghosts that live in my garage

Basta’s Notes • 122 implied HN points • 13 Jan 25
🕹 Technology Machine Learning Artificial Intelligence Self-driving cars Data science Software Development
  1. Machine learning models are good at spotting patterns that humans might miss. This means they can make predictions and organize data in ways that are impressive and often very useful.
  2. However, machine learning can struggle with unclear or messy data. This fuzziness can lead to mistakes, like misidentifying objects or giving unexpected results.
  3. Not every problem needs a machine learning solution, and sometimes simpler methods work better and are more effective. It's important to think carefully about whether machine learning is truly the best tool for the job.