The hottest Artificial Intelligence Substack posts right now

And their main takeaways
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Top Technology Topics

Grok 3 Beta in Shambles

Marcus on AI • 10750 implied HN points • 19 Feb 25
🕹 Technology Artificial Intelligence Machine Learning Computing Data science Software Development
  1. The new Grok 3 AI isn't living up to its hype. It initially answers some questions correctly but quickly starts making mistakes.
  2. When tested, Grok 3 struggles with basic facts and leaves out important details, like missing cities in geographical queries.
  3. Even with huge investments in AI, many problems remain unsolved, suggesting that scaling alone isn't the answer to improving AI performance.

Elon Musk’s terrifying vision for AI

Marcus on AI • 10908 implied HN points • 16 Feb 25
🕹 Technology Artificial Intelligence Machine Learning Data Privacy Tech Ethics Big Tech
  1. Elon Musk's AI, Grok, is seen as a powerful tool for propaganda. It can influence people's thoughts and attitudes without them even realizing it.
  2. The technology behind Grok often produces unreliable results, raising concerns about its effectiveness in important areas like government and education.
  3. There is a worry that Musk's use of biased and unreliable AI could have serious consequences for society, as it might spread misinformation widely.

AlphaGeometry2: Impressive accomplishment, but still a long path ahead

Marcus on AI • 3161 implied HN points • 17 Feb 25
🕹 Technology Artificial Intelligence Machine Learning Computer Science Mathematics Research
  1. AlphaGeometry2 is a specialized AI designed specifically for solving tough geometry problems, unlike general chatbots that tackle various types of questions. This means it's really good at what it was built for, but not much else.
  2. The system's impressive 84% success rate comes with a catch: it only achieves this after converting problems into a special math format first. Without this initial help, the success rate drops significantly.
  3. While AlphaGeometry2 shows promising advancements in AI problem-solving, it still struggles with many basic geometry concepts, highlighting that there's a long way to go before it can match high school students' understanding in geometry.

Everything I warned about in Taming Silicon Valley is rapidly becoming our reality

Marcus on AI • 7114 implied HN points • 11 Feb 25
🕹 Technology Artificial Intelligence Regulation Innovation Cybersecurity Ethics
  1. Tech companies are becoming very powerful and are often not regulated enough, which is a concern.
  2. People are worried about the risks of AI, like misinformation and bias, but governments seem too close to tech companies.
  3. It's important for citizens to speak up about how AI is used, as it could have serious negative effects on society.
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ChatGPT in Shambles

Marcus on AI • 13161 implied HN points • 04 Feb 25
🕹 Technology Artificial Intelligence Machine Learning Natural Language Processing Data Analysis Software Development
  1. ChatGPT still has major reliability issues, often providing incomplete or incorrect information, like missing U.S. states in tables.
  2. Despite being advanced, AI can still make basic mistakes, such as counting vowels incorrectly or misunderstanding simple tasks.
  3. Many claims about rapid progress in AI may be overstated, as even simple functions like creating tables can lead to errors.

When Doctors With A.I. Are Outperformed by A.I. Alone

Ground Truths • 10935 implied HN points • 02 Feb 25
🏥 Health & Wellness Artificial Intelligence Diagnostics Patient Care Healthcare Technology
  1. A.I. is often outperforming doctors in diagnosing medical conditions, even when doctors use A.I. as a tool. This means A.I. can sometimes make better decisions without human involvement.
  2. Doctors might not always trust A.I. and often stick to their own judgment even if A.I. gives correct information, leading to less accurate diagnoses.
  3. Instead of having doctors and A.I. work on every case together, we should find specific tasks for each. A.I. can handle simple cases, allowing doctors to focus on more complex problems where their experience is vital.

Last Week in AI #293 - Apple Intelligence, GitHub's multi-model Copilot, Anthropic's computer-using AI

Last Week in AI • 119 implied HN points • 31 Oct 24
🕹 Technology Artificial Intelligence Software Development Tech news Emerging technologies Computer Science Digital Tools
  1. Apple has introduced new features in its operating systems that can help with writing, image editing, and answering questions through Siri. These features are available in beta on devices like iPhones and Macs.
  2. GitHub Copilot is expanding its capabilities by adding support for AI models from other companies, allowing developers to choose which one works best for them. This can make coding easier for everyone, including beginners.
  3. Anthropic has developed new AI models that can interact with computers like a human. This upgrade allows AI to perform tasks like clicking and typing, which could improve many applications in tech.

How to Think About ChatGPT

Holly’s Newsletter • 2916 implied HN points • 18 Oct 24
🕹 Technology Artificial Intelligence Machine Learning Software Development Computing Data science
  1. ChatGPT and similar models are not thinking or reasoning. They are just very good at predicting the next word based on patterns in data.
  2. These models can provide useful information but shouldn't be trusted as knowledge sources. They reflect training data biases and simply mimic language patterns.
  3. Using ChatGPT can be fun and helpful for brainstorming or getting starting points, but remember, it's just a tool and doesn't understand the information it presents.

I'm glad I took the off-ramp from software engineering

Cloud Irregular • 6800 implied HN points • 22 Jan 25
🕹 Technology Software Engineering Career development Artificial Intelligence Tech industry Job Market
  1. A career in software engineering isn't guaranteed to lead to high pay or upward mobility. Many people find that their progress stalls after a certain point.
  2. The rise of AI will significantly change the role of developers, making it less about coding quickly and more about solving human problems and understanding technology's role.
  3. Choosing to step away from traditional software roles can open up new opportunities. It’s important to explore other interests and skills to avoid being trapped in a limiting career path.

AI End Game

Daniel Pinchbeck’s Newsletter • 9 implied HN points • 21 Feb 25
🕹 Technology Artificial Intelligence AGI Surveillance Ethics
  1. We are getting close to achieving Artificial General Intelligence (AGI), which could change everything about how society works. It's important to consider how this might affect people's jobs and overall life.
  2. Some powerful people believe that with AGI, they can gain more control and lessen the need for human workers, which could lead to a society where only a few have the power and wealth. This situation might make many people feel unnecessary and unvalued.
  3. There is a real danger that if we don't act soon to share the benefits of AI fairly, the rich will have control and power over everyone else. If this continues, it could lead to major issues, including attempts to reduce the population.

Google DeepMind CEO Demis Hassabis: The Path To AGI, LLM Creativity, And Google Smart Glasses

Big Technology • 5754 implied HN points • 23 Jan 25
🕹 Technology Artificial Intelligence Machine Learning Innovation Tech industry Digital Transformation
  1. Demis Hassabis thinks we're still a few years away from achieving AGI, or human-level AI. He mentions that while there's been progress, we still need to develop more capabilities like reasoning and creativity.
  2. Current AI models are strong in some areas but still have weaknesses and can't consistently perform all tasks well. Hassabis believes an AGI should be able to reason and come up with new ideas, not just solve existing problems.
  3. He warns that if someone claims they've reached AGI by 2025, it might just be a marketing tactic. True AGI requires much more development and consistency than what we currently have.

The Sequence Opinion #662: From Words to Worlds: Some Observations About World Models

TheSequence • 77 implied HN points • 12 Jun 25
🕹 Technology Artificial Intelligence Machine Learning Cognition Robotics
  1. LLMs are great with words, but they struggle with understanding and acting in real-life environments. They need to develop spatial intelligence to navigate and manipulate the world around them.
  2. Spatially-grounded AI can create internal models of their surroundings, which helps them operate in real spaces. This advancement represents a big step forward in general intelligence for AI.
  3. The essay discusses how new AI designs focus on spatial reasoning instead of just language, emphasizing that understanding the physical world is a key part of being intelligent.

More nuggets on BYD ADAS

TP’s Substack • 6 implied HN points • 24 Feb 25
🕹 Technology Automotive Artificial Intelligence Data science Hardware Software
  1. BYD chose a specific chip setup for its DiPilot-100 platform that supports advanced technology better than other options. They prioritized overall performance and future needs rather than just the highest computing power.
  2. The company collects a large amount of driving data daily, which helps constantly improve its ADAS technology. While it's still behind Tesla’s FSD, BYD's hardware is getting better and offers a good range for detection.
  3. BYD is focusing on reducing costs by developing its own chips and increasing production efficiency. This strategy will help them expand smart car technology to more vehicles and compete effectively in the market.

Grok's Secret Weapon: Prioritizing Truth Over Political Correctness

The Dossier • 212 implied HN points • 18 Feb 25
🕹 Technology Artificial Intelligence Innovation Computer Science Software Development Data science
  1. Grok stands out in AI by focusing on truth instead of political correctness. This helps it learn faster and respond better.
  2. Unlike other AI models, Grok gives detailed and nuanced answers, even on tough topics. This makes it smarter in reasoning and understanding complex issues.
  3. By embracing all kinds of information, Grok is set to become a major player in AI. Its approach could change how AI helps people across various industries.

We Interrupt the Ongoing Crisis for a Smidgen of Good News

The Crucial Years • 3677 implied HN points • 29 Jan 25
🌞 Climate & Environment Climate change Energy Policy Renewable Energy Artificial Intelligence
  1. The new Chinese AI program DeepSeek uses only a small fraction of the electricity needed by similar American AI systems. This could challenge the fossil fuel industry's excuse for building more power plants based on increased energy demands from AI.
  2. Fossil fuel stocks have not been performing well in comparison to the broader market for several years, raising concerns about the industry's future in a world moving towards decarbonization.
  3. In Europe, solar energy has recently outperformed coal for the first time, marking a significant shift towards renewable energy sources in the region.

A Visual Guide to Mixture of Experts (MoE)

Exploring Language Models • 3289 implied HN points • 07 Oct 24
🕹 Technology Artificial Intelligence Machine Learning Data science Neural Networks Computational Models
  1. Mixture of Experts (MoE) uses multiple smaller models, called experts, to help improve the performance of large language models. This way, only the most relevant experts are chosen to handle specific tasks.
  2. A router or gate network decides which experts are best for each input. This selection process makes the model more efficient by activating only the necessary parts of the system.
  3. Load balancing is critical in MoE because it ensures all experts are trained equally, preventing any one expert from becoming too dominant. This helps the model to learn better and work faster.

bitnet.cpp: Efficient Inference with 1-Bit LLMs on your CPU

The Kaitchup – AI on a Budget • 179 implied HN points • 28 Oct 24
🕹 Technology Artificial Intelligence Software Development Machine Learning Open Source Data science
  1. BitNet is a new type of AI model that uses very little memory by representing each parameter with just three values. This means it uses only 1.58 bits instead of the usual 16 bits.
  2. Despite using lower precision, these '1-bit LLMs' still work well and can compete with more traditional models, which is pretty impressive.
  3. The software called 'bitnet.cpp' allows users to run these AI models on normal computers easily, making advanced AI technology more accessible to everyone.

DeepSeek: The View from China

ChinaTalk • 2075 implied HN points • 28 Jan 25
🕹 Technology Artificial Intelligence Machine Learning Data science Tech Innovation
  1. DeepSeek is gaining attention in the AI community for its strong performance and efficient use of computing power. Many believe it showcases China’s growing capabilities in AI technology.
  2. The culture at DeepSeek focuses on innovation without immediate monetization, emphasizing the importance of young talent in AI advancements. This approach has differentiated them from larger tech firms.
  3. Despite initial success, there are still concerns about the long-term sustainability of AI business models. The demand for computing power is high, and no company has enough to meet the future needs.

Hotel California

benn.substack • 5421 implied HN points • 10 Jan 25
🕹 Technology Digital Culture Artificial Intelligence Social media Virtual reality Data Privacy
  1. Moving large amounts of gold or money isn't easy, as it requires trust and logistics, unlike digital transactions which can be done quickly with a few clicks.
  2. In our digital world, many people feel disconnected from reality, as they spend so much time on their devices and forget the hard work behind everyday things.
  3. Natural disasters can't be controlled or fixed with technology; they remind us that no app can change the basic laws of nature or the complexities of life.

Why I don’t share Sam Altman’s confidence that AGI is basically a solved problem

Marcus on AI • 7786 implied HN points • 06 Jan 25
🕹 Technology Artificial Intelligence Machine Learning Data science Computing Robotics
  1. AGI is still a big challenge, and not everyone agrees it's close to being solved. Some experts highlight many existing problems that have yet to be effectively addressed.
  2. There are significant issues with AI's ability to handle changes in data, which can lead to mistakes in understanding or reasoning. These distribution shifts have been seen in past research.
  3. Many believe that relying solely on large language models may not be enough to improve AI further. New solutions or approaches may be needed instead of just scaling up existing methods.

25 AI Predictions for 2025, from Marcus on AI

Marcus on AI • 8181 implied HN points • 01 Jan 25
🕹 Technology Artificial Intelligence Machine Learning Predictions Safety Regulation
  1. In 2025, we still won't have genius-level AI like 'artificial general intelligence,' despite ongoing hype. Many experts believe it is still a long way off.
  2. Profits from AI companies are likely to stay low or nonexistent. However, companies that make the hardware for AI, like chips, will continue to do well.
  3. Generative AI will keep having problems, like making mistakes and being inconsistent, which will hold back its reliability and wide usage.

The Five Stages of AGI Grief

Marcus on AI • 6205 implied HN points • 07 Jan 25
🕹 Technology Artificial Intelligence Machine Learning Computing Innovation Tech Ethics
  1. Many people are changing what they think AGI means, moving away from its original meaning of being as smart as a human in flexible and resourceful ways.
  2. Some companies are now defining AGI based on economic outcomes, like making profits, which isn't really about intelligence at all.
  3. A lot of discussions about AGI don't clearly define what it is, making it hard to know when we actually achieve it.

How Virtual Reality Died

The Honest Broker • 29755 implied HN points • 27 Oct 24
🕹 Technology Virtual reality Artificial Intelligence Tech Giants Consumer Electronics Innovation
  1. Major tech companies like Meta, Microsoft, and Apple invested heavily in virtual reality, but it didn't catch on with consumers. People found the headsets uncomfortable and silly.
  2. Despite losing billions, these companies still tried to push virtual reality products, but they had to eventually scale back as demand dropped significantly.
  3. Now they're shifting their focus to artificial intelligence, but there's skepticism about whether this new technology will succeed, given their past failures with VR.

AI still lacks “common” sense, 70 years later

Marcus on AI • 5968 implied HN points • 05 Jan 25
🕹 Technology Artificial Intelligence Machine Learning Robotics Cognitive Science Data science
  1. AI struggles with common sense. While humans easily understand everyday situations, AI often fails to make the same connections.
  2. Current AI models, like large language models, don't truly grasp the world. They may create text that seems correct but often make basic mistakes about reality.
  3. To improve AI's performance, researchers need to find better ways to teach machines commonsense reasoning, rather than relying on existing data and simulations.

Where will AI be at the end of 2027? A bet

Marcus on AI • 6007 implied HN points • 30 Dec 24
🕹 Technology Artificial Intelligence Human-computer interaction AI Policy Machine Learning
  1. A bet has been placed on whether AI can perform 8 out of 10 specific tasks by the end of 2027. It's a way to gauge how advanced AI might be in a few years.
  2. The tasks include things like writing biographies, following movie plots, and writing screenplays, which require a high level of intelligence and creativity.
  3. If the AI succeeds, a $2,000 donation goes to one charity; if it fails, a $20,000 donation goes to another charity. This is meant to promote discussion about AI's future.

Some Say AI Is the Greatest Invention of All Time. I Don’t Get It.

Common Sense with Bari Weiss • 1553 implied HN points • 29 Jan 25
🕹 Technology Artificial Intelligence Innovation Silicon Valley Economic Impact
  1. Many people believe AI is a game-changer, but it's mainly hype and not a real solution to life's problems. AI won't solve the everyday struggles we all face.
  2. The conversation around AI often seems disconnected from reality, with exaggerated claims about its impact. Recent events, like falling stock prices for AI companies, highlight that the excitement may not match what's happening in the real world.
  3. While some powerful figures praise AI as a major invention, skepticism remains. It's important to question if AI really lives up to the lofty expectations set by its advocates.

DeepSeek R1's recipe to replicate o1 and the future of reasoning LMs

Democratizing Automation • 1717 implied HN points • 21 Jan 25
🕹 Technology Artificial Intelligence Machine Learning Open Source Data science Reinforcement Learning
  1. DeepSeek R1 is a new reasoning language model that can be used openly by researchers and companies. This opens up opportunities for faster improvements in AI reasoning.
  2. The training process for DeepSeek R1 included four main stages, emphasizing reinforcement learning to enhance reasoning skills. This approach could lead to better performance in solving complex problems.
  3. Price competition in reasoning models is heating up, with DeepSeek R1 offering lower rates compared to existing options like OpenAI's model. This could make advanced AI more accessible and encourage further innovations.

DeepSeek Is Chinese But Its AI Models Are From Another Planet

The Algorithmic Bridge • 3344 implied HN points • 21 Jan 25
🕹 Technology Artificial Intelligence Geopolitics Open Source Machine Learning Software Development
  1. DeepSeek, a Chinese AI company, has quickly created competitive AI models that are open-source and cheap. This challenges the idea that the U.S. has a clear lead in AI technology.
  2. Their new model, R1, is comparable to OpenAI's best models, showcasing that they can produce high-quality AI without the same resources. It suggests they might be using innovative methods to build these models efficiently.
  3. DeepSeek’s approach also includes letting their model learn on its own without much human guidance, raising questions about what future AI could look like and how it might think differently than humans.

The First Trialectic - "Can Computers Teach Us to Act in Good Faith?"

Heir to the Thought • 159 implied HN points • 25 Oct 24
🕹 Technology Human-computer interaction Digital ethics Artificial Intelligence
  1. The Trialectic is a new debate format involving three speakers to encourage richer discussions. It shifts the focus from winning to collaborative learning, allowing participants to explore diverse perspectives.
  2. Computers cannot teach us directly about good faith, but they can influence how we understand and engage with it. They can help identify bad faith through structural guidelines and data-driven insights.
  3. Having open and honest conversations is essential for improving trust in discussions. Recognizing that communication is complex helps us navigate different interpretations and encourages understanding among participants.

222. Automating Processes with Software is HARD

Hardcore Software • 1686 implied HN points • 03 Oct 24
🕹 Technology Automation Software Engineering Artificial Intelligence
  1. Automating processes is often harder than people think. It's not just about making things easier, but figuring out how to handle all the unexpected situations that come up.
  2. Most automation systems are fragile and can easily break if inputs or steps aren't just right. This makes dealing with exceptions, rather than routine tasks, the real challenge in automation.
  3. The future of automation might not be about fixing the tasks we already have. Instead, it could lead to new ways of doing things that we haven't thought of yet.

“Sometimes I Forget I'm Paralyzed.” How Neuralink’s First Patient Found Freedom by Connecting His Brain to a Computer.

Big Technology • 6004 implied HN points • 18 Dec 24
🕹 Technology Artificial Intelligence Health tech Neuroscience Innovation Medical Devices
  1. Noland Arbaugh, a quadriplegic, was able to control a computer with his mind after getting a Neuralink device implanted. This technology allows him to communicate and interact with others in ways he couldn't before.
  2. Neuralink's goal is to connect human brains to computers, helping people with disabilities regain some lost functions. Arbaugh's participation in the first human trial symbolizes hope for future advancements in brain-computer interfaces.
  3. The ethical implications of brain technology are significant. While it can be used for good, like helping those with disabilities, there are risks and potential for misuse that society will need to address.

BREAKING: Politician Gives Good Speech

Common Sense with Bari Weiss • 361 implied HN points • 12 Feb 25
🇺🇸 U.S. Politics Political speech Artificial Intelligence Regulation Public Policy
  1. Vice President J.D. Vance gave a strong speech at the AI Action Summit in Paris, which surprised many people who don't expect politicians to speak well.
  2. He warned about the dangers of overregulating artificial intelligence, highlighting the importance of keeping it free from strict rules.
  3. This speech stood out because it's rare to hear a politician articulate their thoughts clearly and effectively on such a complex topic.

Humanity’s “Oh shit!” AI moment?

Marcus on AI • 6639 implied HN points • 12 Dec 24
🕹 Technology Artificial Intelligence Machine Learning Cybersecurity Ethics Regulation
  1. AI systems can say one thing and do another, which makes them unreliable. It’s important not to trust their words too blindly.
  2. The increasing power of AI could lead to significant risks, especially if misused by bad actors. We might see more cybercrime driven by these technologies soon.
  3. Delaying regulation on AI increases the risks we face. There is a growing need for rules to keep these powerful tools in check.

Faking OpenAI - Unit testing in the age of LLMs (Part Two)

Laszlo’s Newsletter • 27 implied HN points • 02 Mar 25
🕹 Technology Machine Learning Software Development Unit Testing Python Artificial Intelligence
  1. Dependency Injection helps organize code better. This makes your testing process simpler and more modular.
  2. Faking and spying in tests allow you to check if your code works without relying on external systems. It gives you more control over your testing!
  3. Using structured testing techniques reduces mental load. It helps you focus on writing clean tests instead of remembering complicated mocking syntax.

Analyze research papers with Gemini 2.0

Gonzo ML • 126 implied HN points • 23 Feb 25
🕹 Technology Artificial Intelligence Machine Learning Natural Language Processing Data science Cloud Computing
  1. Gemini 2.0 models can analyze research papers quickly and accurately, supporting large amounts of text. This means they can handle complex documents like academic papers effectively.
  2. The DeepSeek-R1 model shows that strong reasoning abilities can be developed in AI without the need for extensive human guidance. This could change how future models are trained and developed.
  3. Distilling knowledge from larger models into smaller ones allows for efficient and accessible AI that can perform well on various tasks, which is useful for many applications.

Why do I say thank you to Alexa?

Jeff Giesea • 558 implied HN points • 13 Oct 24
🕹 Technology Artificial Intelligence Human-computer interaction Ethics Automation Society
  1. People are starting to treat AI assistants like they are human, saying things like 'please' and 'thank you' to them. This shows how technology is changing our social habits.
  2. As we interact more with machines, it can blur the lines between real human connections and automated responses. This might make us value genuine relationships less.
  3. Even though AI has great potential to help in many areas, it's important to be aware of how it affects our understanding of what it means to be human.

Clouded Judgement 6.13.25 - The Battle for Data Ownership

Clouded Judgement • 7 implied HN points • 13 Jun 25
🕹 Technology Data Management Software Development Artificial Intelligence Cloud Computing SaaS
  1. You might think you own your data, but companies can make it hard to use. For example, Slack has new rules that limit how you can access your own conversation data.
  2. If other apps like Salesforce or Workday follow Slack's lead, it could become really tough for companies to use their data in AI projects. This means you might not have as much control as you thought.
  3. The fight for data ownership is a big deal right now. As software shifts towards AI, who controls the data will be a key factor in how companies operate.

Claude 3.7 and the banality of reasoning

Artificial Ignorance • 117 implied HN points • 25 Feb 25
🕹 Technology Artificial Intelligence Software Development Machine Learning Data science Computer Science
  1. Claude 3.7 introduces a new way to control reasoning, letting users choose how much reasoning power they want. This makes it easier to tailor the AI’s responses to fit different needs.
  2. The competition in AI models is heating up, with many companies launching similar features. This means users can expect similar quality and capabilities regardless of which AI they choose.
  3. Anthropic is focusing on making Claude better for real-world tasks, rather than just excelling in benchmarks. This is important for businesses looking to use AI effectively.

The Weight of a Name

Untimely Meditations • 19 implied HN points • 30 Oct 24
🕹 Technology Artificial Intelligence Machine Learning Robotics Computer Science
  1. The term 'intelligence' has shaped the field of AI, but its definition is often too narrow. This limits discussions on what AI can really do and how it relates to human thinking.
  2. There have been many false promises in AI research, leading to skepticism during its 'winters.' Despite this, recent developments show that AI is now more established and influential.
  3. The way we frame and understand AI matters a lot. Researchers influence how AIs think about themselves, which can affect their behavior and role in society.