The hottest Machine Learning Substack posts right now

And their main takeaways
Category
Top Business 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.

Introducing the Model Memo

Artificial Ignorance • 25 implied HN points • 06 Mar 25
🕹 Technology AI Models Machine Learning Software Development Technology Trends Data Analysis
  1. Several new advanced AI models have been released recently, improving reasoning and knowledge. These models, like OpenAI's GPT-4.5 and Google's Gemini 2.0, excel in different areas.
  2. AI is becoming more interactive with features that let it browse the web and perform tasks for users. This shows a shift towards AI that can take action, not just chat.
  3. The best AI models now cost more, with some requiring premium subscriptions. While powerful models like GPT-4.5 have high access fees, other new features may be available for free with some limits.

Grok Grok

Don't Worry About the Vase • 4211 implied HN points • 24 Feb 25
🕹 Technology AI Machine Learning Software Development Social media Data Privacy
  1. Grok can search Twitter and provides fast responses, which is pretty useful. However, it has issues with creativity and sometimes jumps to conclusions too quickly.
  2. Despite being developed by Elon Musk, Grok shows a strong bias against him and others, leading to a loss of trust in the model. There are concerns about its capabilities and safety features.
  3. Grok has been described as easy to jailbreaking, raising concerns about it potentially sharing dangerous instructions if properly manipulated.

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.
Get a weekly roundup of the best Substack posts, by hacker news affinity:

Breaking: OpenAI's efforts at pure scaling have hit a wall.

Marcus on AI • 7825 implied HN points • 13 Feb 25
🕹 Technology AI Machine Learning Software Development Data science Innovation
  1. OpenAI's plan to just make bigger AI models isn't working anymore. They need to find new ways to improve AI instead of just adding more data and parameters.
  2. The new version, originally called GPT-5, has been downgraded to GPT 4.5. This shows that the project hasn't met expectations and isn't a big step forward.
  3. Even if pure scaling isn't the answer, AI development will continue. There are still many ways to create smarter AI beyond just making models larger.

Time to Welcome Claude 3.7

Don't Worry About the Vase • 2419 implied HN points • 26 Feb 25
🕹 Technology AI Models Machine Learning Tech development Software Engineering
  1. Claude 3.7 is a new AI model that improves coding abilities and offers a feature called Extended Thinking, which lets it think longer before responding. This makes it a great choice for coding tasks.
  2. The model prioritizes safety and has clear guidelines for avoiding harmful responses. It is better at understanding user intent and has reduced unnecessary refusals compared to the previous version.
  3. Claude Code is a helpful new tool that allows users to interact with the model directly from the command line, handling coding tasks and providing a more integrated experience.

AI #105: Hey There Alexa

Don't Worry About the Vase • 1120 implied HN points • 27 Feb 25
🕹 Technology AI Machine Learning Automation Robotics Data science
  1. A new version of Alexa, called Alexa+, is coming soon. It will be much smarter and can help with more tasks than before.
  2. AI tools can help improve coding and other work tasks, giving users more productivity but not always guaranteeing quality.
  3. There's a lot of excitement about how AI is changing jobs and tasks, but it also raises concerns about safety and job replacement.

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.

The Weekly Kaitchup #65

The Kaitchup – AI on a Budget • 59 implied HN points • 01 Nov 24
🕹 Technology AI Models Machine Learning Natural Language Text-to-Speech Data science
  1. SmolLM2 offers alternatives to popular models like Qwen2.5 and Llama 3.2, showing good performance with various versions available.
  2. The Layer Skip method improves the speed and efficiency of Llama models by processing some layers selectively, making them faster without losing accuracy.
  3. MaskGCT is a new text-to-speech model that generates high-quality speech without needing text alignment, providing better results across different benchmarks.

Five ways in which the last 3 months — and especially the DeepSeek era — have vindicated “Deep learning is hitting a wall"

Marcus on AI • 7074 implied HN points • 09 Feb 25
🕹 Technology AI Machine Learning Deep Learning Data science
  1. Just adding more data to AI models isn't enough to achieve true artificial general intelligence (AGI). New techniques are necessary for real advancements.
  2. Combining neural networks with traditional symbolic methods is becoming more popular, showing that blending approaches can lead to better results.
  3. The competition in AI has intensified, making large language models somewhat of a commodity. This could change how businesses operate in the generative AI market.

A new generation of AIs: Claude 3.7 and Grok 3

One Useful Thing • 1968 implied HN points • 24 Feb 25
🕹 Technology AI Machine Learning Computing Innovation Research
  1. New AI models like Claude 3.7 and Grok 3 are much smarter and can handle complex tasks better than before. They can even do coding through simple conversations, which makes them feel more like partners for ideas.
  2. These AIs are trained using a lot of computing power, which helps them improve quickly. The more power they use, the smarter they get, which means they’re constantly evolving to perform better.
  3. As AI becomes more capable, organizations need to rethink how they use it. Instead of just automating simple tasks, they should explore new possibilities and ways AI can enhance their work and decision-making.

All paths point downhill

arg min • 218 implied HN points • 31 Oct 24
🕹 Technology Algorithms Optimization Data science Machine Learning Mathematics
  1. In optimization, there are three main approaches: local search, global optimization, and a method that combines both. They all aim to find the best solution to minimize a function.
  2. Gradient descent is a popular method in optimization that works like local search, by following the path of steepest descent to improve the solution. It can also be viewed as a way to solve equations or approximate values.
  3. Newton's method, another optimization technique, is efficient because it converges quickly but requires more computation. Like gradient descent, it can be interpreted in various ways, emphasizing the interconnectedness of optimization strategies.

Grok 3 and an accelerating AI roadmap

Democratizing Automation • 482 implied HN points • 18 Feb 25
🕹 Technology AI Machine Learning Innovation Software Data science
  1. Grok 3 is a new AI model that's designed to compete with existing top models. It aims to improve quickly, with updates happening daily.
  2. There's increasing competition in the AI field, which is pushing companies to release their models faster, leading to more powerful AI becoming available to users sooner.
  3. Current evaluations of AI models might not be very practical or useful for everyday life. It's important for companies to share more about their evaluation processes to help users understand AI advancements.

If AGI Means Everything People Do... What is it That People Do?

Am I Stronger Yet? • 250 implied HN points • 27 Feb 25
🕹 Technology AI Machine Learning Human factors Automation Robotics
  1. There's a big gap between what AIs can do in tests and what they can do in real life. It shows we need to understand the full range of human tasks before predicting AI's future capabilities.
  2. AIs currently struggle with complex tasks like planning, judgment, and creativity. These areas need improvement before they can replace humans in many jobs.
  3. To really know how far AIs can go, we need to focus on the skills they lack and find better ways to measure those abilities. This will help us understand AI's potential.

Hallucinations Are Fine, Actually

Artificial Ignorance • 92 implied HN points • 04 Mar 25
🕹 Technology AI Machine Learning Software Development Data science Automation
  1. AI models can often make mistakes or 'hallucinate' by providing wrong information confidently. It's important for humans to check AI output especially for important tasks.
  2. Even though AI hallucinations are a challenge, they're seen as something we can work to improve rather than an insurmountable problem.
  3. Instead of aiming for AI to do everything on its own, we should use it as a tool to help us do our jobs better, understanding that we need to collaborate with it.

GPT-4.5 Feels Like a Letdown But It’s OpenAI’s Biggest Bet Yet

The Algorithmic Bridge • 605 implied HN points • 28 Feb 25
🕹 Technology AI Machine Learning Software Innovation Data science
  1. GPT-4.5 is not as impressive as expected, but it's part of a plan for bigger advancements in the future. OpenAI is using this model to build a better foundation for what's to come.
  2. Despite being larger and more expensive, GPT-4.5 isn't leading in new capabilities compared to older models. It's more focused on creativity and communication, which might not appeal to all users.
  3. OpenAI wants to improve the basic skills of AI rather than just aiming for high scores in tests. This step back is meant to ensure future models are smarter and more capable overall.

Build AI or Be Buried By Those Who Do

Contemplations on the Tree of Woe • 3574 implied HN points • 30 May 25
🕹 Technology AI Machine Learning Innovation Automation Data science
  1. There are three main views on AI: believers who think it will change everything for the better, skeptics who see it as just fancy technology, and doomers who worry it could end badly for humanity. Each group has different ideas about what AI will mean for the future.
  2. The belief among AI believers is that AI will become a big part of our lives, doing many tasks better than humans and reshaping many industries. They see it as a revolutionary change that will be everywhere.
  3. Many think that if we don’t build our own AI, the narrative and values that shape AI will be dominated by one ideology, which could be harmful. The idea is that we need balanced development of AI, representing different views to ensure freedom and diversity in thought.

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.

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.

On OpenAI's Model Spec 2.0

Don't Worry About the Vase • 985 implied HN points • 21 Feb 25
🕹 Technology AI Machine Learning Software Development Data Ethics Tech Policy
  1. OpenAI's Model Spec 2.0 introduces a structured command chain that prioritizes platform rules over individual developer and user instructions. This hierarchy helps ensure safety and performance in AI interactions.
  2. The updated rules emphasize the importance of preventing harm while still aiming to assist users in achieving their goals. This means the AI should avoid generating illegal or harmful content.
  3. There are notable improvements in clarity and detail compared to previous versions, like defining what content is prohibited and reinforcing user privacy. However, concerns remain about potential misuse of the system by those with access to higher-level rules.

Using AI: Queries, Conversations, and Projects

In My Tribe • 303 implied HN points • 11 Jun 25
🕹 Technology AI Machine Learning Human-computer interaction Software Development Educational Technology
  1. A conversation with AI is different from simply asking a question. You can explore topics more deeply and learn from the back-and-forth interaction.
  2. Using AI for projects is essential to becoming skilled with it. It’s like doing a group assignment, where you can create something together.
  3. Providing clear instructions and materials to AI helps it assist you better. Treating it like a partner, rather than just a tool, can lead to better results.

The latest open artifacts (#7): Alpaca era of reasoning models, China's continued dominance, and tons of multimodal advancements

Democratizing Automation • 63 implied HN points • 19 Feb 25
🕹 Technology AI Machine Learning Open Source Data science Model development
  1. New datasets for deep learning models are appearing, but choosing the right one can be tricky.
  2. China is leading in AI advancements by releasing strong models with easy-to-use licenses.
  3. Many companies are developing reasoning models that improve problem-solving by using feedback and advanced training methods.

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.

The Impact of the Calibration Dataset for AutoRound and AWQ Quantization

The Kaitchup – AI on a Budget • 39 implied HN points • 31 Oct 24
🕹 Technology AI Data science Machine Learning Quantization Model optimization
  1. Quantization helps reduce the size of large language models, making them easier to run, especially on consumer GPUs. For instance, using 4-bit quantization can shrink a model's size by about a third.
  2. Calibration datasets are crucial for improving the accuracy of quantization methods like AWQ and AutoRound. The choice of the dataset impacts how well the quantization performs.
  3. Most quantization tools use a default English-language dataset, but results can vary with different languages and datasets. Testing various options can lead to better outcomes.

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.

Breaking news: AGI is not imminent!

Marcus on AI • 4466 implied HN points • 20 Jan 25
🕹 Technology AI Machine Learning Computing Innovation Digital Trends
  1. Many people believe AGI, or artificial general intelligence, is coming soon, but that might not be true. It's important to stay cautious and not believe everything we hear about upcoming technology.
  2. Sam Altman, a well-known figure in AI, suggested we're close to achieving AGI, but he later changed his statement. This shows that predictions in technology can quickly change.
  3. Experts like Gary Marcus are confident that AGI won't arrive as soon as 2025. They think we still have a long way to go before we reach that level of intelligence in machines.

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.

AGI versus “broad, shallow intelligence”

Marcus on AI • 5019 implied HN points • 13 Jan 25
🕹 Technology AI Machine Learning Intelligence Robotics Data science
  1. We haven't reached Artificial General Intelligence (AGI) yet. People can still easily come up with problems that AI systems can't solve without training.
  2. Current AI systems, like large language models, are broad but not deep in understanding. They might seem smart, but they can make silly mistakes and often don't truly grasp the concepts they discuss.
  3. It's important to keep working on AI that isn't just broad and shallow. We need smarter systems that can reliably understand and solve different problems.

AI Agents: Hype versus Reality, redux

Marcus on AI • 4545 implied HN points • 15 Jan 25
🕹 Technology AI Machine Learning Software Development Innovation Data science
  1. AI agents are getting a lot of attention right now, but they still aren't reliable. Most of what we see this year are just demos that don't work well in real life.
  2. In the long run, we might have powerful AI agents doing many jobs, but that won't happen for a while. For now, we need to be careful about the hype.
  3. To build truly helpful AI agents, we need to solve big challenges like common sense and reasoning. If those issues aren't fixed, the agents will continue to give strange or wrong results.

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.

Grok 3: Another Win For The Bitter Lesson

The Algorithmic Bridge • 817 implied HN points • 18 Feb 25
🕹 Technology AI Development Computing Machine Learning Startup Ecosystem Tech Innovation
  1. Scaling laws are really important for AI progress. Bigger models and better computing power often lead to better results, like how Grok 3 outperformed earlier versions and is among the best AI models.
  2. DeepSeek shows that clever engineering can help, but it still highlights the need for more computing power. They did well despite limitations, but with more resources, they could achieve even greater things.
  3. Grok 3's success proves that having more computing resources can beat just trying to be clever. Companies that focus on scaling their resources are likely to stay ahead in the AI race.

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.

Why reasoning models will generalize

Democratizing Automation • 1504 implied HN points • 28 Jan 25
🕹 Technology AI Machine Learning Data science Natural Language Processing Computing
  1. Reasoning models are designed to break down complex problems into smaller steps, helping them solve tasks more accurately, especially in coding and math. This approach makes it easier for the models to manage difficult questions.
  2. As reasoning models develop, they show promise in various areas beyond their initial focus, including creative tasks and safety-related situations. This flexibility allows them to perform better in a wider range of applications.
  3. Future reasoning models will likely not be perfect for every task but will improve over time. Users may pay more for models that deliver better performance, making them more valuable in many sectors.

Last Words on AI

God's Spies by Thomas Neuburger • 80 implied HN points • 10 Jun 25
🕹 Technology AI Ethics Climate Impact Data Privacy Machine Learning Automation
  1. AI can't solve new problems unless they've been solved by humans before. It relies on previous data and patterns to operate.
  2. AI is largely a tool driven by greed, impacting our environment negatively. Its energy demands could worsen the climate crisis.
  3. Current AI models are not genuinely intelligent; they mimic patterns they've learned without real reasoning ability. This highlights that we are far from achieving true artificial general intelligence.

This Rumor About GPT-5 Changes Everything

The Algorithmic Bridge • 4788 implied HN points • 16 Jan 25
🕹 Technology AI Machine Learning Software Development Data science Tech industry
  1. There's a belief that GPT-5 might already exist but isn't being released to the public. The idea is that OpenAI may be using it internally because it's more valuable that way.
  2. AI labs are focusing on creating smaller and cheaper models that still perform well. This new approach aims to reduce costs while improving efficiency, which is crucial given the rising demand for AI.
  3. The situation is similar across major AI companies like OpenAI and Anthropic, with many facing challenges in producing new models. Instead, they might be opting to train powerful models internally and use them to enhance smaller models for public use.

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.