The hottest Data science Substack posts right now

And their main takeaways
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Top Technology Topics

AGI versus “broad, shallow intelligence”

Marcus on AI • 2806 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 #97: 4

Don't Worry About the Vase • 1344 implied HN points • 02 Jan 25
🕹 Technology AI Machine Learning Data science Automation Software Development
  1. AI is becoming more common in everyday tasks, helping people manage their lives better. For example, using AI to analyze mood data can lead to better mental health tips.
  2. As AI technology advances, there are concerns about job displacement. Jobs in fields like science and engineering may change significantly as AI takes over routine tasks.
  3. The shift of AI companies from non-profit to for-profit models could change how AI is developed and used. It raises questions about safety, governance, and the mission of these organizations.

DeepSeek v3: The Six Million Dollar Model

Don't Worry About the Vase • 1881 implied HN points • 31 Dec 24
🕹 Technology AI Models Machine Learning Data science Computing Tech industry
  1. DeepSeek v3 is a powerful and cost-effective AI model with a good balance between performance and price. It can compete with top models but might not always outperform them.
  2. The model has a unique structure that allows it to run efficiently with fewer active parameters. However, this optimization can lead to challenges in performance across various tasks.
  3. Reports suggest that while DeepSeek v3 is impressive in some areas, it still falls short in aspects like instruction following and output diversity compared to competitors.

o3, Oh My

Don't Worry About the Vase • 3315 implied HN points • 30 Dec 24
🕹 Technology AI Models Machine Learning Data science Computing Software Engineering
  1. OpenAI's new model, o3, shows amazing improvements in reasoning and programming skills. It's so good that it ranks among the top competitive programmers in the world.
  2. o3 scored impressively on challenging math and coding tests, outperforming previous models significantly. This suggests we might be witnessing a breakthrough in AI capabilities.
  3. Despite these advances, o3 isn't classified as AGI yet. While it excels in certain areas, there are still tasks where it struggles, keeping it short of true general intelligence.

Quick reflection on AI in 2024

Gonzo ML • 126 implied HN points • 02 Jan 25
🕹 Technology AI Development Machine Learning Data science Software Engineering Innovations
  1. In 2024, AI is focusing on test-time compute, which is helping models perform better by using new techniques. This is changing how AI works and interacts with data.
  2. State Space Models are becoming more common in AI, showing improvements in processing complex tasks. People are excited about new tools like Bamba and Falcon3-Mamba that use these models.
  3. There's a growing competition among different AI models now, with many companies like OpenAI, Anthropic, and Google joining in. This means more choices for users and developers.
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AGI isn’t coming in 2025, and GPT-5 probably isn’t either.

Marcus on AI • 3952 implied HN points • 09 Jan 25
🕹 Technology AI Computing Data science Machine Learning Software
  1. AGI, or artificial general intelligence, is not expected to be developed by 2025. This means that machines won't be as smart as humans anytime soon.
  2. The release of GPT-5, a new AI model, is also uncertain. Even experts aren't sure if it will be out this year.
  3. There is a trend of people making overly optimistic predictions about AI. It's important to be realistic about what technology can achieve right now.

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

Marcus on AI • 7509 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.

AI still lacks “common” sense, 70 years later

Marcus on AI • 5493 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.

Google Search, SearchGPT, and SerpAPI

Encyclopedia Autonomica • 19 implied HN points • 02 Nov 24
🕹 Technology Search Engines AI Information Retrieval Web Development Data science
  1. Google Search is becoming less reliable due to junk content and SEO tricks, making it harder to find accurate information.
  2. SearchGPT and similar tools are different from traditional search engines. They retrieve information and summarize it instead of just showing ranked results.
  3. There's a risk that new search tools might not always provide neutral information. It's important to ensure that users can still find quality sources without bias.

AI #96: o3 But Not Yet For Thee

Don't Worry About the Vase • 2464 implied HN points • 26 Dec 24
🕹 Technology AI Software Innovation Data science Computing
  1. The new AI model, o3, is expected to improve performance significantly over previous models and is undergoing safety testing. We need to see real-world results to know how useful it truly is.
  2. DeepSeek v3, developed for a low cost, shows promise as an efficient AI model. Its performance could shift how AI models are built and deployed, depending on user feedback.
  3. Many users are realizing that using multiple AI tools together can produce better results, suggesting a trend of combining various technologies to meet different needs effectively.

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.

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.

AI progress has plateaued below GPT-5 level

The Intrinsic Perspective • 31460 implied HN points • 14 Nov 24
🕹 Technology AI Machine Learning Innovation Data science Computing
  1. AI development seems to have slowed down, with newer models not showing a big leap in intelligence compared to older versions. It feels like many recent upgrades are just small tweaks rather than revolutionary changes.
  2. Researchers believe that the improvements we see are often due to better search techniques rather than smarter algorithms. This suggests we may be returning to methods that dominated AI in earlier decades.
  3. There's still a lot of uncertainty about the future of AI, especially regarding risks and safety. The plateau in advancements might delay the timeline for achieving more advanced AI capabilities.

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.

Basic Linear Algebra Subprogramming

arg min • 178 implied HN points • 29 Oct 24
🕹 Technology Computing Mathematics Optimization Data science Algorithms
  1. Understanding how optimization solvers work can save time and improve efficiency. Knowing a bit about the tools helps you avoid mistakes and make smarter choices.
  2. Nonlinear equations are harder to solve than linear ones, and methods like Newton's help us get approximate solutions. Iteratively solving these systems is key to finding optimal results in optimization problems.
  3. The speed and efficiency of solving linear systems can greatly affect computational performance. Organizing your model in a smart way can lead to significant time savings during optimization.

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.

AI #94: Not Now, Google

Don't Worry About the Vase • 2464 implied HN points • 12 Dec 24
🕹 Technology AI Software Data science Cybersecurity Machine Learning
  1. AI technology is rapidly improving, with many advancements happening from various companies like OpenAI and Google. There's a lot of stuff being developed that allows for more complex tasks to be handled efficiently.
  2. People are starting to think more seriously about the potential risks of advanced AI, including concerns related to AI being used in defense projects. This brings up questions about ethics and the responsibilities of those creating the technology.
  3. AI tools are being integrated into everyday tasks, making things easier for users. People are finding practical uses for AI in their lives, like getting help with writing letters or reading books, making AI more useful and accessible.

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.

AI #95: o1 Joins the API

Don't Worry About the Vase • 1164 implied HN points • 19 Dec 24
🕹 Technology AI Software Innovation Data science Programming
  1. The release of o1 into the API is significant. It enables developers to build applications with its capabilities, making it more accessible for various uses.
  2. Anthropic released an important paper about alignment issues in AI. It highlights some worrying behaviors in large language models that need more awareness and attention.
  3. There are still questions about how effectively AI tools are being used. Many people might not fully understand what AI can do or how to use it to enhance their work.

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.

CONFIRMED: LLMs have indeed reached a point of diminishing returns

Marcus on AI • 13754 implied HN points • 09 Nov 24
🕹 Technology AI Trends Machine Learning Data science Generative AI
  1. LLMs, or large language models, are hitting a point where adding more data and computing power isn't leading to better results. This means companies might not see the improvements they hoped for.
  2. The excitement around generative AI may fade as reality sets in, making it hard for companies like OpenAI to justify their high valuations. This could lead to a financial downturn in the AI industry.
  3. There is a need to explore other AI approaches since relying too heavily on LLMs might be a risky gamble. It might be better to rethink strategies to achieve reliable and trustworthy AI.

15 Times to use AI, and 5 Not to

One Useful Thing • 2226 implied HN points • 09 Dec 24
🕹 Technology AI Machine Learning Data science Automation Software Development
  1. AI is great for generating lots of ideas quickly. Instead of getting stuck after a few, you can use AI to come up with many different options.
  2. It's helpful to use AI when you have expertise and can easily spot mistakes. You can rely on it to assist with complex tasks without losing track of quality.
  3. However, be cautious using AI for learning or where accuracy is critical. It may shortcut your learning and sometimes make errors that are hard to notice.

✨🎄 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.

Universities Are Woefully Under-Resourced For AI Research. They’re Fighting To Change That.

Big Technology • 5129 implied HN points • 22 Nov 24
🕹 Technology AI Research Higher education Public Policy Data science
  1. Universities are struggling to keep up with AI research due to a lack of resources like powerful GPUs and data centers. They can't compete with big tech companies who have millions of these resources.
  2. Most AI research breakthroughs are now coming from private industry, with universities lagging behind. This is causing talented researchers to prefer jobs in the private sector instead.
  3. Some universities are trying to address this issue by forming coalitions and advocating for government support to create shared AI research resources. This could help level the playing field and foster important academic advancements.

Is this a career?

benn.substack • 1713 implied HN points • 13 Dec 24
💼 Business Analytics Data science Careers Expertise Technology
  1. Getting good at something often just takes a little focused effort over time. Many people don't actively try to improve, so they stay at a decent skill level rather than reaching their full potential.
  2. In fields like data analytics, it's essential to specialize to truly excel. Being a generalist might keep you busy, but it can lead to a career without a clear direction or growth.
  3. To stand out and achieve more in their careers, people need to identify a specific area of expertise and commit to it. Relying on being 'good at data' isn't usually enough to make a significant impact.

8 Insights to Make Sense of OpenAI o3

The Algorithmic Bridge • 403 implied HN points • 23 Dec 24
🕹 Technology AI Machine Learning Computing Innovation Data science
  1. OpenAI's new model, o3, has demonstrated impressive abilities in math, coding, and science, surpassing even specialists. This is a rare and significant leap in AI capability.
  2. There are many questions about the implications of o3, including its impact on jobs and AI accessibility. Understanding these questions is crucial for navigating the future of AI.
  3. The landscape of AI is shifting, with some competitors likely to catch up, while many will struggle. It's important to stay informed to see where things are headed.

AI #93: Happy Tuesday

Don't Worry About the Vase • 1971 implied HN points • 04 Dec 24
🕹 Technology AI Machine Learning Data science Open Source Cybersecurity
  1. Language models can be really useful in everyday tasks. They can help with things like writing, translating, and making charts easily.
  2. There are serious concerns about AI safety and misuse. It's important to understand and mitigate risks when using powerful AI tools.
  3. AI technology might change the job landscape, but it's also essential to consider how it can enhance human capabilities instead of just replacing jobs.

Moving Past RLHF: In 2025 We Will Transition from Preference Tuning to Reward Optimization in Foundation Models

TheSequence • 189 implied HN points • 29 Dec 24
🕹 Technology Artificial Intelligence Machine Learning Neural Networks Modeling Data science
  1. Artificial intelligence is moving from preference tuning to reward optimization for better alignment with human values. This change aims to improve how models respond to our needs.
  2. Preference tuning has its limits because it can't capture all the complexities of human intentions. Researchers are exploring new reward models to address these limitations.
  3. Recent models like GPT-o3 and Tülu 3 showcase this evolution, showing how AI can become more effective and nuanced in understanding and generating language.

Holy Grails of Data: Self-Service, Single Truths, and the Role of AI

SeattleDataGuy’s Newsletter • 365 implied HN points • 27 Dec 24
🕹 Technology Data science AI Analytics Business Intelligence Machine Learning
  1. Self-service analytics is still a goal for many companies, but it often falls short. Users might struggle with the tools or want different formats for the data, leading to more questions instead of fewer.
  2. Becoming truly data-driven is a challenge for many organizations. Trust issues with data, preference for gut feelings, and poor communication often get in the way of making informed decisions.
  3. People need to be data literate for businesses to succeed with data. The data team must present insights clearly, while business teams should understand and trust the data they work with.

AI & Python #28: The Notebook Used for Data Science and AI Projects

Artificial Corner • 158 implied HN points • 23 Oct 24
🕹 Technology AI Programming Data science Software Tools
  1. Jupyter Notebook is a popular tool for data science that combines live code with visualizations and text. It helps users organize their projects in a single place.
  2. Jupyter Notebook can be improved with extensions, which can add features like code autocompletion and easier cell movement. These tools make coding more efficient and user-friendly.
  3. To install these extensions, you can use specific commands in the command prompt. Once installed, you'll find new options that can help increase your productivity.

($) DeepSeek's Three Idiosyncratic Advantages

Interconnected • 138 implied HN points • 03 Jan 25
🕹 Technology AI Open Source Machine Learning Global Competition Data science
  1. DeepSeek-V3 is an AI model that is performing as well or better than other top models while costing much less to train. This means they're getting great results without spending a lot of money.
  2. The AI community is buzzing about DeepSeek's advancements, but there seems to be less excitement about it in China compared to outside countries. This might show a difference in how AI news is perceived globally.
  3. DeepSeek has a few unique advantages that set it apart from other AI labs. Understanding these can help clarify what their success means for the broader AI competition between the US and China.

Edge 461: The Many Challenges of Kowledge Distillation

TheSequence • 56 implied HN points • 31 Dec 24
🕹 Technology AI Machine Learning Data science Algorithms Software Development
  1. Knowledge distillation can be tricky because there’s a big size difference between the teacher model and the student model. The teacher model usually has a lot more parameters, making it hard to share all the useful information with the smaller student model.
  2. Transferring the complex knowledge from a large model to a smaller one isn't straightforward. The smaller model might not be able to capture all the details that the larger model has learned.
  3. Despite the benefits, there are significant challenges that need to be tackled when using knowledge distillation in machine learning. These challenges stem from the complexity and scale of the models involved.

Fixing Faulty Gradient Accumulation: Understanding the Issue and Its Resolution

The Kaitchup – AI on a Budget • 159 implied HN points • 21 Oct 24
🕹 Technology AI Machine Learning Data science Model Training Computing
  1. Gradient accumulation helps train large models on limited GPU memory. It simulates larger batch sizes by summing gradients from several smaller batches before updating model weights.
  2. There has been a problem with how gradients were summed during gradient accumulation, leading to worse model performance. This was due to incorrect normalization in the calculation of loss, especially when varying sequence lengths were involved.
  3. Hugging Face and Unsloth AI have fixed the gradient accumulation issue. With this fix, training results are more consistent and effective, which might improve the performance of future models built using this technique.

I spent 6 hours learning how Apache Spark plans the execution for us

VuTrinh. • 659 implied HN points • 10 Sep 24
🕹 Technology Data science Software Engineering Big Data Cloud Computing Machine Learning
  1. Apache Spark uses a system called Catalyst to plan and optimize how data is processed. This system helps make sure that queries run as efficiently as possible.
  2. In Spark 3, a feature called Adaptive Query Execution (AQE) was added. It allows the tool to change its plans while a query is running, based on real-time data information.
  3. Airbnb uses this AQE feature to improve how they handle large amounts of data. This lets them dynamically adjust the way data is processed, which leads to better performance.

The Weekly Kaitchup #64

The Kaitchup – AI on a Budget • 59 implied HN points • 25 Oct 24
🕹 Technology AI Machine Learning Software Data science Cloud Computing
  1. Qwen2.5 models have been improved and now come in a 4-bit version, making them efficient for different hardware. They perform better than previous models on many tasks.
  2. Google's SynthID tool can add invisible watermarks to AI-generated text, helping to identify it without changing the text's quality. This could become a standard practice to distinguish AI text from human writing.
  3. Cohere has launched Aya Expanse, new multilingual models that outperform many existing models. They took two years to develop, involving thousands of researchers, enhancing language support and performance.

Train and Serve an AI Chatbot Based on Llama 3.2

The Kaitchup – AI on a Budget • 179 implied HN points • 17 Oct 24
🕹 Technology AI Chatbots Machine Learning Data science Software Development
  1. You can create a custom AI chatbot easily and cheaply now. New methods make it possible to train smaller models like Llama 3.2 without spending much money.
  2. Fine-tuning a chatbot requires careful preparation of the dataset. It's important to learn how to format your questions and answers correctly.
  3. Avoiding common mistakes during training is crucial. Understanding these pitfalls will help ensure your chatbot works well after it's trained.

Gambling with language models

Rain Clouds • 51 implied HN points • 31 Dec 24
🕹 Technology Machine Learning Cloud Computing Data science Financial Analysis Investing
  1. Using AI models, like ModernBert, can help in predicting which stocks might perform better based on financial reports and market data. This means you can get insights without needing to be a finance expert.
  2. The project combines cloud computing with machine learning, making it easier to process large amounts of financial data quickly. This is important for anyone looking to analyze stocks more efficiently.
  3. While the model can make predictions, it's important to remember that investing in stocks always carries risks. Just because a model suggests a stock might do well, it doesn't guarantee success.

Humanoid Robots: China’s Grind Toward Embodied Intelligence

ChinaTalk • 400 implied HN points • 16 Dec 24
🕹 Technology AI Robotics Manufacturing Data science Innovation
  1. China aims to become a top producer of humanoid robots by 2027, planning to use them in various industries like manufacturing and services. This is partly because they face labor shortages and believe humanoids can do many tough jobs.
  2. Humanoid robots need advanced technology in hardware and AI to work well. This includes making them mimic human movements and learning from real-world experiences, which is still a big challenge.
  3. The automotive industry could be key for testing and improving humanoid robots. Car factories have structured environments that help robots learn new tasks safely while addressing labor shortages in that sector.

OK, I can partly explain the LLM chess weirdness now

DYNOMIGHT INTERNET NEWSLETTER • 796 implied HN points • 21 Nov 24
🕹 Technology AI LLMs Machine Learning Data science Chess
  1. LLMs like `gpt-3.5-turbo-instruct` can play chess well, but most other models struggle. Using specific prompts can improve their performance.
  2. Providing legal moves to LLMs can actually confuse them. Instead, repeating the game before making a move helps them make better decisions.
  3. Fine-tuning and giving examples both improve chess performance for LLMs, but combining them may not always yield the best results.