The hottest Machine Learning Substack posts right now

And their main takeaways
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Newsletter #16: PEARL — A LLM brain for large texts

Decoding Coding • 0 implied HN points • 22 Jun 23
🕹 Technology Machine Learning
  1. LLMs can act like a 'brain' for processing and understanding large texts. They help plan and execute tasks by breaking them down into smaller steps.
  2. The process consists of three main parts: discovering the necessary actions, creating a plan using those actions, and finally executing the plan carefully to avoid mistakes.
  3. Though this method shows promise, it still has limitations, like generating incorrect plans and being restricted by the size of information it can handle. Improvements are expected as technology advances.

Newsletter #15: ViperGPT

Decoding Coding • 0 implied HN points • 15 Jun 23
🕹 Technology Machine Learning
  1. ViperGPT is a new AI model that can answer questions about images and videos. It combines powerful text and vision models to understand visual inputs better.
  2. The model generates Python code based on user questions, allowing it to be flexible and efficient. It uses all available online Python code for improvement.
  3. ViperGPT's execution engine runs the generated code and provides results based on the visual content. This helps users make sense of raw data in a more meaningful way.

Newsletter #14: Adding Memory to LLMs

Decoding Coding • 0 implied HN points • 01 Jun 23
🕹 Technology Machine Learning
  1. LLMs can forget information when they get too big, which makes their performance worse. Adding an internal memory can help them remember better and adapt to new tasks.
  2. The new framework, Decision Transformers with Memory (DT-Mem), uses a special memory module to identify and store important information effectively. This helps the model improve its decision-making.
  3. By using techniques like content-based addressing, DT-Mem can selectively add or erase information in its memory, making it smarter and more efficient in handling tasks.

Newsletter #11: System Design for Machine Learning - Part I

Decoding Coding • 0 implied HN points • 04 May 23
🕹 Technology Machine Learning
  1. Before starting on a machine learning project, it's important to define clear goals and understand how ML can help achieve them.
  2. Setting up a data pipeline is crucial; it involves collecting, preparing, and analyzing data to see what features are useful for your model.
  3. When deploying machine learning models, you need to consider both hardware and software needs, including how to handle real-time data for ongoing training.

Newsletter #9: Building a Brain with LLM

Decoding Coding • 0 implied HN points • 20 Apr 23
🕹 Technology Machine Learning
  1. Robots can use language models to understand and navigate their environments better. This setup includes a visual model that acts like an 'eye' to see the world.
  2. The robot has a 'nerve' system that asks questions and plans actions based on what it sees. It makes sense of information and decides what the robot should do next.
  3. Eventually, as language models improve, robots could act more autonomously and make decisions on their own. This could change how we interact with machines in exciting ways.
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Newsletter #5: Backprop from scratch

Decoding Coding • 0 implied HN points • 09 Mar 23
🕹 Technology Machine Learning
  1. Derivatives show how small changes in inputs affect the output of a function. This is important for understanding how neural networks adjust to improve their predictions.
  2. In neural networks, understanding how changes in weights and inputs influence the output helps us optimize performance. By adjusting weights based on calculated gradients, we can make the network learn better.
  3. The chain rule is key when calculating how different layers of a neural network affect the final output. It allows us to connect changes in inputs through to the overall output, helping us to fine-tune the model.

Newsletter #4: Probabilities with Python

Decoding Coding • 0 implied HN points • 02 Mar 23
🕹 Technology Machine Learning
  1. NumPy is a powerful tool for working with probability distributions in Python. You can easily generate data and calculate probabilities using its features.
  2. Common probability distributions like Normal, Binomial, and Poisson can be modeled using NumPy. Each distribution has its own formula to calculate probabilities.
  3. De Morgan's Laws help in calculating probabilities of complements in events. They show how to relate the union and intersection of events, which can be useful in probability theory.

The Week of Small Language Models

Sector 6 | The Newsletter of AIM • 0 implied HN points • 22 Jul 24
🕹 Technology Machine Learning
  1. Small language models are gaining popularity, with companies like Hugging Face and OpenAI participating in their development. This means we could see more accessible and efficient AI tools in the near future.
  2. Mistral AI has launched a new model called Mistral NeMo that can handle a lot of information at once, making it useful for various applications. This could help improve how we use AI in complex tasks.
  3. There's an increasing focus on creating smaller models that still perform well, which suggests a shift in how we think about AI technology. Smaller models could make AI more practical for everyday use.

When LLMs are Super Confident 😎 ✨

Sector 6 | The Newsletter of AIM • 0 implied HN points • 19 Jul 24
🕹 Technology Machine Learning
  1. OpenAI is improving LLM outputs with a new technique called Prover-Verifier Games. This helps make the answers clearer and more trustworthy for users.
  2. Smaller LLMs are taught to check the responses of larger LLMs, similar to a student explaining their homework to a tutor. This approach ensures the solutions are easy to understand.
  3. The focus is on making LLM outputs more legible, especially in areas like grade-school math. This makes it easier for everyone to follow the reasoning behind the answers.

OpenAI is Not Open, will Safe Superintelligence be Safe?

Sector 6 | The Newsletter of AIM • 0 implied HN points • 20 Jun 24
🕹 Technology Machine Learning
  1. OpenAI is not as open as it claims to be, which raises questions about transparency in AI development.
  2. Ilya Sutskever's new company focuses on developing safe superintelligence, although some may joke that if it never happens, it will always be safe.
  3. The conversation around AI safety and superintelligence is becoming more relevant as industry leaders express concerns and start new ventures.

Cheese Sticking, AI Knows 🍕🤖❓

Sector 6 | The Newsletter of AIM • 0 implied HN points • 25 May 24
🕹 Technology Machine Learning
  1. A recent response from Google AI about cheese sticking to pizza caused a lot of debate online. It made people question how well AI understands everyday problems.
  2. This isn't the first time AI has given strange advice. In earlier tests, it suggested weird things like drinking light-colored urine for kidney stones.
  3. These odd suggestions highlight the gaps in AI knowledge and make us think about how we rely on technology for information.

The Unrivalled Leader in GenAI

Sector 6 | The Newsletter of AIM • 0 implied HN points • 25 Mar 24
🕹 Technology Machine Learning
  1. Accenture has made a huge impact in the generative AI space, making $1.1 billion in sales which is more than all the VC-backed startups combined. This shows they are leading the way.
  2. Compared to Accenture, major Indian tech companies like TCS and Infosys show less confidence in generative AI. They haven't reported specific earnings in this area, which raises concerns.
  3. The difference in performance between Accenture and these Indian companies could indicate a possible risk in the outsourcing industry as they navigate new technology trends.

When (Not) to XGBoost

Sector 6 | The Newsletter of AIM • 0 implied HN points • 12 Mar 24
🕹 Technology Machine Learning
  1. XGBoost is a popular tool in machine learning, but it's not always the best choice for every situation. It's important to understand when to apply it and when to use other methods.
  2. Many people now claim to be experts in AI after the rise of large language models, but AI includes a lot more than just these models.
  3. It's essential to know the broader landscape of AI techniques to make better decisions in data science and machine learning projects.

The Week of AI Drama

Sector 6 | The Newsletter of AIM • 0 implied HN points • 11 Mar 24
🕹 Technology Machine Learning
  1. OpenAI has had a busy week with a lot of drama, including Sam Altman returning to its board after being fired as CEO.
  2. Elon Musk is suing OpenAI, which adds to the tension between him and the company.
  3. New AI models like Claude 3 and Inflection 2.5 have been released, competing directly with OpenAI's GPT-4.

Put Some Pants On! 👖👉😳

Sector 6 | The Newsletter of AIM • 0 implied HN points • 31 Jan 24
🕹 Technology Machine Learning
  1. LLMs, or large language models, rely on prompts to function properly, just like people choosing to dress appropriately for work. This analogy shows the importance of setting the right context for success.
  2. Using open-source models is different from closed ones, impacting how they are packaged and function. This means the way we interact with these models, including the prompts we use, can change significantly.
  3. A new course on prompt engineering has been released to help users navigate these differences in LLMs. It's a way for people to learn how to effectively work with these models.

Google’s Q*?

Sector 6 | The Newsletter of AIM • 0 implied HN points • 14 Dec 23
🕹 Technology Machine Learning
  1. Google's AlphaCode 2 has improved significantly, performing better than the earlier version by solving many coding challenges. It shows that Google's advancements in AI are making big leaps.
  2. AlphaCode 2 ranks in the 85th percentile among competitors, meaning it outperforms most human participants in coding competitions. This suggests that AI is becoming very capable in technical problem-solving.
  3. Many people are focused on Google's Gemini project, but AlphaCode 2 might be a game-changer in competitive coding, indicating a shift in how powerful AI tools can be for programmers.

No Tricks, Just M3 Treats

Sector 6 | The Newsletter of AIM • 0 implied HN points • 31 Oct 23
🕹 Technology Machine Learning
  1. Apple has launched three new chips: M3, M3 Pro, and M3 Max. These chips can handle very large AI models thanks to their ability to support lots of memory.
  2. The new chips have a faster neural engine, making machine learning tasks quicker and better at protecting user privacy.
  3. These M3 chips are significantly faster, with improvements of 15% over the previous M2 chips and up to 60% faster than the older M1 chips.

The Cost of Using LLMs

Sector 6 | The Newsletter of AIM • 0 implied HN points • 20 Oct 23
🕹 Technology Machine Learning
  1. Using large language models (LLMs) can be costly, with prices influenced by factors like the number of tokens processed. For example, GPT-4 is much more expensive than other options like Llama 2.
  2. There are many LLMs available today, with some newer open-source models like Llama 2 and Mistral 7B performing well. These models are gradually becoming more popular.
  3. The choice of LLM depends on your specific needs and budget, as different models offer varying costs and performance levels. It's good to explore all available options before deciding.

Researchers Want Galactica Back

Sector 6 | The Newsletter of AIM • 0 implied HN points • 06 Oct 23
🕹 Technology Machine Learning
  1. Meta launched a language model called Galactica, which had many useful features like summarizing papers and solving math problems.
  2. Unfortunately, the model was pulled just three days after its release because it produced inaccurate and random results.
  3. Many researchers now believe that the model should be reintroduced, thinking that the learning challenges are part of its development process.

Benchmarking, the Indian Way

Sector 6 | The Newsletter of AIM • 0 implied HN points • 29 Sep 23
🕹 Technology Machine Learning
  1. Benchmarks are essential for testing the intelligence of large language models (LLMs), like GPT-4 and Llama 2. They help measure how well these models perform on various human-level tasks.
  2. Common benchmarks come from the US and cover a range of subjects, including math and history. For example, MMLU includes 57 tasks that test different knowledge areas.
  3. To create effective benchmarks, they often mimic real-world exams like the SAT or law school tests. This ensures the LLMs are evaluated in ways similar to how humans are tested.

Mojo🔥Steals the Show

Sector 6 | The Newsletter of AIM • 0 implied HN points • 13 Sep 23
🕹 Technology Machine Learning
  1. Mojo is a new programming language that combines the user-friendliness of Python with the speed of C and CUDA. Developers can now download it and see great results.
  2. A developer named Aydyn Tairov got a significant performance boost using Mojo, proving it can be faster than traditional C implementations.
  3. Mojo is designed to work with Python and aims to be even better for AI tasks by significantly increasing performance—up to 68,000 times faster than Python!

Censorship is Killing ChatGPT

Sector 6 | The Newsletter of AIM • 0 implied HN points • 30 May 23
🕹 Technology Machine Learning
  1. Censorship affects chatbots like ChatGPT. When developers try to make AI models align with social values, it can actually limit their ability to perform well.
  2. Using techniques like Reinforcement Learning with Human Feedback can create biased models. This happens because the fine-tuning process often reduces the chatbot's overall effectiveness.
  3. The idea of an 'alignment tax' suggests that trying to fit chatbots to human values may end up harming their true potential, making them less useful in the end.

Stop Comparing AI with A-Bomb

Sector 6 | The Newsletter of AIM • 0 implied HN points • 09 May 23
🕹 Technology Machine Learning
  1. Comparing AI to an atomic bomb creates unnecessary fear and limits innovation. It's important to focus on the real benefits and risks of AI without sensationalizing them.
  2. Many critics of AI lack direct experience with machine learning, which can skew their opinions. Listening to actual AI experts is crucial for informed discussions.
  3. Analogies like the one between AI and atomic bombs can dominate conversations and hinder progress. It's vital to steer discussions towards constructive and realistic views of AI.

Amazon Crashes the GAI Party with a Bang!

Sector 6 | The Newsletter of AIM • 0 implied HN points • 16 Apr 23
🕹 Technology Machine Learning
  1. Amazon was focusing on transfer learning to improve their AI, like making Alexa learn new languages. However, they recently stopped this project because it was losing a lot of money.
  2. The company has experienced several failures in the past, showing that they are not unfamiliar with setbacks. This suggests they are trying to learn and adapt from their mistakes.
  3. Despite their challenges, Amazon's efforts in AI and technology continue to impact the industry, making them a major player in the field.

Why So Quiet?

Sector 6 | The Newsletter of AIM • 0 implied HN points • 30 Mar 23
🕹 Technology Machine Learning
  1. OpenAI is working hard to make a significant impact in AI with tools like ChatGPT, but Apple is surprisingly quiet about its plans for AI technology.
  2. Experts believe that Apple should pay attention to large language models (LLMs) because they can lead to exciting new ways for people to interact with technology.
  3. There's a possibility that LLMs could create a new operating system or ecosystem, similar to how the iPhone changed everything with its touchscreen.

LLaMA Leaked

Sector 6 | The Newsletter of AIM • 0 implied HN points • 07 Mar 23
🕹 Technology Machine Learning
  1. LLaMA, a new language model from Meta, has been leaked online, including its downloadable files.
  2. The leak was first shared on 4chan and gained attention quickly on the internet.
  3. Users can find LLaMA's models, which are smaller and efficient compared to other options, through torrent links.

For Google, Automotive is the New Black

Sector 6 | The Newsletter of AIM • 0 implied HN points • 27 Feb 23
🕹 Technology Machine Learning
  1. Google is focusing on the automotive industry to boost its growth. They are looking to partner with car companies to provide advanced technology.
  2. A significant partnership with Mercedes-Benz was formed to enhance their navigation and geospatial data.
  3. Google will support car manufacturers with AI and machine learning to help develop smarter vehicles quickly.

Say Goodbye to Boring Data​​

Sector 6 | The Newsletter of AIM • 0 implied HN points • 16 Feb 23
🕹 Technology Machine Learning
  1. Data scarcity is a big problem for AI and machine learning. New tools like generative AI can help create more data.
  2. Synthetic datasets can be built using techniques like Stable Diffusion. This can make data less boring and more useful for developers.
  3. Generative AI tools can change how we approach data challenges. They offer creative solutions to improve AI development.

The AGI Blasphemy Saga Continues

Sector 6 | The Newsletter of AIM • 0 implied HN points • 15 Feb 23
🕹 Technology Machine Learning
  1. Yann LeCun, the Meta AI chief, prefers to go against popular trends in AI development. He does not follow the rush to create advanced chatbots like Google and Microsoft are doing.
  2. The failure of the Galactica model has left LeCun feeling disappointed. He believes that while large language models can help with writing, they can't think or act like humans.
  3. Despite the hype around AI models, LeCun is skeptical about their true capabilities. He highlights the gap between what these AI tools can do and what people expect from them.

Back to the Future

Sector 6 | The Newsletter of AIM • 0 implied HN points • 09 Jan 23
🕹 Technology Machine Learning
  1. Scientists are still trying to create a machine that works like the human brain, but they haven't found a solution yet.
  2. Researchers are looking at older AI methods, called Good-Old-Fashioned Artificial Intelligence (GOFAI), to help machines understand like humans do.
  3. Symbolic AI can understand complex ideas and relationships better, while deep learning needs to be retrained often to learn new tasks.

Face-PaLM, ChatGPT 🤦

Sector 6 | The Newsletter of AIM • 0 implied HN points • 29 Dec 22
🕹 Technology Machine Learning
  1. Google has created a new language model called PaLM, which is much larger than OpenAI's GPT-3. PaLM has 540 billion parameters compared to GPT-3's 175 billion.
  2. There is a growing interest in comparing who will lead the AI race, PaLM or the next versions of GPT models.
  3. The popularity of ChatGPT is rising, creating more competition in the language model space.

Decoding Deep Learning with Yoshua Bengio

Sector 6 | The Newsletter of AIM • 0 implied HN points • 25 Dec 22
🕹 Technology Machine Learning
  1. Yoshua Bengio discusses how understanding intelligence can help us create better AI, possibly even surpassing human intelligence. He believes that knowing the fundamental principles is crucial.
  2. He emphasizes that we have built advanced machines like airplanes that don't directly mimic birds. They can perform tasks that birds can't, showing that different systems excel in different areas.
  3. Bengio is skeptical about the term 'AGI' or Artificial General Intelligence. He thinks there is more to be explored beyond that label when discussing the potential of AI.

Top Data Science & AI Trends For 2022

Sector 6 | The Newsletter of AIM • 0 implied HN points • 16 Jan 22
🕹 Technology Machine Learning
  1. The Machine Learning Developers Summit 2022 is happening soon, with many industry experts joining virtually. It's a great chance to learn from the best in the field.
  2. There will be in-depth talks, workshops, and paper presentations during the summit. Participants can gain valuable insights and skills.
  3. A hackathon and individual mentoring sessions are also part of the event. This offers hands-on experience and personalized guidance.

A 100T Language Model?

Sector 6 | The Newsletter of AIM • 0 implied HN points • 27 Dec 21
🕹 Technology Machine Learning
  1. There is a hackathon for data science where participants can showcase their skills. It's a great way to get noticed by top companies in analytics and tech.
  2. The hackathon will last until January 10th, so you have time to join and compete. This could be a fun challenge to sharpen your skills.
  3. By participating, you might not only learn new things but also get a job offer from a leading company. It's a promising opportunity for anyone interested in the field.

Amazon's Transfer Learning, Urban Company & Interpolation 🐉🌊☔

Sector 6 | The Newsletter of AIM • 0 implied HN points • 01 Nov 21
🕹 Technology Machine Learning
  1. Amazon is using transfer learning to improve their AI capabilities. This means they can build smarter models faster by using what they've already learned.
  2. Urban Company is involved in providing various services and is adapting to meet market demands effectively. They are using technology to enhance their service offerings.
  3. Interpolation is being discussed as a technique to make data work better for predictions. It's about filling in gaps so that models can be more accurate.

State Of Artificial Intelligence In India 2021 ▲▼

Sector 6 | The Newsletter of AIM • 0 implied HN points • 24 Oct 21
🕹 Technology Machine Learning
  1. Artificial Intelligence is rapidly growing in India, with various companies investing in it. This shows that the country is embracing technological advancements.
  2. Competitions like the 'Dare in Reality' Hackathon encourage innovation and collaboration in machine learning. They help teams develop quick insights for real-time decision-making.
  3. Partnerships between tech firms and racing companies highlight the practical applications of AI. It's not just theory; AI is being used in exciting and competitive environments.