The hottest AI Development Substack posts right now

And their main takeaways
Category
Top Technology Topics

#7: Binding AI Treaty by 2024 + The UK Takes the Lead + A Constitution for AI?

Navigating AI Risks • 78 implied HN points • 20 Jun 23
🕹 Technology AI Governance Government Relations Legislation AI Development Tech Companies
  1. The world's first binding treaty on artificial intelligence is being negotiated, which could significantly impact future AI governance.
  2. The United Kingdom is taking a leading role in AI diplomacy, hosting a global summit on AI safety and pushing for the implementation of AI safety measures.
  3. U.S. senators are advocating for more responsibility from tech companies regarding the release of powerful AI models, emphasizing the need to address national security concerns.

Data Design For Fine-Tuning To Improve Small Language Model Behaviour

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 19 implied HN points • 17 Apr 24
🕹 Technology AI Development Machine Learning Data science Natural Language Processing Model Training
  1. Small Language Models can be improved by designing their training data to help them reason and self-correct. This means creating special ways to present information that guide the model in making better decisions.
  2. Two methods, Prompt Erasure and Partial Answer Masking (PAM), help models learn how to think critically and correct mistakes on their own. They get trained in a way that shows them how to approach problems without providing the exact questions.
  3. The focus is shifting from just updating a model's knowledge to enhancing its behavior and reasoning skills. This means training models not just to recall information, but to understand and apply it effectively.

GPU-enabled Bioinformatics

LatchBio • 9 implied HN points • 06 Nov 24
🕹 Technology Bioinformatics Data science AI Development Cloud Computing
  1. Bioinformatics is moving towards using GPUs to speed up data processing. This change can save a lot of time and money for researchers.
  2. New molecular techniques generate massive amounts of data that take too long to analyze without faster systems. Using GPUs can make these processes much quicker, especially for large datasets.
  3. There are now cloud platforms that make it easier to use GPU technology without needing special expertise or expensive hardware. This helps more teams access advanced analysis tools.

Evaluating LLM Agents and Applications

LLMs for Engineers • 79 implied HN points • 11 Jul 23
🕹 Technology AI Development Machine Learning Software Engineering Data Analysis
  1. Evaluating large language models (LLMs) is important because existing test suites don’t always fit real-world needs. So, developers often create their own tools to measure accuracy in specific applications.
  2. There are four main types of evaluations for LLM applications: metric-based, tools-based, model-based, and involving human experts. Each method has its strengths and weaknesses depending on the context.
  3. Understanding how well LLM applications are performing is essential for improving their quality. This allows for better fine-tuning, compiling smaller models, and creating systems that work efficiently together.

In cautious defense of LLM-ology

The Counterfactual • 119 implied HN points • 02 Mar 23
🕹 Technology AI Development Machine Learning Natural Language Processing Cognitive Science Human-computer interaction
  1. Studying large language models (LLMs) can help us understand how they work and their limitations. It's important to know what goes on inside these 'black boxes' to use them effectively.
  2. Even though LLMs are man-made tools, they can reflect complex behaviors that are worth studying. Understanding these systems might reveal insights about language and cognition.
  3. Research on LLMs, known as LLM-ology, can provide valuable information about human mind processes. It helps us explore questions about language comprehension and cognitive abilities.
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Pausing AI development?

Digital Epidemiology • 58 implied HN points • 01 Apr 23
🕹 Technology AI Development Ethical Concerns Regulation Future impact Debate
  1. The debate about pausing AI development focuses on concerns about next-gen AI surpassing current technology like GPT-4.
  2. Separate the message from the messenger in the discussions surrounding the call for a pause in AI development.
  3. Managing the rapid advancement of AI requires thoughtful regulation to balance progress and potential risks to society.

#11 - AI in the Public Eye + AI Governance is (mostly) Compute Governance

Navigating AI Risks • 58 implied HN points • 06 Sep 23
🕹 Technology AI Governance AI Policy AI Development
  1. One proposed approach to AI governance involves implementing KYC practices for chip manufacturers to sell compute only to selected companies with robust safety practices.
  2. There is growing public concern over the existential risks posed by AI, with surveys showing varied attitudes towards regulating AI and its potential impact on society.
  3. Nationalization of AI and the implementation of red-teaming practices are suggested as potential strategies for controlling the development and deployment of AI.

How To Regulate AI, If You Must

jonstokes.com • 175 implied HN points • 22 Jun 23
🕹 Technology AI Regulations AI Governance AI Development
  1. AI rules are inevitable, but the initial ones may not be ideal. It's a crucial moment to shape discussions on AI's future.
  2. Different groups are influencing AI governance. It's important to be aware of who is setting the rules.
  3. Product safety approach is preferred in AI regulation. Focus on validating specific AI implementations rather than regulating AI in the abstract.

Microsoft’s New Love

Sector 6 | The Newsletter of AIM • 39 implied HN points • 17 Nov 23
🕹 Technology AI Development Machine Learning Natural Language Data science Software Engineering
  1. Large language models (LLMs) like ChatGPT are powerful but costly to run and customize. They require a lot of resources and can be tricky to adapt for specific tasks.
  2. Small language models (SLMs) are emerging as a better option because they are cheaper to train and can give more accurate results. They also don't need heavy hardware to operate.
  3. Many companies are starting to focus on developing small language models due to their efficiency and effectiveness, marking a shift in the industry.

Which Llama-2 Inference API should I use?

LLMs for Engineers • 39 implied HN points • 31 Oct 23
🕹 Technology AI Development Open Source APIs Machine Learning Cloud Computing
  1. TogetherAI was found to perform the best overall in terms of cost, speed, and accuracy, closely followed by MosaicML.
  2. It's important to understand your specific needs when choosing an API, like cost and speed requirements, to find the best fit.
  3. Experimenting with system prompts can lead to major improvements in performance, so don't hesitate to try different settings!

The Future of Programming: Copilots vs. Agents (Part I)

East Wind • 11 implied HN points • 12 Nov 24
🕹 Technology AI Development Software Engineering Tech industry Product Design
  1. The competition to create better AI coding tools is intense. Companies are racing to attract developers and dominate a huge market.
  2. AI coding tools can be divided into three types: copilots, agents, and custom models. Each type has its own approach to helping programmers finish their work.
  3. User experience is very important for these tools. Small differences in how they function can greatly affect how easy they are to use.

Musk Vs Zuck: Fight is Outside the Cage

Sector 6 | The Newsletter of AIM • 19 implied HN points • 05 Jul 23
🕹 Technology Social media Data Privacy AI Development Platform Competition
  1. Elon Musk and Mark Zuckerberg are in a competition that goes beyond just their online arguments. Zuckerberg is launching a new social media platform called Instagram Threads, which is aimed at rivaling Twitter.
  2. This new platform is part of a bigger trend towards text-based social media, joining others like Mastodon and Bluesky. It shows that there's a growing interest in how people communicate online.
  3. Zuckerberg seems to be focused on collecting valuable data through this platform. As Twitter and Reddit limit data access, his strategy may involve using this data for future tech development.

A Possibly Medieval Future

Via Appia • 2 implied HN points • 25 Jan 25
🕹 Technology AI Development Wealth Inequality Social Structures Labor Market Data Privacy
  1. As AI technology grows, the value of capital will likely become more important, possibly increasing wealth inequality. This means that having money might give some people more power than others.
  2. AI systems will reflect the values and choices of the people who create them. If not carefully designed, these systems can influence society in ways that are hard to change later.
  3. Despite these challenges, right now we have a chance to shape the future positively. People can still learn about AI, influence how it develops, and make choices to enhance individual freedoms.

June Newsletter

RSS DS+AI Section • 11 implied HN points • 02 Jun 23
🕹 Technology Data science Generative AI Open Source AI Development Ethics
  1. June newsletter focuses on Open Source special, including recent developments in the open source community.
  2. The newsletter highlights activities of the committee, discussions on AI ethics and diversity, and advancements in generative AI.
  3. An in-depth exploration of the open source explosion driven by the development of generative AI, showcasing the surge of open source capabilities and research contributions.

Data Science Weekly - Issue 353

Data Science Weekly Newsletter • 19 implied HN points • 27 Aug 20
🕹 Technology Machine Learning Data science AI Development Software Engineering User Experience
  1. Effective testing is crucial for machine learning systems. It's important to understand that these systems require different testing strategies compared to traditional software.
  2. There are hidden challenges in becoming a machine learning engineer. Many of these insights come from the experiences of those already in the field, beyond what you learn in books.
  3. New resources and courses are constantly being developed in data science. For example, fast.ai just released a new deep learning course and libraries, which can help beginners get started.

OpenAI: The Board Expands

Don't Worry About the Vase • 1 HN point • 12 Mar 24
🕹 Technology AI Ethics AI Development AGI Interview OpenAI
  1. The investigation found no wrongdoing with OpenAI and the new board has been expanded, showing that Sam Altman is back in control.
  2. The new board members lack technical understanding of AI, raising concerns about the board's ability to govern OpenAI effectively.
  3. There are lingering questions about what caused the initial attempt to fire Sam Altman and the ongoing status of Ilya Sutskever within OpenAI.

What is Mojo Programming Language?

Machine Economy Press • 3 implied HN points • 04 May 23
🕹 Technology Programming AI Development AI Applications
  1. Mojo Programming Language combines Python syntax with the speed of C, making it ideal for AI development.
  2. Mojo is about 35,000 times faster than Python, offering exceptional AI hardware programmability and model extensibility.
  3. Mojo allows writing portable code faster than C, seamlessly inter-operating with the Python ecosystem, and includes features like a unified inference engine and zero-cost abstractions.

Data Science Weekly - Issue 238

Data Science Weekly Newsletter • 19 implied HN points • 14 Jun 18
🕹 Technology Machine Learning Data science AI Development Statistical Analysis Data Visualization
  1. Neural networks can struggle to tell jokes if they don't have enough examples to learn from. Giving them more data might help improve their humor.
  2. Machine learning is becoming more efficient with smaller, low-power chips, which could solve many current problems. This trend is expected to grow in the future.
  3. Data cleaning takes a lot of time in data science, with up to 80% of the effort spent on it. Learning tools like Python's Pandas can really help with this task.

The Art of Forgetting: How AI Learns to Understand Rather Than Memorize

Nano Thoughts • 0 implied HN points • 27 Jan 25
🕹 Technology AI Development Machine Learning Data science Cognitive Science
  1. AI can struggle with memorization instead of understanding, similar to how students might remember specific math problems without grasping the general concept. When AI memorizes examples too closely, it can't apply knowledge to new situations.
  2. Techniques like regularization help AI focus on important patterns rather than get lost in details. This is like training athletes under various conditions to build real skills instead of just practicing one way.
  3. Understanding how to forget unimportant information is crucial for both AI and human intelligence. The best learning doesn't come from remembering everything, but from knowing which patterns are worth keeping.

Why intelligence isn’t a thing

AI Prospects: Toward Global Goal Convergence • 0 implied HN points • 31 Jan 24
🕹 Technology AI Intelligence Machine Learning Artificial Intelligence AI Development
  1. Intelligence is a resource, not an entity, with two different meanings based on learning and doing.
  2. Intelligence isn't a distinct, autonomous being but rather a capacity within intelligent systems, a resource for solving problems.
  3. Superintelligent-level AI can be managed as a pool of resources, leading to a focus on how we should use AI rather than speculating on what 'it' will do to us.

The Cost of Using LLMs

Sector 6 | The Newsletter of AIM • 0 implied HN points • 20 Oct 23
🕹 Technology AI Development Machine Learning Data science Open Source
  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.

A New Era of Open-Source LLMs Begins

Sector 6 | The Newsletter of AIM • 0 implied HN points • 04 Jun 23
🕹 Technology AI Development Open Source Language Models Tech advancements
  1. A new open-source language model called Falcon has been created, and it performs better than several other models, showing a strong leap in technology.
  2. The model is built with a huge amount of information, having 40 billion parameters and trained on one trillion tokens, making it powerful for research and business.
  3. Falcon is available for free, meaning anyone can use it without paying royalties, which aims to help more people access technology and promote inclusivity.

The Rise of LLM Chatbots

Sector 6 | The Newsletter of AIM • 0 implied HN points • 12 Feb 23
🕹 Technology AI Development Software Computer Science Innovation Internet
  1. Large language models like ChatGPT and Bard have led to the rise of conversational chatbots. These chatbots can interact with users in a more human-like way.
  2. Big tech companies are competing to develop advanced AI models. OpenAI and Microsoft are currently at the forefront of this race.
  3. Google is also entering the chatbot scene with its own conversational AI called Bard. However, it may be released gradually and only to select users.

Big tech Flocks to India's Thriving Digital Landscape

Sector 6 | The Newsletter of AIM • 0 implied HN points • 08 Jan 23
🕹 Technology Digital economy AI Development Tech regulation Global Markets Industry Trends
  1. Big tech companies are actively engaging with the Indian government to navigate regulations. They want to build a positive relationship while facing challenges.
  2. Leaders from companies like Google and Microsoft are impressed by India's focus on digital growth. They see great potential for economic development through technology.
  3. The Indian government's 'Digital India' vision is attracting global tech leaders, indicating a bright future for the country's digital landscape.

What's happening at OpenAI?

Jon’s Newsletter • 0 implied HN points • 21 Nov 23
🕹 Technology AI Development Corporate Governance Investment Trends Tech industry Leadership Changes
  1. Sam Altman was removed as CEO of OpenAI, causing a big shake-up in the company. The board was worried that OpenAI was moving too fast with its business plans.
  2. Greg Brockman, the President, quit in protest and many OpenAI staff members threatened to leave for Microsoft. They even asked for the board to resign.
  3. Microsoft quickly hired Altman and Brockman to lead an AI team, and has seen a big boost in its stock value since its investment in OpenAI.

Develop Generative Apps Locally

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 29 Feb 24
🕹 Technology AI Development Software Engineering Programming Languages Data science Machine Learning
  1. You can create generative apps that run completely on your own computer. This makes development easier and often faster.
  2. Using tools like HuggingFace and TitanML's TakeOff Server, you can access and manage small language models without needing an internet connection.
  3. Running inference locally improves speed, keeps your data private, and lets you work offline when needed.

Bulk Data Discovery

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 25 Jan 24
🕹 Technology Data science Machine Learning AI Development Data Visualization Open Source
  1. Data discovery is crucial for understanding unstructured data. It helps find user intent and classifies interactions effectively.
  2. Using embeddings allows us to visualize data by grouping similar meanings. This helps spot patterns and outliers in conversations.
  3. Data preparation involves identifying, collecting, and analyzing data. This step helps reveal valuable insights that support decision-making.

[in case you missed it] Data Science Weekly - Issue 421

Data Science Weekly Newsletter • 0 implied HN points • 19 Dec 21
🕹 Technology Data science Machine Learning AI Development Data Visualization Research
  1. Lee Wilkinson made big contributions to how we visualize data, helping us understand graphics better.
  2. A new journal for machine learning research will use a transparent review process to improve scholarly communication.
  3. Feature engineering is still important in data science despite the rise of deep learning, showing that sometimes traditional methods still apply.

AI Weekly Update - October 4

Handy AI • 0 implied HN points • 04 Oct 24
🕹 Technology AI Development Software updates Legislation
  1. Meta has launched a new AI tool that can create videos from text, making it easier for filmmakers and content creators to produce content.
  2. ChatGPT has improved its writing and coding features with a new interface, allowing for better collaboration and suggestions in projects.
  3. OpenAI has received a significant amount of funding, which will help advance their research and development in AI technology.

The Evolution of the Creator

Digital Native • 0 implied HN points • 13 Nov 24
🕹 Technology Creator Economy Digital Tools AI Development Social media Content creation
  1. The idea of being a 'creator' is evolving. It's not just about influencing others but also about creativity and self-expression.
  2. New technologies, especially AI, are making it easier for anyone to create content. This means more people can make cool stuff with less effort.
  3. Successful creator businesses need to focus on three main things: making good content, sharing it widely, and finding ways to earn money from it.

Season 4 - Newsletter 06 —> Going from 0 to 0.1 in Product in an AI world

Product Hustle Stack Newsletter • 0 implied HN points • 13 Nov 24
🕹 Technology AI Development Product Management User Experience Entrepreneurship App Development
  1. Building an app using AI can feel easy with all the tools available, but it's crucial to clearly define the problem you want to solve before jumping in. If you focus too much on creating, you might miss the real issue that needs addressing.
  2. Always aim for that 'Aha' moment for users while developing your product. If it doesn't bring joy or clarity to them, it may be worth going back to the drawing board and seeking honest feedback.
  3. Developing a product can be emotionally challenging. Recognizing your feelings during the process is important for navigating both the technical and personal hurdles that come with entrepreneurship.

2025

domsteil • 0 implied HN points • 31 Dec 24
🕹 Technology AI Development Software Engineering Digital Transformation
  1. AI is changing how we interact and build software. It allows developers to create programs much faster and more efficiently than before.
  2. New AI technologies are making it easier for everyone to access and utilize smart systems in their daily tasks, potentially leading to a big shift in how businesses operate.
  3. In the future, software development will focus on using AI to handle tasks automatically. This will not only change how software is built but also how we measure success and pricing in business.