The hottest Data Substack posts right now

And their main takeaways
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No Free Dessert in Machine Learning

Mindful Modeler • 399 implied HN points • 20 Feb 24
🔬 Science Machine Learning Generalization Modeling Data
  1. Generalization in machine learning is essential for a model to perform well on unseen data.
  2. There are different types of generalization in machine learning: from training data to unseen data, from training data to application, and from sample data to a larger population.
  3. The No Free Lunch theorem in machine learning highlights that assumptions and effort are always needed for generalization, and there's no free lunch when it comes to achieving further generalization.

LLM Links, 12/1

In My Tribe • 288 implied HN points • 01 Dec 24
🕹 Technology AI Machine Learning Data Ethics Education
  1. AI systems are being developed to have better memory which would improve conversations with users. If they can remember past interactions, it could lead to more meaningful and deeper exchanges.
  2. Humans have unique qualities like vulnerability and connection that AI can't replicate. This means people will still value human interactions over machines, no matter how advanced they become.
  3. Virtual friends powered by AI can help those who are lonely, but they might also distract from real-life relationships. It's important to balance technology use with human connections.

OpenAI is too cheap to beat (redux)

The AI Frontier • 59 implied HN points • 18 Jul 24
🕹 Technology AI Infrastructure Data Economics Scalability
  1. Data and infrastructure are really important for companies like OpenAI. They collect a lot of data, which helps them improve their models faster than others.
  2. OpenAI is cheaper for fine-tuning models compared to using your own infrastructure. This means most companies will find it more cost-effective to use OpenAI's services instead of trying to run their own setups.
  3. Even though open-source models have potential, big companies will likely stay ahead due to their ability to serve models quickly and cheaply. Switching to a different system is hard and expensive, making it tough for smaller players.

sqlmesh test

davidj.substack • 47 implied HN points • 12 Dec 24
🕹 Technology Software Data Engineering Testing Development
  1. Unit tests and data tests are different. Unit tests check if a function works right with set inputs, while data tests check if the data meets certain conditions.
  2. Running tests locally can save costs and speed things up. If you test your code on your own machine, you don’t have to pay for the cloud data warehouse until you’re ready.
  3. Creating external models in sqlmesh can be automated, making it easier to document source tables. You just run a command to generate the necessary files instead of doing it manually.

Breaking and Non-Breaking Changes

davidj.substack • 47 implied HN points • 11 Dec 24
🕹 Technology Software Development Data Engineering Analytics
  1. When making changes to data models, it's important to identify if they are breaking or non-breaking changes. Breaking changes affect downstream models, while non-breaking changes do not.
  2. SQLMesh automatically analyzes changes to understand their impact on other models. This helps developers avoid manual tracking and reduces the chances of errors.
  3. New features in SQLMesh will allow for more precise tracking of changes at the column level. This means less unnecessary work when something minor is modified.
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The Rise of Open-Sourced R&D, How Communal Resourcefulness Will Protect Us, & Wild Data Provocations

Implications, by Scott Belsky • 432 implied HN points • 23 Jan 24
🕹 Technology AI Data Blockchain Spatial Computing Web Development
  1. 2024 brings significant changes and implications due to societal shifts, innovation speed, and changing human desires.
  2. Customers are increasingly driving R&D by generating ideas, particularly with the help of AI tools and social validation.
  3. Communal resourcefulness, like shared threat models and blocklists, is crucial for enhancing security in the AI era.

Lessons from Cybersyn

Magis • 227 implied HN points • 23 Dec 24
💼 Business Startups Data Economics Innovation Market Trends
  1. Starting a data company can be really challenging because it takes a lot of time and money to create useful products. It’s hard to find customers who are ready to pay for insights quickly.
  2. Big companies have valuable data but making deals can be tough. You often have to convince them to sell data at a good price while also showing them the benefits of monetizing it.
  3. The shift in the market towards valuing profits over growth made it harder to raise funds for data startups. Sometimes, it might be smarter to shut down a project to save capital instead of pushing forward with uncertain outcomes.

5 Cybersecurity Predictions for 2024

Frankly Speaking • 203 implied HN points • 27 Dec 24
🕹 Technology Cybersecurity AI Software Engineering Data
  1. In 2024, cybersecurity companies will focus more on creating platforms instead of using many separate tools. This means they can work faster and solve problems better.
  2. Cybersecurity is moving towards building its own solutions rather than just buying products. This change is necessary to keep up with the evolving threats.
  3. The use of AI in cybersecurity will become more effective. Companies will learn how to use AI to make their security processes better and faster.

Responsible AI in Security

Rod’s Blog • 396 implied HN points • 19 Jan 24
🕹 Technology AI Security Ethics Data Privacy
  1. AI in security offers enhanced threat detection and response capabilities by analyzing data and providing insights.
  2. Responsible AI in security involves principles like transparency, safety, human control, and privacy to ensure ethical use.
  3. Security professionals can leverage responsible AI to improve performance while safeguarding data, privacy, and safety.

The Great Sobriety for Venture Investing, Where To Start with AI, & Undeniable Data

Implications, by Scott Belsky • 707 implied HN points • 19 Sep 23
💼 Business Venture Capital AI Data Investing Startups
  1. The venture capital world is facing harsh realities and there are lessons to be learned about creating great products from failed ventures.
  2. Adopting AI requires a '4 P's' framework: Play, Pilot, Protect, Provoke.
  3. Financing for startups should prioritize product-led growth, focus, and discipline over raising large amounts of capital.

The Marketing Funnel Funeral: How To Replace Dusty Funnels With Dynamic Flywheels That Attract, Engage, And Retain Superconsumers

Category Pirates • 707 implied HN points • 12 Jun 23
💼 Business Marketing Community AI Data
  1. Flywheels focus on attracting customers with value, engagement, and community.
  2. Marketing funnels push customers down a linear path, while flywheels put customers at the center to drive organic growth.
  3. Superconsumers are key in fueling the positive feedback loop of a marketing flywheel.

Uber was Built in Silicon Valley of India

Sector 6 | The Newsletter of AIM • 439 implied HN points • 03 Jan 24
🕹 Technology Software Innovation Mobile Development Data
  1. During the COVID-19 pandemic, Uber's tech team in Bangalore focused on managing both Uber Ride and Uber Eats effectively.
  2. They realized that they could save resources by combining their tech systems instead of using separate ones.
  3. The team found that some tech functions were useful for both services, which allowed them to make improvements in efficiency and performance.

The energy cost of AI, visually explained ⚡️💧

Year 2049 • 13 implied HN points • 21 Jan 25
🕹 Technology AI Energy Sustainability Data Trends
  1. AI requires a lot of energy to function, and this is becoming a bigger concern as it grows. People are curious about why AI even uses water in its processes.
  2. There are new trends and solutions emerging to address the high energy costs associated with AI. It's important to stay informed about these developments.
  3. Understanding the impact of AI on energy consumption can help us find ways to make it more sustainable and efficient in the future. Being aware of these issues is crucial as technology advances.

🚨 The House of Lords could liberate the Postcode Address File if they back this amendment 🚨

Odds and Ends of History • 1139 implied HN points • 14 Feb 24
🕹 Technology Data Government Innovation Policy Legislation
  1. The Postcode Address File (PAF) is a critical database of postal addresses in the UK, owned by Royal Mail and requires expensive licensing fees for access.
  2. An amendment proposed in the House of Lords aims to make UK address data freely available for public use, potentially liberating the PAF.
  3. Individuals are encouraged to reach out to House of Lords members to support the amendment, as it moves through the legislative process towards potential implementation.

It's time to build

benn.substack • 1278 implied HN points • 19 Jan 24
🕹 Technology Data AI Software Startups Analytics
  1. The modern data stack ecosystem is shifting as interest in generative AI takes over.
  2. The hype surrounding data tools can lead to rapid product development but also instability and distraction.
  3. Startups can find success by focusing on rebuilding existing ideas in a more deliberate and stable manner.

($) Stargate is America’s Sovereign AI

Interconnected • 138 implied HN points • 22 Jan 25
🕹 Technology AI Innovation Data Security Automation
  1. Stargate is seen as a key AI technology for America, focusing on improving national capabilities. It aims to make the U.S. more self-sufficient in AI development.
  2. The project emphasizes the importance of sovereign technology, meaning that the U.S. can control and utilize its own AI resources without relying heavily on foreign technologies.
  3. Community support and subscriptions play a crucial role in sharing insights about such technologies, encouraging more people to get involved and informed.

LLMs are becoming commodities

The AI Frontier • 99 implied HN points • 30 May 24
🕹 Technology AI Software Innovation Data Industry
  1. LLMs are growing similar and it's hard to tell them apart. Companies must now find new ways to stand out as features become alike.
  2. The race to create better models is very fast, and some newer models are catching up to the established ones. This means that model quality is no longer the main thing that makes a provider unique.
  3. For businesses and users, having more options is good for getting better deals. But, many people will likely stick with known brands rather than trying new, less familiar choices.

wind and rain and sun and data

Dan Davies - "Back of Mind" • 334 implied HN points • 19 Jan 24
🕹 Technology Energy Data Trading Algorithms Regulation
  1. Supply and demand for electricity become more unpredictable with an increasing proportion of wind and solar energy
  2. The profit motive drives the application of information processing power and bandwidth to solve energy planning problems
  3. Market trading and the profit motive are ways to match the variety of the energy problem with the regulatory system

January 2024: What if ACH had attachments?

Kunle.app • 314 implied HN points • 17 Jan 24
🕹 Technology Payments Messaging Innovation Data
  1. Payments innovation has focused on optimizing speed and cost over the past two decades.
  2. The messaging layers in payment systems have a bandwidth constraint that limits the communication of metadata and important contextual information.
  3. Increasing the bandwidth in the messaging layer of payments could allow for self-reconciling payments and eliminate the need for parallel systems for information exchange.

Artificial Intelligence Weather Model AIFS

Open-Meteo • 843 implied HN points • 29 Feb 24
🕹 Technology AI Data Forecasting
  1. ECMWF released its cutting-edge artificial intelligence weather model AIFS as open-data, marking a significant move in the open-data weather forecasting landscape.
  2. AIFS uses Graph Neural Networks to learn complex weather patterns, showcasing superior accuracy in longer-range forecasts exceeding 5 days.
  3. While AIFS has limitations in weather variables range and interval forecasts, its open availability enables users to compare its forecasts with traditional models, offering a new perspective in weather forecasting.

Expanding AI Horizons: The Rise of Function Calling in LLMs

Gradient Flow • 279 implied HN points • 25 Jan 24
🕹 Technology AI Machine Learning LLMs Data
  1. Function Calling in AI enables models to interact with external functions, going beyond basic text generation to execute actions based on requests.
  2. Combining Retrieval Augmented Generation (RAG) with Function Calling enhances AI systems, allowing them to access external APIs to improve adaptability and assist in various tasks.
  3. Despite its potential, Function Calling in AI faces challenges like security risks, ethical alignment, technical limitations, and the need for advancements in contextual understanding for full potential realization.

Data: the new oil or a new byproduct?

Topsoil • 511 implied HN points • 30 Jun 23
🕹 Technology Data Agriculture Framework Digitization Analytics
  1. Data in agriculture is essential for advancements like Generative AI, automation, and precision agriculture.
  2. Challenges in farm digitization include issues like connectivity, interoperability, data quality, trust, and incentives.
  3. Farmers derive value from data through decision-making, enabling technologies, sharing with advisors, compliance, and future income opportunities.

Import AI 346: Human-like meta-learning; a 3 trillion token dataset; spies VS AI

Import AI • 459 implied HN points • 30 Oct 23
🕹 Technology AI Data Robotics License Research
  1. UK's intelligence services are slightly worried about the safety implications of generative AI technologies, particularly in amplifying existing risks like cyber-attacks and digital vulnerabilities
  2. Research shows that a basic Transformer neural net architecture can meta-learn and match human performance in inferring complex rules from small data, hinting at AI systems increasingly displaying human-like qualities
  3. Facebook's Habitat 3.0 software enables training and testing agents to collaborate with humans by simulating realistic 3D environments with humanoid avatars, human-in-the-loop interactions, and benchmark tasks for human-robot interaction

AI Roundup 098: Flash Thinking

Artificial Ignorance • 29 implied HN points • 20 Dec 24
🕹 Technology AI Innovation Data Software Policy
  1. Google has introduced a new AI model called Gemini Flash Thinking, which aims to improve AI reasoning. This model is part of a trend where companies want AI to think more like humans.
  2. OpenAI is facing legal challenges while trying to shift to a for-profit model, which could affect its future. They are also experimenting with new features and tools despite these issues.
  3. The UK government is pushing for more transparency from AI companies about their training data, while many in the creative industry are resisting this change as it might threaten their copyright protections.

Google Gemini Anti-Whiteness Disaster Is a Cautionary Tale About... Gaming?

The Algorithmic Bridge • 520 implied HN points • 23 Feb 24
🕹 Technology AI Ethics Data Chatbots Alignment
  1. Google's Gemini disaster highlighted the challenge of fine-tuning AI to avoid biased outcomes.
  2. The incident revealed the issue of 'specification gaming' in AI programs, where objectives are met without achieving intended results.
  3. The story underscores the complexities and pitfalls of addressing diversity and biases in AI systems, emphasizing the need for transparency and careful planning.

pip install sqlmesh-cube

davidj.substack • 23 implied HN points • 19 Dec 24
🕹 Technology Software Programming Data Development Systems
  1. A new package called 'sqlmesh-cube' is available for anyone to use. You can easily install it with pip.
  2. This package helps create a CLI command that outputs JSON, showing how sqlmesh models relate to each other. It's important for building a semantic layer.
  3. This was the author's first package, and they learned a lot about the publishing process along the way. They are open to feedback and requests for updates.

The NIH and Data . . .

The Good Science Project • 22 implied HN points • 25 Dec 24
🔬 Science Research Data Innovation Metrics Evaluation
  1. The NIH is starting a program to give scholars access to its internal data. This will help them answer important questions about the economic impact and effectiveness of research policies.
  2. They are creating a new metric called the S-index to reward scientists for sharing data with the wider community. This aims to encourage more collaboration rather than just focusing on personal achievements.
  3. The NIH is offering a $1 million prize for innovative ideas on how to implement the S-index metric, encouraging creativity and participation from the scientific community.

It's A New Year, But Businesses Are Still Baffled By The Same Old Problems With Data And AI

High ROI Data Science • 317 implied HN points • 15 Jan 24
🕹 Technology AI Data Business Digital Transformation
  1. CEOs face challenges with limited skills and expertise in implementing AI initiatives.
  2. Businesses struggle with data complexity and ethical concerns when it comes to utilizing AI.
  3. Companies need to align AI opportunities with business goals, estimate costs upfront, and prioritize continuous reskilling for successful AI implementation.

Edge 460: Anthropic's New Protocol to Link AI Assistants to Data Sources

TheSequence • 119 implied HN points • 26 Dec 24
🕹 Technology AI Software Open Source Data Frameworks
  1. Anthropic has created the Model Context Protocol (MCP) to help AI assistants connect with different data sources. This means AI can access more information to assist users better.
  2. MCP is open-source, which allows developers to use and improve the protocol freely. This encourages collaboration and innovation in AI tools.
  3. Anthropic is expanding its focus beyond AI models to include workflows and developer tools, showing that they're growing in new areas within AI technology.

Import AI 335: Synth data is a bad AI drug; Facebook changes the internet with LLaMa release; and Chinese researchers use AI to figure out chip design

Import AI • 459 implied HN points • 31 Jul 23
🕹 Technology AI Data Social media Semiconductors AI Ethics
  1. Synthetic data during AI training can be harmful if not used in moderation, as shown by researchers from Rice University and Stanford University
  2. Chinese researchers have successfully used AI to design semiconductors based only on input and output data, demonstrating the potential for economic and national security implications
  3. Facebook has released Llama 2, a powerful language model with freely available weights, potentially changing the landscape of AI deployment on the internet

Import AI 343: Humanlike AI; LLaMa 2 protests; the NSA's new AI center

Import AI • 439 implied HN points • 09 Oct 23
🕹 Technology AI Robotics Data Security Generative models
  1. Google DeepMind and 33 labs created a large dataset for training robots, showing that using heterogeneous data and high-capacity models improves robot performance.
  2. Protests have begun against Facebook for releasing AI models that can be easily modified, raising concerns about AI safety becoming a political issue.
  3. Generative image models are displaying human-like qualities in tasks, like shape bias and understanding perceptual illusions, suggesting a convergence between AI systems and humans.

Sunshine and moonbeams, motherfuckers

Alex's Personal Blog • 32 implied HN points • 10 Dec 24
🕹 Technology AI Quantum Computing Data Internet Robotics
  1. We're living in a rapidly advancing tech age. It's getting easier and cheaper to explore space and we're seeing big improvements in AI and robotics.
  2. Quantum computing is becoming more accurate as technology progresses. This means we can expect even more powerful computing capabilities in the future.
  3. C3.AI's new patent could significantly change the game for enterprise AI by protecting important technologies. This could lead to big changes in how businesses use AI.

Language Agent Tree Search — LATS

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 59 implied HN points • 12 Jun 24
🕹 Technology AI NLP Automation Software Data
  1. The LATS framework helps create smarter agents that can reason and make decisions in different situations. It's designed to enhance how language models think and plan.
  2. Using external tools and feedback in the LATS framework makes agents better at solving complex problems. This means they can learn from past experiences and improve their responses over time.
  3. LATS allows agents to explore many possible actions and consider different options before making a choice. This flexibility leads to more thoughtful and helpful interactions.

What Are Companies Doing With Generative AI? A Tale Of 3 Strategies.

High ROI Data Science • 297 implied HN points • 12 Jan 24
🕹 Technology AI Retail Data Strategy E-commerce
  1. Companies are using Generative AI tools to decrease training times and improve customer service in retail.
  2. Some companies are implementing Generative AI without a clear business problem statement, leading to undefined outcomes.
  3. Retailers like Walmart are strategically using Generative AI to change customer workflows, improve online shopping experiences, and increase revenue.

🌻 E45: PDL - New Language for Prompting

Musings on AI • 184 implied HN points • 05 Nov 24
🕹 Technology AI Software Development Innovation Data
  1. Prompt engineering is important because the way a prompt is worded can change the AI's response. Finding the right technique can improve the effectiveness of AI applications.
  2. The Prompt Declaration Language (PDL) is a new tool designed to simplify working with AI. It allows programmers to easily create applications like chatbots using a straightforward, data-oriented approach.
  3. Recent advancements in AI include new architectures that enhance performance in specific tasks, like financial analysis. These innovations are making AI applications more powerful and useful for real-world problems.

Agent AI: Agentic Applications Are Software Systems With A Foundation Model AI Backbone & Defined Autonomy via Tools

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 19 implied HN points • 05 Aug 24
🕹 Technology AI Software Models Applications Data
  1. Agentic Applications are advanced software systems that use AI models to operate more independently. They can navigate and process information effectively using tools.
  2. The MindSearch framework helps break down complex questions into simpler parts, making it easier to find answers online. It simulates how humans think and search for information.
  3. There are special agents in this system, like WebPlanner and WebSearcher, that work together to gather and organize information from the web, enhancing the problem-solving process.