The hottest Data Substack posts right now

And their main takeaways
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Top Literature Topics

Import AI 320: Facebook's AI Lab Leak; open source ChatGPT clone; Google makes a universal translator.

Import AI • 339 implied HN points • 13 Mar 23
🕹 Technology AI Data Robots Decentralization Models
  1. Google is making strides with a universal translator by training models on diverse unlabeled data from multiple languages.
  2. The FTC is calling out companies for lying about AI capabilities, emphasizing the importance of truthful representation in the AI industry.
  3. OpenChatKit, an open-source ChatGPT clone, is released with a focus on decentralized training and customization for chatbot creation.

What even is a data asset?

Entry Level Investing • 16 implied HN points • 10 Dec 24
💼 Business Investing Technology AI Data Startups
  1. AI companies are focusing more on improving data instead of just making bigger models. They realize that using better, unique data can give them an edge.
  2. Having unique data, known as a 'data asset,' means owning valuable information that others can't easily get. This can be essential for success in AI.
  3. Startups are finding creative ways to gather exclusive data, like partnering with others or creating synthetic data. This helps them stand out in a crowded market.

Who Runs the World?

Sector 6 | The Newsletter of AIM • 99 implied HN points • 26 Feb 24
🕹 Technology Computing AI Software Hardware Data
  1. NVIDIA is a major player in the tech industry, affecting many computer companies worldwide. They've made big strides in both hardware and software for computing and AI.
  2. The company's recent financial success is impressive, with revenue growing significantly compared to last year. This shows that more businesses and industries are adopting their technology.
  3. NVIDIA's growth signals a shift to a new era in computing. Many experts believe we are entering a transformative phase in technology.

Edge 438: Meet DataGemma: Google DeepMind's Effort to Ground LLMs in Factual Knowledge

TheSequence • 112 implied HN points • 10 Oct 24
🕹 Technology AI Data Research Models Applications
  1. DataGemma is a new model developed by Google DeepMind that helps large language models (LLMs) use factual information.
  2. It aims to reduce errors, known as hallucinations, and make LLMs more reliable for important tasks.
  3. The model uses a large data source called DataCommons to verify the information it provides.
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Data Points

Technically Optimistic • 59 implied HN points • 19 Apr 24
🕹 Technology Data AI Privacy
  1. Data is essential for AI; you can't have AI without massive amounts of data.
  2. Our relationship with data is complex - it enhances our efficiency and personalization but also raises privacy concerns.
  3. Surveillance capitalism is a reality where tech companies profit from capturing and shaping our private experiences, showcasing the lack of user power and awareness.

Joe's Nerdy Rants #7

Joe Reis • 216 implied HN points • 01 Jul 23
🕹 Technology Data AI Business Startups Events
  1. The data community deserves better events free of vendor influence.
  2. The major data platforms are in an intense competition and push to capture attention.
  3. Attending big-vendor conferences often involves dealing with aggressive selling tactics.

Is AI a Silver Bullet for our Climate Problems?

This Week in MCJ (My Climate Journey) • 216 implied HN points • 07 Mar 23
🔬 Science Climate AI Data Education Energy
  1. AI solutions in climate problems can be biased towards easily accessible data, encouraging broader solution development is crucial.
  2. AI must quantify its confidence in recommendations for climate problem-solving due to the high cost of mistakes.
  3. Encouraging new datasets and AI methods with confidence measurement can lead to more successful projects in addressing climate challenges.

Models

Sriram Krishnan’s Newsletter • 216 implied HN points • 20 Jun 23
🕹 Technology AI Data Models
  1. Large-language models are open-sourced and ranked based on benchmarks like ChatGPT and Google Bard.
  2. Model performance improves with each iteration, leading to better models rising and lesser ones fading out.
  3. Different types of data sources contribute to the creation of unique models, with more gated data leading to more variety.

Joe's Nerdy Rants #8

Joe Reis • 196 implied HN points • 08 Jul 23
💼 Business Tech AI Data Startups Journalism
  1. People skills are becoming increasingly important in the tech industry.
  2. Technical skills are essential, but communication and empathy separate individuals for career success.
  3. Businesses are shifting towards paying tech vendors based on outcomes, emphasizing accountability.

The Politics of Data

Joe Reis • 196 implied HN points • 29 Jul 23
🕹 Technology Data AI Business Ethics Tech
  1. The politics of data often involves using data to push pre-determined agendas.
  2. In organizations, decisions are often driven by politics rather than technical excellence or data.
  3. Understanding the political dynamics within an organization can help navigate potential impacts on one's career.

On a Personal Note

Sarah's Newsletter • 319 implied HN points • 20 Dec 22
💼 Business Career Consulting Startups Marketing Data
  1. The author is relocating to Vermont, excited about being closer to snow for ski season and connecting with local communities.
  2. The author's startup, Versionable, is currently taking a back seat as they focus on settling into new changes and exploring different angles to address marketing challenges.
  3. The author is embarking on a new role as the Growth Lead at Prefect, highlighting their interest in ambitious team goals and a UI-first experience in data tooling space.

China May Stay Permanently Behind the US in Generative AI

Interconnected • 447 implied HN points • 12 Nov 23
🕹 Technology AI Data Regulations China US
  1. China may be permanently behind the US in Generative AI due to factors like blocking quality datasets.
  2. Unique attributes of Chinese Internet data, like linguistic challenges, present additional hurdles for AI developers in China.
  3. New regulatory burdens in China around AI development may hinder progress and keep the country behind the US in generative AI.

How to metacognate

Sunday Letters • 99 implied HN points • 29 Jan 24
🕹 Technology AI Programming Machine Learning Cognition Data Automation
  1. Working with complex models can be hard when they get confused by incorrect or incomplete information. This can lead to mistakes and conflicts in what they remember.
  2. Creating a stable pattern for how tasks are done can help models work better by giving them a solid structure to follow. This is like giving the model a framework to lean on for more complicated tasks.
  3. As models improve, the need for extra coding to guide their thinking may lessen. Better memory strategies will likely help them function more effectively over time.

Why We Founded SaaSGrid

Bottom Up by David Sacks • 541 implied HN points • 06 Sep 23
🕹 Technology SaaS Metrics Data
  1. SaaS companies need a dedicated dashboarding platform for their metrics.
  2. Problems faced by SaaS companies include lack of proper metrics, errors in data, and lack of real-time availability.
  3. SaaSGrid provides a solution by automating the calculation of key SaaS metrics and offering real-time dashboards.

The rise of AI work

The AI Frontier • 5 HN points • 22 Aug 24
🕹 Technology AI Data Productivity Workforce Automation
  1. AI products should focus on automating work that humans often find tedious. This helps measure their true value to consumers and businesses.
  2. Companies can choose to specialize deeply in one area or offer a broad service across multiple tasks. Each approach has its own strengths and weaknesses.
  3. Finding a middle ground might be beneficial, as it allows companies to manage a workflow that spans several tasks, though they should focus on making sure their quality remains high.

The Sequence Chat: Why Transformers are the Best Thing that Ever Happened to NVIDIA

TheSequence • 84 implied HN points • 21 Oct 24
🕹 Technology AI Hardware Data Software Market Trends
  1. Transformers are special because they can learn from a lot of data without hitting a limit. This helps improve AI performance.
  2. NVIDIA has been able to fine-tune its hardware thanks to the widespread use of transformers in AI. This gives them a market edge.
  3. Most advanced transformer models rely on NVIDIA GPUs for their computing needs. This creates a strong connection between transformers and NVIDIA's success.

AI slashes ads by boosting intention (+ weekly update)

12challenges • 171 implied HN points • 09 Mar 24
🕹 Technology AI Advertising Data Innovation Digital marketing
  1. Our intentions can get diluted through different stages like Action and Input before resulting in something happening on a computer.
  2. The use of AI can boost intention by translating inputs into more aligned results and increasing confidence in actions.
  3. AI can help shrink the 'Crapgret Zone' where ads reside by improving intention alignment and reducing unintentional consumption of ads.

Datetime vs Timestamp datatype in databases - Which one is better?

Arpit’s Newsletter • 176 implied HN points • 26 Apr 23
🕹 Technology Data Database Storage
  1. In databases, you can use DATE, DATETIME, or TIMESTAMP data types to store date and time information, each with its own range of values.
  2. DATETIME is best for storing static timestamps like appointment schedules, while TIMESTAMP is ideal for recording event timestamps with efficient storage and automatic timezone handling.
  3. Consider factors like range, storage requirements, and use cases when choosing between DATETIME and TIMESTAMP for accurate and efficient temporal data storage.

RDEL #25: How can engineering managers improve the OKR and goal setting process?

Research-Driven Engineering Leadership • 99 implied HN points • 15 Jan 24
💼 Business Management Teamwork Strategy Communication Data
  1. Improving the OKR process can enhance team development by focusing on effective goal setting methods.
  2. Investing in data quality and transparency and promoting communication can address challenges in working with others and ensuring alignment on goals.
  3. Striving for consistency, promoting learning communities, and guiding teams in OKR implementation can lead to successful adoption and use of OKRs across the organization.

Global Space and Technology Convention 2024: Highlights by Space Ambition

Space Ambition • 59 implied HN points • 22 Mar 24
🕹 Technology Space Tech Investments Innovation Data
  1. The Global Space and Technology Convention is a big event in Asia for space tech, attracting over 1,000 people. It offers great networking opportunities for those interested in the space industry.
  2. There were interesting discussions about how space data is being used in finance and how money pressure can hurt sustainability in startups. It's important to balance profit and environmental concerns.
  3. Panels discussed innovation in space exploration, covering topics like robotics and energy needs in space. It's exciting to think about future missions and technologies that can help us explore beyond Earth.

Data at Depth Newsletter 6: Thankful, Creating Like Crazy, GPT-4/StreamLit Dashboards

Data at Depth • 79 implied HN points • 08 Feb 24
🕹 Technology Data Creation Artificial Intelligence
  1. The author's Substack newsletter is rapidly growing, and they are very active in creating content to keep up with the growth.
  2. The newsletter includes the author's personal journey with data, highlighting successes on platforms like Medium and Substack.
  3. Readers can access the full newsletter and archives with a 7-day free trial subscription.

So... what is multi-modal AI? And why is the internet losing their mind about it? [Math Mondays]

Technology Made Simple • 159 implied HN points • 10 Oct 23
🕹 Technology AI Machine Learning Data Models Embeddings
  1. Multi-modal AI integrates multiple types of data in the same training process, allowing models to represent data in a common n-dimensional space.
  2. Multi-modality adds an extra dimension to data, expanding the search space exponentially, enabling more diverse and powerful AI applications.
  3. While multi-modality enhances model performance, it does not solve fundamental issues with AI models like GPT, and simpler technologies may be more effective for certain use-cases.

AI applications are more than an LLM

The AI Frontier • 19 implied HN points • 20 Jun 24
🕹 Technology AI Software Programming Engineering Data
  1. AI applications are more than just using a big model; they need careful design and planning to be effective. It's like building a nice piece of furniture versus just putting some wood together.
  2. Quality comes with a cost, and building great AI solutions takes more time and resources. Cheaper options might save money now, but they often lead to poorer results.
  3. Not all AI applications perform the same, even if they use the same tools. Good performance comes from thoughtful engineering and working with the data properly.

From bare-bones to holistic machine learning

Mindful Modeler • 159 implied HN points • 08 Aug 23
🕹 Technology Machine Learning Modeling Interpretability Data Tools
  1. Machine learning can range from simple, bare-bones tasks to more complex, holistic approaches.
  2. In bare-bones machine learning, the modeling choices are defined, making it about the model's performance and tuning.
  3. Holistic machine learning involves designing the model to connect with the larger context, considering factors like uncertainty, interpretability, and shifts in distribution.

Quo vadis, Data Open source

timo's substack • 157 implied HN points • 03 Sep 23
🕹 Technology Open Source Data Community Business strategy Software Development
  1. Snowplow, dbt, Rudderstack, and Iceberg are examples of open-source data tools each with unique characteristics.
  2. Open-source data tools face challenges in transitioning to successful go-to-market strategies.
  3. Companies need to focus on identifying customer pain points and developing experience-changing solutions in their GTM strategy.

Synthetic data: Anthropic’s CAI, scaling, OpenAI’s Superalignment, tips, and open-source examples

Democratizing Automation • 332 implied HN points • 29 Nov 23
🕹 Technology AI Data Models Methods Applications
  1. Synthetic data is becoming more important in AI, with a focus on removing human involvement.
  2. Proponents believe that using vast amounts of synthetic data can lead to breakthroughs in AI models.
  3. Open and closed communities are both utilizing synthetic data for different end goals.