The hottest Data science Substack posts right now

And their main takeaways
Category
Top Technology Topics

Data Science Weekly - Issue 6

Data Science Weekly Newsletter • 19 implied HN points • 02 Jan 14
🕹 Technology Data science
  1. Machine learning is becoming really popular in education and helps improve various fields, like online dating and data analysis. Many students at universities, like Stanford, are eager to learn about it.
  2. Deep learning models are advancing quickly, and some can now even beat human players in video games. This shows how powerful these technologies are getting.
  3. Data scientists need to have a mix of skills in business, math, and coding. This combination helps them solve problems and create better algorithms in the industry.

Data Science Weekly - Issue 5

Data Science Weekly Newsletter • 19 implied HN points • 26 Dec 13
🕹 Technology Data science
  1. Data science combines various skills and knowledge, making it important for professionals to share their experiences and lessons learned.
  2. Machine learning can be applied in surprising ways, like developing vaccines or improving image recognition, showcasing its versatility in different fields.
  3. There are valuable resources and guides available for those interested in data science, making it easier for beginners to get started in the field.

Data Science Weekly - Issue 4

Data Science Weekly Newsletter • 19 implied HN points • 19 Dec 13
🕹 Technology Data science
  1. Data analysis can reveal surprising patterns, like how riders use Uber, by looking at location and time data.
  2. Machine learning is being used in innovative ways, such as predicting stock prices and improving email marketing, making processes smarter.
  3. Even in competitive sports like cycling, there's a gap in using data analytics effectively, despite having lots of available data.

Data Science Weekly - Issue 3

Data Science Weekly Newsletter • 19 implied HN points • 12 Dec 13
🕹 Technology Data science
  1. Data science is important for understanding and predicting human behavior, especially in areas like media and health. This helps create better metrics and healthcare solutions.
  2. Big data can revolutionize industries, such as travel and sports, by analyzing large amounts of information to improve decision making and user experiences.
  3. Training and collaboration are key in data science. Courses and mentorship can help upcoming data scientists gain the skills needed to succeed in the job market.
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Databricks acquires AI startup MosaicML in $1.3 billion deal

Machine Economy Press • 1 implied HN point • 27 Jun 23
🕹 Technology Data science
  1. Databricks acquires AI startup MosaicML in a $1.3 billion deal to make generative AI accessible for all organizations.
  2. Major companies are investing in open-source AI tools to stay competitive in the rapidly growing AI sector.
  3. The acquisition of MosaicML by Databricks significantly enhances Databricks' credibility in large language models and generative AI.

LLM of the day: LLAMA 2

Age of AI • 0 implied HN points • 20 Jul 23
🕹 Technology Data science
  1. Facebook's LLAMA 2 is an updated LLM comparable to GPT 3.5 and now available for commercial use for up to 700 million users.
  2. LLAMA 2 is not as advanced as GPT 4, but its availability for commercial use is attracting many companies to use it.
  3. There may not be a clear process for external contributions to improving LLAMA 2, but Facebook's decision to open-source it could be for goodwill or competitive reasons.

Abrupt skill emergence in Large Language Models

Intuitive AI • 0 implied HN points • 31 Aug 23
🕹 Technology Data science
  1. General Large Language Model performance can be predicted based on compute, dataset size, and parameter count.
  2. Task-specific abilities in models show abrupt jumps in proficiency as the parameter count increases.
  3. Abrupt skill emergence is observed in models for tasks like adding numbers or unscrambling words as they reach certain parameter thresholds.

The taxonomy of machine learning paradigms

The Palindrome • 0 implied HN points • 18 Sep 23
🕹 Technology Data science
  1. Machine learning tasks involve three important parameters: the input, the output, and the training data.
  2. The basic machine learning setup consists of a dataset, a true relation function, and a parametric model as an estimation.
  3. Major paradigms of machine learning include supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning.

March Newsletter

RSS DS+AI Section • 0 implied HN points • 05 Mar 23
🕹 Technology Data science
  1. Ethical concerns around the use of AI, especially in the military, continue to be a significant issue.
  2. Research in data science is focusing on efficiency, scalability, and the adaptation of large language models.
  3. Generative AI, like ChatGPT, is a hot topic with advancements in business applications and ethical considerations.

Google's new AI model goes natively multimodal; Meta’s chief AI scientist doesn’t think AI super intelligence is coming soon; AMD forecasts $45 billion AI chip market this year

Computerspeak by Alexandru Voica • 0 implied HN points • 08 Dec 23
🕹 Technology Data science
  1. Google's new AI model, Gemini, is natively multimodal, meaning it can understand complex written and visual information. This could lead to more logical and consistent AI responses.
  2. Integrating a 'world model' into large language models could enhance AI reasoning by simulating the world based on scientific principles and observational data. This could make AI systems more broadly intelligent.
  3. There are ongoing advancements in AI technology across various industries, from using AI to catch fare-dodgers on public transport to creating AI tools for detecting audit frauds. AI's impact is diverse and expanding.

Making Money Online, Learning Data Science, and Building a Wealth Vehicle

Austin's Analects • 0 implied HN points • 09 Jun 21
💼 Business Data science
  1. Building an audience and making money online can be achieved by offering a free product to attract followers and subscribers.
  2. Learning data science involves introspection to identify what aspects you enjoy, such as problem-solving and quick learning, and utilizing valuable resources like Datacamp for skill development.
  3. To achieve financial freedom, it's crucial to identify and invest in a 'wealth vehicle,' which can be any business or system aiming to grow wealth, following a 4-step process of making money, saving, investing, and scaling.

What is Context?

Cybernetic Forests • 0 implied HN points • 17 Oct 21
🕹 Technology Data science
  1. Understanding context in automated systems is critical to identify bias and ensure accurate conclusions are drawn from data.
  2. Data science transforms real-world events into numerical representations, and the way data is collected and interpreted can influence outcomes.
  3. Designing technology needs to consider a broader context beyond just user interactions, including social impacts, environmental considerations, and lasting community value.

Sora: A Data-Powered Physical Engine

AI Disruption • 0 implied HN points • 07 May 24
🕹 Technology Data science
  1. OpenAI's Sora project aims to teach AI to understand videos and simulate the physical world, marking a significant step toward general artificial intelligence.
  2. Sora is a complex and powerful data-powered physical engine with detailed technical aspects that make it intriguing for AI enthusiasts.
  3. Readers have the opportunity to explore more about Sora and AI by subscribing to AI Disruption and accessing free trial content.

GPT-4 Code Interpreter: Your Magic Wand for Instant Python Data Visuals

Data at Depth • 0 implied HN points • 24 Jul 23
🕹 Technology Data science
  1. GPT-4 Code Interpreter generates data visualization code instantly, allowing users to upload a data file, clean it, load it into a data frame, and display the results.
  2. Users can try GPT-4 Code Interpreter with a 7-day free trial by subscribing to Data at Depth.
  3. John Loewen's post explores a case study example using UN population projection data, showcasing the features and capabilities of the GPT-4 Code Interpreter.

This is not the AI we were looking for

AI Prospects: Toward Global Goal Convergence • 0 implied HN points • 07 Feb 24
🕹 Technology Data science
  1. AI has diversified into myriad service providers instead of developing into super-agents, updating our thinking about AI as a valuable resource.
  2. Intelligence is a capacity, not a thing, and AI systems can be easily specialized, frozen, deployed, and composed for different tasks.
  3. Advanced AI systems like GPT-4 can be fine-tuned, leading to diverse AI systems with unique behaviors, challenging the idea of one dominant AI pushing everything else aside.

What is open-source workflow orchestration and when should you move on?

The Orchestra Data Leadership Newsletter • 0 implied HN points • 23 Oct 23
🕹 Technology Data science
  1. Open-source workflow orchestration tools like Apache Airflow have been around for a long time and offer flexibility in developing, scheduling, and monitoring batch-oriented workflows.
  2. Specialized tools are emerging for data operations to improve quality, moving away from the Swiss Army Knife approach of general-purpose orchestration tools.
  3. When considering upgrading from open-source orchestration tools, evaluate if the tool effectively handles monitoring, metadata gathering, and other complex data operation needs; specialized tools may be more suitable in such cases.

London Meetup 19th July

RSS DS+AI Section • 0 implied HN points • 12 Jul 23
🕹 Technology Data science
  1. Upcoming London meetup on 19th July will focus on 'International Standards for AI.'
  2. The event will also cover how to avoid becoming an 'ornamental' data scientist.
  3. Guest speaker Dr. Florian Ostmann from The Alan Turing Institute will lead the discussion.

An Introduction to LSTM with Attention Model

Data Science Daily • 0 implied HN points • 01 Mar 23
🕹 Technology Data science
  1. LSTM models are good for handling input sequences of varied length like in language modeling and translation.
  2. Attention models help LSTM models focus on important parts of a sequence, improving accuracy.
  3. Combining LSTM with attention models can lead to better predictions and performance in tasks like natural language processing and image captioning.

🤓LSTM Networks: The Power of Long-Short-Term Memory and comparisons to ARIMA/XGBoost/Prophet

Data Science Daily • 0 implied HN points • 23 Feb 23
🕹 Technology Data science
  1. LSTM Networks can remember information for long periods and are great for processing sequential data.
  2. LSTMs can handle a wide variety of input and output types, making them flexible for real-world data.
  3. LSTMs are powerful for time series forecasting but can be computationally expensive, especially with large datasets.