Common Sense with Bari Weiss $5 / month

Common Sense with Bari Weiss critiques contemporary issues impacting society from the perspectives of free speech, scientific inquiry, and cultural shifts. It explores themes around gender dysphoria, debate censorship, autism, personal values, public controversies, mental health, political correctness, scientific skepticism, cultural identity, historical context, and societal purpose.

Free Speech and Censorship Gender and Identity Education and Debate Health and Science Cultural and Social Issues Mental Health Politics and Public Opinion Historical and Cultural Identity Personal Values and Purpose Science and Skepticism

The hottest Substack posts of Common Sense with Bari Weiss

And their main takeaways

[in case you missed it] Data Science Weekly - Issue 469

0 implied HN points • 20 Nov 22
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Robotics
  1. Learning machine learning can be a challenging but rewarding journey, and it often involves continuous effort to improve skills and practices.
  2. Robotics and AI are making a big impact in industries like fulfillment, but there are still many challenges to overcome as the technology scales.
  3. Emerging AI capabilities, particularly in large language models, are becoming increasingly action-driven, resembling more advanced forms of intelligence.

[in case you missed it] Data Science Weekly - Issue 377

0 implied HN points • 14 Feb 21
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Data Analytics
  1. Using Active Learning can save time and effort in machine learning. It allows models to learn with less labeled data by letting them ask questions about unclear data.
  2. There is a growing shift from Excel to Python in many industries. This change is driven by the need for more advanced data analysis and the capabilities Python offers.
  3. Understanding the importance of machine learning in healthcare is crucial. Innovations like AI systems that can identify smells may lead to new diagnostic tools and enhance medical practices.

[in case you missed it] Data Science Weekly - Issue 381

0 implied HN points • 14 Mar 21
🕹 Technology Data science Machine Learning Artificial Intelligence Data Visualization Computer Vision
  1. Data sharing in Africa faces challenges due to issues like historical power imbalances and Western-centric policies. It's important to recognize these factors when discussing data access and usage.
  2. Machine learning models can struggle when tested on data that is different from what they were trained on. Research is being done to improve how these models generalize to new situations.
  3. New tools like Dolt combine Git and MySQL to help data scientists collaborate better on datasets. This makes it easier for teams to work together without overwriting each other's changes.

[in case you missed it] Data Science Weekly - Issue 386

0 implied HN points • 18 Apr 21
🕹 Technology Data science Machine Learning AI Accessibility Visualization
  1. Chartability focuses on making data visuals more accessible for people with disabilities. It's about ensuring everyone can understand the information presented.
  2. Data observability is important as companies handle more data, helping them maintain data quality. This can prevent issues like missing or stale data from affecting business decisions.
  3. Using advanced learning techniques like Graph Neural Networks can improve how we process complex data structures. These techniques can reveal deeper insights into various systems.

[in case you missed it] Data Science Weekly - Issue 389

0 implied HN points • 09 May 21
🕹 Technology Data science AI Machine Learning Visualization Ethics
  1. Artificial intelligence is changing healthcare but raises important ethical questions, like the risk of bias and loss of doctors' decision-making power.
  2. Observable Plot is a new library designed to make data visualization easier and more enjoyable, built on the foundations of D3.
  3. Using SQL for data analysis can be very efficient, and it's worth remembering its capabilities compared to popular tools like Pandas.
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[in case you missed it] Data Science Weekly - Issue 390

0 implied HN points • 16 May 21
🕹 Technology Data science Artificial Intelligence Machine Learning Software Development Big Data Data Analysis
  1. AI can solve complex puzzles better than humans, but humans still have unique skills. Don't give up on challenging word games just yet!
  2. Defining trees in biology is tricky because many plants don't fit into clear categories. It's surprising how many things that look like trees actually aren't.
  3. New technology makes searching through large image databases easier. With smart algorithms, you can quickly find the pictures you're looking for without remembering file names.

[in case you missed it] Data Science Weekly - Issue 322

0 implied HN points • 25 Jan 20
🕹 Technology AI Data science Machine Learning Software Development Programming
  1. The Smulemates project suggests a feature for the karaoke app Smule to help users find singing partners that match their style. This could make karaoke more social and enjoyable.
  2. Facebook AI introduced a new method for teaching machines to navigate in real environments without maps. This could lead to better robots that understand complex spaces, helping them perform tasks with ease.
  3. A tool called Manifold was released as open source to help find problems in machine learning models. It allows users to visually debug and improve their models more efficiently.

[in case you missed it] Data Science Weekly - Issue 352

0 implied HN points • 23 Aug 20
🕹 Technology Data science Artificial Intelligence Machine Learning Computing Programming
  1. minGPT is a simple way to understand and train GPT models with only 300 lines of code. It's designed to be clean and educational.
  2. Bias in datasets like CoNLL-2003 can affect how well AI models recognize names. If a model only learns from biased data, it may perform poorly on names that aren't represented.
  3. Real-world challenges in reinforcement learning can hinder its effectiveness. Researchers are working on solutions to make RL more applicable in practical situations.

[in case you missed it] Data Science Weekly - Issue 360

0 implied HN points • 18 Oct 20
🕹 Technology AI Ethics Machine Learning Data science Computer Vision Human-computer interaction
  1. Making machine learning models run fast on GPUs is important for research and production. It can help speed up improvements and make coding more efficient.
  2. Companies like BMW are creating ethical guidelines for AI use to ensure it benefits people. This is a proactive step to use AI responsibly.
  3. There are various learning resources and tools available for anyone interested in data science. These can help you build a solid foundation and advance your career.

[In case you missed it] Data Science Weekly - Issue 450

0 implied HN points • 10 Jul 22
🕹 Technology AI Data science Machine Learning Deep Learning Data Engineering
  1. AI forecasting contests are being used to predict future progress in AI, showing how forecasts can be evaluated based on actual results.
  2. The demand for analytics engineers is growing, shifting from a less desirable role to one of great interest in the job market.
  3. A new multilingual translation model called NLLB-200 helps translate between 200 low-resource languages, making high-quality translation more accessible.

[in case you missed it] Data Science Weekly - Issue 471

0 implied HN points • 04 Dec 22
🕹 Technology Machine Learning Data science Artificial Intelligence Automation Software Development
  1. MLOps is important for automating machine learning products. It helps researchers and practitioners understand the roles and workflows needed in machine learning.
  2. Companies face challenges when moving to realtime machine learning. They need to balance performance, cost, and complexity in their ML pipelines.
  3. The FDA has outlined guiding principles for using AI in medical devices. These principles aim to ensure safety and effectiveness in tech for healthcare.

[in case you missed it] Data Science Weekly - Issue 461

0 implied HN points • 25 Sep 22
🕹 Technology Data science Artificial Intelligence Machine Learning Natural Language Processing Database Management
  1. NLP is a growing field, but using it effectively is still a challenge for many. People are eager to learn how to make NLP useful in their work.
  2. Curating social media accounts can be a rewarding experience. It helps to connect with a community and share insights in fun ways.
  3. Generative AI can boost productivity and creativity significantly. It has the potential to create a lot of economic value by making workers faster and more effective.

[In case you missed it] Data Science Weekly - Issue 454

0 implied HN points • 07 Aug 22
🕹 Technology Data science Machine Learning Artificial Intelligence Big Data Cloud Computing
  1. NASA is using AI to categorize millions of astronaut photos of Earth, making it easier for scientists to find specific images.
  2. Data-driven companies can have a competitive edge, especially in industries where expertise and speed matter.
  3. Understanding and explaining complex models is important for making ethical and business decisions before automating processes.

[in case you missed it] Data Science Weekly - Issue 468

0 implied HN points • 13 Nov 22
🕹 Technology Data science AI Analytics Privacy Machine Learning Software Engineering
  1. Before leaving Twitter, it's a good idea to download and save your data. This way, you can analyze important trends and insights you might miss if you just leave.
  2. The command line can make data processing easier and more readable. New tools like SPyQL help bridge familiarity with SQL and Python for better data analytics.
  3. Federated learning allows multiple users to train models without sharing their raw data. This technology can enhance privacy while still allowing valuable insights from diverse data sources.

[in case you missed it] Data Science Weekly - Issue 464

0 implied HN points • 16 Oct 22
🕹 Technology Data science Artificial Intelligence Machine Learning Data Analysis Software Development
  1. Building a community of R users can greatly enhance collaboration and knowledge sharing, especially in specialized fields like pharmaceuticals.
  2. Generating research ideas often starts with identifying gaps in existing literature, which can be guided by specific frameworks to improve the quality of ideas.
  3. Data cleaning is crucial for model accuracy, and its success relies on effective ETL processes and organizational commitment to maintaining high-quality data.

[in case you missed it] Data Science Weekly - Issue 394

0 implied HN points • 13 Jun 21
🕹 Technology Data science Machine Learning AI Programming Research
  1. The data economy harms our privacy by collecting personal information for profit. It's important to rethink this approach.
  2. New AI methods are improving tasks like chip design, allowing machines to do the work faster and better than humans.
  3. There's a growing interest in data management concepts like data mesh, which focuses on decentralized data ownership and treating data as a product.

[in case you missed it] Data Science Weekly - Issue 467

0 implied HN points • 06 Nov 22
🕹 Technology AI Data science Machine Learning User Experience Software Development
  1. Startups using large language models should focus on improving user experience, as it's currently their biggest hurdle, not the data or algorithms.
  2. Data science notebooks have evolved significantly since they were first created, and there are predictions for how they'll continue to develop in the future.
  3. OpenAI is supporting new AI startups by offering $1 million each and early access to their systems, which could help boost innovation in the field.

[in case you missed it] Data Science Weekly - Issue 466

0 implied HN points • 30 Oct 22
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Open Source
  1. Teaching science should start with the values and virtues of being a good scientist rather than just tools and techniques. Focusing on qualities like curiosity and creativity is key.
  2. Creating a data dictionary before collection is crucial. It helps guide your data collection and makes interpreting results easier later on.
  3. Open source reinforcement learning is evolving with new organizations to improve standardization and support. This effort aims to enhance the quality and usability of available tools.

[in case you missed it] Data Science Weekly - Issue 396

0 implied HN points • 27 Jun 21
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Cloud Computing
  1. Understanding hype cycles can help us see how technology develops over time. It's interesting to look back at these cycles to learn from past trends.
  2. Multi-task learning is beneficial as it allows machines to make multiple predictions. This can lead to more effective and efficient models.
  3. AI struggles with understanding basic concepts like 'same' and 'different.' This limitation raises questions about how truly intelligent AI can become.

[in case you missed it] Data Science Weekly - Issue 452

0 implied HN points • 24 Jul 22
🕹 Technology Data science Machine Learning Artificial Intelligence Statistical Modeling Data Engineering
  1. Data scientists are still in demand and well-paid, with job growth expected to continue into the future.
  2. Large Language Models (LLMs) are playing a big role in innovation and are becoming a part of everyday life.
  3. There's a growing need for domain experts in deep learning, allowing more people without advanced degrees to contribute to the field.

[in case you missed It] Data Science Weekly - Issue 447

0 implied HN points • 19 Jun 22
🕹 Technology AI Data science Machine Learning Natural Language Computer Vision
  1. Natural Language Processing is advancing quickly, with AI starting to mimic human-like conversation. This technology could change how we interact with machines.
  2. DeepMind is using AI for significant medical discoveries, showing real-world applications of machine learning beyond just technology.
  3. There's a debate in the AI community about the limits of scaling language models. Some believe that simply making them bigger may not solve all problems.

[in case you missed it] Data Science Weekly - Issue 463

0 implied HN points • 09 Oct 22
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Data Analysis
  1. To explore a large CSV file, you should use handy tools and methods to quickly understand the data without getting overwhelmed.
  2. AI can help convert messy unstructured text into organized data, speeding up tasks that would usually take a long time manually.
  3. Building a career in data science involves learning not just the technical skills but also how to navigate job opportunities and project management.

[in case you missed it] Data Science Weekly - Issue 446

0 implied HN points • 12 Jun 22
🕹 Technology Data science Machine Learning Artificial Intelligence Programming Software Development
  1. The connection between literature and AI has a long history. There are many examples of how machines have been used to create and assist in writing over the years.
  2. Jupyter Notebooks are versatile tools for data science. They can be used in surprising ways beyond just coding, mixing visualizations and markdown effectively.
  3. Understanding how to use AI responsibly is important. As AI increasingly relies on crowdworkers for data, it raises ethical questions about oversight and compliance.

[In case you missed it] Data Science Weekly - Issue 462

0 implied HN points • 02 Oct 22
🕹 Technology Data science Machine Learning Artificial Intelligence Data Visualization Programming
  1. Teaching students about scientific failure is important. It helps them understand resilience and learn from mistakes.
  2. AI systems are advancing rapidly, with new tools like video generation from text prompts. This opens up new opportunities for creators.
  3. Understanding uncertainties in deep learning is key for improving model performance. It helps practitioners make better decisions.

[in case you missed It] Data Science Weekly - Issue 440

0 implied HN points • 01 May 22
🕹 Technology AI Data science Machine Learning Programming Statistics
  1. AI is getting smarter, but we need better ways to ask it questions about its decisions to understand it better.
  2. Synthetic data can help when there's not enough real data for training, allowing us to create more examples for our models.
  3. Data accessibility is really important because unlocking the data can help solve big problems and improve society as a whole.

[in case you missed it] Data Science Weekly - Issue 460

0 implied HN points • 18 Sep 22
🕹 Technology Data science Machine Learning Artificial Intelligence Software Engineering Data Visualization
  1. Data scientists need soft skills like communication and teamwork. These skills help them work better with others and tell stories from data.
  2. There's a lot of free, live-streamed data science content available on Twitch. This makes it easier for everyone to learn and connect with the data science community.
  3. Understanding how to use AI tools for content generation can open up new creative possibilities. These tools can help enhance projects in various ways.

[in case you missed it] Data Science Weekly - Issue 405

0 implied HN points • 29 Aug 21
🕹 Technology AI Data science Machine Learning Podcasts Analytics
  1. Data teams should treat their work as products for their colleagues, focusing on collaboration to create effective solutions. This helps ensure that the end result meets the needs of those using the data.
  2. Many machine learning funds in finance fail due to common mistakes, but the few that succeed can deliver impressive results for investors. Understanding these pitfalls is key to improving success rates.
  3. OpenAI's Ilya Sutskever has been a major influence in AI, contributing to key advancements in deep learning. His work has played a big role in the evolution of intelligence in machines.

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

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.

[in case you missed it] Data Science Weekly - Issue 410

0 implied HN points • 03 Oct 21
🕹 Technology Data science Machine Learning Artificial Intelligence Statistics
  1. Data science is growing quickly, and the best companies to work for vary depending on your career stage. It's important to find a workplace that helps you grow in your data science career.
  2. Recent research is improving weather prediction by looking at short-term changes, like predicting rain in the next hour. This can be really useful for planning daily activities.
  3. Using statistics can help us understand large groups by studying small samples. It simplifies the data and gives us insights without needing to look at everything.

[in case you missed it] Data Science Weekly - Issue 411

0 implied HN points • 10 Oct 21
🕹 Technology Data science Machine Learning Artificial Intelligence Data Visualization Statistical Analysis
  1. Freelancing in data visualization can be tricky. It's important to learn from mistakes and adjust strategies for better outcomes.
  2. Combining AI with art can bring lost masterpieces back to life. Using algorithms to mimic an artist's style can recreate vibrant colors in old, damaged artworks.
  3. Building a strong data team is essential for businesses. Companies need to focus on data strategy, governance, and analytics to harness the power of data effectively.

[in case you missed it] Data Science Weekly - Issue 400

0 implied HN points • 25 Jul 21
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Data Visualization
  1. A new documentary used AI to generate Anthony Bourdain's voice, raising questions about ethics in media. It's important to think about how technology like this affects what we perceive as real.
  2. Deep learning is becoming more effective despite challenges, and understanding its success can help bridge gaps between traditional statistics and modern AI. Bigger and deeper models often yield better results, even with less data.
  3. Combining different AI models, like Transformers and convolutional neural networks, can lead to better performance in tasks like image recognition. This shows that mixing approaches can help overcome the limitations of each technology on its own.

[in case you missed It] Data Science Weekly - Issue 459

0 implied HN points • 11 Sep 22
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Data Analytics
  1. Organizations should work on improving their data quality because it directly impacts their success and competitive edge. Creating better data can lead to better decisions and outcomes.
  2. The modern data stack's activation layer is crucial for turning data into actionable insights. This allows companies to go beyond just looking at data and actually use it to improve their products and services.
  3. Using the right tools, like ONNX for model deployment, can help make machine learning models more portable and less tied to specific programming environments. This makes it easier to run models across different programming languages.

[in case you missed it] Data Science Weekly - Issue 398

0 implied HN points • 11 Jul 21
🕹 Technology Data science Machine Learning AI Programming Data Analysis
  1. Data science projects can analyze unique datasets, like personal music streaming from Apple Music, helping us understand our listening habits better.
  2. Language affects how cultures understand color, with some languages having fewer words for colors, which is interesting for studying cultural differences.
  3. Using advanced techniques like causal inference can help businesses make better pricing decisions, improving their competitiveness in the market.

[in case you missed it] Data Science Weekly - Issue 388

0 implied HN points • 02 May 21
🕹 Technology Data science Machine Learning Artificial Intelligence Data Visualization
  1. Cluster analysis can be tricky since you often don't know how many groups to create. A new method called clustergram helps visualize data better as you adjust the number of clusters.
  2. Bayesian and frequentist methods in statistics provide different types of results, so they shouldn't be compared directly. They answer different questions rather than yielding similar outputs.
  3. Netflix is working on a feature called 'Play Something' to combat decision fatigue. This feature plays a show automatically, similar to turning on a TV, making it easier for users to start watching.

[in case you missed it] Data Science Weekly - Issue 416

0 implied HN points • 14 Nov 21
🕹 Technology Machine Learning Data science Artificial Intelligence Software Engineering Data Engineering
  1. ML platforms are crucial for turning models into valuable tools, and each tech company has its own approach and tools to integrate machine learning effectively.
  2. While Kubernetes has advantages for managing data engineering, it's not always necessary and can be frustrating for engineers just wanting to help the business use data better.
  3. New large language models are emerging, making GPT-3 less unique; people are working on creating similar models that could soon be available.

[in case you missed it] Data Science Weekly - Issue 456

0 implied HN points • 21 Aug 22
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Research
  1. Machine learning models need regular maintenance. Even after they're deployed, the changing world means they require constant updates to stay effective.
  2. Specialized skills in data science can lead to better job opportunities. Understanding different roles can help you maximize your impact in the field.
  3. Learning resources for machine learning and data science are widely available. Whether through courses, videos, or discussions, there's plenty of help to get started in this exciting area.

[in case you missed it] Data Science Weekly - Issue 387

0 implied HN points • 25 Apr 21
🕹 Technology Data science Machine Learning Artificial Intelligence Computer Science Software Engineering
  1. Goodreads lets users decide what counts as a classic book, showing how the definition has changed over time. This online platform helps readers share their thoughts in various ways.
  2. Scientists are trying to decode whale language using AI, aiming to understand how these marine animals communicate. This research could reveal insights about their behavior and society.
  3. New techniques allow neural networks to solve tough equations much faster. This improvement can help us better model complex systems, making it easier for researchers and engineers.

[in case you missed it] Data Science Weekly - Issue 382

0 implied HN points • 21 Mar 21
🕹 Technology Data science AI Machine Learning Statistics Visualization
  1. Computers can't write good stories. It's a big claim, but they really don't understand literature like humans do.
  2. Using color scales is important when showing data visually. Choosing the right colors can make your data easier to understand.
  3. Data science can help fight illegal fishing with satellite data. By tracking boats, experts can prevent unlawful activities in our oceans.

[in case you missed it] Data Science Weekly - Issue 430

0 implied HN points • 20 Feb 22
🕹 Technology Data science Machine Learning AI Business Models Computing
  1. Data businesses are a big part of tech, but not enough resources explain how they work. Understanding their models can help people navigate the industry better.
  2. Investors are interested in machine learning and see many opportunities and challenges in startups. Talking to them can give insights into what they're looking for.
  3. Learning how to make data visualization easier can help you communicate better. There are ways to think about it that make the process feel more natural.