The hottest Data science Substack posts right now

And their main takeaways
Category
Top Technology Topics

Data Science Weekly - Issue 258

Data Science Weekly Newsletter • 19 implied HN points • 01 Nov 18
🕹 Technology Data science
  1. Reinforcement learning agents can now explore better with curiosity-driven methods, helping them perform beyond human levels in certain games.
  2. Machines can simulate dreaming by recognizing patterns like the human brain, allowing them to create unique visual outputs without direct input.
  3. Choosing the right data science projects is crucial; a good strategy can lead to valuable results while a poor one may just waste resources.

Data Science Weekly - Issue 257

Data Science Weekly Newsletter • 19 implied HN points • 25 Oct 18
🕹 Technology Data science
  1. Neural networks can help create fun and unique Halloween costumes. Using AI for creative tasks can lead to new ideas we might not think of ourselves.
  2. Uber processes massive amounts of data very quickly, showing how big data can improve services and make operations smoother. Their platform manages over 100 petabytes of information.
  3. Learning data science can be made easier with mentorship and flexible payment options. Programs like Springboard's help you get job-ready skills while supporting your career journey.

Data Science Weekly - Issue 256

Data Science Weekly Newsletter • 19 implied HN points • 18 Oct 18
🕹 Technology Data science
  1. The Big Mac Index, which used to be calculated manually, is now done using the R programming language. This change promotes transparency in how data is gathered and shared in journalism.
  2. Compression might become a key application for machine learning on devices like phones. Many people are surprised to learn that it can significantly improve performance in this area.
  3. There is a growing trend of AI chatbots providing medical advice, which raises questions about their effectiveness compared to human doctors.

How I manage information

Perambulations • 3 implied HN points • 06 Feb 24
🕹 Technology Data science
  1. Managing knowledge and information is important for staying organized and productive.
  2. Separating the tasks of finding and reading articles can improve focus and efficiency.
  3. Using parsimonious and opinionated tools for knowledge management can help maintain focus and avoid overwhelm.
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Data Science Weekly - Issue 255

Data Science Weekly Newsletter • 19 implied HN points • 11 Oct 18
🕹 Technology Data science
  1. The ML Engineering Loop helps engineers improve their model development by following a cycle of analyzing, selecting approaches, implementing, and measuring. This cycle allows them to quickly find the best solutions.
  2. Understanding uncertainty in data visualizations is important, and integrating uncertainty estimates can improve how we interpret plots and models. This can lead to better decision-making based on data.
  3. Using tools like TensorFlow.js for practical applications, such as object recognition in games, shows how machine learning can be fun and engaging. These examples help in learning and applying complex concepts in a creative way.

Data Science Weekly - Issue 254

Data Science Weekly Newsletter • 19 implied HN points • 04 Oct 18
🕹 Technology Data science
  1. You can calculate the age of the universe using SQL to analyze data from various databases. It's easier than it sounds and can lead to interesting insights.
  2. Training deep learning models on phones and other small devices is now possible but still challenging. There are teams making it work, but the tools available aren't very user-friendly yet.
  3. Big data is starting to change genetic research a lot. New techniques are creating huge amounts of data, which helps scientists discover new things but also keeps them busy trying to catch up.

Data Science Weekly - Issue 253

Data Science Weekly Newsletter • 19 implied HN points • 27 Sep 18
🕹 Technology Data science
  1. Uber uses forecasting with machine learning and deep learning to enhance its products and services. This means they can predict customer needs better and improve their offerings based on accurate data.
  2. Deep learning is changing software development by requiring fewer lines of code. Instead of writing complicated rules, developers set a foundation and let the system learn from examples.
  3. AI is being influenced by how we sense smell, leading to advancements in both biology and technology. Understanding chemical information can help create more sophisticated AI systems.

Data Science Weekly - Issue 252

Data Science Weekly Newsletter • 19 implied HN points • 20 Sep 18
🕹 Technology Data science
  1. A team found a surprising pattern in prime numbers, linking them to natural crystal patterns. This challenges the idea that prime numbers are completely random.
  2. DeepMind's AI is being used in Android Pie to help improve battery life, showing how AI can impact everyday technology. It's interesting to see if this actually makes a difference for users.
  3. Transfer learning makes it easier to solve problems by using knowledge from similar tasks. This approach saves time and resources in the field of deep learning.

What Is AGI?

Gradient Ascendant • 1 implied HN point • 20 Jan 25
🕹 Technology Data science
  1. There are many definitions of AGI, but they can be quite different from each other. It's important to recognize that people might be talking about different things when they mention AGI.
  2. AGI isn't just about intelligence; it's also about capabilities and outcomes. The effectiveness of AI solutions can be more important than how closely they mimic human thinking.
  3. A practical way to define AGI is by comparing the economic performance of AI to human workers. This approach focuses on measurable results rather than vague qualities of intelligence.

Data Science Weekly - Issue 251

Data Science Weekly Newsletter • 19 implied HN points • 13 Sep 18
🕹 Technology Data science
  1. AI systems, like Amazon's Echo, rely on many factors, including resources and labor. Understanding these can give insights into the complexity of AI.
  2. Fake news can significantly impact politics, and there's now a mathematical model to help simulate how it influences voting. This shows the power of accurate models in understanding societal effects.
  3. There are new tools and techniques in machine learning that make it easier to analyze and improve models. Resources like the 'What-If' tool let users explore machine learning without needing to code.

May Newsletter

RSS DS+AI Section • 5 implied HN points • 01 May 23
🕹 Technology Data science
  1. The May newsletter contains updates on data science and AI developments, including information on the Royal Statistical Society's activities.
  2. There is a focus on ethics, bias, and diversity in data science, along with concerns about AI model safety and regulatory challenges.
  3. Generative AI remains a hot topic, with discussions on training models, practical applications, and real-world impact of AI in healthcare, design, and storytelling.

Data Science Weekly - Issue 250

Data Science Weekly Newsletter • 19 implied HN points • 06 Sep 18
🕹 Technology Data science
  1. There's a growing need for data scientists in the U.S. now that there's a shortage, which is a big change from just a few years ago when there were too many people in the field.
  2. New approaches in machine learning, like unsupervised machine translation, are making it easier to provide fast and accurate translations in many languages, helping people connect better.
  3. Researchers are looking into how small changes in images can confuse computer vision models, and they wonder if the same happens to humans, pointing out potential vulnerabilities in both AI and human vision.

Data Science Weekly - Issue 249

Data Science Weekly Newsletter • 19 implied HN points • 30 Aug 18
🕹 Technology Data science
  1. Netflix is using notebooks for development and collaboration, helping manage many scheduled jobs more effectively.
  2. Understanding the world in 3D is challenging, especially for extending successful technologies like convolutional networks.
  3. There's a creative idea to enhance shopping experiences for color-blind clients by pairing their selections with personalized music.

Data Science Weekly - Issue 248

Data Science Weekly Newsletter • 19 implied HN points • 23 Aug 18
🕹 Technology Data science
  1. AI is changing how we do business, and it's becoming more self-sufficient, meaning it could improve processes on its own without needing human input.
  2. China uses data and AI extensively for surveillance and governance, which raises questions about the balance between democracy and data-driven control.
  3. New tools and technologies are constantly emerging in data science, such as those that help improve the speed of medical procedures like MRIs and enhance gaming graphics.

Data Science Weekly - Issue 247

Data Science Weekly Newsletter • 19 implied HN points • 16 Aug 18
🕹 Technology Data science
  1. Data science is an evolving field, and experts suggest there's still much to learn and improve upon.
  2. New tools and resources, like machine learning platforms, can help workers identify skills they need to develop.
  3. Collaboration between industry and academia can drive more innovation in artificial intelligence.

Data Science Weekly - Issue 246

Data Science Weekly Newsletter • 19 implied HN points • 09 Aug 18
🕹 Technology Data science
  1. Balancing quick changes and long-term planning is tough in data science, and it's important to find ways to adapt without losing sight of the bigger picture.
  2. Coca-Cola successfully used advanced technology like TensorFlow for its marketing efforts, showcasing how big companies can leverage data science for effective campaigns.
  3. Automated machine learning tools, like AutoKeras, help people without deep technical skills to use powerful machine learning models easily.

Data Science Weekly - Issue 245

Data Science Weekly Newsletter • 19 implied HN points • 02 Aug 18
🕹 Technology Data science
  1. Hiring the right people is crucial for data science teams. Companies should look for candidates who can work independently and fit well with the team culture.
  2. Understanding uncertainty in models is important. This helps in interpreting results and debugging any issues that arise in data science projects.
  3. Learning resources are abundant in data science. There are many tools and tutorials available to help beginners and advanced users improve their skills.

Data Science Weekly - Issue 244

Data Science Weekly Newsletter • 19 implied HN points • 26 Jul 18
🕹 Technology Data science
  1. Companies should define data science roles using three tracks: Analytics, Inference, and Algorithms. This helps meet business needs more effectively.
  2. Google's AutoML is a tool that automates machine learning processes, tapping into transfer learning to enhance capabilities and ease of use.
  3. Multi-task learning allows machines to learn multiple tasks at once, making them smarter and better at handling complex problems, similar to how humans learn.

Data Science Weekly - Issue 243

Data Science Weekly Newsletter • 19 implied HN points • 19 Jul 18
🕹 Technology Data science
  1. AI might be able to replace some animal testing by predicting chemical toxicity. This could make testing faster and more ethical.
  2. Understanding what machine learning practitioners do is key to improving their training and tools. This could help more people get into the field of machine learning.
  3. The Netflix workshop highlighted that traditional recommendation methods might be outdated. New techniques are needed to keep up with changing user preferences.

Data Science Weekly - Issue 242

Data Science Weekly Newsletter • 19 implied HN points • 12 Jul 18
🕹 Technology Data science
  1. There's a big focus on how artificial intelligence has evolved in the past year, with many players in the market and new trends shaping its future.
  2. Understanding the difference in approaches to machine learning is crucial for businesses, as many struggle when they don't recognize the distinctions.
  3. New methods in machine learning, like generating detailed ground views from satellite images, show how technology can create innovative solutions to complex problems.

April Newsletter

RSS DS+AI Section • 5 implied HN points • 01 Apr 23
🕹 Technology Data science
  1. Ethical considerations are crucial in Data Science, especially with the rise of generative AI and potential biases.
  2. Research in Data Science is focused on developing large language models and improving their applications.
  3. Practical tips and deep dives into different data science techniques offer valuable learning opportunities.

Data Science Weekly - Issue 240

Data Science Weekly Newsletter • 19 implied HN points • 28 Jun 18
🕹 Technology Data science
  1. AI has become very powerful, even beating expert humans in complex games like Dota 2. This shows how quickly technology is advancing.
  2. Data science can play a meaningful role in addressing social issues, like the problem of public human waste in cities. Mixing social science with data could lead to helpful solutions.
  3. Building a data dictionary is crucial for teams, as it helps clarify key terms and metrics. This can greatly improve communication and reduce confusion within a business.

Data Science Weekly - Issue 239

Data Science Weekly Newsletter • 19 implied HN points • 21 Jun 18
🕹 Technology Data science
  1. AI can win arguments, but it doesn't actually understand what it's saying. This highlights the difference between human reasoning and machine processing.
  2. Researchers are working hard to make sure algorithms are fair and unbiased. This is important as more decisions are made by machines in our everyday lives.
  3. AI and robotics are making a big impact on healthcare. Experts believe they will transform how we treat and manage health issues in the future.

Data Science Weekly - Issue 238

Data Science Weekly Newsletter • 19 implied HN points • 14 Jun 18
🕹 Technology Data science
  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.

Data Science Weekly - Issue 237

Data Science Weekly Newsletter • 19 implied HN points • 07 Jun 18
🕹 Technology Data science
  1. Understanding how the human brain works can improve our grasp of complex environments. This knowledge helps in both neuroscience and technology applications.
  2. The future job landscape will involve more collaboration between humans and machines. Companies need to prepare for a mix of human and automated roles.
  3. Deep learning techniques are evolving, especially in object detection. Innovations in this field show how minor adjustments can lead to significant improvements in performance.

Data Science Weekly - Issue 236

Data Science Weekly Newsletter • 19 implied HN points • 01 Jun 18
🕹 Technology Data science
  1. Improving training data is really important for making machine learning models work well. Focusing on data quality can lead to better results than just tweaking the model itself.
  2. AI tools are making a big difference in healthcare, like the one approved for detecting wrist fractures. These technologies can help doctors diagnose patients more accurately.
  3. Google found that some tricky interview questions didn't actually help in hiring good candidates. It shows that being smart isn't just about solving brainteasers.

Data Science Weekly - Issue 236

Data Science Weekly Newsletter • 19 implied HN points • 31 May 18
🕹 Technology Data science
  1. Natural disasters like Hurricane Maria can have serious health impacts, and it's hard to get an accurate death count afterward.
  2. Improving training data is key to making better machine learning models, and there are practical ways to enhance that data.
  3. Reproducibility in machine learning is important, but it can be tough to achieve and often requires careful planning and work.

Data Science Weekly - Issue 235

Data Science Weekly Newsletter • 19 implied HN points • 24 May 18
🕹 Technology Data science
  1. Deep learning models are making it easier to categorize images, like those used in Airbnb listings.
  2. New research suggests that the brain may store information in a discrete way, which could change our understanding of brain and technology interactions.
  3. There are many resources available for learning data science, including online programs and tutorials that cover various tools and techniques.

Data Science Weekly - Issue 234

Data Science Weekly Newsletter • 19 implied HN points • 17 May 18
🕹 Technology Data science
  1. Teaching AI about cause and effect can help make it smarter and more intelligent. Understanding the 'why' behind actions is crucial for progress.
  2. Self-driving technology is advancing, as seen with MIT's new car that can drive on roads it has never seen before using basic GPS and sensors.
  3. There are resources available to help people start a career in data science, including guides on building a portfolio and creating a standout resume.

Data Science Weekly - Issue 233

Data Science Weekly Newsletter • 19 implied HN points • 10 May 18
🕹 Technology Data science
  1. AI systems can learn from each other by arguing, which might help us understand their behavior better.
  2. In the future, machine learning tools may interact with us more like pets than machines, creating a collaborative experience.
  3. Despite powerful tech companies, skilled programmers can still outperform them in certain AI tasks, showing the value of human creativity.

Data Science Weekly - Issue 232

Data Science Weekly Newsletter • 19 implied HN points • 04 May 18
🕹 Technology Data science
  1. Google's Teachable Machine helps people understand how to make machine learning models easier to use.
  2. Data science in startups needs strong processes for analyzing data and experimenting with models, especially when building from scratch.
  3. There's a powerful method for deep learning that works well with tabular data, and it's starting to be used by many big companies.

Data Science Weekly - Issue 232

Data Science Weekly Newsletter • 19 implied HN points • 03 May 18
🕹 Technology Data science
  1. Using machine learning can be made easier and more accessible through tools like Google's Teachable Machine, which provides useful UX insights.
  2. Deep learning techniques are being adapted for different types of data, including enhancing performance in models working with tabular data.
  3. Focusing on good data practices and proper processes is key for startups looking to build a strong data science platform.

Data Science Weekly - Issue 231

Data Science Weekly Newsletter • 19 implied HN points • 26 Apr 18
🕹 Technology Data science
  1. The efficiency of the human brain surpasses AI due to its ability for massive parallel processing, which is an interesting aspect of studying intelligence.
  2. Using qualitative methods in data science projects can lead to better outcomes by ensuring crucial features are not overlooked before jumping into data analysis.
  3. There are ongoing debates about the reliability of p-values in statistical testing, and some researchers are reconsidering their use in studies.

Data Science Weekly - Issue 230

Data Science Weekly Newsletter • 19 implied HN points • 19 Apr 18
🕹 Technology Data science
  1. You can learn how to become a data scientist with specific guides focused on gaps in knowledge, portfolio building, and resume writing.
  2. There are fun projects in AI, like training models to recognize dogs or create cartoons, showing how diverse applications of data science can be.
  3. Bias in machine learning models is a big issue, and it's important to understand how these biases can affect results in various tasks.

Data Science Weekly - Issue 229

Data Science Weekly Newsletter • 19 implied HN points • 12 Apr 18
🕹 Technology Data science
  1. Using mathematical methods like Markov Decision Processes can help find the best strategies to play games like 2048.
  2. Uber AI Labs has introduced a technique called differentiable plasticity, which allows AI to adapt and learn better over time.
  3. Automating canary analysis, as done by Netflix with their Kayenta platform, can improve testing of new software changes quickly and efficiently.

Data Science Weekly - Issue 228

Data Science Weekly Newsletter • 19 implied HN points • 05 Apr 18
🕹 Technology Data science
  1. Using just $1 of hardware, you can turn a MacBook into a touchscreen with some clever computer vision. It shows how innovative ideas can come from simple solutions.
  2. There's a debate about whether we need new programming languages specifically for machine learning. Current languages are being adapted, but new ones might be better suited for future AI developments.
  3. The NIH is pushing to use data science and AI to improve healthcare initiatives. They’re looking for public input to create a strategy around data science in health and research.