The hottest Data science Substack posts right now

And their main takeaways
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Top Technology Topics

Data Science Weekly - Issue 361

Data Science Weekly Newsletter • 19 implied HN points • 22 Oct 20
🕹 Technology Data science
  1. Modern data infrastructure is becoming crucial for businesses, as they need better ways to analyze data for value. Companies are confused about the best technologies to use.
  2. Many businesses are investing in AI, but few are actually seeing big returns on that investment. About 11% of companies report gaining significant financial benefits from AI.
  3. There are new learning techniques in AI that allow models to learn from very few examples. This could make machine learning more accessible and reduce costs.

Data Science Weekly - Issue 360

Data Science Weekly Newsletter • 19 implied HN points • 15 Oct 20
🕹 Technology Data science
  1. Improving performance on GPUs is crucial for machine learning. It helps speed up both research and development, which leads to better results overall.
  2. BMW is working on ethical guidelines for AI usage. This aims to ensure that as AI evolves, it remains focused on benefiting people.
  3. Data discovery can be a challenge for companies. Facebook built a tool called Nemo to make it easier for engineers to find the information they need quickly.

Data Science Weekly - Issue 359

Data Science Weekly Newsletter • 19 implied HN points • 08 Oct 20
🕹 Technology Data science
  1. Arduino is making machine learning easier for everyone by integrating TensorFlow Lite, which lets people run neural networks on Arduino boards to understand simple voice commands.
  2. Papers with Code is now working with arXiv to connect research papers to related code, making it easier for people to see how studies are applied in practice.
  3. Research shows that machine learning models can help automate tasks like counting craters on Mars, which saves human researchers time and effort, allowing them to focus on more complex questions.

Data Science Weekly - Issue 358

Data Science Weekly Newsletter • 19 implied HN points • 01 Oct 20
🕹 Technology Data science
  1. Data quality is very important for machine learning (ML) operations. It helps ensure that ML systems produce reliable results and builds trust with stakeholders.
  2. The State of AI Report highlights recent developments in AI, focusing on research breakthroughs, talent supply, industry applications, and future predictions.
  3. Diversity in AI and supporting applied statistics students are crucial for improving representation and effectiveness in data science and machine learning fields.
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Data Science Weekly - Issue 357

Data Science Weekly Newsletter • 19 implied HN points • 24 Sep 20
🕹 Technology Data science
  1. Good communication techniques are key for data and engineering teams to solve technical problems effectively. By improving how they express ideas, teams can reach better solutions faster.
  2. Competitions like the C3.ai COVID-19 Grand Challenge encourage teams to use data science for social good. It's a great chance to make a positive impact during tough times by tackling significant challenges like the pandemic.
  3. New tools like TensorFlow Recommenders make it easier for people to build and serve recommendation models. These tools help users get personalized suggestions for things like movies and restaurants quickly.

Data Science Weekly - Issue 356

Data Science Weekly Newsletter • 19 implied HN points • 17 Sep 20
🕹 Technology Data science
  1. ICML is an important conference for those in machine learning, catering to various professionals like researchers and engineers. It's a great place to learn and share knowledge about advancements in the field.
  2. NumPy is a key tool for scientific programming in Python, helping organize and analyze data efficiently. It's widely used and supports various other libraries for data science tasks.
  3. The emergence of generative AI technology is changing the entertainment industry rapidly. Soon, creating movies or shows could be done at a fraction of today's production costs.

Data Science Weekly - Issue 355

Data Science Weekly Newsletter • 19 implied HN points • 10 Sep 20
🕹 Technology Data science
  1. DeepMind and Google Maps are using advanced Graph Neural Networks to improve the accuracy of travel time predictions, making them even more reliable in cities around the world.
  2. AI is now being used to detect deepfake videos by identifying unique signals from the videos, which can help spot how they were made.
  3. There are resources available to help people get started in data science, build their portfolios, and improve their resumes to land jobs in this field.

Smarter Agents, Self-Aware LLMs, and Knowledge from Videos

HackerPulse Dispatch • 2 implied HN points • 24 Jan 25
🕹 Technology Data science
  1. New techniques can shrink the size of data storage without losing accuracy, which helps in finding information faster.
  2. Language models are getting better at learning from their own mistakes, making them smarter and more self-aware.
  3. AI can now learn complex skills just by watching videos, which shows that reading text isn't always necessary for advanced learning.

Data Science Weekly - Issue 354

Data Science Weekly Newsletter • 19 implied HN points • 03 Sep 20
🕹 Technology Data science
  1. A machine learning algorithm recently helped discover 50 new planets from old NASA data, showing how AI can unlock new discoveries.
  2. There has been a noticeable drop in deep learning job postings in the past six months, revealing that many companies are reassessing the importance of this technology.
  3. Apple has introduced a residency program for AI and machine learning, offering training and hands-on experience for those with relevant backgrounds.

Data Science Weekly - Issue 353

Data Science Weekly Newsletter • 19 implied HN points • 27 Aug 20
🕹 Technology Data science
  1. Effective testing is crucial for machine learning systems. It's important to understand that these systems require different testing strategies compared to traditional software.
  2. There are hidden challenges in becoming a machine learning engineer. Many of these insights come from the experiences of those already in the field, beyond what you learn in books.
  3. New resources and courses are constantly being developed in data science. For example, fast.ai just released a new deep learning course and libraries, which can help beginners get started.

Data Science Weekly - Issue 352

Data Science Weekly Newsletter • 19 implied HN points • 20 Aug 20
🕹 Technology Data science
  1. minGPT is a smaller version of the GPT model that aims to be simple and easy to understand. It’s only about 300 lines of code, which makes it a good resource for learning.
  2. Biased training data, like the CoNLL-2003 dataset, can lead AI models to perform poorly on diverse names and future data. This can cause ongoing issues with how these models recognize different groups.
  3. Reinforcement learning has challenges in real-world applications due to assumptions that often don't hold up. Researchers need to address these challenges to make RL more practical and effective.

Data Science Weekly - Issue 351

Data Science Weekly Newsletter • 19 implied HN points • 13 Aug 20
🕹 Technology Data science
  1. Machine learning models need regular maintenance after deployment. It's important to monitor data and model behavior to avoid problems and improve performance.
  2. Collaboration and good understanding of problems are key in AI development. This helps teams create better applications and make profits.
  3. New tools and resources are becoming available for data science, like access to research papers on Kaggle. These can help improve machine learning techniques and open up new possibilities.

Data Science Weekly - Issue 350

Data Science Weekly Newsletter • 19 implied HN points • 06 Aug 20
🕹 Technology Data science
  1. Language models like GPT-3 can do amazing things, such as creating human-like text and writing code, but there's still curiosity about their ability to make analogies.
  2. Data science is increasingly being applied to many fields, like health through biomedical NLP or analyzing complex problems with graph technologies.
  3. As companies build their data tools, there’s a trend toward developing unique solutions tailored to their specific needs, highlighting the importance of data discovery.

Data Science Weekly - Issue 349

Data Science Weekly Newsletter • 19 implied HN points • 30 Jul 20
🕹 Technology Data science
  1. Deep learning has important ideas that have been around for a while. If you're new to it, learning these basics can really help you understand current research.
  2. GPT-3 is creating a lot of buzz, and it's important to think critically about the hype. Understanding the difference between hype and reality helps us navigate new technologies better.
  3. Evaluating machine learning models is similar to testing software. New methods can help us better assess how well these models work, which is key to making them reliable.

Data Science Weekly - Issue 348

Data Science Weekly Newsletter • 19 implied HN points • 23 Jul 20
🕹 Technology Data science
  1. Deep Learning papers can be confusing for beginners, but there's a roadmap to help you choose where to start. It's a good way to navigate through the vast amount of research out there.
  2. Machine Learning is creating a lot of value for businesses, and it's important to understand how this value can be captured. Different companies are finding unique ways to apply ML for their needs.
  3. New techniques in AI, like using neural networks for soundscapes, are not just tech innovations but can also help protect the environment. It shows how technology can contribute to nature conservation.

Data Science Weekly - Issue 347

Data Science Weekly Newsletter • 19 implied HN points • 16 Jul 20
🕹 Technology Data science
  1. Netflix is working on making its data usage more efficient. They have created a dashboard that helps their team understand data costs and trends better.
  2. Using meta-augmentation in machine learning can improve performance more than just changing the model. It's important to focus on enhancing the data we use.
  3. When building robots, the goal should be to assist humans, not replace them. This approach considers the future of robotics in various fields like transportation and healthcare.

Data Science Weekly - Issue 346

Data Science Weekly Newsletter • 19 implied HN points • 09 Jul 20
🕹 Technology Data science
  1. AI training costs are dropping much faster than usual, which means AI technology is becoming easier and cheaper to develop. This could lead to more companies using AI over the next decade.
  2. Training Generative Adversarial Networks (GANs) can be tough, but there are new algorithms that help make it more stable and efficient. This is important for many applications in science and engineering.
  3. Moving from traditional statistics to machine learning involves a different way of thinking. Understanding this shift can help those with a stats background adapt and excel in machine learning.

Data Science Weekly - Issue 345

Data Science Weekly Newsletter • 19 implied HN points • 02 Jul 20
🕹 Technology Data science
  1. Making machine learning useful in real life is a key focus for companies like startups, especially when they provide machine learning as a service.
  2. Documentation is important in machine learning to explain how models work and to clarify their intended use, which helps avoid misuse.
  3. There are ongoing discussions about improving the machine learning community, addressing issues like toxicity, fairness, and the peer-review process.

Data Science Weekly - Issue 344

Data Science Weekly Newsletter • 19 implied HN points • 25 Jun 20
🕹 Technology Data science
  1. As AI systems become more common, it’s important to think about who is responsible when things go wrong. Recent incidents raise questions about how to share accountability between people, companies, and governments.
  2. Scientists are learning more about years of small earthquakes in California, and they found that fluids moving through the ground might have caused them. This shows how understanding these events can help with studying earthquakes around the world.
  3. There are many tools for machine learning, but the landscape is still developing. A study looked at over 200 tools to find out what works best and what challenges people face when using them.

Data Science Weekly - Issue 343

Data Science Weekly Newsletter • 19 implied HN points • 18 Jun 20
🕹 Technology Data science
  1. AI models can now generate images just like they generate text, thanks to advanced training methods. This shows how powerful these technologies have become in creating complex visuals.
  2. MLOps is key for data scientists as it helps them work together better by automating tasks like testing and versioning. This makes their processes smoother and more efficient.
  3. Regulating algorithms is important because they influence many aspects of our lives without any oversight. A new system is needed to ensure they are used fairly and responsibly.

Data Science Weekly - Issue 342

Data Science Weekly Newsletter • 19 implied HN points • 11 Jun 20
🕹 Technology Data science
  1. Recent studies show that there hasn't been a significant change in the types of jobs that get automated, despite the rise of new technology. It seems that many jobs remain unaffected by automation trends.
  2. Tools like OpenAI's API allow easy integration of advanced language tasks without needing extensive data. This makes it simpler for developers to use powerful language models.
  3. Feature engineering and managing technical debt are crucial in machine learning development. Good practices can help to avoid messy code and ensure smoother transitions from development to production.

Data Science Weekly - Issue 341

Data Science Weekly Newsletter • 19 implied HN points • 04 Jun 20
🕹 Technology Data science
  1. Mathematics often requires new methods to solve problems, showing how innovation is crucial in research.
  2. GPT-3 is a massive language model that significantly improves deep learning and natural language processing capabilities.
  3. Many people find data science jobs disappointing, and it's important to manage your expectations in any job field.

Data Science Weekly - Issue 340

Data Science Weekly Newsletter • 19 implied HN points • 28 May 20
🕹 Technology Data science
  1. AI can be limited in business because of how it's researched, but understanding these limits can help identify new business opportunities. This means knowing the business process well can lead to better use of AI to save time and money.
  2. There's a growing belief that humans and machines should work together rather than striving for complete automation. Collaborating with machines can often be more effective and safer than going fully automated.
  3. Basic machine learning skills are still very important, even with all the focus on deep learning. Many companies want solid foundational knowledge rather than just the latest trends, so understanding the basics can be key to success.

How Data Scientists can sell "Tech Debt" to their managers?

Laszlo’s Newsletter • 5 implied HN points • 04 Mar 24
🕹 Technology Data science
  1. Bad code wastes company resources by making the team spend more time on fixes. Refactoring can free up time for working on features.
  2. Mildly bad code slows down new feature delivery. Refactoring can make the team faster and deliver more features in less time.
  3. In data science projects, data quality issues can lead to excessive time spent on bug fixing, emphasizing the need for refactoring to increase efficiency.

Data Science Weekly - Issue 339

Data Science Weekly Newsletter • 19 implied HN points • 21 May 20
🕹 Technology Data science
  1. AI Product Managers need special skills for managing AI products beyond traditional project management. This includes an understanding of machine learning and its real-world applications.
  2. Technical debt in machine learning is important to manage to avoid problems later. New tools can help address this issue, highlighting the need for staying updated over time.
  3. China is actively discussing AI ethics, contrary to popular belief. Their conversations align with global standards, and they are exploring how these principles fit into their own culture and systems.

Data Science Weekly - Issue 338

Data Science Weekly Newsletter • 19 implied HN points • 14 May 20
🕹 Technology Data science
  1. AR and machine learning can be combined to create cool tools, like cutting parts of our surroundings and pasting them into images.
  2. Mapping the connections in the human brain can help scientists understand how our brains work and what happens when they are not healthy.
  3. Data shows that during quarantine, people are not necessarily gaining weight or losing activity, which might surprise some people.

Data Science Weekly - Issue 337

Data Science Weekly Newsletter • 19 implied HN points • 07 May 20
🕹 Technology Data science
  1. Data scientists are in high demand, and job opportunities can be found on platforms like Vettery. It's a good time to consider a career change or advance in this field.
  2. Regularization in linear models is important and can be understood visually. Simple explanations can help grasp how these techniques improve model performance.
  3. Freelancing as a data scientist can be rewarding and productive. Many people share their experiences to help others understand what it's like to work independently.

Smarter Learning Rates, Medical AI, and Compiler Innovations

HackerPulse Dispatch • 2 implied HN points • 20 Dec 24
🕹 Technology Data science
  1. New learning rate techniques, like SGD-SaI, are making AI training more efficient and using less memory. This means large models can learn better and faster.
  2. AI is showing amazing skills in medical tasks, sometimes even better than doctors, but it still has some limitations in certain areas.
  3. There are advancements in AI compilers that help optimize how programs run, making them more efficient. This is important for developing smarter AI systems.

Data Science Weekly - Issue 336

Data Science Weekly Newsletter • 19 implied HN points • 30 Apr 20
🕹 Technology Data science
  1. Tornado plots are a unique way to visualize time series data, showing how values change over time. They help us understand trends in a different way than regular graphs.
  2. Categorizing diverse products efficiently is crucial for platforms like Shopify. Proper categorization helps users find similar products faster, making shopping easier.
  3. Blender is an open-source chatbot by Facebook AI that feels more human and engages users better. It's a leap forward for conversational AI technology.

Data Science Weekly - Issue 335

Data Science Weekly Newsletter • 19 implied HN points • 23 Apr 20
🕹 Technology Data science
  1. Specification gaming is when AI follows rules exactly but misses the main goal. It's important to design AIs that understand the true purpose of their tasks.
  2. There's a growing need to improve how deep learning studies are reported in healthcare. This helps ensure that new AI tools are effective and trustworthy for patients.
  3. Bias in AI language models, like Google Translate, can reflect societal issues. Efforts are being made to address these biases for fairer translations.

Data Science Weekly - Issue 334

Data Science Weekly Newsletter • 19 implied HN points • 16 Apr 20
🕹 Technology Data science
  1. Understanding the risks of SARS-CoV-2 is important. We might see continued outbreaks in winter unless we keep social distancing measures.
  2. There's a strong need for AI systems to be understandable. As complex algorithms are used more, we must ensure they are explainable to avoid issues.
  3. Using data science can help improve how we find live music events. By analyzing music data, we can suggest shows that fit users' tastes.

Data Science Weekly - Issue 333

Data Science Weekly Newsletter • 19 implied HN points • 09 Apr 20
🕹 Technology Data science
  1. Data science roles often don't meet expectations due to issues like unclear job roles and lack of leadership.
  2. Monitoring machine learning models in production is complex and requires careful strategies to ensure effectiveness.
  3. Best practices in time series forecasting help improve the accuracy of predictions by utilizing advanced algorithms and example-driven approaches.

Data Science Weekly - Issue 332

Data Science Weekly Newsletter • 19 implied HN points • 02 Apr 20
🕹 Technology Data science
  1. Agent57 is a new deep learning agent that can beat human scores in all Atari games. It's a big step forward in how we measure AI performance.
  2. During the COVID-19 crisis, it's important to approach data honestly and with curiosity. This helps individuals responsibly discuss topics outside their expertise.
  3. ACM is offering free access to their digital library to support research and learning during the pandemic. This allows more people to access valuable computing resources.

Data Science Weekly - Issue 331

Data Science Weekly Newsletter • 19 implied HN points • 26 Mar 20
🕹 Technology Data science
  1. The AI field has a serious gender imbalance that can lead to inequalities in AI systems. It's important to address this issue to avoid harming underrepresented groups.
  2. Remote work can be tough for data science teams due to challenges in communication and feelings of isolation. It's crucial to create effective systems to keep the team engaged and productive.
  3. New data-sharing approaches, like HealthMap for coronavirus monitoring, can greatly enhance our ability to respond to public health crises. This represents a shift in how we collect and share important data.

Data Science Weekly - Issue 330

Data Science Weekly Newsletter • 19 implied HN points • 19 Mar 20
🕹 Technology Data science
  1. COVID-19 spreads very quickly, especially without measures to control it. Understanding how outbreaks work can help people take action sooner.
  2. Data and models are essential to understanding how COVID-19 will affect local areas. People should act decisively based on available information.
  3. New tools and research in data science are helping track and analyze the impact of COVID-19. These resources are making it easier to study and respond to the pandemic.

Data Science Weekly - Issue 329

Data Science Weekly Newsletter • 19 implied HN points • 12 Mar 20
🕹 Technology Data science
  1. Google has developed a new shoe insole that uses machine learning to analyze soccer players' movements, helping them improve their game in real-time.
  2. Human-in-the-Loop Machine Learning is beneficial in many ways, such as avoiding bias, maintaining accuracy, and making processes easier and safer by involving humans in decision-making.
  3. Reinforcement learning is being explored to optimize trading strategies and financial concepts, showcasing its ability to learn and adapt in complex environments.

Data Science Weekly - Issue 328

Data Science Weekly Newsletter • 19 implied HN points • 05 Mar 20
🕹 Technology Data science
  1. The brain is not like a computer. Many scientists believe we might be misunderstanding how our brains work by using this comparison.
  2. BERT models are widely used in language processing, but we still need to learn more about how they really function.
  3. Understanding machine learning doesn't have to be complicated. There are resources that explain it in simple terms with practical examples for everyone.

AI Risks and Reverse Reasoning

ppdispatch • 2 implied HN points • 06 Dec 24
🕹 Technology Data science
  1. ReVersion is a cool new way to create images that keep specific object relationships, allowing more creative flexibility.
  2. There's a growing concern about malicious AI, and strategies are being developed to protect against potential security threats.
  3. WebAssembly is enhancing AR and VR technology by making it faster and more compatible across different devices.