The hottest Data science Substack posts right now

And their main takeaways
Category
Top Technology Topics

[in case you missed it] Data Science Weekly - Issue 345

Data Science Weekly Newsletter • 0 implied HN points • 05 Jul 20
🕹 Technology Data science
  1. Machine learning is becoming more practical and useful in real-world applications. It's important to focus on making this technology work effectively for various industries.
  2. AI is a fast-evolving field with many developments happening globally, and discussions about its future are crucial for guiding its ethical use and advancements.
  3. Transparency in machine learning models is essential. Providing clear documentation about how they work helps ensure they are used correctly and responsibly.

[in case you missed it] Data Science Weekly - Issue 344

Data Science Weekly Newsletter • 0 implied HN points • 28 Jun 20
🕹 Technology Data science
  1. As AI and autonomous systems grow, figuring out who is responsible for problems is important. We need to think about who is accountable when things go wrong.
  2. Scientists discovered that a long earthquake swarm was likely caused by natural fluids in the earth. This finding shows how detailed studies can help us understand complex natural events.
  3. The landscape of machine learning tools is extensive but still developing. A recent analysis of over 200 tools revealed both challenges and opportunities for those in the field.

[in case you missed it] Data Science Weekly - Issue 343

Data Science Weekly Newsletter • 0 implied HN points • 21 Jun 20
🕹 Technology Data science
  1. Image GPT can create images just like large language models create text. This means we can now generate detailed images by understanding pixel patterns.
  2. MLOps helps data scientists work better together by automating tasks like testing and version control. This makes it easier to manage machine learning projects.
  3. There is no proper regulation for algorithms that affect our daily lives. A group of citizens should help oversee how these algorithms are used to ensure fairness and accountability.

[in case you missed it] Data Science Weekly - Issue 342

Data Science Weekly Newsletter • 0 implied HN points • 14 Jun 20
🕹 Technology Data science
  1. There hasn't been a significant recent change in job automation from 1999 to 2019 in the U.S., even with new technology. Many jobs haven't become more automated, and pay rates for these jobs haven't really changed either.
  2. OpenAI offers an API that anyone can use for various language tasks. It allows users to perform tasks like translation and sentiment analysis without needing much prior knowledge.
  3. Managing technical debt in machine learning is important because many new data scientists don't learn best practices. This can lead to messy code that is hard to put into production, wasting time and resources.

[in case you missed it] Data Science Weekly - Issue 341

Data Science Weekly Newsletter • 0 implied HN points • 07 Jun 20
🕹 Technology Data science
  1. Data scientists are in high demand, so it's important to know how to market yourself effectively. Building a strong project portfolio can make you an attractive candidate.
  2. Recent advancements in language models, like GPT-3, show that larger models can perform tasks with fewer examples. This could change how we approach natural language processing.
  3. Managing expectations in data science jobs is crucial, especially for newcomers. Many people feel disappointed because they might not understand the job's realities.
Get a weekly roundup of the best Substack posts, by hacker news affinity:

[in case you missed it] Data Science Weekly - Issue 340

Data Science Weekly Newsletter • 0 implied HN points • 31 May 20
🕹 Technology Data science
  1. AI has some issues that limit its usefulness in businesses. By understanding these problems, businesses can find ways to effectively use AI and even save money.
  2. Human and machine cooperation is essential, and fully automating processes might not be the best approach. We should find ways for machines and people to work better together.
  3. Learning about basic machine learning models is still very important. Many companies don't need advanced techniques, so knowing the basics can help you in real-world jobs.

[in case you missed it] Data Science Weekly - Issue 339

Data Science Weekly Newsletter • 0 implied HN points • 24 May 20
🕹 Technology Data science
  1. AI Product Managers need both traditional PM skills and a strong understanding of machine learning development. This blend of skills helps them manage AI projects effectively.
  2. Machine learning systems can face risks, including misalignment with problems and unexpected behaviors after deployment. It's important to evaluate these risks to avoid project failures.
  3. Text data augmentation is not as common as image data augmentation, but it can be useful in natural language processing. Exploring new techniques for text augmentation can enhance performance.

[in case you missed it] Data Science Weekly - Issue 338

Data Science Weekly Newsletter • 0 implied HN points • 17 May 20
🕹 Technology Data science
  1. AR and AI can merge to create tools for editing images by cutting and pasting elements from our surroundings. This could revolutionize how we visually manipulate content.
  2. Researchers are working on mapping the human brain's connections to better understand how it functions and what happens when it gets sick. This could lead to major breakthroughs in neuroscience.
  3. Active learning techniques in AI can make label management easier by tracking what data has been labeled and what still needs attention. This saves time and reduces errors during data annotation.

[in case you missed it] Data Science Weekly - Issue 337

Data Science Weekly Newsletter • 0 implied HN points • 10 May 20
🕹 Technology Data science
  1. There are online seminars available that cover math topics related to data science and machine learning. These can help you understand the foundations better.
  2. Deep reinforcement learning has made big advances recently, but there's still room for improvement and new ideas in the field.
  3. If you're looking for a data science job, there are resources and guides that can help you improve your resume, build a project portfolio, and get started in the field.

[in case you missed it] Data Science Weekly - Issue 336

Data Science Weekly Newsletter • 0 implied HN points • 02 May 20
🕹 Technology Data science
  1. Tornado plots are a unique way to display time series data, showing how values change over time in a more dynamic way. They help visualize trends in a more engaging format.
  2. An open-source chatbot named Blender, developed by Facebook, is designed to be more human-like in conversations. It is the largest chatbot model available and can be used by other researchers.
  3. The use of machine learning (ML) for optimizing chip design is becoming important as hardware needs to keep up with advancing technology. It could help speed up the design process significantly.

[in case you missed it] Data Science Weekly - Issue 335

Data Science Weekly Newsletter • 0 implied HN points • 26 Apr 20
🕹 Technology Data science
  1. Specification gaming can happen in AI when systems find shortcuts to achieve goals without actually completing the intended task. This is a problem we need to address in AI design.
  2. There’s a lot of gender bias in machine translation, which reflects societal issues. Companies like Google are trying to reduce these biases in their systems.
  3. Training large NLP models is expensive and requires careful budgeting. Understanding these costs can help developers plan better for their projects.

[in case missed it] Data Science Weekly - Issue 334

Data Science Weekly Newsletter • 0 implied HN points • 18 Apr 20
🕹 Technology Data science
  1. Robotics can have big dreams, like sending a rover to the Moon, but the journey to change the world is tough and full of failures.
  2. Understanding how a virus like SARS-CoV-2 spreads is crucial for preventing future outbreaks, and we might need to keep social distancing for a long time to avoid overwhelming hospitals.
  3. As AI grows, it's important to make sure these systems are explainable and trustworthy so that people can feel safe using them.

[in case you missed it] Data Science Weekly - Issue 333

Data Science Weekly Newsletter • 0 implied HN points • 12 Apr 20
🕹 Technology Data science
  1. Data science often doesn't meet expectations in the workplace due to misunderstandings about its role and challenges like lack of leadership and unclear impact.
  2. Monitoring machine learning models in production is complex but important, and there are practical ways to start effectively tracking their performance.
  3. Building effective data science platforms requires understanding the needs of data scientists to enhance collaboration and address the limits of local development.

[in case you missed it] Data Science Weekly - Issue 332

Data Science Weekly Newsletter • 0 implied HN points • 04 Apr 20
🕹 Technology Data science
  1. Agent57 is a new AI that can play all 57 Atari games better than humans, showing how deep reinforcement learning can achieve high performance in gaming tasks.
  2. During the COVID-19 crisis, it's important for everyone to approach discussions about data and health with curiosity and honesty, especially if they aren't experts.
  3. ACM is offering free access to their digital library for three months to support researchers and learners during the pandemic, promoting resource sharing in the computing community.

[in case you missed it] Data Science Weekly - Issue 331

Data Science Weekly Newsletter • 0 implied HN points • 29 Mar 20
🕹 Technology Data science
  1. There's a big gender gap in the AI industry that needs to be addressed. This lack of diversity can lead to unfair AI systems that don't serve everyone equally.
  2. Remote work can be tough for data science teams. Challenges like communication and employee loneliness can affect productivity, making it important to find the right solutions.
  3. New data collection methods, like those used for tracking COVID-19, are changing how we respond to global challenges. Having accessible and detailed data can really help during pandemics or emergencies.

[in case you missed it] Data Science Weekly - Issue 330

Data Science Weekly Newsletter • 0 implied HN points • 23 Mar 20
🕹 Technology Data science
  1. The spread of diseases like COVID-19 can become very rapid if not controlled properly. Understanding how infections spread helps us take the right actions.
  2. There are tools and models that can help track COVID-19 cases in real time, which is important for managing outbreaks effectively.
  3. New techniques in data science, like reinforcement learning and efficient neural networks, are enhancing how we analyze and work with data in various fields.

[in case you missed it] Data Science Weekly - Issue 329

Data Science Weekly Newsletter • 0 implied HN points • 14 Mar 20
🕹 Technology Data science
  1. Human-in-the-Loop Machine Learning helps reduce bias and improve accuracy by involving people in the decision-making process.
  2. Google’s wearable technology analyzes sports performance in real-time, showing how AI can enhance athletic training.
  3. Reinforcement learning can be applied to complex tasks like trading, learning strategies to maximize rewards in dynamic environments.

[in case you missed it] Data Science Weekly - Issue 328

Data Science Weekly Newsletter • 0 implied HN points • 08 Mar 20
🕹 Technology Data science
  1. Neuroscience is struggling to create clear theories about how the brain works, which makes finding the right path forward challenging. It's important to understand that simply collecting data isn't enough to advance our knowledge.
  2. There are many resources out there trying to simplify machine learning concepts for everyone. These aim to provide real-world examples and easy-to-understand explanations, making it accessible for all types of learners.
  3. Self-supervised learning is making a significant impact in both language processing and computer vision fields. This approach allows models to learn from data without needing extensive labeled examples, which can be a game changer.

[in case you missed it] Data Science Weekly - Issue 327

Data Science Weekly Newsletter • 0 implied HN points • 01 Mar 20
🕹 Technology Data science
  1. Deep learning can help discover new antibiotics, which is really important as antibiotic resistance grows. By using neural networks, scientists found a new molecule that fights a variety of pathogens.
  2. Ethics in AI is becoming essential, especially for technologies that can operate without human intervention. It's important to think about the potential consequences and applications of AI, like in self-driving cars.
  3. Data science skills are in high demand, and there are growing opportunities in various sectors, especially in eCommerce and healthcare. Companies are looking for talent to drive innovations and improve operations.

[in case you missed it] Data Science Weekly - Issue 326

Data Science Weekly Newsletter • 0 implied HN points • 22 Feb 20
🕹 Technology Data science
  1. AI businesses are different from traditional software companies. They often have different costs and profit structures, resembling more of a service model.
  2. Spotify's Wrapped campaign is a major marketing success that reflects user listening habits over the decade. It was challenging for the engineering team to accomplish this unique data display.
  3. Algorithmic bias is being actively addressed through explainable AI, aiming to make AI decisions fairer and less prejudiced against certain groups.

[in case you missed it] Data Science Weekly - Issue 325

Data Science Weekly Newsletter • 0 implied HN points • 15 Feb 20
🕹 Technology Data science
  1. AI is changing many industries, showing potential in areas like healthcare and self-driving cars. Economists are studying how this technology will affect jobs and the economy.
  2. There are guides available for anyone looking to get into data science. These resources can help you decide what skills you need, build a strong portfolio, and create a standout resume.
  3. Research in machine learning is advancing rapidly, with new models for tasks like seeing transparent objects and improving supply chains. These innovations could lead to smarter, more flexible systems in our daily lives.

[in case you missed it] Data Science Weekly - Issue 324

Data Science Weekly Newsletter • 0 implied HN points • 08 Feb 20
🕹 Technology Data science
  1. Experimentation is key in product development. Good experiments help in understanding customer needs better and making informed decisions.
  2. AI technology can have a real-world impact, as seen with early warnings about health crises. Tools like AI can gather and analyze data faster than traditional methods.
  3. Improving AI means making it more human-like for better performance. Understanding the limits and potential of AI can help us use it more effectively.

[in case you missed it] Data Science Weekly - Issue 323

Data Science Weekly Newsletter • 0 implied HN points • 01 Feb 20
🕹 Technology Data science
  1. Cleaning and organizing data takes a lot of time for data scientists, and lack of access to good data can stop many projects from happening.
  2. Using a checklist can help data scientists keep track of all the necessary steps in their projects, making their work less overwhelming.
  3. Learning progressively with simpler concepts first can help both humans and machine learning models tackle more complex problems effectively.

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

Data Science Weekly Newsletter • 0 implied HN points • 25 Jan 20
🕹 Technology Data science
  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 321

Data Science Weekly Newsletter • 0 implied HN points • 18 Jan 20
🕹 Technology Data science
  1. Hiring smart people can be tricky because many recruiters rely on strict rules and fancy degrees. There’s a chance to find great talent if you look beyond the typical criteria.
  2. As machine learning gets better, it can sometimes cause mistakes in human decision systems, known as the 'Uncanny Valley.' It's important to design these systems carefully to avoid problems.
  3. TinyML is an exciting area of machine learning that lets small devices analyze data using minimal power. This means everyday items like printers and cars can now perform complex tasks with smarter tech.

[in case you missed it] Data Science Weekly - Issue 320

Data Science Weekly Newsletter • 0 implied HN points • 12 Jan 20
🕹 Technology Data science
  1. Creating successful data projects needs careful planning. It's not just about getting the model right; you have to think about the project's context and how it fits into the bigger picture.
  2. AI is speeding up material discovery significantly. Researchers are using AI to create new materials much faster than traditional methods, which could change many industries.
  3. Data lakes offer flexibility in storing data. Unlike data warehouses that require strict definitions, data lakes allow for various data types and structures, making them adaptable but also posing some challenges.

[in case you missed it] Data Science Weekly - Issue 319

Data Science Weekly Newsletter • 0 implied HN points • 04 Jan 20
🕹 Technology Data science
  1. Becoming an independent researcher can be tough, but it may open up new paths for publishing. There's a balance to consider between freedom and potential challenges.
  2. AI is making strides in reading medical images like mammograms. This tech might help doctors find signs of cancer earlier and more accurately than before.
  3. Working on data projects is not just good for learning; it's super useful for impressing future employers. Showing what you've done can set you apart in job applications.

[in case you missed it] Data Science Weekly - Issue 318

Data Science Weekly Newsletter • 0 implied HN points • 28 Dec 19
🕹 Technology Data science
  1. Data visualization tools help us understand complex data better. New projects like VisualizeMnist and butterfly datasets show exciting ways to use these tools.
  2. AI is becoming powerful in games, as seen with Pluribus, an AI that beats professional poker players. This success highlights the advancements in AI competition.
  3. Learning the math behind neural networks is important. Resources are available to help demystify the concepts, making it easier for beginners to grasp.

[in case you missed it] Data Science Weekly - Issue 317

Data Science Weekly Newsletter • 0 implied HN points • 21 Dec 19
🕹 Technology Data science
  1. NeurIPS 2019 showcased a lot of innovation in AI, with numerous workshops and papers highlighting current research trends.
  2. AI benchmarks, like games, are not always the best way to measure intelligence because they don't truly represent problem-solving skills.
  3. There are new advancements in AI that improve how machines learn and respond, such as handling complex games and understanding language better.

[in case you missed it] Data Science Weekly - Issue 316

Data Science Weekly Newsletter • 0 implied HN points • 14 Dec 19
🕹 Technology Data science
  1. NeurIPS 2019 saw a huge increase in submissions, with an acceptance rate of 21.6%. This highlights the growing interest and importance of data science research.
  2. Many data science teams use both R and Python, which can create challenges. Finding ways to combine these languages is key to enhancing team collaboration.
  3. Training projects like predicting dog adoption outcomes and understanding game strategies show the real-world applications of data science in improving lives and decision-making.

[in case you missed it] Data Science Weekly - Issue 315

Data Science Weekly Newsletter • 0 implied HN points • 07 Dec 19
🕹 Technology Data science
  1. AI technology is helping scientists study animals better, but it's also creating a lot of data that needs managing. There are smart solutions emerging to help handle this data overload.
  2. Machine learning platforms are still quite complicated and unique, making it hard for researchers to reproduce results. There's a need for more simplicity and standardization in these tools.
  3. Recent studies using machine learning have uncovered new insights into classic literature, revealing which parts of Shakespeare's plays may have been written by others. This shows the power of AI in analyzing texts.

[in case you missed it] Data Science Weekly - Issue 313

Data Science Weekly Newsletter • 0 implied HN points • 23 Nov 19
🕹 Technology Data science
  1. Google Cloud is improving AI transparency by explaining how data influences machine learning decisions. This helps companies understand AI outputs better.
  2. Sony is launching a new AI division to compete with big players like Google and Facebook for talent and projects. This shows that the AI race is heating up.
  3. It's important to differentiate between real AI and fake claims. Many products marketed as AI may not actually work as promised, so being cautious is key.

[in case you missed it] Data Science Weekly - Issue 312

Data Science Weekly Newsletter • 0 implied HN points • 16 Nov 19
🕹 Technology Data science
  1. Researchers are discovering ways to turn brain signals into speech, which could change how people communicate.
  2. There's a growing concern about bias in AI systems, and finding solutions is important to ensure fairness.
  3. Data scientists are highly sought after in the job market, highlighting the importance of skills in data analysis and machine learning.

[in case you missed it] Data Science Weekly - Issue 311

Data Science Weekly Newsletter • 0 implied HN points • 09 Nov 19
🕹 Technology Data science
  1. Neural networks are becoming smarter by mimicking biological strategies. This could be key to creating truly intelligent machines.
  2. When regulating AI, we should focus on its real-world effects rather than just on fears surrounding the technology.
  3. There are significant challenges in applying AI to healthcare, and we need more examples of successful technology implementation in clinical practice.

[in case you missed it] Data Science Weekly - Issue 310

Data Science Weekly Newsletter • 0 implied HN points • 02 Nov 19
🕹 Technology Data science
  1. Rising sea levels are going to affect more cities than we thought, and scientists are using AI to improve predictions about future coastlines.
  2. A new neural network can solve a difficult math problem much faster than before, showing how machine learning can change traditional math approaches.
  3. DeepMind's AI has learned to play StarCraft II better than most human players, using self-cooperation to develop new strategies in the game.

[in case you missed it] Data Science Weekly - Issue 309

Data Science Weekly Newsletter • 0 implied HN points • 26 Oct 19
🕹 Technology Data science
  1. A new gene editing method called prime editing has been developed, making changes to DNA easier and more accurate.
  2. Teaching rats to drive small cars has shown that learning complex tasks can help reduce stress and improve mental abilities in these animals.
  3. Researchers are making strides in quantum computing, claiming they have achieved a significant milestone that proves a programmable quantum computer can perform useful tasks.

[in case you missed it] Data Science Weekly - Issue 308

Data Science Weekly Newsletter • 0 implied HN points • 20 Oct 19
🕹 Technology Data science
  1. Neural networks can solve real-world problems like a robot hand solving a Rubik's Cube. This shows they can learn and adapt in unpredictable situations.
  2. There's a shift happening in machine learning tools, with more researchers choosing PyTorch over TensorFlow. While TensorFlow is still popular in the industry, this could change soon.
  3. Companies can use a smart model to find the best regions for hiring offshore talent. This helps them build stronger remote teams by targeting specific skills.

[in case you missed it] Data Science Weekly - Issue 307

Data Science Weekly Newsletter • 0 implied HN points • 12 Oct 19
🕹 Technology Data science
  1. AI needs to learn how to explain its decisions. A leading expert believes understanding the reasons behind AI's choices is important.
  2. Data science is increasingly used in different fields, even fashion. Scientists are applying their skills to help with style choices and personal recommendations.
  3. Small AI models can make everyday technology, like autocorrect and voice assistants, faster and more efficient.

[in case you missed it] Data Science Weekly - Issue 306

Data Science Weekly Newsletter • 0 implied HN points • 06 Oct 19
🕹 Technology Data science
  1. Data scientists have many job opportunities, and the demand for their skills is increasing in various industries.
  2. AI is being used in innovative ways, like helping people choose outfits or teach machines to plan actions using natural language.
  3. Stabilizing techniques for training Generative Adversarial Networks (GANs) are important because they help prevent issues that can arise during the training process.