The hottest Data science Substack posts right now

And their main takeaways
Category
Top Technology Topics

Data Science Weekly - Issue 327

Data Science Weekly Newsletter • 19 implied HN points • 27 Feb 20
🕹 Technology Data science
  1. AI startups might not be as promising as they seem and should be closely evaluated. A recent review suggests there's a big difference between AI investments and traditional software investments.
  2. Deep learning is being used to discover new antibiotics, which is crucial due to the rise in antibiotic-resistant bacteria. This shows the real-life applications of AI in solving global health issues.
  3. Ethics in AI is becoming more important, especially with autonomous systems. Companies need to think carefully about the implications of their AI technologies and how they are used.

Data Science Weekly - Issue 326

Data Science Weekly Newsletter • 19 implied HN points • 20 Feb 20
🕹 Technology Data science
  1. AI businesses operate differently than traditional software companies and can seem more like service companies.
  2. Spotify Wrapped is a big marketing campaign that shares users' listening habits over the past year, showcasing engineering efforts to handle data.
  3. Addressing algorithmic bias in AI is becoming more important, and companies are working on ways to make AI fairer and more transparent.

Data Science Weekly - Issue 325

Data Science Weekly Newsletter • 19 implied HN points • 13 Feb 20
🕹 Technology Data science
  1. AI is being closely studied for its effects on the economy, including job creation and productivity. Experts are discussing how to ensure the benefits of AI are widely shared.
  2. Machine learning researchers are advised to choose their problems wisely and manage their time effectively. Simple guidance can help them advance in their careers.
  3. New technologies like brain implants are emerging to restore vision in blind individuals. This innovation shows the potential for technology to enhance human capabilities.

LLM Chronicles #5: GPT For Ecommerce Search Engine With Pinecone

Pratik’s Pakodas 🍿 • 8 implied HN points • 09 May 23
🕹 Technology Data science
  1. In certain scenarios, companies use 2 types of hybrid search: weighted scoring and filter and rerank, especially prevalent in e-commerce.
  2. GPT can be leveraged for query understanding to parse out complex queries and populate Elasticsearch/Solr with detected entities.
  3. Although using GPT-4 for this purpose may be costly and slow, training an open-source model like MPT-7B can be a more viable option.
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Data Science Weekly - Issue 324

Data Science Weekly Newsletter • 19 implied HN points • 06 Feb 20
🕹 Technology Data science
  1. Good experiments in product development involve learning from both successes and failures, refining techniques over time.
  2. AI can help detect health crises, as seen with a platform that warned about the Wuhan virus before major health organizations.
  3. Neural networks are being used to enhance older video game graphics, making classic games look modern and appealing again.

Data Science Weekly - Issue 323

Data Science Weekly Newsletter • 19 implied HN points • 30 Jan 20
🕹 Technology Data science
  1. Data cleaning is a big part of a data scientist's job. Many great ideas can get stuck because people can't access or use the right data.
  2. Choosing the right settings, called hyperparameters, greatly impacts a machine learning project's success. There are smarter ways to find these settings than just guessing.
  3. Learning is easier when it's structured step by step. Using a curriculum helps models learn complex tasks bit by bit, just like how people learn.

Data Science Weekly - Issue 322

Data Science Weekly Newsletter • 19 implied HN points • 23 Jan 20
🕹 Technology Data science
  1. Smule is a popular karaoke app and now has a feature called Smulemates that helps users find others with similar singing styles to sing with.
  2. Facebook AI made a big advancement with a new learning algorithm called DD-PPO that helps machines navigate real-world environments using just basic tools like GPS and cameras.
  3. There’s a tool called Manifold from Uber that helps people check if their machine learning models are working well, and they have made it open source for everyone to use.

Data Science Weekly - Issue 321

Data Science Weekly Newsletter • 19 implied HN points • 16 Jan 20
🕹 Technology Data science
  1. Hiring smarter in the job market can be achieved by looking beyond the usual qualifications. There are talented candidates who might not fit the typical mold, and recognizing this can create great opportunities.
  2. Introducing machine learning into human decision systems can lead to issues, often referred to as the 'Uncanny Valley.' It’s important to carefully design these systems to avoid performance problems.
  3. TinyML is a growing field that allows advanced machine learning to happen on small devices. This means everyday products can become smarter without needing a lot of power.

Data Science Weekly - Issue 320

Data Science Weekly Newsletter • 19 implied HN points • 09 Jan 20
🕹 Technology Data science
  1. Creating effective data projects involves more than just building a model; you also need to consider context, strategy, and maintenance.
  2. AI can speed up material discovery by analyzing large datasets and predicting useful combinations, which could change many industries.
  3. Data lakes allow for more flexible data storage than data warehouses, but this flexibility comes with important tradeoffs to think about.

Data Science Weekly - Issue 319

Data Science Weekly Newsletter • 19 implied HN points • 02 Jan 20
🕹 Technology Data science
  1. AI can help detect cancer in mammograms better than humans, which shows the growing role of technology in healthcare.
  2. Working on data projects can help new data scientists stand out to employers and improve their skills.
  3. The AI research community needs to improve transparency by sharing their work, which can help advance the field.

Data Science Weekly - Issue 318

Data Science Weekly Newsletter • 19 implied HN points • 26 Dec 19
🕹 Technology Data science
  1. Visualizing data is important. Tools like MNIST and butterfly datasets help us see patterns and improve recognition using machine learning.
  2. AI is making strides in complex games, like poker. There are now AI that can beat expert players, showing how advanced it's become.
  3. Learning and understanding the math behind neural networks is crucial. It helps us grasp how these systems work and improve our data analysis skills.

Data Science Weekly - Issue 317

Data Science Weekly Newsletter • 19 implied HN points • 19 Dec 19
🕹 Technology Data science
  1. NeurIPS 2019 had a lot of focus on workshops and research, showing that the field of AI is rapidly growing and evolving.
  2. AI's ability to play games like chess may not measure true intelligence since it can't solve everyday problems as easily as humans do.
  3. There's a push for improving AI tools and methods, particularly in language understanding and cooperation in complex tasks.

Data Science Weekly - Issue 316

Data Science Weekly Newsletter • 19 implied HN points • 12 Dec 19
🕹 Technology Data science
  1. NeurIPS 2019 saw a huge increase in submissions, with over 6,700 entries and a 21.6% acceptance rate. This shows how popular and competitive the field of data science has become.
  2. Data Science teams often use both R and Python together, but merging them can be challenging. Finding ways to integrate these languages can help teams be more effective in their projects.
  3. A new method has been discovered for understanding quadratic equations, making it easier for students who struggled with the traditional formula. This could change how math concepts are taught.

Data Science Weekly - Issue 315

Data Science Weekly Newsletter • 19 implied HN points • 05 Dec 19
🕹 Technology Data science
  1. New technology is helping scientists study animals more effectively, but it's also creating a lot of data to handle.
  2. Machine learning tools are still complex and unique, making it tough for researchers to replicate their work easily.
  3. Recent advancements in machine learning are uncovering historical authorship details, like who wrote parts of Shakespeare's plays.

Data Science Weekly - Issue 314

Data Science Weekly Newsletter • 19 implied HN points • 28 Nov 19
🕹 Technology Data science
  1. Data science can be quite tedious and involves a lot of boring tasks. It's important for aspiring data scientists to manage their expectations and be prepared for the long-term commitment.
  2. AI is changing the workplace, especially for white-collar jobs. Many roles in fields like law, marketing, and programming might be disrupted by advancements in artificial intelligence.
  3. Diversity in AI isn't just a technical issue; it's about understanding perspectives and the impact of pronouns and identity in discussions on diversity.

Data Science Weekly - Issue 313

Data Science Weekly Newsletter • 19 implied HN points • 21 Nov 19
🕹 Technology Data science
  1. Google Cloud is improving AI transparency by explaining how machine learning models make decisions. This helps businesses understand and improve their models.
  2. AI is being used to discover ancient symbols in Peru, making the research process faster and more efficient.
  3. Building a data science portfolio can attract potential employers and provide conversation starters during interviews.

Data Science Weekly - Issue 312

Data Science Weekly Newsletter • 19 implied HN points • 14 Nov 19
🕹 Technology Data science
  1. PhD students often face many challenges during their research, making it a tough journey. It's important to recognize that they might not be alone in these struggles.
  2. Scientists are making progress in decoding brain signals into speech, which could help people communicate directly from their thoughts. This could be a game changer for those with communication disabilities.
  3. AI and bias continue to be major topics, especially when systems make mistakes. It's crucial to address these issues and find solutions to prevent hidden biases in AI.

Data Science Weekly - Issue 311

Data Science Weekly Newsletter • 19 implied HN points • 07 Nov 19
🕹 Technology Data science
  1. Neural networks using biological strategies are improving, suggesting that ignoring specific goals could help create smarter machines.
  2. AI in healthcare is growing quickly, but there are challenges in making these technologies actually work in hospitals and clinics.
  3. When applying for data science jobs, resumes should focus more on results and actions rather than just academic achievements.

Data Science Weekly - Issue 310

Data Science Weekly Newsletter • 19 implied HN points • 31 Oct 19
🕹 Technology Data science
  1. Rising sea levels could affect more cities than we realized, based on new research using artificial intelligence to correct earlier mistakes.
  2. Machine learning has made it possible to solve complex math problems, like the three-body problem, much faster than before.
  3. AI can learn to play video games like StarCraft II at a high level by practicing against itself, showcasing advances in gaming and strategy development.

Data Science Weekly - Issue 309

Data Science Weekly Newsletter • 19 implied HN points • 24 Oct 19
🕹 Technology Data science
  1. A new gene editing method called prime editing works better than CRISPR. It can change DNA more accurately, which is a big deal for scientists.
  2. Teaching rats to drive tiny cars can help them feel less stressed and improve their learning. This shows how important the environment is for learning new skills.
  3. Quantum computing is growing and important experiments are being done to show its real potential. Researchers are working to solve complex problems that regular computers can't handle.

Data Science Weekly - Issue 308

Data Science Weekly Newsletter • 19 implied HN points • 17 Oct 19
🕹 Technology Data science
  1. Reinforcement learning can solve real-world problems, like making a robot hand solve a Rubik's Cube. It shows how advanced AI can be applied outside digital spaces.
  2. More researchers are shifting from TensorFlow to PyTorch for experiments, while TensorFlow remains popular in the industry. This could change what tools are most commonly used in future projects.
  3. Companies can use machine learning to find the best regions for hiring offshore talent. This helps them build remote teams with the right skills more effectively.

Data Science Weekly - Issue 307

Data Science Weekly Newsletter • 19 implied HN points • 10 Oct 19
🕹 Technology Data science
  1. Deep learning is great at spotting patterns but struggles to explain the reasons behind those patterns. This is something experts want to improve.
  2. Some scientists are using their skills in machine learning for everyday tasks like fashion recommendations instead of just space research.
  3. Tiny AI models can make phone features like autocorrect and voice assistants work much better and faster.

Data Science Weekly - Issue 306

Data Science Weekly Newsletter • 19 implied HN points • 03 Oct 19
🕹 Technology Data science
  1. Data scientists are in high demand, and platforms like Vettery can help connect them with top employers. It’s a good time to create a profile and name your salary.
  2. New developments in AI are making it easier for algorithms to understand natural language and plan tasks effectively. This approach could lead to smarter AI capable of tackling unfamiliar challenges.
  3. The training process for Generative Adversarial Networks (GANs) is often tricky, but researchers are working on methods to stabilize it. This could improve how GANs are used in various applications.

Data Science Weekly - Issue 305

Data Science Weekly Newsletter • 19 implied HN points • 26 Sep 19
🕹 Technology Data science
  1. Neural networks can create unique artworks, like an unseen Picasso painting, by analyzing and reconstructing based on existing styles.
  2. Explainable AI is important for understanding how AI models make decisions, especially to avoid biases and harmful behaviors.
  3. Anonymous data can still lead to re-identification, meaning privacy is a big concern even when personal information is removed.

Data Science Weekly - Issue 304

Data Science Weekly Newsletter • 19 implied HN points • 20 Sep 19
🕹 Technology Data science
  1. Backpropagation is crucial for how neural networks learn and improve their performance.
  2. AI is evolving rapidly, with successful projects like AlexNet revolutionizing technology and creating buzz among investors.
  3. Real-world data science experience is essential for job seekers, and there are resources available to help bridge the gap between education and practical skills.

Data Science Weekly - Issue 304

Data Science Weekly Newsletter • 19 implied HN points • 19 Sep 19
🕹 Technology Data science
  1. Backpropagation is key to how neural networks learn and work. It's important to understand how it makes AI smarter.
  2. There's a lot of interest in AI startups right now, like those that clean and prepare data for analysis. They are getting significant funding due to the AI boom.
  3. If you want a job in data science, gaining real-world experience is crucial. Many people feel discouraged, but projects and hands-on training can help bridge that gap.

Data Science Weekly - Issue 303

Data Science Weekly Newsletter • 19 implied HN points • 12 Sep 19
🕹 Technology Data science
  1. Machine learning is being used in fashion to create personalized outfits for users, showing how AI can enhance personal style.
  2. AI technology is transforming biology, especially in imaging, which could lead to significant advancements in understanding and treating diseases.
  3. Protection against job displacement from automation is important, with ideas like a robot tax being proposed to safeguard workers' roles.

Data Science Weekly - Issue 302

Data Science Weekly Newsletter • 19 implied HN points • 05 Sep 19
🕹 Technology Data science
  1. Deep learning is a big deal in AI. It's all about machines learning from data, and experts like Yann LeCun are leading the way.
  2. Data scientists are in high demand, and understanding their salaries can help you know what to expect in the job market.
  3. Using AI for face recognition can be surprising, like tracking chimpanzees, and shows how powerful this technology has become.

Data Science Weekly - Issue 301

Data Science Weekly Newsletter • 19 implied HN points • 29 Aug 19
🕹 Technology Data science
  1. Managing data scientists requires unique skills and knowledge that differ from other management roles. It's important for leaders to understand these differences for effective team building.
  2. Research in data science is a long-term commitment, not a quick task. Success often comes from persistence and adaptation over time.
  3. Creating a strong resume for data science roles is crucial. It can be challenging to know what to include, so seeking specific advice is helpful.

Data Science Weekly - Issue 300

Data Science Weekly Newsletter • 19 implied HN points • 22 Aug 19
🕹 Technology Data science
  1. Adversarial Fashion aims to confuse surveillance cameras by using items like license plates. This shows how fashion can be used to challenge technology.
  2. A new AI optimizer called RAdam can improve accuracy for various AI models. It's a helpful update for anyone working with machine learning.
  3. Deep learning is making waves in genetics, showing that it can help explore DNA. This opens new possibilities for understanding and working with genetic data.

Data Science Weekly - Issue 299

Data Science Weekly Newsletter • 19 implied HN points • 15 Aug 19
🕹 Technology Data science
  1. AI is now being used to train models for games like video soccer, building on its success in chess and Go. This shows how far AI technology has come in mastering complex tasks.
  2. Nvidia has made big strides in AI by speeding up the training process for advanced language models. This improvement can help in developing better conversational AI systems.
  3. To become a data scientist, it's more effective to start in a related job and learn along the way. Focusing too much on skills from blog posts can lead to confusion and delay.

Data Science Weekly - Issue 298

Data Science Weekly Newsletter • 19 implied HN points • 08 Aug 19
🕹 Technology Data science
  1. AI is becoming a part of dating apps, helping users find potential matches by analyzing their conversations.
  2. Natural Language Processing is evolving, with new trends emerging from major conferences like ACL 2019.
  3. Tools like Teraport simplify the process of building data pipelines, making it easier to manage data for machine learning projects.

Data Science Weekly - Issue 297

Data Science Weekly Newsletter • 19 implied HN points • 01 Aug 19
🕹 Technology Data science
  1. Integrating data science teams within companies can help improve collaboration and effectiveness. It's important to explore different models to find what works best.
  2. Automated thinking may lead to overdependence on AI, which can cause us to miss critical thinking skills. We should be cautious about relying too much on technology.
  3. Understanding how machine learning models work is crucial for building trust. New techniques are emerging that can help explain complex models better.

EL-Attention: Memory Efficient Lossless Attention for Generation

efficientml • 1 HN point • 30 Apr 23
🕹 Technology Data science
  1. EL-Attention proposes a method to reduce memory usage during inference in Transformer models by caching only past hidden states instead of keys and values.
  2. By re-ordering matrix multiplication steps, EL-Attention can achieve the same results as traditional attention mechanisms with significantly reduced memory requirements.
  3. EL-Attention provides an efficient way to handle attention mechanisms in transformer models, especially for decoder-only models, by halving the amount of caching memory needed.

The True Cost of Data Debt

Data Products • 5 implied HN points • 11 Oct 23
🕹 Technology Data science
  1. Data should be seen as an asset, not just a resource.
  2. Data debt can lead to serious consequences like trust issues and organizational chaos.
  3. Data developers need to focus on data quality tools like data contracts to prevent and manage data debt.

Data Science Weekly - Issue 296

Data Science Weekly Newsletter • 19 implied HN points • 25 Jul 19
🕹 Technology Data science
  1. Machine learning is being used in various industries to improve data handling and application. There's a growing trend of using Python notebooks for these projects.
  2. Facebook created a tool called Map With AI to help speed up the mapping of roads, especially in less-developed areas. It uses satellite imagery to predict road networks.
  3. Leaderboards in Natural Language Processing (NLP) encourage teams to compete, which drives the development of better models for understanding human language.

Data Science Weekly - Issue 295

Data Science Weekly Newsletter • 19 implied HN points • 18 Jul 19
🕹 Technology Data science
  1. Netflix is moving away from traditional collaborative filtering methods to improve its recommendation system.
  2. Using AI and natural language processing (NLP) can help companies better understand and meet customer requests.
  3. It's important to audit AI systems to check for bias, especially when making significant decisions like loans or legal verdicts.

Data Science Weekly - Issue 294

Data Science Weekly Newsletter • 19 implied HN points • 11 Jul 19
🕹 Technology Data science
  1. A new AI poker bot has learned to beat professional players, showing how advanced artificial intelligence has become in understanding complex strategies.
  2. Effective data science managers play a key role in driving team success and impact, focusing on building strong, skilled teams.
  3. Generative adversarial networks, often linked to deepfakes, can also be used positively in medical fields, like improving cancer diagnosis.