The hottest AI Substack posts right now

And their main takeaways
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Top Technology Topics

[in case you missed it] Data Science Weekly - Issue 413

Data Science Weekly Newsletter • 0 implied HN points • 24 Oct 21
🕹 Technology AI
  1. Understanding your tools is essential for success in computer science. Learning how to use the command line and version control can help you a lot.
  2. Improving language models to reduce harmful content is a complex task. It's important to ensure these models are safe while still being effective.
  3. Getting a job in data science is easier when you know what companies look for. Keep an eye on the key skills and experiences employers value most.

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

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

[in case you missed it] Data Science Weekly - Issue 399

Data Science Weekly Newsletter • 0 implied HN points • 18 Jul 21
🕹 Technology AI
  1. There's a growing movement called 'Data for Good', which focuses on using data to help improve society. It's important to understand the different groups and initiatives within this space.
  2. Peer review in data science is crucial, especially for startups, but the process can be tricky. It's good to learn from experiences about what works and what doesn't.
  3. Big companies like Amazon collect a lot of data about their users, often more than people realize. It's important to be aware of how this data is being tracked and used.

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

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

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

Data Science Weekly Newsletter • 0 implied HN points • 13 Jun 21
🕹 Technology AI
  1. The data economy harms our privacy by collecting personal information for profit. It's important to rethink this approach.
  2. New AI methods are improving tasks like chip design, allowing machines to do the work faster and better than humans.
  3. There's a growing interest in data management concepts like data mesh, which focuses on decentralized data ownership and treating data as a product.
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[in case you missed it] Data Science Weekly - Issue 389

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

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

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

[in case you missed it] Data Science Weekly - Issue 385

Data Science Weekly Newsletter • 0 implied HN points • 11 Apr 21
🕹 Technology AI
  1. Building a good machine learning rig can be expensive. But with careful planning and research, you can create an effective setup.
  2. Understanding adaptive data analysis is important for trusting your models. New methods are being developed to address issues with model evaluation.
  3. Model compression techniques can help enhance performance. This includes strategies like quantization and knowledge distillation to make models smaller and faster.

[in case you missed it] Data Science Weekly - Issue 384

Data Science Weekly Newsletter • 0 implied HN points • 04 Apr 21
🕹 Technology AI
  1. AI is improving tools like Google Maps, making them smarter and more helpful with real-time updates.
  2. It's important to focus on building effective machine learning systems that provide real value, instead of just labeling everything as AI.
  3. Data can be powerful for decision-making, but relying too heavily on numbers can lead to mistakes and misinterpretation.

[in case you missed it] Data Science Weekly - Issue 383

Data Science Weekly Newsletter • 0 implied HN points • 28 Mar 21
🕹 Technology AI
  1. AI is making strides in drug discovery by addressing important problems, and there's great research available on the topic.
  2. Jupyter notebooks are loved for data exploration but can be tricky for production use, leading to mixed feelings among data scientists and machine learning engineers.
  3. Detecting names in user messages is a complex challenge that's important for creating better virtual assistants.

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

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

[in case you missed it] Data Science Weekly - Issue 375

Data Science Weekly Newsletter • 0 implied HN points • 31 Jan 21
🕹 Technology AI
  1. Building a machine learning (ML) team starts small but can grow significantly. As projects develop, different challenges arise that require specific team structures to tackle them.
  2. Effective machine learning should help systems generalize beyond the data they are trained on. This means creating algorithms that can learn from observations and apply that knowledge to new situations.
  3. AI is starting to influence many fields, like music technology, by learning characteristics of sound and improving products like guitar amplifiers. This shows how machine learning can apply to real-world problems in creative ways.

[in case you missed it] Data Science Weekly - Issue 370

Data Science Weekly Newsletter • 0 implied HN points • 27 Dec 20
🕹 Technology AI
  1. 2020 saw significant advancements in AI, especially with neural volume rendering and models that can learn rules themselves.
  2. Data scientists are in high demand, and platforms like Vettery can help job seekers connect with employers.
  3. Resources are available to help aspiring data scientists improve their skills, build portfolios, and create impactful resumes.

[in case you missed it] Data Science Weekly - Issue 369

Data Science Weekly Newsletter • 0 implied HN points • 20 Dec 20
🕹 Technology AI
  1. Companies are now changing how they present information because machines and AI read their reports too. They're trying to make it easier for algorithms to understand, sometimes even avoiding negative words that might confuse them.
  2. Monitoring machine learning in production is crucial. It's important to catch any unusual patterns or changes in how models behave to ensure they keep performing well.
  3. Artificial intelligence is being developed to better interact with humans. By using virtual environments, researchers are teaching AI to mimic human behaviors and improve interaction quality.

[in case you missed it] Data Science Weekly - Issue 366

Data Science Weekly Newsletter • 0 implied HN points • 29 Nov 20
🕹 Technology AI
  1. Pinterest improved its data infrastructure by moving from Lambda to Kappa architecture to better handle its visual signals for machine learning. This change aimed to streamline costs and enhance signal availability.
  2. When building machine learning models, companies like DoorDash face huge data challenges. Choosing the right feature store is crucial for managing this data effectively, ensuring performance without overspending.
  3. Differentially private learning still faces challenges in performance compared to traditional models. For effective results, more private data or improved features from public data may be necessary.

[in case you missed it] Data Science Weekly - Issue 364

Data Science Weekly Newsletter • 0 implied HN points • 15 Nov 20
🕹 Technology AI
  1. Organizing data in spreadsheets helps reduce errors. Use consistent formats, avoid empty cells, and save backups to make analysis easier.
  2. AI is creating convincing fake music performances of famous artists. This raises legal concerns as the music industry watches closely.
  3. Monitoring performance is crucial in data science. Tools like Mona help track data and model performance to avoid issues like biases and errors.

[in case you missed it] Data Science Weekly - Issue 355

Data Science Weekly Newsletter • 0 implied HN points • 13 Sep 20
🕹 Technology AI
  1. DeepMind and Google Maps teamed up to improve travel time predictions using advanced technology called Graph Neural Networks. This helps users get even more accurate arrival times in busy cities.
  2. AI technology is now being used to spot edited videos, like deepfakes, by detecting hidden signals called 'deepfake heartbeats'. This could make it easier to tell which video was made with what software.
  3. A new book aims to teach machine learning from scratch, breaking down complex algorithms to make them understandable. It's a good resource for anyone wanting to learn the basics of machine learning.

[in case you missed it] Data Science Weekly - Issue 354

Data Science Weekly Newsletter • 0 implied HN points • 06 Sep 20
🕹 Technology AI
  1. A new machine learning algorithm helped identify 50 new planets by analyzing old NASA data. This shows how AI can unlock discoveries from existing information.
  2. There has been a significant drop in deep learning job postings recently, especially among smaller companies. This indicates a shift in the demand for deep learning talent after the pandemic.
  3. Apple has launched a residency program for people with STEM backgrounds to improve their machine learning skills. This offers participants hands-on experience and personalized training.

[in case you missed it] Data Science Weekly - Issue 353

Data Science Weekly Newsletter • 0 implied HN points • 29 Aug 20
🕹 Technology AI
  1. Testing machine learning systems is different from testing traditional software. It's important to do this testing well to ensure the models work as intended.
  2. Fast.ai has released new resources for deep learning, including a complete course and several libraries. These tools can help people learn and apply deep learning more effectively.
  3. AI systems can make decisions that seem efficient but might also cause unfair outcomes. It's vital to consider ethical implications when using algorithms in important areas like hiring or policing.

[in case you missed it] Data Science Weekly - Issue 350

Data Science Weekly Newsletter • 0 implied HN points • 09 Aug 20
🕹 Technology AI
  1. GPT-3 can create very human-like text and it can even write computer programs with just a few examples. This shows how advanced AI language models are becoming.
  2. Many languages are spoken around the world, but most natural language processing work has focused only on English. It's important to include other languages in research.
  3. Graph technologies are being used to solve complex business problems, such as making recommendations and detecting fraud. They are becoming essential tools in data science.

[in case you missed it] Data Science Weekly - Issue 347

Data Science Weekly Newsletter • 0 implied HN points • 19 Jul 20
🕹 Technology AI
  1. Netflix is improving its data efficiency by using a dashboard that helps everyone see costs and usage trends. This way, decision-makers can make better choices based on clear information.
  2. Creating a strong portfolio and resume is really important for landing a data science job. Focus on showcasing your best skills and experiences to attract employers.
  3. There's a shift in building robots to assist humans instead of replacing them. The future should focus on robots that enhance our capabilities rather than take over our jobs.

[in case you missed it] Data Science Weekly - Issue 346

Data Science Weekly Newsletter • 0 implied HN points • 12 Jul 20
🕹 Technology AI
  1. A workshop at the Santa Fe Institute explored the meaning and understanding in AI, involving participants from different fields to discuss how machines might understand like living beings.
  2. The cost of training AI is dropping much faster than expected, making it easier for companies to adopt AI technology in the coming years.
  3. Training Generative Adversarial Networks (GANs) presents challenges, but new algorithms are being developed to improve stability and performance in machine learning.

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

Data Science Weekly Newsletter • 0 implied HN points • 21 Jun 20
🕹 Technology AI
  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 329

Data Science Weekly Newsletter • 0 implied HN points • 14 Mar 20
🕹 Technology AI
  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 326

Data Science Weekly Newsletter • 0 implied HN points • 22 Feb 20
🕹 Technology AI
  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 322

Data Science Weekly Newsletter • 0 implied HN points • 25 Jan 20
🕹 Technology AI
  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 320

Data Science Weekly Newsletter • 0 implied HN points • 12 Jan 20
🕹 Technology AI
  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 AI
  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 AI
  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 315

Data Science Weekly Newsletter • 0 implied HN points • 07 Dec 19
🕹 Technology AI
  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 AI
  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 308

Data Science Weekly Newsletter • 0 implied HN points • 20 Oct 19
🕹 Technology AI
  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 306

Data Science Weekly Newsletter • 0 implied HN points • 06 Oct 19
🕹 Technology AI
  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.

[in case you missed it] Data Science Weekly - Issue 303

Data Science Weekly Newsletter • 0 implied HN points • 14 Sep 19
🕹 Technology AI
  1. Stitch Fix is using machine learning to help customers pick outfits that match their style. It shows how technology can personalize shopping experiences.
  2. There's a push to protect workers from being replaced by automation. Some suggest taxing companies that use robots to keep people employed.
  3. AI is transforming fields like biology, especially in analyzing images. It highlights how technology is changing research and discovery in science.

[in case you missed it] Data Science Weekly - Issue 302

Data Science Weekly Newsletter • 0 implied HN points • 07 Sep 19
🕹 Technology AI
  1. Yann LeCun is a key figure in deep learning, known for his work on convolutional neural networks, which help machines learn from data.
  2. Data scientists are in high demand, and understanding their salaries is important for those interested in entering the field.
  3. Deep learning techniques can swiftly perform tasks like face recognition, outperforming human experts in speed and accuracy.

[in case you missed it] Data Science Weekly - Issue 300

Data Science Weekly Newsletter • 0 implied HN points • 25 Aug 19
🕹 Technology AI
  1. There's a new AI optimizer called RAdam that can help improve the accuracy of AI models. It automatically adjusts the learning rate based on training conditions.
  2. Deep learning is an area of study that's compared to classical methods and explores various neural network models. Understanding these models can help grasp the foundations of modern AI.
  3. Data scientists are in high demand, and there are resources available to help newcomers prepare for training programs. This can lead to job opportunities in the field.

[in case you missed it] Data Science Weekly - Issue 299

Data Science Weekly Newsletter • 0 implied HN points • 17 Aug 19
🕹 Technology AI
  1. AI is now being used to improve video gaming, like training in soccer using a new football simulator. This shows how far technology has come in understanding games.
  2. Nvidia is making big strides in AI language models, making them faster and more efficient. This means we could see better and more responsive AI conversations soon.
  3. For those wanting to become data scientists, it's smarter to get a related job first and learn on the job. Skills can be built up as you go instead of trying to learn everything at once.

[in case you missed it] Data Science Weekly - Issue 295

Data Science Weekly Newsletter • 0 implied HN points • 20 Jul 19
🕹 Technology AI
  1. Netflix is moving away from collaborative filtering for recommendations, focusing on more effective strategies that drive revenue.
  2. Machine learning can play a big role in tackling climate change, helping us find solutions to one of our biggest challenges.
  3. There is a growing demand for data scientists to know a variety of tools like Python, R, and SQL, so it's important to keep learning and improving your skills.

[in case you missed it] Data Science Weekly - Issue 293

Data Science Weekly Newsletter • 0 implied HN points • 06 Jul 19
🕹 Technology AI
  1. The State of AI Report 2019 highlights how fast AI is growing and important developments from the past year.
  2. Machine learning is now being used to translate languages that were previously lost, opening up new ways to understand history.
  3. There are many resources and guides available for those wanting to get started in data science, covering everything from building a portfolio to writing a great resume.