The hottest Robotics Substack posts right now

And their main takeaways
Category
Top Technology Topics

We Need to Recognize How Profoundly Different The AGI Future Will Be

Am I Stronger Yet? • 31 implied HN points • 11 Jul 23
🕹 Technology AI Robotics Future implications Automation
  1. The AGI future will be profoundly different, shaping both AI and humanity.
  2. An advanced level of AI will automate most jobs, leading to a world with artificial workers surpassing humans.
  3. AI will make decisions, influence events, and potentially replace human interaction, drastically changing society and daily life as we know it.

Interesting things about robots...

Robots & Startups • 19 implied HN points • 14 Feb 23
🕹 Technology Robotics Networking Events
  1. There are private robotics networking events available for those interested in technical discussions and developments in the field.
  2. The Robotics CTO Network is tailored for senior engineers and technical founders to engage in discussions about robotics standards and technology.
  3. A 7-day free trial subscription to Robots & Startups gives access to full post archives for those interested in further exploration.

#38

The Nibble • 12 implied HN points • 17 Dec 23
🕹 Technology AI Models Language Robotics Programming
  1. Interesting developments in Indian Language Models and AI projects
  2. OpenAI bans TikTok for using GPT to train their own AI model
  3. New advancements like Stable Zero123 for 3D Object views and Tesla's Optimus Gen 2 humanoid prototype
Get a weekly roundup of the best Substack posts, by hacker news affinity:

Grounding Large Language Models in a Cognitive Foundation

The Gradient • 20 implied HN points • 15 Apr 23
🕹 Technology AI Robotics Neural Networks Speech Recognition
  1. Intelligent robots have struggled commercially due to the challenge of having meaningful conversations with them.
  2. Recent advancements in AI, speech recognition, and large language models like ChatGPT and GPT-4 have opened up new possibilities.
  3. For robots to effectively interact in the physical world, they need to quickly adapt to context and be localized in their knowledge.

2022 – The Year of ‘Text-to-Anything’

Sector 6 | The Newsletter of AIM • 19 implied HN points • 09 Oct 22
🕹 Technology AI Robotics Innovation Software Digital Media
  1. AI is rapidly advancing and changing many fields like art and gaming. It's exciting to see how much it can do in such a short time.
  2. We can now have complex conversations with AI, making technology feel more human-like. This could change how we interact with machines.
  3. The variety of tasks AI can perform shows the endless possibilities for the future. We're just starting to understand what AI can achieve.

AprilTags: Why Robotics Invented Its Own QR Code

Luminotes • 7 implied HN points • 09 Feb 24
🕹 Technology Robotics Computer Vision Algorithms Open Source Image Processing
  1. AprilTags are similar to QR codes but are used as fiducial markers in robotics for localization purposes.
  2. AprilTags, created by the reputable robotics lab April, enable systems to localize features in 6 degrees of freedom using a single image.
  3. AprilTags differ from QR codes as they are designed for easy detection in low resolution, unevenly lit, or cluttered images and can detect multiple tags.

The Rise of Optimus 🦾🤖⚡

Sector 6 | The Newsletter of AIM • 19 implied HN points • 03 Oct 22
🕹 Technology Robotics AI Innovation Automation Tech industry
  1. Tesla revealed a working prototype of their humanoid robot, Optimus, at their recent AI Day. This is a significant step from just a concept to a real robot that can walk.
  2. The prototype's initial name was 'Bumblecee' and it marks Tesla's first tangible progress in creating humanoid automation.
  3. The development of Optimus shows how far robotics has come and raises excitement about future possibilities for AI in everyday life.

Data Science Weekly - Issue 469

Data Science Weekly Newsletter • 19 implied HN points • 17 Nov 22
🕹 Technology AI Machine Learning Data science Robotics Software Engineering
  1. Learning machine learning can be accomplished without an engineering background. It often requires hard work, perseverance, and adopting good software engineering practices.
  2. Robotics and AI are being increasingly used in fulfillment processes at companies like Amazon. These technologies face challenges but also provide innovative solutions for package handling.
  3. Large language models are evolving to act like agents that make decisions. This shift towards action-driven models may make them resemble artificial general intelligence (AGI) more closely.

AI and robotics for deep automation

AI Prospects: Toward Global Goal Convergence • 1 HN point • 21 May 24
🕹 Technology AI Automation Manufacturing Design Robotics
  1. AI and robotics will transform manufacturing by scaling production, reducing costs, and increasing possibilities.
  2. Humanoid robots are not practical for manufacturing due to cost, clumsiness, and inefficiency compared to specialized machines.
  3. Automation in mass production focuses on designing and constructing machines efficiently, with AI playing a key role in breaking production bottlenecks.

The Belamy | Metaverse, Robot Tax & AI-Ready Youth

Sector 6 | The Newsletter of AIM • 19 implied HN points • 12 Sep 21
🕹 Technology AI Metaverse Robotics Deep Learning
  1. The metaverse is a growing digital space where people can interact and create, much like the real world. It's becoming an important part of our online experience.
  2. There is a discussion about a 'robot tax', which would be a tax on companies that use robots to replace human jobs. This could help address job loss due to automation.
  3. Preparing young people for an AI-driven future is crucial. Education systems are starting to include skills related to AI and technology to better equip the next generation.

Data Science Weekly - Issue 367

Data Science Weekly Newsletter • 19 implied HN points • 03 Dec 20
🕹 Technology AI Machine Learning Data science Software Development Robotics
  1. AlphaFold is a huge breakthrough in biology that helps solve the protein folding problem, which has puzzled scientists for 50 years. It shows how AI can speed up scientific discovery.
  2. Spotify needs good tools to make sense of its massive data from millions of users. Designing user-friendly data tools is key for them to understand and improve their services.
  3. Having high-quality data is essential for companies. New technologies can help businesses maintain data quality without spending huge amounts of money.

Data Science Weekly - Issue 337

Data Science Weekly Newsletter • 19 implied HN points • 07 May 20
🕹 Technology Data science Machine Learning Artificial Intelligence Robotics Programming
  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.

Data Science Weekly - Issue 334

Data Science Weekly Newsletter • 19 implied HN points • 16 Apr 20
🕹 Technology Data science Machine Learning Artificial Intelligence Robotics
  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 Machine Learning AI Big Data Robotics
  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 317

Data Science Weekly Newsletter • 19 implied HN points • 19 Dec 19
🕹 Technology AI Machine Learning Data science Robotics Ethics
  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 308

Data Science Weekly Newsletter • 19 implied HN points • 17 Oct 19
🕹 Technology Data science Machine Learning Artificial Intelligence Programming Robotics
  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.

How to get a million bucks: quick thoughts on cognitive programs and the ARC challenge.

Artificial General Ideas • 1 implied HN point • 13 Jun 24
🕹 Technology Artificial Intelligence Cognitive Science Machine Learning Robotics Problem Solving
  1. The ARC challenge is about understanding abstract concepts from visual inputs and applying them to new situations. It's tricky because it's not based on a strict set of rules, making it harder to solve.
  2. Cognitive programs need a controllable world model to work properly. This means they must be able to run simulations using the information they have about the world.
  3. Abstract reasoning tests, like ARC, are important but not complete measures of intelligence. They need to be systematic and clear to truly assess reasoning skills.

Data Science Weekly - Issue 292

Data Science Weekly Newsletter • 19 implied HN points • 27 Jun 19
🕹 Technology AI Data science Machine Learning Robotics Software Development
  1. Amazon held its first AI conference showcasing robots and their vision for an efficient future. It was a glimpse into how technology can change everyday tasks.
  2. A new method helped process large DNA sequencing data faster using R and AWK. This approach can help researchers avoid common pitfalls.
  3. Machine learning can improve medical devices, like a better prosthetic hand. This shows how technology can help people lead better lives.

Data Science Weekly - Issue 291

Data Science Weekly Newsletter • 19 implied HN points • 20 Jun 19
🕹 Technology AI Machine Learning Data science Robotics Software Development
  1. New AI technology is advancing quickly, enabling robots to be more intelligent and functional. For example, Boston Dynamics has robots that can now actively defend themselves.
  2. Deepfake technology is becoming more sophisticated, allowing a single photo and audio file to create a singing video. This shows how media can be manipulated in exciting and potentially concerning ways.
  3. AI is starting to play roles traditionally held by humans, such as in healthcare. Chatbots are now providing medical advice, which raises questions about their effectiveness compared to real doctors.

Data Science Weekly - Issue 287

Data Science Weekly Newsletter • 19 implied HN points • 23 May 19
🕹 Technology Artificial Intelligence Data science Machine Learning Robotics Healthcare
  1. AI is becoming better at detecting diseases like lung cancer through improved analysis of CT scans. This could help doctors make more accurate diagnoses.
  2. Robots that learn to explore their environment can contribute to advancements in artificial intelligence. Facebook believes this could lead to smarter machines for various uses.
  3. Data analysis is playing a significant role in sports, such as soccer, by helping teams like Liverpool improve their performance and achieve success.

Data Science Weekly - Issue 285

Data Science Weekly Newsletter • 19 implied HN points • 09 May 19
🕹 Technology AI Machine Learning Data science Robotics Algorithms Data Analysis
  1. Machine learning is good at finding patterns in data, but understanding why those patterns exist is still a challenge. This breakthrough could help us understand complex systems better.
  2. Robots can avoid obstacles more effectively with a special type of camera that reduces perception delays. This can help improve how robots navigate through tricky environments.
  3. Stitch Fix uses a game called 'Style Shuffle' to quickly learn about customer preferences. This fun method helps them suggest clothes that people are more likely to buy.

Data Science Weekly - Issue 279

Data Science Weekly Newsletter • 19 implied HN points • 28 Mar 19
🕹 Technology Artificial Intelligence Machine Learning Data science Algorithms Robotics
  1. Three scientists won the Turing Award for their groundbreaking work on neural networks. This award is like the Nobel Prize for computing and comes with a $1 million prize.
  2. Adversarial machine learning could pose security risks by allowing enemies to reverse-engineer AI systems. Experts urge caution as this threat could impact important technologies.
  3. The fast-food giant McDonald's is investing heavily in machine learning by acquiring a startup. This shows how businesses are increasingly using data and AI to improve operations.

Data Science Weekly - Issue 276

Data Science Weekly Newsletter • 19 implied HN points • 07 Mar 19
🕹 Technology AI Machine Learning Data science Software Engineering Robotics
  1. Deep learning can be used to convert imagined words into text using Keras and EEG technology.
  2. There's a new tool called Handtrack.js for quickly creating hand gesture interactions in web apps with TensorFlow.js.
  3. Microsoft Excel now lets you take a picture of a printed spreadsheet and turn it into an editable table, making data handling easier.

Data Science Weekly - Issue 275

Data Science Weekly Newsletter • 19 implied HN points • 28 Feb 19
🕹 Technology Data science Artificial Intelligence Machine Learning Robotics Programming
  1. Artificial intelligence can help humans discover things we couldn't find on our own, making it a powerful tool in various fields.
  2. Creating a strong data science portfolio and tailored resume is crucial for job seekers in the data science field to stand out to potential employers.
  3. Machine learning can significantly improve the efficiency and value of renewable energy sources like wind power, showcasing its practical applications.

Data Science Weekly - Issue 270

Data Science Weekly Newsletter • 19 implied HN points • 24 Jan 19
🕹 Technology Data science Artificial Intelligence Machine Learning Robotics Software Development
  1. Curiosity in data science can lead to big innovations. Instead of just focusing on improving processes, companies should give data scientists the space to explore new ideas.
  2. AI technology is advancing but can also reinforce past mistakes, especially in areas like criminal justice. It's important to use this technology wisely to avoid repeating errors.
  3. Training resources for aspiring data scientists are crucial. Guides that help build a strong portfolio and craft impressive resumes can significantly improve job prospects in this field.

Announcing posetree.py: Wrangling Timestamps and Transforms for Robots

General Robots • 2 HN points • 10 Jul 23
🕹 Technology Robotics Programming Open Source Mathematics Software Development
  1. Posetree.py is a library for dealing with poses and transforms in robotics, making code more readable and reducing common bugs.
  2. Understanding the distinction between transforms, poses, and frames is crucial for clarity in robotics code.
  3. The 'timestamps' capability of posetree.py allows for expressing powerful ideas with simple code by automatically handling frame motion.

Data Science Weekly - Issue 239

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

Data Science Weekly - Issue 225

Data Science Weekly Newsletter • 19 implied HN points • 15 Mar 18
🕹 Technology AI Machine Learning Data science Robotics Automation
  1. Machine learning can create completely new sounds by learning from existing ones, which is really cool for music-making.
  2. AI has a problem where it sometimes sees or hears things that aren't there, which makes using it tricky.
  3. Robots might be the future of farming, helping to automate growing food from start to finish for better efficiency.

Data Science Weekly - Issue 176

Data Science Weekly Newsletter • 19 implied HN points • 06 Apr 17
🕹 Technology Artificial Intelligence Data science Machine Learning Computer Vision Robotics
  1. Image style transfer can turn famous impressionist paintings into more realistic photos, helping us see the world through the artist's eyes.
  2. DeepMind claims to have made a breakthrough in artificial general intelligence, which could have significant impacts on the future of AI.
  3. One-shot imitation learning allows robots to learn new tasks quickly and without needing a lot of examples, making them more adaptable.