How to make an AI rss feed Using the Five Top Theory
As a beginner in the AI field, my current skill set is limited to writing prompts. However, I am determined to gain a deeper understanding of artificial intelligence and gradually build my career in this domain. To achieve this, I have utilized the “Five-top Theory” to help construct my AI RSS feed.
The “Five-top Theory” in the field of learning refers to:
Top People: Identify the experts in the field, learn from their experiences and methods.
Top Works: Read the classic works in the field, absorb the essence.
Top Companies: Study the outstanding companies in the field, understand their operational models.
Top Publications: Read authoritative publications in the field, build a knowledge system.
Top Information: Keep up with the latest information in the field, maintain keen insight.
Top People
twitter rss feed
Twitter is undoubtedly an excellent platform for following top individuals. So, I started following Sam Altman, Elon Musk, Yann LeCun, Andrew Ng, and Geoffrey Hinton, among others
Blog rss feed
Sam Altman（Sam Altman, is the CEO of OpenAI and was previously the president of Y Combinator. His blog shares his insights on entrepreneurship, technology, and future trends.）
Geoffrey Hinton (Geoffrey Hinton, known as the “father of deep learning,” shares his research and achievements in the field of AI on his personal homepage, offering in-depth articles on deep learning and artificial intelligence.)
Jason Brownlee (Jason Brownlee’s blog provides tutorials and articles on machine learning and deep learning, offering insights and guidance in these fields.)
Li’Log: lilian (Basically, this blog can systematically sort out a field, making it easy to understand and accessible.)
Top Companies
There are also official blogs of major AI companies:
OpenAI Blog : OpenAI’s official blog offers research updates and in-depth analysis, providing articles on deep learning and artificial intelligence.
Google Blog : The official blog of Google AI introduces the latest research developments and applications, offering news and research on artificial intelligence and machine learning.
Deepmind Blog: DeepMind’s official blog showcases their research achievements and latest advancements, providing the latest research findings in artificial intelligence and deep learning.
Meta Blog: The official blog of Facebook AI shares their research and new AI technologies, offering the latest research findings and applications in AI.
Nvidia Blog:NVIDIA’s official AI blog shares their latest advancements and research in AI and deep learning, providing in-depth articles on hardware acceleration, AI applications, and deep learning.
Microsoft Blog: Microsoft’s official AI blog shares their latest research and progress in AI and machine learning, offering a wealth of in-depth articles on AI technologies and applications.
Top Information
I found this five-Top AI information website
TechCrunch AI: TechCrunch is a leading technology news website that covers the latest news, startups, and trends in the field of artificial intelligence.
VentureBeat AI: VentureBeat provides in-depth technology news and analysis, with a particular focus on the development and application of artificial intelligence.
WIRED AI: WIRED reports on the latest news, trends, and in-depth analyses related to artificial intelligence.
The Verge AI: The Verge offers the latest news and commentary on artificial intelligence and its impact.
MIT Technology Review AI: MIT Technology Review, a publication of the Massachusetts Institute of Technology, provides cutting-edge research and technical reports on artificial intelligence.
I used an RSS on Chrome Extension called Tidyread to help me subscribe to these sources of information.
You can follow and subscribe directly on Tidyread, or use RSSHub to detect RSS sources + subscribe with Tidyread.
Through these five aspects, we can quickly position and expand our understanding, and build our own knowledge
Top Publications
“Artificial Intelligence: A Modern Approach” (1995) — — The definitive textbook for AI education globally.
Authors: Stuart Russell and Peter Norvig
2. “Pattern Recognition and Machine Learning” (2006) — — Essential for understanding the mathematical foundations of machine learning.
Author: Christopher M. Bishop
3. “Deep Learning” (2016) — — Comprehensive resource on deep learning principles and applications.
Authors: Ian Goodfellow, Yoshua Bengio, and Aaron Courville
4. “Machine Learning: A Probabilistic Perspective” (2012) — — Widely used for its in-depth exploration of probabilistic models in machine learning.
Author: Kevin P. Murphy
5. “Reinforcement Learning: An Introduction” (1998, 2nd edition 2018) — — Seminal text on core concepts and algorithms in reinforcement learning.
Authors: Richard S. Sutton and Andrew G. Barto
Top Works
I directly asked ChatGPT, and it provided relevant papers and download links
“A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence” (1955)
Authors:
John McCarthy: Often considered the father of AI, he coined the term “artificial intelligence.”
Marvin Minsky: A pioneer in AI, co-founder of the MIT AI Laboratory.
Nathaniel Rochester: Developed the first assembler for IBM.
Claude Shannon: Known as the father of information theory.
Significance: This proposal is considered the founding document of AI as a field. It set the stage for research by suggesting that “every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.”
Download Link:
2. “Computing Machinery and Intelligence” (1950)
Author:
Alan Turing: A mathematician and logician, Turing is considered one of the fathers of computer science. He introduced the concept of the Turing Machine and the Turing Test.
Significance: This paper posed the question, “Can machines think?” and introduced the Turing Test, a method for determining whether a machine can exhibit intelligent behavior indistinguishable from that of a human.
Download Link:
3. “Learning Representations by Back-Propagating Errors” (1986)
Authors:
David E. Rumelhart: A cognitive psychologist who made significant contributions to neural networks.
Geoffrey E. Hinton: Known as the “godfather of deep learning,” he has made foundational contributions to neural networks and deep learning.
Ronald J. Williams: A computer scientist known for his work in neural networks.
Significance: This paper popularized the backpropagation algorithm, which is fundamental for training multi-layer neural networks. It laid the groundwork for modern deep learning techniques.
Download Link:
4. “ImageNet Classification with Deep Convolutional Neural Networks” (2012)
Authors:
Alex Krizhevsky: A computer scientist known for his work on deep learning and convolutional neural networks.
Ilya Sutskever: Co-founder of OpenAI and a leading researcher in deep learning.
Geoffrey E. Hinton: Mentioned previously.
Significance: This paper demonstrated the power of deep convolutional neural networks (CNNs) by achieving unprecedented accuracy on the ImageNet dataset. It spurred the widespread adoption of deep learning in computer vision.
Download Link:
5. “Attention is All You Need” (2017)
Authors:
Ashish Vaswani & Team: Researchers at Google Brain who contributed to the development of the Transformer model.
Significance: This paper introduced the Transformer architecture, which eschews recurrence and relies entirely on attention mechanisms to draw global dependencies. It revolutionized natural language processing and led to the development of powerful models like BERT and GPT.
Download Link:
These papers have been instrumental in shaping the field of AI, each contributing foundational concepts and techniques that have driven the field forward.
Conclusion:
This is my first article on Medium. As a non-native English speaker, I’m actively learning the language and look forward to exchanging thoughts with everyone. If you find my article interesting, feel free to comment and share your thoughts!
