As Clay Awakens

As Clay Awakens is a Substack that offers critical perspectives on artificial intelligence, exploring the theoretical risks, practical applications, and philosophical considerations surrounding AI and machine learning. It scrutinizes current AI methodologies, debates on AI ethics and advancements, and reflects on the human aspects of delegating tasks to machines.

Artificial Intelligence Machine Learning AI Ethics Reinforcement Learning Deep Learning Rationality and Decision Making AI Risk and Safety Programming and Technology Tools Academic Practices Peer Review

The hottest Substack posts of As Clay Awakens

And their main takeaways

Thoughts On Rationality

39 implied HN points • 02 Jan 23
  1. Rationality is a decision-making philosophy based on reason and systematization.
  2. Rationality is effective in closed systems but can go awry in complex, real-world scenarios.
  3. One should balance rationality with irrational decision-making strategies like intuition and tradition to avoid blind adherence to flawed frameworks.

Deep Learning Is Better Than Linear Regression

2 HN points • 19 Mar 23
🕹 Technology Deep Learning Machine Learning Data science Neural Networks
  1. Linear regression is a reliable, stable, and simple technique with a long history of successful applications.
  2. Deep learning, especially non-linear regression, has shown significant advancements over the past decade and can outperform linear regression in many real-world tasks.
  3. Deep learning models have the ability to automatically learn and discover complex features, making them advantageous over manually engineered features in linear regression.
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PAGESLAUGHTER

0 implied HN points • 02 Feb 23
  1. Plagiarism involves stealing and lying by passing off someone else's work as your own.
  2. Cheating in school can involve using tools like calculators or getting someone else to complete assignments, but not all cheating is plagiarism.
  3. AI-driven writing agents like ChatGPT offer opportunities to enhance learning by focusing on advanced skills rather than trivial tasks.

On The Road To St. Petersburg

0 implied HN points • 18 Nov 22
  1. The blog post is titled 'On The Road To St. Petersburg'.
  2. The author is Jacob Buckman.
  3. The post was shared on November 18, 2022.

The One Where I Rebut Gary Marcus

0 implied HN points • 15 Jun 22
  1. The author rebutted Gary Marcus in a post on their blog.
  2. The post includes a link to the original blog for further reading.
  3. The main content of the post seems to focus on disputing Gary Marcus's views.

An Actually-Good Argument Against Naive AI Scaling

0 implied HN points • 14 Jun 22
  1. The post discusses an argument against naive AI scaling.
  2. It highlights the importance of considering the limitations of AI scaling.
  3. Jacob Buckman presents a critical view on AI scaling in the post.

Abstraction Enables Thought

0 implied HN points • 21 Feb 22
  1. Abstraction enables thought
  2. Abstract thinking is important
  3. Engaging with abstract concepts is beneficial

How To Think About Replay Memory

0 implied HN points • 13 Feb 21
  1. Replay memory is important in certain contexts.
  2. Understanding the concept can help improve decision-making in machine learning.
  3. Reflection on replay memory can enhance understanding of algorithms.

Conceptual Fundamentals of Offline RL

0 implied HN points • 30 Nov 20
  1. The post discusses conceptual fundamentals of Offline RL.
  2. Jacob Buckman is the author of the post.
  3. The full post can be found on Jacob Buckman's original blog.

Updating the Accepted Wisdom in Offline RL

0 implied HN points • 31 Oct 20
  1. The post discusses updating the accepted wisdom in offline reinforcement learning.
  2. The content is available on the original blog's website.
  3. Jacob Buckman is the author of the post.

Bayesian Neural Networks Need Not Concentrate

0 implied HN points • 22 Jan 20
  1. Bayesian neural networks do not have to concentrate
  2. Bayesian neural networks offer flexibility in their behavior
  3. Understanding Bayesian neural networks can enhance application effectiveness

Three Paradigms of Reinforcement Learning

0 implied HN points • 25 Oct 19
  1. Reinforcement learning can be categorized into three main paradigms.
  2. Each paradigm offers a different approach to solving problems in reinforcement learning.
  3. Understanding these paradigms can help in choosing the right approach for specific tasks in reinforcement learning.

On Peer Review

0 implied HN points • 16 May 19
  1. The post is about peer review.
  2. It includes a link to the original blog.
  3. The post was shared multiple times.

OpenAI Five Takeaways

0 implied HN points • 06 Aug 18
  1. The article discusses OpenAI Five.
  2. Jacob Buckman shares insights from OpenAI Five.
  3. The post can be found on Jacob Buckman's original blog.

More on Graph Inspection

0 implied HN points • 05 Aug 18
  1. The post is about Graph Inspection by Jacob Buckman.
  2. It was shared on August 5, 2018.
  3. Readers can find the original post on Jacob Buckman's blog.

Bad ML Abstractions I (Generative vs Discriminative Models)

0 implied HN points • 19 Apr 22
  1. This post discusses Generative vs Discriminative Models in ML
  2. Understanding the difference between generative and discriminative models is crucial in machine learning
  3. Bad ML abstractions can lead to confusion and suboptimal results

Fair ML Tools Require Problematic ML Models

0 implied HN points • 15 Feb 21
  1. Fair ML tools need problematic ML models.
  2. Addressing fairness in ML requires dealing with challenging models.
  3. Developing fair ML tools is complex due to underlying model issues.

Tensorflow, The Confusing Parts (2)

0 implied HN points • 17 Sep 18
  1. Post discusses confusing aspects of TensorFlow
  2. Author shares insights on navigating complex concepts in TensorFlow
  3. Resource for understanding challenging parts of TensorFlow

Tensorflow, The Confusing Parts (1)

0 implied HN points • 25 Jun 18
  1. The post is about the confusing parts of Tensorflow.
  2. The author is Jacob Buckman.
  3. The post was originally published on jacobbuckman.com.

Moving To Substack

0 implied HN points • 01 Feb 23
  1. The author is moving their blog to Substack for the email distribution list.
  2. Public writing allows for valuable feedback and discussions with different backgrounds and beliefs.
  3. The author chose Substack as a sustainable platform for sharing their writing and engaging with readers.

Automation via Reinforcement Learning

0 implied HN points • 23 Sep 19
  1. The post discusses automation through reinforcement learning.
  2. The author is Jacob Buckman.
  3. The post was shared on September 23, 2019.

Where Bayes Falls Short

0 implied HN points • 30 May 23
🔬 Science Statistics Machine Learning Neural Networks Deep Learning
  1. Deep learning algorithms are powerful for intelligence and learning, especially in contexts where Bayes' theorem falls short.
  2. Simpson's paradox shows how data separation can change conclusions based on initial beliefs.
  3. Deep learning approaches in regression tasks offer solutions without the need for ad-hoc choices, allowing for better predictions and generalization.