Jürgen Schmidhuber's "Matters Computational" provides a comprehensive overview of computer science, spanning its theoretical foundations and practical applications. It delves into topics like algorithmic information theory, computability, complexity theory, and the history of computation, including discussions of Turing machines and the Church-Turing thesis. The book also explores the nature of intelligence and the possibilities of artificial intelligence, covering areas such as machine learning, neural networks, and evolutionary computation. It emphasizes the importance of self-referential systems and universal problem solvers, reflecting Schmidhuber's own research interests in artificial general intelligence. Ultimately, the book aims to provide a unifying perspective on computation, bridging the gap between theoretical computer science and the practical pursuit of artificial intelligence.
This blog post introduces Differentiable Logic Cellular Automata (DLCA), a novel approach to creating cellular automata (CA) that can be trained using gradient descent. Traditional CA use discrete rules to update cell states, making them difficult to optimize. DLCA replaces these discrete rules with continuous, differentiable logic gates, allowing for smooth transitions between states. This differentiability allows for the application of standard machine learning techniques to train CA for specific target behaviors, including complex patterns and computations. The post demonstrates DLCA's ability to learn complex tasks, such as image classification and pattern generation, surpassing the capabilities of traditional, hand-designed CA.
HN users discussed the potential of differentiable logic cellular automata, expressing excitement about its applications in areas like program synthesis and hardware design. Some questioned the practicality given current computational limitations, while others pointed to the innovative nature of embedding logic within a differentiable framework. The concept of "soft" logic gates operating on continuous values intrigued several commenters, with some drawing parallels to analog computing and fuzzy logic. A few users desired more details on the training process and specific applications, while others debated the novelty of the approach compared to existing techniques like neural cellular automata. Several commenters expressed interest in exploring the code and experimenting with the ideas presented.
This Google Form poses a series of questions to William J. Rapaport regarding his views on the possibility of conscious AI. It probes his criteria for consciousness, asking him to clarify the necessary and sufficient conditions for a system to be considered conscious, and how he would test for them. The questions specifically explore his stance on computational theories of mind, the role of embodiment, and the relevance of subjective experience. Furthermore, it asks about his interpretation of specific thought experiments related to consciousness and AI, including the Chinese Room Argument, and solicits his opinions on the potential implications of creating conscious machines.
The Hacker News comments on the "Questions for William J. Rapaport" post are sparse and don't offer much substantive discussion. A couple of users express skepticism about the value or seriousness of the questionnaire, questioning its purpose and suggesting it might be a student project or even a prank. One commenter mentions Rapaport's work in cognitive science and AI, suggesting a potential connection to the topic of consciousness. However, there's no in-depth engagement with the questionnaire itself or Rapaport's potential responses. Overall, the comment section provides little insight beyond a general sense of skepticism.
Roger Penrose argues that Gödel's incompleteness theorems demonstrate that human mathematical understanding transcends computation and therefore, strong AI, which posits that consciousness is computable, is fundamentally flawed. He asserts that humans can grasp the truth of Gödelian sentences (statements unprovable within a formal system yet demonstrably true outside of it), while a computer bound by algorithms within that system cannot. This, Penrose claims, illustrates a non-computable element in human consciousness, suggesting we understand truth through means beyond mere calculation.
Hacker News users discuss Penrose's argument against strong AI, with many expressing skepticism. Several commenters point out that Gödel's incompleteness theorems don't necessarily apply to the way AI systems operate, arguing that AI doesn't need to be consistent or complete in the same way as formal mathematical systems. Others suggest Penrose misinterprets or overextends Gödel's work. Some users find Penrose's ideas intriguing but remain unconvinced, while others find his arguments simply wrong. The concept of "understanding" is a key point of contention, with some arguing that current AI models only simulate understanding, while others believe that sophisticated simulation is indistinguishable from true understanding. A few commenters express appreciation for Penrose's thought-provoking perspective, even if they disagree with his conclusions.
This 2018 paper demonstrates how common spreadsheet software can be used to simulate neural networks, offering a readily accessible and interactive educational tool. It details the implementation of a multilayer perceptron (MLP) within a spreadsheet, using built-in functions to perform calculations for forward propagation, backpropagation, and gradient descent. The authors argue that this approach allows for a deeper understanding of neural network mechanics due to its transparent and step-by-step nature, which can be particularly beneficial for teaching purposes. They provide examples of classification and regression tasks, showcasing the spreadsheet's capability to handle different activation functions and datasets. The paper concludes that spreadsheet-based simulations, while not suitable for large-scale applications, offer a valuable pedagogical alternative for introducing and exploring fundamental neural network concepts.
HN users discuss the practicality and educational value of simulating neural networks in spreadsheets. Some find it a clever way to visualize and understand the underlying mechanics, especially for beginners, while others argue its limitations make it unsuitable for real-world applications. Several commenters point out the computational constraints of spreadsheets, making them inefficient for larger networks or datasets. The discussion also touches on alternative tools for learning and experimenting with neural networks, like Python libraries, which offer greater flexibility and power. A compelling point raised is the potential for oversimplification, potentially leading to misconceptions about the complexities of real-world neural network implementations.
Catalytic computing, a new theoretical framework, aims to overcome the limitations of traditional computing by leveraging the entire storage capacity of a device, such as a hard drive, for computation. Instead of relying on limited working memory, catalytic computing treats the entire memory system as a catalyst, allowing data to transform itself through local interactions within the storage itself. This approach, inspired by chemical catalysts, could drastically expand the complexity and scale of computations possible, potentially enabling the efficient processing of massive datasets that are currently intractable for conventional computers. While still theoretical, catalytic computing represents a fundamental shift in thinking about computation, promising to unlock the untapped potential of existing hardware.
Hacker News users discussed the potential and limitations of catalytic computing. Some expressed skepticism about the practicality and scalability of the approach, questioning the overhead and energy costs involved in repeatedly reading and writing data. Others highlighted the potential benefits, particularly for applications involving massive datasets that don't fit in RAM, drawing parallels to memory mapping and virtual memory. Several commenters pointed out that the concept isn't entirely new, referencing existing techniques like using SSDs as swap space or leveraging database indexing. The discussion also touched upon the specific use cases where catalytic computing might be advantageous, like bioinformatics and large language models, while acknowledging the need for further research and development to overcome current limitations. A few commenters also delved into the theoretical underpinnings of the concept, comparing it to other computational models.
Summary of Comments ( 11 )
https://news.ycombinator.com/item?id=43288861
HN users discuss the density and breadth of "Matters Computational," praising its unique approach to connecting diverse computational topics. Several commenters highlight the book's treatment of randomness, floating-point arithmetic, and the FFT as particularly insightful. The author's background in physics is noted, contributing to the book's distinct perspective. Some find the book challenging, requiring multiple readings to fully grasp the concepts. The free availability of the PDF is appreciated, and its enduring relevance a decade after publication is also remarked upon. A few commenters express interest in a physical copy, while others suggest potential updates or expansions on certain topics.
The Hacker News post titled "Matters Computational (2010) [pdf]" linking to a PDF of Jörg Fliege's book "Matters Computational" has a moderate number of comments, discussing various aspects of the book and computational mathematics in general.
Several commenters praise the book's comprehensive nature and clarity. One user highlights its value as a reference for "all sorts of basic algorithms and data structures," appreciating the detailed explanations and pseudocode provided. They specifically mention its usefulness for understanding fundamental concepts like numerical stability.
Another commenter focuses on the book's treatment of linear algebra, noting its depth and accessibility, even for those without a strong mathematical background. They contrast it with other resources they found less helpful.
A few comments delve into specific topics covered in the book. One user discusses the exploration of floating-point arithmetic and its associated challenges, acknowledging the importance of understanding these concepts for anyone working with numerical computations. Another highlights the chapter on optimization, mentioning its practical value and the inclusion of various optimization algorithms.
Some commenters offer broader perspectives on computational mathematics and its role in computer science. One reflects on the importance of a strong mathematical foundation for software engineers, advocating for more emphasis on these concepts in education.
The discussion also touches on the book's availability. The author's decision to make it freely available is commended, with some users expressing gratitude for open access to such valuable educational resources. A link to the author's webpage is shared, offering further context.
While a number of commenters express interest in the book based on the description and other comments, there isn't extensive engagement in deep technical discussions. The overall sentiment is positive, with the comments primarily focusing on the book's breadth, clarity, and value as a resource for understanding fundamental computational concepts.