Jeff Shrager writes a comprehensive guide to Herbert Simon's 1961 Heuristic Compiler, a program that uses General Problem Solver (GPS) means-end analysis to generate IPL-V code. After being dormant for roughly 65 years, the archival card deck was transcribed and successfully run on a modern Common Lisp interpreter. The system is composed of three parts: a State Description Compiler that derives code from before-and-after memory states, a Functional Description Compiler that modifies existing code based on imperative phrases, and a General Compiler executive that manages the search process. Notably, the program reproduces the exact machine code printed in Simon's 1963 paper, such as the "INSERT AT END OF VALUE LIST" and "SET SIGNAL MINUS" examples, without any modern language rewrites.
- The guide highlights that the compiler treats routine generation as a problem to be solved via operator search rather than a direct translation task.
- A debugging print statement left in routine U113 happens to output the exact stage-by-stage compilation sequence described in the 1963 paper.
- The original 1961 deck fails to compile the J3 routine; a specific card must be modified to erase a redundant description before the state compiler can execute successfully.
- The guide highlights that the compiler treats routine generation as a problem to be solved via operator search rather than a direct translation task.
- A debugging print statement left in routine U113 happens to output the exact stage-by-stage compilation sequence described in the 1963 paper.
- The original 1961 deck fails to compile the J3 routine; a specific card must be modified to erase a redundant description before the state compiler can execute successfully.
Rodney Brooks writes about the seven cognitive errors that lead to wildly mistaken predictions about the future of robotics and artificial intelligence. He frames four categories of predictions—Artificial General Intelligence, the Singularity, misaligned values, and evil AI entities—then dissects the reasoning failures behind them: overestimating short-term impact while underestimating long-term effects, treating far-future technology as unfalsifiable magic, confusing narrow task performance with broad competence, relying on ambiguous "suitcase words" like "learn" or "understand," extrapolating exponentials that will inevitably flatten, imagining Hollywood-style single-disruption scenarios, and ignoring the glacial pace of hardware deployment in the physical world.
- The OpenWorm project spent thirty years attempting to simulate C. elegans (302 neurons, 7,000 connections) bottom-up and was not yet halfway done as of 2017.
- iPod storage followed a perfect five-year exponential (10→160 GB) then collapsed abruptly once a single device could hold a complete music library.
- Modern factory automation still relies on PLCs introduced in 1968 that emulate electromechanical relays; Tesla was actively hiring PLC technicians at its Fremont factory.
- Autonomous vehicles drove on public roads in 1987 and coast-to-coast across the US in 1995, yet no large-scale deployment path had been identified by 2017.
- Brooks observes that humans coexisted with horses—autonomous agents with ongoing existences and super-human strength—for millennia without a single formal theorem about them.
- The OpenWorm project spent thirty years attempting to simulate C. elegans (302 neurons, 7,000 connections) bottom-up and was not yet halfway done as of 2017.
- iPod storage followed a perfect five-year exponential (10→160 GB) then collapsed abruptly once a single device could hold a complete music library.
- Modern factory automation still relies on PLCs introduced in 1968 that emulate electromechanical relays; Tesla was actively hiring PLC technicians at its Fremont factory.
- Autonomous vehicles drove on public roads in 1987 and coast-to-coast across the US in 1995, yet no large-scale deployment path had been identified by 2017.
- Brooks observes that humans coexisted with horses—autonomous agents with ongoing existences and super-human strength—for millennia without a single formal theorem about them.
Publication list for Leigh Klotz from 1982 to 1994, featuring work on digital document storage and user interfaces.
Konstantin Kakaes writes about the transformative impact of artificial intelligence on mathematical research, announcing a new dispatch series called "Transformation." The publication aims to explore how AI-driven discoveries are reshaping the discipline, covering everything from controversial proofs and automated reasoning to the philosophical debates regarding machine co-authorship.
- A recent breakthrough involves an LLM finding a complex structure for $S^6$, solving a problem that had eluded mathematicians since 1947.
- AI is increasingly used to verify or discover solutions to long-standing mathematical conjectures, such as the Navier-Stokes equations.
- The rise of "mechanical reasoning" in math has sparked significant debate within the community regarding peer review and the definition of a mathematician's role.
- A recent breakthrough involves an LLM finding a complex structure for $S^6$, solving a problem that had eluded mathematicians since 1947.
- AI is increasingly used to verify or discover solutions to long-standing mathematical conjectures, such as the Navier-Stokes equations.
- The rise of "mechanical reasoning" in math has sparked significant debate within the community regarding peer review and the definition of a mathematician's role.
This article examines the fundamental differences between how human children acquire values and competencies versus how current artificial intelligence systems are trained. While modern AI training relies on large datasets and software guardrails, humans learn through a combination of innate biological programming, personal experience, and essential social interaction within a community. The author argues that to achieve truly reliable, trustworthy, and human-compatible AI, developers should move beyond mere data containment and instead integrate deep learning with the collaborative, staged experiences found in developmental robotics and social species.
A review exploring how the current artificial intelligence revolution is driven more by capital interests and hype than technological necessity. The piece examines Cory Doctorow’s argument that technology development is often steered toward maximizing investor returns rather than human empowerment, leading to a phenomenon known as "reverse centaurs" where workers lose autonomy and skill to machines. It critiques the industry's perceived inevitabilism and its similarities to the process of enshittification seen in other tech sectors.
* The economic motivations behind artificial intelligence hype
* Concept of reverse centaurs versus automation theory
* Critique of Big Tech business models
* Impact of capitalism on technological progress
* The economic motivations behind artificial intelligence hype
* Concept of reverse centaurs versus automation theory
* Critique of Big Tech business models
* Impact of capitalism on technological progress
Nobel laureate Daron Acemoglu critiques current optimism regarding AI productivity and economic narratives. He argues that much of the prevailing debate is speculative and fails to address critical issues like concentrated corporate power and extractive data models. Rather than focusing on whether capitalism is mutating, he suggests evaluating technology based on whether it fosters inclusive or extractive institutions.
>*Seen through that lens, AI is not troublesome in its own right, but rather whether it is positioned as inclusive or extractive. Today’s AI hyperscalers, he argues, fit the extractive mold almost perfectly: concentrated ownership, regulatory capture, and a business model that extracts data and attention at scale."
- Skepticism toward massive near-term AI productivity gains due to current model limitations
- The distinction between simple automation and true human-complementary tasks
- Potential social instability if significant job displacement occurs among younger generations
- A call for global governance and a focus on socially desirable technological outcomes
>*Seen through that lens, AI is not troublesome in its own right, but rather whether it is positioned as inclusive or extractive. Today’s AI hyperscalers, he argues, fit the extractive mold almost perfectly: concentrated ownership, regulatory capture, and a business model that extracts data and attention at scale."
- Skepticism toward massive near-term AI productivity gains due to current model limitations
- The distinction between simple automation and true human-complementary tasks
- Potential social instability if significant job displacement occurs among younger generations
- A call for global governance and a focus on socially desirable technological outcomes
A new TechBrief from the Association for Computing Machinery's Technology Policy Council examines the rapid rise of agentic AI systems capable of planning and executing multi-step tasks autonomously. As adoption scales among enterprises and consumers, existing legal, regulatory, and technical frameworks are struggling to keep pace with these autonomous capabilities.
The report highlights several critical policy dimensions:
- Ambiguity in legal liability when an autonomous system causes harm without a clear accountable party.
- Serious security risks arising from the inability of models to distinguish between data and malicious commands.
- A lack of consumer transparency regarding agent permissions, authority, and recourse.
- Workforce disruption concerns where productivity claims have not been independently verified at scale.
The report highlights several critical policy dimensions:
- Ambiguity in legal liability when an autonomous system causes harm without a clear accountable party.
- Serious security risks arising from the inability of models to distinguish between data and malicious commands.
- A lack of consumer transparency regarding agent permissions, authority, and recourse.
- Workforce disruption concerns where productivity claims have not been independently verified at scale.
Philosophers Eric Schwitzgebel and Jeremy Pober argue that consciousness is likely not restricted to Earth's specific biological structures. By applying the concept of substrate flexibility, they suggest that sentient experiences could emerge from many different types of materials or chemical compositions found throughout the universe. This "Copernican principle of consciousness" challenges human-centric views by suggesting that various forms of complexity may lead to varied and non-humanoid types of conscious experience.
Key topics:
- Substrate flexibility and its role in potential sentience.
- The Copernican principle applied to consciousness to avoid terrocentrism.
- Implications for the diversity of extraterrestrial life forms.
- Philosophical considerations regarding different modes of AI intelligence.
Key topics:
- Substrate flexibility and its role in potential sentience.
- The Copernican principle applied to consciousness to avoid terrocentrism.
- Implications for the diversity of extraterrestrial life forms.
- Philosophical considerations regarding different modes of AI intelligence.
This encyclical letter from Pope Leo XIV examines the profound challenges posed by artificial intelligence and digital transformation to human dignity and social order. Drawing on the Church's Social Doctrine, the document provides an ethical framework for navigating the technological era, warning against a technocratic paradigm that seeks to reduce humans to data or optimize them for efficiency. It calls for a shared responsibility to ensure technology serves the common good rather than becoming an instrument of dominance or exclusion.
Main topics include:
- Safeguarding human dignity and ontological value in the digital age
- The need for transparency, accountability, and governance in AI
- Upholding truth as a common good against disinformation
- Protecting the dignity of work during digital transitions
- Guarding freedom from commercialization and algorithmic social control
- Addressing new forms of slavery within technological supply chains
- Building a civilization of love through justice and peace
Main topics include:
- Safeguarding human dignity and ontological value in the digital age
- The need for transparency, accountability, and governance in AI
- Upholding truth as a common good against disinformation
- Protecting the dignity of work during digital transitions
- Guarding freedom from commercialization and algorithmic social control
- Addressing new forms of slavery within technological supply chains
- Building a civilization of love through justice and peace
