Ben Recht

Highlights: Ben Recht questions how to prove unpredictability in machine learning, reflecting on engineering philosophy.

Worth reading: Challenges assumptions about predictability in ML systems.

Highlights: Ben Recht announces teaching a class on learning and control after a decade.

Worth reading: Indicates renewed focus on control theory in ML education.

Highlights: Ben Recht's AI reading list remains unchanged since 2019, suggesting foundational texts.

Worth reading: Highlights enduring principles in AI literature.

Highlights: Ben Recht announces teaching a class on learning and control after a long hiatus.

Worth reading: Shows his renewed engagement with teaching at the intersection of learning and control theory.

Highlights: He is working on a theory for organizing both machines and people.

Worth reading: Reflects his interest in the broader implications of machine learning on organizational structures.

Highlights: He discusses the challenges of unquantifiable costs and tradeoffs in decision theory.

Worth reading: Highlights his critical perspective on the limitations of optimization in decision-making.

Highlights: Ben Recht announces teaching a class on learning and control after almost a decade.

Worth reading: Shows his current academic focus on the intersection of learning and control.

Highlights: Ben Recht is working on a theory for organizing machines and people.

Worth reading: Reflects his interest in the broader implications of AI systems.

Highlights: Ben Recht discusses unquantifiable costs and tradeoffs in decision theory.

Worth reading: Highlights his perspective on the limitations of quantitative approaches.

Highlights: Ben Recht announces teaching a class on learning and control after nearly a decade.

Worth reading: Shows his academic focus on control theory and machine learning.

Highlights: Recht discusses developing a theory for organizing machines and people.

Worth reading: Reflects his interest in the intersection of AI, organization, and human systems.

Highlights: Recht addresses unquantifiable costs and tradeoffs in decision theory.

Worth reading: Highlights his perspective on limitations of quantifiable metrics in AI.

Highlights: Recht explains policy gradient as a framework for random search.

Worth reading: Provides insight into his technical work on reinforcement learning.

Highlights: Ben Recht revisits Sutton's Bitter Lesson in the context of GPT-5, likely discussing the implications of scaling and general methods over specialized knowledge.

Worth reading: Provides perspective on a classic AI insight applied to modern large language models.

Highlights: Ben Recht announces teaching a class on learning and control after a long hiatus.

Worth reading: Indicates a renewed focus on the intersection of machine learning and control theory.

Highlights: Ben Recht notes that his recommended AI reading list remains unchanged since 2019.

Worth reading: Suggests that foundational AI knowledge has not been superseded by recent developments.

Highlights: Ben Recht is teaching a class on learning and control after a long hiatus.

Worth reading: Shows his return to teaching a specialized topic combining learning and control theory.

Highlights: Distinguishes between actions and predictions in machine learning contexts.

Worth reading: Highlights a key conceptual difference in ML that is often overlooked.

Highlights: Ben is curating materials for a course on engineering architecture theory.

Worth reading: Indicates his focus on foundational theory in engineering and ML.

Highlights: Ben Recht is teaching a class on learning and control after a long hiatus.

Worth reading: Shows his current academic focus and return to teaching a specific topic.

Highlights: Ben Recht expresses frustration with X/Twitter.

Worth reading: Reflects his candid opinion on the platform.

Highlights: Ben Recht is working on a theory about organizing machines and people.

Worth reading: Indicates his research interest in socio-technical systems.

Highlights: Ben Recht is teaching a class on learning and control after a long hiatus.

Worth reading: Shows a shift in his teaching focus towards control theory.

Highlights: Ben Recht discusses the fundamental difference between actions and predictions.

Worth reading: Highlights a key distinction in machine learning and control.

Highlights: Ben Recht announces teaching a class on learning and control after nearly ten years.

Worth reading: Shows his renewed focus on control theory in machine learning.

Highlights: Ben Recht shares that his AI reading list remains unchanged since 2019.

Worth reading: Indicates his consistent perspective on foundational AI literature.

Highlights: Ben Recht is teaching a class on learning and control after a long hiatus.

Worth reading: Shows his renewed focus on combining learning and control theory.

Highlights: He is developing a theoretical framework for organizing both machines and people.

Worth reading: Reflects his interest in the intersection of AI and organizational design.

Highlights: He explains policy gradient as a random search framework with mathematical detail.

Worth reading: Provides insight into his teaching style and research focus on reinforcement learning.

Highlights: Revisits Sutton's Bitter Lesson in the context of GPT-5.

Worth reading: Provides perspective on scaling and AI progress.

Highlights: Expresses frustration with the platform.

Worth reading: Shows personal sentiment about X/Twitter.

Highlights: Announces teaching a class on learning and control after a long gap.

Worth reading: Indicates a shift in academic focus.

Highlights: Ben Recht announces teaching a class on learning and control after nearly a decade.

Worth reading: Shows his return to teaching a foundational topic in ML and control theory.

Highlights: He is working on a theory for organizing machines and people.

Worth reading: Reflects his interest in the intersection of optimization, control, and human systems.

Highlights: He explains policy gradient as a framework for random search, with equations.

Worth reading: Provides insight into his pedagogical approach to reinforcement learning.

Highlights: He discusses unquantifiable costs and tradeoffs in decision theory.

Worth reading: Highlights his critical perspective on formal decision-making frameworks.

Highlights: Ben Recht revisits Sutton's Bitter Lesson in the context of GPT-5, likely discussing the implications of scaling laws and the role of general methods over specialized knowledge.

Worth reading: Provides insight into how a leading ML researcher views the ongoing relevance of Sutton's argument in the era of large language models.

Highlights: Ben Recht notes the success of Berkeley alumni in the field, tagging several individuals.

Worth reading: Shows community recognition and networking within the ML research community.

Highlights: Ben Recht announces teaching a class on learning and control after a long hiatus, indicating a return to a core research area.

Worth reading: Highlights a shift in focus or renewed interest in control theory combined with machine learning.

Highlights: Ben Recht is teaching a class on learning and control after a long hiatus.

Worth reading: Shows his renewed focus on the intersection of machine learning and control theory.

Highlights: Recht reflects on Sutton's Bitter Lesson in the context of GPT-5.

Worth reading: Connects classic AI philosophy to modern LLM developments.

Highlights: Recht argues that open ML needs both hardware access and community dedication.

Worth reading: Highlights the broader requirements for truly open machine learning.

Highlights: Ben Recht is teaching a class on learning and control after a long hiatus.

Worth reading: Shows his return to teaching a course that bridges machine learning and control theory.

Highlights: He is working on a theory for organizing both machines and people.

Worth reading: Reflects his interest in the intersection of optimization, organization, and human-machine systems.

Highlights: He shares a story about mathematicians solving a long-standing problem using an old paper.

Worth reading: Highlights the value of historical knowledge in mathematical research.

Highlights: Ben Recht announces teaching a class on learning and control after nearly a decade.

Worth reading: Shows his return to teaching a specialized topic, indicating current academic focus.

Highlights: Ben Recht expresses frustration with the X/Twitter platform.

Worth reading: Reflects his candid opinion on the platform's state.

Highlights: Ben Recht discusses Sutton's Bitter Lesson in context of GPT-5.

Worth reading: Connects classic AI insight to current developments.

Highlights: Ben Recht announces teaching a class on learning and control after nearly a decade.

Worth reading: Shows Recht's return to teaching a course combining learning and control theory.

Highlights: Recht highlights the distinction between actions and predictions in machine learning.

Worth reading: Reflects on a key conceptual challenge in decision-making systems.

Highlights: Recht questions how to prove unpredictability in machine learning systems.

Worth reading: Raises fundamental issues about verification and evaluation of ML models.

Highlights: Ben Recht announces teaching a class on learning and control after a long hiatus.

Worth reading: Shows his shift back to teaching a foundational topic combining learning and control.

Highlights: He is working on a theory for organizing machines and people.

Worth reading: Reflects his interest in the intersection of machine learning and organizational design.

Highlights: He argues that open ML needs both GPU access and community commitment.

Worth reading: Highlights a key barrier to open ML beyond hardware.

Highlights: Ben Recht is teaching a class on learning and control after nearly a decade.

Worth reading: Indicates a renewed focus on combining learning and control theory.

Highlights: Recht is developing a theory for organizing machines and people.

Worth reading: Suggests a foundational approach to AI and organizational design.

Highlights: Open ML needs both GPU access and community commitment to openness.

Worth reading: Highlights a key barrier to truly open machine learning.

Highlights: Ben Recht is teaching a class on learning and control after nearly a decade.

Worth reading: It signals a renewed focus on control theory in machine learning education.

Highlights: Ben Recht is working on a theory for organizing both machines and people.

Worth reading: It suggests a broader perspective on AI systems as socio-technical architectures.

Highlights: Open ML requires both GPU access and community commitment to openness.

Worth reading: It emphasizes that openness in ML is a social, not just technical, challenge.

Highlights: Ben Recht humorously acknowledges an 'AI Winter' and plans to analyze the culture and code behind it.

Worth reading: Provides perspective on cyclical hype in AI and the importance of critical analysis.

Highlights: Ben Recht celebrates the success of UC Berkeley alumni in the field.

Worth reading: Shows community engagement and recognition of peers.

Highlights: Ben Recht announces teaching a course on learning and control after a long hiatus.

Worth reading: Indicates a shift in focus or renewed interest in control theory combined with learning.

Highlights: Ben Recht announces teaching a class on learning and control after nearly a decade.

Worth reading: Shows his return to teaching a core topic bridging machine learning and control theory.

Highlights: Recht plans to analyze AI culture and code during the 39th AI Winter.

Worth reading: Provides critical perspective on AI hype cycles and their cultural impact.

Highlights: Recht argues that open ML needs both hardware access and community dedication to openness.

Worth reading: Highlights the social dimension of open science in ML beyond just resources.

Highlights: Ben Recht is teaching a class on learning and control for the first time in nearly a decade.

Worth reading: Shows his renewed engagement with the intersection of control theory and machine learning.

Highlights: Ben Recht revisits Sutton's Bitter Lesson in the context of GPT-5.

Worth reading: Provides insight into how scaling laws and neural approaches continue to dominate AI progress.

Highlights: Ben Recht argues that open ML requires more than just hardware access; it needs a culture of openness.

Worth reading: Highlights the social and communal aspects of open science in ML.

Highlights: Critique of the Trump administration's focus on reproducibility in science, warning it may backfire despite some good faith.

Worth reading: Provides perspective on political influence on scientific standards.

Highlights: Announcement of teaching a class on learning and control after a long hiatus.

Worth reading: Relevant for those interested in his teaching and research focus.

Highlights: Revisiting a classic AI lesson in context of latest GPT advancements.

Worth reading: Connects historical AI insights to current LLM developments.

Highlights: Ben Recht announces he is teaching a course on learning and control after a long hiatus.

Worth reading: Indicates a shift in his teaching focus and potential new research directions.

Highlights: Ben Recht announces teaching a class on learning and control after a long hiatus.

Worth reading: Highlights his return to teaching a core subject in his expertise.

Highlights: Ben Recht announces teaching a class on learning and control after a long hiatus.

Worth reading: Highlights his return to teaching a key subject.

Highlights: Ben Recht questions the persistent appeal of optimization as a framework for decision-making and design.

Worth reading: Reflects his critical perspective on optimization in machine learning and related fields.

Highlights: Ben Recht discusses the distinction between actions and predictions.

Worth reading: Reflects his thinking on control vs. prediction.

Highlights: Ben Recht reflects on the distinction between actions and predictions in machine learning.

Worth reading: Insight into his thinking on decision-making vs. prediction.

Highlights: Ben Recht announces teaching a class on learning and control after nearly a decade.

Worth reading: Highlights a shift in focus from pure machine learning to control theory integration.

Highlights: Ben Recht's AI reading list has remained unchanged since 2019, suggesting stable foundational knowledge.

Worth reading: Indicates his perspective on enduring AI concepts versus fleeting trends.

Highlights: Ben Recht acknowledges Berkeley alumni success at an event.

Worth reading: Shows his community engagement and pride in UC Berkeley alumni.

Highlights: Ben Recht notes the success of Berkeley alumni at an event.

Worth reading: Shows his pride in the Berkeley community and connections.

Highlights: Ben Recht expresses pride in seeing UC Berkeley alumni succeed in their careers.

Worth reading: Shows Recht's connection to the Berkeley community and his support for former students.