Stories with Tag ACM

• Richard Sutton and Andrew Barto Win 2024 Turing Award

  permalink

  Posted: 2025-03-05 10:03:31

  Verbose tl;dr

  Richard Sutton and Andrew Barto have been awarded the 2024 ACM A.M. Turing Award for their foundational contributions to reinforcement learning (RL). Their collaborative work, spanning decades and culminating in the influential textbook Reinforcement Learning: An Introduction, established key algorithms, conceptual frameworks, and theoretical understandings that propelled RL from a niche topic to a central area of artificial intelligence. Their research laid the groundwork for numerous breakthroughs in fields like robotics, game playing, and resource management, enabling the development of intelligent systems capable of learning through trial and error.

  The Association for Computing Machinery (ACM) has bestowed the prestigious 2024 A.M. Turing Award, often referred to as the "Nobel Prize of Computing," upon Richard S. Sutton and Andrew G. Barto for their groundbreaking and foundational contributions to the field of reinforcement learning (RL). Their collaborative work, spanning several decades, has revolutionized the way computers learn and interact with their environment, paving the way for advancements in artificial intelligence that were previously relegated to the realm of science fiction.

  Sutton and Barto's research has been instrumental in establishing reinforcement learning as a distinct and powerful paradigm within machine learning. Their seminal textbook, "Reinforcement Learning: An Introduction," initially published in 1998 and later updated in a second edition in 2018, serves as the definitive guide to the field. This comprehensive work has not only educated generations of researchers and practitioners but has also codified the core principles and algorithms that underpin contemporary reinforcement learning.

  The award specifically recognizes their contributions to the development of temporal-difference learning, a crucial aspect of reinforcement learning that allows agents to learn from ongoing experience without waiting for a final outcome. This methodology enables machines to adapt to dynamic environments and make predictions about future rewards, leading to more efficient and effective learning processes. Their exploration of policy gradient methods has also been pivotal, enabling the direct optimization of control policies within reinforcement learning systems. This further refines the learning process, allowing agents to learn optimal strategies for interacting with complex environments.

  The impact of their work extends far beyond academia. Reinforcement learning, thanks to their pioneering research, is now employed in a diverse array of practical applications. These include robotics, where it allows robots to learn complex motor skills and navigate challenging terrains; game playing, enabling AI agents to achieve superhuman performance in games like Go and chess; resource management, where it optimizes energy consumption and distribution in complex systems; and personalized recommendations, where it tailors online experiences to individual user preferences.

  The Turing Award is a testament to the profound influence Sutton and Barto have exerted on the field of computer science. Their decades-long dedication to the advancement of reinforcement learning has not only enriched our understanding of machine intelligence but has also opened doors to a future where intelligent systems can seamlessly integrate into our lives, solving complex problems and enhancing human capabilities in myriad ways. Their contributions have been fundamental to the ongoing evolution of artificial intelligence and will continue to inspire future generations of researchers and innovators.

  • Richard Sutton
  • Andrew Barto
  • Turing Award
  • ACM
  • Association for Computing Machinery
  • reinforcement learning
  • artificial intelligence
  • AI
  • Computer Science
  • Temporal-Difference Learning
  • TD Learning
  • 2024 Turing Award
  • Awards
  • machine learning

  Summary of Comments ( 53 )
  https://news.ycombinator.com/item?id=43264847

  Verbose tl;dr

  Hacker News commenters overwhelmingly praised Sutton and Barto's contributions to reinforcement learning, calling their book the "bible" of the field and highlighting its impact on generations of researchers. Several shared personal anecdotes about using their book, both in academia and industry. Some discussed the practical applications of reinforcement learning, ranging from robotics and game playing to personalized recommendations and resource management. A few commenters delved into specific technical aspects, mentioning temporal-difference learning and policy gradients. There was also discussion about the broader significance of the Turing Award and its recognition of fundamental research.

  The Hacker News post titled "Richard Sutton and Andrew Barto Win 2024 Turing Award" has generated several comments discussing the significance of their work in reinforcement learning. Many commenters express admiration for Sutton and Barto's foundational contributions, particularly their textbook "Reinforcement Learning: An Introduction," which is widely considered the canonical text in the field. Several people share personal anecdotes about how the book influenced their careers or helped them understand complex concepts. The clarity and accessibility of the book are frequently praised.

  Some comments delve into the technical details of reinforcement learning, highlighting the importance of temporal difference learning, a key concept pioneered by Sutton. They discuss its impact on various fields, including robotics, game playing, and artificial intelligence more broadly. There's acknowledgement of the long trajectory of research in this area and the dedication required to bring these ideas to fruition.

  A few commenters mention the relative lack of public awareness of reinforcement learning compared to other areas of AI, like deep learning, despite its profound impact. They suggest this might be due to the less visually spectacular nature of reinforcement learning applications.

  A recurring theme is the well-deserved nature of the award, with many expressing their respect for Sutton and Barto's intellectual contributions and their influence on subsequent generations of researchers. Some comments also speculate on the future of reinforcement learning and its potential to solve even more complex problems. There's a sense of excitement about the ongoing developments in the field and the possibilities that lie ahead. Finally, several commenters express their congratulations to the award winners.

• A history of APL in the USSR (1991)

  permalink

  Posted: 2025-01-30 13:18:49

  Verbose tl;dr

  This paper chronicles the adoption and adaptation of APL in the Soviet Union up to 1991. Initially hampered by hardware limitations and the lack of official support, APL gained a foothold through enthusiastic individuals who saw its potential for scientific computing and education. The development of Soviet APL interpreters, notably on ES EVM mainframes and personal computers like the Iskra-226, fostered a growing user community. Despite challenges like Cyrillic character adaptation and limited access to Western resources, Soviet APL users formed active groups, organized conferences, and developed specialized applications in various fields, demonstrating a distinct and resilient APL subculture. The arrival of perestroika further facilitated collaboration and exchange with the international APL community.

  This 1991 paper, "A History of APL in the USSR," meticulously chronicles the introduction, adoption, and evolution of the APL programming language within the Soviet Union, painting a picture of a vibrant, albeit isolated, community of users and developers. The narrative begins in the late 1960s, a period marked by the Iron Curtain and restricted access to Western technology. Despite these limitations, the inherent power and conciseness of APL, particularly its suitability for scientific and mathematical computations, captured the attention of Soviet academics and researchers.

  The initial exposure to APL within the USSR happened through limited channels like published literature and occasional international conferences. This scarcity of direct access fostered an environment of independent implementation and adaptation. The authors describe the development of several indigenous APL dialects, each tailored to the specific hardware limitations and computational needs of the time. These included interpreters designed for mainframe systems like the BESM-6 and Minsk-32, often lacking the full functionality of their Western counterparts but nonetheless enabling crucial research and development within the Soviet Union.

  The paper details the painstaking process of creating these APL implementations, often involving manual translation of algorithms and adaptation to the Cyrillic alphabet. This period of independent development fostered a deep understanding of the language's underlying principles and contributed to a unique flavor of APL within the Soviet context. The authors highlight the dedication and ingenuity of the early Soviet APL pioneers who overcame significant obstacles to bring this powerful tool to their research communities.

  As the narrative progresses into the 1970s and 80s, the paper explores the emergence of localized APL communities. These groups, often centered around specific research institutions or universities, facilitated the exchange of knowledge, software, and experiences, playing a crucial role in disseminating APL throughout the Soviet Union. The authors underscore the importance of these informal networks in overcoming the limitations imposed by the political and technological landscape.

  The paper also touches on the challenges faced by the Soviet APL community. The restricted access to Western hardware and software, combined with the inherent complexity of the language itself, presented significant hurdles to wider adoption. Furthermore, the lack of official support and standardization hindered the development of robust and portable APL applications.

  Despite these challenges, the authors depict a resilient and enthusiastic community of APL users within the USSR. They highlight the significant contributions made by Soviet researchers in areas like mathematical modeling, scientific computation, and data analysis, all powered by the unique capabilities of APL.

  Finally, the paper concludes with a glimpse into the future of APL in the post-Soviet era, anticipating the potential for greater collaboration and integration with the international APL community. The authors express optimism that the lifting of restrictions and increased access to Western technology will usher in a new era of growth and innovation for APL within the former Soviet Union, allowing the Soviet APL community to finally connect with and contribute to the wider global APL ecosystem.

  • APL
  • USSR
  • Soviet Union
  • programming language
  • History
  • 1991
  • Computing
  • Software
  • ACM
  • Technology
  • Eastern Bloc
  • Cold War

  Summary of Comments ( 7 )
  https://news.ycombinator.com/item?id=42877430

  Verbose tl;dr

  HN commenters discuss the fascinating history of APL's adoption and adaptation within the Soviet Union, highlighting the ingenuity required to implement it on limited hardware. Several share personal anecdotes about using APL on Soviet computers, recalling its unique characteristics and the challenges of working with its specialized keyboard. Some commenters delve into the technical details of Soviet hardware limitations and the creative solutions employed to overcome them, including modifying character sets and developing custom input methods. The discussion also touches on the broader context of computing in the USSR, with mentions of other languages and the impact of restricted access to Western technology. A few commenters express interest in learning more about the specific dialects of APL developed in the Soviet Union and the influence of these adaptations on later versions of the language.

  The Hacker News post titled "A history of APL in the USSR (1991)" contains several comments discussing various aspects of APL, its history, and its use within the Soviet Union.

  Several commenters reminisce about their experiences with APL. One user recalls encountering APL in the 1980s, initially perceiving it as "line noise" but later appreciating its expressive power for array manipulation. Another shares their experience of learning APL during university in the Soviet Union, highlighting the challenges posed by the specialized keyboard required for its unique symbols. This user also notes the language's prevalence in academic settings, particularly for tasks involving matrix and vector operations. A third user discusses the development of an APL interpreter for the Soviet BESM-6 mainframe computer.

  The discussion also touches upon the practical applications of APL within the Soviet Union. One commenter notes its use in economic planning and optimization, given its strength in handling matrices and performing complex calculations efficiently. Another emphasizes the theoretical nature of much of the APL work in the USSR, suggesting that practical implementations were less common due to hardware limitations and the specialized nature of the language.

  Some commenters delve into more technical details, comparing APL to other languages and discussing its unique features. One user compares APL's array-oriented paradigm to J, a successor language, noting the similarities and differences in their approaches to symbolic computation. Another points out APL's influence on other languages and paradigms, particularly its impact on array programming concepts in more mainstream languages.

  A few comments also provide additional context about the historical backdrop of APL in the USSR. One user suggests the paper offers insight into the challenges faced by Soviet scientists and programmers, particularly in accessing and utilizing Western technology during the Cold War era. The scarcity of resources and the relative isolation of the Soviet scientific community are mentioned as factors influencing the adoption and development of APL within the country.

  The comments overall provide a blend of personal anecdotes, technical insights, and historical context, enriching the understanding of APL's role and impact within the Soviet Union.