Marmof

marmof

Cyrus Harding and his companions slept like innocent marmots

Cyrus Harding and his companions slept like innocent marmots in the cave

Grundsaudaag. Jour de la Marmotte. Ground Hog Day

The Greedy Miner’s Dilemma: Flaws in DAG-Based Consensus Protocols

Table of Links Abstract and 1. Introduction Why ELIZA?
The Intelligence Engineers
Newell, Shaw, and Simon’s IPL Logic Theorist: The First True AIs
From IPL to SLIP and Lisp
A Critical Tangent into Gomoku
Interpretation is the Core of Intelligence
The Threads Come Together: Interpretation, Language, Lists, Graphs, and Recursion
Finally ELIZA: A Platform, Not a Chat Bot!
A Perfect Irony: A Lisp ELIZA Escapes and is Misinterpreted by the AI Community
Another Wave: A BASIC ELIZA turns the PC Generation on to AI
Conclusion: A certain danger lurks there
Acknowledgements and References 13 Acknowledgements This paper developed through extensive discussions over several years with “Team ELIZA”, including Anthony Hay, Art Schwarz, Sarah Ciston, Peggy Weil, Peter Millican, David Berry, and Mark Marino. I am especially indebted to Anthony Hay and Art Schwarz for detailed discussion about technical aspects of ELIZA and SLIP, such as how symbol manipulation worked in SLIP. Terry Winograd and John Markoff participated in helpful background discussions, and Anne Rawls and Clemens Eisenmann provided access to the Garfinkel archives, and Andrei Korbut introduced us to them. Many anonymous commenters in the Hacker News community helped clarify concepts, and provided additional pointers to information that I could not have easily discovered on my own. I am especially indebted to the MIT Archivists, Myles Crowley, Mikki Macdonald, and Allison Schmitt. References [1] https://grammarist.com/new-words/chatbot/. Accessed 2024-04-18. [2] https://sites.google.com/view/elizagen-org/news#h.ykbzq5nuiccs. Accessed 2024-04-18. [3] https://plato.stanford.edu/entries/church-turing/. Accessed 2024-04-18. [4] https://en.wikipedia.org/wiki/Von_Neumann_architecture. Accessed 2024-04-18. [5] https://en.wikipedia.org/wiki/Information_Processing_Language. Accessed 2024-04-18. [6] https://en.wikipedia.org/wiki/Dartmouth_workshop. Accessed 2024- 04-18. [7] https://en.wikipedia.org/wiki/John_McCarthy_(computer_scientist). Accessed 2024-04-18. [8] https://plato.stanford.edu/entries/meaning/. Accessed 2024-04-18. [9] https://en.wikipedia.org/wiki/Conceptual_blending. Accessed 2024- 04-18. [10] https://sites.google.com/view/elizagen-org/news#h.74lv9le8r29u. Accessed 2024-04-18. [11] https://en.wikipedia.org/wiki/ARPANET. Accessed 2024-04-18. [12] https://en.wikipedia.org/wiki/History_of_personal_computers. Accessed 2024-04-18. [13] https://en.wikipedia.org/wiki/HAL_9000. Accessed 2024-04-18. [14] https://sites.google.com/view/elizagen-org/. Accessed 2024-04-18. [15] https://en.wikipedia.org/wiki/HyperCard. Accessed 2024-04-18. [16] https://sites.google.com/view/elizaarchaeology/. Accessed 2024- 04-18. [17] D. G. Bobrow. “Natural language input for a computer problem solving system”. PhD Dissertation. Cambridge, MA: Massachusetts Institute of Technology, May 1964. [18] L. Cameron. What to know about the scientist who invented the term ”software engineering”. https://www.computer.org/publications/tech-news/events/what-to-know-aboAccessed: 2024-04-18. Oct. 2018. [19] D. Chisnall. Influential programming languages, Part 4: Lisp. https://www.informit.com/articles/artAccessed 2024-04-18. 2011. [20] Noam Chomsky and George A. Miller. “Introduction to the formal analysis of natural languages”. In: Handbook of Mathematical Psychology. Ed. by R. Duncan Luce, Robert R. Bush, and Eugene Galanter. Vol. 2. Hoboken, NJ: John Wiley and Sons, 1963, pp. 269–321. [21] K. M. Colby. “Modeling a paranoid mind”. In: The Behavioral and Brain Sciences 4 (1981), pp. 515–560. [22] K. M. Colby and J. P. Gilbert. “Programming a computer model of neurosis”. In: J. Math Psych 1 (1964), pp. 405–416. [23] Ada Augusta Countess of Lovelace. Notes on L. F. Menabrea’s “Sketch of the analytical engine invented by Charles Babbage”. http://imaginaryinstruments.org/lovelace-anaAccessed 2024-04-18. Sept. 1843. [24] G. Dyson. Turing’s cathedral. Vintage Books, 2012. [25] C. Eisenmann et al. ““Machine Down”: making sense of human–computer interaction—Garfinkel’s research on ELIZA and LYRIC from 1967 to 1969 and its contemporary relevance”. In: AI & Soc (2023). doi: 10.1007/s00146-023-01793-z. [26] E. Feigenbaum. An information processing theory of verbal learning. Paper P-1817. The RAND Corporation, 1959. [27] H. Gelernter, J. R. Hansen, and L. L. Gerberich. “A Fortran-compiled list-processing language”. In: JACM 7.2 (Apr. 1960), pp. 87–101. [28] D. Gentner and A. L. Stevens. Mental models. Hillsdale, NJ: Lawrence Erlbaum Associates, 1983. [29] B. F. Green et al. “Baseball: An automatic question-answerer”. In: Name of Journal or Conference Volume Number.Issue Number (1961). Additional Information, Page Numbers. [30] F. J. Gruenberger. The history of the JOHNNIAC. RAND memorandum RM-5654-PR. 1968. [31] P. Hayward. Flexible discussion under student control in the ELIZA computer program. Educational Research Center, Massachusetts Institute of Technology, Cambridge. 1967. [32] D. R. Hofstadter. G¨odel, Escher, Bach: An eternal golden braid. New York: Basic Books, 1979. [33] P. McCorduck. Machines who think. W.H. Freeman and Co., 1979. [34] M. T. McGuire, S. Lorch, and G. C. Quarton. “Man-machine natural language exchanges based on selected features of unrestricted input—II. The use of the time-shared computer as a research tool in studying dyadic communication”. In: J. Psychiat. Res. 5.2 (1967). [35] A. Newell and H. A. Simon. “Computer science as empirical inquiry: Symbols and search”. In: Communications of the ACM 19.3 (1976), pp. 113– 126. [36] S. North. “ELIZA”. In: Creative Computing (1977), pp. 100–103. [37] C. Petzold. The annotated turing. Wiley, 2008. [38] G. C. Quarton, M. T. McGuire, and S. Lorch. “Man-machine natural language exchanges based on selected features of unrestricted input—I. The development of the time-shared computer as a research tool in studying dyadic communication”. In: J. Psychiat. Res. 5.2 (1967). [39] R. C. Schank and R. P. Abelson. Scripts, plans, goals, and understanding: An inquiry into human knowledge structures. Hillsdale, NJ: Lawrence Erlbaum Associates, 1977. [40] J. R. Searle. “Minds, brains, and programs”. In: Behavioral and Brain Sciences 3.3 (1980), pp. 417–457. [41] P. Seibel. Coders at Work. APress, 2009. [42] SLIP: University of Michigan executive system for the IBM 7090 computer. Vol. 2. University of Michigan Computing Center, 1965. [43] E. F. Taylor. ELIZA: A skimmable report on the ELIZA conversational tutoring system. Educational Research Center, Massachusetts Institute of Technology, Cambridge. 1968. [44] A. Turing. “Computing machinery and intelligence”. In: Mind 49 (1950), pp. 433–460. [45] D. Walden and R. Nickerson, eds. A culture of innovation: Insider accounts of computing and life at BBN. A Sixty Year Report 18 October 1948 to 1 July 2010. Waterside Publishing, 201. [46] J. Weizenbaum. Computer power and human reason. W.H. Freeman and Co., 1976. [47] J. Weizenbaum. “ELIZA—a computer program for the study of natural language communication between man and machine”. In: Communications of the ACM 9.1 (1966), pp. 36–45. doi: 10.1145/365153.365168. [48] J. Weizenbaum. “How to make a computer appear intelligent”. In: Datamation (Feb. 1962), pp. 24–26. [49] J. Weizenbaum. “Symmetric list processor”. In: Comm. ACM 6.9 (1963), pp. 524–536. Author:
(1) Jeff Shrager, Blue Dot Change and Stanford University Symbolic Systems Program (Adjunct)( jshrager@stanford.edu). This paper is available on arxiv under CC BY 4.0 license. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction Why ELIZA? The Intelligence Engineers Newell, Shaw, and Simon’s IPL Logic Theorist: The First True AIs From IPL to SLIP and Lisp A Critical Tangent into Gomoku Interpretation is the Core of Intelligence The Threads Come Together: Interpretation, Language, Lists, Graphs, and Recursion Finally ELIZA: A Platform, Not a Chat Bot! A Perfect Irony: A Lisp ELIZA Escapes and is Misinterpreted by the AI Community Another Wave: A BASIC ELIZA turns the PC Generation on to AI Conclusion: A certain danger lurks there Acknowledgements and References Why ELIZA? Why ELIZA? The Intelligence Engineers The Intelligence Engineers Newell, Shaw, and Simon’s IPL Logic Theorist: The First True AIs Newell, Shaw, and Simon’s IPL Logic Theorist: The First True AIs From IPL to SLIP and Lisp From IPL to SLIP and Lisp A Critical Tangent into Gomoku A Critical Tangent into Gomoku Interpretation is the Core of Intelligence Interpretation is the Core of Intelligence The Threads Come Together: Interpretation, Language, Lists, Graphs, and Recursion The Threads Come Together: Interpretation, Language, Lists, Graphs, and Recursion Finally ELIZA: A Platform, Not a Chat Bot! Finally ELIZA: A Platform, Not a Chat Bot! A Perfect Irony: A Lisp ELIZA Escapes and is Misinterpreted by the AI Community A Perfect Irony: A Lisp ELIZA Escapes and is Misinterpreted by the AI Community Another Wave: A BASIC ELIZA turns the PC Generation on to AI Another Wave: A BASIC ELIZA turns the PC Generation on to AI Conclusion: A certain danger lurks there Conclusion: A certain danger lurks there Acknowledgements and References Acknowledgements and References 13 Acknowledgements This paper developed through extensive discussions over several years with “Team ELIZA”, including Anthony Hay, Art Schwarz, Sarah Ciston, Peggy Weil, Peter Millican, David Berry, and Mark Marino. I am especially indebted to Anthony Hay and Art Schwarz for detailed discussion about technical aspects of ELIZA and SLIP, such as how symbol manipulation worked in SLIP. Terry Winograd and John Markoff participated in helpful background discussions, and Anne Rawls and Clemens Eisenmann provided access to the Garfinkel archives, and Andrei Korbut introduced us to them. Many anonymous commenters in the Hacker News community helped clarify concepts, and provided additional pointers to information that I could not have easily discovered on my own. I am especially indebted to the MIT Archivists, Myles Crowley, Mikki Macdonald, and Allison Schmitt. References [1] https://grammarist.com/new-words/chatbot/. Accessed 2024-04-18. [2] https://sites.google.com/view/elizagen-org/news#h.ykbzq5nuiccs. Accessed 2024-04-18. [3] https://plato.stanford.edu/entries/church-turing/. Accessed 2024-04-18. [4] https://en.wikipedia.org/wiki/Von_Neumann_architecture. Accessed 2024-04-18. [5] https://en.wikipedia.org/wiki/Information_Processing_Language. Accessed 2024-04-18. [6] https://en.wikipedia.org/wiki/Dartmouth_workshop. Accessed 2024- 04-18. [7] https://en.wikipedia.org/wiki/John_McCarthy_(computer_scientist). Accessed 2024-04-18. [8] https://plato.stanford.edu/entries/meaning/. Accessed 2024-04-18. [9] https://en.wikipedia.org/wiki/Conceptual_blending. Accessed 2024- 04-18. [10] https://sites.google.com/view/elizagen-org/news#h.74lv9le8r29u. Accessed 2024-04-18. [11] https://en.wikipedia.org/wiki/ARPANET. Accessed 2024-04-18. [12] https://en.wikipedia.org/wiki/History_of_personal_computers. Accessed 2024-04-18. [13] https://en.wikipedia.org/wiki/HAL_9000. Accessed 2024-04-18. [14] https://sites.google.com/view/elizagen-org/. Accessed 2024-04-18. [15] https://en.wikipedia.org/wiki/HyperCard. Accessed 2024-04-18. [16] https://sites.google.com/view/elizaarchaeology/. Accessed 2024- 04-18. [17] D. G. Bobrow. “Natural language input for a computer problem solving system”. PhD Dissertation. Cambridge, MA: Massachusetts Institute of Technology, May 1964. [18] L. Cameron. What to know about the scientist who invented the term ”software engineering”. https://www.computer.org/publications/tech-news/events/what-to-know-aboAccessed: 2024-04-18. Oct. 2018. [19] D. Chisnall. Influential programming languages, Part 4: Lisp. https://www.informit.com/articles/artAccessed 2024-04-18. 2011. [20] Noam Chomsky and George A. Miller. “Introduction to the formal analysis of natural languages”. In: Handbook of Mathematical Psychology. Ed. by R. Duncan Luce, Robert R. Bush, and Eugene Galanter. Vol. 2. Hoboken, NJ: John Wiley and Sons, 1963, pp. 269–321. [21] K. M. Colby. “Modeling a paranoid mind”. In: The Behavioral and Brain Sciences 4 (1981), pp. 515–560. [22] K. M. Colby and J. P. Gilbert. “Programming a computer model of neurosis”. In: J. Math Psych 1 (1964), pp. 405–416. [23] Ada Augusta Countess of Lovelace. Notes on L. F. Menabrea’s “Sketch of the analytical engine invented by Charles Babbage”. http://imaginaryinstruments.org/lovelace-anaAccessed 2024-04-18. Sept. 1843. [24] G. Dyson. Turing’s cathedral. Vintage Books, 2012. [25] C. Eisenmann et al. ““Machine Down”: making sense of human–computer interaction—Garfinkel’s research on ELIZA and LYRIC from 1967 to 1969 and its contemporary relevance”. In: AI & Soc (2023). doi: 10.1007/s00146-023-01793-z. [26] E. Feigenbaum. An information processing theory of verbal learning. Paper P-1817. The RAND Corporation, 1959. [27] H. Gelernter, J. R. Hansen, and L. L. Gerberich. “A Fortran-compiled list-processing language”. In: JACM 7.2 (Apr. 1960), pp. 87–101. [28] D. Gentner and A. L. Stevens. Mental models. Hillsdale, NJ: Lawrence Erlbaum Associates, 1983. [29] B. F. Green et al. “Baseball: An automatic question-answerer”. In: Name of Journal or Conference Volume Number.Issue Number (1961). Additional Information, Page Numbers. [30] F. J. Gruenberger. The history of the JOHNNIAC. RAND memorandum RM-5654-PR. 1968. [31] P. Hayward. Flexible discussion under student control in the ELIZA computer program. Educational Research Center, Massachusetts Institute of Technology, Cambridge. 1967. [32] D. R. Hofstadter. G¨odel, Escher, Bach: An eternal golden braid. New York: Basic Books, 1979. [33] P. McCorduck. Machines who think. W.H. Freeman and Co., 1979. [34] M. T. McGuire, S. Lorch, and G. C. Quarton. “Man-machine natural language exchanges based on selected features of unrestricted input—II. The use of the time-shared computer as a research tool in studying dyadic communication”. In: J. Psychiat. Res. 5.2 (1967). [35] A. Newell and H. A. Simon. “Computer science as empirical inquiry: Symbols and search”. In: Communications of the ACM 19.3 (1976), pp. 113– 126. [36] S. North. “ELIZA”. In: Creative Computing (1977), pp. 100–103. [37] C. Petzold. The annotated turing. Wiley, 2008. [38] G. C. Quarton, M. T. McGuire, and S. Lorch. “Man-machine natural language exchanges based on selected features of unrestricted input—I. The development of the time-shared computer as a research tool in studying dyadic communication”. In: J. Psychiat. Res. 5.2 (1967). [39] R. C. Schank and R. P. Abelson. Scripts, plans, goals, and understanding: An inquiry into human knowledge structures. Hillsdale, NJ: Lawrence Erlbaum Associates, 1977. [40] J. R. Searle. “Minds, brains, and programs”. In: Behavioral and Brain Sciences 3.3 (1980), pp. 417–457. [41] P. Seibel. Coders at Work. APress, 2009. [42] SLIP: University of Michigan executive system for the IBM 7090 computer. Vol. 2. University of Michigan Computing Center, 1965. [43] E. F. Taylor. ELIZA: A skimmable report on the ELIZA conversational tutoring system. Educational Research Center, Massachusetts Institute of Technology, Cambridge. 1968. [44] A. Turing. “Computing machinery and intelligence”. In: Mind 49 (1950), pp. 433–460. [45] D. Walden and R. Nickerson, eds. A culture of innovation: Insider accounts of computing and life at BBN. A Sixty Year Report 18 October 1948 to 1 July 2010. Waterside Publishing, 201. [46] J. Weizenbaum. Computer power and human reason. W.H. Freeman and Co., 1976. [47] J. Weizenbaum. “ELIZA—a computer program for the study of natural language communication between man and machine”. In: Communications of the ACM 9.1 (1966), pp. 36–45. doi: 10.1145/365153.365168. [48] J. Weizenbaum. “How to make a computer appear intelligent”. In: Datamation (Feb. 1962), pp. 24–26. [49] J. Weizenbaum. “Symmetric list processor”. In: Comm. ACM 6.9 (1963), pp. 524–536. Author: (1) Jeff Shrager, Blue Dot Change and Stanford University Symbolic Systems Program (Adjunct)( jshrager@stanford.edu). Author: Author: (1) Jeff Shrager, Blue Dot Change and Stanford University Symbolic Systems Program (Adjunct)( jshrager@stanford.edu). This paper is available on arxiv under CC BY 4.0 license. This paper is available on arxiv under CC BY 4.0 license. available on arxiv available on arxiv

Who Made ELIZA Possible? 

A few years ago, a proficient designer needed skills in Adobe Photoshop for retouching, Adobe Illustrator for drawing, and Figma for creating landing pages. However, the demand for highly specialized designers has surged. Now, a product designer focuses solely on product design, and a graphic designer concentrates on graphic elements, without overlapping roles. The need for all-in-one designers is declining, which is a positive shift. However, this trend hasn’t yet affected the game development industry. GameDev still requires versatile specialists who possess a broad skill set. If you’re aiming for a career in the game industry, you must be adept at a wide range of skills. In this article, I will delve into the essential skills required for GameDev and illustrate each with examples from my own experience. Graphic Design: From Banners to Brand Identity Graphic design encompasses many elements. It includes identity and branding, as well as all promotional materials, from avatars and banners to merchandise and billboards. As part of graphic design in game development, you will develop a project’s identity and brand book, create banners, design social media assets, and develop merchandise. What you need to know in graphic design: you need a comprehensive understanding of typography, color theory, and composition, and the ability to integrate these elements beautifully into the right format. UX/UI and Web Every game has a website, usually a multi-page site that needs to be designed for easy user navigation. Here too, the designer must think about structure and design interfaces. In web design, you will handle the development of promotional sites, special projects, and landing pages for new game updates. For long-running projects that are frequently updated, you’ll need to create a design system. For more straightforward, one-off sites, you’ll build a UI kit. This category also includes creating email newsletters, as the tasks are similar to web design. What you need to know about web design: Interface design skills are essential, from research to visualization. 3D Design for Marketing For web and graphic design layouts, you need various 3D graphics—cannons, chests, 3D icons, renders of characters, weapons, and achievements. Where do you get them with the perfect angle and lighting? You create them yourself. Within the scope of 3D tasks, you’ll need to: Render in-game models with the right angle and lighting.
Model and render materials for promotions, including lighting and action boosts.
Create 3D graphics for special projects.
Develop 3D marketing materials that are later integrated directly into the game, such as chevrons and badges for user customization.
Create 3D sticker packs for Discord. What you need to know about 3D design: At a minimum, you should be proficient in one 3D software package, but preferably two. One for modeling, such as Cinema 4D, Blender, or Maya, and another for texturing, like Adobe Substance 3D Painter or Marmoset. Sticker Pack Case.
My first challenging 3D job was a duck sticker pack. I spent sleepless nights grappling with complex materials and shapes while trying to convey anthropomorphic emotions on the ducks’ faces. It was tough, but I succeeded, and I will never forget those ducks. Retouching for Anything That's Not Perfect With 3D models, things can go wrong: polygons may be out of place, lighting may be off, or the model may come from the developer’s engine. To make everything look attractive and polished, retouching is essential. Knowledge of retouching allows you to quickly assemble a story art banner from existing assets or enhance them for further use. What you need to know about retouching: A basic level of retouching involves using Photoshop sliders, masks, adjustment layers, and a graphics tablet, and understanding the basics of light shading and composition. Neural Networks as an Additional Retouching Tool Neural networks can now be used for retouching. For example, you may have a 3D render of poor quality based on a ready-made model from developers. You can take the long route and retouch it yourself, or the short route by using a neural network like SD. In just five minutes, you can achieve a good result: the model will have beautiful hair, appropriate facial expressions, and correct color corrections. What you need to know about neural networks: Using them effectively requires time and resources to train the model. You need to feed the neural network about 100 photos of the object from different angles with prompts, review the results, make edits, and review everything again. Motion Design Let’s say the website for a game is ready, but it looks boring. What to do? Add IDLE animation to the main art. For this, you need to know how to create such animations. Knowledge of motion graphics allows you to create animated logos, art for promotional materials, and showreels. What you need to know to excel in motion graphics: Learn After Effects and master layering art in Photoshop. The More You Know, The More You Can Do Of course, on a design team, everyone has an area in which they are highly skilled experts. For example, I wouldn’t expect high-quality AAA art from a web designer. However, in game development, a web designer might still get AAA-art tasks and will need to handle them. Therefore, the more diverse tasks you can manage without relying on colleagues, the more valuable you will be as a specialist. Consequently, your grade, salary, and recognition will grow faster. I remember vividly how, as a designer with over a decade of experience, I retouched characters late into the night, struggled with 3D models, or nervously created my first landing page. Now I realize that it was a crucial experience. Acquired skills significantly broaden your horizons and enhance your design capabilities. There emerges an ironclad confidence that you can handle any task (even 3D ducks). So, I highly recommend developing multidisciplinary skills, especially if you want to enter game development. Even if you don’t, the more you know, the more you can achieve. A few years ago, a proficient designer needed skills in Adobe Photoshop for retouching, Adobe Illustrator for drawing, and Figma for creating landing pages. However, the demand for highly specialized designers has surged. Now, a product designer focuses solely on product design, and a graphic designer concentrates on graphic elements, without overlapping roles. A few years ago, a proficient designer needed skills in Adobe Photoshop for retouching, Adobe Illustrator for drawing, and Figma for creating landing pages. However, the demand for highly specialized designers has surged. Now, a product designer focuses solely on product design, and a graphic designer concentrates on graphic elements, without overlapping roles. The need for all-in-one designers is declining, which is a positive shift. However, this trend hasn’t yet affected the game development industry. GameDev still requires versatile specialists who possess a broad skill set. If you’re aiming for a career in the game industry, you must be adept at a wide range of skills. The need for all-in-one designers is declining, which is a positive shift. However, this trend hasn’t yet affected the game development industry. GameDev still requires versatile specialists who possess a broad skill set. If you’re aiming for a career in the game industry, you must be adept at a wide range of skills. In this article, I will delve into the essential skills required for GameDev and illustrate each with examples from my own experience. In this article, I will delve into the essential skills required for GameDev and illustrate each with examples from my own experience. Graphic Design: From Banners to Brand Identity Graphic Design: From Banners to Brand Identity Graphic design encompasses many elements. It includes identity and branding, as well as all promotional materials, from avatars and banners to merchandise and billboards. As part of graphic design in game development, you will develop a project’s identity and brand book, create banners, design social media assets, and develop merchandise. What you need to know in graphic design: you need a comprehensive understanding of typography, color theory, and composition, and the ability to integrate these elements beautifully into the right format. What you need to know in graphic design: you need a comprehensive understanding of typography, color theory, and composition, and the ability to integrate these elements beautifully into the right format. UX/UI and Web Every game has a website, usually a multi-page site that needs to be designed for easy user navigation. Here too, the designer must think about structure and design interfaces. In web design, you will handle the development of promotional sites, special projects, and landing pages for new game updates. For long-running projects that are frequently updated, you’ll need to create a design system. For more straightforward, one-off sites, you’ll build a UI kit. This category also includes creating email newsletters, as the tasks are similar to web design. What you need to know about web design: Interface design skills are essential, from research to visualization. What you need to know about web design: Interface design skills are essential, from research to visualization. 3D Design for Marketing For web and graphic design layouts, you need various 3D graphics—cannons, chests, 3D icons, renders of characters, weapons, and achievements. Where do you get them with the perfect angle and lighting? You create them yourself. Within the scope of 3D tasks, you’ll need to: Render in-game models with the right angle and lighting. Model and render materials for promotions, including lighting and action boosts. Create 3D graphics for special projects. Develop 3D marketing materials that are later integrated directly into the game, such as chevrons and badges for user customization. Create 3D sticker packs for Discord. Render in-game models with the right angle and lighting. Model and render materials for promotions, including lighting and action boosts. Create 3D graphics for special projects. Develop 3D marketing materials that are later integrated directly into the game, such as chevrons and badges for user customization. Create 3D sticker packs for Discord. What you need to know about 3D design: At a minimum, you should be proficient in one 3D software package, but preferably two. One for modeling, such as Cinema 4D, Blender, or Maya, and another for texturing, like Adobe Substance 3D Painter or Marmoset. What you need to know about 3D design: At a minimum, you should be proficient in one 3D software package, but preferably two. One for modeling, such as Cinema 4D, Blender, or Maya, and another for texturing, like Adobe Substance 3D Painter or Marmoset. Sticker Pack Case. My first challenging 3D job was a duck sticker pack. I spent sleepless nights grappling with complex materials and shapes while trying to convey anthropomorphic emotions on the ducks’ faces. It was tough, but I succeeded, and I will never forget those ducks. Sticker Pack Case. Sticker Pack Case. My first challenging 3D job was a duck sticker pack. I spent sleepless nights grappling with complex materials and shapes while trying to convey anthropomorphic emotions on the ducks’ faces. It was tough, but I succeeded, and I will never forget those ducks. Retouching for Anything That's Not Perfect Retouching for Anything That's Not Perfect With 3D models, things can go wrong: polygons may be out of place, lighting may be off, or the model may come from the developer’s engine. To make everything look attractive and polished, retouching is essential. Knowledge of retouching allows you to quickly assemble a story art banner from existing assets or enhance them for further use. What you need to know about retouching: A basic level of retouching involves using Photoshop sliders, masks, adjustment layers, and a graphics tablet, and understanding the basics of light shading and composition. What you need to know about retouching: A basic level of retouching involves using Photoshop sliders, masks, adjustment layers, and a graphics tablet, and understanding the basics of light shading and composition. Neural Networks as an Additional Retouching Tool Neural networks can now be used for retouching. For example, you may have a 3D render of poor quality based on a ready-made model from developers. You can take the long route and retouch it yourself, or the short route by using a neural network like SD. In just five minutes, you can achieve a good result: the model will have beautiful hair, appropriate facial expressions, and correct color corrections. What you need to know about neural networks: Using them effectively requires time and resources to train the model. You need to feed the neural network about 100 photos of the object from different angles with prompts, review the results, make edits, and review everything again. What you need to know about neural networks: Using them effectively requires time and resources to train the model. You need to feed the neural network about 100 photos of the object from different angles with prompts, review the results, make edits, and review everything again. Motion Design Let’s say the website for a game is ready, but it looks boring. What to do? Add IDLE animation to the main art. For this, you need to know how to create such animations. Knowledge of motion graphics allows you to create animated logos, art for promotional materials, and showreels. What you need to know to excel in motion graphics: Learn After Effects and master layering art in Photoshop. What you need to know to excel in motion graphics: Learn After Effects and master layering art in Photoshop. The More You Know, The More You Can Do Of course, on a design team, everyone has an area in which they are highly skilled experts. For example, I wouldn’t expect high-quality AAA art from a web designer. However, in game development, a web designer might still get AAA-art tasks and will need to handle them. Therefore, the more diverse tasks you can manage without relying on colleagues, the more valuable you will be as a specialist. Consequently, your grade, salary, and recognition will grow faster. I remember vividly how, as a designer with over a decade of experience, I retouched characters late into the night, struggled with 3D models, or nervously created my first landing page. Now I realize that it was a crucial experience. Acquired skills significantly broaden your horizons and enhance your design capabilities. There emerges an ironclad confidence that you can handle any task (even 3D ducks). So, I highly recommend developing multidisciplinary skills, especially if you want to enter game development. Even if you don’t, the more you know, the more you can achieve.

Why GameDev Designers Are the Swiss Army Knives of Game Development

Artificial Intelligence has been advancing at an unprecedented rate, causing fear about whether AI will dominate various aspects of our lives in the near future and “take our jobs.” In this article, by leveraging Moore's Law and other technological growth principles, I’m going to make an educated estimation about the state of AI in the coming years. Let’s explore these predictions and explain why AI may not completely take over within the next five years, and what the timeline might look like for AI to reach a truly advanced state. The Current State of AI AI technology has made significant strides in recent years. From natural language processing to computer vision, AI applications are becoming increasingly sophisticated, whether that’s the recent GPT-4o model, Google’s Gemini, or any one of the models in the race to become the most capable AI model. There are lots of people using these models in positive ways to unlock new methodologies in research and education, but there are also lots of people using these technologies for evil such as deepfakes. However, despite these advancements, there are still considerable challenges and limitations that need to be addressed. Current AI systems need massive amounts of data, computational power, and energy to function effectively. Moore's Law and Its Implications for AI Moore's Law, the observation that the number of transistors on a microchip doubles approximately every two years, has been a reliable predictor of technological progress for decades. More generally, Moore’s Law points to technology getting twice as accessible, cheaper, and efficient every two years. According to Moore's Law, we can expect computational power to continue increasing exponentially. However, there are physical and economic limits to this growth that may impact the pace of AI development. Estimating AI's Growth Using Known Laws By extrapolating from Moore's Law and considering other technological growth trends, we can make predictions about AI's capabilities in the coming years. Computational Power If we consider top-of-the-notch GPUs today, let’s say NVIDIA A100, it provides approximately 312 teraflops (TFLOPS) of performance. Moore's Law suggests a doubling of transistors every two years, which implies a potential 8-fold increase in computational power over five years (2^2.5 ≈ 5.66). Future GPU Performance = 312 TFLOPS × 5.66 ≈ 1766 TFLOPS However, the physical limitations of silicon and the economic costs of producing ever-smaller transistors could slow this growth. The increase might realistically be closer to 4 times due to these constraints: Realistic Future GPU Performance=312 TFLOPS×4≈1248 TFLOPS Data Availability and Quality According to projections by IDC (International Data Corporation), the size of the global datasphere will increase from 64.2 zettabytes in 2020 to 175 zettabytes by 2025. It is essential for the training of increasingly complex AI models that data is growing exponentially. But there are still a lot of obstacles to overcome, including concerns about data privacy, data quality, and effectively labeling and processing this data.The availability of high-quality, labeled data is critical, even with an increase in data output. Preprocessing and data labeling are obstacles that will not scale linearly with data increase, according to current trends. Energy Consumption AI training models like GPT-3 require substantial energy. GPT-3 used approximately 1,287 MWh to train. GPT-4, developed approximately 3 years after its predecessor required over 50 gigawatt-hours, so around 50 times the amount it took to train GPT-3. Assuming AI models become more complex and require more data and computational power, energy consumption could grow significantly. If we estimate a similar 50x increase or a 5000% increase in complexity to around 2500 gigawatt-hours for the next model that could be released in the next 5 years, this would require large improvements in energy production and efficiency. Given current energy efficiency improvements of around 10% per year (source: Lawrence Livermore National Laboratory), this only translates to approximately a 60% increase in energy efficiencies over a 5-year period if the energy production technologies don’t improve significantly. The Need for Increased Complexity in AI Models Current AI models struggle with many major issues today. Lack of Up-to-date Information: Current models are trained on static datasets and do not have real-time access to the internet for the most up-to-date information. Hallucinations: AI models sometimes generate information that is plausible-sounding but incorrect or nonsensical. Understanding Context and Nuance: Handling nuanced human language, context, and intent remains a challenge for AI. To address these issues, future AI models will need to incorporate: Real-Time Data Integration: Access to and processing of real-time data from the internet to provide up-to-date responses. Improved Model Architectures: Enhancements in model architectures to reduce hallucinations and improve the reliability of generated content. Contextual Understanding: Advanced natural language processing techniques to better understand and maintain context over long conversations. Increased Model Complexity Solving these problems will significantly increase the complexity of AI models. For example: Real-Time Data Integration: Requires constant data updates and real-time processing capabilities.
Improved Model Architectures: Likely to involve larger and more complex models with more parameters.
Contextual Understanding: Demands more sophisticated training algorithms and potentially longer training times. Let's assume each of these improvements independently doubles the complexity of the AI model. If we start with the current complexity (C), integrating these advancements might lead to: Future Complexity=C×2(real-time data)×2(improved architectures)×2(contextual understanding)=C×2^3 =C×8 Let’s estimate a 5x increase in complexity as a more conservative yet substantial figure. Data Quality and Ethics AI systems depend heavily on high-quality, unbiased data for training and decision-making. While the quantity of data is increasing rapidly, the quality and ethical considerations surrounding this data are equally crucial. Data Generation and Processing With the increase from 64.2 to 175 zettabytes, data generation will likely outpace the current capabilities of processing and labeling. Ensuring data quality and addressing biases require significant resources. Let's consider the human labor and computational resources needed for data labeling. Assuming 1 hour of human labor is needed to label 1 GB of data, labeling 175 zettabytes would require: 175 ZB×1 hour/GB×10^9  GB/ZB =175×10^9  hours This is not feasible with current resources, indicating a substantial gap in data quality and ethical processing. Conclusion: When Will AI Be in a Truly Advanced State In the Next, Say 5 Years? The idea that AI will "take over" in the next five years seems improbable, even if it will surely continue to advance and become more deeply integrated into many industries. There are several obstacles to overcome, including issues with energy usage, data quality, and processing power limitations. AI will not be able to evolve as fast without significant advancements in energy efficiency, data ethics, and technology.It seems more reasonable to anticipate that AI will become truly advanced within the next ten to fifteen years, based on existing patterns and reasonable development estimates. This prolonged period enables the development and successful implementation of essential ethical, technological, and resource-based breakthroughs. Sources and References: Moore, G. E. (1965). Cramming more components onto integrated circuits. Electronics, 38(8), 114-117.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
Markoff, J. (2015). Machines of Loving Grace: The Quest for Common Ground Between Humans and Robots. HarperCollins.
IDC. (2020). Global DataSphere Forecast 2021-2025.
Lawrence Livermore National Laboratory. (2021). Energy Efficiency Trends in Computing. Artificial Intelligence has been advancing at an unprecedented rate, causing fear about whether AI will dominate various aspects of our lives in the near future and “take our jobs.” In this article, by leveraging Moore's Law and other technological growth principles, I’m going to make an educated estimation about the state of AI in the coming years. Let’s explore these predictions and explain why AI may not completely take over within the next five years, and what the timeline might look like for AI to reach a truly advanced state. The Current State of AI AI technology has made significant strides in recent years. From natural language processing to computer vision, AI applications are becoming increasingly sophisticated, whether that’s the recent GPT-4o model, Google’s Gemini, or any one of the models in the race to become the most capable AI model. There are lots of people using these models in positive ways to unlock new methodologies in research and education, but there are also lots of people using these technologies for evil such as deepfakes. However, despite these advancements, there are still considerable challenges and limitations that need to be addressed. Current AI systems need massive amounts of data, computational power, and energy to function effectively. Moore's Law and Its Implications for AI Moore's Law , the observation that the number of transistors on a microchip doubles approximately every two years, has been a reliable predictor of technological progress for decades. More generally, Moore’s Law points to technology getting twice as accessible, cheaper, and efficient every two years. According to Moore's Law, we can expect computational power to continue increasing exponentially. Moore's Law However, there are physical and economic limits to this growth that may impact the pace of AI development. Estimating AI's Growth Using Known Laws By extrapolating from Moore's Law and considering other technological growth trends, we can make predictions about AI's capabilities in the coming years. Computational Power If we consider top-of-the-notch GPUs today, let’s say NVIDIA A100, it provides approximately 312 teraflops (TFLOPS) of performance. Moore's Law suggests a doubling of transistors every two years, which implies a potential 8-fold increase in computational power over five years (2^2.5 ≈ 5.66). Future GPU Performance = 312 TFLOPS × 5.66 ≈ 1766 TFLOPS However, the physical limitations of silicon and the economic costs of producing ever-smaller transistors could slow this growth. The increase might realistically be closer to 4 times due to these constraints: Realistic Future GPU Performance=312 TFLOPS×4≈1248 TFLOPS Data Availability and Quality According to projections by IDC (International Data Corporation), the size of the global datasphere will increase from 64.2 zettabytes in 2020 to 175 zettabytes by 2025. It is essential for the training of increasingly complex AI models that data is growing exponentially. But there are still a lot of obstacles to overcome, including concerns about data privacy, data quality, and effectively labeling and processing this data. The availability of high-quality, labeled data is critical, even with an increase in data output. Preprocessing and data labeling are obstacles that will not scale linearly with data increase, according to current trends. Energy Consumption AI training models like GPT-3 require substantial energy. GPT-3 used approximately 1,287 MWh to train. GPT-4, developed approximately 3 years after its predecessor required over 50 gigawatt-hours , so around 50 times the amount it took to train GPT-3. Assuming AI models become more complex and require more data and computational power, energy consumption could grow significantly. 50 gigawatt-hours If we estimate a similar 50x increase or a 5000% increase in complexity to around 2500 gigawatt-hours for the next model that could be released in the next 5 years, this would require large improvements in energy production and efficiency. 2500 gigawatt-hours Given current energy efficiency improvements of around 10% per year (source: Lawrence Livermore National Laboratory), this only translates to approximately a 60% increase in energy efficiencies over a 5-year period if the energy production technologies don’t improve significantly. The Need for Increased Complexity in AI Models Current AI models struggle with many major issues today. Lack of Up-to-date Information: Current models are trained on static datasets and do not have real-time access to the internet for the most up-to-date information. Lack of Up-to-date Information: Hallucinations: AI models sometimes generate information that is plausible-sounding but incorrect or nonsensical. Hallucinations: Understanding Context and Nuance: Handling nuanced human language, context, and intent remains a challenge for AI. Understanding Context and Nuance: To address these issues, future AI models will need to incorporate: Real-Time Data Integration: Access to and processing of real-time data from the internet to provide up-to-date responses. Real-Time Data Integration: Access to and processing of real-time data from the internet to provide up-to-date responses. Real-Time Data Integration: Improved Model Architectures: Enhancements in model architectures to reduce hallucinations and improve the reliability of generated content. Improved Model Architectures: Enhancements in model architectures to reduce hallucinations and improve the reliability of generated content. Improved Model Architectures: Contextual Understanding: Advanced natural language processing techniques to better understand and maintain context over long conversations. Contextual Understanding: Advanced natural language processing techniques to better understand and maintain context over long conversations. Contextual Understanding: Increased Model Complexity Increased Model Complexity Solving these problems will significantly increase the complexity of AI models. For example: Real-Time Data Integration: Requires constant data updates and real-time processing capabilities. Improved Model Architectures: Likely to involve larger and more complex models with more parameters. Contextual Understanding: Demands more sophisticated training algorithms and potentially longer training times. Real-Time Data Integration: Requires constant data updates and real-time processing capabilities. Real-Time Data Integration: Improved Model Architectures: Likely to involve larger and more complex models with more parameters. Improved Model Architectures: Contextual Understanding: Demands more sophisticated training algorithms and potentially longer training times. Contextual Understanding: Let's assume each of these improvements independently doubles the complexity of the AI model. If we start with the current complexity (C), integrating these advancements might lead to: Future Complexity=C×2(real-time data)×2(improved architectures)×2(contextual understanding)=C×2^3 =C×8 Future Complexity=C×2(real-time data)×2(improved architectures)×2(contextual understanding)=C×2^3 =C×8 Let’s estimate a 5x increase in complexity as a more conservative yet substantial figure. Let’s estimate a 5x increase in complexity as a more conservative yet substantial figure. Data Quality and Ethics AI systems depend heavily on high-quality, unbiased data for training and decision-making. While the quantity of data is increasing rapidly, the quality and ethical considerations surrounding this data are equally crucial. Data Generation and Processing With the increase from 64.2 to 175 zettabytes, data generation will likely outpace the current capabilities of processing and labeling. Ensuring data quality and addressing biases require significant resources. Let's consider the human labor and computational resources needed for data labeling. Assuming 1 hour of human labor is needed to label 1 GB of data, labeling 175 zettabytes would require: 175 ZB×1 hour/GB×10^9  GB/ZB =175×10^9  hours 175 ZB×1 hour/GB×10^9  GB/ZB = 175×10^9  hours 175×10^9  hours This is not feasible with current resources, indicating a substantial gap in data quality and ethical processing. Conclusion: When Will AI Be in a Truly Advanced State In the Next, Say 5 Years? The idea that AI will "take over" in the next five years seems improbable, even if it will surely continue to advance and become more deeply integrated into many industries. There are several obstacles to overcome, including issues with energy usage, data quality, and processing power limitations. AI will not be able to evolve as fast without significant advancements in energy efficiency, data ethics, and technology. It seems more reasonable to anticipate that AI will become truly advanced within the next ten to fifteen years, based on existing patterns and reasonable development estimates. This prolonged period enables the development and successful implementation of essential ethical, technological, and resource-based breakthroughs. Sources and References : Sources and References Moore, G. E. (1965). Cramming more components onto integrated circuits. Electronics, 38(8), 114-117. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. Markoff, J. (2015). Machines of Loving Grace: The Quest for Common Ground Between Humans and Robots. HarperCollins. IDC. (2020). Global DataSphere Forecast 2021-2025. Lawrence Livermore National Laboratory. (2021). Energy Efficiency Trends in Computing. Moore, G. E. (1965). Cramming more components onto integrated circuits. Electronics , 38(8), 114-117. Electronics Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning . MIT Press. Deep Learning Markoff, J. (2015). Machines of Loving Grace: The Quest for Common Ground Between Humans and Robots. HarperCollins . HarperCollins IDC. (2020). Global DataSphere Forecast 2021-2025. Lawrence Livermore National Laboratory. (2021). Energy Efficiency Trends in Computing.

Predicting the Future of AI With Moore's Law: When Will It Really Take Over?

Performance management season always brings a push for data-driven approaches, and it’s usually not lost on people managers the truly subjective nature of the process. As previously written, I think embracing this subjectivity can be more beneficial than attempting to mask a subjective process with vanity metrics. There still is an opportunity to improve flawed data-driven approaches by being data-informed when data supports good arguments rather than replaces them. The Pitfall of Vanity Metrics Vanity metrics are appealing because they provide quantifiable data that seems to offer clear insights. However, they often obscure the subjective nature of performance management. This reminds me of the ongoing discussion on the distinction between math and science. Math relies on deductive reasoning, starting with basic principles and logically deducing consequences. If you accept the initial premises, the conclusions are certain.
In contrast, science uses inductive reasoning, drawing general conclusions from observations.For example, if every swan observed is white, one might conclude all swans are white, but a single black swan disproves this theory (1). The Advantages of Being Data-Informed Being data-informed means using data as a valuable tool to support well-reasoned arguments, not as the sole determinant of decisions, e.g.: Comprehensive Understanding: Data provides insights, but a comprehensive understanding of the factors driving performance is essential. Are all factors driving your metric well understood? If not, data may be misleading (2). By being data-informed, you use data to enhance your understanding rather than replace it.


Supporting Good Arguments: Good arguments are often based on a mix of data, context, and expertise. By supporting arguments with data, you ensure that decisions are well-rounded and consider multiple perspectives. This holistic approach is more robust than relying solely on data, which might overlook important nuances (2).


Encouraging Experimentation: Can you run an experiment to test your assumptions? Controlled experiments are invaluable for establishing causality. When data is used to inform hypotheses and guide experiments, it becomes a powerful tool for validation and learning (2).


Enhancing Communication: Data can help communicate complex ideas clearly and persuasively. Good data storytelling combines narrative with explanatory visuals, making insights more accessible and actionable (3).


Maintaining Motivation: Metrics that align with an individual’s or team’s convictions can enhance motivation. By focusing on meaningful metrics that truly reflect performance and contribution, organizations can boost morale and intrinsic motivation (2). Challenges with Overemphasis on Data While data is invaluable, an overemphasis on data-driven decisions can lead to: Misinterpretation: Without proper context, data can be misinterpreted. It’s crucial to ensure that data is understood correctly and used appropriately (2).


Streetlight Effect: Organizations may focus on easily measurable improvements, neglecting more significant but harder-to-measure changes. This can lead to superficial enhancements rather than meaningful progress (2).


Suspension of Disbelief: Introducing metrics in areas where they don’t belong can lead to a culture of ignoring the limitations and meaninglessness of certain data points (2).


Weak Leadership: Relying too heavily on data can be a sign of ineffective leadership. Strong leaders should be able to use their judgment and observations to make decisions, using data to support rather than dictate those decisions (2). The Value of Good Arguments Good arguments, even without extensive data, can often lead to better decisions. Here’s why: Correlation vs. Causation: Basic statistical principles remind us that correlation does not imply causation. It’s possible to make great decisions with good arguments and minimal data, but bad arguments can easily lead to poor decisions, even with good data (3,2).


Cultural Impact: An overemphasis on data can harm organizational culture. Metrics can lead to superficial improvements at the expense of more nuanced, meaningful changes (2).


Holistic Decision Making: Good arguments consider a wider range of factors, including context, expertise, and judgment, which are often overlooked in data-driven approaches (2). Embracing a Balanced Approach Data has its place and can be a useful tool for supporting arguments. However, it should not be the sole basis for decision-making. Strong arguments, grounded in observation and theory, often provide a more reliable foundation for decisions. Resist the temptation to rely solely on data, and maintain a healthy skepticism towards metrics that promise easy answers. Being data-informed rather than data-driven means using data to enhance and support well-reasoned arguments. This balanced approach ensures that decisions are both informed by data and enriched by context and expertise. Performance management benefits from a balanced approach that leverages data to support strong arguments. By being data-informed, organizations can make better decisions that consider both quantitative and qualitative factors. Embrace the subjectivity of the process and use data as a tool to enhance, not replace good arguments. This approach will lead to more meaningful, well-rounded decisions that align with organizational goals and values. For further reading, check out my other writings: Embracing Followership and Technical Fluency in Modern Leadership
Rethinking Performance Management: Embracing Subjectivity Over Objectivity
Improving Engineering Culture by Building Products, Not Projects University of Houston, THE ENGINES OF OUR INGENUITY: Math Versus Science ↩︎
Richard Marmorstein: Be good-argument-driven, not data-driven ↩︎
Forbes: 8 Pitfalls In The Data-Driven Decision-Making (DDDM) Process ↩︎ Performance management season always brings a push for data-driven approaches, and it’s usually not lost on people managers the truly subjective nature of the process. As previously written, I think embracing this subjectivity can be more beneficial than attempting to mask a subjective process with vanity metrics. There still is an opportunity to improve flawed data-driven approaches by being data-informed when data supports good arguments rather than replaces them. I think embracing this subjectivity I think embracing this subjectivity The Pitfall of Vanity Metrics Vanity metrics are appealing because they provide quantifiable data that seems to offer clear insights. However, they often obscure the subjective nature of performance management. This reminds me of the ongoing discussion on the distinction between math and science. Math relies on deductive reasoning, starting with basic principles and logically deducing consequences. If you accept the initial premises, the conclusions are certain. In contrast, science uses inductive reasoning, drawing general conclusions from observations. For example, if every swan observed is white, one might conclude all swans are white, but a single black swan disproves this theory (1). Math relies on deductive reasoning, starting with basic principles and logically deducing consequences. If you accept the initial premises, the conclusions are certain. Math relies on deductive reasoning, starting with basic principles and logically deducing consequences. If you accept the initial premises, the conclusions are certain. In contrast, science uses inductive reasoning, drawing general conclusions from observations. In contrast, science uses inductive reasoning, drawing general conclusions from observations. For example, if every swan observed is white, one might conclude all swans are white, but a single black swan disproves this theory (1). For example, if every swan observed is white, one might conclude all swans are white, but a single black swan disproves this theory (1). The Advantages of Being Data-Informed Being data-informed means using data as a valuable tool to support well-reasoned arguments, not as the sole determinant of decisions, e.g.: Comprehensive Understanding: Data provides insights, but a comprehensive understanding of the factors driving performance is essential. Are all factors driving your metric well understood? If not, data may be misleading (2). By being data-informed, you use data to enhance your understanding rather than replace it. Supporting Good Arguments: Good arguments are often based on a mix of data, context, and expertise. By supporting arguments with data, you ensure that decisions are well-rounded and consider multiple perspectives. This holistic approach is more robust than relying solely on data, which might overlook important nuances (2). Encouraging Experimentation: Can you run an experiment to test your assumptions? Controlled experiments are invaluable for establishing causality. When data is used to inform hypotheses and guide experiments, it becomes a powerful tool for validation and learning (2). Enhancing Communication: Data can help communicate complex ideas clearly and persuasively. Good data storytelling combines narrative with explanatory visuals, making insights more accessible and actionable (3). Maintaining Motivation: Metrics that align with an individual’s or team’s convictions can enhance motivation. By focusing on meaningful metrics that truly reflect performance and contribution, organizations can boost morale and intrinsic motivation (2). Comprehensive Understanding: Data provides insights, but a comprehensive understanding of the factors driving performance is essential. Are all factors driving your metric well understood? If not, data may be misleading (2). By being data-informed, you use data to enhance your understanding rather than replace it. Comprehensive Understanding: Data provides insights, but a comprehensive understanding of the factors driving performance is essential. Are all factors driving your metric well understood? If not, data may be misleading (2). By being data-informed, you use data to enhance your understanding rather than replace it. Comprehensive Understanding: Supporting Good Arguments: Good arguments are often based on a mix of data, context, and expertise. By supporting arguments with data, you ensure that decisions are well-rounded and consider multiple perspectives. This holistic approach is more robust than relying solely on data, which might overlook important nuances (2). Supporting Good Arguments: Good arguments are often based on a mix of data, context, and expertise. By supporting arguments with data, you ensure that decisions are well-rounded and consider multiple perspectives. This holistic approach is more robust than relying solely on data, which might overlook important nuances (2). Supporting Good Arguments: Encouraging Experimentation: Can you run an experiment to test your assumptions? Controlled experiments are invaluable for establishing causality. When data is used to inform hypotheses and guide experiments, it becomes a powerful tool for validation and learning (2). Encouraging Experimentation: Can you run an experiment to test your assumptions? Controlled experiments are invaluable for establishing causality. When data is used to inform hypotheses and guide experiments, it becomes a powerful tool for validation and learning (2). Encouraging Experimentation: Enhancing Communication: Data can help communicate complex ideas clearly and persuasively. Good data storytelling combines narrative with explanatory visuals, making insights more accessible and actionable (3). Enhancing Communication: Data can help communicate complex ideas clearly and persuasively. Good data storytelling combines narrative with explanatory visuals, making insights more accessible and actionable (3). Enhancing Communication: Maintaining Motivation: Metrics that align with an individual’s or team’s convictions can enhance motivation. By focusing on meaningful metrics that truly reflect performance and contribution, organizations can boost morale and intrinsic motivation (2). Maintaining Motivation: Metrics that align with an individual’s or team’s convictions can enhance motivation. By focusing on meaningful metrics that truly reflect performance and contribution, organizations can boost morale and intrinsic motivation (2). Maintaining Motivation: Challenges with Overemphasis on Data While data is invaluable, an overemphasis on data-driven decisions can lead to: Misinterpretation: Without proper context, data can be misinterpreted. It’s crucial to ensure that data is understood correctly and used appropriately (2). Streetlight Effect: Organizations may focus on easily measurable improvements, neglecting more significant but harder-to-measure changes. This can lead to superficial enhancements rather than meaningful progress (2). Suspension of Disbelief: Introducing metrics in areas where they don’t belong can lead to a culture of ignoring the limitations and meaninglessness of certain data points (2). Weak Leadership: Relying too heavily on data can be a sign of ineffective leadership. Strong leaders should be able to use their judgment and observations to make decisions, using data to support rather than dictate those decisions (2). Misinterpretation: Without proper context, data can be misinterpreted. It’s crucial to ensure that data is understood correctly and used appropriately (2). Misinterpretation: Without proper context, data can be misinterpreted. It’s crucial to ensure that data is understood correctly and used appropriately (2). Misinterpretation: Streetlight Effect: Organizations may focus on easily measurable improvements, neglecting more significant but harder-to-measure changes. This can lead to superficial enhancements rather than meaningful progress (2). Streetlight Effect: Organizations may focus on easily measurable improvements, neglecting more significant but harder-to-measure changes. This can lead to superficial enhancements rather than meaningful progress (2). Streetlight Effect: Suspension of Disbelief: Introducing metrics in areas where they don’t belong can lead to a culture of ignoring the limitations and meaninglessness of certain data points (2). Suspension of Disbelief: Introducing metrics in areas where they don’t belong can lead to a culture of ignoring the limitations and meaninglessness of certain data points (2). Suspension of Disbelief: Weak Leadership: Relying too heavily on data can be a sign of ineffective leadership. Strong leaders should be able to use their judgment and observations to make decisions, using data to support rather than dictate those decisions (2). Weak Leadership: Relying too heavily on data can be a sign of ineffective leadership. Strong leaders should be able to use their judgment and observations to make decisions, using data to support rather than dictate those decisions (2). Weak Leadership: The Value of Good Arguments Good arguments, even without extensive data, can often lead to better decisions. Here’s why: Here’s why: Correlation vs. Causation: Basic statistical principles remind us that correlation does not imply causation. It’s possible to make great decisions with good arguments and minimal data, but bad arguments can easily lead to poor decisions, even with good data (3,2). Cultural Impact: An overemphasis on data can harm organizational culture. Metrics can lead to superficial improvements at the expense of more nuanced, meaningful changes (2). Holistic Decision Making: Good arguments consider a wider range of factors, including context, expertise, and judgment, which are often overlooked in data-driven approaches (2). Correlation vs. Causation: Basic statistical principles remind us that correlation does not imply causation. It’s possible to make great decisions with good arguments and minimal data, but bad arguments can easily lead to poor decisions, even with good data (3,2). Correlation vs. Causation: Basic statistical principles remind us that correlation does not imply causation. It’s possible to make great decisions with good arguments and minimal data, but bad arguments can easily lead to poor decisions, even with good data (3,2). Cultural Impact: An overemphasis on data can harm organizational culture. Metrics can lead to superficial improvements at the expense of more nuanced, meaningful changes (2). Cultural Impact: An overemphasis on data can harm organizational culture. Metrics can lead to superficial improvements at the expense of more nuanced, meaningful changes (2). Holistic Decision Making: Good arguments consider a wider range of factors, including context, expertise, and judgment, which are often overlooked in data-driven approaches (2). Holistic Decision Making: Good arguments consider a wider range of factors, including context, expertise, and judgment, which are often overlooked in data-driven approaches (2). Embracing a Balanced Approach Data has its place and can be a useful tool for supporting arguments. However, it should not be the sole basis for decision-making. Strong arguments, grounded in observation and theory, often provide a more reliable foundation for decisions. Resist the temptation to rely solely on data, and maintain a healthy skepticism towards metrics that promise easy answers. Being data-informed rather than data-driven means using data to enhance and support well-reasoned arguments. This balanced approach ensures that decisions are both informed by data and enriched by context and expertise. Performance management benefits from a balanced approach that leverages data to support strong arguments. By being data-informed, organizations can make better decisions that consider both quantitative and qualitative factors. Embrace the subjectivity of the process and use data as a tool to enhance, not replace good arguments. This approach will lead to more meaningful, well-rounded decisions that align with organizational goals and values. For further reading, check out my other writings: For further reading, check out my other writings: Embracing Followership and Technical Fluency in Modern Leadership Rethinking Performance Management: Embracing Subjectivity Over Objectivity Improving Engineering Culture by Building Products, Not Projects Embracing Followership and Technical Fluency in Modern Leadership Embracing Followership and Technical Fluency in Modern Leadership Embracing Followership and Technical Fluency in Modern Leadership Rethinking Performance Management: Embracing Subjectivity Over Objectivity Rethinking Performance Management: Embracing Subjectivity Over Objectivity Rethinking Performance Management: Embracing Subjectivity Over Objectivity Improving Engineering Culture by Building Products, Not Projects Improving Engineering Culture by Building Products, Not Projects Improving Engineering Culture by Building Products, Not Projects University of Houston, THE ENGINES OF OUR INGENUITY: Math Versus Science ↩︎ Richard Marmorstein: Be good-argument-driven, not data-driven ↩︎ Forbes: 8 Pitfalls In The Data-Driven Decision-Making (DDDM) Process ↩︎ University of Houston, THE ENGINES OF OUR INGENUITY: Math Versus Science ↩︎ Math Versus Science Math Versus Science Richard Marmorstein: Be good-argument-driven, not data-driven ↩︎ Be good-argument-driven, not data-driven Be good-argument-driven, not data-driven Forbes: 8 Pitfalls In The Data-Driven Decision-Making (DDDM) Process ↩︎ 8 Pitfalls In The Data-Driven Decision-Making (DDDM) Process 8 Pitfalls In The Data-Driven Decision-Making (DDDM) Process

How to Balance Data and GoodJudgment in Effective Performance Management

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Hyerin Cho (조혜린), Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA;
(2) Ben S. Prather, CCS-2, Los Alamos National Laboratory, PO Box 1663, Los Alamos, NM 87545, USA;
(3) Ramesh Narayan, enter for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA;
(4) Priyamvada Natarajan, Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA, Department of Astronomy, Yale University, Kline Tower, 266 Whitney Avenue, New Haven, CT 06511, USA and Department of Physics, Yale University, P.O. Box 208121, New Haven, CT 06520, USA;
(5) Kung-Yi Su, Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA;
(6) Angelo Ricarte, Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA;
(7) Koushik Chatterjee, Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA. Table of Links Abstract and Introduction Numerical Methods Hydrodynamic Bondi Accretion Magnetized Bondi Accretion Feedback Via Reconnection-Driven Convection Summary and Conclusions Acknowledgements Appendix A. GRMHD Primer and Definitions B. Numerical Set-up C. Resolution and Initial Condition Study References REFERENCES Angl´es-Alc´azar, D., Quataert, E., Hopkins, P. F., et al. 2021, ApJ, 917, 53, doi: 10.3847/1538-4357/ac09e8 Anile, A. M. 1989, Relativistic fluids and magneto-fluids: With applications in astrophysics and plasma physics Bisnovatyi-Kogan, G. S., & Ruzmaikin, A. A. 1974, Ap&SS, 28, 45, doi: 10.1007/BF00642237 Bondi, H. 1952, MNRAS, 112, 195, doi: 10.1093/mnras/112.2.195 Booth, C. M., & Schaye, J. 2009, MNRAS, 398, 53, doi: 10.1111/j.1365-2966.2009.15043.x Chatterjee, K., & Narayan, R. 2022, ApJ, 941, 30, doi: 10.3847/1538-4357/ac9d97 Chatterjee, K., Chael, A., Tiede, P., et al. 2023, Galaxies, 11, 38, doi: 10.3390/galaxies11020038 Dav´e, R., Angl´es-Alc´azar, D., Narayanan, D., et al. 2019, MNRAS, 486, 2827, doi: 10.1093/mnras/stz937 Event Horizon Telescope Collaboration, Akiyama, K., Algaba, J. C., et al. 2021, ApJL, 910, L13, doi: 10.3847/2041-8213/abe4de Ferrarese, L., & Merritt, D. 2000, ApJL, 539, L9, doi: 10.1086/312838 Ferri`ere, K. 2020, Plasma Physics and Controlled Fusion, 62, 014014, doi: 10.1088/13616587/ab49eb Fiacconi, D., Sijacki, D., & Pringle, J. E. 2018, MNRAS, 477, 3807, doi: 10.1093/mnras/sty893 Gammie, C. F., McKinney, J. C., & T´oth, G. 2003, ApJ, 589, 444, doi: 10.1086/374594 Gebhardt, K., Bender, R., Bower, G., et al. 2000, ApJL, 539, L13, doi: 10.1086/312840 Grete, P., Dolence, J. C., Miller, J. M., et al. 2023, 37, 465, doi: 10.1177/10943420221143775 Guerra, J. A., Lopez-Rodriguez, E., Chuss, D. T., Butterfield, N. O., & Schmelz, J. T. 2023, AJ, 166, 37, doi: 10.3847/1538-3881/acdacd Guo, M., Stone, J. M., Kim, C.-G., & Quataert, E. 2023, ApJ, 946, 26, doi: 10.3847/1538-4357/acb81e Hopkins, P. F., & Quataert, E. 2010, MNRAS, 407, 1529, doi: 10.1111/j.1365-2966.2010.17064.x Hopkins, P. F., Grudic, M. Y., Su, K.-Y., et al. 2023, arXiv e-prints, arXiv:2309.13115. https://arxiv.org/abs/2309.13115 Igumenshchev, I. V., & Narayan, R. 2002, ApJ, 566, 137, doi: 10.1086/338077 Igumenshchev, I. V., Narayan, R., & Abramowicz, M. A. 2003, ApJ, 592, 1042, doi: 10.1086/375769 Kaaz, N., Murguia-Berthier, A., Chatterjee, K., Liska, M. T. P., & Tchekhovskoy, A. 2023, ApJ, 950, 31, doi: 10.3847/1538-4357/acc7a1 Komissarov, S. S. 1999, MNRAS, 303, 343, doi: 10.1046/j.1365-8711.1999.02244.x Kormendy, J., & Ho, L. C. 2013, ARA&A, 51, 511, doi: 10.1146/annurev-astro-082708-101811 Lalakos, A., Gottlieb, O., Kaaz, N., et al. 2022, ApJL, 936, L5, doi: 10.3847/2041-8213/ac7bed Li, Y., & Bryan, G. L. 2014, ApJ, 789, 153, doi: 10.1088/0004-637X/789/2/153 Magorrian, J., Tremaine, S., Richstone, D., et al. 1998, AJ, 115, 2285, doi: 10.1086/300353 Meszaros, P. 1975, A&A, 44, 59 Michel, F. C. 1972, Ap&SS, 15, 153, doi: 10.1007/BF00649949 Narayan, R., Chael, A., Chatterjee, K., Ricarte, A., & Curd, B. 2022, MNRAS, 511, 3795, doi: 10.1093/mnras/stac285 Narayan, R., Igumenshchev, I. V., & Abramowicz, M. A. 2000, ApJ, 539, 798, doi: 10.1086/309268—. 2003, PASJ, 55, L69, doi: 10.1093/pasj/55.6.L69 Ni, Y., Di Matteo, T., Bird, S., et al. 2022, MNRAS, 513, 670, doi: 10.1093/mnras/stac351 Pen, U.-L., Matzner, C. D., & Wong, S. 2003, ApJL, 596, L207, doi: 10.1086/379339 Penna, R. F., Kulkarni, A., & Narayan, R. 2013, A&A, 559, A116, doi: 10.1051/0004-6361/201219666 Porth, O., Chatterjee, K., Narayan, R., et al. 2019, ApJS, 243, 26, doi: 10.3847/1538-4365/ab29fd Prather, B. S., Wong, G. N., Dhruv, V., et al. 2021, 6, 3336, doi: 10.21105/joss.03336 Quataert, E., & Gruzinov, A. 2000, ApJ, 539, 809, doi: 10.1086/309267 Ressler, S. M., Quataert, E., White, C. J., & Blaes, O. 2021, MNRAS, 504, 6076, doi: 10.1093/mnras/stab311 Ressler, S. M., White, C. J., Quataert, E., & Stone, J. M. 2020, ApJL, 896, L6, doi: 10.3847/2041-8213/ab9532 Ricarte, A., Tremmel, M., Natarajan, P., & Quinn, T. 2019, MNRAS, 489, 802, doi: 10.1093/mnras/stz2161 Ripperda, B., Liska, M., Chatterjee, K., et al. 2022, ApJL, 924, L32, doi: 10.3847/2041-8213/ac46a1 Rosas-Guevara, Y., Bower, R. G., Schaye, J., et al. 2016, MNRAS, 462, 190, doi: 10.1093/mnras/stw1679 Russell, H. R., Fabian, A. C., McNamara, B. R., & Broderick, A. E. 2015, MNRAS, 451, 588, doi: 10.1093/mnras/stv954 Shapiro, S. L., & Teukolsky, S. A. 1983, Black holes, white dwarfs, and neutron stars : the physics of compact objects Shvartsman, V. F. 1971, Soviet Ast., 15, 377 Sijacki, D., Vogelsberger, M., Genel, S., et al. 2015, MNRAS, 452, 575, doi: 10.1093/mnras/stv1340 Talbot, R. Y., Bourne, M. A., & Sijacki, D. 2021, MNRAS, 504, 3619, doi: 10.1093/mnras/stab804 Tchekhovskoy, A., McKinney, J. C., & Narayan, R. 2012, in Journal of Physics Conference Series, Vol. 372, Journal of Physics Conference Series, 012040, doi: 10.1088/1742-6596/372/1/012040 Tchekhovskoy, A., Narayan, R., & McKinney, J. C. 2011, MNRAS, 418, L79, doi: 10.1111/j.1745-3933.2011.01147.x Trott, C. R., Lebrun-Grandi, D., Arndt, D., et al. 2022, 33, 805, doi: 10.1109/TPDS.2021.3097283 Wang, Q. D., Nowak, M. A., Markoff, S. B., et al. 2013, Science, 341, 981, doi: 10.1126/science.1240755 Weinberger, R., Springel, V., Pakmor, R., et al. 2018, MNRAS, 479, 4056, doi: 10.1093/mnras/sty1733 Weinberger, R., Su, K.-Y., Ehlert, K., et al. 2023, MNRAS, 523, 1104, doi: 10.1093/mnras/stad1396 Wellons, S., Faucher-Gigu`ere, C.-A., Hopkins, P. F., et al. 2023, MNRAS, 520, 5394, doi: 10.1093/mnras/stad511 Wong, G. N., Du, Y., Prather, B. S., & Gammie, C. F. 2021, ApJ, 914, 55, doi: 10.3847/1538-4357/abf8b8 Yuan, F., Gan, Z., Narayan, R., et al. 2015, ApJ, 804, 101, doi: 10.1088/0004-637X/804/2/101 This paper is available on Arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. Authors: (1) Hyerin Cho (조혜린), Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA; (2) Ben S. Prather, CCS-2, Los Alamos National Laboratory, PO Box 1663, Los Alamos, NM 87545, USA; (3) Ramesh Narayan, enter for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA; (4) Priyamvada Natarajan, Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA, Department of Astronomy, Yale University, Kline Tower, 266 Whitney Avenue, New Haven, CT 06511, USA and Department of Physics, Yale University, P.O. Box 208121, New Haven, CT 06520, USA; (5) Kung-Yi Su, Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA; (6) Angelo Ricarte, Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA; (7) Koushik Chatterjee, Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA. This paper is available on arxiv under CC 4.0 license. Authors : Authors (1) Hyerin Cho (조혜린), Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA; (2) Ben S. Prather, CCS-2, Los Alamos National Laboratory, PO Box 1663, Los Alamos, NM 87545, USA; (3) Ramesh Narayan, enter for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA; (4) Priyamvada Natarajan, Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA, Department of Astronomy, Yale University, Kline Tower, 266 Whitney Avenue, New Haven, CT 06511, USA and Department of Physics, Yale University, P.O. Box 208121, New Haven, CT 06520, USA; (5) Kung-Yi Su, Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA; (6) Angelo Ricarte, Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA; (7) Koushik Chatterjee, Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA. Table of Links Abstract and Introduction Abstract and Introduction Numerical Methods Numerical Methods Hydrodynamic Bondi Accretion Hydrodynamic Bondi Accretion Magnetized Bondi Accretion Magnetized Bondi Accretion Feedback Via Reconnection-Driven Convection Feedback Via Reconnection-Driven Convection Summary and Conclusions Summary and Conclusions Acknowledgements Acknowledgements Appendix Appendix A. GRMHD Primer and Definitions A. GRMHD Primer and Definitions B. Numerical Set-up B. Numerical Set-up C. Resolution and Initial Condition Study C. Resolution and Initial Condition Study References References REFERENCES Angl´es-Alc´azar, D., Quataert, E., Hopkins, P. F., et al. 2021, ApJ, 917, 53, doi: 10.3847/1538-4357/ac09e8 Anile, A. M. 1989, Relativistic fluids and magneto-fluids: With applications in astrophysics and plasma physics Bisnovatyi-Kogan, G. S., & Ruzmaikin, A. A. 1974, Ap&SS, 28, 45, doi: 10.1007/BF00642237 Bondi, H. 1952, MNRAS, 112, 195, doi: 10.1093/mnras/112.2.195 Booth, C. M., & Schaye, J. 2009, MNRAS, 398, 53, doi: 10.1111/j.1365-2966.2009.15043.x Chatterjee, K., & Narayan, R. 2022, ApJ, 941, 30, doi: 10.3847/1538-4357/ac9d97 Chatterjee, K., Chael, A., Tiede, P., et al. 2023, Galaxies, 11, 38, doi: 10.3390/galaxies11020038 Dav´e, R., Angl´es-Alc´azar, D., Narayanan, D., et al. 2019, MNRAS, 486, 2827, doi: 10.1093/mnras/stz937 Event Horizon Telescope Collaboration, Akiyama, K., Algaba, J. C., et al. 2021, ApJL, 910, L13, doi: 10.3847/2041-8213/abe4de Ferrarese, L., & Merritt, D. 2000, ApJL, 539, L9, doi: 10.1086/312838 Ferri`ere, K. 2020, Plasma Physics and Controlled Fusion, 62, 014014, doi: 10.1088/13616587/ab49eb Fiacconi, D., Sijacki, D., & Pringle, J. E. 2018, MNRAS, 477, 3807, doi: 10.1093/mnras/sty893 Gammie, C. F., McKinney, J. C., & T´oth, G. 2003, ApJ, 589, 444, doi: 10.1086/374594 Gebhardt, K., Bender, R., Bower, G., et al. 2000, ApJL, 539, L13, doi: 10.1086/312840 Grete, P., Dolence, J. C., Miller, J. M., et al. 2023, 37, 465, doi: 10.1177/10943420221143775 Guerra, J. A., Lopez-Rodriguez, E., Chuss, D. T., Butterfield, N. O., & Schmelz, J. T. 2023, AJ, 166, 37, doi: 10.3847/1538-3881/acdacd Guo, M., Stone, J. M., Kim, C.-G., & Quataert, E. 2023, ApJ, 946, 26, doi: 10.3847/1538-4357/acb81e Hopkins, P. F., & Quataert, E. 2010, MNRAS, 407, 1529, doi: 10.1111/j.1365-2966.2010.17064.x Hopkins, P. F., Grudic, M. Y., Su, K.-Y., et al. 2023, arXiv e-prints, arXiv:2309.13115. https://arxiv.org/abs/2309.13115 Igumenshchev, I. V., & Narayan, R. 2002, ApJ, 566, 137, doi: 10.1086/338077 Igumenshchev, I. V., Narayan, R., & Abramowicz, M. A. 2003, ApJ, 592, 1042, doi: 10.1086/375769 Kaaz, N., Murguia-Berthier, A., Chatterjee, K., Liska, M. T. P., & Tchekhovskoy, A. 2023, ApJ, 950, 31, doi: 10.3847/1538-4357/acc7a1 Komissarov, S. S. 1999, MNRAS, 303, 343, doi: 10.1046/j.1365-8711.1999.02244.x Kormendy, J., & Ho, L. C. 2013, ARA&A, 51, 511, doi: 10.1146/annurev-astro-082708-101811 Lalakos, A., Gottlieb, O., Kaaz, N., et al. 2022, ApJL, 936, L5, doi: 10.3847/2041-8213/ac7bed Li, Y., & Bryan, G. L. 2014, ApJ, 789, 153, doi: 10.1088/0004-637X/789/2/153 Magorrian, J., Tremaine, S., Richstone, D., et al. 1998, AJ, 115, 2285, doi: 10.1086/300353 Meszaros, P. 1975, A&A, 44, 59 Michel, F. C. 1972, Ap&SS, 15, 153, doi: 10.1007/BF00649949 Narayan, R., Chael, A., Chatterjee, K., Ricarte, A., & Curd, B. 2022, MNRAS, 511, 3795, doi: 10.1093/mnras/stac285 Narayan, R., Igumenshchev, I. V., & Abramowicz, M. A. 2000, ApJ, 539, 798, doi: 10.1086/309268—. 2003, PASJ, 55, L69, doi: 10.1093/pasj/55.6.L69 Ni, Y., Di Matteo, T., Bird, S., et al. 2022, MNRAS, 513, 670, doi: 10.1093/mnras/stac351 Pen, U.-L., Matzner, C. D., & Wong, S. 2003, ApJL, 596, L207, doi: 10.1086/379339 Penna, R. F., Kulkarni, A., & Narayan, R. 2013, A&A, 559, A116, doi: 10.1051/0004-6361/201219666 Porth, O., Chatterjee, K., Narayan, R., et al. 2019, ApJS, 243, 26, doi: 10.3847/1538-4365/ab29fd Prather, B. S., Wong, G. N., Dhruv, V., et al. 2021, 6, 3336, doi: 10.21105/joss.03336 Quataert, E., & Gruzinov, A. 2000, ApJ, 539, 809, doi: 10.1086/309267 Ressler, S. M., Quataert, E., White, C. J., & Blaes, O. 2021, MNRAS, 504, 6076, doi: 10.1093/mnras/stab311 Ressler, S. M., White, C. J., Quataert, E., & Stone, J. M. 2020, ApJL, 896, L6, doi: 10.3847/2041-8213/ab9532 Ricarte, A., Tremmel, M., Natarajan, P., & Quinn, T. 2019, MNRAS, 489, 802, doi: 10.1093/mnras/stz2161 Ripperda, B., Liska, M., Chatterjee, K., et al. 2022, ApJL, 924, L32, doi: 10.3847/2041-8213/ac46a1 Rosas-Guevara, Y., Bower, R. G., Schaye, J., et al. 2016, MNRAS, 462, 190, doi: 10.1093/mnras/stw1679 Russell, H. R., Fabian, A. C., McNamara, B. R., & Broderick, A. E. 2015, MNRAS, 451, 588, doi: 10.1093/mnras/stv954 Shapiro, S. L., & Teukolsky, S. A. 1983, Black holes, white dwarfs, and neutron stars : the physics of compact objects Shvartsman, V. F. 1971, Soviet Ast., 15, 377 Sijacki, D., Vogelsberger, M., Genel, S., et al. 2015, MNRAS, 452, 575, doi: 10.1093/mnras/stv1340 Talbot, R. Y., Bourne, M. A., & Sijacki, D. 2021, MNRAS, 504, 3619, doi: 10.1093/mnras/stab804 Tchekhovskoy, A., McKinney, J. C., & Narayan, R. 2012, in Journal of Physics Conference Series, Vol. 372, Journal of Physics Conference Series, 012040, doi: 10.1088/1742-6596/372/1/012040 Tchekhovskoy, A., Narayan, R., & McKinney, J. C. 2011, MNRAS, 418, L79, doi: 10.1111/j.1745-3933.2011.01147.x Trott, C. R., Lebrun-Grandi, D., Arndt, D., et al. 2022, 33, 805, doi: 10.1109/TPDS.2021.3097283 Wang, Q. D., Nowak, M. A., Markoff, S. B., et al. 2013, Science, 341, 981, doi: 10.1126/science.1240755 Weinberger, R., Springel, V., Pakmor, R., et al. 2018, MNRAS, 479, 4056, doi: 10.1093/mnras/sty1733 Weinberger, R., Su, K.-Y., Ehlert, K., et al. 2023, MNRAS, 523, 1104, doi: 10.1093/mnras/stad1396 Wellons, S., Faucher-Gigu`ere, C.-A., Hopkins, P. F., et al. 2023, MNRAS, 520, 5394, doi: 10.1093/mnras/stad511 Wong, G. N., Du, Y., Prather, B. S., & Gammie, C. F. 2021, ApJ, 914, 55, doi: 10.3847/1538-4357/abf8b8 Yuan, F., Gan, Z., Narayan, R., et al. 2015, ApJ, 804, 101, doi: 10.1088/0004-637X/804/2/101 This paper is available on Arxiv under CC 4.0 license. This paper is available on Arxiv under CC 4.0 license. Arxiv

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