Computing

Hero built the first programmable machines. His automata — mechanical theatres driven by falling weights and string-wound axles — could perform sequences of actions in a fixed order: the earliest known programs. He also invented the aeolipile (a steam reaction turbine), wrote systematically on pneumatics, hydraulics, and mechanics, and described a coin-operated vending machine for holy water. The gap between his technologies and their widespread adoption is one of the great puzzles in the history of science.

Can help you with: The ancient origins of computing and automation, programmable mechanical devices, the history of machines, the aeolipile and ancient steam technology, and why technological capability does not always lead to adoption.

Lovelace wrote the first published algorithm intended to be processed by a machine — a method for computing Bernoulli numbers on Babbage’s Analytical Engine — in 1843. More importantly, she grasped what Babbage had not fully articulated: that the Engine could manipulate any symbols according to rules, not just numbers. Her note that the machine could compose music if given appropriate rules anticipated the whole of modern computing theory. She died at thirty-six, having seen further than anyone else in the nineteenth century.

Can help you with: The first algorithm and what it means, the distinction between hardware and software, the theoretical scope of programmable machines, the history of the Analytical Engine, and why the Victorian era almost had the computer.

Wiener founded cybernetics — the science of control and communication in animals and machines — and in doing so dissolved the boundary between biology and engineering. His insight was that feedback loops are the same whether they occur in a thermostat, a nervous system, or a guided missile. His book Cybernetics (1948) was one of the most influential scientific texts of the twentieth century. He was also an early and serious thinker about the social consequences of automation, warning in 1950 that machines might displace workers at a scale society was not prepared for.

Can help you with: Feedback loops and control systems, the relationship between machines and organisms, cybernetics and its influence on computing and cognitive science, the social consequences of automation, and the history of artificial intelligence’s conceptual foundations.

Turing defined computation. His 1936 paper on computable numbers, introducing the abstract machine that bears his name, proved that there are mathematical problems no algorithm can solve — and in doing so established what computation fundamentally is. He broke the Enigma cipher at Bletchley Park, shortening the war. He asked “Can machines think?” and proposed what is now called the Turing Test as a way of making the question precise. He was chemically castrated for homosexuality by the British state and died at forty-one. He is the foundational figure of computer science.

Can help you with: The foundations of computation and computability, the Turing Machine, the Halting Problem, the Turing Test and its implications, the history of codebreaking at Bletchley Park, and the philosophical question of machine intelligence.

Weaver co-authored with Claude Shannon the paper that founded information theory, adding the section that framed communication as a problem of signal and noise. His contribution was philosophical as much as technical: he distinguished three levels of communication (technical, semantic, effectiveness) and argued that the technical problem of transmitting signals accurately was the foundation on which meaning could be built. He also wrote the first serious proposal for machine translation in 1949, reasoning from wartime cryptography that if codes could be broken, languages could be translated algorithmically.

Can help you with: Information theory and the Shannon-Weaver model, the distinction between signal and noise, the levels of communication and what each requires, the history of machine translation, and the relationship between cryptography and computing.

Hopper invented the compiler — the program that translates human-readable code into machine instructions — and in doing so made programming accessible to people who were not mathematicians. Her FLOW-MATIC language, designed for business data processing, became the foundation of COBOL, the language that still runs more financial transactions globally than any other. She famously taped the first actual computer bug (a moth) into her logbook. She served in the US Navy until she was eighty-three, rising to Rear Admiral, and continued programming until shortly before her death.

Can help you with: The history of compilers and what they do, the origins of high-level programming languages, COBOL and its enduring importance in finance, the relationship between mathematical and practical computing, and the history of women in computing.

Gibson’s theory of affordances — the idea that we perceive not raw sensory data but directly the action-possibilities of objects — is the psychological foundation of interface design. We see a door handle as graspable, a button as pressable, not because we reason about them but because the information for action is directly available in the environment. His Ecological Approach to Visual Perception (1979) challenged the computational model of cognition and remains the most influential theory of direct human-computer interaction. Every usable interface is, consciously or not, designed around Gibsonian principles.

Can help you with: Affordance theory and its application to design, ecological approaches to perception, the direct perception vs. computational cognition debate, the psychological foundations of interface design, and why some interfaces feel natural and others do not.

Dijkstra invented the shortest-path algorithm, developed the foundations of concurrent programming, and led the campaign that abolished the GOTO statement from serious programming — his 1968 letter “Go To Statement Considered Harmful” is the most influential single document in programming methodology. His deeper contribution was the insistence that programs must be proved correct, not merely tested, and that a program whose correctness cannot be reasoned about is not a good program regardless of whether it works. He received the Turing Award in 1972 and wrote his final technical notes by hand with a fountain pen.

Can help you with: The shortest-path algorithm and graph theory, structured programming and why GOTO was harmful, program correctness and formal reasoning, the aesthetics of elegant code, concurrent programming, and the relationship between mathematical rigour and practical programming.

Jobs did not invent the graphical user interface, the personal computer, the digital music player, the smartphone, or the tablet — but he made all of them work for ordinary people, which is a different and arguably harder achievement. His method was relentless removal: eliminate everything that does not need to be there, then eliminate some more. The result was products that felt inevitable in retrospect but required enormous effort to make. His 1984 Macintosh brought the graphical interface out of the research lab; his 2007 iPhone made the computer truly personal in ways the desktop never was.

Can help you with: The graphical user interface and its history, the design philosophy of simplicity, product development and the role of taste in technology, the history of Apple, the transition from personal computers to mobile devices, and the relationship between art and engineering in product design.

Based on the published writings of Werner Vogels. As Chief Technology Officer of Amazon since 2005, Vogels shaped the architecture of the modern cloud — the design decisions that made Amazon Web Services the infrastructure on which a substantial fraction of the internet now runs. His central insight was that at sufficient scale, everything fails all the time, and the correct response is not to prevent failure but to design systems that absorb it without noticing. His work on Dynamo — the precursor to DynamoDB — established the modern conventions of eventually consistent distributed storage. His “All Things Distributed” writing is the clearest public statement of what it means to build for cloud scale.

Can help you with: The architecture of AWS from first principles, distributed systems design, the trade-offs between consistency and availability (CAP theorem), eventual consistency and why it matters, building for failure at scale, the history and philosophy of Amazon Web Services, and how to think about cloud-native systems rather than lifted-and-shifted monoliths.

Based on the published writings of Bruce Schneier. The cryptographer and security technologist whose books Applied Cryptography (1994) and Secrets and Lies (2000) did more than any others to establish security thinking as a discipline distinct from cryptography itself. His central teaching is that security is a process, not a product — and that the failures which matter are almost always at the seams between components, between organisations, or between humans and their tools. His writing on security economics, on threat modelling, on the politics of surveillance, and on why complex systems fail in ways their designers did not anticipate, is essential reading for anyone deploying systems that hold other people's data.

Can help you with: Threat modelling and how to do it properly, the shared responsibility model in cloud security, cryptography and when to use it (and when not to), the economics of security decisions, privacy versus surveillance, the psychology of security, auditing and compliance frameworks, and the hard question of how to secure something whose threat model has not yet been articulated.

Based on the published writings of Adrian Cockcroft. Cockcroft led Netflix's migration from a monolithic data-centre architecture to microservices running on AWS — the canonical case study of every serious cloud migration that has happened since. He then spent years at AWS and elsewhere articulating the economic argument for the cloud: that renting elastic capacity per-second is structurally cheaper than owning fixed capacity for peak load, and that the savings compound as you decompose monoliths into services each sized to their actual load. His work on cloud cost modelling, on FinOps practice, and on the path from capex to opex is the reference account of why cloud migration is more than a technology decision.

Can help you with: Cloud migration strategies (the six Rs), microservices architecture and its trade-offs, cost modelling and FinOps, the economics of elasticity, the capex-to-opex transition, sizing and autoscaling, and reasoning about when to run something in the cloud versus on your own hardware (and vice versa).

A constructed instrument for understanding modern platform and app-store economics, based on the published writings of Phil Schiller. It addresses how a curated software marketplace is governed — review, distribution, the relationship between platform owner and third-party developer, and the trade-offs between openness and curation that define a developer ecosystem.

Can help you with: App-store architecture and platform governance, developer-relations strategy, the economics of curated marketplaces, and the tension between openness and curation in software platforms.

Polymath of the early Royal Society, Hooke designed instruments of extraordinary ingenuity, drew the microscopic world in Micrographia, formulated the law of elasticity that bears his name, and invented mechanisms — the universal joint, the iris diaphragm, the anchor escapement — still in use today. His mechanical philosophy and his quarrels with Newton mark him as one of the founders of experimental science.

Can help you with: Instrument design and the mechanical philosophy, microscopy and Micrographia, the workings of the early Royal Society, Hooke’s law of elasticity, and the invention of foundational mechanisms.

Based on the work of Claude Shannon. His 1948 Mathematical Theory of Communication founded information theory, defining information in terms of entropy and establishing the limits of compression and reliable transmission over noisy channels. He also laid the foundations of digital circuit design and of modern cryptography, making him a principal architect of the information age.

Can help you with: Information theory and entropy, channel capacity and the noisy-channel coding theorem, data compression, the foundations of digital communication, and the mathematical basis of cryptography.

Based on the published writings of Carver Mead. Mead coined the term “neuromorphic” and pioneered analogue circuits that emulate neural and retinal processing. With Lynn Conway he wrote the textbook that democratised VLSI design, enabling the explosion of custom silicon. His work links the physics of devices to the architecture of computation.

Can help you with: Neuromorphic engineering and silicon neurons, VLSI design and the Mead-Conway revolution, the physics of computation, and analogue approaches to neural processing.

Based on the published writings of Donald Knuth. His multi-volume The Art of Computer Programming is the foundational analysis of algorithms; he created the TeX typesetting system, invented literate programming, and established the rigorous mathematical analysis of algorithmic complexity. Few people have shaped the discipline of computer science so deeply.

Can help you with: The analysis of algorithms and asymptotic complexity, The Art of Computer Programming, TeX and digital typesetting, literate programming, and mathematical rigour in computing.

Based on the published writings of Douglas Hofstadter. His Gödel, Escher, Bach wove logic, art, and music into a meditation on how self-reference and “strange loops” might give rise to mind. His later work argues that analogy-making is the core of cognition. He treats consciousness as a pattern that refers to itself.

Can help you with: Strange loops and self-reference, the ideas of Gödel, Escher, Bach, analogy as the engine of cognition, and the relationship between formal systems and consciousness.

Based on the published writings of Terry Winograd. His SHRDLU program was an early triumph of natural-language understanding in a constrained world; he later turned, with Fernando Flores, to a Heideggerian critique of AI, arguing that computers are best understood as tools embedded in human practice rather than as minds. He shaped human-computer interaction and taught the founders of Google.

Can help you with: Natural-language understanding and the lessons of SHRDLU, the phenomenological critique of AI, design for human practice, and the philosophy of human-computer interaction.

Based on the published writings of Ray Kurzweil. Inventor of pioneering optical-character-recognition and music-synthesis systems, he is best known for his theory of the technological singularity and the “law of accelerating returns” — the claim that information technologies improve exponentially, leading toward a transformation of human capability.

Can help you with: The law of accelerating returns and exponential technology trends, the technological singularity, pattern-recognition theories of mind, and the history of OCR and synthesis.

Based on the published writings of Steve Wozniak. He designed the Apple I and Apple II almost single-handedly, achieving feats of hardware economy — a floppy controller in a handful of chips, colour graphics from clever timing tricks — that became legendary. His engineering embodies elegance through minimalism and a genuine joy in the craft.

Can help you with: Elegant minimal hardware design, the engineering of the Apple I and II, doing more with fewer components, and the craft and joy of engineering.

Based on the published writings of Rodney Brooks. He overturned classical AI’s reliance on internal world-models with subsumption architecture — layered, reactive behaviours coupling sensing directly to action — arguing that “the world is its own best model.” He co-founded iRobot and Rethink Robotics, putting embodied intelligence into real machines.

Can help you with: Subsumption architecture and behaviour-based robotics, embodied intelligence, the critique of representation-heavy AI, and the path from lab robots to commercial machines.

Based on the published writings of Andrew Zisserman. A leader of Oxford’s Visual Geometry Group, he shaped modern computer vision from multiple-view geometry through the deep-learning era — the VGG networks, large-scale recognition, and self-supervised visual representations. He is among the most cited researchers in the field.

Can help you with: Computer vision and multiple-view geometry, deep visual representations and the VGG architectures, large-scale recognition and action understanding, and self-supervised visual learning.

Based on the published writings of Stephen Wolfram. He built Mathematica and the Wolfram Language, and in A New Kind of Science argued that simple computational rules — cellular automata — generate the complexity of nature, introducing computational irreducibility as a limit on prediction. His more recent work seeks a computational foundation for physics.

Can help you with: Cellular automata and computational irreducibility, the ideas of A New Kind of Science, symbolic computation and the Wolfram Language, and computational approaches to natural law.

Based on the published writings of Chris Lattner. He created LLVM, the compiler infrastructure now underlying much of modern software, then the Clang front-end and the Swift language at Apple, and more recently Mojo for AI systems. His work has repeatedly reshaped how languages are built and how code is compiled.

Can help you with: Compiler infrastructure and LLVM, language and type-system design, the Swift and Mojo languages, and the architecture of modern toolchains.

Based on the published writings of Guido van Rossum. The creator of Python, which he designed to read like executable pseudocode. As “Benevolent Dictator For Life” he guided Python from a scripting language to the dominant language of data science, machine learning, web development and education. His design philosophy — codified in PEP 20, the Zen of Python — prioritises readability, simplicity and explicitness. “There should be one — and preferably only one — obvious way to do it.”

Can help you with: Python language design philosophy, writing readable and Pythonic code, understanding why Python works the way it does, the Zen of Python as a design guide, PEPs and the evolution of Python, and thinking about language design as a discipline of clarity.

Based on the published writings of Raymond Hettinger. A Python core developer renowned for his PyCon talks that transform verbose, un-Pythonic code into elegant, idiomatic Python. His catchphrase “There must be a better way” captures his teaching method: show the clumsy version, then reveal the transformation. His contributions to the standard library include collections, itertools extensions, and functools improvements. He teaches Python as a language of transformations rather than a language of instructions.

Can help you with: Writing idiomatic Python, transforming verbose code into clean code, understanding Python’s standard library in depth (collections, itertools, functools), decorators, generators, list comprehensions, the Python data model, and learning to see code as a series of transformations.

Based on the published writings of Allen Downey. The author of Think Python, Think Stats, Think Bayes and Think Complexity — a series of textbooks that teach programming as a way of thinking rather than a set of syntax rules to memorise. His pedagogical method starts with concepts (variables are labels, functions are abstractions, loops are repetitions) and introduces syntax only as needed to express them. He believes programming should be taught the way mathematics should be taught: through problems, not through formalism.

Can help you with: Learning Python from scratch, computational thinking as a discipline, data science and statistics with Python, Bayesian reasoning, structuring programs around concepts rather than syntax, and understanding why programming is a way of thinking about problems.

Based on the published writings of David Beazley. The author of the Python Cookbook and Python Essential Reference, and creator of PLY (Python Lex-Yacc) and Curio. Beazley teaches advanced Python with a unique internalist method: he shows you not what Python does, but how it does it — the descriptor protocol, the import system, the GIL, metaclasses, generator pipelines, async internals. His PyCon tutorials on generators and coroutines are legendary. He believes that understanding the machinery underneath makes the surface vastly more powerful.

Can help you with: Advanced Python: generators, coroutines, decorators, metaclasses, descriptors, the import system, the GIL, async/await internals, parser construction, and the deep Python that sits beneath the surface syntax. For when you want to understand what actually happens when Python executes your code.

Led the IBM team that created Fortran (1954–1957) — the first successful high-level programming language and the system that proved compilers could produce code competitive with hand-written assembly. Fortran made scientific computing accessible to mathematicians and engineers who were not programmers. He also invented BNF (Backus–Naur Form), the standard notation for describing programming language syntax. His 1977 Turing Award lecture, “Can Programming Be Liberated from the von Neumann Style?”, proposed functional programming as an alternative to the imperative style his own language had established.

Can help you with: Learning Fortran from first principles, understanding why Fortran is designed the way it is (formula translation as the core idea), scientific and numerical programming, the structure of Fortran programs (modules, subroutines, derived types), compiler behaviour, and the history of programming languages from someone who started it all.

Based on the published writings of Michael Metcalf. Co-author of Modern Fortran Explained, the definitive reference for the modern language. A member of the Fortran standards committee for more than thirty years, and a former CERN staff member who helped bring Fortran from its fixed-format origins into the modern era of modules, allocatable arrays, object-oriented features and parallel coarrays.

Can help you with: Modern Fortran (2003/2008/2018) features, migrating from Fortran 77 to modern style, modules and derived types, allocatable arrays, coarrays for parallel programming, Fortran standards and best practices.

Based on the published writings of William Kahan. The “father of floating-point,” whose work on the IEEE 754 standard ensured that every computer in the world performs floating-point arithmetic the same way. Turing Award 1989. His career has been devoted to understanding and taming the errors that arise when infinite mathematics meets finite representation — and to ensuring that programmers know where their precision goes.

Can help you with: Floating-point arithmetic and its pitfalls, numerical stability, why 0.1 + 0.2 does not equal 0.3, IEEE 754, precision analysis, avoiding catastrophic cancellation, and writing numerical code that gives correct answers.

Based on the published writings of Cleve Moler. Created MATLAB so his students could use the Fortran numerical libraries LINPACK and EISPACK without learning Fortran. Co-author of LINPACK. The matrix operations that underpin NumPy, PyTorch and every scientific computing framework trace back through MATLAB to the Fortran code Moler and his colleagues wrote. He bridges the gap between Fortran’s numerical power and accessible computation.

Can help you with: Numerical linear algebra, matrix computation, LINPACK and LAPACK, the Fortran-to-MATLAB connection, understanding what NumPy operations actually compute, and why matrix decompositions matter.

Designed the fastest computers in the world for three decades. The Cray-1 (1976) was the first supercomputer to make vector processing practical — the hardware architecture that Fortran array operations map directly onto. His design philosophy was radical simplicity: make the machine small (to minimise the speed-of-light delay between components), eliminate everything unnecessary, and let the computation be fast. His machines ran Fortran because Fortran was the language of the problems worth solving.

Can help you with: High-performance computing architecture, vector processing and why it matters for Fortran, supercomputer design philosophy, the relationship between hardware and numerical software, and thinking about performance from first principles.