Skip to content

Part XI – Emerging Technologies

Technologies still being decided: quantum and reversible computing, the Internet of Things, cryptocurrencies with their frauds and collapses, and self-driving cars. A second section covers computing as a tool of science, from numerical methods and computer algebra to statistics with R, bioinformatics and climate models.

  • Quantum Computing — Feynman, Shor, and the limits of classical computers — and the limits of quantum hype
  • Reversible Computing — Landauer’s price for forgetting a bit, Bennett’s uncomputation, Fredkin and Toffoli’s billiard balls, the MIT Pendulum chips, and Vaire’s 2025 energy-recovering test chip
  • The IoT Revolution — MQTT, Zigbee, the Mirai botnet, and 15 billion devices with no patch path
  • Mark Weiser and Ubiquitous Computing — program slicing (1981), the 1988 coinage at PARC, tabs, pads and boards, calm technology, and the drummer of the first band on the MBone
  • The Cryptocurrency Revolution — Satoshi, blockchain, Ethereum, and speculation meeting technology
  • Sam Bankman-Fried — FTX’s $8B fraud, “effective altruism” as alibi, and a 25-year sentence
  • Ruja Ignatova: The Missing Cryptoqueen — OneCoin’s $4B+ Ponzi with no blockchain, and a vanished FBI fugitive
  • Mark Karpelès and Mt. Gox — 850,000 lost bitcoins and crypto’s first great custodian collapse
  • Craig Wright — the man who sued the world to be Satoshi, until a court ruled he forged the proof
  • The Autonomous Vehicle Race — DARPA, Waymo, Tesla Autopilot, and why full autonomy is always ten years away

Computing × Science

  • Scientific and Numerical Computing — Monte Carlo, floating point, BLAS/LAPACK/MATLAB, and the math of computing the continuous on a discrete machine
  • Computer Algebra Systems — symbolic instead of numerical math: Veltman’s Nobel-winning Schoonschip, Macsyma, Maple and Mathematica, algebra in a calculator, and Axiom’s elegant dead end
  • Stephen Wolfram — the prodigy who classified cellular automata, shipped Mathematica in 1988, kept his employee’s Rule 110 proof out of print, and self-published a new kind of science in 2002
  • Joel Moses — SIN, the 1967 integration program, eleven years directing Macsyma, the largest Lisp program of its day, then EECS head, dean, and MIT provost
  • R and Statistical Computing — SPSS and SAS on mainframes, Chambers’s S at Bell Labs, the free clone from Auckland, CRAN, the tidyverse, and R against Python
  • Data Science and the Notebook — Tukey’s 1962 complaint, a job title from 2008, the Netflix Prize nobody deployed, NumPy, pandas and scikit-learn, IPython’s 259 lines, Jupyter, and the 4 percent of notebooks that reproduce
  • Ross Ihaka and Robert Gentleman — two Auckland statisticians who could not afford S-PLUS for a teaching lab, wrote R on a Scheme engine, and let it go under the GPL in 1995; Bioconductor
  • John Chambers — S at Bell Labs from a five-person meeting on May 5, 1976, the ACM Software System Award in 1998, and a career spent improving R, the free copy of his own language
  • Hadley Wickham — ggplot2’s grammar of graphics, tidy data, dplyr’s verbs, and the 2016 tidyverse: the dialect most people who learned R afterwards learned first
  • James Goodnight — the NC State graduate student who has run SAS Institute since 1976, the largest private software company in the world, with a 35-hour week and no shareholders
  • Bioinformatics and the Human Genome — Dayhoff, sequence alignment, BLAST, the $2.7B race to read human DNA, and biology as a data science
  • Weather and Climate Modeling — Richardson’s forecast factory, the 1950 ENIAC forecast, GCMs, the butterfly effect, and Manabe’s Nobel
  • The Limits to Growth and World3 — Forrester’s system dynamics, the Club of Rome’s World3 model, the 1972 report, its critics, and fifty years of comparisons
  • Volunteer Computing — SETI@home’s screensaver supercomputer, BOINC, and Folding@home reaching exascale from strangers’ gaming rigs