connection machine

David Beazley's PyData Global 2021 keynote, an oral history of early scientific Python. He tells how Python ended up running on a Connection Machine supercomputer at Los Alamos.

In this 2019 GDC talk, Alex Schwartz and Ziba Scott explain how they flooded the app stores with over 1,500 auto-generated slot machine games to find the lowest bar for success. They tour the dark side of the mobile app market.

Dan Bornstein's Google I/O 2008 talk on the internals of Dalvik, the virtual machine that runs Android code. It covers the motivation for its design, the dex format and its register-based interpreter, plus tips for writing code that suits the platform.

Chris Lattner and Brennan Saeta present Swift for TensorFlow at the TensorFlow Dev Summit 2019, covering language-integrated automatic differentiation and tooling for machine learning in Swift.

Hans Rosling makes the case that the washing machine was the greatest invention of the industrial revolution. Using Gapminder graphics, he shows how electricity and economic growth free up time for reading and learning.

Ali Rahimi accepts the NIPS 2017 Test of Time Award and argues that machine learning has become alchemy, with practice running ahead of understanding.

Geoffrey Hinton's Google Tech Talk linking machine learning to biology, explaining dropout as a form of model averaging and why sexual reproduction and stochastic spiking neurons may prevent overfitting.

Cliff Click, author of HotSpot's JIT compiler, shares hard-won advice for people writing virtual machines at the VMSS16 workshop. Topics include interpreters, JITs and performance lessons.

Bryan Cantrill of Oxide Computer Company gives a Stanford EE380 talk on rethinking the server. He argues that hyperscaler-style rack-scale machines with open firmware, RISC-V and Rust should be available to everyone, not just the giants.

Johann Hari's TED talk arguing that addiction is driven largely by disconnection and environment rather than chemical hooks alone. He draws on his personal experience with addicted relatives and on research and policy examples from around the world.

A talk by Yuri Iozzelli at a WebAssembly meetup on CheerpX, an x86 virtual machine running in the browser. It uses a JIT compiler that emits WebAssembly at runtime to run real native applications. It covers the architecture and demos such as porting a multiplayer C++ game.

Peter Norvig's 2007 Google talk on theorizing from data, covering what huge datasets can do for language understanding, translation, information extraction and inference. It argues that more data often beats cleverer models.

Kalman Reti, one of the last Symbolics developers, gives a roughly hour-long talk on Symbolics Lisp machines. It covers the machines and their environment from an insider's perspective.

James Mickens's humorous USENIX Security keynote argues that improving security and trusting opaque technology like machine learning is far harder than the industry admits.

Chuck Moore, inventor of Forth, presents GreenArrays' 144-core asynchronous chip at Strange Loop 2013. He explains how idle cores draw almost no power and how tight code minimizes energy use.

David Beazley's PyCon India closing keynote, in which he live-codes a stack machine and grows it into an interpreter that runs WebAssembly, then uses it to play a Rust-written game. It requires no prior WebAssembly knowledge.

A short SIGBOVIK 2017 presentation by Tom Wildenhain showing that PowerPoint animations are Turing complete, with a working Turing machine built in slides. The talk is delivered deadpan as a serious research result.

Valve's John McDonald presents at GDC 2018 on how Valve uses deep learning (VACnet) to detect cheaters in Counter-Strike: Global Offensive. He covers training on human-reviewed replays and how model output is confirmed by manual review.

A GDC 2018 talk by Valve's John McDonald on VACNet, a deep learning system that analyzes CS:GO gameplay to detect cheaters. It covers how the model is trained on human-reviewed Overwatch cases and how its output feeds human moderation.

Ali Rahimi's NIPS 2017 Test-of-Time award talk, in which he argues that modern machine learning has become alchemy, working without being rigorously understood. He calls for more rigor and understanding in the field.

Max Tegmark's MIT CBMM talk on why deep learning works so well. He argues that the laws of physics and the symmetries in physical data make it compressible into small neural networks, tying physics to network structure.

Joscha Bach's 35C3 talk offers an AI perspective on the soul, covering perception, mental representation, agency, consciousness and selfhood as things that can arise in a computational system like the brain.

Jose Valim introduces Nx, the project bringing numerical computing to Elixir, covering multi-dimensional tensors, automatic differentiation and JIT compilation to CPU and GPU.

David Ditzel of Esperanto Technologies presents their massively parallel, energy-efficient RISC-V processors at the 7th RISC-V Workshop in 2017. It covers the high-performance many-core design aimed at machine learning.

Rob Pike shows how the Go text/template lexer is built, using state functions, goroutines and channels. The talk was given at a Google Technology User Group in Sydney in 2011.

Tom7 explains how he built a SIGBOVIK 2017 paper made only of printable ASCII bytes that is also a valid DOS executable. He writes a C compiler targeting printable x86 without self-modifying code and solves the puzzles the constraint creates.

Drew Berry shows scientifically accurate animations of molecular processes inside cells, such as DNA replication, at TEDxSydney.

Dave Ackley gives a high-level introduction to robust-first computing, which prioritizes robustness over efficiency in computer architecture. A sorting demo illustrates the idea.

George Hotz of Comma AI gives a 2017 conference talk on lessons from building a self-driving system. He argues about how driving should be defined and why the common approach to autonomous vehicles is flawed.

Peter Norvig, then Director of Research at Google, talks at Startup School 2008 about leveraging large amounts of data and about technical challenges in Google projects such as image search. A central idea is that data can beat hand-written programming.

Geoff Hinton's 2010 Google Tech Talk on deep belief networks, learning layers of representation one at a time from unlabeled data, and extending them with multiplicative interactions.

Chris Stucchio presents the ethical principles behind algorithmic decision making and shows with examples from lending, criminology and education that they cannot all be satisfied at once. The talk frames AI fairness as a set of quantitative tradeoffs.

Tom Dale explains the internals of the Glimmer VM, Ember's rendering engine, which compiles templates to opcodes. The talk includes a performance benchmark demo.

Jeff Hawkins presents his view that the neocortex works on principles different from traditional computers. He describes sparse distributed representations and how they are learned, then shows how Numenta applies them to predict from high-velocity data streams.

Historian Doron Swade tells the story of Charles Babbage's quest to build the Difference Engine and the construction of Difference Engine No. 2 at the Science Museum. The talk covers Babbage's motivation, his machines and Ada Lovelace's insight into the Analytical Engine.

A Google Tech Talk by Laurens van der Maaten, co-creator of t-SNE, on visualizing high-dimensional data by mapping it to two dimensions. It explains how the technique works and how it compares with other visualization approaches.

Andrej Karpathy's Databricks talk on Software 2.0, the shift from human-written code to code produced by neural network training. It covers how developer work moves toward curating datasets, the advantages and challenges, and opportunities for new tooling.

Marcin Jakubowski of Open Source Ecology presents his plan to open-source blueprints for 50 cheap, buildable farming tools, a "civilization starter kit". He argues that opening the means of production can enable self-sustaining lives.

A Curry On talk by Lars Bak, known for V8 and Dart, on building a secure and robust IoT development platform. It discusses language and VM design choices for small devices.

Emery Berger argues that performance is hard to measure and deliver on modern systems, showing how code layout and link order can swamp optimization effects. He presents tools that evaluate performance more reliably.