AI milestones in 2011
7 documented milestones from 2011.
IBM Simulates 4.5 Percent of Human Brain Activity Using Blue Gene Supercomputer
In November 2011, IBM researchers led by Dharmendra Modha at IBM Research Almaden demonstrated a cortical simulation on the Blue Gene/P supercomputer that modelled approximately 4.5 percent of human-scale neural activity, using 147,456 processors to represent 1.617 billion neurons and 8.87 trillion synapses.
Apple Releases Siri with iPhone 4S
On 4 October 2011, Apple announced Siri as an integrated feature of the iPhone 4S, making a conversational voice assistant, capable of natural-language queries, task execution, and third-party service calls, available to a mass consumer audience for the first time at that scale.
D-Wave Systems Announces D-Wave One, a 128-Qubit Quantum Annealing Computer Available for Commercial Purchase
In May 2011, D-Wave Systems announced the D-Wave One, a 128-qubit quantum annealing processor codenamed Rainier, marketed as the first commercially available quantum computer. Lockheed Martin purchased a system for approximately US $10 million, making it the first known commercial transaction for a quantum computing system.
IBM Watson Defeats Human Champions on Jeopardy!
In February 2011, IBM's Watson system defeated Jeopardy! champions Ken Jennings and Brad Rutter across three televised episodes, demonstrating that a computer could parse ambiguous natural-language clues and retrieve factual answers competitively against expert human players.
AeroVironment Nano Hummingbird: DARPA-funded Flapping-Wing Micro Air Vehicle
In February 2011, AeroVironment unveiled the Nano Hummingbird, a DARPA-funded flapping-wing micro air vehicle weighing 19 grams, less than a AA battery, that carried a video camera and used control systems to mimic hummingbird flight, including hover and omnidirectional movement.
Google Brain Founded by Andrew Ng and Jeff Dean
In 2011, Andrew Ng and Jeff Dean co-founded Google Brain, an internal research group at Google dedicated to large-scale deep learning. The project demonstrated that deep neural networks trained on substantial compute could learn useful representations without labelled data, reshaping how the industry approached machine learning research.
Microsoft Research Develops Real-Time Human Pose Estimation for Kinect
In 2011, Jamie Shotton and colleagues at Microsoft Research Cambridge published a method for real-time human pose estimation from a single depth image, using randomised decision forests trained on synthetic data. The technique powered the skeleton-tracking feature of Microsoft Kinect and was presented at CVPR 2011.