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Support-Vector Clustering [5] and other kernel methods [6] and unsupervised machine learning methods become widespread. [7] 2010s: Deep learning becomes feasible, which leads to machine learning becoming integral to many widely used software services and applications. Deep learning spurs huge advances in vision and text processing. 2020s
Cloud computing extended this boundary to cover all servers as well as the network infrastructure. [7] As computers became more diffused, scientists and technologists explored ways to make large-scale computing power available to more users through time-sharing. [6]
Cloud bursting is an application deployment model in which an application runs in a private cloud or data center and "bursts" to a public cloud when the demand for computing capacity increases. A primary advantage of cloud bursting and a hybrid cloud model is that an organization pays for extra compute resources only when they are needed. [ 68 ]
Amazon launches Amazon Elastic Compute Cloud (EC2), which forms a central part of Amazon.com's cloud-computing platform, Amazon Web Services (AWS), by allowing users to rent virtual computers on which to run their own computer applications. The service initially includes machines (instances) available for 10 cents an hour, and is available only ...
Timeline of computing presents events in the history of computing organized by year and grouped into six topic areas: predictions and concepts, first use and inventions, hardware systems and processors, operating systems, programming languages, and new application areas.
Leonard Uhr and Charles Vossler published "A Pattern Recognition Program That Generates, Evaluates, and Adjusts Its Own Operators", which described one of the first machine learning programs that could adaptively acquire and modify features and thereby overcome the limitations of simple perceptrons of Rosenblatt.
The solution is to align the machine's goal function with the goals of its owner and humanity in general. Thus, the problem of mitigating the risks and unintended consequences of AI became known as "the value alignment problem" or AI alignment. [272] At the same time, machine learning systems had begun to have disturbing unintended consequences.
Machine operators in Britain were mostly women into the early 1970s. [89] As these perceptions changed and computing became a high-status career, the field became more dominated by men. [90] [91] [92] Professor Janet Abbate, in her book Recoding Gender, writes: Yet women were a significant presence in the early decades of computing.