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The Frontiers of Machine Learning 2017 Raymond and Beverly Sackler U.S.-U.K. Scientific Forum (2018) / Chapter Skim
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1 Introduction
Pages 4-10

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From page 4...
... Participants included industry leaders, ma chine learning researchers, and experts in privacy and the law. They represented strong propo nents of widespread adoption of machine learning as well as those with concern for the societal tensions that arise with expanding the use of machine learning.
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From page 5...
... While specific definitions vary, artificial intelligence is, generally speaking, any method for programming computers to enable them to carry out tasks or behaviors that would require intelligence if performed by humans. Early work in this field focused on automated reasoning; using these approaches, programs were written as sets of logical statements against which queries were processed by theorem proving and search.
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From page 6...
... There are count less applications of machine learning in society that help people become more organized and make processes more efficient -- for example, in organizing digital photo collections, managing spam in email platforms, and supporting navigation devices. Machine learning can solve prob lems related to classification, regression, clustering, dimensionality reduction, semi-supervised learning, and reinforcement learning.
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From page 7...
... These approaches map high-dimensional data onto low dimensions, while preserving relevant information, and have a range of applications -- for example, in data mining, scientific analysis, and image recognition. Typical methods include principal components analysis, factor analysis, multidimensional scaling, IsoMAP, and Gaussian process latent variable models.
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From page 8...
... 2 Neural networks are "computing system[s] made up of a number of simple, highly interconnected processing elements, which process information by their dynamic state response to external inputs." (M.
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From page 9...
... Deep learning has a number of similarities to the human brain: artificial neural networks act like neurons in the brain to connect various input concepts with potential outputs. E-mail spam filters, Internet search engines, speech recognition devices, translators, photo recognition programs, automatic email repliers, object detection in automated vehicles, and navigation apps all rely on deep learning to simplify tasks for the user.
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From page 10...
... This raises one facet of a broader question about vehicle safety standards: potentially dangerous should new standards be developed, or do situations current road safety standards translate for automated vehicles? Machine learning is also finding new applications in data science itself.
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Key Terms

  • deep learning
  • neural networks
  • automated vehicles
  • artificial intelligence
  • reinforcement learning

  • typical methods
  • methods include
  • dimensionality reduction
  • learning methods
  • learning draws

  • using deep
  • input data
  • create artificial
  • gaussian process
  • probabilistic models

  • reduce vehicle
  • logical rules
  • object detection
  • apply machine
  • unsupervised machine

  • applications machine
  • including machine
  • basic machine
  • developing machine
  • learning reinforcement

  • learning experts
  • learning provides
  • vised learning
  • learning demands
  • learning addresses


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