Introduction to special issue

The scientists' apprentice

See allHide authors and affiliations

Science  07 Jul 2017:
Vol. 357, Issue 6346, pp. 16-17
DOI: 10.1126/science.357.6346.16

eLetters is an online forum for ongoing peer review. Submission of eLetters are open to all. eLetters are not edited, proofread, or indexed.  Please read our Terms of Service before submitting your own eLetter.

Compose eLetter

Plain text

  • Plain text
    No HTML tags allowed.
  • Web page addresses and e-mail addresses turn into links automatically.
  • Lines and paragraphs break automatically.
Author Information
First or given name, e.g. 'Peter'.
Your last, or family, name, e.g. 'MacMoody'.
Your email address, e.g. higgs-boson@gmail.com
Your role and/or occupation, e.g. 'Orthopedic Surgeon'.
Your organization or institution (if applicable), e.g. 'Royal Free Hospital'.
Statement of Competing Interests
CAPTCHA

This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.

Image CAPTCHA
Enter the characters shown in the image.

Vertical Tabs

  • "GPU" and "open source software" play a key role for advancing deep learning

    Tim Appenzeller described the introduction to special issue, entitled "The scientists' apprentice" published in Science (1). Appenzeller overstated " Unlike earlier attempts at AI, such 'deep learning' systems don't need to be programmed with a human expert's knowledge" (1). Actually, in deep learning systems, we have to examine the performance of possible system candidates by trial-and-error methods with expert's knowledge. In addition to the progress of deep learning algorithms, GPU (graphic processing unit) and open source software have played a key role for advancing deep learning. GPU parallel computing can accelerate the computation of deep learning (2). An inexpensive GPU board with 3584 CUDA cores can be purchased for less than $1,000 where a CUDA core is most commonly referring to the single-precision floating point units in an SM (streaming multiprocessor). A CUDA core can initiate one single precision floating point instruction per clock cycle. Many of deep learning software (TensorFlow, Theano, Torch,...) are all based on open source and supports parallel executions with enabled CUDA cores (3).

    References:
    1. Tim Appenzeller, The scientists' apprentice, Science, 07 Jul 2017, Vol. 357, Issue 6346, pp. 16-17
    2. Y. Takefuji, GPU parallel computing for machine learning in Python, amazon, June 2017
    3. htt...

    Show More
    Competing Interests: None declared.