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1-10 of 1318 reviews |
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On the model of computation: counterpoint: parallel programming wall and multicore software spiral: denial hence crisis
Vishkin U. Communications of the ACM 65(9): 32-34, 2022. Type: Article
Vishkin’s counterpoint to Dally [1] talks about what the model of computation should now be in light of multicore processors (and their programming difficulties) and parallel processing. His view recommends a new model of computation, other ...
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Jan 24 2023 |
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On the model of computation: point: we must extend our model of computation to account for cost and location
Dally W. Communications of the ACM 65(9): 30-32, 2022. Type: Article
The model of any process demonstrates its functionality and is useful for its study and analysis. For example, the Turing machine is a model for computation. However, the random-access machine (RAM) model is close to the architecture of serial pro...
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Jan 23 2023 |
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Declarative machine learning systems
Molino P., Ré C. Communications of the ACM 65(1): 42-49, 2022. Type: Article
This article presents future directions for how machine learning (ML) can be easily adopted across applications to take advantage of artificial intelligence (AI). In the past two decades, ML has been improving at a tremendous pace thanks to many c...
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Jan 3 2023 |
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Algorithmic poverty
Kirkpatrick K. Communications of the ACM 64(10): 11-12, 2021. Type: Article
The lack of transparency in algorithms and the data they use is a matter of current interest. In addition to a nontransparent process, results are also nontransparent, lacking explanation or justification. These problems lead to unfairness and bia...
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Nov 22 2022 |
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 Biases in AI systems
Srinivasan R., Chander A. Communications of the ACM 64(8): 44-49, 2021. Type: Article
As Srinivasan and Chander discuss, software packages and algorithms encounter many biases related to images on the web. This is then an article on computer application control, not human user control. Machine learning (ML) and artificial intellige...
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Oct 24 2022 |
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Can AI learn to forget?
Greengard S. Communications of the ACM 65(4): 9-11, 2022. Type: Article
Nowadays, we can assume readers of Computing Reviews are familiar with the ideas behind machine learning, where neural networks are trained with large training sets so that they “learn” to recognize patterns ...
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Aug 8 2022 |
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What every engineer and computer scientist should know: the biggest contributor to happiness
Picard R. Communications of the ACM 64(12): 40-42, 2021. Type: Article
Obviously, people in this world go through periods of happiness and unhappiness. But do computer engineers and scientists really understand the factors that account for true happiness in daily life? Perhaps there are useful features fo...
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Jul 25 2022 |
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The long road ahead to transition to post-quantum cryptography
LaMacchia B. Communications of the ACM 65(1): 28-30, 2022. Type: Article
In this article, the author asserts the following:...
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Jul 14 2022 |
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Fifty years of P vs. NP and the possibility of the impossible
Fortnow L. Communications of the ACM 65(1): 76-85, 2022. Type: Article
The P versus NP problem is one of the most fundamental and well-known unresolved questions in computer science. In comparison with the 2009 Communications article by the same author [1], the current survey is less about progress...
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Jun 27 2022 |
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Here we go again: Why is it difficult for developers to learn another programming language?
Shrestha N., Botta C., Barik T., Parmin C. Communications of the ACM 65(3): 91-99, 2022. Type: Article
It is common for programmers to switch programming languages--quite often, new work happens to involve a new programming language and the programmer has to just dive into it. Many have experienced this, along with the ups and ...
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Jun 7 2022 |
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