Communications of the ACM
From Communications of the ACM
The Four Horsemen of an Ailing Software Project
Don't let the pale rider catch you with an exception.
From Communications of the ACM
The Context Problem in Artificial Intelligence
The artificial intelligence design challenge of teaming humans and machines is difficult because machines cannot read the context of use.
From ACM Opinion
Software Engineering in Physics Research
A discussion on how physics research scientists use software.
From Communications of the ACM
Rethinking Silicon Valley: Stewart Brand's Lost Journal
Discovering a hidden chapter in computing history.
From Communications of the ACM
Mobile-App Privacy Nutrition Labels Missing Key Ingredients for Success
Android and iOS privacy labels confuse developers and end users.
From Communications of the ACM
Data Platforms and Network Effects
How data-network effects create opportunities and inflate expectations.
From ACM Opinion
Linguistics and the Development of NLP
An interview with Christopher Manning, director of the Stanford University AI Lab and an associate director of Stanford's Human-Centered Artificial Intelligence...
From ACM Opinion
The AI Researcher Giving Her Field Its Bitter Medicine
Anima Anandkumar wants computer scientists to move beyond the matrix, among other challenges.
From ACM Opinion
Supercomputer Emulator: AI's New Role in Science
Chris Bishop, Microsoft's head of AI4Science, sees machine learning partially supplanting simulation.
From ACM Opinion
Interpretable Machine Learning
A conversation with Been Kim, staff research scientist at Google Brain.
From Communications of the ACM
On the Model of Computation: Counterpoint: Parallel Programming Wall and Multicore Software Spiral: Denial Hence Crisis
Parallel programming wall and multicore software spiral: Denial hence crisis.
From Communications of the ACM
On the Model of Computation: Point: We Must Extend Our Model of Computation to Account for Cost and Location
We must extend our model of computation to account for cost and location.
From ACM Opinion
Can Neural Networks Learn Better than Human Neurons?
When AI programmers stop trying to copy the human neuron, neural networks make much better progress.
From ACM Opinion
How to Solve AI's 'Common-Sense' Problem
AI systems without common sense will make mistakes when they reach the limits of where they've been trained.
From Communications of the ACM
When Should a Black Box Be Transparent?
When is a replacement not a replacement?
From Communications of the ACM
An Interview with Dana Scott
ACM Fellow and A.M. Turing Award recipient Dana Scott reflects on his career.
From Communications of the ACM
An Interview with Dana Scott
ACM Fellow and A.M. Turing Award recipient Dana Scott reflects on his career.
From Communications of the ACM