Artificial Intelligence and Machine Learning
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Autonomous Shipping Charts Its Course
The maritime industry wades into digitalization; will it float?
Geometric Deep Learning Advances Data Science
Researchers are pushing beyond the limitations of convolutional neural networks using geometric deep learning techniques.
Conversational interfaces provide spoken responses to queries of business databases.
Technologies for the Visually Impaired
The last decade has seen major advancements in technology for the blind and visually impaired, but problems remain.
Giving robots soft, artificial skin would enable them to work more closely with people.
Tracing the contacts of those who come into contact with the coronavirus is not that simple.
Creating efficiency in AI research will decrease its carbon footprint and increase its inclusivity as deep learning study should not require the deepest pockets.
Technical Perspective: XNOR-Networks – Powerful but Tricky
How to produce a convolutional neural net that is small enough to run on a mobile device, and accurate enough to be worth using? The strategies in "Enabling AI at the Edge with XNOR-Networks" are the best known to date.
Enabling AI at the Edge with XNOR-Networks
We present a novel approach to running state-of-the-art AI algorithms in edge devices, and propose two efficient approximations to standard convolutional neural networks.
Time to Assess National AI Policies
The Center for AI and Digital Policy has undertaken the first comparative review of national artificial intelligence policies.
Regulation of AI Remains Elusive
The U.S. and EU are considering regulations on artificial intelligence, but none have been implemented...yet.
Bringing Industry Back to Conferences, and Paying for Results
David Patterson wants to boost industry submissions to conferences, while Yegor Bugayenko suggests productivity should govern coders' pay when they work from home.
Terahertz Networks Move Closer to Reality
The desire for faster, higher-frequency wireless networking is a constant. Terahertz technology could deliver large gains.
Seeking Artificial Common Sense
The long-standing goal of providing artificial intelligence some measure of common sense remains elusive.
Generative Adversarial Networks
In this overview paper, we describe one particular approach to unsupervised learning via generative modeling called generative adversarial networks. We briefly review applications of GANs and identify core research problems related to convergence in games necessary to make GANs a reliable technology.
Technical Perspective: When the Adversary Is Your Friend
The key insight of the "Generative Adversarial Networks," by Ian Goodfellow et al., is to learn a generative model's loss function at the same time as learning the model.
AI Shakes Up the Seismology World
Artificial intelligence is reshaping our understanding of earthquakes and other seismological events
Prepare Your Meal Together with a Robot
Julie Shah and her team at the Massachusetts Institute of Technology build robots that can learn from demonstration, understand humans, and truly collaborate.
Shape the Future of Computing
ACM encourages its members to take a direct hand in shaping the future of the association. There are more ways than ever to get involved.
Get InvolvedCommunications of the ACM (CACM) is now a fully Open Access publication.
By opening CACM to the world, we hope to increase engagement among the broader computer science community and encourage non-members to discover the rich resources ACM has to offer.
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