How Did People Write Machine Learning Code in the Past?
Summarizes popular programming languages and libraries that have been used for machine learning throughout the past two decades.
Artificial Intelligence and Machine Learning
Advertisement
Computer Architecture: Disruption from Above
For the research community, opportunities to shape a profoundly different, new age of computer architecture are emerging.
New data protection laws raise questions about whether certain jurisdictions are trying to thwart the ability of foreign governments to obtain data on their citizens.
Scientists are developing tiny medical machines that stretch the definition of the term "robot."
Exploiting the techniques of game theory to come up with the superior poker hand.
A Domain-Specific Architecture for Deep Neural Networks
Tensor processing units improve performance per watt of neural networks in Google datacenters by roughly 50x.
Research for Practice: FPGAs in Datacenters
Expert-curated guides to the best of CS research.
Workload Frequency Scaling Law: Derivation and Verification
Workload scalability has a cascade relation via the scale factor.
Can Beyond-CMOS Devices Illuminate Dark Silicon?
The "new Dark Silicon" model benchmarks transistor technologies at the architectural level for multi-core processors.
An Overview of Deterministic Database Systems
Deterministic database systems show great promise, but their deployment may require changes in the way developers interact with the database.
MIT's Josh Tenenbaum on how people think and learn, and how to make machines do likewise.
Artificial software can outperform human doctors at reading medical imaging.
Irrational Exuberance and the ‘FATE’ of Technology
Questions of verification, safety, and trust must be central when we embody intelligence in physical systems.
The U.S. is getting serious about filling the skies with unmanned aircraft.
Research for Practice: Prediction-Serving Systems
What happens when we wish to actually deploy a machine learning model to production?
Queueing theoretic models can guide design trade-offs in systems targeting tail latency, not just average performance.
Technical Perspective: Graphs, Betweenness Centrality, and the GPU
"Accelerating GPU Betweenness Centrality" by McLaughlin and Bader ably addresses the challenges to authors of efficient graph implementations in the important context of a computation called betweenness centrality.
Accelerating GPU Betweenness Centrality
We present a hybrid GPU implementation that provides good performance on graphs of arbitrary structure rather than just scale-free graphs as was done previously.
Regulating Automated Decision Making
A group assembled by Informatics Europe and the policy committee of the ACM Europe Council produced a report entitled "When Computers Decide" that makes 10 recommendations to policy leaders regarding automated decision making.
Rethinking Autonomous Vehicles
Consumer fears intensify as self-driving car fatalities dent the driverless dream.
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.
Learn More