Practical Applications of AI Agents
Decision-capable AI agents operate within bounds, learn from data, and escalate to humans when needed.
Artificial Intelligence and Machine Learning
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Who is Liable When AI Goes Wrong?
Increasingly capable generative AI tools have created a gap between the technology's power and what many users understand about the legal liabilities of its use.
Generated data that starts forgetting tail events can lead to a concentration of higher probability distributions, which causes a model to fail.
AI in the Era of Climate Change: Solution or Problem?
Will AI lead to a net increase or decrease in energy consumption, and what effect will that have on climate change?
From Prompt Engineering to Prompt Science with Humans in the Loop
Demonstrating how to have scientific rigor in developing a reliable prompt and getting a trustworthy response for a downstream application.
AI’s Next Leap: Agentic Intelligence
The evolution from simple LLM-powered assistants to systems with growing agentic capabilities marks a significant step towards more versatile and impactful AI.
Thoughts about Some Surprising AI-Era Technology Readiness Findings
Survey findings define the essence of risk in companies' business-technology plans and readiness.
Different presumptions underlie successive waves of AI research that claim intelligence via computation is within reach.
Shedding light on the future of programming in the age of generative AI.
Privacy, performance, and security benefits have everyone from academic computer scientists to technology giants racing to develop more efficient ways of pulling AI out of the cloud and closer to users.
Machine Learning Framework Integrates Geometry into Fast PDE Solving
DIMON works by solving a PDE over a template domain, then predicting new solutions on other domains that are diffeomorphic to the template.
Envisioning Recommendations on an LLM-Based Agent Platform
The Rec4Agentverse paradigm for LLM-based agent platforms brings significant changes to recommender systems in areas such as user preference modeling and collaboration mechanisms.
To put civilization back on the right track, a boy relies on a visit from a very intelligent relative from the past.
The use of AI systems developed without a primary consideration of accountable explainability could have the polluting effect of nudging people toward superficial and thus dogmatic thinking.
How Liquid Networks Make Robots Smarter
The architecture of the liquid network includes recurrences which support adaptation.
Developing the Foundations of Reinforcement Learning
2024 Turing laureates Andrew G. Barto and Richard S. Sutton discuss the theoretical background and practical application of reinforcement learning.
Sutton and Barto developed reinforcement learning, a machine learning method that trains neural networks by offering them rewards in the form of numerical values.
Can We Build AI That Does Not Harm Queer People?
Outlining some ways to build queer-friendly systems.
Most IT professionals worry their jobs will be replaced by AI tools, while more companies attribute reductions to "technology updates" rather than AI.
You Can Get There From Here (But It Really Makes a Mess of Things)
Technology isn't helping navigation apps address the problem of rerouting heavy traffic from highways to local roads.
The Rise of Adaptive Phishing: When AI Learns to Manipulate
Tailored phishing messages that appeal directly to the target's interests or match the tone of a trusted contact are harder to spot than traditional phishing.
AI is being used to open, and close, vulnerable points in production systems.
Quantum Computing’s Impact on Algorithmic Complexity
Quantum computing is poised to gut the foundations of modern software development.
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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