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TechCrunch Founder Summit happens on November 4 in Boston for a founder-focused agenda and impactful connections. Save up to $100 or get discounts with group passes.
The artificial intelligence buildout is making an enormous bet on what businesses will eventually be willing to pay for intelligence. In our research this summer on forecasting the AI bubble, we argued that a key indicator to watch is productive utilization by enterprise customers. Specifically, we said at the time, if infrastructure supply catches up […] The post Can enterprise AI pay for the boom? appeared first on SiliconANGLE.
Agentic solutions for RF design. The post Planning Your AI Design Journey In RF EDA appeared first on Semiconductor Engineering.
Professor Paris Smaragdis develops computers with superhuman hearing for applications like speech recognition, music understanding, and environmental monitoring.
Prabha Jha used her experience with MIT Open Learning’s program for senior executives to develop AI solutions for her employer, Verizon, and to write a book.
Katelyn Lesse and Angela Jiang on Opus 5.5, the Claude Platform, and the engineering behind AI agents.
How to set up and run an offline coding agent using OpenCode, Ollama and Qwen.
General Compute is betting on inference chips from outside the Nvidia ecosystem.
A cross-section of the MIT community convened discussions on topics ranging from data centers and super pollutants to agriculture and education.
Learn AI literacy via Google’s latest AI Quests lesson on neuroscience, “Polar Peaks.”
Verisk modernized decades of insurance data, from mainframe to dbt, to get AI-ready.
Coyotec is bilingual, swears, and tells listeners how to defend themselves against ICE. Its popularity comes as more broadcasters are experimenting with artificial personalities.
arXiv:2610.31701v1 Announce Type: new Abstract: Enterprise customer journeys are shaped not only by product experiences and marketing, but also by recurring resource-allocation decisions. In B2B go-to-market (GTM) organizations, territory carving determines which accounts receive seller coverage, which seller-account relationships are preserved, and how limited field capacity is distributed. Yet the process often remains manual, making it difficult to compare opportunities, value continuity, and evaluate tradeoffs among growth, balance, capacity, and feasibility. We present a production decision system for tradeoff-aware customer-to-seller allocation in enterprise GTM planning. It integrates three components: a multidimensional account-value layer that combines predictive signals to score
