We’re tracking notable developments in the democratization of AI, open source supply chain attacks, brain-computer interfaces, and more.
Attacking NLPs, Data Kit, Quantum Computing and Computation, and What-If Question Archive
Michael Jordan details several recent results that blend gradient-based methodology with game-theoretic goals.
Ananth Sankaranarayanan discusses three three key shifts in the AI landscape, how to navigate them, and when to explore hardware acceleration.
Sahika Genc dives deep into state-of-the-art techniques in deep reinforcement learning for a variety of use cases.
Kenneth Stanley discusses how open-ended algorithms can offer an entirely different level of automated creation.
Bayesian Philosophy, Combining Features, Quantum INTERCAL, and Universal Decay of Memory
Daniel Russakoff discusses how AI is being used to predict age-related macular degeneration progression.
Andrew Feldman discusses the Wafer Scale Engine, the largest chip ever built.
Triveni Gandhi explores the collective and individual responsibilities the builders of AI systems must bear.
Eric Gardner shares a four-step journey that all kinds of organizations can use to evaluate their unique paths from data to insights.
Experts discuss new trends, tools, and issues in artificial intelligence and machine learning.
Srinivas Narayanan takes a deep look into the next change we’re seeing in AI—going beyond fully supervised learning techniques.
Sarah Bird discusses the major challenges of responsible AI development and examines promising new tools and technologies to help enable it in practice.
Dinesh Nirmal examines how organizations can unlock the value of their data for AI with a unified, prescriptive information architecture.
Distributed Consistency, Face Anonymization, Game Mechanic Discovery, and Images of Images
Mapping Values, Crawlers are Legal, Laser Tripwire, and Coercion-Resistant Design
Machine learning, artificial intelligence, data engineering, and architecture are driving the data space.