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What would it take to build machines that partner with humans? Can we design AI assistants to be thought partners? In a new perspective, we describe how computational cognitive science can help build AI systems that learn and think *with* people! 🧠🫱🏿‍🫲🏾🤖 arxiv.org/abs/2408.03943

Had a great time in Dakar for my second Deep Learning Indaba! It was fun to lead a practical session on responsible AI and meet so many wonderful people in Senegal 🇸🇳 🔥 #Indaba2024 #DLI2024

CDS Faculty Fellow Umang Bhatt (Umang Bhatt) explores when AI should step back for cultural fit. His "algorithmic resignation" concept highlights where human judgment should prevail. "To align AI, we must grasp cultural context of its use," he says. nyudatascience.medium.com/when-should-ai…

I’m in Philadelphia this weekend at #AAAI2025 🔔 If you’re around, come by and chat with Valerie Chen and me today (and tomorrow) about personalizing access to AI assistance! arxiv.org/abs/2304.06701

CDS Faculty Fellow Umang Bhatt (Umang Bhatt), CDS visiting researcher Valerie Chen (Valerie Chen), & colleagues developed MODISTE, a system that personalizes when AI should assist you—learning your preferences from your behavior. Presented at AAAI 2025. nyudatascience.medium.com/ai-isnt-always…

I’m excited to share new work from Datadog AI Research! We just released Toto, a new SOTA (by a wide margin!) time series foundation model, and BOOM, the largest benchmark of observability metrics. Both are available under the Apache 2.0 license. 🧵

📢 New Preprint: What happens at a population level when humans learn from AI systems that themselves learned from us? We extended the classic "Rogers' Paradox" to explore human-AI learning networks—and found some surprising results! 🤖🧠 (Paper link in 🧵)