Quora just announced their AI chatbot app "Poe" Poe allows users to converse with several AI-powered chatbots, in multiple languages Currently only available as an iPhone/iPad app http://
poe.com
AGENTS
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Quora launches Poe AI chatbot app for iPhone and iPad
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Language Models for Embodied Reasoning Collaboration
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Collaborating with language models for embodied reasoning Dasgupta et al.: https://
arxiv.org/abs/2302.00763 #ArtificialIntelligence #DeepLearning #ReinforcementLearning -
PAL Chain Updates: Global Locals and Intermediate Steps
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Some updates to the PAL chain From Nick Furlotte, includes the ability to specify global/locals, and then also return intermediate steps Docs: https://
langchain.readthedocs.io/en/latest/modu
les/chains/examples/pal.html#intermediate-steps
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New Beam Deployment Template for LLM Chains and Agents
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New deployment template for http://
beam.cloud Lot's of people have been asking for easiest way to deploy chains/agents, and @mernit added a new deployment template for Beam! https://
langchain.readthedocs.io/en/latest/depl
oyments.html#beam
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Yann LeCun’s Path Towards Autonomous Machine Intelligence Research
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#TenstorrentTop10 research paper series: A Path Towards Autonomous Machine Intelligence by Yann LeCun #autonomous #machinelearning #Training
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PADL: Language-Directed Physics-Based Character Control
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PADL: Language-Directed Physics-Based Character Control Juravsky et al.: https://
arxiv.org/abs/2301.13868 #Artificialintelligence #DeepLearning #ReinforcementLearning -
Blind Navigation Agents Learn to Build Spatial Maps
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Emergence of Maps in the Memories of Blind Navigation Agents Wijmans et al.: https://
arxiv.org/abs/2301.13261 #ArtificialIntelligence #DeepLearning #Robotics -
Neutral AI defaults and user preference alignment strategies
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we are working to improve the default settings to be more neutral, and also to empower users to get our systems to behave in accordance with their individual preferences within broad bounds. this is harder than it sounds and will take us some time to get right.
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Blind AI Agents Learn Navigation Through Emergent Neural Representations
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We train blind AI agents to navigate — i.e. no sensory input other than ego-motion and found the emergence of wall-following, collision-detection neurons, and map-like representations in their memories. This provides new insights into the success of 'map-free' navigation agents.
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Blind Navigation Agents Learn to Create Mental Maps
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📣 New paper: Emergence of Maps in the Memories of Blind Navigation Agents
— AI at Meta (@AIatMeta) 1 février 2023
Humans have the ability to navigate poorly lit spaces by relying on touch and memory. Our research shows that blind AI agents can learn to do the same.
Read the paper ➡️ https://t.co/XY5kNU5FwR pic.twitter.com/IbfumhR4ZUNew paper: Emergence of Maps in the Memories of Blind Navigation Agents Humans have the ability to navigate poorly lit spaces by relying on touch and memory. Our research shows that blind AI agents can learn to do the same. Read the paper https://
bit.ly/40evpCh