🛃Generic vs custom cognitive architectures To improve the planning ability of an agent, you may try to improve the cognitive architecture. You can do this in a generic or custom way Agents in production that we see have custom cognitive architectures https://lnkd.in/gwi3WAK3
Harrison Chase’s Post
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Director of Data Science | Applying generative AI and traditional ML to drive innovation and growth.
From the author: “ I believe that no matter how powerful the model becomes, you will always need to communicate to the agent, in some form, what it should do. As a result, I believe prompting and custom architectures are here to stay”, do you agree?
🛃Generic vs custom cognitive architectures To improve the planning ability of an agent, you may try to improve the cognitive architecture. You can do this in a generic or custom way Agents in production that we see have custom cognitive architectures https://lnkd.in/gX9cjBGF
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🛃Generic vs custom cognitive architectures To improve the planning ability of an agent, you may try to improve the cognitive architecture. You can do this in a generic or custom way Agents in production that we see have custom cognitive architectures https://lnkd.in/gX9cjBGF
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In the emerging world of AGI (artificial general intelligence), one of the most essential elements is developing world models. We look at the two leading contenders: LeCun's JEPA (Joint Embedding Predictive Architecture, 2022) and APD( Action Perception Divergence, Hafner et al., 2022). Key differentiators are that (1) all JEPA modules are required to be differentiable, and (2) JEPA is NOT generative, whereas active inference (on which APD is based) is explicitly generative. https://lnkd.in/gqrKHt77
AGI: Comparing JEPA (Joint Embedding Predictive Architecture) with Action Perception Divergence
https://www.youtube.com/
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🏗️Build application specific cognitive architectures, not generic agent infrastructure A custom cognitive architecture will absolutely make your application differentiated, while the generic agent infrastructure is less likely to What's the difference? https://lnkd.in/gMRGpiz4
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Over 75% of Developers Trust Artificial Intelligence - Parametric Architecture: In light of skyrocketing usage within the developer community, the question of trust Artificial Intelligence is front and center. http://dlvr.it/TFmQ6v
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First steps to AGI involve a thoughtful contrast-and-compare between JEPA and APD (Action Perception Divergence, based on active inference). This vid begins the process by identifying that JEPA is (1) explicitly differentiable throughout and (2) non-generative, whereas active inference IS generative.
In the emerging world of AGI (artificial general intelligence), one of the most essential elements is developing world models. We look at the two leading contenders: LeCun's JEPA (Joint Embedding Predictive Architecture, 2022) and APD( Action Perception Divergence, Hafner et al., 2022). Key differentiators are that (1) all JEPA modules are required to be differentiable, and (2) JEPA is NOT generative, whereas active inference (on which APD is based) is explicitly generative.
AGI: Comparing JEPA (Joint Embedding Predictive Architecture) with Action Perception Divergence
https://www.youtube.com/
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The next cohort of my IA:WTF? course starts a week from today! It's never been more important to learn about information architecture — reserve your spot now: https://bit.ly/49WuZ7n
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This article explains how the Container Storage Interface (CSI) works in Kubernetes, detailing the API specification, architecture, deployment models, and communication mechanisms. More: https://lnkd.in/gTqmgkb6
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I am sharing some of the technical details of the implementation behind the Nature paper I published last week. This includes architecture and key design decisions and the technology that allowed processing 2.2 TB worth of data in a single VM with 2 vCPUs and 2 GB RAM. https://lnkd.in/dz4kkkC2
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Founder, Innerverse AI | McKinsey Alum | Google for Startups | VentureBeat Top Woman in AI
4moWe are very excited to have partnered with you, and are looking forward to entering closed beta in two weeks! We are developing bespoke cognitive architecture due to the support of technologists such as LangChain (you), LastMile AI, Google DeepMind, and ElevenLabs!