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LLM models & agents
- Thread starter πππππππ
- Start date
Been following your posts since you've joined. Always a pleasure reading your inputs.
Started offering AI automation as service to my agency clients. Involved in digital marketing for the past decade in different capacities. Been toying around with this same idea your working on.
Let's connect and see what comes from this iA.
Nelson
Chrisp street market alumni
Started offering AI automation as service to my agency clients. Involved in digital marketing for the past decade in different capacities. Been toying around with this same idea your working on.
Let's connect and see what comes from this iA.
Nelson
Chrisp street market alumni
Brilliant, shall drop you a note.
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How far are you into the training? How much data do you use?
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A good resource to create datasets & sources with .doc, .pdf, .ppt, links, audio & video files.
notebook LM -> https://notebooklm.google.com
notebook LM -> https://notebooklm.google.com
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Artificial General Intelligence (AGI) aims for domain-independent reasoning and problem-solving capabilities, and can perform intellectual task that a human being can do, exhibiting generalization, adaptability, and autonomous learning across diverse domains (Goertzel, 2007).
Cognitive Architectures: computational frameworks designed to emulate the structural and functional aspects of human cognitionβsuch as memory, learning, reasoning, and decision-makingβin artificial systems.
Foundations
ACT-R (Adaptive Control of ThoughtβRational): Simulates human cognition through production rules and declarative memory (Anderson et al., 2004).
SOAR: Focuses on problem-solving and learning through chunking and reinforcement (Laird, 2012).
ARS (AGI Agent Architecture): Emphasizes simulation-based testing of human-like information processing (Schaat, 2014)
Cognitive Architectures: computational frameworks designed to emulate the structural and functional aspects of human cognitionβsuch as memory, learning, reasoning, and decision-makingβin artificial systems.
Foundations
ACT-R (Adaptive Control of ThoughtβRational): Simulates human cognition through production rules and declarative memory (Anderson et al., 2004).
SOAR: Focuses on problem-solving and learning through chunking and reinforcement (Laird, 2012).
ARS (AGI Agent Architecture): Emphasizes simulation-based testing of human-like information processing (Schaat, 2014)
llm arena -> https://llmarena.ai/
If interested in learning at MiT -> https://hanlab.mit.edu/courses/2024-fall-65940
Lecture videos:
www.youtube.com
Transformers & LLMs
Lecture videos:
MIT HAN Lab
MIT HAN Lab: Hardware, AI and Neural-nets Accelerate Deep Learning Computing Group website: hanlab.mit.edu TinyML Course: efficientml.ai
www.youtube.com
Transformers & LLMs
open source llms -> ollama.com/search
other dev. tools -> flowiseai.com | n8n.io
other dev. tools -> flowiseai.com | n8n.io
