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22
The slide presents a critique of the 'emergent capabilities' phenomenon in LLMs, citing Stanford research that suggests these are artifacts of metric choice.2023 Air Street Capital The State of AI Report 2023 · Air Street Capital
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23
The slide discusses technical methods like FlashAttention, ALiBi, RoPE, and Positional Interpolation in the context of LLM scaling.2023 Air Street Capital The State of AI Report 2023 · Air Street Capital
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24
The slide references the 'Lost in the Middle' research paper by Samaya.ai, UC Berkeley, Stanford, and LMSYS.org.2023 Air Street Capital The State of AI Report 2023 · Air Street Capital
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25
Part of the State of AI 2023 report.2023 Air Street Capital The State of AI Report 2023 · Air Street Capital
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26
The slide highlights the shift from 'more data/parameters' to 'better data' in LLM training, specifically referencing the 'phi' model series.2023 Air Street Capital The State of AI Report 2023 · Air Street Capital
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27
The slide uses a visual comparison (meme vs. truth) to explain the shift from dense models to Mixture of Experts (MoE) architectures.2023 Air Street Capital The State of AI Report 2023 · Air Street Capital
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28
The slide uses three separate line charts to illustrate data exhaustion timelines for different data types.2023 Air Street Capital The State of AI Report 2023 · Air Street Capital
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29
The chart shows Top-1 Accuracy vs Parameters (M) for models trained on 'Real' vs 'Real + Generated' data, indicating performance gains.2023 Air Street Capital The State of AI Report 2023 · Air Street Capital
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30
Includes logos of University of Maryland, Google, Google DeepMind, ETH Zurich, and Princeton.2023 Air Street Capital The State of AI Report 2023 · Air Street Capital
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31
Discusses the trade-off between training epochs, model size, and overfitting.2023 Air Street Capital The State of AI Report 2023 · Air Street Capital
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32
The slide contrasts structured quantitative evaluation (HELM/Hugging Face) with qualitative user sentiment ('vibes').2023 Air Street Capital The State of AI Report 2023 · Air Street Capital
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33
Includes a process diagram showing the training pipeline for Code Llama models.2023 Air Street Capital The State of AI Report 2023 · Air Street Capital
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34
The slide uses two process diagrams to explain how AlphaDev functions as an RL agent.2023 Air Street Capital The State of AI Report 2023 · Air Street Capital
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35
The slide compares different prompting architectures (IO, CoT, CoT-SC, ToT) visually.2023 Air Street Capital The State of AI Report 2023 · Air Street Capital
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36
The slide uses bar charts to compare accuracy, verbosity, and mismatch metrics for two model versions over time.2023 Air Street Capital The State of AI Report 2023 · Air Street Capital
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37
Includes a small example box illustrating tool usage (QA and Calculator).2023 Air Street Capital The State of AI Report 2023 · Air Street Capital
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38
The chart shows the 'Minecraft Tech Tree' progression of different agents, with Voyager (ours) showing the highest performance.2023 Air Street Capital The State of AI Report 2023 · Air Street Capital
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39
The slide contrasts standard Chain of Thought (linear) with RAP (tree-based planning).2023 Air Street Capital The State of AI Report 2023 · Air Street Capital
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40
Includes a diagram of the reasoning module (DAG) and a performance comparison table.2023 Air Street Capital The State of AI Report 2023 · Air Street Capital
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41
Includes a technical diagram of the BLIP-2 architecture.2023 Air Street Capital The State of AI Report 2023 · Air Street Capital
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42
The slide explains a technical approach to visual reasoning using LLMs as code generators for visual APIs.2023 Air Street Capital The State of AI Report 2023 · Air Street Capital
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43
The slide features a conceptual diagram of the LINGO-1 architecture and two real-world examples of the model providing driving commentary.2023 Air Street Capital The State of AI Report 2023 · Air Street Capital
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44
The slide uses a diagram to explain the multimodal architecture (PaLM + ViT) and provides examples of robotic manipulation and visual Q&A.2023 Air Street Capital The State of AI Report 2023 · Air Street Capital
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45
The slide explains the architecture and capabilities of the RT-2 model, highlighting its ability to generalize to novel objects and its deployment efficiency.2023 Air Street Capital The State of AI Report 2023 · Air Street Capital
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46
The slide uses a circular process diagram to explain the self-improving training loop of the RoboCat agent.2023 Air Street Capital The State of AI Report 2023 · Air Street Capital
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47
The slide highlights a milestone in AI robotics, specifically in competitive drone racing.2023 Air Street Capital The State of AI Report 2023 · Air Street Capital
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48
The slide highlights a specific research finding regarding emergent map-building in blind navigation agents.2023 Air Street Capital The State of AI Report 2023 · Air Street Capital
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49
The slide explains the technical architecture of CICERO, including its use of dialogue history, board state, and a 2.7B-parameter BART-like model.2023 Air Street Capital The State of AI Report 2023 · Air Street Capital
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50
The slide compares diffusion-based models vs. masked transformer models in the context of video generation.2023 Air Street Capital The State of AI Report 2023 · Air Street Capital
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51
The slide highlights the shift from pure generation to instruction-based editing using models like InstructPix2Pix and Imagen Editor.2023 Air Street Capital The State of AI Report 2023 · Air Street Capital
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