OpenAI · conference-presentation
OpenAI | Product Presentation Deck | 64 slides
64 pages · 0 arc beats · 2 loops
OpenAI | Product Presentation Deck | 64 slides
OpenAI · 2018-09 arc beats above · slides in the middle · loops below · scroll → 2 LOOPS
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Deck intelligence map
1 coverage by narrative range · generated from this deck JSON
Narrative range 64 total
Metadata
Components
Metrics
Tools
Frameworks
Beats
Loops
Whole deck 64 slides 100% 64/64 slides 100% 64/64 slides · 182 hits — 0/64 slides
31.3% 20/64 slides · 26 hits 45.3% 29/64 slides — 0/64 slides
28.1% 18/64 slides Slide inventory
64 every slide · same image gating as the playbook
02
The slide includes a UI element indicating a 5-second duration, likely from a video presentation.present_framework
07
The slide includes a '5 seconds' timer element, likely indicating a timed transition or pacing marker.establish_context
08
The image features the OpenAI logo prominently in the center of the arena.establish_context
12
The slide uses visual examples of simple simulated environments (MuJoCo-style physics and Atari games) to illustrate the early scope of RL.establish_context
Open slide detailLoop · Aha Moment
13
The chart illustrates that different random seeds lead to vastly different performance outcomes, undermining the reliability of reported results.expose_contradiction
Open slide detailLoop · Aha Moment
15
The diagram uses a custom visual notation where squares represent Value, triangles represent Reward (R), and circles represent Actor (A) and State (S) componentpresent_framework
Open slide detailLoop · Aha Moment
16
The diagram shows a sequence of inputs feeding into a sequence of hidden states, which are connected sequentially.present_framework
Open slide detailLoop · Aha Moment
17
The diagram shows a complex neural network architecture with multiple input branches, embedding layers, and a multi-output head structure.present_framework
Open slide detailLoop · Aha Moment
18
Likely related to AlphaZero or similar reinforcement learning training methodologies.present_framework
Open slide detailLoop · Aha Moment
20
The chart illustrates that identical experimental setups can yield vastly different results, undermining the reliability of reported RL performance metrics.expose_contradiction
21
The diagram shows a standard unrolled RNN/LSTM structure with input nodes feeding into hidden state nodes that are connected sequentially.present_framework
22
The slide explains a technical adjustment in multi-agent reinforcement learning (LSTM agents) to improve cooperation.summarize
23
The chart tracks performance improvement from May to August 2018, highlighting key milestones and opponent levels.analyze_data
27
The slide illustrates the concept of domain randomization in machine learning, specifically for robotics, by showing visual diversity in training data.illustrate_case
28
The slide illustrates a machine learning training architecture involving distributed data collection, a control policy (LSTM), and a perception model (CNN).present_framework
29
The diagram shows a multi-view input processed by convolutional layers, feeding into object pose estimation, which is then combined with fingertip locations to present_solution
31
The slide uses a Venn diagram to show overlap between two AI projects.present_framework
32
The slide serves as a visual aid to demonstrate the hardware configuration of a vision-based robotic system.illustrate_case
33
The diagram represents a deep learning architecture for multi-camera vision processing.present_framework
34
The diagram represents a standard deep reinforcement learning policy architecture.present_framework
37
The slide contains a screenshot of a document or email thread with three distinct sections labeled 'Bank', 'Mattress', and 'Coffee'.illustrate_case
39
The slide shows a 12x repeated transformer block on the left and specific input/output processing pipelines for four different NLP tasks on the right.present_framework
41
The slide uses a minimalist design to emphasize the central argument.frame_situation
Open slide detailLoop · Cost Of Inaction
42
The slide uses a blue background and includes a book cover image.establish_context
Open slide detailLoop · Cost Of Inaction
43
The slide uses a historical anecdote to preemptively address skepticism about innovation.expose_contradiction
Open slide detailLoop · Cost Of Inaction
44
The slide uses a historical case study to illustrate the concept of 'aversion to scale' as a strategic failure.illustrate_case
Open slide detailLoop · Cost Of Inaction
45
The slide uses historical quotes to preemptively address potential skepticism from the audience regarding the presenter's proposal.preempt_rebuttal
Open slide detailLoop · Cost Of Inaction
46
The slide uses a quote to frame a historical perspective on the limitations of deep learning.cite_precedent
Open slide detailLoop · Cost Of Inaction
47
The slide presents a personal narrative of AI history, contrasting historical milestones with photos of prominent figures in the field.establish_context
Open slide detailLoop · Cost Of Inaction
48
The slide uses a historical photograph of Frank Rosenblatt and the Mark I Perceptron to provide context for the quote.establish_context
Open slide detailLoop · Cost Of Inaction
49
The slide features the book cover of 'Perceptrons' and a portrait of Seymour Papert.cite_precedent
Open slide detailLoop · Cost Of Inaction
50
The slide uses a quote to provide historical context on the AI winter/funding skepticism.cite_precedent
51
The slide features portraits of David Rumelhart, Geoffrey Hinton, James McClelland, and James Anderson.cite_precedent
52
The slide uses a historical anecdote about Gerald Tesauro to challenge the conventional narrative of AI development.establish_context
53
The slide uses a blue background with white text, typical of a section divider or transition slide.transition
54
The slide uses a before-after-bridge framework to contrast traditional domain-specific feature engineering (HOG) with the deep learning approach.illustrate_case
55
Uses a classic before-after-bridge structure to highlight the paradigm shift in AI research.illustrate_case
56
The slide uses a before-after-bridge framework to illustrate the paradigm shift in deep learning capabilities.illustrate_case
57
Uses a classic before-after-bridge structure to highlight the breakthrough of AlphaGo.illustrate_case
58
Uses a before-after-bridge framework to show the evolution of RL capabilities.illustrate_case
59
Uses a before-after-bridge structure to highlight the breakthrough in robotics.illustrate_case
61
The chart uses a logarithmic scale on the Y-axis to visualize the 300,000x increase in compute.analyze_data
62
The chart illustrates the exponential growth in compute requirements for AI training, particularly starting around 2017.analyze_data
63
The slide uses a dark blue background with white text, emphasizing the gravity of the topic.frame_situation