Air Street Capital · consulting-deck
2022 Air Street Capital The State of AI Report 2022
114 pages · 3 arc beats · 18 loops
2022 Air Street Capital The State of AI Report 2022
Air Street Capital arc beats above · slides in the middle · loops below · scroll → 18 LOOPS
15101520253035404550556065707580859095100105110114
Deck intelligence map
3 coverage by narrative range · generated from this deck JSON
Narrative range 105 total
Metadata
Components
Metrics
Tools
Frameworks
Beats
Loops
The Facts (What) 76 slides 100% 76/76 slides 100% 76/76 slides · 455 hits — 0/76 slides
76.3% 58/76 slides · 102 hits 7.9% 6/76 slides · 7 hits 100% 76/76 slides · 152 hits 93.4% 71/76 slides The Implications (So What) 26 slides 100% 26/26 slides 100% 26/26 slides · 159 hits — 0/26 slides
69.2% 18/26 slides · 33 hits 15.4% 4/26 slides 100% 26/26 slides · 52 hits 92.3% 24/26 slides The Action (Now What) 3 slides 100% 3/3 slides 100% 3/3 slides · 10 hits — 0/3 slides
100% 3/3 slides · 7 hits — 0/3 slides
100% 3/3 slides · 6 hits 33.3% 1/3 slides Slide inventory
114 every slide · same image gating as the playbook
03
The slide serves as an introduction/agenda for the report, outlining the structure of the document.establish_context
07
The slide uses a structured list format to categorize key takeaways across four domains.summarize
Open slide detailBeat · The Facts (What)
09
The slide uses a color-coded 'Grade' column (green for Yes, red for No) to summarize the outcome of past predictions.summarize
12
The slide describes a human-AI collaborative workflow for mathematical discovery.illustrate_case
13
Part of a series of slides tracking 2021 predictions.illustrate_case
14
The slide tracks a previous year's prediction and provides a technical explanation of the breakthrough, supported by a bar chart comparing speed-ups.illustrate_case
15
The slide details the application of RL to magnetic coil control in fusion reactors (TCV/ITER).illustrate_case
16
The chart shows a significant jump in predicted structures in 2022 compared to historical published data.summarize
17
Includes a bar chart showing perplexity decrease with model size and a table of ESM-2 performance metrics.illustrate_case
18
Includes logos of research institutions at the bottom.illustrate_case
19
Includes a time-lapse visual of plastic degradation and a schematic of the ML model used for enzyme engineering.illustrate_case
20
The slide references a Princeton study on data leakage in 329 papers across 17 fields.diagnose
21
The slide explains the methodology of training an Inverse Dynamics Model (IDM) to scale up training data for a foundation model.illustrate_case
22
The slide highlights the competitive landscape of LLMs applied to software engineering tasks.summarize
23
The slide features a Venn diagram mapping various 'Efficient Transformer' research papers into categories like Recurrence, Memory/Downsampling, Low Rank/Kernelsanalyze_data
24
Includes a specific example of a math problem solved by Minerva.summarize
25
The slide includes three small scatter plots showing different benchmark saturation dynamics.summarize
26
The chart illustrates the relationship between model parameters and compute (FLOPs), comparing DeepMind's findings with previous OpenAI scaling laws.analyze_data
27
The slide discusses the non-linear performance gains in LLMs as they scale, using four specific task examples (Arithmetics, Fig. of speech, Multi-task NLU, Trananalyze_data
28
The chart compares truthfulness and informativeness across different model sizes and prompting strategies, highlighting WebGPT's performance.illustrate_case
29
The chart uses a logarithmic scale for the y-axis to visualize the massive acceleration in compute requirements.analyze_data
30
Includes a list of academic paper titles as a visual reference for the trend.summarize
31
The slide uses visual examples to demonstrate the capabilities of different AI models.illustrate_case
32
The slide uses visual evidence (image generation examples) to support the claim of competitive parity.illustrate_case
33
Slide from State of AI 2022 report.establish_context
34
The slide highlights the speed at which the open-source community has replicated or improved upon proprietary landmark models.illustrate_case
35
The timeline highlights the speed at which the open-source community (e.g., DALL-E mini/Craiyon, Stable Diffusion) caught up to or surpassed closed-source modelillustrate_case
36
The slide highlights the speed of open-source community adoption and improvement of proprietary AI models.summarize
37
Includes visual diagrams of the SayCan decision process and success metrics.illustrate_case
38
The chart compares model performance (ImageNet accuracy) vs computational cost (GFLOPs) for various architectures.analyze_data
39
Discusses the technical approach of masking large patches of pixels to train Vision Transformers (ViT).summarize
40
The slide discusses the evolution of transformer models from task-specific to generalist architectures.summarize
41
The slide validates a previous prediction using the IRIS model as a case study, supported by visual evidence of world model simulation and comparative performanillustrate_case
42
Data source: Zeta Alpha. The chart illustrates the diversification of transformer model applications.analyze_data
43
The chart shows a significant increase in NeRF-related research papers from 2019 to 2022.analyze_data
44
The slide uses three bar charts (E, F, G) to demonstrate the efficacy of ML-recommended antibiotics compared to physician-prescribed ones.illustrate_case
45
The slide highlights the performance gap between traditional methods and property-prediction transformers in drug discovery.illustrate_case
46
The slide highlights the dual-use nature of AI in pharmaceutical research, specifically referencing the MegaSyn model.illustrate_case
47
The charts use a diverging bar chart format where red bars indicate higher prevalence in China and blue bars indicate higher prevalence in the US.compare_peers
48
The slide uses two horizontal bar charts to contrast national output vs institutional output.compare_peers
49
The chart uses a stacked bar approach to show the difference between standard corpus and CNKI-included corpus for China.quantify_impact
51
The slide uses a comparison of valuation vs revenue to illustrate the scale gap between incumbents and startups.compare_peers
52
Logarithmic scale used for the y-axis.analyze_data
53
Data source: stateof.ai 2022 report.analyze_data
54
The slide uses a combination of a line chart for stock price and bar/line charts for financial metrics (Revenue, R&D) to illustrate the growth during the deal tillustrate_case
55
The slide highlights the synergy between AI research and hardware engineering, specifically referencing the H100 GPU and PrefixRL.illustrate_case
56
The slide uses a mapping of compute providers to AI labs, highlighting the trend of strategic partnerships.present_framework
57
The slide highlights the shift in compute power from public/national infrastructure to private corporate entities.analyze_data
58
The chart shows a correlation between government contracts and AI software upgrades in China.analyze_data
59
Includes screenshots of Google's AI Test Kitchen app.frame_problem
60
The slide uses a logo-grid to illustrate the proliferation of startups founded by alumni from DeepMind and OpenAI.summarize
61
The slide maps individual researchers from the original paper to the companies they founded or joined, and lists the capital raised by those companies.illustrate_case
62
Includes a mix of internal Google metrics and a controlled experiment conducted by GitHub Copilot.quantify_impact
63
The slide uses a stacked bar chart for company-specific pipelines and a horizontal bar chart for the aggregate percentage distribution of assets.quantify_impact
64
The slide uses a combination of narrative text and two charts to illustrate the 'physical bottlenecks' of drug development.frame_problem
65
The slide features a grid of 16 small area charts showing variant submission counts over time, with red (EWS prediction) and green (WHO designation) markers.illustrate_case
66
The slide uses a process flow diagram to explain the AI's role in triaging chest X-rays.illustrate_case
67
Includes data from Beauhurst and GOV.UK. Shows sector breakdown, university-specific spinout counts vs equity stakes, and historical trend of average equity staillustrate_case
68
The slide uses three distinct charts to illustrate the 'spinout problem' (time, equity, and NPS).illustrate_case
69
The slide uses a timeline-based case study to demonstrate the efficacy of a specific academic research funding model.illustrate_case
70
Data source: dealroom.co. The slide uses stacked bar charts to show investment by round size.analyze_data
71
Data source: dealroom.co. The charts show a significant decline in large-scale funding compared to the relative stability of smaller rounds.analyze_data
72
The slide uses stacked bar charts to show EV by launch year cohorts.compare_options
73
The slide uses a bar-chart-in-table format to visualize the data.compare_peers
74
Data source: dealroom.co. The chart shows a decline in absolute investment in 2022 YTD compared to 2021, but maintains a >50% share for the US.analyze_data
75
The slide uses a combination of table and bar chart elements to visualize investment data across 24 industries.analyze_data
76
Data source: dealroom.co. The chart shows a clear shift from public market exits to M&A activity in 2022.analyze_data
77
Data source: dealroom.co. The chart shows a clear trend of increasing AI exits globally, with a specific focus on the performance of European regions versus theanalyze_data
78
The chart uses a stacked bar format to show funding rounds by size, with a projection for the full year 2022.analyze_data
79
Data source: dealroom.co. The chart tracks EV by launch year cohorts.analyze_data
80
The slide uses a stacked bar chart to show EV growth by launch year cohorts and a grid of company logos with valuation details.size_opportunity
82
The slide uses two scatter plots with trend lines to visualize the divergence in compute power and research output share.analyze_data
83
The slide contrasts institutional bureaucracy with agile research collectives.establish_context
84
The chart shows a clear decline in academic dominance (blue) and a rise in industry/collective collaborations (red/grey) in recent years.summarize
85
The slide illustrates the ecosystem of Stability AI, showing how it supports various research groups (LAION, EleutherAI) and commercializes the output (DreamStupresent_solution
87
Part of the State of AI 2022 report.summarize
88
The slide uses a process diagram to explain the 'Uber-like' dispatch model of the software.illustrate_case
89
Data source: CSET, stateof.ai 2022 report.compare_peers
90
Includes a table summarizing the relative need for reshoring across different semiconductor device categories.analyze_data
91
Slide from the State of AI 2022 report.establish_context
92
Part of the 'State of AI 2022' report series.summarize
95
Features quotes from Alan Turing, I.J. Good, and Marvin Minsky.establish_context
96
Includes quotes from the UK National AI Strategy document.cite_precedent
97
The slide uses a series of diverging stacked bar charts to visualize survey agreement levels.summarize
98
The chart uses a stacked bar to compare 2021 and 2022 researcher counts across different venues, highlighting a 30x gap between NeurIPS and Safety.quantify_impact
99
The slide uses two bar charts to contrast the scale of safety-focused funding (in millions) against general capabilities funding (in billions).quantify_impact
100
Includes a chart showing Elo scores vs parameters and a chat example of a safety-aligned model.present_solution
101
Includes a process diagram for RLHF and a bar chart comparing model performance.present_framework
102
Includes a conceptual diagram of the red teaming process and a bar chart showing harmlessness by model size.illustrate_case
103
The slide discusses the reverse-engineering of neural networks into human-interpretable programs.summarize
104
The slide uses the CoinRun experiment to illustrate a specific AI safety failure mode.diagnose
105
The slide highlights the risk of AI being trained in environments that reward immoral behavior and proposes a solution to mitigate this.illustrate_case
106
Part of the State of AI 2022 report.illustrate_case
108
The slide uses a numbered list format to present forward-looking statements.summarize