Datasets:
pretty_name: AI Engineer Conference Talks - Private v2
language:
- en
task_categories:
- text-generation
- summarization
- question-answering
- feature-extraction
- text-classification
tags:
- ai-engineering
- conference-talks
- technical-transcripts
- public-source-grounded
- retrieval-augmented-generation
- technical-evaluation
- timestamped-transcripts
- private-review
configs:
- config_name: talks
data_files:
- split: train
path: data/talks/train.parquet
- split: validation
path: data/talks/validation.parquet
- split: test
path: data/talks/test.parquet
- config_name: segments
data_files:
- split: train
path: data/segments/train.parquet
- split: validation
path: data/segments/validation.parquet
- split: test
path: data/segments/test.parquet
- config_name: topics
data_files:
- split: train
path: data/topics/train.parquet
- split: validation
path: data/topics/validation.parquet
- split: test
path: data/topics/test.parquet
- config_name: entities
data_files:
- split: train
path: data/entities/train.parquet
- split: validation
path: data/entities/validation.parquet
- split: test
path: data/entities/test.parquet
- config_name: summaries
data_files:
- split: train
path: data/summaries/train.parquet
- split: validation
path: data/summaries/validation.parquet
- split: test
path: data/summaries/test.parquet
- config_name: chapters
data_files:
- split: train
path: data/chapters/train.parquet
- split: validation
path: data/chapters/validation.parquet
- split: test
path: data/chapters/test.parquet
- config_name: retrieval
data_files:
- split: train
path: data/retrieval/train.parquet
- split: validation
path: data/retrieval/validation.parquet
- split: test
path: data/retrieval/test.parquet
- config_name: benchmark
data_files:
- split: validation
path: data/benchmark/validation.parquet
- split: test
path: data/benchmark/test.parquet
- config_name: corrected_transcripts
data_files:
- split: train
path: data/corrected_transcripts/train.parquet
- split: validation
path: data/corrected_transcripts/validation.parquet
- split: test
path: data/corrected_transcripts/test.parquet
AI Engineer Conference Talks: the applied-AI engineering corpus
Private organizer-controlled dataset. Organizer-authorized noncommercial model training and evaluation are permitted for these transcripts; public distribution, commercial use, audio release, and image redistribution each require separate organizer approval.
A first-party AI Engineer conference-talk dataset with timestamped technical transcripts and publicly grounded metadata.
The underlying conference talks are already publicly available on the official AI Engineer YouTube channel. This private organizer-controlled dataset adds high-quality provider-generated retranscription, structured public-source metadata, word- and character-level timestamps, grounded topics and entities, summaries, chapters, and recording-level joins. It does not include source audio.
Important limits: Transcripts, derived annotations, and benchmark candidates are machine-generated; annotation and evaluation share a generator and can share its biases. There is no human gold or measured accuracy. Automated privacy filtering is best-effort, timing comes from the transcription provider, and source audio is not included.
| Conference talks | Recorded hours | Timestamped words | Transcript segments | Entity mentions | Conference editions |
|---|---|---|---|---|---|
| 1,041 | 448.38 | 4,663,555 | 147,853 | 82,340 | 7 |
252,918 rows across 8 purpose-built configurations; counts include multiple complementary views of the same talks, not independent recordings. Median recording length: 19.05 minutes across 7 conference editions (2023–2026).
Start with the data
Access requires membership in the private AI Engineer Hugging Face organization and a signed-in Hugging Face account with permission to this repository. Install the datasets client and authenticate once:
pip install -U datasets
hf auth login
Named configurations load as native Parquet datasets. Talks, transcript segments, and retrieval examples join using stable talk and segment identifiers:
from datasets import load_dataset
repo = "aiDotEngineer/ai-engineer-datasets"
talks = load_dataset(repo, "talks", split="train", token=True)
segments = load_dataset(repo, "segments", split="train", token=True)
retrieval = load_dataset(repo, "retrieval", split="train", token=True)
benchmark = load_dataset(repo, "benchmark", split="test", token=True)
print(talks.num_rows, segments.num_rows) # 836, 114905
print(segments.features["word_timings"]) # native nested word + character timings
talk_id = talks[0]["id"]
talk_segments = segments.filter(lambda row: row["talk_id"] == talk_id)
For this release, the training views contain 836 talks and 114,905 timestamped segments. The benchmark intentionally has no training split.
Eight joined views of the same corpus
| Configuration | Total rows | Train | Validation | Test | What it contains |
|---|---|---|---|---|---|
talks |
1,041 | 836 | 94 | 111 | Full talks, public presenters, conference metadata, and presentation dates |
segments |
147,853 | 114,905 | 13,102 | 19,846 | Timestamped transcript passages with native nested word and character timing |
topics |
5,932 | 4,763 | 536 | 633 | Publicly grounded technical topic labels and generation provenance |
entities |
82,340 | 65,288 | 7,601 | 9,451 | Publicly grounded technical entity mentions and aliases |
summaries |
1,041 | 836 | 94 | 111 | Grounded talk-level summaries linked to transcript passages |
chapters |
5,697 | 4,563 | 510 | 624 | Timestamped chapter boundaries linked to transcript passages |
retrieval |
5,960 | 4,792 | 534 | 634 | Synthetic queries, relevant passages, and split-safe hard negatives |
benchmark |
3,054 | — | 1,401 | 1,653 | Held-out machine-generated retrieval, entity, and topic candidates |
All derived records inherit their source talk's partition. Each of 3 reviewed repeated-presentation families stays within one partition; unknown duplicate presentations have not been exhaustively ruled out. Retrieval negatives cannot cross a split or reuse another recording from the same known presentation family. Held-out benchmark examples are machine-generated review candidates, not human-gold annotations.
Conference coverage
7 conference editions (2023–2026) contribute 1,041 speech recordings. Event assignment and session identification are separate claims: every event is assigned, while session matches are separately reported as first-party exact or machine-corroborated.
| Conference edition | Recordings | Share of corpus |
|---|---|---|
| AI Engineer World's Fair 2025 | 298 | 28.6% |
| AI Engineer World's Fair 2026 | 270 | 25.9% |
| AI Engineer Europe 2026 | 187 | 18.0% |
| AI Engineer World's Fair 2024 | 112 | 10.8% |
| AI Engineer Code 2025 | 67 | 6.4% |
| AI Engineer Summit 2025 | 67 | 6.4% |
| AI Engineer Summit 2023 | 40 | 3.8% |
How much material is here?
448.376 hours of original recording time, approximately 18.7 days, contain 4,663,555 timestamped words and 20,789,178 character-level timestamp pairs across 147,853 transcript segments. Recording time includes pauses and non-speech events; it is not a claim of speech-only duration. The median talk lasts 19.05 minutes.
| Recording length | Recordings | Share of corpus |
|---|---|---|
| 5–10 min | 64 | 6.1% |
| 10–20 min | 546 | 52.4% |
| 20–30 min | 270 | 25.9% |
| 30–45 min | 45 | 4.3% |
| 45–60 min | 30 | 2.9% |
| 60–90 min | 51 | 4.9% |
| 90+ min | 35 | 3.4% |
Grounded enrichment, with a visible paper trail
Each recording receives organizer-aware research before retranscription and a second, publicly web-grounded research pass after the improved transcript is available. Generated topics, entities, summaries, chapters, and retrieval examples point back to supporting transcript segments; unsupported candidate claims are excluded rather than promoted to facts.
| Research measure | Observed value |
|---|---|
| Post-transcription, web-grounded recordings | 1,041 |
| Completed second-pass public web searches | 1,363 |
| Unsupported named-entity candidates omitted | 115 |
| Unsupported retrieval candidates omitted | 16 |
| Human review of generated research | Not completed |
Generated annotations remain synthetic and unreviewed. A public-source citation or technical label supports research traceability; it does not make a model-generated assertion equivalent to human verification.
What technical problems do the talks cover?
These categories use the same governed category and subcategory assignments as the topic rows and taxonomy chart. Each topic row belongs to one category, while a talk can appear in multiple categories; distinct-talk shares always use the complete recording count.
| Technical category | Topic rows | Distinct talks | Share of talks |
|---|---|---|---|
| Developer tools | 1,050 | 515 | 49.5% |
| RAG, context, and search | 876 | 483 | 46.4% |
| Evals | 424 | 267 | 25.6% |
| Infrastructure and deployment | 413 | 272 | 26.1% |
| Agent engineering | 380 | 291 | 28.0% |
| Safety and governance | 372 | 262 | 25.2% |
| APIs, MCP, and protocols | 336 | 230 | 22.1% |
| Architecture | 309 | 246 | 23.6% |
| Leadership | 282 | 189 | 18.2% |
| Reasoning and models | 263 | 218 | 20.9% |
| Enterprise | 253 | 174 | 16.7% |
| Speech and audio | 162 | 77 | 7.4% |
| Data and model adaptation | 159 | 100 | 9.6% |
| Observability and reliability | 131 | 107 | 10.3% |
| Creative and generative media | 107 | 43 | 4.1% |
| Vision and video | 101 | 80 | 7.7% |
| Industry applications | 92 | 66 | 6.3% |
| Finance | 78 | 50 | 4.8% |
| Healthcare | 68 | 30 | 2.9% |
| Robotics and world models | 55 | 24 | 2.3% |
| Other / unclassified | 21 | 19 | 1.8% |
Developer tools contains Coding agents and assistants (444 topic rows; 299 talks), Developer workflows, testing, and review (349 topic rows; 240 talks), IDEs, SDKs, and developer platforms (257 topic rows; 201 talks).
Grounded entity mentions
| Entity type | Grounded mentions |
|---|---|
| concept | 38,209 |
| product | 22,997 |
| organization | 10,316 |
| person | 10,290 |
| repository | 378 |
| paper | 150 |
Entity rows describe grounded occurrences spanning technical concepts, commercial and open-source products, organizations, public figures, code repositories, and research papers. Person mentions are not counts of unique people; organization mentions are not counts of unique companies.
Publicly listed speakers and companies
Official conference rosters and public-source research identify 1,030 distinct named presenters and 610 distinct presenter-affiliated organizations across 1,031 recordings, including 987 recordings with a listed affiliation. This affiliation-only organization count can include universities and research institutes and reflects historical conference context, not inferred current employment.
Across presenter affiliations and companies that are a clear central subject of the public talk title, official playlist, or track, the corpus represents 691 distinct companies across 976 talks. Each canonical company is counted at most once per talk; incidental transcript mentions, generic social platforms, and universities are excluded.
Canonical speaker headshots
761 distinct event-scoped organizer speaker identities have a verified official conference portrait, covering 811 presenter appearances across 696 recordings.
The talks rows expose presenters[].canonical_photo_url, presenters[].alternate_photo_urls, and aligned presenters[].alternate_photo_metadata together with exact photo_source_event_id, photo_source_person_id, photo_source, photo_rights_status, source-checksum, review, and opt-out metadata. Canonical portraits use event-qualified organizer speaker identifiers and first-party conference speaker records; unsupported name matches, attendee guesses, and inferred employment are excluded.
The full first-party source audit covered 1,607 organizer identities and 1,469 restricted portrait sources across 8 conference rosters.
The same organizer records link 972 verified public professional-profile URLs. Emcees, moderators, hosts, and speakers retain their organizer-confirmed participation roles when available.
The release additionally includes 3,422 distinct verified conference gallery photos attributed to 161 exact organizer-identified speakers, with 3,952 gallery-photo links across 145 talks. Gallery references are limited to speaker-attributed first-party conference photographs; unknown attendees and time-only guesses are excluded.
The complete reviewed conference gallery contains 7,213 preserved person–photo assignments across 607 known identities. Its 4 exact-photo-only exceptions remain displayable but cannot seed identity matching; no verified gallery assignments were discarded.
| Conference | Named presenters | Verified portraits | Conference gallery photos |
|---|---|---|---|
| AI Engineer World's Fair 2026 | 320 | 229 | 3,422 |
| AI Engineer World's Fair 2025 | 350 | 151 | 0 |
| AI Engineer Europe 2026 | 198 | 127 | 0 |
| AI Engineer World's Fair 2024 | 141 | 127 | 0 |
| AI Engineer Code 2025 | 76 | 49 | 0 |
| AI Engineer Summit 2025 | 81 | 41 | 0 |
| AI Engineer Summit 2023 | 46 | 37 | 0 |
Headshots and verified conference gallery photos are HTTPS references, not bundled image files. Every photo is marked restricted; linking to a photo does not grant a license or authorize image redistribution, commercial use, or model training. No image bytes or biometric descriptors are included.
Public-source metadata coverage
| Measure | Recordings | Share of talks |
|---|---|---|
| First-party recording-to-session matches | 207 | 19.9% |
| Publicly corroborated recording-to-session matches | 359 | 34.5% |
| Recordings without an exact or corroborated session match | 475 | 45.6% |
| Observed presentation dates | 511 | 49.1% |
| Organizer-inferred online-track presentation dates | 215 | 20.7% |
| Unavailable presentation dates | 315 | 30.3% |
| Observed YouTube upload dates (separate from presentation dates) | 1,041 | 100.0% |
| Observed YouTube publication timestamps | 1,041 | 100.0% |
| Recordings with publicly identified presenters | 1,031 | 99.0% |
| Recordings with publicly identified presenter affiliations | 987 | 94.8% |
| Recordings with official organizer-hosted presenter headshots | 696 | 66.9% |
An exact organizer recording ID, a machine-corroborated schedule match, an observed presentation date, an organizer-inferred online-track date, and a YouTube upload timestamp are different kinds of source information. Online-track dates remain explicitly inferred; upload dates never substitute for presentation dates.
Research, editorial, and model-development value
- Editorial discovery: locate relevant public talks and timestamped passages across seven conference editions.
- Conference research: study grounded topic labels, public speakers, organizations, products, and session metadata.
- Archive navigation: connect full transcripts, chapters, summaries, and detailed word/character timings without redistributing audio.
- Provenance review: distinguish official presenter rosters, observed session matches, inferred dates, and synthetic annotations.
- Model development and evaluation: use the organizer-authorized private transcript corpus for noncommercial training, retrieval, and held-out testing.
Held-out evaluation candidates
The benchmark contains 3,054 machine-generated examples: 1,401 validation and 1,653 test. There is no benchmark training split, and no case from a training talk is included.
| Evaluation task | Held-out candidates |
|---|---|
| Transcript retrieval | 1,018 |
| Technical entity extraction | 1,025 |
| Technical topic classification | 1,011 |
The generator excluded 74,843 training-source rows before constructing held-out candidates and capped each task at 5 examples per talk.
These are synthetic review candidates, not human-gold labels or adjudicated relevance judgments. Human-reviewed benchmark rows: 0. Word error rate (WER), diarization error rate (DER), Jaccard error rate (JER), other transcription or diarization accuracy, and benchmark scores have not been measured.
Separate transcription-system comparison
Our AI Engineer transcription and diarization benchmark is a separate historical comparison of five speech-to-text systems across 100 talks. Its silver-reference disagreement scores were Speechmatics 8.98, NVIDIA Parakeet v3 9.19, AssemblyAI 10.19, NVIDIA Parakeet v2 10.99, and Deepgram 12.42; lower disagreement is better against that particular silver reference.
The comparison has no human-adjudicated gold transcript and does not establish this dataset's word error rate or diarization accuracy.
Transcription provenance and limits
All speech recordings are English and received high-quality machine retranscription. Word and character timings retain observed absolute millisecond positions as native nested Parquet arrays. Public presenter metadata does not establish who spoke each individual passage. Human-measured transcription and diarization accuracy are unavailable.
Provider-observed non-speech audio-event timestamps use a controlled label set; 8,954 events were observed across 924 recordings. Event labels are metadata only; original audio is never included.
The original inventory contains one instrumental/non-speech recording that is preserved privately and excluded from this speech-only release. Source audio, provider responses, normalized source transcripts, sensitive-entity quarantine, operational receipts, credentials, and JSONL intermediates are never included in the dataset.
Privacy-preserving release
No unresolved sensitive transcript leakage was reported by the release validator.
| Privacy measure | Count |
|---|---|
| Sensitive entities masked | 822 |
| Talks containing a masking operation | 311 |
| Segments containing a masking operation | 2,261 |
| Masked words | 4,781 |
| Masked characters | 24,978 |
| Unresolved sensitive-text validation issues | 0 |
Masking applies to exported views only. Original source artifacts remain immutable, and restricted provider quarantine stays outside the Hugging Face bundle.
Charts describe the completed speech-only release and contain aggregate counts only; they never expose transcript text, private identifiers, named speaker lists, or audio.
Access and permitted use
This is a private, organizer-controlled dataset. Organizer-authorized noncommercial model training and evaluation of the transcript corpus are permitted. Public redistribution, commercial use, source-audio release, image redistribution, biometric identification, and any open-license declaration require separate organizer approval and are not granted here.
Transcript authorization does not sublicense third-party websites, external materials, or separately hosted conference photographs; their independent restrictions remain in force.
Corrected transcript layer
The separate corrected_transcripts configuration contains 1,041 recordings and 147,853 source-linked segments. It carries 11,244 lexical edits and 1,697 separate speaker annotations; 2,847 candidates remain unresolved. Model-adjudicated corrections are unreviewed, not human gold. Original and corrected text retain privacy masking; source voice-cluster IDs are unchanged. Speaker annotations are separate text-context inferences, not verified voice identities. Timings inherit the original video clock, not new forced alignment.
9 recordings have 11 extra observed event labels in the original full-text representation relative to the word stream. The original view preserves those labels; the corrected view follows the verified word stream. These representation differences are not counted as lexical edits.
The eight pre-existing configurations retain their original source content, with private generation labels removed from exported identifiers: entities, summaries, chapters, retrieval and benchmark rows have not been regenerated from corrected text. Their charts and aggregate statistics describe that frozen snapshot. The new layer does not change rights or grant public distribution, commercial use, audio, or image permissions.
Exported identifiers use opaque deterministic IDs where necessary to withhold private generation labels. No talk content, transcript text, timestamps, row counts, or entity labels were changed by this publication cleanup.