Interview-Based AI Agents and the Limits of Simulating People

Matthew Berman discusses agents built from interviews and imagines using them to simulate societies.

MBMatthew BermanVortrag · Forward Future
Kanal
Matthew Berman
Veröffentlicht
5. Dez. 2024
Video
0:47
Lesezeit
1 Min.
Original
Englisch
Auf YouTube öffnen
Beim Scrollen schrumpft das Video in die rechte untere Ecke und läuft weiter. Untertitel folgen Ihrer Lesesprache.
LesespracheIn 2 Sprachen verfügbar
Diesen Artikel gibt es noch nicht auf Deutsch, daher wird das Original (Englisch) angezeigt.
ZUSAMMENFASSUNG

In 30 Sekunden

Matthew Berman describes research using two-hour interviews to build AI agents that imitate aspects of people’s responses, then speculates about using such agents to model societies. His short describes the result as 85% accuracy; the [research paper](https://arxiv.org/abs/2411.10109) reports a more specific relative measure: on the General Social Survey, agents replicated answers 85% as accurately as participants replicated their own answers two weeks later.

  1. 1Interviews as inputBerman describes two-hour AI-conducted interviews used to create agents that simulate participants.
  2. 2What the 85% measuresThe paper reports survey response replication relative to participants’ own repeat answers, not universal accuracy.
  3. 3A possible future applicationBerman imagines simulating societies to explore their reactions; that extrapolation requires further validation.

Das Wichtigste

  1. Researchers built agents from interviews with 1,052 participants.
  2. The reported 85% figure compares agents’ survey replication to participants’ own two-week test-retest consistency.
  3. The result does not establish that an agent duplicates an entire personality or predicts any response with 85% accuracy.
  4. Simulating society-wide reactions is the presenter’s proposed application, not a result established by this short.
VOLLTEXT

Den bearbeiteten Text nach Kapiteln lesen

Nur das Transkript
01

Interview-based agents

Berman describes researchers’ use of two-hour interviews to build agents. The study evaluates how closely the agents replicate selected responses.

Stanford researchers used two-hour interviews conducted by an AI to gather information about people, then used those interviews to build AI agents. I describe this as cloning personalities and say the agents answer like the people they represent.

I describe the result as 85% accuracy, but the study’s measure is narrower: on the General Social Survey, the agents replicated participants’ answers 85% as accurately as the participants replicated their own answers two weeks later. That does not establish a complete copy of a person or 85% accuracy on every question.

02

Imagining societal simulations

He imagines using agents to explore how societies might react. The short does not demonstrate reliable predictions across arbitrary events.

Imagine building agents representing whole societies and running simulations to explore how they might react to events. This is a possible application I’m excited about, rather than a demonstrated guarantee that arbitrary societal reactions can be predicted.

Mehr aus diesem Kanal

12 Folien34:21EN
Matthew Berman· 27. Okt. 2026Matthew Berman Examines Anthropic’s IPO Filing, AI Growth and GovernanceMatthew Berman, Sam Altman · Forward FutureThe presenter reviews the S-1’s financial metrics, compute strategy and voting structure while questioning the tension between rapid AI development and safety.AnthropicAI SafetyEnterprise-KI+2
2 Folien0:36EN
Matthew Berman· 25. Okt. 2026Kimi K3.1 and a Rubik’s Cube Solver DemonstrationMatthew Berman · Forward FutureMatthew Berman shows a scrambled virtual cube being solved and gives the Kimi K3.1 test a favorable assessment.EvalsCoding-AgentenOpen-weight models
6 Folien25:50EN
Matthew Berman· 24. Okt. 2026Kimi K3.1 Pro Tested: Strong 3D Demos, Uneven Results and Competitive PricingMatthew Berman · Forward FutureMatthew Berman tests the open-weight model on coding, 3D rendering and visual explanations, then compares benchmark results and prices.Open-weight modelsBenchmarksAI cost & performance+1
2 Folien32:20EN
Matthew Berman· 23. Okt. 2026Who Is Local AI For? The Costs, Trade-offs and a Hybrid FutureMatthew Berman · Forward FutureMatthew Berman weighs data control and independence against hardware cost, setup complexity and inference speed.Local inferenceOpen-weight modelsAI cost & performance+1
6 Folien1:03:37EN
Matthew Berman· 23. Okt. 2026Matthew Berman’s Team Tests Kimi K3.1 on Animation, Games, and ChartsMatthew Berman, Alex, Brian · Forward FutureA live team AMA turns into an informal test of Kimi K3.1, from personal-agent workflows to motion graphics, a browser game, and a benchmark chart.Coding-AgentenGenerative mediaEvals+2
0:53EN
Matthew Berman· 22. Okt. 2026Sam Altman on AI and the Discovery of New KnowledgeSam Altman · OpenAIAltman expects progress in mathematical discovery to extend to biology, materials and energy.AI for scienceMathematicsComputational Biology+1