Verified claim · AI-ML · 100% confidence
AlphaZero published in: Science journal December 2018.
Last verified 2026-05-16 · Methodology veritas-v0.1 · b2dbbb7283a89f21
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Structured fields
- Subject
- AlphaZero
- Predicate
published_in- Object
- Science journal December 2018
- Confidence
- 100%
- Tags
- alphazero · deepmind · reinforcement-learning · self-play · foundational · 2018 · science
Sources (2)
[1] peer reviewed · Science (Silver et al. / DeepMind) · 2018-12-07
A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play“Starting from random play and given no domain knowledge except the game rules, AlphaZero convincingly defeated a world champion program in the games of chess and shogi as well as Go.”
[2] official blog · Google DeepMind · 2018-12-06
AlphaZero: Shedding new light on chess, shogi, and GoGoogle DeepMind is rated by SourceScore — see its reliability →
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AlphaZero published in: Science journal December 2018. — SourceScore Claim b2dbbb7283a89f21 (verified 2026-05-16). https://sourcescore.org/api/v1/claims/b2dbbb7283a89f21.jsonEmbed this claim
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Frequently asked questions
Is the claim "AlphaZero published in: Science journal December 2018." verified?
Yes — SourceScore verified this claim with 100% confidence as of 2026-05-16. The verification uses 2 primary sources cross-referenced against the SourceScore methodology (version veritas-v0.1). Full source list + signed JSON envelope linked below.
What is the evidence for "AlphaZero published in: Science journal December 2018."?
Evidence comes from 2 primary sources: Science (Silver et al. / DeepMind), Google DeepMind. Each source is listed below with verbatim excerpts and URLs. The signed JSON envelope at https://sourcescore.org/api/v1/claims/b2dbbb7283a89f21.json includes an HMAC-SHA256 signature for audit verification.
When was this claim last verified by SourceScore?
Last verified 2026-05-16 under methodology version veritas-v0.1. The signed JSON envelope is dated and cryptographically signed for audit trail. Re-verification cadence depends on the claim type and source freshness.
How can I cite this SourceScore claim in my code or article?
Fetch the signed JSON envelope from https://sourcescore.org/api/v1/claims/b2dbbb7283a89f21.json which includes the verbatim claim, primary sources, confidence, methodology version, last-verified date, and HMAC-SHA256 signature for audit. The CC-BY-4.0 license permits commercial use with attribution to SourceScore.
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cURL
curl https://sourcescore.org/api/v1/claims/b2dbbb7283a89f21.jsonJavaScript / TypeScript
const r = await fetch("https://sourcescore.org/api/v1/claims/b2dbbb7283a89f21.json");
const envelope = await r.json();
console.log(envelope.claim.statement);
// "AlphaZero published in: Science journal December 2018."Python
import httpx
r = httpx.get("https://sourcescore.org/api/v1/claims/b2dbbb7283a89f21.json")
envelope = r.json()
print(envelope["claim"]["statement"])
# "AlphaZero published in: Science journal December 2018."LangChain (retrieve-then-cite)
from langchain_core.tools import tool
import httpx
@tool
def get_alphazero_fact() -> dict:
"""Fetch the verified SourceScore claim for AlphaZero."""
r = httpx.get("https://sourcescore.org/api/v1/claims/b2dbbb7283a89f21.json")
return r.json()