Verified claim · AI-ML · 100% confidence
AlphaZero published in: Science journal December 2018.
Last verified 2026-05-16 · Methodology veritas-v0.1 · b2dbbb7283a89f21
SourceScore rates how reliable a source is to cite — for AI answers and research. This is one verified claim from the catalog.
Related verified claims
More verified claims related to this one — keep exploring.
AlphaGo defeated: Lee Sedol 4-1 in March 2016.
100% confidence · shares 3 tags (deepmind, reinforcement-learning, foundational)
BERT (Bidirectional Encoder Representations from Transformers) introduced in paper: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (Devlin et al., 2018).
100% confidence · shares 2 tags (foundational, 2018)
Chinchilla scaling laws introduced in paper: Training Compute-Optimal Large Language Models (Hoffmann et al., 2022).
100% confidence · shares 2 tags (foundational, deepmind)
Proximal Policy Optimization (PPO) introduced in paper: Proximal Policy Optimization Algorithms (Schulman et al., 2017).
100% confidence · shares 2 tags (reinforcement-learning, foundational)
SentencePiece tokenizer introduced in paper: SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing (Kudo & Richardson, 2018).
100% confidence · shares 2 tags (foundational, 2018)
ELMo (Embeddings from Language Models) introduced in paper: Deep contextualized word representations (Peters et al., 2018).
100% confidence · shares 2 tags (foundational, 2018)
GLUE benchmark introduced in paper: GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding (Wang et al., 2018).
100% confidence · shares 2 tags (foundational, 2018)
JAX publicly released on: 2018-12-10 by Google Research.
100% confidence · shares 2 tags (deepmind, 2018)
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 →
Cite this claim
Ready-to-paste citation (Markdown / plain text):
AlphaZero published in: Science journal December 2018. — SourceScore Claim b2dbbb7283a89f21 (verified 2026-05-16). https://sourcescore.org/api/v1/claims/b2dbbb7283a89f21.jsonEmbed this claim
Drop this iframe into any blog post, docs page, or knowledge base. The widget renders the claim record + top cited source + click-through to this canonical page. CC-BY 4.0; attribution included.
<iframe src="https://sourcescore.org/embed/claim/b2dbbb7283a89f21/" width="100%" height="360" frameborder="0" loading="lazy" title="AlphaZero published in: Science journal December 2018."></iframe>Preview: open in new tab
Frequently asked questions
How has SourceScore reviewed the claim "AlphaZero published in: Science journal December 2018."?
SourceScore records this assertion with 100% legacy editorial confidence as of 2026-05-16, under methodology veritas-v0.1. That metadata is not a truth guarantee. Inspect the 2 cited source record(s), excerpts, and live evidence below.
What is the evidence for "AlphaZero published in: Science journal December 2018."?
The record lists 2 cited source(s): Science (Silver et al. / DeepMind), Google DeepMind. Each is shown below with a short excerpt and URL. The JSON record at https://sourcescore.org/api/v1/claims/b2dbbb7283a89f21.json includes SourceScore-issued HMAC integrity metadata, not a public verification proof.
When was this claim record last reviewed by SourceScore?
Last reviewed 2026-05-16 under methodology version veritas-v0.1. The dated JSON record includes SourceScore-issued HMAC metadata, which is not publicly recomputable. Refetch the record and inspect current evidence before relying on it.
How can I cite this SourceScore claim in my code or article?
Fetch the JSON record from https://sourcescore.org/api/v1/claims/b2dbbb7283a89f21.json, including the verbatim claim, cited evidence, confidence, methodology version, and last-verified date. Refetch the canonical HTTPS record and inspect cited evidence; the HMAC tag is not publicly independently verifiable. The CC-BY-4.0 license permits commercial use with attribution to SourceScore.
Use this claim in your code
Fetch this record from your application. The response includes verbatim excerpts, primary-source URLs, and SourceScore-issued HMAC integrity metadata. Refetch the canonical HTTPS record and inspect cited evidence; public users cannot independently verify the HMAC tag.
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()