Đề Thi Thử Tốt Nghiệp 2026 Tiếng Anh Online-Đề 12 Có Lời Giải

licensing frameworks are universally harmonised across all major geopolitical blocs today

software algorithms remain inherently opaque and thus cannot be independently audited

its physical nature allows access to be restricted through hardware control and policy

small developer collectives can always circumvent controls by pooling dispersed resources

tightly controllable

broadly permissive

loosely supervised

minimally regulated

Compute caps eliminate all competitive pressures by restricting data access globally.

Verification replaces intellectual-property law by mandating disclosure of model code.

Frontier AI expansion mainly depends on public trust rather than technical governance.

Verification that protects confidentiality can justify compute caps to reduce escalation.

Providers must publish proprietary model weights once training throughput exceeds thresholds.

Developers pre-notify authorities; providers verify notification before provisioning large compute.

Developers disclose training datasets in full; providers audit algorithmic source code line-by-line.

Providers auction access; developers submit bids that determine caps for each training epoch.

Open-source licences only

Personnel background checks

Selected training properties securely

Consumer end-use declarations

compute oligopoly

safety protocols

model weights

export controls

Should attestation mechanisms exhibit fragmentation and narrow scope, confidence deficits compound systemically, progressively undermining voluntary compliance norms among rivals.

If verification achieves international coordination, competitive pressures intensify paradoxically as transparency exposes asymmetries, prompting states to abandon collaborative restraint.

When verification mandates exhaustive disclosure, adversaries demonstrate greater compliance because comprehensive transparency reduces ambiguity and enables precise trust-building.

Provided verification remains fragmented nationally, inter-state tensions naturally attenuate because opacity enables face-saving ambiguity without triggering accountability pressures.

Compute caps are unnecessary once interpretability tools fully expose internal model mechanisms everywhere.

Concentrated chip manufacturing and hyperscale provision make cooperation from fewer actors potentially sufficient.

Confidential computing eliminates all risks associated with the transparency-security trade-off in perpetuity.

Neutral data centres cannot contribute because they invariably compromise national security prerogatives.

[I]

[II]

[III]

[IV]

Interpretability research alone can stabilise geopolitics; compute governance adds little practical value.

Data provenance controls are superior to compute caps for every frontier-AI risk scenario.

Compute-anchored verification – via reporting and hardware attestations – can operationalise calibrated caps despite real trade-offs.

Export controls guarantee equitable access, making multilateral verification frameworks redundant worldwide.

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struck

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a set

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Ancient Mesopotamian cuneiform emerged alongside Egyptian hieroglyphics, both systems designed to document administrative and liturgical texts

Cuneiform development by Mesopotamians coincided with Egyptian hieroglyphic creation, systems serving administrative and religious documentation

Ancient Mesopotamians pioneered cuneiform while Egyptians devised hieroglyphics for recording administrative operations and religious observances

Administrative documentation utilized hieroglyphics, whereas ancient Mesopotamians and Egyptians collaboratively developed cuneiform for liturgical purposes

Sumerian scribes transitioned from representing tangible objects to encoding phonetic elements rather than semantic concepts

Physical object depiction gave way to sound representation as Sumerian scribes shifted from pictographic to phonetic notation

Rather than encoding spoken sounds, Sumerian scribes initially depicted concrete objects to convey semantic meaning directly

Sumerian scribes initiated phonetic representation instead of depicting physical objects or semantic concepts through pictographic means

Complex scripts underwent simplification, facilitating mercantile exchange and educational access previously restricted to privileged classes

Trade and educational opportunities remained confined to elites until complex scripts necessitated orthographic simplification

Script simplification catalyzed commercial expansion, which had previously encouraged education limited to literate elite classes

Orthographic simplification promoted commercial activity and educational proliferation, domains previously monopolized by elite literate classes

Alphabetic systems encoded complete phonemic inventories using merely two dozen characters, contrasting with syllabaries requiring hundreds

Unlike syllabaries necessitating hundreds of symbols, alphabetic systems required approximately two dozen characters to represent comprehensive phonemic ranges

Syllabaries demanded hundreds of symbols for sound representation, whereas alphabetic systems necessitated roughly two dozen characters

Approximately two dozen characters sufficed for alphabetic systems, contrasting with syllabaries requiring hundreds of symbols for complete phonemic coverage

Unicode standards preserve linguistic diversity by enabling diverse script coexistence within digital environments throughout the internet age

Diverse scripts maintain linguistic diversity; consequently, Unicode standards facilitate internet-age digital space functionality

Unicode standards facilitate diverse script coexistence in digital spaces, preserving linguistic diversity throughout the internet age

The internet age maintains diverse scripts through Unicode standards enabling linguistic diversity coexistence in digital spaces

They are designed mainly for amusement.

They are easy for viewers to spot as fake.

They typically appear as obvious, joking alterations.

They require expert authorization from platforms.

likeness

deviation

obscurity

discord

harmful

trivial

gentle

innocuous

emergency systems

deepfake videos circulating online

government officials

fact-checking organizations

AI detection improves human observation by highlighting anomalies that trained analysts can then verify independently.

Automated systems complement human expertise by flagging suspicious patterns for further forensic examination.

Machine-learning tools, once trained, detect subtle cues invisible to people and thereby identify fabricated media.

Machine learning identifies manipulation traces imperceptible to unaided vision, enabling reliable forgery detection.

Deepfake models emulate patterns from large datasets rather than perceiving reality directly, somewhat like infant learning.

The public widely understands how deep learning operates, so the risks are already minimal in everyday contexts.

Deepfakes depend on specialized hardware that completely prevents any misuse by untrained individuals online.

Because outputs are always imperfect, deepfakes cannot meaningfully influence public trust or social norms.

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