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The Download: metric weaknesses and AI elephant warnings

View original at technologyreview.com
MIT Technology Review - Ai Research Title: The Download: metric weaknesses and AI elephant warnings Date: 2026-06-29 12:10 Source: https://www.technologyreview.com/2026/06/29/1139834/the-download-metric-weaknesses-ai-elephant-warnings/ <p><em>This is today’s edition of </em><a href="https://forms.technologyreview.com/n…
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  • India is home to about 60% of the world's wild Asian elephants, with around 80% of their habitat outside protected areas, leading to deadly human-wildlife conflicts.

    60% confidence
  • Google capping Meta's access to Gemini AI compute has disrupted and delayed some Meta AI projects.

    60% confidence
  • Powerful AI capable of altering the landscape of cyberwarfare cannot remain solely in American hands; Chinese AI firms must match US capabilities.

    60% confidence
  • Research shows Gen Z are more likely to believe and pass on misinformation when they feel a sense of common identity with the person who shared it, with social media shifting credibility from community to identity-based authority.

    60% confidence
  • Zhipu AI's model still cannot match Anthropic or OpenAI on general tasks despite matching Mythos in security bug detection.

    60% confidence
  • Chipmakers are profiting off AI at the expense of everyone else.

    60% confidence
  • Severe weather is now the leading cause of loss for data centers, as extreme heat puts AI-heavy data centers under significant operational pressure.

    60% confidence
  • Appropriate safeguards are now in place for the release of Anthropic's Mythos 5 model to trusted organizations.

    60% confidence
  • External metrics and data can never capture what is truly important; worse, they inevitably redefine one's core sense of what is important, whether or not one is aware of the trap.

    60% confidence
  • Security researchers say Zhipu AI is poised to reset the AI race after its model matched Anthropic's Mythos in finding security bugs.

    60% confidence
  • There have been some 3,000 human casualties from human-elephant conflicts in India in the last five years, and over 1,000 elephant deaths since 2014.

    60% confidence
  • A new study suggests 6.4 to 7.8 hours of sleep is the optimal range; too much sleep may be as harmful as too little.

    60% confidence
  • Android phones gave Venezuelan earthquake victims between seconds and up to two minutes of advance notice before the seismic events struck.

    60% confidence
  • US restrictions on AI models have sparked alarm that they are boosting China's AI progress.

    60% confidence
Ce que nous savons · l'intelligence derrière cette page
En direct du substrat
Ce que nous observons
The Agentic Takeover of the CFO's Office
Enterprise finance software vendors—BlackLine, OneStream, Numero AI, and Oracle—are racing to embed autonomous AI agents into core financial operations (close, consolidation, reporting), backed by consolidation M&A (Numero-Royu, BlackLine-WiseLayer), fresh leadership hires, and survey data showing nearly a quarter of CFOs plan to boost AI spending over 50%. Adoption momentum is strong even as at least one bellwether (Oracle) sees its stock lag year-to-date, suggesting the market hasn't yet fully priced in the shift from AI-as-feature to AI-as-agent in finance.
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Signaux que nous suivons
Satellite-Terrestrial Network Integration Acceleration
Increased investment and launches in hybrid satellite-cellular networks across telecom industry; competitive responses from other carriers; regulatory activity around satellite spectrum; expansion of emergency/rural connectivity use cases
Tendances que nous surveillons ›
Là où les sources divergent
Morgan Stanley & Co. LLC
Two significantly different EPS values (10.21 vs 2.68 USD_per_share) are reported for Morgan Stanley on the same observation date (2025-12-31). Fact A specifies FY 2025, while Fact B's 'N/A' fiscal period is ambiguous. If both represent FY 2025 annual EPS, these values directly conflict. The magnitude of the difference (3.8x) is too large to attribute to rounding or minor calculation variations. The missing fiscal period in Fact B raises data quality concerns, but same-date observation + same attribute should reference the same period.
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