Tag: Generative AI
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AI Won’t Be a True Coworker Until It Finishes Tasks, Not Just Answers
Current AI chatbots shine at answering but fail to complete complex tasks. True AI coworkers require persistent environments and reusable skills for full task management.
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OpenAI’s Delayed IPO Reveals Bigger Market Insecurities
OpenAI’s IPO delay highlights wider instability in tech markets and investor caution, emphasizing risks that go beyond mere valuation targets.
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Rising AI Chatbot News Use Masks Persistent Trust Deficit
AI chatbots are increasingly used for news, but low trust and poor source transparency limit their credibility and user engagement.
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Why Microsoft’s Mirage Misses the Mark on Object Tracking for Video AI
Microsoft’s Mirage reduces compute needs in video AI by using latent space memory but fails at reliable moving object tracking across scenes.
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Public Listings Will Drive More AI Token Price Increases
The public listing of major AI companies will drive up token prices, forcing businesses to prepare for higher costs and more concentrated vendor control.
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OpenAI Declares Chat Dead to Push Autonomous Agent Superapp
OpenAI plans to transform ChatGPT into an autonomous agent superapp, raising concerns about vendor lock-in, integration complexity, and loss of software flexibility.
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Microsoft’s Data Claims Don’t Hold Up Under Detailed Scrutiny
Microsoft claims its MAI models use only licensed, clean data, but closer analysis reveals reliance on unlicensed web sources similar to other AI labs.
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Why Proactive AI Will Fail to Solve Employee Adoption and Cost Issues
Proactive AI promises constant background action, but the real problems of employee adoption and cost remain unaddressed and may get worse.
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UK’s AI Search Opt-Out Rule Reveals Bigger Risks For Data Control
The UK’s new rule letting publishers opt out of AI search unveils deeper risks around data control, vendor lock-in, and rising AI costs globally.
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Why AI Taste Pairing Depends on Data Source, Not Just Tech
Kaikaku.AI’s Epicure shows AI food pairing depends more on training data type—recipe or molecular—than on algorithm sophistication alone.