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Defining AI

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Synthetic insights are not research. They are the fastest path to destroy the margins on your product.

Article by Pavel Samsonov
No, AI User Research is Not “Better Than Nothing”—It’s Much Worse
  • The article critiques the growing trend of substituting human-centered design processes with AI-generated insights, highlighting the potential pitfalls and implications for design quality and user experience.
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6 min read
Article by Savannah Kunovsky
How AI Can Help Us Solve the Climate Crisis
  • The article delves into the transformative intersection of generative AI and the Climate Era, highlighting their potential to reshape economies, influence consumer behaviors, and address sustainability challenges.
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5 min read

Since personal computing’s inception in the 80s, we’ve shifted from command-line to graphical user interfaces. The recent advent of conversational AI has reversed the ‘locus of control’: computers can now understand and respond in natural language. It’s shaping the future of UX.

Article by Jurgen Gravestein
How Conversational AI Is Shaping The Future of UX 
  • The article discusses the transformative impact of conversational AI on UX design, emphasizing the need for user-centric approaches and the emerging societal changes driven by AI technology.
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3 min read

ChatGPT can identify and describe human emotions in hypothetical scenarios.

Article by Marlynn Wei
ChatGPT Outperforms Humans in Emotional Awareness Test
  • New research found ChatGPT was able to outperform humans on an emotional awareness test.
  • Emotional awareness is the cognitive ability to conceptualize one’s own and others’ emotions.
  • Given the psychological risks of artificial intimacy, AI augmentation, and human-AI collaboration—keeping humans in the loop—will be a safer and more beneficial approach for now.
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3 min read

New ML models emerge hourly but this fast pace comes with drawbacks; hypothesis-driven development can help to mitigate those.

Article by Benjamin Thurer
The Inflation of AI: Is More Always Better?
  • The article challenges the need for the rapid growth of AI and ML models, highlighting risks and advocating for hypothesis-driven development to ensure meaningful contributions, enhance quality, and foster innovation in the AI community.
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7 min read

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