How to move up when the only way is down

• Verfasst von: Taub, Judah [VerfasserIn]
• Titel: How to move up when the only way is down
• Titelzusatz: lessons from artificial intelligence for overcoming your local maximum
• Mitwirkende: Block, Frank [ErzählerIn]
• Verf.angabe: by Judah Taub
• Ausgabe: [First edition].
• Verlagsort: [New York]
• Verlag: Ascent Audio
• E-Jahr: 2024
• Jahr: [2024]
• Umfang: 1 online resource (1 sound file (6 hr., 31 min.))
• Fussnoten: Online resource; title from title details screen (O’Reilly, viewed October 22, 2024)
• ISBN: 978-1-6637-4757-0
• ISBN: 1-6637-4757-1
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Prise de décision
• Sach-SW: Intelligence artificielle
• Sach-SW: decision making
• Sach-SW: artificial intelligence
• Sach-SW: Audiobooks
• Sach-SW: Livres audio
• K10plus-PPN: 1910509884
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext
• Lokale URL UB: Bibliothek der Medizinischen Fakultät Mannheim der Universität Heidelberg
• Bibliothek/Idn: UW / m4629554297
• Lokale URL Inst.: Zum Volltext

Abstract

A well-paid executive feels trapped in her very respected but unsatisfying job. A startup founder has paying customers, but knows that unless he "fires" them and pivots the business, his startup won't make it. A senior government planner is tasked with undoing the nation's reliance on outdated infrastructure. These are all examples of individuals stuck in a Local Maximum; we've reached a peak, but not the one that fulfills the highest potential. In order to move up in our pursuits, we must first move back down—a realization which can lead to frustration, decision-making paralysis, and lost opportunity. In How to Move Up When the Only Way Is Down, Judah Taub draws from his perspective guiding early stage AI startups, his years serving in military intelligence, and experiences leading innovation throughout his career. With his off-the-beaten path perspective, Judah shares insights into how humans can achieve better decision-making by learning how AI overcomes local maximums.