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-扩展人工智能的经过验证的概念 Proven concepts for scaling AI - From experimentation to engineering discipline

# 扩展 # 人工智能 # 概念验证 大小:0.29M | 页数:12 | 上架时间:2024-01-04 | 语言:英文

IBV -扩展人工智能的经过验证的概念 Proven concepts for scaling AI - From experimentation to engineering discipline.pdf

IBV -扩展人工智能的经过验证的概念 Proven concepts for scaling AI - From experimentation to engineering discipline.pdf

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类型: 行研

上传者: 李琳琳

撰写机构: IBV

出版日期: 2024-01-04

摘要:

AI growth continues apace The number of AI adopters has grown 65 percent in the past four years, a trend the pandemic’s business disruption is accelerating relative to other technology priorities.1 AI demands engineering discipline Companies need to embrace AI holistically to address the scaling problem—rooting it in business strategy, innovation, and competitive differentiation, then deeply integrating it into evolving business operating models and workflows.AI proofs-of-concept (POCs) must evolve As AI technology continues to mature, many concepts have been proven, so organizations can redirect early-stage trials toward market-ready pilots.Poor, misunderstood artificial intelligence (AI). It’s alternately overhyped as a digital nirvana or vilified as a dystopian menace. Yet in the pragmatic here and now, it is neither.Primarily, AI is a way to augment human capabilities and performance, creating better outcomes for people— customers, employees, partners, and other stakeholders —and better financial returns for businesses. Think human aid, not humanoid.

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