Strengthening AI competitiveness for midsize businesses is critical given their role in national economies and global employment. Plus, ‘collaborative intelligence’ may be your next objective related to data-driven decision making. And understanding data lab manager decisions. Lastly, the end of 2022 will see significant changes for big data and analytics spend.
By Yannick Bammens and Paul Hunermund, contributing writers for HBR.org
In the upcoming age of AI, two very different classes of companies appear well-positioned to leverage AI’s capabilities: startup ventures and multi-billion-dollar giant corporations. Promising AI startups are being launched at an increasing pace in areas like health care, finance, retail, media, and cross-industry tech, to name a few. And alongside tech giants like Google or Microsoft, traditional large corporations are employing AI to digitalize their business model and processes. Examples of AI-driven automation and augmentation range from automated customer loan approval and smart infotainment systems at car manufacturer Daimler to predictive maintenance at oil and gas behemoth Shell and AI-assisted medical image reading at industrial manufacturer Siemens. Corporate AI innovation is fairly concentrated with the top-10 patenting firms in the world accounting for more than 15% of AI patents in the period 2011 to 2016.
By Helena Schwenk, contributing writer for technative.io
In the early 19th century, textile workers in Nottingham rebelled against their factory owners.
As factory owners began to use new machinery that reduced the number of employees and factories they needed, workers felt that their skillset was being wasted and their livelihoods threatened.
By Sridhar Iyengar, contributing writer for labmanager.com
Buried in a working paper scribed in October 1980 by sociologist Charles Tilly was a consequential union—a marriage of two words forged out of necessity to describe a concept not yet named with repercussions not yet imagined.
By A contributing writer for it-online.co.za
The increasing mobile data and cloud computing traffic and the rapid development of technologies such as artificial intelligence (AI) and the Internet of Things (IoT) significantly increased the volume and complexity of data sets, driving the growth of the big data and business analytics market.
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