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<oembed><version>1.0</version><provider_name>Microsoft Research</provider_name><provider_url>https://www.microsoft.com/en-us/research</provider_url><author_name>Ece Kamar</author_name><author_url>https://www.microsoft.com/en-us/research/people/eckamar/</author_url><title>Hybrid Intelligence and the Future of Work - Microsoft Research</title><type>rich</type><width>600</width><height>338</height><html>&lt;blockquote class="wp-embedded-content" data-secret="pp6QtxEYY4"&gt;&lt;a href="https://www.microsoft.com/en-us/research/publication/hybrid-intelligence-future-work/"&gt;Hybrid Intelligence and the Future of Work&lt;/a&gt;&lt;/blockquote&gt;&lt;iframe sandbox="allow-scripts" security="restricted" src="https://www.microsoft.com/en-us/research/publication/hybrid-intelligence-future-work/embed/#?secret=pp6QtxEYY4" width="600" height="338" title="&#x201C;Hybrid Intelligence and the Future of Work&#x201D; &#x2014; Microsoft Research" data-secret="pp6QtxEYY4" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"&gt;&lt;/iframe&gt;&lt;script type="text/javascript"&gt;
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</html><description>Computational and algorithmic advances in machine learning methods including deep neural networks have led to a new wave of excitement for Artificial Intelligence (AI). The performance of automated approaches on perception tasks such as objection recognition and speech recognition is approaching human level abilities. However, AI systems still have limitations in carrying out complex activities [&hellip;]</description></oembed>
