Machine intelligence is here, and we’re already using it to make subjective decisions. But the complex way AI grows and improves makes it hard to understand and even harder to control. In this cautionary talk, techno-sociologist Zeynep Tufekci explains how intelligent machines can fail in ways that don’t fit human error patterns — and in ways we won’t expect or be prepared for. "We cannot outsource our responsibilities to machines," she says. "We must hold on ever tighter to human values and human ethics."
a16z Podcast: Making Sense of Big Data, Machine Learning, and Deep Learning by a16z | Free Listening on SoundCloud
a16z Podcast: Making Sense of Big Data, Machine Learning, and Deep Learning
published on 2015/05/01 21:50:55 +0000
"Machine learning is to big data as human learning is to life experience," says Christopher Nguyen, the co-founder and CEO of big data intelligence company Adatao. Sure, but then, what IS big data? (especially as it’s become a buzzword that captures so many things)…
On this episode of the a16z Podcast, Nguyen puts on his former computer science professor hat to describe ‘big data’ in relation to ‘machine learning’ — as well as what comes next with ‘deep learning’. Finally, the former Google exec shares how Hadoop and Spark evolved from the efforts of companies dealing with massive amounts of real-time information; what we need to make machine learning a property of every application (why would we even want to?); and how we can make all this intelligence accessible to everyone.
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