How to Train a Machine to See Dogs

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An algorithm is really just a set of logical rules to arrive at a conclusion. In machine learning those functions generally map to again mathematical models that are tuned to reflect patterns discovered in the world based on mountains of data. And so if you train the machine to see dogs it will tend to see dogs in all kinds of things. So we have to be careful how we feed these machines data and be mindful of that their responses are based on the data that they're trained on.

Josh Clark enlightens us to everything we need to know about the current state of UX in AI. He takes us on an unsettling stroll through uncanny valley. He encourages us to let machines do what they're really good at, and humans do what they're really good at. He guides us into how to begin getting our hands dirty with AI/Machine-Learning. He also articulates how our software/machines are embedded with values, and inspires us (for future's sake) to be intentional about the kinds of values we embed into them.

Josh passes the Turing Test (07:33)
What is AI/Machine Learning? (10:04)
Is AI’s uncertainties driving engineers nuts? (18:28)
Listener Q: How do we address AI's failure in presenting errors? (27:00)
Taking a stroll down "uncanny valley” + “mechanophilia" (34:35)
Is there hope for the UX of Voice UI? (42:05)
Will the machines we’ve built eventually overtake and destroy us? (55:08)
Will AI eventually take our jobs? (68:35)
Is there a specific area of focus that will benefit designers interested in AI? (77:50)
Listener Q: At what point in the AI development process should UX get involved and how? (91:09)
Do we take these awesome technologies for granted? If so, why do we do that? (100:13)
If today was your last day, what would be your final plea to designers and developers building the future of AI? (105:03)

Check out the detailed show notes including the transcript at userdefenders.com/uxai

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