
Mick Champayne | "Okay Google..."31.07.2019
At the I/O Developer Conference 2019, Google only sees artificial intelligence as its future, “building a more helpful Google for everyone.” But there still seems to be a great deal of confusion about machine learning and where it is taking us. With many of the presentations touching on how to use machine learning to help disadvantaged people, what kind of ethical issues should they bear in mind as the technology creeps further into our lives?
“Okay Google, are the robots going to take over the world?”
I, for one, welcome our new robot overlords. I’ve been waiting all my life for a robot nanny/maid/life coach-in-one à la Rosie from The Jetsons, and while we’re not quite there yet, it’s safe to say we’re entering a new age of AI.
According to CEO Sundar Pichai, Google's ultimate goal is to “
Life has become so much simpler, easier, and more efficient with machine learning
Machine learning is the power behind every one of today’s important emerging technologies: voice assistants, image recognition, predictive interfaces, bots, AR/VR, the Internet of Things, even the human genome. In simple terms,
As evidenced by
“Uh-oh Google, is that such a good idea?”
Life has become so much simpler, easier, and more efficient with machine learning: it’s how YouTube serves up related content to watch, filters your email for spam, and lets you checkout with one-click. But it brings an assortment of design challenges that come with the sometimes problematic nature of imprecise and unpredictable inputs.
We are often designing and building faster than we can realize or understand the implications.
Alexandria Ocasio-Cortez
The unquestionable part AI will play in perpetuating systemic inequalities is an extremely pressing issue. Computing power has doubled on average
“Well then Google, what are you going to do about it?”
Google is currently developing a robust ethical framework of
The only way to influence a better future with AI is to ensure the people creating them are designing critically and challenging deeply embedded exclusionary social systems.
To help achieve these goals, the I/O presentations focused on two areas: putting machine learning models directly onto devices; and using machine learning to help disadvantaged people—including people who are deaf, illiterate, or
Highlights from the conference included:
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Live-captioningall videos, from streaming to your camera roll. It’s built for people who are deaf or hard of hearing but could be used in public scenarios or transcribing phone calls.
-
A
Google Lensupdate that will now contextualize text and content, translate it, and read it out loud. That in conjunction with being all done on-device as opposed to cloud-based means it can be done even with poor network connectivity and more privacy. -
Decentralizing data with
federated learning. Instead of gathering data in the cloud from users to train data sets, federated learning trains AI models on mobile devices in large batches, then transfers those learnings back to a global model without the need for data to leave the device. -
TCAV, or Testing with Concept Activation Vectors, is an open-source technology that dissects machine learning models in order to try and understand why they make certain decisions.
But it’s not enough to rely solely on the technology. Amber Case, a design researcher and author of
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