Google is a monstrosity. Once you start looking at them closely, every algorithm betrays the myth of unitary simplicity and computational purity. You may remember the Netflix Prize , a million dollar competition to build a better collaborative filtering algorithm for film recommendations. Netflix VP Todd Yellin explained to Madrigal why the process of generating altgenres is no less manual than our own process of reverse engineering them.
Theory of multiple intelligences
Netflix trains people to watch films, and those viewers laboriously tag the films with lots of metadata, including ratings of factors like sexually suggestive content or plot closure. These tailored altgenres are then presented to Netflix customers based on their prior viewing habits. But the overall work of the Netflix recommendation system is distributed amongst so many different systems, actors, and processes that only a zealot would call the end result an algorithm. The same could be said for data , the material algorithms operate upon.
Today, conventional wisdom would suggest that mystical, ubiquitous sensors are collecting data by the terabyteful without our knowledge or intervention. Once you adopt skepticism toward the algorithmic- and the data-divine, you can no longer construe any computational system as merely algorithmic.
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Think about Google Maps, for example. Like metaphors, algorithms are simplifications, or distortions. They are caricatures. And they couple to other processes, machines, and materials that carry out the extra-computational part of their work. They want to be innovators, disruptors, world-changers, and such zeal requires sectarian blindness.
The exception is games, which willingly admit that they are caricatures—and which suffer the consequences of this admission in the court of public opinion. Games know that they are faking it, which makes them less susceptible to theologization. Imagine the folly of thinking otherwise! Striphas and Manovich are right—there are computers in and around everything these days.
But the algorithm has taken on a particularly mythical role in our technology-obsessed era, one that has allowed it wear the garb of divinity. Of treating computation theologically rather than scientifically or culturally.
Three ways human-centered design elevates AI
This attitude blinds us in two ways. First, it allows us to chalk up any kind of computational social change as pre-determined and inevitable. It gives us an excuse not to intervene in the social shifts wrought by big corporations like Google or Facebook or their kindred, to see their outcomes as beyond our influence. Second, it makes us forget that particular computational systems are abstractions, caricatures of the world, one perspective among many.
The first error turns computers into gods, the second treats their outputs as scripture.
Myths of British ancestry | Prospect Magazine
We were surrounded by people every day who were all speaking the same language; thinking deep thoughts about AI-enabled futures. When the context of use is novel to the user [figure A], bias for dependability.
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When there are a lot of new UI tricks to learn [figure B], make sure the primary use cases are super relatable. And when the functionality of the product is especially dynamic [figure C] , your UI should be flush with familiar patterns. Over time, we snapped out of it. We began fiercely reducing complexity in the UI, and made control and familiarity cornerstones of our experiential framework. We added a software viewfinder and a hardware capture button to the camera. We made sure that the user had the final say in curation; from the best still frame within a clip to its ideal duration.
Through this process we discovered another critically important finding for testing an AI-powered product: fake it till you make it. Users preview their clips by streaming them from the camera. On the far left, users choose which clips they want saved to their phone.
On the right, users can pinpoint the exact frame they want to save as a still photo. We really hope users go out and play with it. It can become a tool for unprecedented exploration and innovation; a tool to help us seek out patterns in ourselves and the world around us. As human-centered practitioners, we have a tremendous opportunity to shape a more humanist and inclusive world in concert with AI, and it starts by remembering our roots: finding and addressing real human needs, upholding human values, and designing for augmentation, not automation.
Google Design is a cooperative effort led by a group of designers, writers, and developers at Google. We work across teams to publish original content, produce events, and foster creative and educational partnerships that advance design and technology. Share Facebook Twitter Email. The UX of AI.
Using Google Clips to understand how a human-centered design process elevates artificial intelligence. By Josh Lovejoy. Real moments of parents, kids, and pets captured by the Google Clips camera. Back to basics Consistency is the name of the game when trying to teach anything. Capture We needed to train models on what bad looked like: hands in front of the camera, quick and shaky movements, blurriness. Composition We needed to train models about stability, sharpness, and framing.
Social norms Familiarity is such a cornerstone of photography. Trust and self-efficacy One of the reasons we invested in Clips was because of how deeply important it was to demonstrate the importance of on-device and privacy-preserving machine learning to the world—not to mention its remarkable capabilities e.
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So I thought it would be worth writing a piece to explain the difference. Both terms crop up very frequently when the topic is Big Data , analytics, and the broader waves of technological change which are sweeping through our world. Machine Learning is a current application of AI based around the idea that we should really just be able to give machines access to data and let them learn for themselves.
Artificial Intelligence has been around for a long time — the Greek myths contain stories of mechanical men designed to mimic our own behavior. As technology, and, importantly, our understanding of how our minds work, has progressed, our concept of what constitutes AI has changed. Rather than increasingly complex calculations, work in the field of AI concentrated on mimicking human decision making processes and carrying out tasks in ever more human ways. Artificial Intelligences — devices designed to act intelligently — are often classified into one of two fundamental groups — applied or general.
Applied AI is far more common — systems designed to intelligently trade stocks and shares, or maneuver an autonomous vehicle would fall into this category. Generalized AIs — systems or devices which can in theory handle any task — are less common, but this is where some of the most exciting advancements are happening today. It is also the area that has led to the development of Machine Learning.
Two important breakthroughs led to the emergence of Machine Learning as the vehicle which is driving AI development forward with the speed it currently has.
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One of these was the realization — credited to Arthur Samuel in — that rather than teaching computers everything they need to know about the world and how to carry out tasks, it might be possible to teach them to learn for themselves. The second, more recently, was the emergence of the internet, and the huge increase in the amount of digital information being generated, stored, and made available for analysis.
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