Machines for creative enablement

January 29, 2018 Sam DeBrule

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This Week’s Special Guest

A little while back, Howie Liu of Airtable, wrote a piece about machines being used for creative enablement. I think it’s important enough to warrant re-surfacing. Read it in the “Where we’re going.” section below!

Awesome, not awesome.

#Awesome
“AI is probably the most important thing humanity has ever worked on. I think of it as something more profound than electricity or fire…I think a lot of things will play out in more positive ways than people think. But the risks are important. Any time you work with technology, you need to learn to harness the benefits while minimizing the downsides.” Sundar Pichai, Google CEO Learn More on CNN >

#Not Awesome
“China’s advantage over the West in the biotech, artificial intelligence, and machine learning race: They’re not pausing to have key ethics debates, where we take years. This is a fight between democracies and techno-authoritarians.” — Melissa Chan, Reporter Learn More on Twitter >

What we’re reading.

1/ Concerns around AI algorithms’ ability to explain how they make decisions have slowed their introduction into fields ranging from medicine to military systems — and one investor argues we shouldn’t worry about this ‘black box.” Learn Why on The New York Times >

2/ Another investor, speaking about his experience as a Google Now user thinks just the opposite — “what I want…everywhere that machine learning touches me, is a “why” button I can push (or speak) to know why I got that recommendation.” Learn Why on AVC >

3/ As we embed more machine learning algorithms in our technology experiences, we need to optimize for “augmenting human capability,” not just making the algorithm smarter. Learn Why on Google Design >

4/ If you live in the US, and someone uses an AI app that doctors your face onto an actor in a pornographic video, you have no ground for legal recourse against them. Learn Why on WIRED >

5/ For the foreseeable future, we should expect machine learning to reduce costs rather than increase revenues since “we haven’t found a way, yet, to have computers do [unpredictable and novel] things for us…” Learn Why on Tomasz Tunguz >

6/ Now that closed circuit TVs can use machine learning to analyze endless streams of video footage without human intervention, we as a society will have a new brand of privacy issues to grapple with. Learn Why on The Verge >

7/ Facebook makes major changes to its AI leadership, as Yann LeCun steps down as head of their research organization, and Jerome Pesenti takes over. Learn Why on Quartz >

What we’re building.

We think the future workplace will be one that’s led by people who excel at finding and acting quickly with their information.

But today, our work is fractured. Millions of unnecessary steps stand between us and the information we need to do our jobs.

Think of Journal as a companion app that connects your tools. It helps you get to all your information fast so you can use your time and energy on the things that matter.

Join our waitlist, and we’ll give you early access.

Links from the community.

“How should brands use future technologies?” by Michaela Fiasová. Learn More on Noteworthy >

“How to solve 90% of NLP problems: a step-by-step guide” submitted by Geneviève Smith (@smithgk). Learn More on the Insight Data Science blog >

“Algorithms are making American inequality worse” submitted by Avi Eisenberger. Learn More on MIT Technology Review >

“Engineers design artificial synapse for “brain-on-a-chip” hardware” submitted by Samiur Rahman (@samiur1204). Learn More on MIT News >

“The accuracy, fairness, and limits of predicting recidivism” submitted by Cecelia Shao. Learn More on ScienceAdvances >

“Ep 42 — Deep learning, autonomous weapons, and the future of AI with Stuart Russell” submitted by James Connell. Learn More on YouTube >

Where we’re going.
Machines for creative enablement (not human replacement)

The information we publish on the web pales in comparison to the totality of information inside our heads. Companies like Jelly and Quora have jumped on the opportunity to expose that knowledge in a people-powered search engine, but if we take a step back, there’s a more fundamental question to be answered: what about making tools that help us better harness the stuff of our own minds?

Can we use technology to fundamentally think and express more powerfully, and not just to make our lives marginally more convenient (one pizza at a time)?

The rise of software and the web has opened up many more potential dimensions for idea conception and expression. Yet the narrow digital channels we have today — whether self-expiring video or 140 character snippets of text — barely scratch the surface of what’s possible. As far as pushing the limits of what web-connected software can do (for us), we’re still splashing about in the kiddie section of the pool.

This is not to deny the profound emotional significance in the act of experiencing the world through another person’s eyes, or in the value of realtime access to global events. But it would be comically absurd to replace high school literature and writing with training courses for how to express oneself on Twitter, Facebook or Snapchat — because we’d be foregoing opportunities to grow the depth of our thinking.

Yet this sort of deprivation is exactly what happens when we only use software to think about and communicate simple things faster and further. In your pocket and on your desk sit devices already brimming with the latent potential for interactive, multidimensional idea expression, but we’ve instead turned them into buckets full of single-purpose, push-button appliances…

Read the full post on the Machine Learnings blog >

Howie Liu
Co-Founder and CEO of Airtable


Machines for creative enablement was originally published in Machine Learnings on Medium, where people are continuing the conversation by highlighting and responding to this story.

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