Machine Learnings — Melanie Hendrix joins us this week to discuss sports and AI


Awesome, not awesome.

“Scientists from the Scripps Institution of Oceanography in California introduced an algorithm that was able to analyze 52 million dolphin clicks and identify seven distinct groups of sound. These click types, the authors speculate, may correspond to different kinds of dolphins…While it previously took her three weeks to analyze a year’s worth of recordings from a single site, the algorithm took about four days to sort through two years of data from five sites…Improving scientists’ ability to track dolphin populations is not just a matter of helping dolphins, she said. Along with other marine mammals, dolphins reflect overall ocean health, meaning researchers can use them as a window into understanding changing ocean conditions.”— Steph Yin, JournalistLearn More on The New York Time >

#Not Awesome
“Among researchers studying how AI can be used to lie and manipulate the world, there’s a feeling that 2017 has been the calm before the storm. The past few years have brought example after example from research labs of how AI can generate videos of politicians saying literally anything, or potentially trick self-driving cars into speeding past stop signs. But nobody, to the field’s knowledge, has actually used the technology for malicious purposes…Hundreds of AI researchers will gather … at Neural Information Processing Systems (NIPS), AI’s largest and most influential conference, to acknowledge the potential deceptive powers of artificial intelligence, and discuss countermeasures against them.” — Dave Gershgorn, Reporter Learn More on Quartz >

What we’re reading.

1/ If the US wants to maintain its position as the world’s leader in AI, it must invest more in long-term AI research, attract top talent to work in the US, develop relevant educational programming in schools, and monitor Chinese investments in and acquisitions of companies with serious AI chops. Learn More on Foreign Affairs >

2/ As YouTube, Facebook, and Twitter’s moderation algorithms fail to keep horrible content off their platform, it’s time for management to introduce best practices that might make the internet a better place. Learn More on Hunter Walk >

3/ Doubt that humans and AI are already merging? Remember that the “phones [that] control us and tell us what to do when; social media feeds determine how we feel; search engines decide what we think” are powered by algorithms that no one person understands. Learn More on Sam Altman >

4/ Automation won’t necessarily lead to mass-unemployment in the food services industry — it all depends on whether human employees improve or worsen the customer experience. Learn More on The Atlantic >

5/ For all that’s made of AI-powered programs dominating humans at complex games, we’re a long ways away from creating AI systems that can act with the “complete randomness” of the human mind. Learn More on OM >

6/ The mindset of Silicon Valley is one of great optimism and hopefulness, but what could this mean for the economy (and every employee that’s part of it) if future downsides of AI aren’t considered? Learn More on The Economist >

7/ If we figure how the brain integrates all the silo’d information that flows into our brain, we could be one step closer to building artificial general intelligence. Learn More on Axios >

What we’re building.


From powering optical sorters that block unripe tomatoes to the algorithms that determine our Netflix recommendations, machine learning will be used to shape the products and experiences that we can’t live without.

Like @Journal on Facebook to see awesome real-life applications of machine learning, and to understand the people and ideas that shape the products you use every day.

Where we’re going.

Highlights from Melanie Hendrix’s “The gift that sports gives to AI”

“How do you say who is the best athlete in a decathlon?” asks Alexander, a math professor at DePaul University and a mathematical engineer at Nousot, an AI-based tech startup. “You take every event that those athletes do. Then you score all of them, and the highest total gives you the best athlete. But in our case, the highest total gives you the best algorithm.”

Decathlons are both an analogy for and the basis of a solution that Alexander and Akhmametyeva didn’t set out to build, but designed and published nonetheless: a common, comprehensive standard to quantitatively assess the performance of clustering algorithms.

The two mathematicians and computer scientists’ original goal was development, not measurement. Nousot had already built an autonomous forecasting algorithm that used deep learning to deliver high initial accuracy and then improve over time, and the company wanted to do the same with a clustering algorithm.

“Clustering is perfect for big data,” says Akhmametyeva, Nousot’s lead machine learning engineer. “There’s a ton of data out there, and the user doesn’t have to be lost in it. An algorithm figures out the groupings in the data, and the user creates stories from the groupings.”

In fact, users have created literal world change from the groupings. Meaningful data clusters — those groups of elements that reveal something conclusive or useful — have helped people and organizations to do things like develop vaccines, discover species, run election campaigns, and see a tsunami coming, even before the advent of AI.

Now that AI is here, so is the technology to build algorithms that find even more precise and powerful groups in ever growing volumes of data, with little or no human input. But soon after Alexander and Akhmametyeva began the work of creating such an algorithm, they discovered that they had to move the goalposts…Read Melanie Hendrix’s full post >

Links from the community.

“What Happens When an Algorithm Helps Write Science Fiction” submitted by Avi Eisenberger. Learn More on WIRED >

“What Happens When an Algorithm Helps Write Science Fiction” by Capital One Tech. Learn More on Machine Learnings >

“How can investors use machine learning to pick the right startups?” submitted by Jane Del Ser. Learn More on Catalyst Fund >

Noteworthy Featured Cross-post: “The Secrets of Patreon’s High Performance Product Teams” by Tal Raviv. Learn More on Noteworthy >

“How We Are Monitoring Political Ads on Facebook” submitted by Samiur Rahman. Learn More on ProPublica >

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Machine Learnings – Medium

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