Awesome, not awesome.
“Drones equipped with a shark detection system powered by artificial intelligence will start patrolling some Australian beaches next month in a bid to improve safety… by provid[ing] a live-video feed to a drone operator who then uses the shark-spotting software to identify sharks in real time… and warn[ing] swimmers through a megaphone when a shark is detected. Studies have shown that people have a 20–30 percent accuracy rate when interpreting data from aerial images to detect shark activity. Detection software can boost that rate to 90 percent.” — Reuters StaffLearn More on Reuters >
“Two prominent research-image collections display a predictable gender bias in their depiction of activities such as cooking and sports. Images of shopping and washing are linked to women, for example, while coaching and shooting are tied to men. Machine-learning software trained on the datasets didn’t just mirror those biases, it amplified them…if replicated in tech companies, these problems could affect photo-storage services, in-home assistants with cameras like the Amazon Look, or tools that use social media photos to discern consumer preferences.” — Tom Simonite, Reporter Learn More on WIRED >
What we’re reading.
1/ Negative impacts will be felt throughout all of society — from bigoted hiring practices to unfair teacher evaluations — if we put blind faith in “opinions embedded in code.” Learn More on TED > (video)
2/ While the most notable tech and automobile companies race to build the first fully autonomous vehicle, progress in machine learning may be the reason we get there faster than experts predict. Learn More on Benedict Evans >
3/ Elon Musk’s reported push to promise a “full self-driving” car before it’s ready is driving top engineers to leave Tesla. Learn More on The Wall Street Journal >
4/ Apple sets its once lofty sights for its self-driving car that disrupts the entire automobile industry a bit lower — building a self-driving shuttle for employees. Learn More on The New York Times >
5/ The beauty of Netflix’s recommendation system lies in the fact that it uses both the data that you literally tell it when you rate a movie, and behavioral patterns you may not even be aware of, when determining what show you might enjoy next. Learn More on WIRED >
6/ Machine learning algorithms cement the power of companies (and governments) with massive data troves, and we should probably mistrust them — if we want to preserve our fundamental human rights. Learn More on ACLU >
7/ In possibly the cutest tweet of the week, two little league players — one from the Dominican Republic and one from South Dakota — communicate with each other via the power of machine learning and Google Translate. Learn More on Twitter >
Links from the community.
“Big data analytics platform Databricks raises $ 140M Series D round led by Andreessen Horowitz” submitted by Dakota McKenzie (@dakotamckenzie). Learn More on TechCrunch >
“Inside Waymo’s Secret World for Training Self-Driving Cars” submitted by Samiur Rahman. Learn More on The Atlantic >
“Churn Prediction” submitted by Piotr Płoński. Learn More on MLJAR >
“3 Ways Companies Are Building a Business Around AI” submitted by Anna Kohnen. Learn More on Harvard Business Review >
“The dos and dont’s of building an AI startup” by Chris Corbishley. Learn More on Medium >
“Machine Learning for Humans” submitted by Vishal Miani (@v_maini). Learn More on Medium >
“Deep Learning for Siri’s Voice: On-device Deep Mixture Density Networks for Hybrid Unit Selection Synthesis” submitted by Avi Eisenberger. Learn More on Machine Learning Journal >
“Poker & AI: The Rise of Machines Against Humans” submitted by Karthik Reddy. Learn More on Poker Sites >
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