Awesome, not awesome.
“A new study reveals that a machine learning tool can help to identify which breast lesions, already classified as “high-risk,” are likely to become cancerous…The model correctly predicted cancer upgrades in 37 of the 38 lesions or 97 percent. The team found that, had the model been used, it would have helped to prevent nearly one-third of the surgeries conducted on benign lesions.” — Karla Lant, Writer Learn More on Futurism >
“Public agencies responsible for areas such as criminal justice, health, and welfare increasingly use scoring systems and software to steer or make decisions on life-changing events like granting bail, sentencing, enforcement, and prioritizing services…but a scoring system used in sentencing and bail by multiple states was biased against black people…. [and] it appears unlikely that the US federal government will join efforts to engage with concerns about the effects and use of algorithms and AI in public life.” — Tom Simonite, Writer Learn More on Wired >
What we’re reading.
1/ With recent advances in machine learning and automation, manufacturing roles transform from robots assisting humans to humans assisting robots. Learn More on The New Yorker >
2/ Conversational AI systems might extend the reach of human caregivers far enough that we could provide elderly people with the care they actually deserve. Learn More on The Washington Post >
3/ In past technological revolutions, low-wage jobs were most threatened by automation — today, algorithms slowly begin to replace traders at Wall Street’s largest firms. Learn More on Bloomberg >
4/ AlphaGo, a program that used machine learning to beat a human world champion at Go earlier this year, is beaten 100 games to none by a new version that didn’t study any human-played games. Learn More on The Atlantic >
5/ In a reminder to take all AI-related news with a mountain of salt, a major Australian publication spins an outlandish tale of our supposedly imminent future. Learn More on Gizmodo >
6/ Polls indicate that Americans enjoy newfound comforts that automation makes possible, but deeply fear the threat it poses to their jobs. Learn More on WIRED >
7/ Machine learning algorithms will eventually become widespread and easy to deploy throughout organizations, making it increasingly important for all employees to understand how and when algorithms should be used to solve business problems. Learn More on Harvard Business Review >
What we’re building.
At work, our inboxes fill up quicker than we can empty them, key decisions are posted and immediately lost in Slack, we forget the thousands of useful articles we’ve read, and we struggle to be present when we’re surrounded by friends and family.
Attention Jacking wreak havoc on our lives and it’s about time we do something about it.
Journal is a new way to new way to search and remember information across your work apps and beyond.
Where we’re going.
Highlight from “Routes to Defensibility for your AI Startup”
“…AI brings a new type of network effects that some call “data network effects”. Machine learning algorithms need data to work. While the relationship is not linear, the prediction/classification work done by the machine learning algorithms increases in accuracy as they ingest more data.
As a company adds more customers, it gains more data from each of them to train and refine their algorithms. With more data, the accuracy of the prediction goes up, as well as the overall quality of the product. A better product helps convince new customers to purchase it and contribute their data. The loop is closed.”