Awesome, not awesome.

#Awesome
"In a matter of seconds, a new algorithm read chest X-rays for 14 pathologies, performing as well as radiologists in most cases, a Stanford-led study says...“I could see this working in a few ways. The algorithm could triage the X-rays, sorting them into prioritized categories for doctors to review, like normal, abnormal or emergent,” Lungren said. Or the algorithm could sit bedside with primary care doctors for on-demand consultation, he said. In this case, Lungren said, the algorithm could step in to help confirm or cast doubt on a diagnosis.” - Hanae Armitage, Science Writer Learn More from Stanford Medicine >

#Not Awesome
"Amazon’s algorithm has allegedly been raising the price of fire safety equipment in response to increased demand during the California wildfires...
Freestone believes dynamic pricing makes sense for more discretionary purchases, but is troubling when the practice creeps into commodity items such as fire extinguishers. “You have unleashed the beast of the algorithm and it is very effectively doing what it does in setting a price according to supply and demand,” says Freestone. “But at some point you are going to need to muzzle these algorithms.” - Matthew Chapman, Journalist Learn More from WIRED >

What we're reading.

1/ The "new generation" of low-wage workers will be made up of people who tag data that's fed into machine learning algorithms. Learn More from The New York Times >

2/ Companies that promise to use machine learning to boil down a job applicant - like a babysitter - into a single risk assessment score can be seen as both preying on the fears of potential customers and "supercharging" bias in algorithms. Learn More from The Washington Post >

3/ Unfortunately, no matter how biased and pseudo-scientific a risk assessment score may be, there's a huge group of companies willing to pay to see the results. Learn More from The Intercept >

4/ A major airline uses an algorithm to detect passengers with the same last names - and split up their seats -  so families would pay them extra money to sit together. Learn More from The Independent >

5/ A lack of research about arousal signals may make the Sex Toy industry's promises to use machine learning algorithms to create toys that "learn what we like" impossible to keep. Learn More from Motherboard >

6/ UPS uses a combination of AI algorithms and direct intervention from engineers to re-route your packages in extreme weather situations. Learn More from MIT Technology Review >

7/ Researchers use a machine learning algorithm to search through millions of FDA medical device reports and uncover deaths that were the result of technical malfunctions. Learn More from ABC >

What we're building.

Meet Journal
We started Journal excited to answer a single question.

What would be possible if our information — about people, projects, and ideas — was connected and easily accessible?

In a year after closing our Seed funding round, we’ve started moving towards an answer. We are building a new kind of Journal. You write notes in it, save interesting links, and drop in important documents and messages for later. When you need something, ask Journal, and it will actually help you find it.

Journal integrates with the services where your information lives (like Slack, Gmail, Evernote, Pocket, and Dropbox) — so that you have one connected home for all your stuff. You can use the best services for messaging, documents, and more — and Journal will tie them all together so you stay in control.

For the past few years we’ve seen ourselves, friends, and colleagues stretched thin trying to manage many different silos of information. Journal is the new way forward. It’s a connected home to enhance the way we gather and share knowledge.

So far, beta community members have used Journal to launch new products, prepare for a new baby, remodel a home, write a book, teach a graduate course, plan a vacation, prepare for meetings, and much more.


On the surface, Journal looks and feels simple.

Beneath the surface, the Journal platform has two unique attributes that will enable more flexible and personalized product experiences than we’ve seen from existing knowledge management services:

1. A state-of-the-art machine learning and natural language processing model that conceptually understands people’s digital information across formats
    
2. An architecture and UX that understands and shows information as distinct types (e.g. files, tasks, articles, messages, products, and more)

Today, these attributes mean less friction for people interacting with their information in Journal. You organize links, emails, and other types of information without losing visual context, find anything easily across the apps you integrate, and see relevant items automatically grouped alongside your contacts and events. They are also the building blocks to make Journal the connected home for people to gather their thoughts, get organized, discover and share knowledge. We will use them to empower people to harness their information rather than to be burdened by it.

In the future, Journal will change depending on where you are and what you’re doing. When you check your phone from bed, it may look like a blank pad to dump thoughts. When you’re assigned a task at work it could become a list of suggested resources for you and your team. On the weekend, it may just show a map of nearby bakeries and long-form articles recommended by friends — it will help you do more with your information no matter the shape it takes.

We are so grateful to the nearly 2,000 beta users from our Machine Learnings and Noteworthy communities whose feedback has helped shape our product. In recent weeks we’ve observed an inflection point in deepening engagement, retention, and enthusiastic user feedback — So, starting tomorrow, we’re going to share Journal for Mac, our Web app, and our Chrome extension beyond our own communities. Our iOS app will follow in the coming weeks.

It’s been a hell of a challenge building Journal so far, and we have a lot of work ahead of us. We’ll only be able to make our vision for Journal a reality with the contributions of passionate, smart teammates and a supportive community. If you’re a distributed systems or machine learning engineer interested in building the future of knowledge management, please say hello. If you’d like early access to Journal, add your name to our waitlist >.

We would love for you to come a long for the ride.

Samiur Rahman
CEO, Co-founder
Journal