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Machine Learnings

Awesome, not awesome.

#Awesome
",,,An algorithm ingests thousands of songs in a specific genre and rapidly cranks out chord progressions and melodies optimized for that style...While these programs might not be consciously driving or tapping into musical trends the way human artists do, they churn out content much more efficiently than, say, an exhausted musician who’s been touring for months... few on the AI development side believe that their creations will replace artists altogether. Instead, they see their algorithms as a supplement to human efforts, offering new arrangements of notes and tunes that can help songwriters clarify their own ideas — enhancing, not neutering, their creative power.” - Cherie Hu, Tech Columnist Learn More from Rolling Stone >

#Not Awesome
"Companies whose business model includes harvesting our data are building self-driving supercomputers that will prowl city streets, bristling with sensors, recording absolutely everything they encounter.” - Christopher Mims, Tech Columnist Learn More from Twitter >

What we're reading.

1/ Algorithms can cause people great harm when they display inappropriate advertisements - like maternity-wear ads shown to people who've experienced a miscarriage. Learn More from The Washington Post >

2/ To reduce bias in algorithms - that would lead to fair treatment for some people and unfair treatment for others - we must help the people building the next generation of AI systems to navigate ethical dilemmas that they'll face all along the way. Learn More from Harvard Magazine >

3/ Since there are no laws governing the use of facial recognition technology by police, if an image of your face exists in a database, "you are effectively a suspect every time it's searched." Learn More from The New Yorker >

4/ Lyft is testing new ways for cars to project messages (like "safe to cross") on their windshields so that pedestrians and autonomous vehicles can safely share the same streets. Learn More from TechCrunch >

5/ Citing Amazon's patent application for a doorbell facial recognition system that scans the face of anyone who walks by, the ACLU argues the company is "chasing profit at the expense of safety and civil rights." Learn More from ACLU >

6/ Researchers use cutting edge machine learning and graphics processors technology to create photo-realistic images of people who never existed. Learn More from Motherboard >

7/ China's ability to make specialized AI computer chips continues to progress despite repeated attempts by the US to disrupt it. Learn More from MIT Technology Review >

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

Links from the community.

"A radical new neural network design could overcome big challenges in AI" submitted by Samiur Rahman (@samiur1204). Learn More from MIT Technology Review >

"A.I. as Talent Scout: Unorthodox Hires, and Maybe Lower Pay" submitted by Avi Eisenberger (@aeisenberger). Learn More from The New York Times >

"The Data Science Workflow" submitted by Cecelia Shao (@ceceliashao). Learn More from Towards Data Science >

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