Mobile-first. Absolutely need a notes app that syncs across computer and phone. That’s just how I function.
Ideally, there should be a backup option that keeps my notes unlocked if when the app starts degrading a few years from now.
OrgMode is ideal, but images cause Emacs scrolling to be wonky on the desktop, and recreating agenda mode on the mobile would be a challenge. But glad to see apps like Orgzly take on it.
Since my blog and books are already in Markdown, it would make sense to just stick to Markdown for notes as well.
What I tried before
Evernote sync was so unreliable that I had stopped using it.
OneNote wouldn’t let me even create an account (would complain about password, regardless of how small or long, how simple or complicated a password I try)
Quiver seemed promising, but the iOS app is still in beta and currently only provides a read-only view to the notes. And it does not support iCloud sync, only third-party sync mechanisms, which is strange for a Mac+iOS app combination.
Currently using Apple Notes. The downside is that exporting notes for publishing / sharing is a pain. For example, I can’t copy a note for sharing as text on messaging platforms like Slack, because it loses all the links and formatting.
Long-term availability? I’m glad they have a subscription model, so that they are encouraged to maintain the app, instead of creating an upgrade treadmill (I’m looking at you, Alfred app ). Worst case, they have a really good backup feature, that also exports attachments.
Last time I checked, Evernote does a bad job at this. The “export” menu command only exports the text of the notes as an xml file. How can you not include attachments in the backup?
My soothsayer friend BG told me last year that “deep learning is the next big thing”. I didn’t know what that meant. A few days ago, I attended the AIFrontiers conference in Santa Clara, California. Now I have a glimpse of what he meant :-)
What is Intelligence?
In this context, by “intelligence”, I interpret it as “smart”. Yes, we have smart phones, smart TVs, and smart speakers. But imagine way more smarter software and devices… like self-driving cars!
This was best explained by the legendary Jeff Dean in his keynote speech, talking about how many products at Google use deep learning:
What is Machine Learning?
Machine learning is one technique to achieve intelligence.
What is machine learning? My understanding is: it is about making computer programs whose behavior is learned from data instead of solely based on lines of code written by humans. Think spam filters – whenever we click on “Spam” or “Not Spam” buttons, the spam filtering system learns from this and the behavior changes over time to reflect that, without somebody explicitly writing code for every single email. On top of this idea, design the system to learn by itself, and it can learn and improve orders of magnitude faster.
What makes Machine Learning special? Because the system is now learning behaviors that is more accurate for the task and can handle more situations than the algorithms we humans could have imagined! Think converting sentences from one human language to another, self-driving cars, etc. Think of all the situations that such systems need to handle. We could have not written code to handle every situation.
What is deep learning? It is a machine learning technique that is based on “layers of neurons”, i.e. think of millions of neurons in your human brain that work together to understand, perceive, store knowledge… deep learning tries to simulate your brain. At least, that’s the way I understood it.
What do you want in a Machine Learning System?
Jeff Dean talked about their first internal machine learning system, the problems they faced, and what they ideally wanted:
And eventually they designed TensorFlow to achieve those desirable features.
He went on to mention the algorithms they use for different products, which I found interesting, not because I understood what they meant, but because they are pointers in case you want to learn more. After all, the whole point of attending conferences and meetups is to know what is happening out there.
He talked about how deep learning has dramatically changed the field of NLP. Focused on “end-to-end” deep learning methods.
Computer Vision (Perception)
An example of using computer vision is from Jeff Dean’s keynote speech – https://www.google.com/get/sunroof – enter your address, it will tell you how much roof area you have and how much money you can save by switching to solar energy!
OpenCV is a popular open source computer vision library:
Impact of AI on jobs
Internet of Things
Vion Vision was the most interesting. They are deploying machine learning models to devices like cameras. They demonstrated their bus-counting cameras that helps bus operators to get real-time traffic so that they can deploy more buses in high-traffic routes, etc. They even had a demo of public-area cameras that auto-detect a crowd beating up a person and sending an alert to the local police station.
Deep Learning Frameworks
This was an amazing session where creators or prominent members of each Deep Learning Framework came up and talked about their thoughts on the framework status and future.
Scalability – How do I train on multiple GPUs and CPUs? OpenMPI, NCCL, ZeroMQ, etc.
Augmented Computation Patterns – more than float dense math – quantized computation, sparse math libs, model compression, rethinking existing ops (ResNEXT)
Augmented Math Challenges
Modularity – reusability
Thank You AIFrontiers Organizers
It was an excellent conference, with well-chosen topics and the best speakers imaginable – the platform creators themselves. People who were expecting deep-dives or technical details were disappointed, but it was a great “state of the industry” conference for people like me who know nothing about the topic.
In the coming decade all global enterprises, both private and public, will target the trapped value in their ineffective and inefficient outward-facing relationships with their targeted constituencies, be they consumers, clients, customers, patients, students, or citizens. Authentic sustainable engagement will become the new scarce ingredient. The as-a-service model will expand from commodity transactions to incorporate more significant life interests as well—education, health, personal development, family relationships, wealth management, safety and security, and the like. Machine learning and artificial intelligence will be the new keys to the kingdom, enabling institutions to operate at global scale with unprecedented speed, relevance, and accuracy. Operating models will prioritize customer relationship effectiveness over the supply chain efficiency, causing CRM to displace ERP as the most prominent information system, and the hot expertise will lie in user experience design, data analytics, machine learning, and artificial intelligence.
I am a common journalism student from CYU, Beijing. And actually, I am an absolute newbie in Python programming when I start to translate this book. Initially, it was just a whim, but when I done this work, I realized that a decision triggered by interest had prompted me to go so far. With the help of my predecessors’ translations and the vast amount of information provided by the developed Internet, and with the help of my friends, I prudently presented this translation edition. I just hope my translation work will help other newcomers in learning Python. At the same time, I am always waiting for my translation of the comments and suggestions, and ready to change or improve this superficial work.