I read The Daily Stoic book by Ryan Holiday around a year ago. It changed my perception of reality with the very first page:
Stoicism is an ancient philosophy that focuses on practical life instead of theoretical questions. It is concerned with everyday life and does not concern itself about the stars, heaven, hell, etc. That is what I found remarkable about this philosophy.
This was immediately apparent when my car broke down. I dropped my daughter to preschool and when I came back to the parking lot, the car wouldn’t start! Immediately, I was frustrated. This is the part where I would usually curse a lot and mutter “waste of time”, blah blah.
This time, I did something different. I quickly shut down that line of thinking. I thought “What’s the Stoic way?” – as the first page indicates, the principle is simple:
“If you cannot change something, move on.
If you can change something, decide what you want to do about it, and move on.
So I calmed down quickly and asked myself “What is the next right action?”. “Duh, call AAA (roadside assistance).”
So I did that – they came in an hour (I listened to a podcast in the meanwhile), analyzed that the battery had died, they called for a flatbed truck, where they loaded the car onto it, and dropped us off at the nearby service center, and then I walked back home.
Things that I was actually happy about after this incident:
I’m glad the car broke down when I was driving, and not when my wife was driving. She would have panicked and I would have been 60 km away in San Francisco and won’t be able to arrive quickly enough to help.
I’m glad the car broke down when it was parked, not when I was driving.
My daughter and her friends were excited on seeing a flatbed truck and seeing how a car is loaded onto it. The truck driver honked at the kids and they ran around the playground to see the truck drive by. The kids talked about it for weeks!
I didn’t really miss anything at work. I had to attend one meeting, which I connected via phone and was listening in when I was walking home.
Other than spending money on a new battery (which would’ve been inevitable), I thought to myself – that this was not a bad situation!
This comes back to another Stoic principle:
Things are not good or bad. It’s our perception (impression) of things that make it good or bad!
If you can properly turn a problem upside down, every “bad” becomes a new source of good.
Suppose for a second that you are trying to help someone and they respond by being surly or unwilling to cooperate. Instead of making your life more difficult, the exercise says, they’re actually directing you towards new virtues; for example, patience or understanding. Or, the death of someone close to you; a chance to show fortitude.
Marcus Aurelius described it like this:
“The impediment to action advances action. What stands in the way becomes the way.”
About philosophy
I had benefitted from the book so much (which explains many more principles and ideas than what I mentioned here), and was so thankful to the author, that when I read that Ryan Holiday was doing a book tour for his new book “Conspiracy”, I went to get his autograph:
Only those who make time for philosophy are truly alive. — Ryan Holiday
Four years ago, I migrated this blog from WordPress to Jekyll, with the intention of using whatever format I want to use inside Emacs… Subsequently, my posting rate dropped drastically to just 13 posts in 4 years!
I don’t think that was a coincidence. Tools matter.
I believe the speed and ease of writing dropped drastically. Even simple steps like using photos in a post meant using a separate tool such as Finder.app (on macOS) or command-line to move it to the right directory and then linking to it from the main post. In WordPress, that’s one drag-and-drop and done.
Similarly, no comments was demotivating as well. While there tends to be more nitpicking these days, I would still like to benefit from the wisdom of the crowds.
So now I have migrated back to WordPress. Let’s see how this goes.
That’s when I started going down the rabbit hole of the cryptocurrency space 😬
What is blockchain, cryptocurrency and Ethereum?
The way I understand it is that cryptocurrency is digital money. So why is it different from PayPal or Paytm? Because this is not a national currency like rupees or dollars, this is a currency “for the people, by the people, of the people”. No government has sanctioned it or vouches for it. Sounds nuts, right?
But that’s what so exciting. Think of how people tinkering with technology can start a transformation like Steve Wozniak designing the Mac or Tim Berners-Lee creating the world wide web. People are now tinkering with creating a virtual currency that nobody can control, except by the participants agreeing to make changes, which makes it democratic and hence chaotic at the same time.
Note that I could have probably learned the same stuff online such as going through the Blockchain @ Berkeley’s Decal videos, etc. I just preferred a 2-day immersion, so I went to the in-person course.
The first day was an introduction and tons of questions by the audience. Everything from architecture to economics and incentives to security. Then we got an introduction to Solidity language and used the Truffle framework to practice writing a simplistic ecommerce shop smart contract.
Also, Ethereum founders are shifting focus from the solidity language which is javascript-y to a new language called viper (that also runs on the Ethereum Virtual Machine) which is python-y. Maybe there’s a moral in there somewhere…
The second day was an overview of oracles, web3.js, metamask, security (how not to ICO), authentication. There was so much to absorb here.
Special thanks to the instructors Ali Mousa and Collin Chin for a useful course. In fact, they had just finished a smart contract project on an internal supply-chain system for Airbus, and had plenty of practical advice to offer.
After attending the AI Frontiers conference at the beginning of this year, I was amazed, fascinated and befuddled at what actually is machine learning and deep learning and all of the associated buzzwords at an implementation level. I wanted to learn more about this. So, on a whim, I downloaded the TWiML podcast to listen during my commute and happened to be listening to an interview with Siraj Raval. Next thing you know, I checked out Siraj’s YouTube channel and followed him on Twitter. On Twitter, he kept talking about big news coming up in a few days, and turns out that he was co-creating the Udacity’s Deep Learning Foundation course (a MOOC). I was excited by Siraj’s and Mat’s intro video, and I immediately signed up and waited in trepidation.
The good part about the course was that there is a weekly schedule of lessons and projects. As I keep saying to friends and colleagues, nothing in the modern world ever gets done without a deadline (don’t tell your boss that).
The bad part was that the course was literally being built while we were enrolled, so we would see a mad rush by the instructors to write and create the content every week for the upcoming week, which was okay by me, because getting introduced to a topic that has only become feasible in the recent years and making it accessible in a way for people who don’t have Ph.D in machine learning, was exciting and I was grateful.
In the first few weeks, we dove into Anaconda (I’ve been doing Python for 10 years and had never heard of it), Jupyter notebooks (again, had never paid attention to or used it before), and started learning about perceptrons and neural networks. I was lost in the first few weeks. The course was advertised as 3 hours / week which was clearly insufficient, I had to spend like ~15 hours/week to catch up on the course and make sense of it all.
Like a tortoise, slowly I caught up, and reading Andrew Trask’s brilliant introductory book which was the course’s prescribed text book, I started understanding a little. We started off mostly with supervised learning, where we provide the training data set and the expected output. The lessons got into higher gear with learning convolutional neural networks (CNNs) and recurrent neural networks (RNNs). The way I understood is that CNNs are useful for working on the full input such as individual images, because you’re extracting and condensing patterns with several layers and getting a condensed representation of the full input. RNNs are useful for sequences where there is a dependency such as text, where a sentence can depend on a previous sentence.
Whenever motivation was low, Siraj’s videos kept the enthusiasm and fascination flowing!
The projects also kept me going throughout the course because that’s where the understanding is really put to the test. Since I was taking copious notes during the course, I was forced to pay attention to the details, and that helped a lot during the project.
The lessons combined with the great idea of using a forum and dedicated forum mentors who guide you on questions that you have, about both lessons and projects, was just a perfect learning environment. I can’t thank the forum mentors enough.
The last topic of the course was generative adversarial networks (GANs), a type of unsupervised learning, which is actually a relatively recent concept, the paper came out in 2014! It applies game theory to neural networks to make two neural networks to compete with each other, the generator creates new patterns and the discriminator (trained on real data) decides whether it is realistic enough or not, forcing the generator to create realistic data after sufficient training.
Unfortunately, life happened, and I was delayed by a month to work on the last project. So it took immense effort to get back into the groove. The project was to generate faces! Imagine that! That invigorated me and was so glad to finally see this screen:
Graduated!
There was plenty of other concepts we learned along the way such as autoencoders and reinforcement learning, it would take an entire article to list all the concepts we encountered.
I’m thankful to Udacity for this course, I could see that not all students were satisfied with this course, but this course was oh so worth it for me. Getting introduced to data science, machine learning and deep learning in a few months has been a gruelling and happy experience.
I signed off in the course slack community with this:
5 years ago, I joined Thejo on his (then) next adventure, Automatic which launched 4 years ago, that story is here. The premise sounded interesting – what can you do when you tap into the data generated from your car. The vision was “owning a car can be safer, cheaper, and smarter”.
Two years ago, we had a real API and events platform and mobile apps that our customers are happy with. Customers especially use it with IFTTT integration and do things like log their business trips to a spreadsheet for expense reporting, for generating SMS messages to friends or family, to switch on/off their thermostat at home, and so on.
Last year, we launched our 3G version of the device. I personally built our core ingest servers that takes in all the real-time data being uploaded from our connected devices plugged into cars, massages that data and sends it down to all the internal microservices, and we’re talking lots of different types of data and interaction models. That core ingest server is now the foundation of all our products. It was a fun and challenging project.
Did you know that 3 out of 4 new cars sold in the USA have Sirius XM satellite radio installed? So, while yes, Sirius XM is a “content and music” company, it is equal parts a “car chip and entertainment system” and “satellite technology” company.
They have scale: 30+ million paying customers
They have a consistently well-performing business – growing between 9-13% in each of the past five years and $1.5 billion in free cash flow last year.
How can one single person be so brilliant that their career spans from law to entrepreneurship to satellite radios to mathematically proving electric-powered helicopters to producing movies to learning biochemistry to create a biotech firm to cure his child’s illness to a Ph.D in medical ethics to creating lungs from pig genes to cybernetic companions!?
It has been a privilege to work in the trenches with Thejo (the visionary, the deal maker), Dr. Jerry (putting the science in data), Ljuba (how to do UX right), Ram J (the original 10x engineer), and several other brilliant folks.
This is a quick note on why I have started using Bear notes app:
What I want
Notes app needed.
Must support images and attachments.
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 [1]). 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?
No Siri integration, not sure if Apple has provided a Siri “intent” for note-taking though.
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:
Deep Learning at Google
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.
Lots of computers – which we have now thanks to cloud computing.
Lots of parallel processing power (think matrix multiplications) – which we have now thanks to Graphics Processing Units (GPUs).
What is Deep Learning?
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.
Jeff Dean explains deep learning
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:
What do you want in a Machine Learning System? Computation Time and Research Productivity
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.
Speech Recognition Google Photos Search Google Search Language Translation
Jeff Dean also mentioned the kind of impact they have had on products, esp. converting April Fool’s Day jokes into reality:
Google Inbox Smart Reply Algorithms behind Google Inbox Smart Reply
Jeff Dean expects more reuse of machine learning-developed models across different tasks, described as zero-shot learning:
Zero-shot learning
And more compute-based model generation:
More compute
Jeff Dean also gave a glimpse of what kind of queries they hope to achieve in the future:
Google Search queries of the future
Autonomous Driving
There was a lot of info throughout the day, so I’ll only post what I found were interesting topics / slides in the discussions:
Speakers were from Waymo (Google), Tesla Motors (not in official capacity), Baidu Autonomous Driving Unit. Google / Waymo designing a car specifically for autonomous driving
The car is one node of the Internet of Things. It will connect and interact with the cloud.
This is very familiar to me because that is what we do at Automatic.
Speech-Enabled Assistants
Speakers were from Microsoft, Baidu, Amazon Alexa.
Microsoft:
Speech is not the same as text processing, there are more nuances. Types of chatbots
Baidu:
Why deep learning Handle issues such as background noise and multiple people speaking Handle issues such as person speaking from other end of room They converted existing voice recordings to far-field and used that to train models How much compute power, you ask? GPUs to the rescue Deep Speech works for Mandarin Deep Speech works for multiple languages Why focus on speech? More inclusive and faster than typing. Speech recognition can be more accurate than typing for non-technical people Try the TalkType app for Android Baidu’s Goal is AI for 100 million people
He talked about how deep learning has dramatically changed the field of NLP. Focused on “end-to-end” deep learning methods.
Computer Vision (Perception)
Speakers were from OpenCV, Bosch and Google
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:
OpenCV 3 Deep Learning comes to OpenCV
Google:
Street View to Vision processing to Local Business discovery, cars, cameras, vision, and maps – all in one sentence New machine learning techniques, better data and compute, you get the idea. Future of Perception
Impact of AI on jobs
Speaker was from McKinsey McKinsey study focus Based on current AI/ML capabilities: Few jobs will be fully automatable. Most jobs will only be partially automatable. That’s a relief!
Internet of Things
Speakers were from Bosch, Nervana (Intel) and Vion
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.
Vion Vision cameras Camera counting Custom chip for deep learning
Deep Learning Frameworks
Speakers were from Google, Facebook and Amazon
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.
Rethinking slow float-based computation Math Challenges Unframework? MAPS
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
No silver bullet
Amazon mxnet:
Why another framework? Core philosophy of mxnet Current state of industry Future direction Torch next generation Another vote for sharing components
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.
