Let the machines do the routine and brute work, we think. Sounds like a reasonable argument. But machines are not capable of replacing humans in every aspect of work. In some cases (like fighting Covid19, it will be nice if machines can completely take over, but they can’t). They can do parts, but not the whole.
The trick is in figuring out the division of labor between the man and the machine. A consensus (for now) seems to be that certain tasks may be automated but and not jobs. We can start with that assumption.
This week I read two articles which talk about this topic.
Working smarter describes what machines are good at and where humans excel. The author recommends personal knowledge mastery as a way to stay ahead of machines using human capabilities like sharing, and network connections.
“There is not much more need for machine-like human work which is routine, standardized, or brute. But certain long-term skills can help us connect with our fellow humans in order to learn and innovate — curiosity, sense-making, cooperation, and novel thinking.”
In 2017, the researchers in DeepMind created AlphaZero, a single program that masters three distinct board games: chess, shogi and Go. To be fair, AlphaZero isn’t the first algorithm created that can beat human in a board game. However, it is the first model that can master three games at the same time, with the performance far better than professional human players as well as all algorithms existed at that time. As a matter of fact, AlphaZero is not only good at these games. People who have witnessed it played described that the deep reinforcement learning algorithm was “toying” with its opponents, “tricking” them, even making one of them (i.e., Stockfish) crashed during the game. This leads some of them to believe that AlphaZero is insightful, and it has actually “understood” the nature of chess, shogi and Go.
Slowly more and more products are incorporating elements of Machine Learning and AI. Product Managers need to understand what the technologies are about and their impact. These articles ease you gently into the role of a product manager and provide some great tips.
Even simple machine learning projects can be difficult, and managing these projects in a real business is much harder than most people realize; that’s why Venture Beat claims 87% of machine learning products never make it into production, and Harvard Business Review says that “The first wave of corporate AI is bound to fail.” Machine learning is not fairy dust you can sprinkle on your existing product.
In “The AI Hierarchy of Needs,” Monica Rogati argues that you can build an AI capability only after you’ve built a solid data infrastructure, including data collection, data storage, data pipelines, data preparation, and traditional analytics.
Start with a simple project, build your infrastructure, learn how to use your data effectively.
Assuming that the selected machine learning technique is suitable, the product manager will have to make several important decisions about the model.
A product manager must decide whether to refactor the research code, determine the scope of the ML model’s inference engine, decide on model format , ensure that the modeling technique can support the service level agreement of the AI system, and plan for deployment and maintenance.
The foundation of any data product consists of “solid data infrastructure, including data collection, data storage, data pipelines, data preparation, and traditional analytics.” A product manager for this phase prepares the way for putting products into production by building the infrastructure needed to support the design, development, and use of future products.
So what did I learn? Start a Machine Learning product without using ML! And manage expectations carefully (your own and others). But the most essential learning is that we should understand the technologies and their current state of development. Building ML products is a lot of work.
I first encountered the third article, read it, and followed two links and read the other two.
I also tried a small experiment and cheated a bit. I fed each one article to a text summarizer, reviewed these summaries and took portions of them to use as context.
The three quoted blocks above were written by a Machine Learing app!
Yesterday I tweeted about “learning to learn” and mentioned that AI would help us learn better. One of my students asked me to share some useful links. Here are a few starting points. I am going to use Education as a proxy for Learning even though they are not exactly the same.
Discovered an International Journal of Artificial Intelligence in Education. Lost of useful resources and they have conferences.
A search for “Artificial Intelligence in Education” got me a bunch of tweets. Here is a link. This is snapshot as of 16th May. If you are interested in these tweet streams, let me know. Will maintain a separate page for these tweets.
I like it when one of my students (current or past) asks me a question. It makes me research a bit more and helps me learn too. Thanks, Hemamalini. This is the first post to answer a small part of your question. There will be more!
If you are just starting out programming, you may want to learn a language, and start exploring by building small applications.
Don’t worry that you don’t know object-oriented analysis/modeling and design. You can learn them when you need them. This is true of algorithms, data structures, object-orientation, software engineering as well.
You can build simple apps with procedural programming and learn to reuse first using functions. As your apps grow in complexity, you appreciate the need for organizing these functions as classes/methods. (You may not even get there if you start out with a functional programming language).
As you deploy your apps and fix bugs, you will come to appreciate testing. If you need to update your app frequently, you will understand the need for automated testing.
As your apps get used more and more, you will understand the need for improving the app. You will learn the need to simplify the code (helping you fix problems faster), and the need for profiling to identify slow performing subsystems.
Learning to build computer application by step-wise refinement is a path to learning and improving. If you need to rework the code, you will learn to re-factor. Automated tests will improve your confidence in refactoring without breaking existing functionality.
Programming is a creative endeavor. It is enjoyable, because you are solving problems big and small. Puzzling over the parts that do not work, trying out things, learning from others, is all part of the experience.
Programming is a journey of self-discovery. The little bits of happiness during the journey add up.
Just finished reading this book. I was tweeting a few quotes as I was reading the book. Here is the full list.
Before you read the quotes, please remember the context Charlie was mostly answering questions about being an Investor.
Let us first look at these quotes.
There is no way you can live an adequate life without making many mistakes.
The best thing a human being can do is to help another human being know more. Being an effective teacher is a high calling.
Those of us who have been very fortunate have a duty to give back. Whether one gives a lot as one goes along as I do, or a little and then a lot (when one dies) as Warren does, is a matter of personal preference.
Spend each day trying to be a little wiser than you were when you woke up. Day by day, and at the end of the day-if you live long enough-like most people, you will get out of life what you deserve.
What are the secret of success? One word answer: rationality.
There’s only one way to the top: hard work. Do what you like and are good at.
Understanding both the power of compound interest and the difficulty of getting it is the heart and soul of understanding a lot of things.
Quickly eliminate the big universe of what not to do, followup with a fluent, multidisciplinary attach on what remains, then act decisively when, and only when, the right circumstances appear.
If you always tell people why, they’ll understand it better, they’ll consider it more important, and they’ll be more likely to comply.
…it never ceases to amaze me to see how much territory can be grasped if one merely masters and consistently uses all.
My habit of committing far more time to learning and thinking than to doing is no accident.
Some people are extraordinarily good at knowing the limits of their knowledge, because they have to be.
So the game is to keep learning, and I don’t think people are going to keep learning who don’t like the learning process. You need to like the learning process.
If you’re capable of understanding the world, you have a moral obligation to become rational.
For me there were many takeaways. There is a note of warning. Whoever edited this book did not do a good job. There are too many repetitions. Please remember it if you buy and read the book. Despite bad editing, the book is full of gems. So it is worth a read.
A few reflections.
The importance of reading. I do read though not anywhere near being “a book with a couple of legs sticking out.”.
When we were programming in early 70s, we spent far more time learning and thinking than doing. Now it is very different. There is a lot more doing. Charlie’s quote “This habit of committing far more time to learning and thinking than to doing” is right on.
Spend each day trying to be a little wiser than you were when you woke up. This can be a powerful micro-habit and a daily goal to shoot for.
I think of life as a series of small experiments. When you frame it that way, you are not devastated by failures. Some experiments succeed and others fail. You don’t have to stress too much about the outcome. Whether they fail or succeed, you always gain a bit, because you learn something.
I think I have had more failures in my life than successes. But a few successes more than wiped out most failures. The rest were swallowed by time.
It is not a happy ending, though (as happens in some movies). I am still living with the psychological effects of some of those failures.
Today, social media is my primary news source for many areas I’m interested in. That’s not a function of the platforms themselves, but rather of who I’m connected to—experts in particular fields who have ready access to information that would be hard to find anywhere else.
Let me mention three instances on the influence of these people I follow on Twitter, Facebook, LinkedIn and other sources (blogs):
A lot of books I bought last year, came from mentions in podcasts I like and blogs I read (Tim Ferriss and Maria Papova for instance)
Most of the technical books I read and the webinars I attend come from some of the most influential people I follow on Twitter.
A lot of literature I collect and research for Data Science comes from @kdnuggets and other similar sources
I do have a List called News on Twitter and follow most of the popular news sources in India and US. However, I visit it a lot less and prefer to read from my “Daily” list.
The people I choose to follow are my filters. I read their posts more than I do the “other” reading.