Will Machines Take Our Jobs?

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.”

Working smarter

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.

Consciousness – An obstacle to the understanding of intelligence.

Sometimes it is difficult to differentiate between intelligence and the perception of intelligence.

LinkLog – Machine Learning for Product Managers

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.

Machine Learning for Product Managers

As Martin Zinkevich’s document on best practices for ML engineering describes, you should not be afraid to ship a product that does not use machine learning.

Many products can collect useful customer feedback using simple baselines; in the document, Martin quotes an example of sorting apps in an app store by download count.

What you need to know about Product Management for AI

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.

Practical Skills for the AI Product Manager

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!