Robotics and Your Job

Now that word has gotten around that I’ve been working with both data science projects (Python for Data Science for Dummies) and machine learning projects (Machine Learning for Dummies), people have begun asking me hard questions, such as whether a Terminator style robot is possible (it isn’t, Ex Machina and The Terminator notwithstanding) or whether they’ll be without work sometime soon (the topic of this post). (As an aside, deus ex machina is a literary plot device that has been around for a long time before the movie came out.)

Whether your job is secure depends on the kind of job you have, whether robotics will actually save money, what you believe as a person, and how your boss interprets all the hype currently out there. For example, if your claim to fame is flipping burgers, then you’d better be ready to get another job soon. McDonald’s is currently opening a store that uses robots in Phoenix and plans to have 25,000 more moved to robotics by the end of 2016. Some jobs are simply going to go away, no doubt about it.

However, robots aren’t always the answer to the question. Many experts see three scenarios: humans working for robots (as in a doctor collaborating with a robot to perform surgery more accurately and with greater efficiency), humans servicing robots (those McDonald’s jobs may be going away, but someone will have to maintain the robots), and robots working for humans (such as that Roomba that’s currently keeping your house clean). The point is that robots will actually create new jobs, but that means humans will need new skills. Instead of boring jobs that pay little, someone with the proper training can have an interesting job that pays moderately well.

An interesting backlash against automation has occurred in several areas. So, what you believe as a person does matter when it comes to the question of jobs. The story that tells the tale most succinctly appears in ComputerWorld, Taxpayer demand for human help soars, despite IRS automation. Sometimes people want a human to help them. This backlash could actually thwart strategies like the one McDonald’s plans to implement. If McDonald’s finds that the robots cost too much to run or that people are going to the competition to obtain food from other humans, it might need to reevaluate what appears to be a winning strategy. However, the backlash would need to involve a significant part of the population of people who buy food at McDonald’s to induce the company to make the change.

There is also the boss’ perspective to consider. A boss is only a boss as long as there is someone or something to manage. Even though your boss will begrudgingly give up your job to automation, you can be sure that giving up a job personally isn’t on the list of things to do. Some members of the press have resorted to viewing the future as a time when robots do everything and humans don’t work, but really, this viewpoint is a fantasy. However, it’s not a fantasy that companies such as Hitachi are experimenting with robot managers. Some employees actually prefer the consistent interaction of a robot boss. It’s unlikely that managers will take this invasion of their domain sitting down and do something to make using robots untenable.

It really is too soon to tell where robots will go for one simple reason. The algorithms used to make robots functional are still works in progress. In addition, society must decide the place that robots will take. The interaction between business and the people that businesses serve will play a distinct role in how things play out. However, all this said, your job will likely be different in the future due to the influences of robots. For the most part, I feel that life will be better for everyone after the adjustment, but that the adjustment will be quite hard. Let me know your thoughts on robots at John@JohnMuellerBooks.com.

 

Technology and Child Safety

I recently read an article on ComputerWorld, Children mine cobalt used in smartphones, other electronics, that had me thinking yet again about how people in rich countries tend to ignore the needs of those in poor countries. The picture at the beginning of the article says it all, but the details will have you wondering whether a smartphone really is worth some child’s life. That’s right, any smartphone you buy may be killing someone and in a truly horrid manner. Children as young as 7 years old are mining the cobalt needed for the batteries (and other components) in the smartphones that people seem to feel are so necessary for life (they aren’t you know).

The problem doesn’t stop when someone gets the smartphone. Other children end up dismantling the devices sent for recycling. That’s right, a rich country’s efforts to keep electronics out of their landfills is also killing children because countries like India put these children to work taking them apart in unsafe conditions. Recycled wastes go from rich countries to poor countries because the poor countries need the money for necessities, like food. Often, these children are incapable of working by the time they reach 35 or 40 due to health issues induced by their forced labor. In short, the quality of their lives is made horribly low so that it’s possible for people in rich countries to enjoy something that truly isn’t necessary for life.

I’ve written other blog posts about the issues of technology pollution. One of the most recent is More People Noticing that Green Technology Really Isn’t. However, the emphasis of these previous articles has been on the pollution itself. Taking personal responsibility for the pollution you create is important, but we really need to do more. Robotic (autonomous) mining is one way to keep children out of the mines and projects such as The Utah Robotic Mining Project show that it’s entirely possible to use robots in place of people today. The weird thing is that autonomous mining would save up to 80% of the mining costs of today, so you have to wonder why manufacturers aren’t rushing to employ this solution. In addition, off world mining would keep the pollution in space, rather than on planet earth. Of course, off world mining also requires a heavy investment in robots, but it promises to provide a huge financial payback in addition to keeping earth a bit cleaner (some companies are already investing in off world mining, but we need more). The point is that there are alternatives that we’re not using. Robotics presents an opportunity to make things right with technology and I’m excited to be part of that answer in writing books such as Python for Data Science for Dummies and Machine Learning for Dummies (see the posts for this book).

Unfortunately, companies like Apple, Samsung, and many others simply thumb their noses at laws that are in place to protect the children in these countries because they know you’ll buy their products. Yes, they make official statements, but read their statements in that first article and you’ll quickly figure out that they’re excuses and poorly made excuses at that. They don’t have to care because no one is holding them to account. People in rich countries don’t care because their own backyards aren’t sullied and their own children remain safe. So, the next time you think about buying electronics, consider the real price for that product. Let me know what you think about polluting other countries to keep your country clean at John@JohnMuellerBooks.com.

 

Making Algorithms Useful

I’m currently engaged writing Machine Learning for Dummies. The book is interesting because it turns math into something more than a way to calculate. Machine learning is about having inputs and a desired result, and then asking the machine to create an algorithm that will produce the desired result from the inputs. It’s about generalization. You know the specific inputs and the specific results, but you want an algorithm that will provide similar results given similar inputs for any set of random inputs. This is more than just math. In fact, there are five schools of thought (tribes) regarding machine learning algorithms that Luca and I introduce you to in Machine Learning for Dummies:

  • Symbolists: The origin of this tribe is in logic and philosophy. This group relies on inverse deduction to solve problems.
  • Connectionists: The origin of this tribe is in neuroscience. This group relies on backpropagation to solve problems.
  • Evolutionaries: The origin of this tribe is in evolutionary biology. This group relies on genetic programming to solve problems.
  • Bayesians: This origin of this tribe is in statistics. This group relies on probabilistic inference to solve problems.
  • Analogizers: The origin of this tribe is in psychology. This group relies on kernel machines to solve problems.

Of course, the problem with any technology is making it useful. I’m not talking about useful in a theoretical sense, but useful in a way that affects everyone. In other words, you must create a need for the technology so that people will continue to fund it. Machine learning is already part of many of the things you do online. For example, when you go to Amazon and buy a product, then Amazon makes suggestions on products that you might want to add to your cart, you’re seeing the result of machine learning. Part of the content for the chapters of our book is devoted to pointing out these real world uses for machine learning.

Some uses are almost, but not quite ready for prime time. One of these uses is the likes of Siri and other AIs that people talk with. The more you interact with them, the better they know you and the better they respond to your needs. The algorithms that these machine learning systems create get better and better as the database of your specific input grows. The algorithms are tuned to you specifically, so the experience one person has is different from an experience another person will have, even if the two people ask the same question. I recently read about one such system under development, Nara. What makes Nara interesting is that she seems more generalized than other forms of AI currently out there and can therefore perform more tasks. Nara is from the Connectionists and attempts to mimic the human mind. She’s all about making appropriate matches—everything from your next dinner to your next date. Reading about Nara helps you understand machine learning just a little better, at least, from the Connectionist perspective.

Machine learning is a big mystery to many people today. Given that I’m still writing this book, it would be interesting to hear your questions about machine learning. After all, I’d like to tune the content of my book to meet the most needs that I can. I’ve written a few posts about this book already and you can see them in the Machine Learning for Dummies category. After reading the posts, please let me know your thoughts on machine learning and AI. Where do you see it headed? What confuses you about it? Talk to me at John@JohnMuellerBooks.com.

 

Warnings in Python and Anaconda

It seems as if Python developers are having more than a few problems at the moment from a number of sources. I recently wrote about the potential issues for readers of Beginning Programming with Python For Dummies and Python for Data Science for Dummies from Windows 10 (Python and Windows 10). However, some readers have come back afterward to say they’re still seeing warnings. It wasn’t until one of the beta readers for Machine Learning for Dummies also saw some of these warnings that it became apparent that some other problem is at work. A recent upgrade to NumPy 1.10.1 has created these warnings. You can see some message threads about the issue at:

The important thing to remember is that you’ll see warnings, not errors (unless there is a problem Luca, my coauthor for Python for Data Science for Dummies, and I haven’t seen yet). For now, updating all of the Anaconda components is the only way to actually get rid of the warnings, which can prove to be quite a pain. However, the warnings are just that, warnings. The code in the books will still run just fine. The best way to avoid a lot of work and potentially creating yet more problems is to ignore the warnings for now. In order to ignore the warnings, type the following two lines of code:

import warnings
warnings.simplefilter("ignore")

Obviously, the situation is inconvenient for everyone, but the various libraries will get in sync sometime soon and then the warnings will disappear until the next set of updates. Please let me know if you continue to see problems after making this fix at John@JohnMuellerBooks.com.

 

A Future Including Virtual Reality

Seeing is believing—at least, that’s how it’s supposed to be. However, seeing may not mean believing anything in the future. During the building of the PC for Build Your Own PC on a Budget, I investigated various new technologies, including virtual reality, where what you see may not exist at all. Of course, gamers are eagerly anticipating the Oculus Rift, which promises to transform gaming with a monitor into an experience where you really feel as if you’re there. This kind of technology isn’t quite available yet, but will be soon. Even when the hardware is ready and the drivers work as promised, truly immersive games will take time to create. Look for this experience to evolve over time to the point where the Holodeck featured in Star Trek actually does become a reality.

To attract attention and become viable, however, technology must answer specific needs today. It was with great interest that I read Marines test augmented reality battlefield. Unlike the Oculus Rift, this technology actually does exist today and it demonstrates some of the early uses of virtual reality that you can expect to see. In this case, the background is real—it’s an actual golf course. The virtual reality system adds the hardware of war to the scene, including tanks, mortars, and features, such as smoke. What the marine sees is a realistic battlefield that doesn’t exist anywhere but the viewer’s glasses. This is the sort of practical use of virtual reality that will continue to drive development until we get a holodeck sometime in the future.

Virtual reality for gamers and the armed services is nice, but it’s also becoming a reality for everyone else. Samsung and Facebook are introducing a virtual reality solution for movie goers. That’s right, you’ll be able to strap some glasses to your head and get transported to a comfy living room with a big screen TV where you can watch the latest movies offered by Netflix. The Gear VR device promises to change the way that people see movies forever. This particular device actually works with your smartphone, so you need a compatible smartphone to use it. In addition to movies, Gear VR also promises to let you play virtual reality game and become involved in other immersive environments. All you really need is the right app.

An immersive experience, where you eventually won’t be able to tell real from created, is what virtual reality promises. Using virtual reality, you could travel to other parts of the world, explore the ocean depths, or even saunter through the solar system as if you’re really there, but still be in your own home. Virtual reality will eventually transform all sorts of environments, including the classroom. Imagine children going to school, interacting with other students, learning from the best instructors, and never leaving their home. A student could get a top notch education for a fraction of the cost that students pay today.

Coupling virtual reality with other technologies, such as robotics, could also allow people to perform a great many unsafe tasks in perfect safety. A human could guide a robot through a virtual reality connection to perform real world tasks that would be unsafe for a human to perform alone. Think about the use of the technology in fighting fires or responding to terrible events that currently put first responders at risk. Virtual reality will eventually change the way we view the world around us and I hope that the experience is as positive as vendors are promising today. Let me know your thoughts about virtual reality at John@JohnMuellerBooks.com.

 

Beta Readers Needed for Machine Learning for Dummies

Do machines really learn, or do they simply give the appearance of learning? What does it actually mean to learn and why would a machine want to do it? Some people are saying that computers will eventually learn in the same manner that children do. However, before we get to that point, it’s important to answer these basic questions and consider the implications of creating machines that can learn.

Like many seemingly new technologies, machine learning actually has its basis in existing technologies. I initially studied about artificial intelligence in 1986 and it had been around for a long time before that. Many of the statistical equations that machine learning relies upon have been around literally for centuries. It’s the application of the technology that differs. Machine learning has the potential to change the way in which the world works. A computer can experience its environment and learn how to avoid making mistakes without any human intervention. By using machine learning techniques, computers can also discover new things and even add new functionality. The computer is at the center of it all, but the computer output affects the actions of machines, such as robots. In reality, the computer learns, but the machine as a whole benefits.

Machine Learning for Dummies assumes that you have at least some math skills and a few programming skills as well. However, you do get all the basics you need to understand and use machine learning as a new way to make computers (and the machines they control) do more. While working through Machine Learning for Dummies you discover these topics:

  • Part I: Introducing How Machines Learn
    • Chapter 1: Getting the Real Story about AI
    • Chapter 2: Learning in the Age of Big Data
    • Chapter 3: Having a Glance at the Future
  • Part II: Preparing Your Learning Tools

    • Chapter 4: Installing a R Distribution
    • Chapter 5: Coding in R Using RStudio
    • Chapter 6: Installing a Python Distribution
    • Chapter 7: Coding in Python Using Anaconda
    • Chapter 8: Exploring Other Machine Learning Tools
  • Part III: Getting Started with the Math Basics

    • Chapter 9: Demystifying the Math behind Machine Learning
    • Chapter 10: Descending the Right Curve
    • Chapter 11: Validating Machine Learning
    • Chapter 12: Starting with Simple Learners
  • Part IV: Learning from Smart and Big Data
    • Chapter 13: Preprocessing Data
    • Chapter 14: Leveraging Similarity
    • Chapter 15: Starting Easy with Linear Models
    • Chapter 16: Hitting Complexity with Neural Networks
    • Chapter 17: Going a Step Beyond using Support Vector Machines
    • Chapter 18: Resorting to Ensembles of Learners
  • Part V: Applying Learning to Real Problems
    • Chapter 19: Classifying Images
    • Chapter 20: Scoring Opinions and Sentiments
    • Chapter 21: Recommending Products and Movies
  • Part VI: The Part of Tens
    • Chapter 22: Ten Machine Learning Packages to Master
    • Chapter 23: Ten Ways to Improve Your Machine Learning Models
    • Online: Ten Ways to Use Machine Learning in Your Organization

As you can see, this book is going to give you a good start in working with machine learning. Because of the subject matter, I really want to avoid making any errors in book, which is where you come into play. I’m looking for beta readers who use math, statistics, or computer science as part of their profession and think they might be able to benefit from the techniques that data science and/or machine learning provide. As a beta reader, you get to see the material as Luca and I write it. Your comments will help us improve the text and make it easier to use.

In consideration of your time and effort, your name will appear in the Acknowledgements (unless you specifically request that we not provide it). You also get to read the book free of charge. Being a beta reader is both fun and educational. If you have any interest in reviewing this book, please contact me at John@JohnMuellerBooks.com and will fill in all the details for you.