Machine Learning For Dummies. John Paul Mueller

Чтение книги онлайн.

СКАЧАТЬ technology is in its infancy, now is the time to consider the potential pitfalls and do something about them before they materialize. The last section of the chapter doesn’t discuss problems that will happen; rather, it discusses problems that could happen and that people can certainly avoid by using the technology correctly.

Creating Useful Technologies for the Future

      To survive, a technology must prove useful. In fact, it must prove more than useful; it must meet perceived needs in a manner that existing technologies don’t as well as build a base of adherents who provide a monetary reason to continue investing in the technology. For example, the Apple Lisa was an interesting and useful piece of technology that demonstrated the usefulness of the GUI to business users who had never seen one before. It solved the need to make computers friendly. However, it failed because it didn’t build a base of adherents. The computer simply failed to live up to the hype surrounding it (see https://www.inexhibit.com/case-studies/different-fate-apples-lisa-macintosh-design-matters/ for details). The next system that Apple built, the Macintosh, did live up to the hype a bit better — yet it built on the same technology that the Lisa used. The difference is that the Macintosh developed a considerable array of hard-core adherents.

      Machine learning solves a considerable number of problems in a manner that other technologies would have a hard time copying. However, to become the must-have technology that everyone wants to invest in, machine learning must build that cadre of hard-core adherents. The bar chart at https://www.grandviewresearch.com/industry-analysis/machine-learning-market shows that machine learning is growing significantly, with services leading the way. The associated free downloadable report (you must fill in a request to get it) details just how much machine learning has grown in the past few years. The following sections discuss some of the ways in which machine learning is already affecting you personally and how this use will increase in the future — making machine learning a must-have technology.

      Considering the role of machine learning in robots

A goal of machine learning today is to create useful, in-home robots. Now, you might be thinking of something along the lines of Rosie the robot found in The Jetsons (see https://thejetsons.fandom.com/wiki/Rosey for a quick description). However, real-world robots need to solve practical and important problems to attract attention. To become viable and attract funding, a technology must also amass a group of followers, and to do that, it must provide both interaction and ownership. According to MarketsAndMarkets (see https://www.marketsandmarkets.com/Market-Reports/household-robot-market-253781130.html), the market for home robots will go from $1.2 billion in 2016 to $3.3 billion in 2019 (estimated) to $9.1 billion by 2024 (predicted), so home robots are becoming popular.

      An example of a successful in-home robot is the Roomba from iRobot (https://www.amazon.com/exec/obidos/ASIN/B005GK3IVW/datacservip0f-20/). You can actually buy a Roomba today; it serves a useful purpose; and it has attracted enough attention to make it a viable technology. The Roomba also shows what is doable at a commercial, in-home, and autonomous level today. Yes, the Roomba is a fancy vacuum cleaner — one with built-in smarts based on simple but very effective algorithms. The Roomba can successfully navigate a home, which is a lot harder to accomplish than you might think. It can also spend more time on dirtier areas of the home. However, you still need to empty the Roomba when full; current robot technology does only so much. (And, just in case you’re interested, you can also find the WORX robotic mower on Amazon at https://www.amazon.com/exec/obidos/ASIN/B07VC44C68/datacservip0f-20/.)

      

You can find other real-world robots that people are using to perform specialized tasks, but you won’t find them in your home. The video at https://www.youtube.com/watch?v=PfvXKXSAsUM shows 15 humanoid robots that are interesting, but not particularly useful today (still, you have to admit that they’re fun to look at). Other sites, such as https://shareably.net/70-robots-that-actually-exist/ and https://www.cnn.com/2020/09/23/asia/japan-gundam-robot-test-scli-intl-scn/index.html, present other robots, but you won’t find general-purpose uses in any of them. Before robots can enter a home and work as a generalized helper, machine learning needs to solve a wealth of problems, and the algorithms need to become both more generalized and deeper thinking. By now, you should see that robots will become a part of daily life, but it won’t happen right away.

      Using machine learning in health care

      An issue that is receiving a lot of attention is the matter of elder care. People are living longer, and a nursing home doesn’t seem like a good way to spend one’s twilight years. Robots will make it possible for people to remain at home yet also remain safe. Some countries are also facing a critical shortage of health care workers, and Japan is one. As a result, the country is spending considerable resources to solve the problems that robotics present. (Read the story at https://www.bbc.com/worklife/article/20200205-what-the-world-can-learn-from-japans-robots for details.)

The closest that technology currently comes to the vision presented by an in-home nurse robot is the telepresence robot, which is also found in hospitals (see https://spectrum.ieee.org/automaton/robotics/medical-robots/telepresence-robots-are-helping-take-pressure-off-hospital-staff for details). In this case, the robot is an extension of a human doctor or nurse, so it’s not even close to what the Japanese hope to create in the future.

      Creating smart systems for various needs

      Many of the solutions you can expect to see that employ machine learning will be assistants to humans. They perform various tasks extremely well, but these tasks are mundane and repetitive in nature. For example, you might need to find a restaurant to satisfy the needs of an out-of-town guest. You can waste time looking for an appropriate restaurant yourself, or you can access an AI to do it in far less time, with greater accuracy and efficiency. Siri (https://www.apple.com/siri/) is one of the more popular and well-known solutions. Another such solution is Nara (http://www.news.com.au/technology/innovation/meet-your-artificial-brain-the-algorithm-redefining-the-web/news-story/6a9eb73df016254a65d96426e7dd59b4), an experimental AI that learns your particular likes and dislikes as you spend more time with it. Unlike Siri, which can answer basic questions, Nara goes a step further and makes recommendations.

      Using machine learning in industrial settings

      Machine learning СКАЧАТЬ