Machine Learning For Dummies. John Paul Mueller

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      Defining machine learning limits based on hardware

Huge datasets require huge amounts of memory. Unfortunately, the requirements don’t end there. When you have huge amounts of data and memory, you must also have processors with multiple cores and high speeds. One of the problems that scientists are striving to solve is how to use existing hardware more efficiently. In some cases, waiting for days to obtain a result to a machine learning problem simply isn’t possible. The scientists who want to know the answer need it quickly, even if the result isn’t quite right. With this in mind, investments in better hardware also require investments in better science. This book considers some of the following issues as part of making your machine learning experience better:

       Obtaining a useful result: As you work through the book, you discover that you need to obtain a useful result first, before you can refine it. In addition, sometimes tuning an algorithm goes too far and the result becomes quite fragile (and possibly useless outside a specific dataset).

       Asking the right question: Many people get frustrated in trying to obtain an answer from machine learning because they keep tuning their algorithm without asking a different question. To use hardware efficiently, sometimes you must step back and review the question you’re asking. The question might be wrong, which means that even the best hardware will never find the answer.

       Relying on intuition too heavily: All machine learning questions begin as a hypothesis. A scientist uses intuition to create a starting point for discovering the answer to a question. Failure is more common than success when working through a machine learning experience. Your intuition adds the art to the machine learning experience, but sometimes intuition is wrong and you have to revisit your assumptions.

      

When you begin to realize the importance of environment to machine learning, you can also begin to understand the need for the right hardware and in the right balance to obtain a desired result. The current state-of-the-art systems actually rely on Graphical Processing Units (GPUs) to perform machine learning tasks. Relying on GPUs does speed the machine learning process considerably. A full discussion of using GPUs is outside the scope of this book, but you can read more about the topic at https://devblogs.nvidia.com/parallelforall/bidmach-machine-learning-limit-gpus/ and https://towardsdatascience.com/what-is-a-gpu-and-do-you-need-one-in-deep-learning-718b9597aa0d.

Overcoming AI Fantasies

As with many other technologies, AI and machine learning both have their fantasy or fad uses. For example, some people are using machine learning to create Picasso-like art from photos using products like NightCafé (https://creator.nightcafe.studio/), which supports people who really enjoy this art form. You can read all about using machine learning to create art at https://www.washingtonpost.com/news/innovations/wp/2015/08/31/this-algorithm-can-create-a-new-van-gogh-or-picasso-in-just-an-hour/. Of course, the problems with such use are many. For one thing, most people wouldn’t really want a Picasso created in this manner except as a fad item (because no one had done it before). The point of art isn’t in creating an interesting interpretation of a particular real-world representation, but rather in seeing how the artist interpreted it. The end of the article points out that the computer can only copy an existing style at this stage — not create an entirely new style of its own. The following sections discuss AI and machine learning fantasies of various sorts.

      Discovering the fad uses of AI and machine learning

      AI is entering an era of innovation that you used to read about only in science fiction. It can be hard to determine whether a particular AI use is real or simply the dream child of a determined scientist. For example, The Six Million Dollar Man (https://en.wikipedia.org/wiki/The_Six_Million_Dollar_Man) is a television series that looked fanciful at one time. When it was introduced, no one actually thought that we’d have real-world bionics at some point. However, Hugh Herr (https://www.smithsonianmag.com/innovation/future-robotic-legs-180953040/) and others (https://www.fiercebiotech.com/medtech/using-onboard-ai-to-power-quicker-more-complex-prosthetic-hands) have other ideas — bionic legs and arms really are possible now. Of course, they aren’t available for everyone yet; the technology is only now becoming useful. Muddying the waters is The Six Billion Dollar Man movie, based partly on The Six Million Dollar Man television series (https://www.cinemablend.com/new/Mark-Wahlberg-Six-Billion-Dollar-Man-Just-Made-Big-Change-91947.html), which has suffered delays for various reasons (https://screenrant.com/mark-wahlberg-six-billion-dollar-man-delays-updates/). The fact is that AI and machine learning will both present opportunities to create some amazing technologies and that we’re already at the stage of creating those technologies, but you still need to take what you hear with a huge grain of salt.

      One of the more interesting uses of machine learning for entertainment purposes is the movie B (https://www.cinemablend.com/news/2548939/one-sci-fi-movie-will-be-able-to-film-during-the-pandemic-thanks-to-casting-an-ai-robot-as-its-lead), which stars an android named Erica. The inventors of Erica, Hiroshi Ishiguro and Kohei Ogawa, have spent a great deal of time trying to make her lifelike by trying to implement the human qualities of intent and desire (https://www.yoichimatsuyama.com/conversation-with-evolving-robotic-species-interview-with-hiroshi-ishiguro/). The result is something that encroaches on the uncanny valley (https://www.scientificamerican.com/article/why-uncanny-valley-human-look-alikes-put-us-on-edge/) in a new way. The plot of this movie will be on the same order as Ex Machina (https://www.indiewire.com/2020/06/ex-machina-real-robot-erica-science-fiction-movie-1234569484/).

To make the future uses of AI and machine learning match the concepts that science fiction has presented over the years, real-world programmers, data scientists, and other stakeholders need to create tools. Nothing happens by magic, even though it may look like magic when you don’t know what’s happening behind the scenes. In order for the fad uses for AI and machine learning to become real-world uses, developers, data scientists, and others need to continue building real-world tools that may be hard to imagine at this point.

      Considering the true uses of AI and machine learning

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