Algorithms For Dummies. John Paul Mueller

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      A math coprocessor and a GPU are two examples of common special-purpose chips in that you don’t see them used to perform tasks such as booting the system. However, algorithms often require the use of uncommon special-purpose chips to solve problems. This isn’t a hardware book, but it pays to spend time showing you all sorts of interesting chips, such as the artificial neurons that UCSD is working on (https://www.marktechpost.com/2021/06/03/ucsd-researchers-develop-an-artificial-neuron-device-that-could-reduce-energy-use-and-size-of-neural-network-hardware/). Imagine performing algorithmic processing using devices that simulate the human brain. It would create an interesting environment for performing tasks that might not otherwise be possible today.

      Neural networks, a technology that is used to simulate human thought and make deep learning techniques possible for machine learning scenarios, are now benefitting from the use of specialized chips (the article at https://analyticsindiamag.com/top-10-gpus-for-deep-learning-in-2021/ outlines ten of them). These kinds of chips not only perform algorithmic processing extremely fast but also learn as they perform the tasks, making them faster still with each iteration. Learning computers will eventually power robots that can move (after a fashion) on their own, akin to the robots seen in the movie I Robot. Some robots even sport facial expressions now (https://www.sciencedaily.com/releases/2021/05/210527145244.htm).

      

No matter how they work, specialized processors will eventually power all sorts of algorithms that will have real-world consequences. You can already find many of these real-world applications in a relatively simple form. For example, imagine the tasks that a pizza-making robot would have to solve — the variables it would have to consider on a real-time basis. This sort of robot already exists (this is just one example of the many industrial robots used to produce material goods by employing algorithms), and you can bet that it relies on algorithms to describe what to do, as well as on special chips to ensure that the tasks are done quickly (https://www.therobotreport.com/picnic-pizza-making-robot-is-now-available/).

      

Eventually, it might even be possible to use the human mind as a processor and output the information through a special interface. At one point, people experimented with putting processors directly into the brain, but the latest innovation relies on the use of veins to make the connection (https://www.independent.co.uk/life-style/gadgets-and-tech/brain-computer-interface-vein-als-stent-neuralink-b1556167.html). Imagine a system in which humans can solve problems using algorithms at the speed of computers, but with the creative “what if” potential of humans.

      Networks: Sharing is more than caring

      Unless you have unlimited funds, using some algorithms effectively may not be possible, even with specialized chips. In that case, you can network computers together. Using special software, one computer, a host, can use the processors of all client computers running an agent (a kind of in-memory background application that makes the processor available). Using this approach, you can solve incredibly complex problems by offloading pieces of the problem to a number of client computers. As each computer in the network solves its part of the problem, it sends the results back to the host, which puts the pieces together to create a consolidated answer, a technique called cluster computing.

      Lest you think this is the stuff of science fiction, people are using cluster computing techniques (https://www.geeksforgeeks.org/an-overview-of-cluster-computing/) in all sorts of interesting ways. For example, the article at https://turingpi.com/12-amazing-raspberry-pi-cluster-use-cases/ details how you can build your own supercomputer (among other tasks) by combining multiple Raspberry Pi (https://www.raspberrypi.org/) boards into a single cluster.

      Distributed computing, another version of cluster computing (but with a looser organization) is also popular. In fact, you can find a list of distributed computing projects at https://en.wikipedia.org/wiki/List_of_distributed_computing_projects. The list of projects includes some major endeavors, such as Search for Extraterrestrial Intelligence (SETI). You can also donate your computer’s extra processing power to work on a cure for cancer. The list of potential projects is amazing. Most of these projects are hosted by Berkeley Open Infrastructure for Network Computing (https://boinc.berkeley.edu/), but you can find other sponsors.

      Leveraging available data

      Part of solving problem using an algorithm has nothing to do with processing power, creative thinking outside the box, or anything of a physical nature. To create a solution to most problems, you also need data on which to base a conclusion. For example, in the toast-making algorithm, you need to know about the availability of bread, a toaster, electricity to power the toaster, and so on before you can solve the problem of actually making toast. The data becomes important because you can’t finish the algorithm when missing even one element of the required solution. Of course, you may need additional input data as well. For example, the person wanting the toast may not like rye. If this is the case and all you have is rye bread to use, the presence of bread still won’t result in a successful result.

Data comes from all sorts of sources and in all kinds of forms. You can stream data from a source such as a real-time monitor, access a public data source, rely on private data in a database, scrape the data from websites, or get it in myriad other ways too numerous to mention here. The data may be static (unchanging) or dynamic (constantly changing). You may find that the data is complete or missing elements. The data may not appear in the right form (such as when you get imperial units and require metric units when solving a weight problem). The data may appear in a tabular format when you need it in some other form. It may reside in an unstructured way (for instance, in a NoSQL database or just in a bunch of different data files) when you need the formatting of a relational database. In short, you need to know all sorts of things about the data used with your algorithm in order to solve problems with it.

      

Because data comes in so many forms and you need to work with it in so many ways, this book pays a lot of attention to data. Starting in Chapter 6, you discover just how data structure comes into play. Moving on to Chapter 7, you begin looking at how to search through data to find what you need. Chapters 12 through 14 help you work with big data. However, you can find some sort of data-specific information in just about every chapter of the book because without data, an algorithm can’t solve any problems.

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