An introduction to algorithms and use cycles


Algorithms and Science

 Algorithms: Thinking Machines

Have you ever wondered what lies inside all the multitude of electronic and automated devices that we encounter in everyday life? The answer is that most people don’t, especially since they seamlessly weave and wind their way into our world without making us aware of their inner workings.

That is the nature of machines. They are all around us but we barely notice the complexity and incredible effort that it takes to keep them beeping and bopping to serve our every need. In this lesson we will have a closer look at the differences between machines and tools and how algorithms play a key role in transforming knowledge into action.

One definition sees tools as “any physical item that can be used to achieve a goal, especially if the item is not consumed in the process” [1]. In other words, tool are instruments or utensils that allow a user to perform a specific task. By contrast, a machine can be described as “a tool containing one or more parts that uses energy to perform an intended action”[2]. Machines can also be divided into simple machines that redirect or convert motion, such as a drill or more complex automated machines such as a loom or a calculator, where the embedded knowledge is encoded into instructions that control a mechanism.

Machines, whether biological o mechanical, can be thought of as programmable systems. In the case of biological machines like human beings, tools are an extension of ourselves, meaning that the knowledge needed to use a tool is embedded in the form of memories and experience. For instance, when a carpenter uses a chisel, thinking and doing are one and the same. Through trial and error they learn how much pressure to apply to carve a particular type of wood. They can do this by encoding sensory information and emotions through a neural network that connects hands and brain simultaneously.

By contrast, mechanical systems perform each tasks as separate discrete operations. One part of the machine computes and interprets information while another performs a command or task based on instructions from a central processor. The set of instructions that control a system are known as an algorithm, which are procedures or formulas used for executing a task[3]. In humans, algorithms are partially encoded as genes or fractals, but we also rely on learning and heuristics as a form of cultural social programming that allows us to adapt. Mechanical systems on the other hand use a more direct form of logical algorithms, also known as programming, which are mathematical expressions that encode symbolic information so  processor engines or gates can convert instructions into action.

One last thing we should consider are factors that separate biological and mechanical systems. For instance, mechanical systems have an intrinsic advantage in terms of reliability, repeatability and accuracy which means that machines can be programmed to perform a simple task faster and with more precision than a human counterpart; however, their strength is also their weakness since mechanical systems rely on repetition and are not able to react to changing stations. Humans use both social algorithms and emotions as part of their programming, meaning that results are more variable but we can also adapt to changing threats or opportunities. While some progress is being made in helping machines to learn and adapt, most mechanical systems lack this trait, a result of being dependent on discrete algorithms to run their functions.   




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