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Intelligence, Artificial and Otherwise*

The process of teaching a machine to simulate human thoughts, reasoning, and emotions is known as Artificial Intelligence or AI, wherein the machine demonstrates intelligence through agents or bots that mimic human communications, learning, and problem solving skills. Just as with classical disciplines, AI is broken into many endeavors, such as reasoning, analysis, machine learning, and knowledge representation. Programmers have to teach a machine (machine learning) to think (reason) and then demonstrate that it understands (analyze) the concept (knowledge representation) and can work to a solution (problem solving) on its own. Independent Problem Solving is one of the key goals of AI.

A second, and increasingly important goal of AI is Knowledge Generation, using speech recognition, perception, and Natural Language Generation (NLG) to create better outcomes faster and more efficiently. This can be seen in applications for Technical Support, Agriculture, and finding common medical solutions that have worked in the past for other patients with similar disease profiles. Obviously, Natural Language Processing is another key sub-discipline of AI. The AI Revolution in computing will make your business smarter in many ways, as data gathering advances alongside the increased scale in processing power driven by cloud and distributed computing initiatives.

But you cannot teach a machine to think and act like a human without first understanding what human intelligence is. And this means that Computer Scientists, whether they like it or not, are going to have to collaborate with the Humanities. The Social and Anthropological disciplines offer the best insights into what makes the human mind function, along with Linguistics and Philosophy. The whole debate also engenders questions of ethics: should we create artificial entities endowed with human properties like intelligence, emotion, and choice? Clearly, automating a DevOps function with AI is not going to give birth to SkyNet, but the groundwork of ethical choices is still a relevant topic that will be addressed in future posts.

How is this Intelligence?

There is no end to the debates, philosophical and otherwise, as to what constitutes intelligence. For the purposes of our discussion, we will rely on a simple definition from “It is a capacity to acquire, adapt, modify, extend and use information in order to solve problems.” Therefore, intelligence is the ability to cope with unpredictable. In other words, to take the unpredictable and make it known and predictable. This concept encapsulates one of the disciplines of AI, which is Predictive Analytics, where the machine takes data and analyzes it in order to surface trends, make them more apparent, and therefore enable predictions. At the base of every debate is the assumption that machines are communicating with other machines (M2M) or with humans (M2H). In this discussion, we shall first look at how humans acquire language and communicate to exchange knowledge, and then how computer languages are modeled on human languages and therefore essentially work on the same structural principles.

When examining the human disciplines, a natural separation between the hard and soft sciences exists where on one hand you have Neurology, Biophysics, and Linguistics which study how the brain (human “hardware”) processes language and on the other hand Communications, Sociology, Psychology and Anthropology which study how humans use language within social context to convey knowledge.

As shown below, we can see that a parallelism can be drawn between the common view of the machine and our view of classic human knowledge: each has a classic 7-layer stack when it comes to communications. The following diagram illustrates how logic machines mimic human systems and make it possible to teach a machine to understand human languages. Obviously, there is a lot more to explore in this topic, such as how anthropologists study toolmaking and comparing applications as tools. This parallelism is what makes Computational Linguistics possible at a conceptual level.

The Language Instinct

It is in the heart of man to know and be known by others, at a minimum by at least one other person. This pursuit of community is at the heart of Pascal’s dilemma. (Go look it up.) Humans have the need to communicate, to share their innermost thoughts through words, signs, and signals. This instinct, sometimes referred to as the “Language Instinct” implies that communicating is not an invention like writing or tools, but rather an innate capacity for sharing thoughts and experiences. Many see the brain as a “neural network” where language resides in one region, denoted as Broca’s and Wernicke’s areas, supported by other functions such as the motor skills necessary to move the mouth and tongue, reasoning skills to process high-order concepts, and so forth. Hence the desire to simulate language in computers with circuitry, neural networks, and programming.

The structure, or grammar of languages however is quite different and reflects the culture in which it evolved. For example, Germanic grammar is quite different in nature from Swahili. The ordering of words into sentences, formal rules for writing, and the like are able to be grouped into language families and can be taught and codified, whereas the urge to speak and communicate is a natural part of a baby’s reasoning and development. Some linguists posit a “universal grammar” as part of this innate ability, a debate we will not digress into. However, suffice it to say that there is no need to have a universal grammar to understand the difference between language as an ability of humans and grammar as a structural arrangement of words.

Programmers fight over languages all the time some preferring Java to C++ or Python to Perl. This a debate over grammar first and nothing more. Operating systems also have grammars, witnessed by the wars between Linux aficionados, Apple fanboys, and Windows diehards. These communities of practice have agreed to use a particular way of speaking to the machine in order to give the hardware instructions. Those instructions have to be translated into a language the machine can understand. This is the job of the compiler which takes the human-readable code, such as Basic or Java and turns it into Machine Code. The machine code is then interpreted by the CPU as 1s and 0s that that the circuitry can use. Of course, it’s far more complicated than that. If you want to talk to the graphics card and tell it to render a particular picture on the screen you use different commands (verbs) than if you are talking to the math coprocessor asking it to calculate a function. You can talk to the operating system and tell it to open a port, you can use an API to send commands and data to other systems and programs through that port, and so forth. The possibilities are as creative as any human communications. You just have to know how to talk the talk.

Based on the ability to talk to machines via code, and knowing how to parse human speech with NLP, the goal of AI is to create agents that take care of everyday tasks and independently operate in the background with little to no human intervention. Scheduling appointments, answering simple questions, alerting a robot to refill the supply line, and so forth. These may sound like the stuff of science fiction, but each example has already been realized in the current business climate by Amazon, Google, Tesla, and others.


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*Reposted with permission from


About the Author

Sharon Bolding is the CTO of, an A.I.-powered Enterprise SaaS company. She is a serial entrepreneur, with experience in AI, SaaS, FinTech, and Cybersecurity. With two successful exits of her own, she is a trusted advisor to startups and growing companies. An invited speaker at universities and tech conferences, she focuses on educating users about the ethical use of their data and how AI impacts privacy and security.


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