If your business has not begun thinking of how artificial intelligence will affect your bottom line, then it may be the right time to start putting some thought behind it. 

About Collective Intelligence

Collective intelligence is the term being used to define the human/machine cooperative interaction, meaning how machines and humans work together and the interactions between human intelligence and artificial intelligence. The current environment of AI is based on collective intelligence, and its current and future implications for business will affect every industry. From job creation to job loss, from new manufacturing to replacement models, AI has the potential to shift the industrial landscape in a few concise years.

Understanding Artificial Intelligence as a Business

There are three main architectures that are currently affecting business; each of these presents opportunities for differentiation and challenges to each industry. However, knowing, at least the fundamental structures behind these artificial intelligence architectures is key to the planning of your business.

Machine Learning

The ability of machines to learn, in a supervised or unsupervised environment is what machine learning is currently about. Machine learning is methods enabled by constraints that support models of thinking, which is part of the definition of Artificial Intelligence, according to MIT Professor, Thomas Malone. What this means, in lay man’s terms, is the ability of a machine to learn how to respond to different interactions based on rules we provide it or based on learnings it can develop based on the rules we provide it. We may still be far away from the days where machines can learn on their own, but when that day comes, we may be up against our creation. In essence, machines can be provided with structures or rules for them to learn how to do a specific task. That unique task could be as simple-sounding but very complicated as determining whether an image is a dog or a cat. Humans can help machines understand the difference between the photos by “training” them on characteristics that define the differences between the two at a micro-level. Those questions could be examples of the differences between a cat’s ear and a dog’s ear. We can define rules around the shape, the color, specific features, that’s probably what most of us would do. Machines, however, require a much deeper understanding to learn, starting with questions like “Does it exist?” or “Is this a tip” before it can ask if “Is the tip rounded” for example, so the level of detail is micro-level. It could be as deep as looking at a micro-structure portion of the ear and learning the shape, depth, and other features.

Among the learning portion of machine learning, we also find visual learning, as in sensing in automated vehicles learning to recognize specific patterns on the ground to guide them on a road, and predictive, where based on a set of rules, the learned behavior or task can predict what will happen next, as in a stock market prediction, which can be made given a large set of information and knowledge of how we, as humans and investors, behave based on information, news, and trends. The behavioral modeling behind AI for machine learning can then be considered as supervised, unsupervised, semi-supervised, active, transfer, and reinforcement learning. The latter three are not considered within this article as they’re more complex than what an average business should be worrying about in the immediate future.

Natural Language Processing

Natural language processing is the ability of machines not only to understand the language but to translate the intent within the sentences, the relationship between the words, and the probability of meaning or context trying to be communicated. This is the job of what we see IOT devices like Alexa-enabled home devices attempting to achieve. Alexa, for example, is based on an NLP called LEX, which is Amazon’s version. Same as Siri, and others, these NLP’s look to gain an understanding not only by leveraging learning but by leveraging connected information. Other NLP’s in the market include architectures like Chatbots, which are trained in different, defined structures of processing in order to provide a guided conversation that feels, as much as possible as a human interaction, in order to assist us in resolving issues, finding answers, ordering items, and other day-to-day operational functions.


Robotics is mostly what we all expect it to be, machines that can produce and execute tasks generally considered labor or laborious. We see robotics in places like automotive and manufacturing to build cars at plants or fill plastic bottles. Add an element of probability to those robotics, and you start creating an environment where the machines can select based on trends in orders or purchases whether they should produce more red cars or black cars than others.

All-in-all artificial intelligence is not a thing of the future. It is here and now. Looking at how your business can leverage tools that serve as part of the collective intelligence of the future of your business should not only be on your radar, schedule, and budget; not having it as part of your long-range plan could be a serious detriment to your business prospects. To us, Artificial intelligence is now a total boon.

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