How does artificial intelligence actually work? How does chatGPT work? Let’s be honest: everyone is talking about AI changing their industry, but not many really know the specifics of how that will happen. We all know that machines can perform at a faster rate and higher consistency than people. We can look at this and say “Okay, it’s likely this can be automated… and that can be augmented… but is my boss going to be the one controlling the system?”
At the moment, based on what’s publicly available, AI tools need to be managed by people who know how to input, what to take from an AI-generated output, how to train algorithms, and then monitor KPIs and adjust methods accordingly. Again, we’re heading down a path of skepticism and unanswered questions, so let’s simplify this.
Many products and services use artificial intelligence, but if we were to look at them from an outsider's perspective, how they work can be very confusing. “How does this chatbot put these ideas together?” “How can this create an elaborate picture based on a few words I provided?” “How am I getting product recommendations that are so relevant to me?” These are all common and valid questions. Well, we can answer those questions for you.
AI operates and depends on 5 simple components: learning, perception, problem-solving, reasoning, and language understanding. Software engineers use these pillars as a framework when they create solutions that lead to products that can perform a seemingly endless amount of tasks.
Of course, there are programming languages and software frameworks going on behind the scenes but that is useless without these factors at play.
Breaking Down The 5 Components
Ok, so we know that Artificial Intelligence is able to perform the way it does because of learning, perception, problem-solving, reasoning, and language understanding. But these are all the same fundamentals of how people function— what is it about the machine mind that makes them special?
Learning
We’re taught from a young age that in order to thrive, you have to enter a learning environment. The same principle applies to artificial intelligence and machine learning - trial and error, getting comfortable with certain skills, introducing new skills, and adapting to different contexts are all part of how AI systems develop. While people learn through experiences, machines learn through algorithms and data which undergo continuous innovation.
An example of the learning phase for AI could be training a neural network by teaching it a big dataset meant to classify images the system is fed. The machine would need to learn by continuously being exposed to various images and their respective labels. Over time, the system will adjust its parameters so the accuracy can improve.
Perception
We know perception as how we view the world and various topics or situations. In the case of AI systems, perception is the ability to understand information and interpret that information. This involves not only being able to recognize different datasets— whether it be image, text, or sound— but knowing what to do with that data.
When AI systems perceive data, they need to provide valuable insights from that data. For instance, a natural language processor (NLP) allows AI to understand human language. So if you think about when you’re using Chat GPT, the NLP lets the system know what you’re saying which it is then able to provide you with insights.
Problem-solving
The simplest explanation is that AI uses and manipulates data to find solutions. Many will argue that the most appealing aspect of AI is its ability to provide solutions and do so rapidly, but what makes that happen?
Many key factors and mechanisms at play dictate the problem-solving ability of artificial intelligence to provide solutions rapidly. The one to pay attention to is the immense computational power of AI systems. Take for instance the utilization of specialized hardware, such as graphics processing units (GPUs) or tensor processing units (TPUs), which are designed to handle large-scale computations and accelerate AI algorithms. These hardware advancements enable AI systems to process massive amounts of data all at once, significantly speeding up the problem-solving process.
Reasoning
AI relies on software programs that can draw conclusions and make inferences entirely on their own. These inferences can be categorized as either inductive or deductive reasoning.
With inductive reasoning, conclusions are made based on specific observations and patterns, often referred to as a “bottom-up approach”. On the other hand, deductive reasoning is a funnel, taking broad information and cutting it down to find specific conclusions.
Inductive reasoning has been particularly useful in creating AI products and systems that are consistent with delivering results for specific problems. Tools like Grammarly are a great example, a cloud-based grammar and writing assistant that uses inductive reasoning to analyze patterns in the text and provide the appropriate suggestions.
Language Understanding
We touched upon it briefly but let's dive deeper into it. AI systems need to be able to interpret language to effectively interact with users. But language understanding involves more than just recognizing words, it’s understanding the context, intent, and meaning behind that language.
This brings us back to NLP which AI systems use to understand the structure and meaning of sentences, extract relevant information, and derive insights which then produce the output to the user.
What’s Most Important For You To Know
Every aspect of these 5 pillars contributes to the materialized AI application. Like any project, while these components can build the system, the premise needs to be focused on a specific use case. Yes, businesses need to have an AI strategy to survive the rapid change of the business landscape but to rely solely on its implementation is irresponsible.
An AI strategy needs to align with the business, for instance, IKEA built a visual experience for consumers curious about their products. This use case addresses a specific customer need and enhances the overall shopping experience, something that will allow them to remain ahead in the future. How can your company do the same?
Written By Ben Brown
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ISU Corp is an award-winning software development company, with over 17 years of experience in multiple industries, providing cost-effective custom software development, technology management, and IT outsourcing.
Our unique owners’ mindset reduces development costs and fast-tracks timelines. We help craft the specifications of your project based on your company's needs, to produce the best ROI. Find out why startups, all the way to Fortune 500 companies like General Electric, Heinz, and many others have trusted us with their projects. Contact us here.
