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According to Gartner, by 2025, at least half of the organizations in the world will have devised their own Artificial Intelligence (AI) platforms. AI will become the top category driving infrastructure decisions, due to the rapid growth of the AI market. Now that artificial intelligence is actively entering our lives, it is high time we sorted things out about this emerging technology. 

How is AI different from traditional computing? This is the most important thing to understand before digging into the topic. In traditional computing, we enter data, add an operator or a rule or model and get an exact answer as an output. The best example here would be a simple mathematical operation. Say, our data is numbers 3 and 5. We add an additional operation and get an exact answer which is 8. With AI, things are different. What we enter here is the data and the answer, to get an operator as an output. That is, if we enter 3 and 5 as the data and 8 as the answer, the computer will tell us how we arrived at 5 and give us an operator – addition. In other words, AI does not simply do what we tell it to do, but it makes decisions by analyzing data and applying logic-based techniques. 

Treat It Like Your Baby 

Human intelligence provides us with a capacity for identification, comprehension, classification, decision, and prediction. Due to our intelligence, we can get things done, communicate, behave properly, and make decisions. AI can make decisions and take action too, just like humans. And just like us, before it starts making the right decisions, it needs to grow up. AI can be compared to a baby who develops its intelligence first through the five senses and then during the life-long journey of learning. Artificial intelligence, though being intelligent, has not advanced beyond humans. It also has to learn things.

As mentioned earlier, in traditional coding, we make the computer “learn by instructions”, while with AI, it “learns by example”. Let’s understand this by comparing AI to teaching swimming. When you teach a kid to swim, you don’t just give them instructions like “float with your face in the water” or “stack your hands.” The best way to do it is to show how exactly they should move their body, arms, and legs not to drown. So, the famous pedagogical method “show by example” can be well applied to artificial minds too, but we need to provide examples in the way AI understands – we provide data. 

While humans grow their intelligence through the five senses, life experience, education, reading, TV, and other things, AI develops itself by learning from the data we feed it. Applying mathematical models to data, it extracts knowledge and finds patterns to predict the result. The more patterns it identifies, the better its problem-solving skills. 

AI Like Cooking 

Operating artificial intelligence is very similar to cooking. You should know what dish you want to make, and a recipe for this dish. You should also decide what appliances and tools are best to use and prepare the ingredients needed.  

The ingredients, or data, are the very essence of AI. You need to acquire data from various places – CRM, SAP systems, or marketing databases. The process of data acquisition is as important as selecting vegetables for a salad – both should be fresh and relevant. 

The tools we use for cooking are the platforms and algorithms on which we build our AI while the recipe is neither more nor less than a model or a strategic formula we apply to the data. In order to get the expected output and not spoil the ingredients, you need to find the right platform and the right model, given that there are thousands of them.  

The dish we finally cook is the outcome we expected from our AI – the predictions, recommendations, or any anomaly detection – all those things AI was supposed to help us with. What is next? Longing for praise, we offer the dish to our guests or family members. “Maybe try to add a lemon here,” “It’s a little bit too salty,” “Great with merlot, actually!” – you learn from the feedback and next time try to make them all long for more. Similarly, AI will learn from its experience and retrain.  

Understanding AI Terminology 

There are many different definitions of AI. If we put it simply, artificial intelligence is any technique that enables computers to mimic human behavior. One of the critical techniques that enable AI to solve problems is machine learning (ML), which is the ability to learn without explicitly being programmed. ML algorithms learn through training by identifying patterns in data and using them to provide new predictions and recommendations. One of the variants of ML algorithms is deep learning, which solves problems by extracting patterns from data using neural networks. In other words, it recognizes different kinds of patterns, just like a human does. Deep learning can outperform traditional ML working with such complex data as images, speech, and text. 

Though AI can learn “on its own,” we still can talk about supervised and unsupervised learning. Supervised learning involves a humans training the computer to recognize patterns. For instance, if the task is to identify cats and dogs in the pictures, a human gives the system a training dataset – a set of examples where the cats and dogs are identified and labeled by themselves. This way, with a human as a supervisor, the machine will learn how to do the task independently. 

The process of unsupervised learning happens without humans being involved, meaning that the machine is forced to find patterns on its own, with the data being untagged. This learning type is relevant for cases where humans cannot identify the pattern by themselves and have to rely on the aid of the computer. 

If you ask your virtual assistant Alexa to tell you the weather, it will, because it is trained to do it. This is what a pre-trained model is. Ask it to order an Uber for you, it will. But what if you ask Alexa to name your favorite movie? Most likely, it will not have an answer because nobody has trained it on your personal context. So, a pre-trained model is a model which is pre-trained on a certain set of data. For example, it can be trained on the English language, Wikipedia, or some specific domains, like banking accounts.  

Technology of the Present 

Artificial Intelligence is no longer just a technology of the future. It is steadily entering our everyday lives: digital personal assistance, music recommendation services, purchase prediction algorithms, self-driving cars, and more. Over the next five years, organizations will be adopting innovative techniques for smarter and more reliable, responsible, and environmentally sustainable artificial intelligence applications. For years, Orion has been helping firms all over the globe to implement the best practices of AI. We apply Deep Learning techniques to create leading-edge data-driven solutions that will transform your business.  

Contact us to learn more about our expertise and services in AI and Machine Learning. 

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