“You’re only as good as the tools you use.”
A phrase that’s been echoed for many years highlighting the significance of using the right tool for the right job.
Like a painter knows about selecting the right color for their painting, so does the technologists building the AI system. As much as we’ve been harkening buzzwords such as AI and machine learning, do we even know what AI truly is? Or are we all blindfolded by the trending technology that we barely know the logic that runs behind AI?
Therefore, it is certainly important for humans to understand AI and the mechanism behind it. Certain people still don’t know that we’re already incorporating AI in our lives one way or the other. Ironic, yet true.
Here’s what you need to know. Behind AI there’s always those intelligent technologies. Oh, wait, which technologies are you talking about? Well, there aren’t five but six technologies you need to be well acquainted with AI.
Many of the AI jargon include machine learning, predictive analytics, and deep learning. These cutting-edge technologies are transforming the way humans interact with data. Not to mention, it has taken a toll on revolutionizing the jobs market.
The super technologies of artificial intelligence:
1. Machine learning:
Machine learning, the first step most organizations are adding to their AI-related technologies within the IT portfolio. ML, a subset of AI gives the ability to the system to automatically learn and improve from experience without needing to be programmed. Machine learning’s focus is solely on developing algorithms that analyze data to make future predictions.
- Role of machine learning in AI
As mentioned, it is an AI technique that allows the computer system to learn without being explicitly programmed.
Netflix, you’ve probably heard this one – using machine learning technologies, Netflix generates multiple variations of high-probability click-through images (thumbnails) directly interacting with the user. Besides Netflix, Amazon, Spotify, and Facebook use the same AI-related solutions to interact with consumers daily.
2. Deep learning:
Deep learning otherwise called self-educating machines employs artificial neural networks that learn by processing data. The artificial neural network helps mimic the biological neural networks that go on inside the human mind.
- Role of deep learning in AI
With the help of multiple layers of the artificial neural network, single output from multiple inputs can easily be determined. For instance, it can identify the image of a face from a mosaic of tiles. These machines learn from negative and positive reinforcement of the functions they perform. Now, this requires constant processing and reinforcement for progression.
Image recognition, speech recognition, recommendation systems in Netflix and Amazon, and drug discovery and toxicology.
3. Natural language processing (NLP):
NLP helps the computer system interpret, understand, and develop the human language. They do not just speak in zeros or ones; however, the goal of machine learning is to understand what humans speak.
- Role of natural language processing in AI
The core focus of natural language processing is to enable seamless interaction with machines by teaching the system how to recognize and comprehend human language and produce logical results.
The best example is the Skype translator – it helps interpret the speech of multiple languages that too in real-time.
4. Neural Network:
The neural network acts as the enabler for deep learning. Simply said, neural networks are computer systems that have been modeled after the neural connections present in the brain.
- Role of neural network in AI
The neural network learns by processing the training examples. In a similar manner where neurons create neural networks inside the brain, there is apile of perceptron that helps develop an artificial neural network in the computer system.
Imagine the dataset composes of thousands of cat photos. By processing multiple images (inputs) the machine can detect one single output, whether the photo is a cat or not.
5. Cognitive Computing:
Another major component of AI that imitates and improves the interaction between both the machine and human. Cognitive modeling is here to recreate what humans are thinking and compile it in a computer model.
- Role of cognitive computing in AI
Cognitive computing helps in making inferences from the context by understanding the meaning of the images and understanding human language.
With the help of data mining, cognitive computing can be used in identifying risk points such as fraud detection and chatbots. Industries like insurance, healthcare, and customer service have started adopting cognitive computing at every level.
6. Computer Vision:
It is a unique technique that uses deep learning along with pattern identification to make interpretations of images – this includes images within PDFs, tables, graphs, texts, videos, and documents. It is all about recognizing images the way humans can, this includes not just the pixels but what the pixel represents.
- Role of computer vision in AI
Being an integral part of AI, computer visions helps the computers identify the data, process it, and finally make an interpretation of the data.
The utilization of computer vision is already seen within industries such as healthcare and research and development.
The impact of AI is already being felt across the industries. The technology is inevitably transforming lives today.
AI is here to make us more human, and not less human.