Artificial intelligence, or AI, is a field of computer science that is aimed at developing computer systems or soft wares that are capable of producing value added output from a specific input. AI can be regarded as the comer stone of modern invention. With numerous diversified industries like medicine and commerce experiencing revolutionary transformations due to AI.
The concept of behind the functioning of AI seems to require a lot of intelligence, which may or may not surpass human intelligence, depending on the tasks at hand. This techniques is brought about by a set of methods and algorithms that are used to develop intelligent systems that possess the ability to learn, make computations, identify trends and offer predictions. One could say AI is a clone of the human brain hidden from plain, nerves replaced by algorithms.
Therefore, to make the most of AI, it would help to understand how its techniques works.
Artificial Intelligence techniques connotes the methods, algorithms and data science approaches that allow computers to perform tasks that traditionally require human beings. Such techniques include:
- Machine learning.
- Natural language processing.
- Computer vision.
- Deep learning.
Machine learning (ML)
This is an AI technique that has the ability to induce learning to improve results over time by using algorithms and artificial neural networks to study data. It flatly imitates the human usual learning process, by improving from previous experience autonomously without any need for reprogramming at every turn. This is the actually the guiding principle behind the concept of AI. And with the timely analysis of large amounts of data, it is just as accurate as it is fast.
There are several types of ML with distinct characteristics and applications.
Supervised machine learning
Its no surprise it is called Supervised. This type of machine learning deals with training data that has already been labelled by the operator, making the trending detection and learning process less strenuous for AI. An application of this can be seen in the email spam detection, where a system has to label certain emails as spam. By using a labelled dataset, the system can monitor and learn the patterns to make the necessary decisions.
Semi-superivised machine learning
This is a machine learning algorithm that intermediates between the supervised and the unsupervised learning in that it uses both a labelled and an unlabeled dataset. This is particularly helpful as the manual labelling of data can prove costly, time consuming and resource intensive.
Unsupervised Machine Learning
Unsupervised machine learning is a machine learning technique that uses unlabeled data. Supervised learning does not involve such inputs, unlike supervised machine learning where a trend or similarities is already known from the trained data. On the contrary, it relies on the autonomy of AI to identify and discover hidden patterns, similarities, or clusters within the data. This can aid serendipitous discoveries and foster new inventions.
Reinforcement Machine learning
You have probably heard of an AI that was able to compete and defeat the best chess players in the world. How it do it? This algorithm learns by interacting with the a defined set of actions, parameters or end values. It will then seek to explore different options and possibilities, whilst taking note of the outcomes. The outcome is vital for this learning method as the machine learns from trial and error, success and failure. On discovering a success pattern, the model continually repeats the behavior or pattern using the Reward Feedback.
Natural Language Processing
This method uses algorithms to enable machines comprehend, interpret and generate human language. Despite bridging the gap between human and computer, this model faults as its inevitable conversion of unstructured human language to the computer-understandable binary. This AI technique has solidly proven itself instrumental for virtual assistance, chatbots and language translation tools. One could say this AI technique is popular but it is much easier to say – Who can claim not to know Siri or Alexa?
Forms of NLP include:
Text Preprocessing:
The translation of raw text into a format that machines can understand. In addition, it involves the breakdown and reduction of individual sentences to their most basic form. Then eliminating words irrelevant to the main idea of the text. This leaves only meaningful text that go into further processing
Part of Speech Tagging:
Here, it takes a part of a sentence to understand the meaning behind each individual word. It is mostly used to determine the context in which a word, with multiple meanings, was used.
Named entity recognition:
This is almost like a search engine, where data relevant to a specific input are generated. This generation of data is achieved through the recognizing and extracting of specific items in text.
Sentiment analysis:
NLP analyzes the sentiment in a text. It does this by determining what mood or emotion is being conveyed in an input. In fact, this is irreplaceable when used in social media to determine how the masses feel about a certain topic or a recent event by analyzing their enormous amount of comments about said topic.
Computer or Machine Vision
If natural language was a writer, this AI technique would be a photographer no doubt. Computer or Machine vision teaches machines to comprehend visual data and images to extract information and find patterns to make decisions. However, this makes the ability to find patterns and differentiate between those patterns very crucial to the success of this model.
Sensitivity is an AI application’s ability to pick out small details in visual information. Therefore, the higher the sensitivity, the more thorough the pattern, the more accurate the decision. Details, in the visual world, corresponds with Resolution. This means the little childish riddle above also applies here.
In essence, the resolution of the input and the sensitivity of the AI tool play a larger-than-life role in the integrity of the outcome.
Deep Learning
Lastly, Deep learning. This is a branch of Machine learning which is based on Neural Networks (NNs) architecture. NNs uses layers of interconnected nodes, likened to a neuron, that work together to process and learn from input data. This interconnected nodes occur in multiple layers to model and analyze patterns within data
Furthermore, Deep learning can be applied to the other AI techniques. Deep learning facilitates the recognition ability of objects within images or videos in computer vision. In NLP, deep learning enables the understanding and generation of human language. However, its most influence is in reinforcement learning. There, it generates action based on the reward feedback to increase the patterns frequency.
Conclusion
AI has seen its home in our everyday life. From our house gardening to our Business maintenance. It would, no doubt, help us to understand the reason behind its splendid performance and its limitations. To adapt to the world and better our personal applications of it. AI, contrary to what it may seem, does not seek to replace us. It only exists to empower us.
