In this article, we’re going to dive into the basics of the artificial intelligence, its main sub-practices and how they’re affecting our lives.
While the term isn’t new and has been used in research laboratories for many years, artificial intelligence has become a buzzword due to the increased data volume, advanced algorithms and improvements in computing power and storage. In fact, the creation of the Logic Theorist is an important step towards the development of modern AI.
Machines demonstrate artificial intelligence with the simulation of human intelligence. Trained by humans to do so, machines can demonstrate certain aspects of human intelligence, such as learning, reasoning, self-correcting. Real-life examples of AI are problem solving,speech and text recognition, learning and planning.
There are two major type in research and development work in AI:
Seeks to develop machine intelligence that can turn their hands to any task, much like a human or even better. But, the truth is, most experts say we’re still a decade or two away from achieving true general AI because of its complexity.
Which is highly focused and permeates our world today. It uses principles of simulating human thought to carry out few specific things. However, if you give it another different task, it will fail miserably.
So, let me introduce you to the three basic AI concepts, which we’ll explore together in this article: Machine Learning, Deep Learning, and Big data.
Although AI and machine learning seem to be interchangeable terms, AI is usually considered a broad term, of which machine learning is a subset. So, the main difference between the two is that machine learning is just one of the AI applications, which enables machines/robots to learn automatically and improve from experience.
Above all, the main idea is that computer systems can learn on their own from the information acquired by performing past assignments and experience. So, this implies you don’t have to pre-program the AI device each time you need to work on a specific task.
Machine learning algorithms use statistical information to find patterns in large amounts of data. This data contains a lot of numbers, words, images, clicks, things you own. So, if we can store them digitally, then it can enter into a machine learning algorithm.
Speech recognition is the ability for a machine or program to convert into text by identifying spoken words. Vocal assistants generally use this technology, like Apple’s Siri, Google Assistant and Microsoft’s Cortana, which can respond to voice commands and provide users with relevant information about their queries. Currently, voice assistants can perform services such as processing product orders, answer questions and perform tasks such as playing music, or make simple phone calls with friends.
Machine learning can be employed in procedures and devices that help diagnose diseases.
Statistical arbitrage is a short-term trading strategy that uses a mean regression model. In these strategies, users focus on implementing trading algorithms reached from a set of securities. Based on quantity like historical correlation and general economic variables, rather than implying directional bets or exposure to broader market moves.
Machine learning plays a major role in the financial and banking sector, from approving loans, to credit scores, to managing assets, and assessing risks.
It can help banks and financial institutions to make smarter decisions, spot an account closure before it occurs, track the spending pattern of the customers and perform the market analysis.
Artificial neural networks are one of the basic means used in machine learning. They’re brain-modeling systems designed to replicate the way biological neural networks learn.
Input and output layers compose ANN. The input layer collects different forms of information from the external environment. Then, a hidden layer composed of units, converts the input into a format that the output layer can operate. They are the feedforward network. They’re excellent tools for finding patterns that are too complex or too much for human programmers to extract.
Many fields apply ANN:
This subset of AI refers to the ability to take unstructured data from multiple sources, analyze it, and apply it to solve new problems.
Most deep learning methods use neural network architecture, just like neurons compose the human brain. It’s said that the network is deeper according to the number of hidden layers it has. The traditional neural network only contains 2–3 hidden layers, while the deep network can contain up to 150.
Deep learning systems require powerful hardware because the models are trained by using a large amount of labeled data and neural network architecture. Which can learn features directly from the data without manually extracting features.
Big data refers to massive complex structured and unstructured data sets that have to be processed and analyzed. It includes data mining, data storage, data analysis, data sharing, and data visualization.
And that leads us to the five Vs behind big data:
Read here to learn more about the subject.
Most Big Data is unstructured, for that reason, Big Data requires specialized NoSQL databases with the ability of storing data in a way that doesn’t require strict adherence to a particular model analysis. Organizations and businesses analyze Big Data for reasons like discovering details on buying patterns and trends related to consumer behavior and our interactions with technology, which can then be used to make decisions that affect our lives.
The importance of big data in healthcare and medicine can’t be ignored. It helps doctors, physicians, etc. to maintain reliable tracking of the entire patient’s medical history in some way. For example, if one of the patients goes to the doctor, he can easily obtain the patient’s medical history. In addition, all data obtained for any patient can be permanently saved and stored and doctors can access the data at any time in the future.
Nowadays, in the age of the internet and technology, most courses are available online for students. It’s used to identify educational problems that students must face. Moreover, it can help educational institutions to obtain and store student data. Big data also enables educational institutions to assess student progress and academic development. Also, it helps to collect feedback from students and improve the performance of the institution based on the collected feedback.
One of the major uses of big data is in the banking sector. With big data, banks are able to spot fraud and reduce gaps in their systems. Besides, it helps the banking sector in finding out any misuse of debit cards, credit cards and other services such as enhanced compliance reporting, personalized product offerings and other areas improved thanks to Big Data.
From machine learning to big data, artificial intelligence is changing the world in several ways, having a huge impact on many industries today, and playing a greater role in our daily lives.
As these different sub-practices gradually develop into more powerful functions, artificial intelligence will eventually be able to solve more complex tasks completely and independently and will continue to grow in future for us to leverage them.