What is Artificial Intelligence (AI)?
Artificial intelligence technology is the simulation of human intelligence for machines, particularly computer systems. This is achieved by using a combination of hardware and software, which communicate with one another in order for the machine to receive and process the information given to it. The type of information the machine receives includes various types of complex code, logic, algorithms, and more, all of which are designed to dictate how the machine works within the context of other machines and humans.
For example, a machine learning algorithm may be asked to perform a very specific task, such as transport materials onto a conveyor belt, or it may be comprised of more sophisticated coding and logic, where the machine has the capacity to perform a wide range of tasks, learn from its mistakes, mimic the behaviors of other people, and potentially even reach its own conclusions – or learn how to perform new functions – based on past experiences.
How capable a machine is of simulating human intelligence comes down to the quality of the machine itself and the quality of the code used to instruct the machine.
How exactly does AI work?
Generally speaking, AI technology ingests large amounts of data and then use their computing power to interpret the data presented to them, make decisions based on the provided information, and potentially even predict future behaviour or events by being able to identify patterns in the data.
This kind of machine learning is already present in chatbots, where the software is fed data from users (i.e., in the form of questions, responses given, and general comments), to which the software then learns how to respond accordingly to different text prompts. When taught correctly, chatbots can be designed to provide better customer service to online users by being able to answer their questions promptly and correctly, guide the user to other useful sources of relevant information, and even arrange for the user to be transferred to a human customer service representative for further help.
Types of machine learning algorithms
There are many ways we can train machine learning algorithms to function in various real-world applications. This involves applying specific rules, guidelines, and procedures when it comes to training the machine learning algorithm. How the training is applied will have a major influence on not just how well the algorithm learns and adapts but also how much manual labor is required to help it learn.
The following types of machine learning algorithms are:
Supervised learning involves giving the machine learning algorithm labeled data to learn from. For this to work, the data must be labeled accurately. It also requires regular manual labor in order for the algorithm to gain an idea of how the data works and the relationship between the different types of data. But when done properly, supervised learning is an extremely effective way to help machine learning algorithms to adapt and improve the way they function.
Unsupervised learning involves giving the machine learning algorithm unlabelled data to work with. The benefit to unsupervised learning is that there is no need for manual intervention in order for the algorithm to learn. As a result, it allows for a much larger dataset to be worked on, and it also enables the algorithm to determine the relationship between different data points in a more abstract manner. Of course, the potential downside is that it could lead to the algorithm reaching unpredictable conclusions in regards to how it perceives the data presented to it.
Reinforcement learning takes inspiration from how human beings learn from data. It involves applying an algorithm that improves upon itself and learns from new situations over time, applying a sort of trial-and-error approach. As a result, favorable outputs are encouraged or reinforced, while unfavorable outputs are either discouraged or ‘punished.’ This is also similar to the ‘conditioning’ approach to learning, where a given stimulus becomes more effective over time in evoking a specific response (for example, conditioning a dog to associate the sound of a ringing bell with being given food).
The four must-have technologies for developing AI systems
Building sophisticated AI technology requires a lot of software. Developers need to use a combination of different frameworks, tools, libraries, and plugins in order to create AI systems that have the capability to give machines a realistic simulation of human intelligence.
These days, there is a wide variety of tools and software to choose from. Many of these tools are highly compatible with one another, which makes it easy for developers to pick and choose the right tools for them and then implement them in a way that helps them achieve their desired outcomes. Most importantly, the easier it is for developers to create complex and useful AI systems, the easier it will be for businesses to integrate AI into their products and services.
Below is a quick breakdown of some of the most used AI technologies out there:
TensorFlow is a Python-friendly open-source library that enables developers to write, develop, and implement effective and user-friendly machine learning models. Created by Google, TensorFlow is designed to streamline the many processes required to build an AI system, such as acquiring data, training models, serving predictions, and refining future predictions. Since TensorFlow is built off the back of Python, it is easy to use, predictable, lightweight, and incredibly versatile, giving developers near-limitless possibilities in terms of building AI systems tailored to a business’s individual needs.
Keras is a high-level neural network API built off the back of TensorFlow as a means to streamline the AI building process by promoting fast development and experimentation for better results. Keras simplifies many of the tasks you can perform in TensorFlow, such as build modules like neural layers, optimizers, activation functions, and regularisation schemes to name a few. It is also very user-friendly and supports a range of production employment options, including five back-end engines (TensorFlow, Theano, MXNet, PlaidML, and CNTK).
spaCy is a type of Natural Language Processing (NLP) technology. It is designed to make it easier for machines to understand, process, and produce language in the same way that a human does. spaCy automatically extracts information from machine-readable documents, and it features a wide range of tools to assist with NLP tasks, such as Tokenization (The process of breaking down a portion of text into words, commas, spaces, symbols, and so forth), Part-of-Speech Tagging (Giving individual words grammatical properties such as nouns, adjectives, and adverbs), and Entity Recognition (Categorising words into differently labeled entities. For example, labeling certain words as events, people, places, and nationalities).
PyTorch is a type of deep learning library. Deep learning is a subfield of machine learning, where the algorithms used are inspired by the way in which the human brain works. They are referred to as artificial neural networks. PyTorch specializes in Tensor computation and GPU acceleration as well as automatic differentiation for building and training neural networks. Put simply; a tensor is a mathematical object used to describe physical properties. Therefore, tensor computation is the process of calculating the arithmetic operations within those tensors, which helps developers manipulate them using tools like Python.
Advantages of Artificial Intelligence (AI) technology
As AI systems become better at processing, interpreting, and turning data into actionable information, both businesses and society as a whole will continue to integrate AI into society in a way that offers numerous advantages. Some of the most important advantages include the ability to:
- Automate highly detailed, repetitive, and even physically strenuous tasks without requiring human intervention.
- Prevent human workers from having to work in potentially dangerous environments – i.e., Working near hazardous waste, saving victims from collapsed buildings.
- Businesses can deliver consistent results on a more frequent basis by operating 24x7.
- Enable the execution of highly complex tasks in less time and reduced labor costs.
- Facilitate the decision-making process much faster and more accurately.
Engage the right AI development team
Currently, one of the main hurdles of artificial intelligence is cost, as businesses need a lot of money, infrastructure, and resources in order to build, integrate, and maintain an AI system that meets their specific needs.
However, with the right development team and software solutions, businesses can implement the right AI system for them in the most efficient, affordable, and hassle-free way so as to provide a better customer experience and innovation in their respective field.
Furthermore, AI in the form of big data can help companies gain a deeper understanding of what their customers think of their brand, which can help them reposition their brand in a more positive light and adjust their marketing strategy to better target their ideal customers.
You are looking for an IT partner that can help you utilize the power of AI to help your business be more productive, efficient, safe, and competitive? Contact Orient Software today, and see what our experienced AI development team can do for you.