Technology has come a long way in the past few centuries. It is simply a field that is constantly evolving. When considering the current trends, Artificial Intelligence or AI is a concept that stands out of the rest. Moreover, artificial intelligence has been taking over the field of software development lately. AI software development is an emerging trend in the Sri Lankan IT industry as well. Hence, we thought of focusing this read on AI software development. Continue reading to know all about it!
What is AI?
Artificial Intelligence, popular as AI, is a concept that demonstrates how to build programs that have very similar characteristics to human decision-making behavior. In fact, these computer programs have the ability to learn various data patterns and insights. Hence, there are many smart systems that have emerged with the help of Artificial Intelligence. And yes, people worldwide use them to run businesses and offices, and even to manage chores at home. Therefore, there is nothing wrong in mentioning that Artificial Intelligence has been transforming the field of technology over the past few years.
Types of AI Software
There are different types of AI software. So, first of all, let’s get to know about these four main types of AI software.
1. Artificial Intelligence Platforms
The main concept behind artificial intelligence platforms is using machines to replace the tasks that are usually performed by human beings. The human mind directs the functions of the body using cognitive processes. So, artificial intelligence platforms also follow a similar structure to stimulate the cognitive functions that human minds perform. Such cognitive functions that these AI systems deal with are mainly problem-solving, learning, reasoning, social intelligence, and general intelligence. Furthermore, it involves other expert systems such as speech recognition and machine vision. Accordingly, we can identify it as a set of services mainly focused on supporting the machine in learning the life cycle.
In order to make this happen, Artificial Intelligence platforms are mainly focusing on subject areas like machine learning, deep learning, prebuilt algorithms, and code frameworks.
Besides, the machine learning life cycle in artificial intelligence platforms mainly consists of 7 steps.
- Gathering data
- Data preparation
- Data wrangling
- Analyzing data
- Training model
- Testing model
- Deployment
Many organizations face a lot of difficulties when it comes to productising machine learning models. That is where AI platforms come in handy. On the other hand, this has made many organizations work more effectively and it is becoming an imperative for business survival.
2. Chatbots
This is a very interesting type of computer program that has the ability to stimulate and process human conversation. Here, the program can process both written and spoken conversations. Accordingly, with the help of a chatbot, humans can interact with a device like they are interacting with a human.
In fact, they can use their voice to give commands to the device and the device also has the ability to respond with a chatbot program. So, now there are such simple programs where the chatbot can respond with simple answers and also complex programs where the chatbot can even provide personalized information. Therefore, the chatbot has become a program that we commonly use in our day-to-day life
Chatbots are of two main types.
- Task-oriented (declarative) chatbots
- Data-driven and predictive (conversational) chatbots
Task-oriented chatbots are highly specific and structured. They focus on one main function and support with service functions. Data-driven and predictive chatbots are digital assistants like Apple’s Siri and Alexa. They are more sophisticated and consumer-driven programs. These chatbots have the ability to use predictive intelligence. However, the most common chatbots are task-oriented chatbots.
Many consumers prefer chatbots nowadays because of their convenience and personalization abilities. After all, chatbots are a revolutionary creation of AI software development.
3. Deep Learning Software
Deep learning software is helpful for data processing. This is mainly a concept that tries to imitate the human brain. While the software does not have the ability to attain the total capabilities of a human brain, this software has the ability to bring more accuracy and speed into data processing. In addition, they can also make predictions and classifications. Hence, deep learning software is useful to gain intelligence from data which in return can be used in innovations as well.
Owing to this reason, many organizations, businesses, research facilities, and universities use deep learning software. Now that automation is taking over the field of technology, people find more ease in finding solutions with the help of deep learning software.
When considering the back end of the deep learning software, it consists of neural networks with multiple layers. These multiple layers increase the accuracy which results in more refined results. They use a combination of weights, data, and bias that gives the ability to accurately describe, recognize and classify objects from data. This kind of software mainly supports AI and MI-based services and applications.
However, the most interesting feature of deep learning software is its ability to perform physical and analytical tasks with less to no human intervention.
4. Machine Learning Software
Machine learning software is able to predict more accurate outcomes even without a program guiding them. The process behind this software is by using algorithms to learn historical data as input to predict new output values. Hence, machine learning software is most commonly used as a recommendation engine. They are also used in fraud detection, spam filtering, malware threat detection, business process automation, and predictive maintenance. Many enterprises use the software to identify trends in customer behavior and business operational patterns.
Leading companies such as Facebook, Google, Amazon, and Uber use machine learning software for their operational processes. Facebook is probably the most popular application that uses machine learning software as a recommendation engine.
There are different kinds of machine learning software categorized according to the way its algorithm learns to become more accurate in its predictions. Accordingly, there are four main types of machine learning software:
- Supervised learning
- Unsupervised learning
- Semi-supervised learning
- Reinforcement learning
Data scientists use suitable machine learning software, in accordance with the type of software they need to predict. Machine learning software has evolved over time under AI software development and it continues to intensify supporting many businesses and enterprises.
Software Development Process with Artificial Intelligence Integration
The traditional processes that were used in software development were quite complex. Therefore, artificial intelligence enhances these traditional processes with high reliability. There are several steps in the machine learning model development process. They are as follows.
1. Problem and Goals Definition
In this first step, it is important to understand the business and how the model would help to improve the current process. However, it should be noted that this step needs human intervention. Usually, the method used in problem and goal definition is natural language processing. The importance of this step is that it helps to understand the user’s requirements. Furthermore, it automatically converts natural software into high-level software models. There are several factors that need to be taken into consideration under problem and goals definition such as availability of the data, applicability, legal constraints, robustness and scalability, explainability, and availability of resources.
2. Data Collection
Data comes as a very important factor in software development. The data that is used in applying ML techniques to software development is called ‘machine data’ and usually they are in the form of logs, metrics, etc. In the data collection step, it is vital to decide how to collect data. In order to find the correct patterns, it is important that the machine learning model has access to reliable data. When the model acquires high-quality data, the accuracy of the model also increases. Similarly, if the data is incorrect or irrelevant, it is impossible to get relevant outcomes or predictions from the model. Therefore, it is essential to get the data from reliable sources. Some characteristics of good data are its relevance, containing only a few missing and repeated values, and the availability of a good representation of various subcategories/ classes present.
3. Data Preparation
This step is all about gathering and preparing the right data. Establishing data reduction, complete data cleaning, data decomposition, and data rescaling are the important steps within data preparation. So first, in order to make sure that the collected data is evenly distributed, they should be randomized as the order does not matter for the learning process. Then, this data should be cleaned properly by removing unwanted data. In this step, you may also need to restructure the dataset. Afterward, it will be helpful to understand the structure of the dataset and the relationship between various variables and classes present where visualization of data comes in handy. Finally, the cleaned data should be categorized into two, the training data set and the testing data set. Testing data is usually just a subset.
4. Model Learning
This is an integral step in a machine learning model where the machine learns to follow the data and not the human instructions. The machine acquires this ability from an iterative and less predictable process. This step also mainly focuses on improving accuracy and efficiency. In fact, the machine can learn to fit specific goals, tasks, or use cases.
Then, the assessment and reconfiguration of model hyperparameters are called model learning optimization. This optimization results in more efficiency in the way the model will perform its tasks.
5. Model Deployment and Integration
This last step is about moving the model into a live environment. Here, it begins to deal with new and unseen data. There are mainly 4 types of techniques in model deployment such as feature engineering, algorithm selection, optimization, and model deployment. In this step, the model starts to actually perform the task that it is trained to do with the data which can bring a return on investment to the business.
Containerization is often used as a tool for machine learning deployment. The benefit of this process is that it makes it easier to update or deploy different parts of the model more straightforwardly. It is also considered a consistent environment for the model to function.
6. Model Management
Model management is a very important step that is responsible for the development, training, versioning, and deployment of machine learning models. This process makes sure that the model is consistent and it meets all the business requirements. Furthermore, model management interprets important features and patterns of the data.
There are several benefits of model management. First, it enables us to proactively address common business concerns. Then, it also enables reproducible experiments. This is usually done by tracking metrics, losses, code, data, and model versioning. It also has the ability to package and deliver models on repeatable configurations to support reusability.
AI-Powered Tools for Software Development
Software applications are very common among many organizations and businesses at present. Since software development is a complex and difficult process, there can be several difficulties and issues that can be quite challenging. Hence, many organizations, including Sri Lankan companies like LakMobile Technologies, sought artificial intelligence for software development as it provides better outcomes. You can find below some such popular tools that are popular in the AI software development field.
1. Google ML Kit
Google introduced Google ML Kit in 2020 after disapproving two of its existing solutions. The beta version of this was available in 2018. This is basically a machine-learning solution that is free of cost. Both iOS and Android applications can use them. Google ML Kit mainly focuses on ‘on-device’ ML APIs.
Furthermore, this has the ability to be a more convenient interface for additional google cloud AI solutions such as Google Cloud Vision API, TensorFlow Lite, and Neural Network APIs.
There are many benefits of using Google ML kit in comparison with other cloud-based solutions. Some of them are latency, data processing in real-time without any network latency, offline capability, and data security, etc. It should also be noted that iOS and android ML kit libraries are published as dynamic dependencies.
2. Tensorflow
This is another popular end-to-end open-source platform for machine learning. It is a free and open-source software library for machine learning and artificial intelligence. This mainly focuses on the training and inference of deep neural networks. The Google brain team first developed Tensorflow for the internal use of Google.
Many researchers prefer TensorFlow as there is a comprehensive, flexible ecosystem of tools, libraries, and community resources which gives them the ability to build and deploy ML-powered applications with more convenience. Another interesting feature of TensorFlow is that both beginners and experts can use TensorFlow to create machine learning models. High-level APIs, availability of numerous languages such as python, javascript, c++, and Java, and easy model building are some of the most sought-after features of TensorFlow.
3. MxNet
This is another popular open-source deep-learning software framework. It is also well-known as Apache MxNet.
This is very helpful in training and deploying deep neural networks. There are many advantages of MxNet such as its scalability, allowing for fast model training, supporting a flexible programming model, and also multiple programming languages such as C++, Python, Java, Julia, MATLAB, JavaScript, etc. The portability of MxNet is also a very efficient feature as it supports an efficient deployment of a trained model to low-end devices for inference. Furthermore, public cloud providers including Amazon web services and Microsoft Azure support it. In addition, MxNet supports both imperative and symbolic programming.
4. Infosys Nia
The next-generation platform of Infosys Nia is a knowledge-based AI platform. This along with machine learning deals with several issues that arise in businesses. Basically, it helps businesses to simplify data management and automate complicated procedures. With the help of this cloud-based AI platform, professionals can get access to data from multiple sources. This in return helps to automate business processes through machine learning. One of the most useful functions of Infosys Nia is that it can allow customers to build custom experiences to suit their business needs. As a whole, this can help businesses to sort out issues within a wide range.
5. H2O
This is also an open-source machine-learning platform. This advanced platform makes it easy to build smart applications. H2O is an AI platform that is well-known for its accuracy, speed, and transparency. The mission of this cloud company is to democratize artificial intelligence for everyone. Many customers use the platform for functions such as rapidly making, operating, and innovating software. This information in return can be helpful for complex business problems and accelerate the discovery of new ideas. This fully open-source distributed in-memory machine learning platform is available in languages such as Python, R, etc. It mainly works on existing big data infrastructure.
The Bottom Line
Artificial Intelligence or AI has provided many beneficial opportunities for the field of software development. Hence, AI has become a necessity in the field of software development today. In fact, it has become a core component of computer science. Looking at the way artificial intelligence has had a huge impact on the digital revolution of the present world, it is kind of easier to predict the impacts on tomorrow’s technology.
In a way, it is only getting started in the field of technology. Therefore, we can only assume that artificial intelligence can have a considerable impact on the way people live, work and communicate in the future. So, it will have the potential to help billions of people to live in a more productive manner. So, it is important to have a general idea of how artificial intelligence supports various sectors of the world.
