Software engineering implies writing codes that are capable of solving deterministic problems applying logic. But owing to the recent growth of AI, the way software programmers program codes have changed recently. Thanks to the revolution of AI and machine learning in software development for problem-solving, using AI software tools has changed the mindset of the programmer.
The evolution of AI has changed the software development circuit for real. Artificial Intelligence has facilitated the processing of large amounts of data and it is an application of individual technologies operating in tandem allowing the computer to perform actions generally reserved by humans.
AI Software Intelligence has been helping the software development arena in various verticals:
How AI is used in Software Development?
AI can be used in Software Development for a multitude of purposes:
1. Automated Debugging
AI software and tools can be used in automated debugging through pattern detection for an in-depth analysis, identification, and categorizing of error types. The deep learning algorithm can easily flag-off errors and speeds up the debugging procedure.
It can even go toe-to-toe with software developers and even learn how to fix each type of bug. Post-training, the machine will be able to automatically spot and correct a huge number of mistakes. Think of it as similar to the text auto-correct feature in smartphones.
I’m comparing it to text auto-correct because AI software suffers a similar issue as seen in the former- the same annoying effect of correcting that user might experience especially in the case of no correction requirement.
2. Smart assistants
AI software assistants are gaining popularity day by day since they intelligently fasten the development process. It even helps novices to learn about the development environment more speedily than the trial and error method.
AI software assistant tools can play the role of a trainer and come up with recommendations, offer code examples and prevent simple coding mistakes like closing parenthesis.
A great example of this is Kite, an assistant developed for Python.
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3. Automate Code Generation
Coding from scratch is time-consuming and labor-intensive. To combat the issues of money and time constraints, researchers have built programs that can write code beforehand. But these solutions are not very good with ambiguity.
In this case, AI software programs came into the picture. It will reduce the workload by a significant amount. Once it masters the underlying patterns, more than code completion, an AI-based software could generate a program by putting together some predefined modules just like LEGO pieces.
In the future, AI software has the potential to replace the task of junior software programmer. This could be the baby steps toward a self-programming machine.
Throughout the development cycle, testing has been one of the most crucial elements while you build a quality software product. One of the major challenges that software programmers face while testing out software products is creating a thorough list of use-cases as well as situations that could have an impact on the program’s performance.
AI software tools can perform this by going through the previous logs and automatically generating a list of test cases for running through the system. It can also predict the outcomes of testing without even performing the actual tests.
It only focuses on the solution, hence saving a huge chunk of time.
5. Designing strategy
Usually, software developers spend a significant amount of time deciding the features and functionality to be added to a software product.
AI software development tools trained on past software development projects and business factors can analyze existing software and can find a solution, fulfilling the requirements for both the clients and software engineers.
It can produce a hierarchy of the best features for the success of a solution based on user reviews of similar solutions and client marking.
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Popular Software Development Tools using AI
Now that you have gone through the differences that using AI makes in Software Development, it’s time for some AI-developed software tools that one should consider adding in their toolbox:
TensorFlow is an open-source numerical computational tool. This machine learning library is primarily for production and research.
It is backed up by community support and hence the functionality of this tool is pretty decent. It utilizes the arrangement of a multi-layered hub that enables you to rapidly set-up, train, and send counterfeit neural systems with huge datasets.
The only downside is that it takes time to learn this AI Software.
The AI-based solution can be deployed on the following:
- Edge Devices
- CPUs, TPUs, and GPUs.
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H20.AI is an open-source deep learning platform. It is an AI software that is business-oriented and helps them to make a decision from data and enables the user to draw insights. There are two open versions of it: one is standard H20 and the other one is the paid version known as Sparkling Water.
H20.AI is used for predictive modeling, risk and fraud analysis, insurance analytics, advertising technology, healthcare, and customer intelligence.
Cortana is an AI-enabled voice-search assistant which performs varied tasks such as answering your queries, setting reminders, and others.
Its voice recognition feature proves to be a time saver and also supports different operating systems such as Windows, iOS, Xbox, and OS.
It supports different features such as:
- Supporting a wide range of tasks like switching on the light to placing an order.
- Takes voice inputs.
- Uses Bing Search Engine.
- Support multiple languages such as French, German, English, Japanese, and much more.
4. IBM WATSON
IBM Watson is a question-answering software. It gives support to SUSE Linux Enterprise Server 11 OS using the Apache Hadoop framework.
Post-training your model with Watson, it deeply understands the core notions. IBM Watson is a robust system that assists in making business processes smarter.
Some of the predominant features of IBM Watson are:
- Supports distributed computing.
- It can operate with existing tools.
- Provides an API for app development.
5. Infosys Nia
It accumulates organizational data on the legacy system and piles it up into a self-learning knowledge base. One that business analysts and software developers in your team can leverage for creating high-performing, scalable ML models. And the team could do all of this even if they don’t have data science expertise.
Some of the predominant features of Infosys Nia are:
- Extensibility: for data preparation, visualizations, and machine learning methods.
- Self-Service Provisioning: Elastic Cloud Deployments.
- GUI-based features enable your AI solution team to build precise ML models.
- Integrated Enterprise Framework: for data preparation, deployment, and modeling.
- Streaming Fast Predictions: Infosys Nia Prediction Server.
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Deeplearning4j is an open-source, customizable, and distributed deep learning library written for Java and Scala. It is developed as a plug-and-play AI software for faster prototyping.
Some of the predominant features of Deeplearning4j are:
- It can be utilized in business environments on distributed GPUs and CPUs.
- Tailored to fit into a micro-service architecture.
- It supports GPU for scaling on AWS.
- Python, Java, and ScalaAPIs.
- Scales on Hadoop.
- Imports neural net models from other frameworks — Caffe, TensorFlow, Theano — via Keras.
- It comes with a cross-team toolkit for DevOps, data scientists, data engineers.
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7. Google Assistant
It is a virtual assistant by Google which can be utilized on smartphones and smart devices. It supports the following Operating Systems- iOS, Android, KaiOS.
It even supports different languages like Italian, German, French, English, Hindi, Swedish, Russian and more.
Some predominant features of Google Assistant are:
- Browse for the information on the internet through the powerful Google search engine.
- Supports two-way communication.
- Schedule Events.
- Set Alarms.
- Capable of displaying your Google Account information.
- It can perform hardware settings on your device.
- It can recognize songs, objects and read visual information.
This is one of the best Artificial Intelligence tools that allow you to create and train a model using data. Next, you are able to generate new predictive insights based on that same model or use it in your business as-is. One of its biggest strengths is how easy it is to train a big dataset quickly.
Even though BigML is a paid service, there’s no limit on data or features, and training time for datasets can be completed within minutes.
It’s perfect for companies who don’t have expertise in ML (Machine Learning) but need to try out AI solutions, develop prototypes and understand large datasets at scale.
Some of the key features of BigML:-
- Built machine learning algorithms
- Flexible deployment options
- Visualizations and Insights
- Integrations with popular programming languages
- Low-code interface for ease of use
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Microsoft’s artificial intelligence (AI) platform is called Microsoft Azure AI Platform. It encompasses an array of tools and services, including a set of pre-trained neural networks that can be utilized in a broad range of use cases.
This includes recognizing objects and patterns in video streams, translating between languages, text analysis, and sentiment analysis, as well as image tagging. The data layer contains services for both structured and unstructured data.
The former uses SQL, while Spark provides support for batch processing large volumes of unstructured data from document management systems or other sources like IoT devices.
The Key Features Of the Microsoft Azure AI Platform:-
- Built machine learning algorithms
- Data labeling services
- Pre-trained models
- Custom model training
- Model management and deployment
- Integrated development environment (IDE) support
- Real-time predictions
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Salesforce’s Einstein is a set of artificial intelligence and machine learning tools that are designed to offer developers an easy way to add these capabilities to their Salesforce apps.
The main tool, Einstein Vision, is designed to analyze images and then recognize objects within those images. The platform is designed to understand individual objects and the relationships between them.
For example, it can tell you that it sees a picture of a cat sitting next to some food on a table. Then, it can classify both images and objects, so you know exactly what you’re looking at from image A (cat) or image B (food on the table).
It sounds easy enough, but there’s quite a bit of power behind these tools, and they work incredibly well.
Key Features of Salesforce Einstein:
– Pre-trained models
– Custom model training
– Easy to use interface
– Support for a wide range of programming languages
In the end
With this, I have come to the end of the blog. These are some of the top AI software development tools that you can look forward to as a platform for software developers to solve real-life problems which will make the underlying architecture better and more robust.
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FREQUENTLY ASKED QUESTIONS
What is AI software development?
Artificial Intelligence software development is a field of computer science that focuses on building intelligent systems. This includes developing computer programs that can perceive and understand their environment, plan and make decisions, solve problems, learn from experience, etc.
These systems are used for a variety of practical applications such as planning and scheduling, manufacturing, controlling robots and other devices, aircraft or vehicle navigation, medical diagnosis, and treatment.
The scope of AI is restricted to tasks that are amenable to statistical or rule-based methods; however, in practice, it often involves many other techniques, including neural networks, case-based reasoning (CBR), natural language processing), machine learning, among others.
What are the benefits of using AI software?
Artificial intelligence software can help solve some of today’s software problems but also increase development costs. The following are some possible benefits and challenges of using artificial intelligence in your project.
In some cases, more information on how to work with these technologies is available. This should assist you in making a more informed decision about what you would like to include in your projects.
However, if you require additional service or want to learn more about any of these prospects, please contact us. We are always pleased to discuss ways we can help your business become even more successful!
What challenges occur while AI software development?
When using artificial intelligence software, there are two critical challenges you must overcome. The first is to develop algorithms that define how an application functions.
You’ll then need to write code that manipulates data and transfers knowledge among your artificial neural network nodes or units of computation.
You’ll also want to test your artificial intelligence software and tweak your algorithmic and coding strategies as you work out potential bugs in your designs.
Finally, there is what’s known as explainability. Machine learning systems often use deep learning approaches in which insights are extracted from various levels of abstraction; human programmers can explain why a particular classifier performs well but not how it came up with its conclusions.