Artificial Intelligence (AI) Software | How to Create ?

Artificial Intelligence (AI) Software, How to Create?

Introduction

Artificial intelligence (AI) has been present for over half a century. Today, AI technology is widespread and this technology is the foundation for innovative “successors”, such as neural networks and deep learning. AI is a reality that is here to stay for the foreseeable future. However, developing AI apps and other digital products is quite challenging due to the rapid advances in technology. This blog is a guide that tells you how to develop AI efficiently.

Artificial Intelligence (AI) ?

In a broad sense, AI refers to the capability of machines to imitate the creative and cognitive function of the human brain and intellect. This concept is applied to a wide range of innovative services and products today. The most prominent breakthroughs in AI practice are connected with Machine Learning (ML).

Successful Examples of Artificial Intelligence (AI) Integration into Software

There are numerous examples of AI implementation and the following are some successful projects:

  • IBM Watson

    : This IBM research project for business requirements is a portfolio of pre-built tools, apps, and runtimes. Watson reduces the costs and hurdles in AI adoption and supports Natural Language Processing (NLP).

  • Microsoft Cognitive Services

    : This is a platform of ML/AI implementation to integrate with Bing capabilities, such as spellcheck, autosuggest, entities, image, news, video search, and so on.

  • Google AI for Social Good

    : AI for Social Good is an enterprise focused on non-profit engineering and research in AI.

What do you Need to Start

The first step for developing AI is to identify what you need to start. Following are the essentials to start developing AI software:

  • Data

    : To create an AI software, one needs a dataset – data for execution.

  • Platforms

    : Multiple AI platforms provide developers with ready-made tools for product building. Commonly used platforms to develop AI software are:

    • Google’s platform
    • Microsoft Azure
    • Amazon Machine Learning
  • Programming Languages, Frameworks, and Libraries

    : The programming languages used commonly for AI apps are Python, Java, and C++.

How to Build a Software With AI

Creating ML/AI solutions is an iterative process. The development pipeline in its basic form can be represented as:

  • Research, discovery, and team planning;
  • Data mining
  • Modeling
  • Minimum viable product (MVP) and product-with-improvements development
  • Launching and support

Team Planning

For a comprehensive product, one has to hire a great team for:

  • Management and research – business analyst and project manager
  • Data analysis —dataset markup team, data scientists, and ML engineers
  • Development —frontend and backend developers, solution architect, CV, NLP, ML Ops, and DevOps engineers
  • Testing — Quality Assurance (QA) engineers

Data Mining and Modeling

Even the advanced algorithms need suitably collected and prepared data. Mostly, engineers use the Cross-Industry Standard Process for Data Mining (CRISP-DM) comprising a sequence of steps:

  • Business Understanding
  • Data Understanding
  • Data Preparation
  • Modeling
  • Evaluation
  • Deployment

Engineers, typically apply these steps and repeat them multiple times (besides deployment). There is nothing except a project description in the discovery phase and business and data understanding enable one to understand the issue and identify its solution. The data preparation stage consists of choosing and uploading raw data, highlighting, picking annotation tools, labeling data blocks, choosing and saving file formats.

The dataset enables one to compare solution options chosen by the client. For this type of problem, the metric choice for model comparison is essential. One should establish the success criteria and select an option that is appropriate to the client’s business goals. The next step is modeling. Previously collated data is used to train ML models via various methods.

Conclusion

If you want to adopt AI for your business, it is best and most appropriate take the help of professionals. A company focusing on AI-based software development can find the most effective, customer-centric and resource saving solution for you.