CognitiveBricks is a proprietary library of AI algorithms, which works on the concept of building blocks or bricks, optimized for computing, power and memory usage that works with higher accuracy as compared to open source algorithms. We can curate an AI-based cognitive platform tailored for you.

AI (Artificial Intelligence) is a technology that feels like it came out of a comic book. What we once considered to be the future, is here now. AI as we know it today has footprints that date back to the classic philosophers, who attempted to explain human thinking as a symbolic system. However, the term AI was formally coined in the year 1956, at a conference at Dartmouth College in Hanover, New Hampshire. In a report by PWC, it is stated that AI-enabled activities could raise the global GDP to 14percent by the end of 2030, which sums up to $15.7 Trillion. This is evidence of the potential that AI software development has today and in the future to come.

This post is the first in a series that will highlight the similarities and differences of AI software development with regards to non-AI software development. In this article, we will focus on the software architecture of a complete AI solution. Developing Artificial Intelligence (AI) software components using techniques such as Deep Learning (DL) or Machine Learning (ML) implies some changes in the way you produce a software solution. In "traditional" software development (later written non-AI software), software engineers write source code in a programming language (python, java, C, etc.) to implement an algorithm. On the other hand, AI software development does not involve that much coding.

Coders are busy these days as entire software infrastructures transition to the development and deployment of applications incorporating AI. Thankfully, there are powerful tools to help. Google Cloud, for example has recently added to its AI Hub launched in April in response to concerns about reducing redundant AI development efforts, and managing a growing number of machine learning tools. The added collaboration tools are aimed at promoting greater collaboration of data science and machine learning developers, as they manage their pipelines and trained models, as described in an account in Enterprise AI. Enhancements to the hub are said to allow great sharing of trained ML models and pipelines from the Kubeflow workflow automation tool.

Statistics state that 38% of companies have already invested in AI and using AI technologies for various operations, meanwhile, 62% of them plan to use those in the next 12 months, i.e. by the end of the year. Helped by the power of big data and cloud computing, AI is revolutionizing the digital world faster than anyone can imagine. We are already witnessing the change- from Amazon's Alexa to Google Photos and Tesla's self-driving cars. However, how does AI effect the development of the software that underlies most of these services? What are the things that from the perspective of a developer, that are necessary to be considered for AI software development? Each year the amount of data produced is doubled in amount and it is estimated that by the next decade there will be 150 billion networked sensors.

Nevertheless, a growing number of AI-based software tools are becoming increasingly available for business leaders. Many organizations create smart business applications developed on top of tools that Google, Apple, Amazon, and other tech corporations create. Amazon's Alexa has already solved a complex problem of speaker-independent voice recognition, and its noise canceling technology allows using voice commands in noisy places. In a business setting, Alexa is widely used for a wide range of tasks on command, e.g. to start a meeting, control the equipment in a conference room or notify an IT department of an equipment issue. Whereas AI platforms such as IBM Watson can help you easily integrate AI into your application to store, train and manage your data in the secure cloud.