Artificial Intelligence

Software development (but also the development of Machine Learning and AI) is combat against chaos and complexity. It is bad enough when software becomes chaotic and does unexpected things, but it is even worse, when software becomes too complex to be fixed.

I have a vision. A vision where my digital assistant’s only task is to understand me, to support me, like a head butler in the household. Where it is in control of an army of specialist AI agents, each with its own task to make my life easier. To me this is the true digital happiness: innovation to make my life easier.

One of the things that are important to consider when you’re testing artificial intelligence is the possibility that you have unintentional bias in your training data. Because if there is, you’ll end up with a biased system… So, how can this happen and what should you look for?

This article showcases the AI capabilities of Capgemini Engineering Services. It aims to add unique differentiation of “high availability” feature in Distributed AI context.

The amount of software systems that are using artificial intelligence (AI) and in particular machine learning (ML) is increasing. AI algorithms outperform people in more and more areas, causing risk avoidance and reducing costs. Despite the many successful AI applications, AI is not yet flawless.

In this post, I explore an approach to help organizations develop a roadmap in their journey from BI to AI.

Are you better off if AI took over your routine tasks so that you could focus on higher value-added activities?

Today, Artificial Intelligence (AI) is far more than a buzzword in the business world. Let’s put aside what is possible with AI and look at what is the foundation of an AI solution? Yes, you have got it right – the answer is data!

According to this McKinsey article, 70% of the organizations might adopt at least one form of Artificial Intelligence-based technology by 2030. By the end of the same decade, PWC predicts that AI could contribute up to $15.7 trillion to the global economy, which is more than the current GDP of India and China put together. Perhaps no other technology has been rapidly absorbed as AI but there are challenges at different levels as we adopt AI enabled applications and devices in our daily lives.

Artificial Intelligence (AI) in one guise or another is beginning to permeate our everyday activities.  Whether it’s a visit to the doctor’s office to get an initial diagnosis of eye problems or trying to find a new job, AI is either already making decisions on your behalf or will be in the not too distant future.

Sogeti's annual TextExpo event discussed the future outlook for the software testing industry, led by top professionals in the field. Read about my compilation of how artificial intelligence, machine learning or intelligent systems affect testing now and in the future.

We all know the highly advanced algorithm Google employs to test the millions of pieces of content pieces submitted on its platform every day. But, recently an article mentioned that Google is now planning to hire human readers in the backend to ensure the quality of the content. This strengthened my belief in human testers, who still have an important place in the testing value chain, in spite of growing concerns that automation will take over the role of human testers.