How to make AI work in QA
AI has radically changed Quality Assurance, breaking old inefficient ways of test automation, promising huge leaps in speed and the ability to test things we otherwise couldn’t easily test before.
The no code rubric covers software that is generated through means rather than programmed directly, typically using adaptive behaviors that come from human interaction.
AI has radically changed Quality Assurance, breaking old inefficient ways of test automation, promising huge leaps in speed and the ability to test things we otherwise couldn’t easily test before.
Interview with Patrick Stingley During this very special 6th episode of the AI Think Tank Podcast, I had the honor to speak with Patrick Stingley, a… Read More »Zero Trust Architecture and AI
“Our goal is to redefine the interaction between technology and personal data. We envision an AI that is not just a tool used by the masses but an extension of the individual, respecting their privacy and enhancing their autonomy.” -Reza Rassool
For our 4th episode of the AI Think Tank Podcast, we explored cybersecurity and artificial intelligence with the insights of Tim Rohrbaugh, an expert whose career has traversed the Navy to the forefront of commercial cybersecurity. The discussion focused on the strategic deployment of AI in cybersecurity, highlighting the use of open-source models and the benefits of local deployment to secure data effectively.
Introduction The fast-paced digital world requires effective and user-friendly software solutions more than ever. Custom software development is changing business communication, especially email design. This… Read More »Custom Software Development for No-Code Tool Email Design
Low code development is getting a lot of traction. Low code platform development provides a more accessible (and typically a graphical) interface for developing applications.
If you are employed as a data scientist and have survived (or strived!) in your position for more than a year, chances are you are… Read More »18 Differences Between Good and Great Data Scientists