Midnight, December 31, 1999, marked the beginning of a millennium where aircrafts would not fall out of the sky and bank ATMs would not spew millions of dollars from their cash withdrawal slots. Preemptive software testing and remediation on a global scale provided the automated world with some assurance that the machines would continue to run as they had for decades.
Deep learning has become a buzzword in the field of artificial intelligence (AI) in recent years. It has achieved impressive results in a variety of tasks, including image and speech recognition, natural language processing, and even playing games.
The cost of DNA sequencing continues to decline exponentially. With the average cost of sequencing mammalian DNA hovering around $1,000 in the beginning of 2023, startups like Ultima Genomics and Illumina are working to decrease the cost to between $100-$200. That’s about the same as a new pair of Brooks running shoes! As the sequencing cost drops, the quantity of genetic data to study and analyze explodes, making it even more important to leverage machine learning techniques.
The promise of Artificial Intelligence (AI) in software testing is that an intelligent agent will one day replace humans. Instead of the struggle of manual labor involved in the endless unit and integrated proofing of software quality, machines will test computer systems without human intervention.
We are going to look at how performance testing can work hand in hand with Artificial Intelligence and Machine Learning: As there are many Artificial Intelligence solutions to choose from and for the purposes of this post this is the easiest way to discuss the principles of Artificial Intelligence working with performance testing rather than discussing a particular framework.
Improving patient care is right up there with the importance of optimizing the allocation and efficiency of precious resources when it comes to today’s healthcare. But that’s been difficult for people alone to accomplish, even with automation. The good news is that machine learning is now addressing these challenges and a company called Iodine Software is leading the innovation.
As the field of software development continues to evolve, one cannot help but wonder about the future of the profession. With the integration of AI tools, like ChatGPT, and Machine Learning, tasks that were once exclusive to human developers are now being handled by machines. While this has the potential to greatly increase efficiency and productivity, it also raises important questions about the future of the developer profession. How will developers adapt to this new reality?
