A secret to adopting machine learning that has nothing to do with the actual technology. Machine learning has the potential to transform your business. To automate processes, uncover new insights, make your products and services better, and customers happier. Integrating the capability into your organization requires operational transformation and lots (and lots) of experimentation. But, you know this already.
If software's been eating the world for the past twenty years, it's safe to say machine learning has been eating it for the past five. But what exactly is machine learning? Why should a web developer care? This article by Julie Kent answers these questions. I don't have kids yet, but when I do, I want them to learn two things: Whether or not you believe that the singularity is near, there's no denying that the world runs on data.
The theoretical bases for Machine Learning have existed for decades yet it wasn’t until the early 2000’s that the last AI winter came to an end. Since then, interest in and use of machine learning has exploded and its development has been largely democratized. Perhaps not so coincidentally, the same period saw the rise of Big Data, carrying with it increased distributed data storage and distributed computing capabilities made popular by the Hadoop ecosystem.
The introduction of AI into products and services – across all sectors – is creating new capabilities at a scale that software developers could never have dreamed of just a decade ago. But this development is not just about the tech.
In today’s business world, marketing segmentation is a must have for every organisation. It helps you process and aim different targets in a market into multiple customer or prospect segments to enhance your marketing actions. Through this discipline, you can hold a crucial competitive advantage over your competitors because you can adapt your offer and your communication according to the identified groups of personas you want to address.
Artificial intelligence works on the principle of human intelligence. The machines are programmed in such a way that they think like humans and can imitate our actions. They can be designed to execute all types of tasks from complex to simple ones. The primary task machines can perform are learning, reasoning and perception.
We are excited to release Deep Learning for Anomaly Detection, the latest applied machine learning research report from Cloudera Fast Forward Labs. Anomalies, often referred to as outliers, are data points or patterns in data that do not conform to a notion of normal behavior. Anomaly detection, then, is the task of finding those patterns in data that do not adhere to expected norms. The capability to recognize or detect anomalous behavior can provide highly useful insights across industries.
Software Testing has changed a lot! Earlier, manual testing ruled the world of testing, however, test automation increasingly became a reality in most organizations developing software. Testing continued to evolve, and it took advantage of technology innovations. Artificial Intelligence (AI) is one such technology that has made a substantial contribution to automation in general. But, can AI-driven test automation significantly enhance test automation?
Readers of this blog have probably read about allegro.ai’s partnership with NetApp. Earlier this year the two companies showcased an integrated AI solution at the NVIDIA GPU Technology Conference (GTC) which took place in San Jose, California.
