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.
Machine learning is a branch of Artificial Intelligence that makes it possible for systems to learn without being programmed. Applying this technology to real world Marketing is often misunderstood and is within your reach.
Great to hangout with Noah Learner and Jordan Choo and talk about automation in agency life, with some thoughts around Machine Learning thrown in there too.
AI and its enabled tools continue to enthrall business with its promise of efficiency and innovation. But, one of the things AI is also clearly enabling is the bias. We’ve all read the news and heard the scaremongering stories around potential flaws and biases in Artificial Intelligence systems. I believe for this technology to reach its full potential, addressing bias will need to be a top priority.
As machine learning continues to address common use cases it is crucial to consider what it takes to operationalize your data into a practical, maintainable solution. This is particularly important in order to predict customer behavior more accurately, make more relevant product recommendations, personalize a treatment, or improve the accuracy of research.
