Legal Knowledge Management and the Rise of Artificial Intelligence
Robotic Refactoring the Workplace
Why Your Next Insurance Claims Processor Could be a Robot
Building an AI Based Machine Learning for Global Economics
The Forgotten Element in Your Big Data Strategy
HK Bain, CEO, Digitech Systems
Natural Language Processing and the Future of Customer Service
Eugene Willard, CIO, LaserShip, Inc.
AI is not just for Silicon Valley: Powering Changes in the Energy...
Chris Shelton, President, AES Next, The AES Corporation
Beyond the Hype - Exploring AI Potential and Pitfalls
Chuck Monroe, Head Of AI Enterprise Solutions, Wells Fargo
Thank you for Subscribing to CIO Applications Weekly Brief
The Know-Abouts of the Advancing AI
FREMONT, CA: As business acceptance of Artificial Intelligence (AI) is expanding rapidly, so is the vocabulary used to define the technology and the myriad ways that companies put it into action. Thus terms like algorithm, machine learning, and neural networks have become as familiar as cloud, SaaS, and IoT today, lots of new AI terms and trends are now entering the field or growing in importance.
Processes that use Big Data analytics to automate IT operations in real time are AIOps. AIOps utilizes data analysis and pattern recognition to allow IT teams, to streamline many of their traditional management functions to maximize the efficiency of their systems. By 2022, Gartner anticipates that 40 percent of large firms will replace existing human-led IT services with automated AIOps systems. Shadow auditing or algorithmic auditing is the method used to classify "blind spots" of AI. As concerns about the hidden bias in AI systems are growing, algorithmic verification is a way to detect flaws in structural design, coding and training data sets and to evaluate the system for consistency, transparency, accuracy, and equity. Such auditing is often used to detect bias in AI tools used in financial services, the criminal justice system, and hiring practices.
Edge AI is used to train ML algorithms on devices such as sensors or smartphones, facilitating faster decision-making and responsiveness in real time. Edge AI removes latency delays and significantly reduces data vulnerability and storage costs by eliminating the need to connect to cloud-based systems. Evolving applications include automobiles, robots, and industrial equipment powered by AI. Human-in-the-loop (HITL) testing is a method to train an ML algorithm to check its results and refine them. An image-recognition system trained on manually labeled pictures requires people to verify and score results accuracy. It is also possible to use the technique to enhance mapping technology accuracy, speech recognition, and product categories. That was all about the advancing AI, transforming almost every sphere of the world in the long run.