Legal Knowledge Management and the Rise of Artificial Intelligence
Robotic Refactoring the Workplace
"AI -The Future of Automotive Industry"
WiFi Networks: Shifting from Providing a Service to Improving the...
Breaking the Stereotypes in the Development of AI
Yves Jacquier, Executive Director, Production Studio Services, Ubisoft
Operationalize Machine Learning
Zongjie Diao, Director of Product Strategy and Management, Data Center Compute Group, CISCO
Where is AI Already Having an Impact on Business?
David Wirt, VP, ASEAN & Greater China, Pure Storage
Artificial Intelligence in the Biopharma and Healthcare Sector
Ronald Dorenbos, Associate Director Materials & Innovation, Takeda
Thank you for Subscribing to CIO Applications Weekly Brief
Lightning the Future of Deep Learning
Some fascinating breakthroughs could make AI technology accessible to many more companies and enterprises.
FREMONT, CA: As the influx of structured and unstructured data increases in the conventional ecosystem, the balance between information gathered and information harnessed becomes profoundly dissimilar. However, to reduce the gap between operational and computational knowledge, professionals and experts have started integrating artificial intelligence technology into the functional frameworks.
Recently, the industry has seen a considerable effort to fix the "big data issue" of AI. And some exciting breakthroughs have started to arise that could render AI available to many more companies and organizations. Big data Technology is a sophisticated AI technology that enables computers to discover information interactions and trends on their own.
In order to execute their duties correctly, deep learning systems often involve millions of training examples. However, several businesses and organizations, for training their models, do not have significant exposure to such massive caches of annotated data. Furthermore, the limitations to attain proper customer profiles are in direct relation to the sophistication of machine learning. In addition, information is divided and dispersed in many fields, needing tremendous attempts and financing to strengthen and clean up AI learning. Data is subject to data protection laws and other restrictions in several areas, which may place it outside the reach of AI technicians. Furthermore, over the past few years, AI investigators have been under pressure to maintain technical solutions for the substantial information demands of deep learning.