Mobile phone jammers are necessary for the storage and protection of information
In the past few months, I have discussed with industry leaders the potential scope of AI and machine learning, and the role that humans need to play. Most of what I heard heralded a pandemic. Jason Phippen, global marketing manager for SUSE products and solutions, said: ``There are always false assumptions in artificial intelligence systems that will reduce performance or data availability. "It is also possible that the data comes from the wrong combination or poor learning, leading to bad business or operational decisions. The worst case may be that a free-running system moves the data to cold storage,
Artificial intelligence and machine learning cannot be integrated into existing infrastructure or process concentration at all. DataKitchen CEO Chris Bergh warned that the existing system needs to be adjusted and adjusted. He said: "In the traditional architecture, artificial intelligence and machine learning systems consume the data environment to meet data requirements." We need to change this architecture slightly by letting AI manage the data environment. This transition must be smooth to avoid catastrophic failure of the existing system and put a robust system in place".
Artificial intelligence and machine learning systems “must treat the development of software for managing the data environment as a critical system and must be developed very carefully,” Krisberg continued. "As data drives today's business decisions, the data environment will become the core of the business. Therefore, due to the loss of operating time, other resources and user confidence, even minor failures in data management will result in significant business costs." A company uses cell phone jammer to block other information. And soon they saw the results.
Another problem highlighted by Chris Bergh is that data experts have a large knowledge gap between AI and machine learning, while AI and machine learning experts have very little knowledge. In data management. Most importantly, qualified personnel are still needed to manage the process and ensure the quality of data injected into AI and machine learning systems. The data management mechanism will be autonomous, but the data context requires human involvement.
"We can look at examples of using Google's DeepMind, such as self-driving cars or data center energy optimization, and have confidence in the same opportunities in database management," said Erik Brown, one of West Monroe Partners' main technical directors. "However, a fully autonomous database seems to belong to the more distant future, and human participation should become more strategic and focused on better equipped areas."
Jeremy Wortz, chief architect of West Monroe's Technical Design Office, agrees that a fully autonomous data environment "may take many years to achieve." "Machine learning is far from solving large and complex problems. However, the methods of developing narrow and in-depth use cases will vary over time, and the development of self-management systems will begin. Most organizations can adopt mobile jammers. Method, but you need to make sure that they have a way to list narrow use cases and have the right skills and talents to make these use cases a reality."