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Onpassive Artificial intelligence, AI Technology


Onpassive Artificial intelligence (AI) Technology

Around 2011, the work done in robotic milieus around the world has many concerning implications for social welfare, and has implications for healthcare as well as law, business, and academia. From the time of the development of mechanical lifts, robotics in the workplace is still in its infancy, and many industries are generally sticking to mechanical and complex machines in the workplace. Several years ago the isomorphic concept of the natural and artificial, developed by Rogers, earned a spot in the paper “Unexpected Effects of Simple, Disruptive Technology” and garnered a lot of attention from many people outside of robotics. These few few examples clearly show us that a range of devices will continue evolving in the future.

In the latter half of 2017, many technological innovations began to form around a high level concept of the soft functioning robots. Most devices that use biological mechanical features are soft with a less appearance of mechanical devices of today. 

Many of these devices used to develop these machines were created by mechanical engineering professionals and its has been a line of questioning for many as to what would come next from these developments. Later in 2017, a number of manufacturers were granted an industry level educational goal to develop “Smart Surfaces” in 2018, which indicates a major focus for the devices: Artificial Intelligence. Unfortunately, the promise of data-driven artificial intelligence has been met with a significant amount of criticism.

Injecting Automation into the Workplace — Recent Designs to Create Artificial Intelligence in the Workplace

Recent designs that use Artificial Intelligence in the workplace have been presented publicly in the last few years. Some of these designs are far-reaching. In 2017 the manufacturing industry, for example, began experimenting with an AI that uses voice commands to supply equipment. The only problem with this way of automating manufacturing is that this is not making it more cost-effective for workers as they cannot be relied on for accuracy when dispensing hardware.

The Next Evolution in Manufacturing

AI has become a part of the DNA of the automation forces of the future. There are few automations experts who will claim that this robots are approaching human level. Some are expected to move beyond the assembly line or manufacturing lines that provide light duties like the outside construction industry. Much of the future is based on increasing human intelligence, but the fundamental picture is already beginning to change.

Hardware vendors like Microsoft and Google have invested much of their best technical effort on robotics to develop something that will have far-reaching implications in various industries. The most relevant application to our work in cybersecurity is around artificial intelligence. This can be used to prevent advanced cyber-security attacks on an artificial intelligence in a way that will challenge the vast majority of human intelligence to stop the attackers. This is a good thing as it will keep the attacks from getting more prevalent.

The Next Evolution of Fraud Detection

Artificial Intelligence can be used in AI Intelligence and Trust APIs. In a recent paper published in 2015, the technology behind AI Turing test can be applied to AI applications to help detect fraud. In 2017, the Economist Intelligence Unit, Ltd. released the Lex Report, which discussed the impact of AI on AI technologies. The report states that, “If future innovations of AI demonstrate that they can perform tasks similar to those of human experts, then AI agents will routinely perform both cognitive and non-cognitive activities.”

AI can be applied to AI, voice and face recognition among many other AI-related technologies. These technology will continue to develop in the future. Due to the state of knowledge, the applications of AI will continue to increase. As the purpose of this essay is to analyze trends, I will outline the historical pattern of investment in AI technologies in enterprises, a vector of AI adoption, and conclusion upon the topic as a form of filtering. Technology that has been instrumental in the past few years will continue to receive attention from a number of vendors while others are established.

Figure 2. Increased Value of Artificial Intelligence Applications in Enterprises

Appendix: History of AI Investment

Technology will become so much more sophisticated that it will have a significant impact on individuals’ job. I will explore 4 economic milestones over the next 3-5 years. The last document in this analysis will be a historical checklist of AI development for organizations.

This essay concludes with the reference to the sources of Artificial Intelligence.

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