Artificial intelligence technology is gradually reaching its peak of productivity. Among the first signs is a voice recognition service with a working chat bot. Users are also interested in automated machine learning technology and business applications with integrated AI mechanisms. The demand for artificial intelligence platforms provided by cloud-related services and services is growing. However, some applications, such as automatic driving cars, can only be implemented after ten years.
Artificial intelligence is aimed at the public
Gartner’s latest research in the development of artificial intelligence points to a wide range of AI applications. According to a study by the company, this is logical because the percentage of organizations that implemented AI in 2019 increased from 4% to 14% compared to last year. And despite being relatively young in the overall AI market, Gartner analysts immediately rolled out two technologies on the “productivity board”: “voice recognition” and “GPU-based AI accelerator.” System instead of processor).
Other AI applications that want quick success are interactive AI tools that help the success of virtual assistants like Amazon Alexa, Google Assistant, and more. I’m interested in new technologies like extended intelligence, “Edge AI”, automatic data tags that are becoming more popular in peripherals, and “descriptive” AI (artificial intelligence systems that can explain human solutions). . However, autonomous cars that many people think will soon appear on the road, according to Gartner, will “leave” the peak of productivity for more than a decade.
Blue chip AI
In general, many new technologies have emerged for the “curve” of artificial intelligence, many of which are marked with blue circles, and Gartner hopes to soon approach the plateau of productivity. In addition, many of them received predictions about “two to five years before their implementation”, which is still to be expected.
However, analysts point out that not all new technologies are used for easy-to-understand purposes, which can benefit the company. In a realistic way, it is necessary to approach the predictions and analyze their feasibility.
Either way, analysts advise companies that want to keep time to prepare a minimum financial and economic basis for AI implementation. And those who have already done the initial implementation should consider the projects to increase the amount.
Gartner curve for artificial intelligence
Source: Gartner, 2019
Artificial intelligence technology that needs special attention of all the AI technologies, Gartner analysts chose five things that could seriously change business processes in the near future and advised CIOs to keep a close eye on their development.
Analysts designate automation systems as advanced intelligence systems that help increase the productivity of human intelligence work. They help organize the ‘partnership’ between people and AI, and the dominant role plays the dominant role.
Using artificial intelligence in this role can reduce the amount of errors you have during implementation and routine workload. And artificial participation reduces the risks associated with automated decision-making, because AI can solve questions that are not yet eligible to be answered.
Chatbots, the “face” of artificial intelligence that we encounter every day, also change the processes involved in interacting with customers. For example, at Kia, it helps solve the problems of 115,000 vehicle owners each week, and at German discount chain Lidl, a bot called Margot advises customers on wine and snack options.
Chat bots can be text-to-speech and, based on pre-written scenarios, standard operators can answer standard questions, taking into account the experience gained by live operators. It can be used to solve human resource work or technical support services and help employees adjust to new positions. However, most of these AI solutions have changed the customer service process. If the user previously had to study the user interaction interface with the system, the chat now “evaluates the user” and leads to more actions.
One of the tasks the machine teaches is how to improve customer service, get more robust prices, diagnose the disease and respond to costs. The principle behind the use of machine learning tools is to use mathematical operations to identify patterns in data. Machine learning is a powerful application, promoted by the rapid growth of data in organizations and the effective development of computer systems.
Machine learning helps to drive multiple processes in different industries and find solutions to business problems. For American Express, for example, algorithms and machine learning analytics recognize relatively simple tests, prevent losses, save millions of companies. Alternatively, the Volvo test system improves safety by noting the chances of failure and the need to repair and repair various parts of the vehicle.
AI control system
According to experts, setting up an AI management system in business cannot be avoided. In particular, it is necessary to understand and manage the potential risks associated with control and the potential for reputation damage. Explained by Gartner, AI control systems are based on rules designed specifically to prevent AI system errors (“bias”), user preferences or user groups, and the adverse consequences of artificial use. Wisdom.
When designing an AI management system, experts urge analysts and IT leaders to pay attention to three areas: principles of trust, transparency and cultural bias. Being able to provide reliable performance for data sources and AI applications is one of the key elements of successful implementation and the need to develop its transparency can reduce the risk of data sources and algorithms. Adherence to the principles of data differentiation and algorithms ensures better control and accuracy of the results of AI solutions.
Over the years, the only way to develop artificial intelligence was through the independent development of AI systems. However, many companies today are reluctant to develop such a solution and purchase AI money as part of their company’s application without buying the “standalone” AI system.
First, “smartstatty” was analyzed using built-in AI technology. However, recently, various business application application providers such as ERP, CRM processes, employee management and office centers have started to acquire AI tools and build an AI platform. As such, Gartner analysts need CIOs to integrate advanced analytics tools and equipment to improve user engagement in order to incorporate AI tools into their product development plans.
Author bio :
Vinodh Kumar, postgraduate from the Business Administration background. He is currently working as a content contributor for Encodingcompiler.com and loves to write tech related niches.
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