Lie detectors are not widely used in the business, but Chinese insurance company Ping An believes that will be able to identify fraud. The company allows customers to apply for loans through your application. Potential borrowers to answer questions about their income and payment plans with video that tracks about 50 tiny facial expressions to determine the sincerity of their decisions. The program is based on artificial intelligence (AI) and helps accurately identify clients that should continue.
AI will replace most of the mandatory audits of States of Bank accounts of the borrowers. Johnson & Johnson, a company for the production of consumer goods, and Accenture, a consulting company that uses AI to sort resumes and select the best candidates. AI helps Caesars group of companies engaged in casinos and hotels, to guess the likely costs for customers and offer personalized promotions to attract them. Bloomberg, the media and financial-information companies, uses AI to scan the earnings reports of companies and automatically generate news articles. Vodafone, mobile operator, can predict problems with communication and end-user devices until the moment they arise. Companies from each economic sector using AI to monitor cyber security threats and other risks, such as emotional burnout of employees.
Instead of relying on intuition and approximate projections, it is more reasonable and fast predictions based on the AI promise to make the business much more effective. In Leroy Merlin, a French home goods store, managers conducted a new promotion on Friday, but by default use the same products as last week to hasten its output. Currently, the firm uses algorithms to obtain the past sales data and other information which may affect sales. For example, weather forecasts for more efficient use of space on the shelves. This has helped the company to reduce its holdings by 8%, even if sales grew by 2%, says Manuel Davy from the company Vekia, AI is a startup engaged in the development of the program.
AI and machine learning (terms that are often used interchangeably) includes the computers, accumulating huge amounts of data to find patterns and make predictions without being explicitly programmed for the task. Large amounts of data, more sophisticated algorithms and enormous computing power gave the AI a big impact and a huge opportunity. The results are often similar to those that could provide the army of extras with unlimited time and resources, but they are achieved much faster, cheaper and more efficient.
One of the main advantage of AI will be a sharp decline in the value of forecasting, says Ajay Agrawal of the University of Toronto and co-author of the new book “Machine predictions”. Just as electricity made lighting much more affordable — this level of coverage now is about 400 times cheaper than in 1800, and AI will make forecasting more accessible, reliable and widely used.
Computers can read text and numbers for decades, but only recently learned to see, hear and speak. "AI is a generic term for "salad bowl" of different segments and disciplines," says FEI-FEI Li, Director AI Lab at Stanford and head of unit for cloud computing Google. The topics in AI include robotics, which replaces the factories and the conveyor, and machine vision, used in applications from identification of anything or anyone in the pictures to the technology of self-driving car. According to Ms. Lee, computer vision — a "killer" AI, because it can be used in many situations, however, AI also improves their skills in the technology of speech recognition. It is the basis of voice assistants on phones and home loudspeakers, and allows the algorithms to listen to calls and to perceive the tone and context of the speaker.
So far the main beneficiary of the AI technology was the technology sector. Most of the leading technology companies such as Google and Amazon in the West, Alibaba and Baidu in China, would not have become so large and successful without the AI used for product recommendations, targeted advertising and demand forecasting. Amazon, for example, is widely used AI to control robots in their warehouses, optimization of packaging and delivery, for the detection of counterfeit goods, and for its voice assistant — Alexa. Alibaba, a Chinese competitor, is also widely used AI, for example, in logistics; and its subsidiary company for online payments, Ant Financial, is experimenting with facial recognition for the approval of the transaction. Sandar Pichai, head of Google, said that AI will have a "deeper" impact than electricity or fire.
Heads of technological companies from various sectors of the economy begin to worry about what the AI can remove them from the market and buying up promising technology start-UPS, to ensure its leading position. According to PitchBook, a data provider, in 2017, companies worldwide spent 21.8 billion dollars on mergers and acquisitions related to AI, approximately 26 times more than in 2015 (see Chart). Startups with no revenue attracted by its cost, which ranges from 5 to 10 million dollars for an expert in the technology AI.
As AI goes beyond the technology sector, it will have an impact on the growing number of new companies that challenge the current. This is already happening in the automotive industry, with startups driving cars and such companies as Uber. This will also change the working methods of other companies, transforming the traditional functions such as supply chain management, customer service and recruitment.
The course for the development of this technology in the future is encouraging, but has its risks. About 85% of companies believe that AI will provide a competitive advantage, but only one of twenty companies using it today, according to a report by MIT Sloan Management Review and Boston Consulting Group. Large companies and industries such as Finance, generating a lot of information, often occupy a leading position, therefore creating their own system with improved AI. But many firms prefer to work with a growing army of independent AI providers, including cloud providers, consultants and startups.
It is not only corporate race, but also international, especially between America and China. Chinese firms have a huge advantage, which is crucial, consisting in the fact that the Chinese government operates an extensive database of individuals that can help in learning algorithms for face recognition. In China, privacy is not as important as in the West.
In the future there will be many chances to take a wrong decision. One of the difficult issues for companies is the question of timing. Roy Bahat of Bloomberg Beta, a venture capital Fund, draws a parallel between today and the first dotcom boom in the late 1990s: "Companies are looking to understand how to spend money." If they had put a huge amount of AI they run the risk of severely limit yourself or pay large amounts of money for useless start-UPS, as did many in the early days of the Internet. But if they wait too long, they can technologically to keep up with the quickly attained success, as well as from competitors, who quickly mastered the technology.
Someone may have been misled by the beautiful messages in the mass media, believing that AI is a magic wand that can be installed as easily as a piece of software to Microsoft, says Gautam Shroff of the Indian company Tata Consultancy Services. AI systems require careful preparation of data, careful monitoring algorithms and a large number of settings to benefit. Gurdip Singh from Microsoft says the AI systems as "mad scientists": they can easily do the job that people find incomprehensible their mind, for example, to detect tiny flaws in industrial products or to quickly classify millions of photos individuals, but they have problems with things that people seem to be easy, such as basic reasoning. In 1956, when researchers held their first meeting to discuss AI, they were looking for a way to give machines human General intelligence, including complex reasoning. But it remains a distant aspiration.
The excitement around artificial intelligence complicates the separation of hype from reality. In the last quarter of 2017 for public companies around the world have mentioned AI and machine learning in their income statements over 700 times, seven times more than in the same period in 2015 (see Chart). So many firms are capitalizing on the abilities of the AI, while not providing specific evidence in this regard, someone needs to start a new channel "an AI fake news," says Tom Sibel, a veteran of Silicon valley.
Executives must think in the years ahead. In the near future, AI will replace the usual business processes such as Finance, human resources and customer service, said Michael Chui of McKinsey Global Institute, analytical consulting center. But over time, it also will replace entire industries, for example, increasing the growth of unmanned vehicles or the opening of a brand new drugs. While people may be biased opinion on the design of industrial products or combinations of medicinal drugs, which might bring great benefits, the algorithms are more likely to be able to find new and acceptable solutions.
In particular, many managers are more interested in cutting costs and saving labor than in the wider opportunities that may be offered AI, says John Hagel of Deloitte. This, of course, will impact negatively on employees, but, consequently, on business. "If you just reduce costs and increase value to customers, you'll be out," he says. Some companies will in the end not to cut existing jobs, but to use technology to avoid creating a new one. And employees who will retain their workplace, are more likely to feel under the patronage of their employers. Some firms are already using AI to centralize communications of their employees without violating the law. This practice will spread, raising questions of confidentiality.
A huge problem lies in the fact that the AI creates the effect of a virtual funnel or "flywheel" allowing companies that use it to work more efficiently: generate more data to improve their services, attract more clients and offer lower prices. It sounds nice, but it can also lead to greater corporate concentration and monopoly influence, as has happened in the technology sector.
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