Non-technology companies are beginning to use artificial intelligence on a large scale.
According to Alexandra Suych Bass, artificial intelligence extends beyond the technological sector, with serious consequences for companies, workers and consumers.
Lie detectors are not very widely used in business, but Ping An, the Chinese insurance company, believes it can detect fraud. The company allows customers to apply for loans through their application. Potential borrowers answer questions about their income and repayment plans with the help of video broadcasting, which tracks about 50 tiny facial expressions, in order to determine the sincerity of their decisions. The program works on the basis of artificial intelligence (AI) and helps to accurately identify customers with whom to continue working.
AI will replace most of the mandatory checks on the state of bank accounts of borrowers. Johnson & Johnson, a consumer goods company, and Accenture, a consulting company, use AI to sort out resumes and select the best candidates. AI helps Caesars, a casino and hotel company group, to guess clients' potential expenses and offer personalized promotions to attract them. Bloomberg, a media holding and financial information company, uses AI to scan company earnings reports and automatically create news articles. Vodafone, a mobile operator, can predict communication problems and user devices before they occur. Companies from every economic industry use AI to monitor cybersecurity threats and other risks, such as employees' emotional burnout.
Instead of relying on intuition and rough predictions, more reasonable and faster AI-based predictions promise to make the business much more efficient. In Leroy Merlin, a French home goods store, managers held new promotions on Fridays, but by default used the same products as last week to speed up their weekend offensive. The firm currently uses algorithms to obtain past sales data and other information that may affect sales. For example, weather forecasts, for more efficient use of shelf space. That helped the company cut its inventory by 8%, even if sales grew by 2%, says Manuel Davey of Vekia, an AI startup program developer.
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AI and machine learning (terms that are often used interchangeably) contains computers that accumulate huge amounts of data to find models and make predictions without being explicitly programmed for this task. Large amounts of data, more complex algorithms and tremendous computational power have provided AI with a great deal of influence and tremendous possibilities. The results are often similar to those that an army of extras could provide with unlimited time and resources, but they are achieved much faster, cheaper, and more efficiently.
One of the main advantages of AI will be a sharp decrease in the cost of forecasting, says Ajay Agrawal of the University of Toronto and co-author of the new book “Machines of Predictions”. Just as electricity made lighting much more affordable — this level of lighting now costs about 400 times less than in 1800, so AI will make forecasting more affordable, reliable and widely used.
Computers have been able to read text and numbers for decades, but only recently have learned to see, hear and speak. “AI is a universal term for“ salad bowl ”from different segments and disciplines,” said Fey-Fei Lee, director of AI Lab at Stanford and head of the cloud computing division of Google. The AI ​​subsections include robotics, which replaces plants and the conveyor, and computer vision used in applications, from identifying something or someone in the photo to self-driving technology. According to Ms. Li, computer vision is the “killer” of AI, as it can be used in many situations, however, AI also improves its abilities in speech recognition technology. It is the basis of voice assistants on telephones and home speakers, and also allows algorithms to listen in on calls and perceive the tone and context of the speaker.
Until now, the technology industry has been the main beneficiary of AI technologies. Most of the leading technology companies, such as Google and Amazon in the West, Alibaba and Baidu in China, could not have become so large and successful without AI used for product recommendations, targeted advertising and demand forecasting. Amazon, for example, makes extensive use of AI for managing robots in its warehouses, optimizing packaging and shipping, for detecting counterfeit goods, and also for the operation of its voice assistant, Alexa. Alibaba, a Chinese competitor, also makes extensive use of AI, for example, in logistics; and its online payment subsidiary, Ant Financial, is experimenting with face recognition to approve transactions. Sandar Pichai, the head of Google, said that AI will have a “deeper” effect than electricity or fire.
The leaders of non-technology companies from various sectors of the economy are beginning to worry that AI can remove them from the market and are buying up promising technology start-ups in order to ensure their leading position. According to PitchBook, the data provider, in 2017, companies around the world spent about 21.8 billion dollars on mergers and acquisitions related to AI, about 26 times more than in 2015 (see the Chart). Startups without income attract with their value, which ranges from 5 to 10 million dollars for an expert in AI technology.
As AI goes beyond the technology sector, it will influence the growth in the number of new companies that challenge existing ones. This is already happening in the automotive industry, with unmanned vehicle startups and companies like Uber. It will also change the way other companies work, transforming traditional functions such as supply chain management, customer service and recruitment.
The course on the development of this technology in the future inspires, but has its own risks. About 85% of companies believe that AI will provide a competitive advantage, but only one in twenty companies use it today, according to a report from the MIT Sloan Management Review and the Boston Consulting Group. Large companies and industrial enterprises, such as finance, generating a lot of information, often occupy a leading position, and therefore create their own systems with improved AI. But many firms will prefer to work with a growing army of independent AI providers, including cloud providers, consultants and startups.
This is not only a corporate race, but also an international one, especially between America and China. Chinese firms have a huge advantage, which is crucial, and that the Chinese government manages an extensive database of people who can help in learning facial recognition algorithms. In China, privacy is not as important as in the West.
In the future there will be many chances to make the wrong decision. One of the difficult questions for companies will be the issue of distribution over time. Roy Bahat of Bloomberg Beta, a venture capital fund, draws a parallel between the present day and the first dot-com boom in the late 1990s: "Companies seek to understand what to spend money on." If they invest huge amounts of money in AI early, they risk limiting themselves greatly or paying large amounts for useless startups, as many did in the early days of the Internet. But if they wait too long, they may technologically fall behind companies that have quickly achieved success, as well as competitors who have mastered technology faster.
Someone may have been deceived by beautiful media reports, believing that AI is a magic wand that can be installed as easily as part of Microsoft software, says Gautam Shroff from Tata Consultancy Services in India. AI systems require careful data preparation, careful monitoring of algorithms and a large number of settings in order to benefit. Microsoft's Gurdip Singh speaks about AI systems as “crazy scientists”: they can easily do the work that people find incomprehensible to their minds, such as finding tiny flaws in manufactured goods or quickly classifying millions of photos of people, but they have problems with things that seem easy to people, such as basic reasoning. Back in 1956, when research scientists held their first meeting to discuss AI, they were looking for a way to fill machines with “human” general intelligence, including complex reasoning. But it remains a remote aspiration.
The agiotage around artificial intelligence makes it difficult to separate HYIP from reality. In the last quarter of 2017, public companies around the world mentioned AI and machine learning in their income reports more than 700 times, seven times more often than in the same period of 2015 (see the Chart). So many companies are speculating on AI's capabilities, while not providing concrete evidence of this, in this regard, someone has to launch the “an AI fake news” channel, ”says Tom Sibel, a veteran of Silicon Valley.
Company executives must think for years to come. In the near future, AI will replace familiar business processes, such as finance, personnel management, and customer service, according to Michael Chui from the McKinsey Global Institute, an analytical consulting center. But over time, it will also replace entire industries, for example, by increasing the growth of unmanned vehicles or the discovery of completely new drugs. While people may have a biased opinion about the design of industrial products or a combination of drugs that may be more useful, the algorithms are more likely to find new and acceptable solutions.
In particular, many managers are more interested in reducing costs and saving labor than in the greater opportunities that AI can offer, says John Hagel from Deloitte. This will certainly have a negative impact on employees, but, consequently, on business. “If you just cut costs and don’t increase customer value, you’ll be out of the game,” he says. Some companies will not be able to reduce existing jobs as a result, but use technologies that allow them to avoid creating new ones. And workers who keep their jobs are more likely to feel under the protection of their employers. Some firms are already using AI to centralize the communications of their employees without breaking the law. This practice will spread, raising issues of confidentiality.
The huge problem lies in the fact that AI creates the effect of a virtual funnel or flywheel, allowing companies that use it to work more efficiently: generate more data, improve their services, attract more customers and offer lower prices. It sounds great, but it can also lead to greater corporate concentration and monopoly influence, as has already happened in the technology sector.
Lie detectors are not very widely used in business, but Ping An, the Chinese insurance company, believes it can detect fraud. The company allows customers to apply for loans through their application. Potential borrowers answer questions about their income and repayment plans with the help of video broadcasting, which tracks about 50 tiny facial expressions, in order to determine the sincerity of their decisions. The program works on the basis of artificial intelligence (AI) and helps to accurately identify customers with whom to continue working.
AI will replace most of the mandatory checks on the state of bank accounts of borrowers. Johnson & Johnson, a consumer goods company, and Accenture, a consulting company, use AI to sort out resumes and select the best candidates. AI helps Caesars, a casino and hotel company group, to guess clients' potential expenses and offer personalized promotions to attract them. Bloomberg, a media holding and financial information company, uses AI to scan company earnings reports and automatically create news articles. Vodafone, a mobile operator, can predict communication problems and user devices before they occur. Companies from every economic industry use AI to monitor cybersecurity threats and other risks, such as employees' emotional burnout.
Instead of relying on intuition and rough predictions, more reasonable and faster AI-based predictions promise to make the business much more efficient. In Leroy Merlin, a French home goods store, managers held new promotions on Fridays, but by default used the same products as last week to speed up their weekend offensive. The firm currently uses algorithms to obtain past sales data and other information that may affect sales. For example, weather forecasts, for more efficient use of shelf space. That helped the company cut its inventory by 8%, even if sales grew by 2%, says Manuel Davey of Vekia, an AI startup program developer.
')
AI and machine learning (terms that are often used interchangeably) contains computers that accumulate huge amounts of data to find models and make predictions without being explicitly programmed for this task. Large amounts of data, more complex algorithms and tremendous computational power have provided AI with a great deal of influence and tremendous possibilities. The results are often similar to those that an army of extras could provide with unlimited time and resources, but they are achieved much faster, cheaper, and more efficiently.
One of the main advantages of AI will be a sharp decrease in the cost of forecasting, says Ajay Agrawal of the University of Toronto and co-author of the new book “Machines of Predictions”. Just as electricity made lighting much more affordable — this level of lighting now costs about 400 times less than in 1800, so AI will make forecasting more affordable, reliable and widely used.
Computers have been able to read text and numbers for decades, but only recently have learned to see, hear and speak. “AI is a universal term for“ salad bowl ”from different segments and disciplines,” said Fey-Fei Lee, director of AI Lab at Stanford and head of the cloud computing division of Google. The AI ​​subsections include robotics, which replaces plants and the conveyor, and computer vision used in applications, from identifying something or someone in the photo to self-driving technology. According to Ms. Li, computer vision is the “killer” of AI, as it can be used in many situations, however, AI also improves its abilities in speech recognition technology. It is the basis of voice assistants on telephones and home speakers, and also allows algorithms to listen in on calls and perceive the tone and context of the speaker.
Technological change
Until now, the technology industry has been the main beneficiary of AI technologies. Most of the leading technology companies, such as Google and Amazon in the West, Alibaba and Baidu in China, could not have become so large and successful without AI used for product recommendations, targeted advertising and demand forecasting. Amazon, for example, makes extensive use of AI for managing robots in its warehouses, optimizing packaging and shipping, for detecting counterfeit goods, and also for the operation of its voice assistant, Alexa. Alibaba, a Chinese competitor, also makes extensive use of AI, for example, in logistics; and its online payment subsidiary, Ant Financial, is experimenting with face recognition to approve transactions. Sandar Pichai, the head of Google, said that AI will have a “deeper” effect than electricity or fire.
The leaders of non-technology companies from various sectors of the economy are beginning to worry that AI can remove them from the market and are buying up promising technology start-ups in order to ensure their leading position. According to PitchBook, the data provider, in 2017, companies around the world spent about 21.8 billion dollars on mergers and acquisitions related to AI, about 26 times more than in 2015 (see the Chart). Startups without income attract with their value, which ranges from 5 to 10 million dollars for an expert in AI technology.
As AI goes beyond the technology sector, it will influence the growth in the number of new companies that challenge existing ones. This is already happening in the automotive industry, with unmanned vehicle startups and companies like Uber. It will also change the way other companies work, transforming traditional functions such as supply chain management, customer service and recruitment.
The course on the development of this technology in the future inspires, but has its own risks. About 85% of companies believe that AI will provide a competitive advantage, but only one in twenty companies use it today, according to a report from the MIT Sloan Management Review and the Boston Consulting Group. Large companies and industrial enterprises, such as finance, generating a lot of information, often occupy a leading position, and therefore create their own systems with improved AI. But many firms will prefer to work with a growing army of independent AI providers, including cloud providers, consultants and startups.
This is not only a corporate race, but also an international one, especially between America and China. Chinese firms have a huge advantage, which is crucial, and that the Chinese government manages an extensive database of people who can help in learning facial recognition algorithms. In China, privacy is not as important as in the West.
In the future there will be many chances to make the wrong decision. One of the difficult questions for companies will be the issue of distribution over time. Roy Bahat of Bloomberg Beta, a venture capital fund, draws a parallel between the present day and the first dot-com boom in the late 1990s: "Companies seek to understand what to spend money on." If they invest huge amounts of money in AI early, they risk limiting themselves greatly or paying large amounts for useless startups, as many did in the early days of the Internet. But if they wait too long, they may technologically fall behind companies that have quickly achieved success, as well as competitors who have mastered technology faster.
Someone may have been deceived by beautiful media reports, believing that AI is a magic wand that can be installed as easily as part of Microsoft software, says Gautam Shroff from Tata Consultancy Services in India. AI systems require careful data preparation, careful monitoring of algorithms and a large number of settings in order to benefit. Microsoft's Gurdip Singh speaks about AI systems as “crazy scientists”: they can easily do the work that people find incomprehensible to their minds, such as finding tiny flaws in manufactured goods or quickly classifying millions of photos of people, but they have problems with things that seem easy to people, such as basic reasoning. Back in 1956, when research scientists held their first meeting to discuss AI, they were looking for a way to fill machines with “human” general intelligence, including complex reasoning. But it remains a remote aspiration.
The agiotage around artificial intelligence makes it difficult to separate HYIP from reality. In the last quarter of 2017, public companies around the world mentioned AI and machine learning in their income reports more than 700 times, seven times more often than in the same period of 2015 (see the Chart). So many companies are speculating on AI's capabilities, while not providing concrete evidence of this, in this regard, someone has to launch the “an AI fake news” channel, ”says Tom Sibel, a veteran of Silicon Valley.
Company executives must think for years to come. In the near future, AI will replace familiar business processes, such as finance, personnel management, and customer service, according to Michael Chui from the McKinsey Global Institute, an analytical consulting center. But over time, it will also replace entire industries, for example, by increasing the growth of unmanned vehicles or the discovery of completely new drugs. While people may have a biased opinion about the design of industrial products or a combination of drugs that may be more useful, the algorithms are more likely to find new and acceptable solutions.
In particular, many managers are more interested in reducing costs and saving labor than in the greater opportunities that AI can offer, says John Hagel from Deloitte. This will certainly have a negative impact on employees, but, consequently, on business. “If you just cut costs and don’t increase customer value, you’ll be out of the game,” he says. Some companies will not be able to reduce existing jobs as a result, but use technologies that allow them to avoid creating new ones. And workers who keep their jobs are more likely to feel under the protection of their employers. Some firms are already using AI to centralize the communications of their employees without breaking the law. This practice will spread, raising issues of confidentiality.
The huge problem lies in the fact that AI creates the effect of a virtual funnel or flywheel, allowing companies that use it to work more efficiently: generate more data, improve their services, attract more customers and offer lower prices. It sounds great, but it can also lead to greater corporate concentration and monopoly influence, as has already happened in the technology sector.
Source: https://habr.com/ru/post/442350/
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