Artificial intelligence is still in its early stages
Many dialogues and discussions revolve around artificial intelligence. However, most European companies participating in a recent survey by SAS, the analyst company, indicated that the adoption of artificial intelligence is still in its early stages and can be said to be still in the planning stage.
The positive side is that the vast majority of institutions have begun to talk about artificial intelligence, and some have even begun implementing appropriate projects. There is a great deal of optimism about the potential of artificial intelligence, despite the lack of confidence among some of their institutions' willingness to take advantage of that potential.
The lack of available technology is not the only factor in slowing the pace of reliance on artificial intelligence. Most of the companies participating in the study indicated that many technical options were available. Often, the challenges are the lack of data science skills to maximize the value of emerging artificial intelligence technology, and the deeper institutional and community barriers to artificial intelligence.
These results were included in the Enterprise Artificial Intelligence Survey, a telephone survey of executives from 100 institutions and companies across Europe in the banking, insurance, manufacturing, retail, government and other sectors. The SAS study was conducted in August to gauge the reaction of business people to the potential of artificial intelligence, how they use it today, their plans for future use, and what challenges they face.
Against the background of the automation and independence of artificial intelligence, 55% of the participants in the study considered that the biggest challenge is the changing range of human employment. This potential impact of artificial intelligence on jobs includes loss of employment and the development of new jobs requiring new artificial intelligence skills.
Work ethics were cited as the second biggest challenge, with 41% of respondents questioning whether robots and artificial intelligence systems should work "for the good of humanity" rather than working for a single company, and ways to sponsor those who lost their jobs after the introduction of intelligence systems Artificial.
Are enterprise data scientists prepared to meet the challenges of emerging artificial intelligence? Only 20% considered that their data science teams were ready, while 19% of the participants did not have any data science teams at all.
The use of data scientists to build organizational skills was the plan adopted by 28% of the participants, while 32% indicated that they may seek to enhance the skills of artificial intelligence in their teams specialized in analysis through training, conferences and workshops.
In addition, trust has emerged as the fundamental challenge for many institutions. Almost half (49%) of the study respondents mentioned the cultural challenges posed by the lack of confidence in the production of artificial intelligence and, more broadly, the lack of confidence in the results of so-called "black box" solutions.
The study sought to assess the readiness of artificial intelligence in terms of the required infrastructure. The study noted a marked disparity among respondents who felt they had the appropriate infrastructure for artificial intelligence (24%), those who felt they needed to modernize and adapt their existing AI-related platform (24%) or did not have a specific platform related to artificial intelligence 29%).
Oliver Schapenberger, Executive Vice President and Chief Technology Officer of SAS, said: "We have seen a remarkable progression to drive algorithms to accomplish tasks that humans can do with great accuracy. It is surprising to see the algorithm outweigh the best players in the world. We thought the game of "atmosphere" was unmanageable to human computing; but the machine did so on our behalf. Once the system has realized the rules, learn how to play, and play better than the best human players. We can use this knowledge to build systems that solve corporate problems, or that are superior to the fixed systems used today. We can also build systems that learn corporate rules, learn to play according to laws, and are designed to make subsequent improvements. That's what SAS is doing right now.
The positive side is that the vast majority of institutions have begun to talk about artificial intelligence, and some have even begun implementing appropriate projects. There is a great deal of optimism about the potential of artificial intelligence, despite the lack of confidence among some of their institutions' willingness to take advantage of that potential.
The lack of available technology is not the only factor in slowing the pace of reliance on artificial intelligence. Most of the companies participating in the study indicated that many technical options were available. Often, the challenges are the lack of data science skills to maximize the value of emerging artificial intelligence technology, and the deeper institutional and community barriers to artificial intelligence.
These results were included in the Enterprise Artificial Intelligence Survey, a telephone survey of executives from 100 institutions and companies across Europe in the banking, insurance, manufacturing, retail, government and other sectors. The SAS study was conducted in August to gauge the reaction of business people to the potential of artificial intelligence, how they use it today, their plans for future use, and what challenges they face.
Against the background of the automation and independence of artificial intelligence, 55% of the participants in the study considered that the biggest challenge is the changing range of human employment. This potential impact of artificial intelligence on jobs includes loss of employment and the development of new jobs requiring new artificial intelligence skills.
Work ethics were cited as the second biggest challenge, with 41% of respondents questioning whether robots and artificial intelligence systems should work "for the good of humanity" rather than working for a single company, and ways to sponsor those who lost their jobs after the introduction of intelligence systems Artificial.
Are enterprise data scientists prepared to meet the challenges of emerging artificial intelligence? Only 20% considered that their data science teams were ready, while 19% of the participants did not have any data science teams at all.
The use of data scientists to build organizational skills was the plan adopted by 28% of the participants, while 32% indicated that they may seek to enhance the skills of artificial intelligence in their teams specialized in analysis through training, conferences and workshops.
In addition, trust has emerged as the fundamental challenge for many institutions. Almost half (49%) of the study respondents mentioned the cultural challenges posed by the lack of confidence in the production of artificial intelligence and, more broadly, the lack of confidence in the results of so-called "black box" solutions.
The study sought to assess the readiness of artificial intelligence in terms of the required infrastructure. The study noted a marked disparity among respondents who felt they had the appropriate infrastructure for artificial intelligence (24%), those who felt they needed to modernize and adapt their existing AI-related platform (24%) or did not have a specific platform related to artificial intelligence 29%).
Oliver Schapenberger, Executive Vice President and Chief Technology Officer of SAS, said: "We have seen a remarkable progression to drive algorithms to accomplish tasks that humans can do with great accuracy. It is surprising to see the algorithm outweigh the best players in the world. We thought the game of "atmosphere" was unmanageable to human computing; but the machine did so on our behalf. Once the system has realized the rules, learn how to play, and play better than the best human players. We can use this knowledge to build systems that solve corporate problems, or that are superior to the fixed systems used today. We can also build systems that learn corporate rules, learn to play according to laws, and are designed to make subsequent improvements. That's what SAS is doing right now.
