After the outbreak of artificial intelligence, it is estimated that many people are starting to panic, because the power of artificial intelligence will surpass humans. Hawking also said that artificial intelligence will likely replace humans. But others say that those who are worried that AI will steal their jobs don't have to be so nervous, because AI will also spawn new jobs. Under the AI ​​wave, there are still five major positions that still stand.
The term “artificial intelligence†often makes people feel fearful and worried. People are afraid of the unknown possibilities brought about by artificial intelligence, fearing the emergence of dystopian scenes in movies like Terminator. Artificial intelligence may steal our work someday. This fear has not just emerged recently, nor is it completely unfounded. Artificial intelligence, like any other disruptive technology invention, has resulted in faster, more efficient machines that will inevitably replace some human workers. However, those who are worried that AI will steal their jobs are not necessarily so nervous, because AI will also spawn new jobs, and they can at least develop in these new jobs. According to a recent Gartner report, although AI technology will replace 1.8 million jobs, it will also create 2.3 million new jobs. Gartner Principal Investigator Peter Sondergaard predicts that AI will strengthen the ability of employees to work and may become the “net work creator†of 2020. I believe that AI, like all other disruptive technologies of the past, will bring us many new jobs.
Thanks to the rise of AI technology, the following five industry positions will show a significant growth trend:
1. Data scientistData scientists are a new category among analytics data experts who analyze data to understand complex behaviors, trends, and inferences, and discover hidden insights to help companies make smarter business decisions. As SAS, which is dedicated to business analytics and business intelligence software, says, data scientists are "a collection of some mathematicians, some computer scientists, and some trending scientists."
Here are some examples of data science applications:
Netflix uses the data mining movie viewing model to understand user interests and then use this data to make decisions about the production of Netflix original drama.
Target uses consumer data to identify key customer segments and analyzes unique shopping behaviors in the customer base to direct messages to different audiences.
P&G's time-series model provides a clearer picture of future product needs, helping companies plan the most appropriate production volume.
As AI drives the trend of creating and collecting data, we can also see that the demand for data scientists will increase in the future. According to IBM forecasts, by 2020, the demand for data scientists will grow by 28%, and the annual demand for data scientists, data developers and data engineers will reach 700,000. Among the general AI field experts, including doctoral students who have just stepped out of the campus and relatively low-educated professionals with years of work experience, the annual salary plus company stock may range from $300,000 to $500,000.
2, AI / machine learning engineerIn most cases, machine learning engineers work with data scientists to synchronize their work. As a result, the demand for machine learning engineers may also appear to be similar to the growing demand for data scientists. Data scientists have more skills in statistics and analysis, while machine learning engineers should have expertise in computer science, and they often need more powerful coding capabilities.
If you entered the field of machine learning ten years ago, it was difficult to find other jobs besides academia. But now, every industry wants to apply AI to their field, and the need for machine learning expertise is everywhere, so AI will continue to promote the development of high demand trends for machine learning engineers. In addition, companies in different vertical industries, including image recognition, speech recognition, medicine, and cybersecurity, face the challenge of a labor force that lacks the right skills and knowledge. According to the Gartner report, one CIO wanted to hire AI technology professionals in New York, only to find that there were only 32 talent pools, of which only 16 met the potential candidate criteria. Of the 16 people, only 8 are actively looking for new employment opportunities.
3, data label professionalsAs data collection becomes more common in every vertical area, the demand for data tag professionals will also proliferate in the future. In fact, in the AI ​​era, data tags may become blue-collar jobs.
Guru Banavar, head of the IBM Watson team, said that "data tags will become data management, you need to get raw data, clean up the data, and use machines to collect." Tags allow AI scientists to train new machines.
Banavar continues to explain: "Assuming you want to train a machine to identify the plane, you have 1 million photos, some of which have planes inside and some have no planes. Then you need someone to teach the computer first which images have planes, There are no planes." This is where the label is used.
4, AI hardware expertsAnother growing blue-collar job in the AI ​​space is the industrial operations that are responsible for creating AI hardware such as GPU chips. Big technology companies have taken steps to build their own professional chips.
Intel is building a chip specifically for machine learning. At the same time, IBM and Qualcomm are creating a hardware architecture that reflects neural network design and can operate like a neural network. According to Yann LeCun, director of research at Facebook AI, Facebook is also helping Qualcomm develop technology related to machine learning. As the demand for artificial intelligence chips and hardware continues to grow, the demand for industrial manufacturing jobs dedicated to the production of these specialty products will increase.
5, data protection expertsAs data, machine learning models, and code continue to grow, so does the need for data protection in the future, and the need for database protection IT experts.
Many levels and types of information security controls apply to databases, including access control, auditing, authentication, encryption, integration control, backup, application security, and database security application statistics.
Databases are largely protected against hacker attacks through network security measures such as firewalls and network-based intrusion detection systems. Protecting the database system and the security of its programs, functions, and data will become increasingly important as network open programs become more and more expensive.
Always need human judgment
Although artificial intelligence can be used to speed up the pace of everyday work, and may replace some of the staff in the future, it creates more work than it does. Whether it's analyzing, organizing, or reaching a viable conclusion based on data, the role of humans in these processes is still necessary. It is precisely because of this that the role of human beings in creating, implementing and protecting artificial intelligence will become even more important.
As Andrew Milroy, senior vice president of Frost & Sullivan, puts it: “The lack of human resources to achieve transformation will reduce the speed of technology adoption and automation. AI will create jobs. With the emergence of new, disruptive technologies, new high-skilled jobs Jobs will also appear. Without human workers, the implementation of these technologies is impossible."
Artificial intelligence is a step in the future of human beings to achieve a unified goal. The work created by AI technology can make life easier and free human workers from trivial tasks. While the current speed and popularity of AI technology is creating more jobs for us, it also means that we are facing a new challenge. We need to train staff to turn to these new positions.
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