on 09 January 19
Robots may be coming for some jobs, but they will likely create new roles as well. While the former is the subject of debate, one thing is certain: there will need to be an entire new support system of professionals required to build and guide AI efforts.
It has become common to joke about how robots are going to take our jobs, and rightfully so: Oxford University researchers estimate that 47% of all current US employment is at high risk to become automated over the next decade or so.
But there is positive news: Of the 1.8 million jobs AI will get rid of, the emerging field will create 2.3 million by 2020, according to a recent report from Gartner. And a recent Capgemini report found that 83% of companies using AI say the technology is already adding jobs.
A lot of that growth is coming from the technology itself.
"We'll continue to see job growth in anything AI-related for the next five to 10 years, which is one of the things that will mitigate the oft-publicized inevitable job loss due to AI-led automation," said Brandon Purcell, an analyst at Forrester.
In their latest book, Human + Machine, Paul Daugherty and H. James Wilson explored the practices and approaches of 1,500 organizations, and found new categories of jobs emerging. "These new jobs are not simply replacing old ones," they find. "They are entirely novel positions, requiring skills and training never needed before. Specifically, sophisticated AI systems are necessitating new business and technology roles that train, explain, and sustain AI behavior. 'Symbiotic with AI, the new roles draw on distinctly human skills."
Daugherty and Wilson outline three broad new career categories that are emerging with the rise of AI:
"In the past, people had to adapt to how computers worked. Now, the reverse is happening -- AI systems are learning to adapt to us. To do so, these systems need extensive training, and the jobs of trainers may involve activities including data cleaning, data discovery, working with HR for work design, error correcting, and defining personalities." As AI creeps across industries, more businesses will need trainers for their physical and software-based systems. Daugherty and Wilson say roles as "information modelers" will "help train the behavior of machines by using expert employees as models."
"These new jobs need to bridge the gap between technologists and business leaders. These jobs will become more important as AI systems become increasingly opaque." A role such as "transparency analyst" will be "responsible for classifying the reasons a particular AI algorithm acts as a black box." Another role, an "explainability strategist," will be responsible for "making important judgment calls about which AI technologies might best be deployed for specific applications"
These individuals will be charged with the proper use of AI. They must "continually work to ensure that AI systems are functioning properly as tools that exist only to serve us, helping people in their work to make their lives easier." A sustainer will engage in such tasks as setting limits or override decisions based on profitability or legal or ethical compliance. In addition, sustainers will oversee applying critical thought to AI performance and designing interfaces for AI-amplified workforces. Job titles may include "ethics compliance managers" who will "act as watchdogs and ombudsmen for upholding generally accepted norms of human values and morals."
Candidates interested in pursuing jobs in this field require specific education based on foundations of math, technology, logic, and engineering perspectives. Written and verbal communication skills are also important to convey how AI tools and services are effectively employed within industry settings. To acquire these skills, those with an interest in an AI career should investigate the various career choices available within the field.
How to get a job in AI
Outside of having the standard skillset and education, there are other ways to be a strong candidate for these lucrative gigs. Here are five things experts recommend to improve your application, whether you're working in the field already or not.
1. Check out online courses. Like many tech-based fields, there are several online courses for AI topics, allowing someone to learn more about the field as a whole or gain more specialized knowledge. Some options offer certifications that can bolster a resume.
Here are Some of the courses that can help you learn and master Reinforcement Learning
- Artificial Intelligence: Reinforcement Learning in Python
- Learn Reinforcement Learning From Scratch
- Artificial Intelligence AI: Reinforcement Learning In Python
- Practical Reinforcement Learning
- Advanced Machine Learning Specialization
- Reinforcement Learning
2. Join outside organizations. Learning from others in the field can help improve your skills, so check out local hackathons or similar meetups. Getting involved with organizations like DataKind allow data scientists to work on new data, practicing and growing their skills while learning from their peers.
3. Add standard business knowledge. Many of the in-demand AI jobs are technical by nature, but knowing how to translate those developments to other businesses or consumers is important for any organization.
Since these folks are typically very technical, they don't necessarily have the business acumen necessary to translate the results of these models into operationalized AI systems that impact the bottom line while having a positive impact on the customer experience as well. So there's also increasing demand for a different skill set that includes familiarity with AI techniques as well as deep business and domain expertise.
4. Read—a lot. Multiple experts agreed: Those working in AI should always be learning, and reading is a way to do that. Roman Yampolskiy, director of the Cybersecurity Laboratory at the University of Louisville, recommended subscribing to scientific publications.
The real trick is to read. Read a lot. Not just your own area but all the related areas. And a few things even more distant, recommending science and nature topics across multiple platforms.
5. Be a sponge. AI is a rapidly developing field, so being prepared to explore as many experiences and opportunities in AI as possible.
Keep up to date on latest research in AI - there is so much advancement happening every single day. This may include looking for opportunities inside their current workplace or outside. Make sure that they do not get pigeon holed into one area."
Career Advice From Machine Learning & Data Science Experts
There is no one size fits all solution. Your situation could be different (educational background, experience etc.), this is where experts and mentors from Machine Learning & Data Science field understand your profile better and guide you in the right path.
Here are some of the experts who are willing to help you achieve your career goals. They conduct a personal one–to–one over Skype and discuss your profile and areas for improvement.
- Arihant Jain (Specialist: Machine Learning, Data Science, Predictive Modelling)
- Jose Munoz Mata (Specialist: Data Science, AI, Career Development)
- Ujjyaini Mitra (Specialist: Data Science, Career movement to Data science)
- Mario Lewis (Specialist: Data Science, Big Data and Analytics)
- Sabrish Surender (Specialist: Data Science, Big Data, IoT, Cloud)
- Harpreet Dua (Specialist: Data Science, Business Analytics)
- Ganapathy Govindan (Specialist: Data Science, Big Data)
- Tarun Sukhani (Specialist: Data Science)
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