Artificial intelligence. It’s everywhere. As Frank Newport, senior scientist at Gallup, expressed it: “Whether they know it or not, AI has moved into a big percentage of Americans’ lives in one way or another already.” Newport made the comment in light of the results of a 2018 Gallup consumer survey. It found almost nine of ten (85%) US adults use at least one service regularly that features some element of artificial intelligence.
Approximately 84%, for example, use navigation apps, such as Google Maps and Waze. Seventy-two percent stream music or video using services, such as Netflix and Spotify. Almost half (47%) said they used smartphone personal assistants while 32% use ride-sharing apps, such as Uber and Lyft. Twenty-two percent have home personal assistants, such as Alexa and Google Home, and 20% use smart home devices, such as smart lights or smart thermostats.
(Image source: nytimes.com)
Artificial intelligence is transforming the business landscape, too. Machine learning, in particular, is one of the most common types of AI in development for business purposes. Used to process large amounts of data quicker and with greater accuracy than a human being could ever dream of achieving, machine learning systems and algorithms are being deployed left, right and center to enhance performance across a wide range of verticals in practically every industry. Business intelligence is being automated. Banks and financial institutions are applying artificial intelligence to automate fraud detection and credit assessments. Cybersecurity professionals are using machine learning to monitor systems, detect anomalies and adapt and respond to threat alerts.
Businesses also have available AI-powered chatbots, marketing automation technologies, robotics and autonomous vehicles. Artificial intelligence is permeating every industry, and many believe that AI will eventually be bigger than the Internet revolution. The question, however, is – who will build it?
The Artificial Intelligence Skills Gap
Most organizations are completely invested in artificial intelligence, according to 2019 research from SnapLogic. The report – entitled The AI Skills Gap – finds 93% of US and UK organizations consider AI and machine learning to be a top business priority, and have various projects planned or already in production.
(Image source: snaplogic.com)
More than half (51%), however, of the IT leaders surveyed for the study acknowledge they don’t have the right mix of AI skills in-house to launch their strategies. In fact, 40% of respondents said a lack of skilled talent was the number one barrier to advancing artificial intelligence initiatives.
To create the right artificial intelligence team, 68% of organizations said they are investing in retraining and improving the skills of existing employees, 58% indicated they are identifying and recruiting skilled talent from other companies and almost half (49%) think recruiting from universities is the most effective means of finding eligible candidates to join an AI team.
What sort of skills do these organizations want their candidates to have? According to the report, coding, programming and software development (35%) are the priority skills and attributes organizations are seeking. Thirty-four percent consider an understanding of governance, security and ethics as a priority too, while 33% think data visualization and data analytics are necessary skills. Slightly more than one-quarter (27%) are looking for talent with an advanced degree in a field closely related to artificial intelligence or machine learning.
(Image source: snaplogic.com)
Gaurav Dhillon, CEO at SnapLogic, comments on the findings: “The AI uptake figures are very encouraging, but key barriers to execution remain in both the US and UK. For organizations to accelerate their AI initiatives, they must up-skill and recruit the right talent and invest in new technology and tools. Today’s self-service and low-code technologies can help bridge the gap, effectively democratizing AI and machine learning by getting these transformative capabilities into the hands of more workers at every skill level and thus moving the modern enterprise into the age of automation.”
Top Jobs Involving AI Skills
As organizations race to implement artificial intelligence and machine learning into products and services, the demand for AI and ML skills is also increasing. AI job postings on Indeed increased 29.1% since 2018. Interest from job seekers, however, is moderating, says Indeed’s analytics team. From May 2018 to May 2019, searches for AI-related jobs decreased 14.5%. Why? Most likely because of the skills gap – there are more open jobs than qualified artificial intelligence workers to fill them. Much of tomorrow’s AI workforce may still be in school or awaiting training, Indeed’s analysts suggest.
The Indeed team stated, “For example, consider data scientists, whose job is to take raw data and apply programming, visualizations and statistical modeling to extract actionable insights for organizations. Given that data is ‘the new oil,’ data scientists are in high demand, and our research shows job postings jumped 31% from 2017 to 2018. During the same period, however, job searches only increased about 14%.”
What are the most sought AI-related jobs posted on Indeed from 2018 to 2019? Indeed’s analytics team identified the top ten positions with the highest percentage of job descriptions that include the words “artificial intelligence” or “machine learning”. Here they are:
(Image source: blog.indeed.com)
While still in demand, Indeed notes director of analytics, statistician, principal scientist, computer scientist, research engineer and data engineer remain contenders for the top ten list, though they weren’t on it for 2019.
Machine learning engineer job postings take the top spot. What does a machine learning engineer do? As Indeed describes it: “Machine learning engineers develop devices and software that use predictive technology, such as Apple’s Siri or weather-forecasting apps. They ensure machine learning algorithms have the data that needs to be processed and analyze huge amounts of real-time data to make machine learning models more accurate.”
Perhaps, the most noteworthy finding of the entire list, however, is it reflects the increasing demand from all types of companies for data scientists. Indeed’s analysts explain that many employers now need an entire data science team – with staffing from junior to director levels – to take advantage of the many opportunities in the new data-driven business world. Data scientists collect, analyze and interpret data from multiple sources and apply analytics models to understand better how various business functions perform. The business can then make better-informed decisions based on the gathered insights. Essentially, the goal of the data scientist is to discover hidden patterns in raw data to help businesses improve and increase profits. Companies are now hiring for a range of experience levels in data science, says Indeed, which will help many companies better compete in the tight labor market.
Building a Career in Artificial Intelligence
Naturally, a career in artificial intelligence is not one-size-fits-all. There are numerous avenues available to you, and different industries and job positions will require different skillsets. That said, anyone interested in starting a career in artificial intelligence should be developing his or her computer skills and programming skills, as well as an understanding of algorithms, at a minimum. Mathematics is also essential, as well as knowing how to prepare data. Good business knowledge is a must, as well as soft skills, such as communication, collaboration, time management and adaptability.
There are ample job opportunities in the field of AI. Let’s consider the skills, qualifications and experience you’ll need to start building a career in artificial intelligence.
Many organizations will require specific education based on foundations of math, technology, logic and engineering perspectives. In practice, this often means at least a bachelor’s degree in mathematics, computer science and/or information technology.
If you’re thinking of attending a school to become an AI specialist, then you should seek degree courses that offer specific majors in artificial intelligence, or pursue an AI specialization from within majors, such as computer science, information technology or engineering.
A college degree, however, is not a prerequisite for a career in artificial intelligence. Some of the world’s top tech companies – including Google, Apple and IBM – have started to waive their requirement for bachelor’s degrees when hiring. These and other innovative companies recognize the candidates they want to hire are self-driven, passionate and eager to take the initiative – none of which necessarily require a university education. What’s more, with the vast amount of resources now available on the Internet – in particular, the huge number of specialized AI certifications available via online programs – it is now possible to learn almost anything at home, if you expend the effort.
Whether or not you have a relevant bachelor’s degree or are in the process of pursuing one, know that you will be able to acquire the necessary technical skills to land a job in artificial intelligence with a master’s degree or by registering for either online or offline courses (from accredited institutions).
What technical skills will you require?
Technical Skills Required for Jobs in Artificial Intelligence
Machine Learning Engineer
The role of the machine learning engineer is at the heart of the majority of AI projects, which is why the position is one of the most sought across industries. Machine learning engineers need strong software skills and the ability to apply predictive models and to utilize natural language processing (NLP) while working with massive data sets.
To pursue a career in machine learning, you will need working knowledge of programming languages, such as Python, R, Java and C++. Hiring companies are also looking for candidates with degrees or certifications in computer science or mathematics, and those with experience in agile development practices.
First and foremost, data scientists work with data. They are responsible for collecting, analyzing and interpreting large amounts of data. They leverage both predictive analytics and the tools and algorithms machine learning engineers have developed to unearth insights from data that lead to strategic decision-making.
The most desired technical skills for those pursuing a career in data science are in-depth knowledge of SAS; programming languages, such as SQL, Python and R; and experience using big data platforms and tools, such as Hadoop and MapReduce.
Research scientists will require skills in multiple artificial intelligence disciplines, including machine learning, computational statistics and applied mathematics. Candidates must demonstrate extensive knowledge and experience in graphical models, data representation, reinforcement learning and natural language processing.
Additional skills in parallel computing and distributed computing and working knowledge of algorithms and computer architecture will be an advantage.
Business Intelligence Developer
Business intelligence developers analyze complex data sets to identify current business and market trends to increase an organization’s profitability and efficiency. Responsible for designing, modeling and maintaining data in cloud-based data platforms, business intelligence developers must have strong technical, problem-solving and analytical skills.
Bachelor’s degrees or certifications in computer science, mathematics or a related field will be required to land a job as a business intelligence developer. Experience in data warehouse design, data mining and BI technologies will also be preferred.
Though hard skills are, of course, imperative for anyone looking to start a career in artificial intelligence, non-technical soft skills – those that enable a candidate to interact and work effectively with others – are still important.
In working scenarios, artificial intelligence professionals must be able to communicate with others about the work they are doing and why they are doing it. They must also translate data insights into language that’s easy for non-technical people to understand. Excellent communication skills are, therefore, a must.
Companies are looking for people who can think of new and better solutions to old problems. Creativity applies to practically every job – including artificial intelligence. As Paul Petrone, senior editor at LinkedIn Learning explains, “Companies don’t want business analysts who just crunch numbers; they want analysts who can think of creative solutions based off what the numbers are telling them.”
Candidates should research the industry that interests them. Though practically every sector is looking to implement artificial intelligence, they are all doing so to solve different problems. Healthcare has different problems than finance; finance has different problems than education; and education has different problems than manufacturing. Gaining an understanding of the real-world pain points of your chosen industry and how AI applications can solve them will be hugely advantageous when choosing courses to pursue, and, of course, when you write your résumé and sell yourself at interviews.
Courses and Resources
Today, people who want to start a career in artificial intelligence are able to find an abundance of online courses and educational resources to help them gain experience and become certified.
Here are just a few you should consider:
- Learn with Google AI: This recently-launched course from Google is intended to broaden the understanding of artificial intelligence among the general public. Designed so that even those with no prior knowledge of AI can start learning, these courses are a great place to start.
- Columbia University – Machine Learning: Available for free online with an option to pay for certification, this course teaches machine learning models, methods and applications for solving real-world problems, using probabilistic and non-probabilistic methods as well as supervised and unsupervised learning.
- Stanford University – Artificial Intelligence: Principles and Techniques: Learn the foundational principles that drive real-world AI applications, including search, speech recognition, face recognition and autonomous driving.
- GeekForge – Daily Email List of AI and ML Coding Tasks: This is an excellent opportunity to educate yourself and build experience with AI technology. GeekForge emails you artificial intelligence tasks created by specialists, which you can solve independently or discuss with the community.
- Udemy – Artificial Intelligence A-Z: Learn How to Build an AI: This course teaches you how to combine the power of data science, machine learning and deep learning to create powerful AI for real-world applications.
- Nvidia – Fundamentals of Deep Learning for Computer Vision: An introduction to the practice of deep learning through the applied theme of computer vision. You will learn and deploy neural networks and implement deep-learning workflows, such as image classification and object detection.
With the increasing proliferation of artificial intelligence in practically every industry, you’ve chosen the right time to start building a career in AI. The number of job opportunities available will only increase, as does the need for the technology. Each position, however, requires education, experience and training – so dedicate a healthy portion of your time to begin today, learn as much as you can and start expanding your skills. While demand for top AI talent is currently outstripping supply, this is likely to change during the coming years as more and more organizations start training their existing employees, universities start offering more AI courses and more self-motivated individuals begin the self-learning process to gain an advantage over other candidates. Be one of them, for artificial intelligence is set to create some of the most exciting job opportunities available during the very near future.