Internet job portal ‘Indeed.com’ links potential employers with people who are looking to take the next step in their careers. The proportion of job posts on their site, relating to ‘Data Science’, a specific job in the AI category, is growing fast (see chart below). More broadly, Artificial Intelligence & machine learning skills, of which ‘Data Scientist’ is just one example, are in demand.
No wonder that it has been termed as the sexiest job role of the 21st century. Interest comes from an explosion of jobs in the field from big companies and Start-Ups, all of which are competing to come up with the best AI business and to earn the money that comes with software that automates tasks.
The skills shortage associated with Artificial Intelligence represents an opportunity for any developer. There has never been a better time to consider whether reskilling or upskilling in AI could be a lucrative path for you.
Below : Indeed.com. Proportion of job postings containing Data Scientist or Data Science.
The AI skills gap the market is experiencing comes from the difficulty associated with finding an individual demonstrating a competent mixture of the very disparate faculties that AI roles require. Artificial Intelligence and it’s near equivalents such as Machine Learning and Neural Networks operate at the intersection of what have mostly been two very different disciplines – statistics and software development. In simple terms, they are half coding, half maths.
Hamish Ogilvy, CEO of AI based Internal Search company Sajari is all too familiar with the problem. He’s on the front line, hiring AI developers. “The hardest part”, says Ogilvy,
“is that AI is pretty complex and the average developer/engineer does not have the background in maths/stats/science to actually understand what is happening. On the flip side the trouble with the stats/maths/science people is that they typically can’t code, so finding people in that sweet spot that have both is pretty tough.”
He’s right. The New York Times suggests that the pool of qualified talent is only 10,000 people, worldwide. Those who do have jobs are typically happily ensconced, paid well, treated appropriately and given no reason whatsoever to want to leave.
In fact, an instinct to develop AI skills will serve any technology employee well. No One can have escaped the many estimates, from reputable consultancies, suggesting that Automation will replace up to 30% of jobs in the next 10 years. No job is safe. Every industry is touched by AI in some form. Any responsible individual with a view to the management of their own skills could learn ML and AI skills to stay relevant in current times. Even if you don’t want to move out of your current job, learning ML will probably help you adapt better in your industry.
What is a typical AI job and what will it pay?
OpenAI, a world class Artificial Intelligence research laboratory, revealed the salaries of some of its key Data Science employees recently. Those working in the AI field with a specialization can earn $300 to $500k in their first year out of university. Experts in Artificial Intelligence now command salaries of up to $1m.
Source: The New York times
Indraneil Roy, an Expert in AI and Talent Acquisition who works for Edge Networks puts it this way when outlining the difficulties of hiring the right skills and to explain why wages in the field are so high.
“The challenge is the quality of resources. As demand is high for this skill, we are already seeing candidates with fake experience and work pedigree not up to standards.”
The phenomenon is also causing a ‘brain drain’ in Universities. About a third of jobs in the AI field will go to someone with a Ph.D., and all of those are drawn from universities working on the discipline, often lured by the significant pay packages which are available. So, with huge demand and the universities drained, where will future AI employees come from?
3 ways to skill up to become an AI expert (And earn all that money?)
There is still not a lot of agreed terminology or even job roles and responsibility in the sector. However, some things are clear. Those wishing to evolve in to the field of AI must understand the conceptual thinking involved, as a starting point, whether that view is found on the job or as part of an informal / formal educational course.
Specifically, most jobs in the specialty require a working knowledge of neural networks, data / analytics, predictive analytics, with some basic programming and database skills. There are some great resources available online to train you up. Most, as you’d expect, are available on your smartphone so there really is no excuse for not having a look.
1. Free online course: Machine Learning & Statistics and probability
Hamish Ogilvy summed the online education which is available in the area well.
There are “so many free courses now on AI from Stanford,” he said, “that people are able to educate themselves and make up for the failings of antiquated university courses. AI is just maths really,” he says “complex models and stats. So that’s what people need grounding in to be successful.”
Microsoft offer free AI courses for technical professionals:
Microsoft’s training materials are second to none. They’re also provided free and provide a shortcut to a credible understanding in an area simply because it comes from a technical behemoth.
Importantly, they also have a list of AI services which you can play with, again for free. For example, a Natural Language engine offers a facility for you to submit text from Instant Messaging conversations and establish the sentiment being felt by the writer. Practical experience of the tools, processes and concepts involved will set you apart. See below.
Google are taking a proactive stance on Machine Learning. They see it’s potential to improve efficiency in every industry and also offer free ML training courses on their site.
2. Take courses on AI/ML
Packt’s machine learning courses, books and videos:
Packt is working towards a mission to help the world put software to work in new ways, through the delivery of effective learning and information services to IT professionals. It has published over 6,000 books and videos so far, providing IT professionals with the actionable knowledge they need to get the job done – whether that’s specific learning on an emerging technology or optimizing key skills in more established tools.
You can choose from a variety of Packt’s books, videos and courses for AI/ML. Here’s a list of top ones:
- Artificial Intelligence by Example [Book]
- Artificial Intelligence for Big data [Book]
- Learn Artificial Intelligence with TensorFlow [Video]
- Introduction to Artificial Intelligence with Java [Video]
- Advanced Artificial Intelligence Projects with Python [Video]
- Python Machine learning – Second Edition [Book]
- Machine Learning with R – Second Edition [Book]
Coursera’s machine learning courses
Coursera is a company which make training courses, for a variety of subjects, available online. Taken from actual University course content and delivered with tests, videos and training notes, all accessed online, each course is roughly a University Module. Students pick up an ‘up to under-graduate’ level of understanding of the content involved.
Coursera’s courses are often cited as merit worthy and are recognizable in the industry. Costs vary but are typically between $2k and $5k per course.
3. Learn by doing
Familiarize yourself with relevant frameworks and tools including Tensor Flow, Python and Keras.
TensorFlow from Google is the most used open source AI software library. You can use existing code in your experiments and experiment with neural networks in much the same way as you can in Microsoft’s.
Python is a programming language written for a big data world. Its proponents will tell you that Python saves developers hundreds of lines of code, allowing you to tie together information and systems faster than ever before. Python is used extensively in ML and AI applications and should be at the top of your study list.
Keras, a deep learning library is similarly ubiquitous. It’s a high level Neural Network API designed to allow prototyping of your software as fast as possible.
Finally, a lesser known but still valuable resources is the Accord.net. It is one final example of the many software elements with which you can engage with to train yourself up. Accord Framework.net will expose you to image libraries, natural learning and real time facial recognition.
Earn extra points with employers
AI has several lighthouse tasks which are proving the potential of the technology in these still early stages. We’ve included a couple of examples, Natural Language processing and image recognition, above. Practical expertise in these areas specifically, image or voice recognition or pattern matching are valued highly by employers.
Alternatively, have you patented something? A registered patent in your name is highly prized. Especially something to do with Machine Learning. Both will help you showcase Extra skills / achievements that will help your application.’ The specifics of how to apply for patents differ by country but you can find out more about the overall principles of how to submit an idea here.
Passion and engagement in the subject are also, clearly appealing characteristics for potential employers to see in applicants. Participating in competitions like Kaggle, and having a portfolio of projects you can showcase on facilities like GitHub are also well prized.
Of all of these suggestions, for those employed, any on the job experience you can get will stand you in the best stead. Indraneil says
“Individual candidates need to spend more time doing relevant projects while in employment. Start ups involved in building products and platforms on AI seem to have better talent.”
The fact that there are not many AI specialists around is a bad sign
There is a demand for employees with AI skills and an investment in relevant training may pay you well.
Unfortunately, the underlying problem this situation reveals could be far worse than the problems experienced so far. Together, once found, all these AI scientists are going to automate millions of jobs, in every industry, in every country around the world.
If Industry, Governments and Universities cannot train enough people to fill the roles being created by an evolving skills market, we may rightly be concerned to worry about how they will deal with retraining all those displaced by AI, for whom there may be no obvious replacement role available.
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