If you’re looking for to progress your career and are now averse to earning money, then Artificial Intelligence (AI) and Machine Learning (ML) may well be a field worth considering.
Stanford University has just completed its inaugural AI study, comprising a review of the last 100 years of research, development and progress in to the subject. It is a thorough piece of work. One of the most interesting aspects of the document they produced was information sourced through job search platform ‘Indeed.’ (See graph below) Artificial Intelligence and Machine Learning are the most desirable jobs in the IT industry today.
Those with the necessary skill are earning a great deal of money for their talent. The number of opportunities in AI related profession is rising fast, at a time of a skills shortage. The result is sky rocketing wages. Some experts are earning as much as $1 Million per year. Stanford’s research also found that while there is a huge demand for this talent in the IT industry, there are only a few thousands of such competent people around the world.
Therefore, the organizations that possess such talent go to great lengths to retain them and ensure that they remain happy enough to stay.
What is Machine Learning?
Machine Learning falls under the umbrella of the broader concept – Artificial Intelligence (AI). It primarily consists of giving software systems the ability to ‘learn’ by themselves.
AI makes it quicker to ‘train’ computers to execute tasks in a real world. Computers struggle with reality. Neither physical environments, or people, often behave in the sort of clear cut, ways that software tend to like.
AI uses the same sort of ‘fuzzy logic’ pattern matching that we do, as humans, (although the mechanism they use to ‘learn’ it is very different) to discern the right answer using a lot of clever mathematics and statistics in the background.
The learning experience for the machine begins with examples that the experts provide. Each instruction is associated with direct experience or instruction. To put that in more human terms, an operator might ‘train’ an AI to recognise ice-creams by showing it 10,000 pictures of ice creams and 10,000 images of ‘other things’. (Critically, the operator, initially, has to tell the AI algorithm they are training that each image is or is not an ice cream.)
The machine then goes through patterns of data that are associated with ‘ice cream’ and ‘other things’ to make better decisions in the future about which is which, when shown a picture.
As you can see, any job in AI, revolves heavily around statistics and software development. This means that the job requires not only an efficient coder, but someone excellent with numbers and abstract thinking. Does that sound like you?
How can you go about acquiring AI & ML skills?
Some of the most efficient ways to get yourself acquainted with machine learning are shown her. All education comes at a cost of time and very often money. Bear in mind, any resources we show below can be accessed from your mobile phone. So long as you have a mobile data plan with enough data, you could acquire the skills you need in your ‘downtime’ – on the bus to work, for example.
- Free Online Courses
Ultimately, AI is likely to automate a large number of job roles. A proportion of the money companies save by automating employees work will end up in the hands of the companies which provide the automation software. That means there is a lot of work in Silicon Valley, and the rest of the world (notably, China) from technology corporations who are trying to have their AI based services become the industry norm.
To you, that means there are a lot of free digital resources available. Additionally, since it’s free to distribute the course material content their professors have produced, some higher education universities make their AI curricula available online.
Anyone with the sheer will to learn can enrol in an online program to pick up the skills required to succeed in the area. Here are two of the best.
- Stanford University: Stanford (yes, again) has made available some of their own professors that come close to offering (at least the material, educational components of) a university under-graduate in the field. Their free ‘Introduction to AI’ is a phenomenal place to start your retraining.
- Microsoft: Gate’s company too has free AI related training materials aplenty online that can provide a credible understanding of the subject. Moreover, there are tonnes of AI tools that Microsoft lets you play with to get familiar with the technology. For instance, you can import your conversations from an instant messaging app and establish the sentiment behind a particular text with the help of a tool called Natural Language engine. Microsoft also mark their courseware clearly as either ‘Beginner’ or ‘Advanced’ which, when you’re stating out is the sort of thing you need to know.
- Take Paid Courses for Artificial Intelligence or Machine Learning
If online material doesn’t seem to be your thing then you could alternatively opt for paid educational books and videos delivered as part of a program which is structured for you and delivered by a professional trainer.
There is a broad range of educational material when it comes to Machine Learning and Artificial Intelligence for you to choose from if you have pockets deep enough.
Kaggle’s Machine Learning course is one example of some of the best AI content which is available online.
It’s not cheap in terms of either dollars or time invested (it lasts 6 months) but it will show potential employers just how seriously you are taking the move and could open the door to higher wages.
- Learn by Doing
As obvious it might sound, learning by doing, at your existing place of work, remains as one of the preferred choices for many employers. It’s an easy route which provides appropriating Machine Learning skills, for a lot of working professionals who may not be able to afford 6 month out of work to follow either the free or paid courses.
Employers value a preparedness to roll sleeves up and to ‘play’ with the concepts and software involved in a real world context. While the courses above have significant ‘real world experience’ there is no substitute for making all the mistakes you can in a beta environment at work.
