Even if you haven’t read any other article in this issue, you could not have missed the cover. Artificial Intelligence is not something that needs to be explained all over again, even to a layman. If you have been observing the buzzwords of the last few years, you’ll have no problem in recognising words such as big data, machine learning etc. You would know by now where we were headed. Where AI is not just a reality, but an attractive career choice as well.
Every industry has a need for AI. In such a new but hot job area, it is no shocker that the demand is high but supply is low. On the other hand, there are ample ways to gain the right skills and make a career in this right now. But before you do that, you have to understand what a career in artificial intelligence really involves.
You cannot ask someone ‘to go and learn artificial intelligence’ or ‘do you know artificial intelligence?’. The phrase, by itself, mostly invokes the outcome of its applications, rather than the intricacies of its development. So, you’re more likely to think of the Terminator rather than machine learning when asking yourself ‘what is artificial intelligence?’. This umbrella term also encompasses the field of data science.
Machine learning, as the name implies, enables the ‘learning’ part in artificial intelligence. This is different from just getting insights from data (which is in the domain of data science). Machine learning enables a program to change based on the data it encounters, without the requirement of explicitly programming every possibility. For example, the News Feed on Facebook uses machine learning to personalise your feed. If you frequently stop scrolling in order to read or “like” a particular friend’s posts, the News Feed will start to show more of that friend’s activity in your feed. Behind the scenes, the software is simply using statistical analysis and predictive analytics to identify patterns in your data and use the patterns to populate the News Feed. Should you have a fight and no longer want to read, like or comment on the friend’s posts, that new data will be included in the data set and the News Feed will adjust accordingly to incorporate your enemity.
The other technical component is data science – this is where you actually deal with the data to get results that enable machine learning. To achieve the level of intelligence, you need a practical AI implementation, vast dataset processing to cover every possible event and establish a predictable behavior in your AI.
While machine learning and data science are broad fields that constitute AI, they also consist of the actual skills that you will need to master in order to work in AI. One of the first things that you’ll need is a background in engineering, to be more precise – software engineering. While the exact language may not matter, a fundamental understanding of programming and how to work with code will go a long way in getting a job in AI. Pro tip – try to go for open source projects and keep your code available for potential interviewers to see.
While there are more technical skills that you will need (ones that we are coming to) at this point, it is more important to talk about the non-technical skills that will be expected. Working in AI requires a constant urge to solve problems. Think of it like a riddle solving addiction – hobbies like game development or solving crossword puzzles are generally a bonus.
Mathematics forms another important part of the required skillset. If you’re not aware how machine learning works, it is understandable if you don’t really understand the significance of mathematics in AI. However, in either case, a strong grasp in certain areas of mathematics is a must to solve actual problems with AI. A solid grasp of probability, statistics, linear algebra, mathematical optimisation, data sets and algorithms will be crucial in your understanding, and as a consequence, your construction of an AI system. In many other situations, treating an algorithm as a black box might not be a big deterrent but that isn’t the case with AI.
|AI Skills Master List|
|A list of skills and areas to focus on to build an expertise in AI
Credits: Dr.Debashis Guha – Program Director – Machine Learning – SP Jain School of High Technology
If you’re not working in research, working with AI will almost always involve the need for an industry expertise based on where it is being applied. For example, if you want to work at Waymo, expertise in automobiles is a bonus. In fact, a lot of AI based companies look at hiring experts in another field and then training them on the job in areas like machine learning and data mining. This is due to the lack of clear academic direction when it comes to treating artificial intelligence as a subject.
As a result, the most reliable source from where you can learn more about AI related topics and develop skills is the internet. Platforms like Udacity and Coursera offer some really useful courses in this area. Udacity’s Artificial Intelligence Nanodegree is developed in collaboration with IBM Watson, Amazon Alexa and Didi. It is a two-term program that first introduces the fundamentals of AI, then further allows you to focus on your area of interest. The areas include Natural Language Processing, computer vision, or speech recognition.
Working it out
Quite similar to the cloudiness surrounding the skills required for AI, there is a confusing mist that covers the kind of roles that you will get if you are looking for a job in artificial intelligence. To clear that up, let us start off by saying that the most common roles that you will see will be closely related to the skills that you’ve seen so far – machine learning and data science.
When it comes to the kind of responsibilities and the work that you’ll have, this is where you will find quite a few familiar terms. Broadly, these are the areas you’ll most likely be working in (along with some of the most prominent companies, both Indian and global, working in that area):
- Natural Language Processing and Generation –
> Generation: Attivio, Automated Insights, Cambridge Semantics, Digital Reasoning, Lucidworks, Narrative Science, SAS, Yseop
> Speech Recognition: NICE, Nuance Communications, OpenText, Verint Systems.
- Computer vision – Affectiva, Blippar, Blue River Technology, Captricity, uSens Solutions
- Virtual agents/assistants: Amazon, Apple, Artificial Solutions, Assist AI, Creative Virtual, Google, IBM, IPsoft, Microsoft, Satisfi
- Machine learning: Amazon, Fractal Analytics, Google, H2O.ai, Microsoft, SAS, Skytree
- Deep learning: Deep Instinct, Ersatz Labs, Fluid AI, MathWorks, Saffron Technology, Sentient Technologies.
- Decision management: Advanced Systems Concepts, Informatica, Maana, Pegasystems, UiPath
- Robotic process automation: Advanced Systems Concepts, Automation Anywhere, Blue Prism, UiPath, WorkFusion
While the global artificial intelligence industry has gone ahead by leaps and bounds, India is not too far behind. This pace is being achieved by startups who are doing some really innovative work in this area. “India is in the middle of an artificial intelligence revolution”, explains Ishan Gupta, MD, Udacity India, “Along with the biggies, which are heavily investing into AI, more and more startups have come up that use or deal with artificial intelligence. Some of the companies in India that one should look out for are Google, Microsoft, LinkedIn, Adobe, Amazon, Ola, Flipkart and Myntra to name a few.” Even though the major focus right now is to address customer issues (like implementing customer care systems) using AI, more advanced use-cases are beginning to surface. It is evident that India is a considerably complex market to implement AI due to the diversity of languages involved.
“There will be huge growth in job demand in machine learning and data science”, says Dr. Debashis Guha, Program Director, Machine Learning, SP Jain School of High Technology, “The global machine learning market is expected to reach $12.5 billion in 2019, up from $2.5 billion in 2014, at a CAGR (Compounded Annual Growth Rate) of 38%. The job market should see at least a 30% CAGR. Job openings range from positions requiring only about a year of experience in R or Python, to seasoned professionals with more than 10 years of experience. Serving as Product Managers, they should be capable enough to define the project requirements and the solution architecture. This also suggests a career arc, starting from positions involved with the nitty gritty of coding and implementation. This evolves to people entering senior positions who are the ones defining requirements, plannig the architecture, and carrying out the projects.”
When it comes to compensation, the low demand-supply ratio in this area ensures that they are quite competitive. For example, according to Glassdoor, a machine learning scientist at Amazon in India could make about 33 to 36 lakhs per annum. Looking at more popular estimates, it would be safe to say that one could easily expect a starting compensation of more than six lakhs for an entry level Data Scientist role which could effectively go up to 15-17 lpa depending on the employer. Although, if you were to look for a job abroad, you would be looking at more matured and demanding markets. For instance, according to Ishan Gupta, the average salary of an Artificial Intelligence Engineer in Silicon Valley is around $158,123 per year.
Not necessarily, as this article must have told you by now. Just like any other technological revolution, the advent of AI in the workplace has created a panic-driven situation where people are worried which jobs will be stolen by robots next. Now that you’ve understood what AI signifies as a profession, it must be clear that the demand for eligible people in the field of AI is only increasing the number of jobs in the market. Down the line, the people replaced by AI implementations will be absorbed again in new expertise areas which are being created everyday due to the advent of AI.
On the other hand, the industry is benefitting greatly from AI technologies. Downtimes are reducing, scope of human error is decreasing with corresponding increase in efficiency. “Companies are increasingly taking on the data-driven approach and there is a vast demand for professionals who can use artificial intelligence and convert it into meaningful business outcomes,” says Ishan Gupta. “There has been a rising adoption of machine learning and artificial intelligence technologies in multiple industries such as healthcare, finance, education, retail, media, advertising and education. Global developments in the field of artificial intelligence are going to define the future. Graduates of AI programs will be uniquely poised to contribute to these developments in innovative and awe-inspiring ways.” With interest in this field rising everyday, now would be the time to get started on the skills mentioned earlier, so that someday you can be remembered in history as the person who brought about the rise of the robots and the end of humanity.
This article was first published in the April 2017 issue of Digit magazine. To read Digit’s articles first, subscribe here or download the Digit app for Android and iOS. You could also buy Digit’s previous issues here.