Working With AI: Your Guide to a Potential Future Career

It should not come as a surprise that quite a few people consider artificial intelligence to play a prominent role in our futures. In fact, AI has already established itself as a significant player.

Some parts of modern society might be against robots and everything else that artificial intelligence brings, but such a trend has been shifting. More and more people are starting to realize that monotonous jobs can be left to machines.

Automotive manufacturing is a great example of having robots do the heavy lifting and making it easier for us humans. One should not consider AI a fad.

If anything, it will continue to make leaps and bounds, growing in the future. And since the industry is expected to grow, it will need professionals who can do various jobs. Is artificial intelligence something that you might be interested in doing yourself? If so, this article should give you a general idea of what you can expect.

In this article we cover:

1. AI Definitions
2. The Necessary Skills
3. The Top Jobs

1. AI Definitions

Let’s start with the definition. Artificial intelligence aims to surpass real human intelligence with the help of machines. Looking up AI, you are likely to encounter the phrase “machine learning,” which is more or less a synonym for artificial intelligence (we may say that machine learning is a subset of AI which is most used in the industry today).

AI learns by recognizing patterns and makes efficient decisions immediately. The purpose of smart AI is to build machines that can adjust and think for themselves rather than follow predetermined protocols. Ultimately, each subsequent decision coming from an AI should be smarter.

Other than the aforementioned automotive manufacturing, other examples of AI include:

• Speech recognition on smartphones and smart home devices
• Chatbots used in ecommerce stores
• Email spam blockers and plagiarism checkers
• Self-driving cars
• The computer software that detects redundant junk and deletes it to increase available storage

Since the likes of Google, Amazon, Microsoft, Facebook, and other giant tech companies are hiring AI talents and offering them solid salaries, it works as another indication of AI’s prominence in our society. 

The question is, what does one need to work in the artificial intelligence industry? Would a college degree suffice?

2. The Necessary Skills

Having a proper education is a significant plus, particularly when it comes to such scientific work. You have to keep in mind that mimicking the human brain using machines is no simple feat. The problems are difficult to solve, and it is even harder to become someone who can call themselves a master with AI. 

While studying for years at a university or college is pretty much a must, as well as keeping up with the latest available research, it is not always enough. Some traits should be present for those who wish to become AI professionals.

As a true AI professional, you should possess the following:

• Love for math and science – Hard sciences are one of the AI foundations, so those who are gifted in maths, physics, chemistry, and other sciences are bound to find more success in excelling in AI.
• Data Analytics and Data Visualization Skills – As an AI engineer you should be able to analyse and prepare data for various algorithms.
• Knowlage of basic Machine Learning Algoritms – The foundation of 
• Programming skills – Python and R are the most popular programming languages that are used for AI development. You should be familliar and comfortable with at least one of them. 
• Deep Learning – Knowlage of Neural Networks and various architectures is one of the most demanding AI skills today.
• Reinforcement Learning – Often observed as the future of AI expecially in the combination with deep learning.

All these skills can be learned from our ULTIMATE GUIDE TO MACHINE LEARNING WITH PYTHON. Check it out today and kickstart your AI career.

Apart from that, as an AI professional you should have:

• Calm mind – Since AI is still a relatively new concept, there are lots to learn about it. The process can be quite difficult and overwhelming at times. Those who fail to keep a clear head are likely to get frustrated.
• Natural curiosity – The desire to learn more and strive for constant improvements in developing AI is also a commonly expected trait.
• Critical thinking – Trial and error are inevitable. The odds of encountering a problem and struggling to solve it will be common, but if you have a critical mind, you are more likely to overcome these obstacles.

3. The Top Jobs

Looking at the top jobs in the industry should also give you an idea of what you can expect. There are new opportunities emerging regularly, but for now, these five disciplines seem to be at the top.

3.1 Business Intelligence Development

Businesses hire intelligence developers to determine trends and find formulas that will increase profits. There is a lot of data to analyze, and manual work would not cut it. Whether it is competition or customer behavior, aspiring businesses need to involve AI in their efforts.

3.2 Research Science

Research science has a wide range of possible fields that one can focus on. Applied mathematics, statistics, machine learning, computer perception, languages, graphical models are a few examples of what you can expect to work as a research scientist in AI.

3.3 Data Science

Data scientists combine tools at their disposal and use machine learning to go beyond what traditional analytical tools have to provide. After all, the more insight one gains from data, the more one can do with it.

3.4 Robotic Science

Job automation is one area where robots excel, but someone has to create machines that can actually do the work properly. Building mechanical devices and developing prototypes will be the majority of work you would be doing as a robot scientist.

3.5 Machine Learning/Deep Learning Engineer

Machine learning engineers should have exceptional software and coding skills in addition to knowing maths. This particular position is one of the most sought-after in the industry, meaning that a potential candidate should stand out from its competitors if they want to get the job.

3.6 MLOps Engineer

Deployment of Machine Learning models is an art for itself. In fact, to successfully put a machine learning model in production goes beyond data science knowledge and engages a lot of software development and DevOps skills. Why should you care about all this? Well, at the moment one of the most valued roles in data science teams are Machine Learning engineers. This role gathers the best of both worlds.

Conclusion

In this article we explored necessary skills and job positions in the vast world of AI.

Thanks for reading!