Artificial Intelligence (AI) is not here to replace humans; it is here to create a better world for us. Technology is evolving rapidly and revolutionising the way in which our world operates. From rocket launches into space to tiny chatbots on websites, AI is changing predefined norms. The impact of AI has been significant and because of its varied applications across industries, many technology professionals try for artificial intelligence jobs. Some of them come from an AI background and some prepare themselves for a career transition. If you are reading this blog, you probably fall into the second category. In this case, there are two factors that will help you build a successful career in artificial intelligence. First is getting necessary training and certifications. Second is avoiding the mistakes that other people on this career path have made.
You can get the necessary training for building a career in artificial intelligence through a reliable training program and a skilled mentor. Along with this, you can follow people from the field of AI on networking platforms like LinkedIn, to get more insights about the industry. It is also important that you stay updated about the developments in the field of AI and make self-learning a practice. Following expert AI sources that keep you updated can help you with this. After doing the hard work of going through training and self-learning, you should work on avoiding certain mistakes that people commonly make in their career transition into AI. A few of these mistakes along with tips to avoid them are explained below.
Mistakes to Avoid for Entry-Level Professionals Starting a Career in Artificial Intelligence
Use the tips mentioned below to avoid making the mistakes highlighted here and start your artificial intelligence career in the right way.
1. Not knowing about your basic focus areas: Building a focus area means knowing your strengths while working with AI. For example, your understanding of neural networks could be your strength or you could be a natural critical thinker. But how will you identify your strengths?
- Know the types of AI: Artificial intelligence has subsets, i.e. machine learning, deep learning and neural networks. Although interlinked, they differ in some basic principles. For example, a neural network is a set of algorithms used in machine learning which uses artificial neurons or nodes to identify and interact with data patterns. Study these basic principles carefully and select the ones that suit your interests. Then, start developing your skills in that particular technology to make it your speciality.
- Find a niche to focus on: Another way of building your focus area is to find the job role and industry that intrigues you the most. For example, if you are interested in finance, learn about AI jobs in the financial sector and build your expertise around it.
2. Not focusing on application based learning: Make sure you focus on practical applications as much as the theoretical aspects. Learning the theories will provide you in building your basics and practical knowledge will help you apply them in different situations. In artificial intelligence, practical experience will help you understand the different types of problems and develop new solutions. Here are a few tips:
- Run your programs: It is necessary to correctly identify the problems in your codes to build flawless programs. For this, you should run the codes you write and note the results. Executing the codes can introduce you to the problems that exist in your program after which, you can work on resolving them. Eventually, you will get better at programming and understanding the technology.
Running and testing your programs will also help you perform better in your job interviews. This is because learning the nuances of codes or projects will help you explain them clearly to your interviewers and showcase your expertise.
- Collaborate with others: Take up projects that will let you work with others in your field. This way, you will be able to explore a variety of approaches to solve problems and understand different perspectives. This process can also help you refine your soft skills as you will be communicating and coordinating with a team.
For collaborations, you can look at assistance from the LinkedIn communities you are a part of. If you have taken up a course, you can connect with the other like-minded peers and work with them.
- Find a mentor: A mentor can guide you through the assignments in your course and personal projects, and even help you prepare for jobs to start your career in artificial intelligence. The right mentor will give you an unbiased opinion of the situation, help you think and work better, and also suggest improvements, wherever required. To start your career in artificial intelligence in the right way, you need the right guide and hence, you should select your mentor carefully.
3. Directly working on advanced problems: Working on advanced problems before mastering your basics can discourage you. It might be a good idea to take it one step at a time. First, work on your basics and problem-solving skills. Then, gradually move towards solving advanced problems.
Here are some ways to improve your basics:
- Tighten your command over programming languages: To become an expert, start by practising the most popular languages like Python, Java and R. Try to work with algorithms every day, without fail. You can start by writing simple codes in the beginning, followed by the more complex ones.
If you feel that a programming language is hard to grasp, ask your mentor or fellow students to help you out. Alternately, you can reach out to the online communities that you are a part of and post your doubts there.
- Enter hackathons and similar competitions: Several platforms like HackerEarth have their own hackathons and AI competitions that can help you understand your shortcomings to help you work on them. Moreover, you can leverage such platforms to network with other people.
- Work on your data literacy: An AI that you build will be as good as the data you have used while building it. This is where data literacy comes in. Data literacy is the ability to derive useful information from an available set of data. You can work more efficiently on varied aspects of AI like data classification, pattern recognition and finding useful data sets, if you have a sound understanding of data literacy. Also, many times, the data sets from which an AI model is drawn can be biased and your data literacy can help you identify such biases with ease.
Mistakes to be Avoided by Experienced Professionals in Their AI Career
Professionals looking for artificial intelligence jobs should work on avoiding the following mistakes by using the tips mentioned below:
1. Not focusing on soft skills: Soft skills like critical thinking and creativity add value for the end-user. Good soft skills can also help you earn some brownie points during your interview.
Here a few ways in which you can practise AI soft skills:
- Converse with the people in your field: Social interaction will help improve your communication skills, creativity and critical thinking ability. Talk to your colleagues or join online communities for more interaction. Check out social platforms like Facebook and LinkedIn communities where people share updates about developments in AI, their problems and relevant solutions.
- Practise your soft skills whenever you are with people: Developing any skill needs practice and soft skills are no exception. Try to socialise more as it will help boost your confidence. Start reading and learning more, and respond promptly to people and try to solve their problems even if they are not related to AI. In the process of helping others, your mind will get conditioned to different perspectives which will help you develop creative solutions.
2. Not researching enough the problem at hand: There may be times when you try to resolve the problems in a code without researching what caused the problem in the first place. For example, when a chatbot gives an incorrect response, the issue can be identified. But, due to conditions like deadlines, you might skip the identification stage which may lead to more errors or can leave the error unresolved.
Here’s how you can focus on identifying the problem at hand precisely:
- Consult other people: Taking advice from your seniors, colleagues, etc. who can provide you with a new perspective and help you resolve the problem at its core. You can also talk about this in your online community and request different viewpoints from the experts.
- Ask more: Ask yourself, what is the origin of this problem? Which errors in the previous code caused this problem? This will help you get to the bottom by identifying the root cause of the problem and you will be able to resolve things easily.
3. Not being up-to-date with developments in the sector: Always lookout for the latest trends in the industry to increase your knowledge. This will give you a better perspective of the latest tools and methods.
Keep yourself updated in this way:
- Allot enough time to read and research: Read about your domain and research about the newer aspects and developments. Follow relevant publications and online resources as well.
- Attend social events for networking opportunities: Social events like seminars and webinars can keep you up-to-date about the developments and innovations in AI. This can also help you implement new observations in your projects.
AI is a booming industry which can offer fulfilling careers to professionals from diverse backgrounds. It may seem overwhelming as you need to learn continuously and practise your skills regularly. But, if you have enrolled in a good learning program and have access to a skilled mentor, your career transition into this field can be very smooth. Once you have selected a reliable source for learning, work on your shortcomings by avoiding the mistakes mentioned above. A good learning program can help you identify the mistakes you are making and rectify them so that you can have a successful start to a career in artificial intelligence.
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