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Becoming an AI Developer: Skills and Tips for Success

Tina Lj.8 min readApr 19, 2024Business & Life
Becoming an AI Developer: Skills and Tips for Success

AI engineers are in high demand. We see ads urging us to "Hire AI developers," and countless articles discuss the advancements in AI development. But what exactly is an AI developer? Or, better yet, who is an AI developer?

The AI market is growing, which would indicate the importance of acquiring skills in this field. Being an AI developer now offers opportunities for career growth and contributing to technological innovation.

In this blog post, we'll explore the beginnings of AI, how the collaboration of neuroscientists and logicians has shaped the field, and why it's important to hone your math skills (your parents were right: you need math in life). We'll also share some tips for beginners aiming for an AI developer career.

Part 1: Foundational skills

1. How did the AI concept start?

There is a high chance you already know the basics of AI and how artificial intelligence works, but starting from the bases, let's sum it up: AI is built upon the foundation of machine learning algorithms, which allow these systems to learn from and adapt to new data without being explicitly programmed.

AI technologies comprise a set of tools and methods designed to simulate human intelligence in machines. Key technologies include neural networks and machine learning models.

Neural (foundational for deep learning) mimics the structure of the human brain to process data. They are important for applications in natural language processing and computer vision. Machine learning models use algorithms to analyze data, learn from it, and make predictions or decisions without being explicitly programmed.

A neuroscientist and logician walk into a bar...

The concept of neural networks was first introduced in the 1940s. One of the earliest models was the McCulloch-Pitts neuron, proposed in 1943 by Warren McCulloch, a neuroscientist, and Walter Pitts, a logician. However, the idea of networks that could learn didn't take off until 1958 with the introduction of the Perceptron by Frank Rosenblatt. The perceptron was the first algorithmically described neural network, which laid the groundwork for future developments in neural networks. This model marked a milestone as it was capable of supervised learning.

Arthur Samuel coined the term "machine learning" in 1959. He developed checkers programs to explore game-based learning strategies for solving complex problems, given the game's mix of simplicity and strategic depth. His work demonstrated that machines could improve at tasks over time with experience, which is the core idea behind machine learning.

2. Is python lingua franca of AI?

In 2021, Python celebrated its 30th anniversary. It is said that it is one of the most important languages for AI development. What makes Python so well-suited to AI and ML? Primarily due to its comprehensive set of libraries and simple syntax, which streamline the development process and make it easier for developers to collaborate on complex projects. This ease of use and the ability to quickly adapt makes Python a great choice for AI applications. Additionally, Python's wide array of data visualization tools are essential for analyzing and presenting large sets of data.

According to some forums, there is an old saying, "Python is the second-best language for anything." Learning it certainly couldn't hurt, right?

While, according to many, Python is pivotal, familiarity with other programming languages can also be beneficial. Java offers robustness and portability in large-scale systems. R is preferred for statistical analysis and data visualization. Additionally, knowledge of C++ can optimize software performance, which is crucial for time-sensitive AI applications.

Many people find Python better than R, Java, or C++.

3. Don't run from math; it's beneficial for AI

Linear algebra is important in AI, particularly in areas like computer vision and machine learning. It deals with vectors, matrices, and operations that are essential for model training and transformations.

AI heavily relies on probability and statistics to make predictions and understand data patterns. Knowing how to perform statistical analysis enables AI developers to estimate the likelihood of outcomes and make more accurate predictions.

Calculus is used in AI to find the optimum parameters during model training through processes like gradient descent, ensuring the best performance of AI models in real-world applications.

4. Data skills are a way to go

An AI developer must be adept at manipulating and analyzing data, as it forms the core of AI development. This includes cleaning data, transforming variables, and using data science tools to uncover insights that drive AI solutions.

Data visualization is crucial for data analysis and for communicating complex concepts and results effectively. Tools and techniques in visualization help in understanding large volumes of data quickly and making informed decisions (which, you could say, are essential skills for any AI professional).

There are different AI tools for data visualization that can help you out. If you have tried something, feel free to send us a recommendation.

Part 2: What are core AI skills?

1. Machine learning and algorithms

Machine learning (as you know, a crucial component of AI development) can be categorized into supervised and unsupervised learning. Supervised learning involves training a model on a labeled dataset and providing the model with the correct answers during training. This method is commonly used for classification and regression tasks.

In contrast, unsupervised learning deals with unlabeled data and helps discover hidden patterns or intrinsic structures within the data set. It is often used for clustering and association tasks.

Any aspiring AI developer must be familiar with certain algorithms. For supervised learning, algorithms like linear regression, decision trees, and support vector machines are foundational. In unsupervised learning, k-means clustering and principal component analysis (PCA) are widely utilized.

2. Deep learning and neural networks

We already talked about neural networks near the beginning and how they are pivotal in deep learning (which is a subset of machine learning specializing in interpreting large volumes of data). Neural networks, particularly deep neural networks, are the primary architecture used in deep learning, consisting of multiple layers of interconnected nodes that process data in successive stages.

That way, they enhance the AI's ability to make decisions and predictions.

Several frameworks simplify deep learning development by providing pre-built functions and structures. TensorFlow and PyTorch are the most notable, offering extensive tools for building and training complex neural networks.

3. Natural language processing (NLP)

Natural Language Processing (NLP) allows machines to understand and interpret human language. The challenge in NLP is enabling computers to understand the context and meaning behind words in text and speech. This AI technology is behind applications such as chatbots, translation services, and sentiment analysis.

Several NLP libraries, such as NLTK (Natural Language Toolkit) and spaCy, simplify the implementation of NLP features. These libraries provide text processing tools capable of tasks ranging from tokenization to semantic analysis (all important for developing NLP applications).

4. Computer vision

Computer vision allows machines to understand and process visual data like humans. It involves tasks such as image recognition and object detection, which are important in healthcare diagnostics.

OpenCV is a widely used library for computer vision, providing a comprehensive suite of image processing capabilities.

Part 3: Advanced AI technologies

1. Reinforcement learning

Reinforcement learning (RL) is a type of machine learning where an agent learns to make decisions by interacting with an environment to achieve a goal. The agent receives rewards or penalties based on its actions (learning optimal behaviors through trial and error). This learning process mirrors the way humans learn from the consequences of their actions.

In gaming, RL algorithms have been used to develop AI that can defeat human players in complex games like Go and Poker. In autonomous vehicles, RL helps in decision-making systems for safer navigation.

2. Robotics and AI

AI enables robots to process sensory data and perform tasks autonomously. By incorporating machine learning models, robots can improve their precision and adapt to new tasks over time without human intervention.

Several tools and platforms facilitate the integration of AI with robotics. ROS (Robot Operating System) provides libraries and tools to help build robot applications. It offers functionalities for hardware abstraction, low-level device control, and commonly used functionalities in robotics.

Part 4: Building AI projects

Don't be afraid to be a beginner

Giving advice on where to start as a beginner is hard because it depends on your level of skills, knowledge, and, well, the ability to try and fail.

During my research, I found that many people recommended creating a spam detector using natural language processing techniques, which involves basic classification tasks.

Another project could be building a recommendation system, like those used by streaming services (we all used Netflix at some point), to understand machine learning algorithms applied to real-world data.

Clean your data

The first step in any AI project is acquiring the right data. Data can be sourced from various places, including public data repositories like Kaggle or generated through simulations.

Once data is acquired, cleaning is important as it directly impacts the performance of AI models. Cleaning involves handling missing values, removing duplicates, and correcting errors. For AI developers, clean data is the backbone of accurate AI systems.

Model building and evaluation

Building your first AI model involves selecting the appropriate algorithm and tuning parameters to best fit your data. For instance, a simple linear regression could be used for predictive tasks with numerical data, while deep learning models might be better suited for complex tasks like image recognition.

Once a model is built, evaluate your model's performance using relevant metrics. Common metrics include accuracy, precision, recall, and F1 score for classification tasks, and mean squared error for regression. Advanced models might require more specific metrics that are tailored to that task.

Understanding these metrics allows AI developers to refine their models and ensure they perform well in real-world applications.

Part 5: How to enter the AI industry?

1. The path of education

For those aspiring to become an AI engineer, courses and certifications can pave the way. Online platforms like Coursera, Udacity, and edX offer specialized courses in machine learning, deep learning, and data science, often taught by industry experts.

Or, of course, you can go by the book and pursue a bachelor’s or master's degree in computer science, data science, or a related field. Look into countries with strong educational backgrounds.

2. Gain practical experience

Hands-on experience is important for every job position, including AI. You will get real-world experience and learn from senior-level people. Check out the careers page from your favorite companies, or send them open applications.

3. Build a portfolio

A portfolio should include a variety of projects that demonstrate your skills in data analysis, model building, and AI solutions.

The portfolio should demonstrate your problem-solving skills, and if you need any help in building a strong portfolio, feel free to reach out to our Talent Acquisition Specialist at [email protected].

ai engineer

AI meetups for learning and networking

For someone wanting to become an AI developer, various skills could be considered important, from programming skills, mathematical and statistical knowledge, and data manipulation and analysis. But apart from these skills, cultivating a problem-solving mindset helps AI developers address challenges effectively. In fact, we based our employer branding strategy on the idea that we are problem solvers and engineers at heart.

We also suggest researching AI communities and events that can help expand your network. Of course, there are AI conferences such as NeurIPS or CVPR, but you don't need to look so far away. You can check local meetups, which offer various groups where you can join discussions and meet like-minded individuals. Not long ago, we had a PHP meetup at Devōt. Stay tuned because, who knows, maybe an AI meetup is on the horizon at Devōt.

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