Then, Now, and Beyond: A Journey Through Time

In today's digital age, computer literacy is a basic requirement for most people. Centuries ago, people relied on oral communication to pass on information from one generation to the next. The ability to read and write was a privilege reserved for the elite few because language literacy was not considered an essential skill. Those who were literate were often looked up to and held in high regard. This created a stark division between the literate and the illiterate, with the latter relying on the former for even the most basic communication needs. It wasn't until several centuries later that society began to see the value in literacy for all.

The spread of literacy was slow and gradual, with schools and educational institutions being established to teach the masses how to read and write. At first, literacy was only available to the privileged few, but as time went on, more and more people gained access to education. This brought about a significant change in society, as people became more informed and knowledgeable about the world around them. Consequently, more opportunities have emerged, especially with the advent of technology and the internet, making educational resources increasingly accessible to all. This shift has ushered in a new era of opportunities for those who previously lacked access to educational materials, paving the way for a more level playing field for the benefit of all. The democratization of knowledge has facilitated a more connected and equitable global population, breaking down traditional barriers to education and promoting greater social justice.

This proliferation of education has had far-reaching positive impacts on society, creating a virtuous cycle of innovation, empowering individuals to pursue new career paths and entrepreneurial ventures. While it is true that the proliferation of education has had some positive impacts on society, it has also had negative impacts. This has led to increased competition for jobs, and has resulted in many people being underemployed or unemployed. At the same time, these negative impacts can also be seen as a sign of progress, as they are indicative of a highly educated and competitive workforce that is eager to leverage their skills to be successful.

However, as technology continues to advance, new skills are needed to thrive in the modern world. One such skill that is rapidly becoming essential is coding artificial intelligence (AI). The ability to code AI has become the new literacy. Just as the ability to read and write human language was once reserved for the elite, this new form of literacy is emerging in the present - the ability to write code is currently limited to a small group of technologists and computer scientists. But someday, we hope, this skill will become as common as reading and writing human language. 

AI is becoming an increasingly integral part of our lives, from autonomous vehicles to virtual assistants and it's showing no signs of slowing down. With the growing importance of AI and its branches in almost every sphere of our lives, from healthcare to finance, transportation to education, it is essential that we invest the time and effort in developing the skills necessary to create intelligent systems that can solve complex problems and improve the quality of our lives. Learning to use technology is similar to learning a new language; while it can seem intimidating at first, with enough practice and immersion it can quickly become second nature. In the same way that becoming fluent in a human language requires understanding of grammar and pronunciation, being able to write code requires understanding of the basics, syntax and structure of the code in order to create a functioning program. But what does it take to become proficient in coding AI, and what skills are required? As the famous American writer Eudora Welty once wrote: “Indeed, learning to write may be part of learning to read. For all I know, writing comes out of a superior devotion to reading.“ I would be happy to write a more technical and detailed blog post on coding AI as a new literacy and explain how learning programming languages, algorithms, data structures, machine learning, and mathematics is essential to coding AI.

😲 "The robots are coming, and they're going to change everything. But if we're smart, they'll change everything for the better." - Andrew McAfee

What is coding AI?

Coding AI involves programming machines to perform tasks that typically require human-like abilities, such as reasoning, perception, and decision-making. Teaching AI to code is like teaching a baby to speak—you start with simple words and gradually introduce more complex ideas until the AI can understand and follow instructions. AI is an umbrella term that covers a wide range of specialties, from natural language processing (NLP) and computer vision to machine learning and deep learning. Each area helps machines mimic human intelligence, making them smarter and more capable of handling various tasks. The focus is on developing algorithms and models that can understand, generate, or improve computer code.

Why is AI programming so crucial?

AI is transforming every aspect of our lives, from healthcare and transportation to finance and education. Learning to code AI is essential for staying competitive and contributing to this transformation.

😟 "AI is the new electricity." - Andrew Ng: Founder of Coursera and deeplearning.ai

Skills

Learning how to code AI requires a combination of theoretical knowledge and practical skills.

To become proficient in coding AI, you need to have a range of skills, including:

  1. Programming Skills: To excel in AI development, one must possess a strong foundation in programming languages such as Python, C, and C++. Python, in particular, is a popular choice among developers for AI applications because of its simplicity and rich ecosystem of libraries and frameworks, such as TensorFlow, PyTorch, and Scikit-Learn, which simplify the implementation of complex AI models, making it easier to create efficient and scalable code. However, it's worth noting that AI applications can also be developed using other programming languages such as R and MATLAB. For example, the C and C++ languages are widely used in AI development due to their flexibility and high-performance capabilities. In general, it is crucial to have the ability to write efficient and scalable code that can run on distributed systems to handle the large amounts of data typically involved in AI projects.
    2. Python Roadmap Learning Resources
    Beginner Python Bootcamp - Python Full Course by freeCodeCamp.org, Python for Everybody by Dr. Chuck Severance, Codecademy's Learn Python 3, Harvard CS50 Introduction to Computer Science with Python [YouTube playlist], MIT Introduction to Computer Science and Programming in Python [YouTube playlist]
    Intermediate Python Intermediate Python by Muhammad Yasoob Ullah Khalid, Python Intermediate by Udacity, Google Automation Using Python by Coursera
    Advanced Python Fluent Python by Luciano Ramalho, Python Advanced Topics by Fred Baptiste
    Web Development with Python Django for Beginners by William S. Vincent, Full Stack Python by Matt Makai
    Data Science with Python Python Data Science Handbook by Jake VanderPlas, Data Science with Python by edX
    Machine Learning with Python Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron, Machine Learning with Python by Coursera
    Python Tutorials Corey Schafer's YouTube channel
  2. Mathematical Skills: Is math a crucial skill for AI? Of course it is! In fact, it's hard to overstate just how important math is in the world of artificial intelligence and machine learning. It's like the air that AI breathes. Math is to AI as oxygen is to humans: essential for life and without it, nothing would survive. But why is math so important? Well, for one thing. Everything we do in AI and ML is based on mathematical algorithms and models, from simple linear regression to complex deep neural networks. And when it comes to building those algorithms, math is the language that computers speak. Of course, with so much to learn, it can be daunting to figure out where to start. But the key is to focus on the areas that are most important for your particular goals. Whether you're building a machine learning model, architecting a system, or trying to land your dream job, understanding the right math is absolutely crucial. For example, understanding the mathematics behind gradient descent helps you to optimize your model's weights, while understanding the mathematics behind linear algebra helps you to understand the underlying operations of neural networks, such as matrix multiplication and dot products.

    😟 "Mathematics is the language in which God has written the universe." - Galileo Galilei

    To excel in machine learning, it's crucial to have a solid grasp of certain mathematical concepts. Some of the key areas that are essential for success in this field include linear algebra, probability and statistics, calculus, and optimization. Linear algebra is a fundamental concept in AI, involving vectors, matrices, and other mathematical structures. It is essential to gain a solid understanding of linear algebra in order to be able to represent and transform data in a way that is useful for machine learning. Probability and statistics provide the framework for understanding uncertainty and making predictions based on data. Concepts like discrete and continuous probability, standard probability distributions, and hypothesis testing are essential for analyzing data and building effective models. Calculus is also important in AI and ML, particularly in the optimization of models. Concepts like gradient descent, momentum, and the Adam optimization algorithm rely heavily on calculus. Still, deep learning is an emerging technology, and when you train a neural network, understanding the math behind the algorithms can help you make better decisions. Additionally, exploratory data analysis is a skill often underrated but incredibly valuable in data-centric AI development. Through systematic exploration of the data, EDA enables the discovery of errors and insights that can drive progress. But it's not just about knowing the math itself. It's also about having the skills to use that math to solve problems, to debug code, and to make better decisions about how to proceed. However, as machine learning becomes more sophisticated, it's getting easier to use and requires less debugging. This means that you don't need to have an in-depth knowledge of the complex math behind it to make it work. Even so, the fundamentals can still prove to be extremely helpful if you encounter problems or need to make tweaks. Ultimately, it's up to you to decide how deep you want to dive into the math and how it fits into your overall goals for using machine learning techniques.
  3. Data science Skills: Data science has become a crucial aspect of modern business, research, and technology. The ability to manipulate and visualize data is essential for anyone who needs to code in AI or work with data. Pandas, NumPy, and Matplotlib are three of the most widely used data science tools that enable efficient data manipulation, analysis, and visualization. Pandas offers several data structures and functions for easy and efficient data manipulation and analysis. NumPy offers a powerful array data structure for numerical computations, and Matplotlib offers several plot types and customization options for data visualization. Whether you are a data scientist, a programmer, or a researcher, mastering these tools can help you streamline your workflow and make your data science projects more efficient and effective. So if you want to work with data more effectively, then it is worth spending some time learning some of these powerful tools that will help you to achieve your goals.
  4. Machine Learning Skills: Have you heard of linear regression, logistic regression, decision trees, and clustering? These are some of the most widely used machine learning algorithms that are used for a variety of tasks, such as predicting numerical values, classifying data, and grouping similar data points. By mastering these algorithms, you can build and train models that can analyze complex data sets and make predictions with high accuracy.
    But how do you implement these algorithms in practice? This is where machine learning frameworks such as TensorFlow, Keras, and PyTorch come in. These tools provide a high-level interface that enables you to build, train, and evaluate machine learning models quickly and efficiently. With these frameworks, you can focus on high-level concepts and let the framework handle the low-level implementation details. TensorFlow, developed by Google, is an open-source machine learning framework that has gained significant popularity in recent years. It provides a powerful platform for building and training neural networks, and it supports both high-level APIs like Keras and low-level APIs for more advanced users. Keras, on the other hand, is a high-level neural network API that runs on top of TensorFlow. It provides a user-friendly interface that makes it easy to build and train neural networks without requiring extensive knowledge of the underlying implementation details. PyTorch is another popular machine learning framework that has gained significant traction in recent years. It provides an efficient and flexible platform for building and training neural networks. It's particularly popular among researchers due to its dynamic computation graph and support for automatic differentiation. Personally, I find PyTorch to be an excellent framework for building deep learning applications in the healthcare industry. Its dynamic computation graph and support for automatic differentiation make it easy to experiment with different models and architectures, and its performance optimizations make it possible to train models quickly and efficiently. However, each framework has its strengths and weaknesses, and the choice of the right framework depends on the specific use case and project requirements.
  5. Deep Learning Skills: Deep learning is a rapidly expanding field that has enabled significant breakthroughs in areas such as computer vision, natural language processing, and robotics. To become a successful deep learning practitioner, you need to have experience with fundamental deep learning algorithms and frameworks. Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs) are the most popular and widely used deep learning algorithms. Meanwhile, TensorFlow, Keras, and PyTorch are essential deep learning libraries that provide a high-level interface for building, training, and evaluating deep learning models quickly and efficiently. Mastering these skills can enable you to analyze complex datasets and make predictions with high accuracy, making a significant impact on your business or research. So, dive into these essential skills today!

    6. 😟 "Deep learning is not a black box, it's a glass box - you can see what's happening inside if you know where to look." - Yann LeCun

  6. Problem-Solving Skills: The ability to solve problems is a crucial skill that is applicable in every aspect of our lives, including artificial intelligence. It is crucial in AI that you approach problems systematically and develop solutions that are robust, scalable, and efficient. By mastering this skill, you can become more effective in identifying problems, analyzing them, and developing solutions that meet the requirements of a project. To become a proficient problem solver, you need to be able to evaluate different approaches and select the best one based on performance, accuracy, and computational resources. This involves comparing the strengths and weaknesses of each approach and assessing the trade-offs involved in terms of time, resources, and performance. Developing problem-solving skills takes time and practice. It involves breaking down complex problems into smaller, more manageable sub-problems, identifying the key requirements and constraints, and developing a plan of attack. It also requires the ability to test and validate solutions, iterate on them, and refine them until they meet the project's objectives.
  7. Collaboration Skills: You need to be able to work effectively in teams and collaborate with other professionals such as data scientists, software engineers, and project managers. To become an effective collaborator, you need to be able to understand and appreciate the perspectives of others. This involves active listening, empathy, and an openness to feedback and constructive criticism. You should also be able to contribute to a team's collective goals, whether that involves sharing knowledge, skills, or resources. Effective collaboration also requires the ability to communicate effectively, both verbally and in writing. You need to be able to explain technical concepts to non-technical stakeholders and convey the implications of technical decisions to other team members. This involves breaking down complex ideas into simple terms, using visual aids, and adapting your communication style to suit your audience.

Learning how to code AI has several benefits, including providing career opportunities, contributing to innovation, developing problem-solving skills, and gaining a competitive advantage. With the growing demand for AI professionals, learning how to code AI can position individuals for a lucrative and rewarding career. Furthermore, by learning the skill of programming AI, individuals might be able to develop innovative solutions that will revolutionize industries, and provide solutions to complex problems. Furthermore, developing problem-solving skills while coding AI can be transferable to other domains, making individuals more efficient, scalable, and robust in their approach to problem-solving. For instance, AI engineers can use their problem-solving skills to quickly identify bugs in the code and rapidly develop efficient solutions to any programming problems. Finally, gaining expertise in coding AI can offer a competitive advantage over other professionals in the field. This will enable individuals to develop innovative solutions that give their organizations a competitive edge.

😟 "The key to addressing job displacement is to focus on the opportunities created by technology, not just the challenges." - Tony Robbins

Conclusion

AI is transforming every aspect of our lives, and the ability to code AI has become the new literacy. People are writing codes to create incredible things, from self-driving cars to advanced medical technologies. By learning how to code AI, you can develop the skills needed to create intelligent systems that can solve complex problems and improve our lives. To learn coding AI, you need to have a strong foundation in programming fundamentals, a deep understanding of machine learning algorithms and AI applications, and a range of technical and non-technical skills. There are many benefits to learning how to code AI, including career opportunities, innovation, problem-solving skills, and a competitive advantage. As a person with the right training and dedication, it is possible to develop a proficient AI coder and contribute to the development of novel solutions that will have a lasting impact on the world we live in.

Citation

Cited as:

kibrom, Haftu. (Jan 2022). Coding AI: The New Literacy. Kb’s Blog. https://kibromhft.github.io/posts/2022-12-19-coding/.

Or

@article{kibrom2022coding,
  title   = "Coding AI: The New Literacy",
  author  = "kibrom, Haftu",
  journal = "Kb's Blog",
  year    = "2022",
  month   = "Dec",
  url     = "https://kibromhft.github.io/posts/2022-12-19-coding/"
}