AI and machine learning (ML) are reshaping industries and unlocking new opportunities at an incredible pace. There are countless routes to becoming an artificial intelligence (AI) expert, and each person’s journey will be shaped by unique experiences, setbacks, and growth. For those with no prior experience eager to dive into this compelling technology, it’s important to know that success is possible with the right mindset and approach.
In the journey to AI proficiency, it’s crucial to develop and utilize AI ethically to ensure the technology benefits organizations and society while minimizing harm. Ethical AI prioritizes fairness, transparency, and accountability, which builds trust among users and stakeholders. By following ethical guidelines, learners and developers alike can prevent the misuse of AI, reduce potential risks, and align technological advancements with societal values.
Despite the importance of using AI ethically, among tens of thousands of people learning how to use AI, research has shown that less than 2% actively searched for how to adopt it responsibly. This divide between those learning how to implement AI and those interested in developing it ethically is colossal. Outside our research, Pluralsight has seen similar trends in our public-facing educational materials with overwhelming interest in training materials on AI adoption. In contrast, similar resources on ethical and responsible AI go primarily untouched.
How to Begin Your Journey as a Responsible AI Practitioner
There are three main components that responsible AI practitioners should focus on — bias, ethics, and legal factors. The legal considerations of AI are a given. Using AI to launch a cyberattack, commit a crime, or otherwise behave illegally is against the law and would only be pursued by malicious actors.
In terms of biases, an individual or team should determine whether the model or solution they are developing is as free of bias as possible. Every human is biased in one form or another, and AI solutions are created by humans, so those human biases will inevitably reflect in AI. AI developers should focus on consciously minimizing those biases.
Addressing ethical considerations can be more complex than addressing bias, as ethics are often closely tied to opinions, which are personal beliefs shaped by individual experiences and values. Ethics are moral principles intended to guide behavior in the quest to define what is right or wrong. Real-world examples of ethics could include whether it is ethical for a companion robot to care for the elderly, for a website bot to give relationship advice, or for automated machines to eliminate jobs performed by humans.
Getting Technical
With ethics and responsible development in mind, aspiring AI developers are ready to get technical. It’s common to initially think that learning to develop AI technologies requires an advanced degree or a background working in a research lab. However, drive, curiosity, and the willingness to take on a challenge are all that’s required to start. The first lesson many AI practitioners learn is that ML is more accessible than one might think. With the right resources and a desire to learn, individuals from various backgrounds can grasp and apply even complex AI concepts.
Aspiring AI experts may find that learning by doing is the most effective approach. It’s helpful to start by choosing a project that is both interesting and manageable within the scope of ML. For example, one might build a model to predict the likelihood of a future event. Such a project would introduce concepts that include data analysis, feature engineering, and model evaluation while also providing a deep understanding of the ML lifecycle—a key framework for systematically solving problems.
As an individual delves into AI, experimenting with different tools and technologies is essential to tackling the learning curve. While no-code and low-code platforms, such as those from cloud providers like AWS, can simplify model-building for people with less technical expertise, individuals with a programming background may prefer to get more hands-on. In such cases, learning Python basics and utilizing tools like Jupyter Notebooks can be instrumental in developing more sophisticated models.
Immersing oneself in the AI community can also greatly enhance the learning process and ensure that ethical AI application methods can be shared with those who are new to the field. Participating in meetups, joining online forums, and networking with fellow AI enthusiasts provide opportunities for continuous learning and motivation. Sharing insights and experiences also helps clarify the technology for others and strengthen one’s own understanding.
Choose a Project that Piques Your Interests
There’s no set roadmap to becoming a responsible AI expert, so it’s important to start wherever you are and build skills progressively. Whether you have a technical background or are starting from scratch, the key is to take that first step and stay committed.
The first project should be something that piques interest and is fueled by motivation. Whether predicting a stock price, analyzing online reviews, or developing a product recommendation system, working on a project that resonates with personal interests can make the learning process more enjoyable and meaningful.
Grasping the ML lifecycle is essential to developing a step-by-step approach to problem-solving, covering stages such as data collection, preprocessing, model training, evaluation, and deployment. Following this structured framework helps guide the efficient development of ML projects. Additionally, as data is the cornerstone of any AI initiative, it’s essential to locate cost-free, public datasets relevant to the project that are rich enough to yield valuable insights. As the data is processed and cleaned, it should be formatted to enable machines to learn from it, setting the stage for model training.
Immersive, hands-on tools like AI Sandboxes allow learners to practice AI skills, experiment with AI solutions, and identify and eliminate biases and errors that may occur. These tools give users the chance to safely experiment with preconfigured AI cloud services, generative AI notebooks, and a variety of large language models (LLMs), which help organizations save time, reduce costs, and minimize risk by eliminating the need to provision their own sandboxes.
When working with LLMs, it’s important for responsible practitioners to be aware of biases that may be embedded in these vast caches of data. LLMs are like expansive bodies of water, containing everything from works of literature and science to common knowledge. LLMs are exceptional at producing text that is coherent and contextually relevant. Yet, like a river moving through diverse terrains, LLMs can absorb impurities as they go—impurities in the form of biases and stereotypes embedded in their training data.
One way to ensure that an LLM is as bias-free as possible is to integrate ethical principles using reinforcement learning from human feedback (RLHF). RLHF is an advanced form of reinforcement learning where the feedback loop includes human input. In simplest terms, RLHF is like an adult helping a child solve a puzzle by actively intervening in the process, identifying why certain pieces don’t fit, and suggesting where they might be placed instead. In RLHF, human feedback guides the AI, ensuring that its learning process aligns with human values and ethical standards. This is especially crucial in LLMs dealing with language, which is often nuanced, context-dependent, and culturally variable.
RLHF acts as a critical tool to ensure that LLMs generate responses that are not only contextually appropriate but also ethically aligned and culturally sensitive. This instills ethical judgment in AI by teaching it to navigate the gray areas of human communication where the line between right and wrong is not always definitive.
Non-Technical Newcomers Can Turn Their Ideas Into Reality
Many AI professionals without IT backgrounds have successfully transitioned from diverse fields, bringing fresh perspectives and skillsets to the domain. No-code and low-code AI tools make it easier to create models without requiring extensive coding experience. These platforms allow newcomers to experiment and turn their ideas into reality without a technical background.
Individuals with IT experience, but lacking coding expertise, are in a strong position to move into AI. The first step is often learning the basics of programming, particularly Python, which is widely used in AI. High-level services from platforms like AWS can provide valuable tools for building models in a responsible way without deep coding knowledge. IT skills like understanding databases or managing infrastructure are also valuable when dealing with data or deploying ML models.
For those who are already comfortable with coding, especially in languages like Python, the transition into AI and ML is relatively straightforward. Learning to use Jupyter Notebooks and gaining familiarity with libraries like Pandas, SciPi, and TensorFlow can help establish a solid foundation for building ML models. Further deepening one’s knowledge in AI/ML concepts, including neural networks and deep learning, will enhance expertise and open the door to more advanced topics.
Tailor the AI Journey to Personal Goals
Although starting from scratch to become an AI expert can seem daunting, it is entirely possible. With a strong foundation, commitment to ongoing learning, hands-on experience, and a focus on the ethical application of AI, anyone can carve their way into the field. There is no one-size-fits-all approach to AI, so it’s important to tailor the journey to personal goals and circumstances. Above all, persistence and dedication to growth and ethics are the keys to success in AI.
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