What AI and robotics skills will you need for the future?

Python coding skills will be necessary for the foreseeable future, but I wonder if the most valuable skill set will be the ability to work with generative AI to quickly produce high quality code Gen AI can code, but it really can't architect. So, you will have to structure the logic and feed it bite sized instructions for producing the right code. Then, you'll have to go back and edit what you get from gen AI to make sure it is exactly what you need. So, a combination of architecting, prompt engineering, and coding will be the right "language" to learn.

The development of AI and robotics should not solely rely on technical skill sets like mathematics and statistics. While these disciplines are undoubtedly important, it is equally critical to recognize the significance of linguistics in this field. Understanding language and its nuances is essential for effective human-robot interaction, natural language processing, and sentiment analysis. By incorporating linguistic expertise, we can enhance the conversational abilities and empathy of AI systems, promoting a more inclusive and user-centric experience. Linguistics allows us to delve into the intricate layers of communication, ensuring that AI and robotics systems accurately comprehend and respond to human needs and intentions.

Perhaps the single most important skill for an aspiring AI engineer, data scientist, or business analyst will be the ability to translate the relevant math into plain and simple English for non-technical stakeholders. It is crucial, for instance, that you understand how backpropagation works. Now, can you describe it simply enough for your non-technical boss to understand when he or she asks how neural networks actually learn? Being able to do this will greatly advance your career in real world settings.

Everyone seems to be talking about vector databases and how they can be paired with generative AI to yield better outcomes. Understand how embeddings can be used with these databases to improve and customize the results that Gen AI produces, and you will be closer to the state of the art (at least as of September 2023).

Semantics play a vital role in enabling machines to understand and interpret meaning from human language, which is fundamental for natural language processing and effective human-robot interaction. By focusing on semantics, we can enhance AI and robotics systems' ability to comprehend context, capture nuances, and accurately respond to user queries or commands. By incorporating semantic understanding, we can bridge the gap between machine intelligence and human communication, leading to more intuitive and contextually appropriate AI and robotics systems.

Understanding how to use specific tools has a short shelf life, but understanding the fundamentals and being able to explain it will enable you to have a long and productive career. As we move towards pretrained models accessed via API calls, there is less of a need to train your own models. However, you really do need to get the logic right, making sure you are invoking the right calls to the right models. Then, you need to be able to explain to non-technical bosses what you are doing and why you ate doing it. So, don't just learn how to use a black box, but rather how it actually works.

This is a key skill to teach the new generation of students, but the one that is least taught. I am currently developing and teaching a new core course on AI and society at the Hong Kong University of Science and Technology, as well as teaching the core business ethics course here. What I have found in classes elsewhere is that there is a tendency to teach the high-level theory OR the practical application but NOT what is really needed -- how to apply the theory in real-world contexts and make good decisions using them. Practice the practice of making good decisions based on high-level theory and low-level concerns. Then, make sure you are communicating key implications, working backwards from key implications.

Promoting accessibility is another critical skill for AI and robotics. As designers and developers, we must prioritize inclusivity and ensure that our AI and robotics systems are accessible to users with diverse needs and abilities. This includes considering factors such as usability for individuals with disabilities, designing for multiple languages and cultures, and addressing barriers related to socioeconomic factors. By incorporating accessibility from the outset, we can create AI and robotics solutions that benefit all users, fostering equal opportunities and empowering individuals across different backgrounds.

individuals looking to excel in AI and robotics should consider developing below skills. 1) Machine Learning and Deep Learning. 2) Computer Vision. 3) Natural Language Processing (NLP). 4) Data Analysis and Data Science. 5) Mathematics and Statistics. 6) Ethical AI and AI Governance: Awareness of ethical considerations and regulations surrounding AI and robotics is increasingly important. 6) Collaboration and Communication. 7) Continuous Learning: The field of AI and robotics is constantly evolving, so a commitment to continuous learning and staying updated is essential. These skills will not only prepare individuals for careers in AI and robotics but also enable them to adapt to the evolving demands of these fields.

I believe that user experience (UX) should be at the forefront of AI and robotics development. Understanding and empathizing with users' needs, preferences, and challenges is essential for creating meaningful and impactful technologies. By prioritizing UX, we can ensure that AI and robotics solutions are intuitive, engaging, and inclusive, ultimately leading to greater user adoption and satisfaction. Of course, this does not diminish the significance of ethical and social considerations; rather, it emphasizes the importance of integrating these factors within a user-centered framework.