What skills do you need to work with AI?

In my experience, knowing how to use modern APIs is also an important skill to working in AI. These APIs include, HuggingFace, OpenAI and Co:Here. Vector databases are also important in AI systems.

AI can be categorized into three branches: Theoretical AI - Primarily academic and research-driven, it requires strong math skills such as Linear Algebra, Calculus, and Statistics. Proficiency in Python and the ability to conduct experiments to validate theories are essential. Applied AI - This engineering-focused branch demands a grasp of the theories from branch #1. It requires traditional software engineering skills and an understanding of AI domains. Knowledge in distributed computing, such as API integration and cloud services, is vital. Infrastructure AI - Specialists in this area design infrastructures for scalable AI applications. Skills in GPU clustering, optimization, and quantization are crucial

Applied ML: In addition to programming, familiarity with the ecosystem such as Co:here and Hugging Face and Octo ML will help in rapid prototyping. Fundamentals of ML: Neural Vectors form the basic building blocks for all ML algorithms and having proficiency in vector data bases to effective quantization techniques can help choose the right approach for the problem one is trying to solve.

The integration of fundamental industry knowledge, programming expertise, mathematical proficiency, process improvement, and financial acumen within the purview of a business strategist is paramount for achieving success in artificial intelligence. A crucial aspect should involve department heads from the affected areas with the technology department to ascertain the precise set of skills required to effectively tackle AI-related challenges. Companies would do well to prioritize utilizing AI for developing innovative products, augmenting revenue, and minimizing expenditures. Recognizing that no single individual possesses the entirety of the proficiencies necessary for AI, adopting a collaborative team-oriented approach is advised.

Python: The Powerhouse of AI Development At the forefront of AI development, Python emerges as a powerhouse programming language. Renowned for its simplicity and readability, Python has become the lingua franca of AI practitioners. Its extensive libraries, including TensorFlow, PyTorch, and scikit-learn, provide a robust ecosystem for machine learning, deep learning, and data analysis. Python's flexibility allows researchers and developers to swiftly prototype and experiment with various AI models. Its clean syntax and large community support make it an ideal choice for beginners diving into the realm of AI programming.

Close to math skills, a specific background in statistics and statistical learning could be differentiating, and a true advantage. Knowing why, and how, run the appropriate Cross Validation, or handling unbalanced data, or testing distribution for equality of means or homogeneity of variance, and hypothesis testing, just to name a few concepts.

Modern frameworks allow people with almost no mathematical background to work with AI. That is one of the worst dangers for AI newbies because it amplifies the Dunning Kruger effect a lot!

One might argue that an in-depth understanding of mathematics seems less critical for developing AI applications in the era of democratized AI. It's akin to driving a car; you don’t have to be an automotive engineer to be a competent driver. However, just as a deeper understanding of a car's mechanics can save you considerable time and trouble when things go awry, a solid grasp of mathematics and statistics can significantly enhance your ability to navigate challenges in AI applications. While AI may seem like magic at first glance, understanding its math-based foundation clears up the mystery. In short, a good grasp of math not only helps you use AI more effectively but also prepares you for challenges you might encounter.

Working with AI requires a solid foundation in mathematics, as many AI techniques are rooted in mathematical concepts. The key mathematical skills you'll need include a set if algebra, statistics, probability While a deep understanding of these mathematical concepts is important, it's also worth noting that many AI frameworks and libraries abstract away some of the intricate mathematical details. However, having a strong mathematical foundation will help you understand the inner workings of algorithms, troubleshoot issues, and come up with innovative solutions when working on AI projects

In my early days exploring AI, I was heavily reliant on ready-made tools and libraries. But during a challenging project, I hit a roadblock when tweaking a machine learning model. A mentor, well-versed in AI Implementation, pinpointed the issue to be related to linear algebra. He explained the underlying math, making complex concepts seem intuitive. I took up Subjects in AI during my MBA, strengthening my mathematical foundation, diving into topics like calculus and statistics. As I delved deeper, AI techniques began making more sense, and I could optimize models with greater precision. This experience was a revelation: AI isn't just about algorithms; it's about understanding the mathematical magic behind them.

Domain knowledge is important. At Vectra.ai we marry security research with data science in order to create our algorithms. Having intimate knowledge of a subject matter area like attacker behavior drives how we create purpose built algorithms. An example of a purpose built algorithm is being able to detect command and control communications inside a encrypted tunnel without decrypting traffic. In the area of security research if you understand MITRE ATT&CK techniques you can start looking at the appropriate AI as the No free lunch theorem applies.

To work with AI and make a positive impact for businesses, in addition to domain knowledge you need functional and algorithm knowledge. These skills allow you to understand the problem, the context, and the tools you need to build the AI system. For e.g., a data scientist with functional knowledge of the retail space could use data to predict which products are likely to sell well and which are likely to sit on the shelves, to optimize their inventory levels. With domain knowledge, one would know about the different types of products to understand the specific context of the problem, and then having algorithm knowledge would know which algorithm is the best fit for predicting product sales and choose the right one to solve the problem.

Domain knowledge is not just extremely important in general but it also goes hand in hand with critical thinking. If you lack domain knowledge, you won't be able to critically assess and evaluate AI-generated material. This is something that needs to be explicitly addressed in (higher) education institutions where students might happily hand in AI-generated essays without noticing that there might be serious flaws in them.

AI is so powerful and there is so much help available, I will rate the Domain skills as the key skill required more than any scientific and programming skills. It’s important to know deeply what problems are we solving and how to decompose that problem.

Domain knowledge also plays a vital role in transferring a trained model in a specific domain into other related domains. By knowing the domains and their similarities, we may use some data from a source domain to train a learning model and use it in a target domain where we do not have any historical data.

Your ability to clearly communicate how your algorithms work and what value they bring to the business is essential for AI professionals. Often, you will be required to take complex technical concepts and communicate it to a non-technical audience. To do this effectively requires you use analogies and common language in a way that your audience can understand and relate to your problem you are trying to solve

Communication and developing project management skills are crucial for the correct implementation, application, and collaboration between stakeholders, peers, and other professionals. It is essential to articulate the project's process and overall goals using simple and universally understandable language.

Communication skills matter a lot! You should know how and what to discuss with the customer. When working with AI, the members from other teams wouldn't be much familiar with the words specially being used in AI. So while giving a demo or during discussions, try to explain them the hot words with 1 line sentence if they seems to be confused. Try to explain models and metrics in simple words. Most important out of everything is - You should know how to convey the limitations to the stakeholders.

Considering AI as an answer to all questions is a widespread misconception. To get the right answer and guidance, you need to be able to ask the right question or instruct the AI platform in the right manner. This is where communication as a skill plays a critical role. This is especially true when you’re utilizing AI platforms to spitball ideas or spark inspiration.

Communication is the bridge that transforms AI from complex codes and jargon into a digestible shared understanding. It's more than just explaining technical terms – it's about crafting a story that resonates. Yes, you need to talk to clients and stakeholders but it's bigger than that: you're the translator between AI experts as well. Imagine data scientists, engineers, and designers as players on a team. They each bring unique skills to the game, but communication is what passes the ball. When an analyst talks to a designer, that's where the magic happens. Ideas spark, questions fly, and innovation thrives. So communication is more than just saying the right words, it's properly translating that effectively to create something bigger.

Problem solving is the core skill when it comes to data. The ability to identify key issues, ask the right questions and evaluate the solutions are at the root of critical thinking. It's more than just coding or understanding machines, it's about identifying the practical and ethical implications of AI solutions. Never underestimate the power of logical reasoning and critical thinking. It will take you far.

Today, AI can automate most tasks. But critical thinking is a deeply human skill that most machines lack (as of date). This skill helps design AI systems by picking the right business problems and factoring in the likely risks and implications. Critical thinking helps assess the recommendations from AI and interpret them for action.

In addition to the core domain skills of Object-Oriented Programming (OOP) or other categories, soft skills are also indispensable for programmers and developers. They empower individuals to analyze complex problems, evaluate different solutions, and make informed decisions.

- Analyze and evaluate information logically. - Question assumptions and limitations. - Solve problems with informed decisions. - Anticipate unintended consequences. - Embrace interdisciplinary insights. - Uncover hidden patterns in data. - Drive ethical AI advancements.

We often get 'lost in the sauce' so to speak when building these thinking machines. It's easy to fall so far down the stack that the ultimate vision or roadmap is hard to grasp. The ability to reason, often on the fly, whether to invest time and efforts into one aspect of a project is crucial, and is a great example of how critical thinking can best be applied to these projects.

To remain informed and stay up-to-date in the realm of creativity, it’s crucial to engage in a multifaceted approach. This involves reading books and publications, enrolling in online courses, following research journals, listening to relevant podcasts, participating in online communities, attending conferences and workshops, watching TED Talks, networking with like-minded individuals, experimenting with creative ideas, and maintaining an idea journal. By embracing these practices, you can continuously nurture and expand your creative thinking, ensuring that you remain at the forefront of innovation and problem-solving in the field of AI.

Don't underestimate the power of creativity. Designing innovative AI solutions often requires one to think outside of the box, exploring new possibilities and anticipating future scenarios. Being able to create, imagine and inspire become just as important as all the other skills and perhaps the most important.

In my experience , one needs to unleash the untapped potential of artificial intelligence, a vast universe of endless possibilities. Beyond coding, lies a thrilling journey of exploration and creativity. Join a vibrant community of unconventional thinkers, where diverse ideas converge and ignite electrifying moments of inspiration. Elevate your problem-solving abilities to unimaginable heights, surround yourself with a close-knit team or a network of enthusiasts. Embrace the adventure, and watch as your creative prowess grows. Discover the power of collaboration and unlock moments that redefine what's possible.

AI is set to enhance creativity, offering fresh avenues for creation. Artistic minds will soon be less constrained by technical abilities. Furthermore, emerging techniques like prompt engineering will join traditional methods, expanding our creative horizons.

Early in my AI journey, I was tasked with developing a recommendation system. While most went the traditional route, I felt a creative urge to try something different. Drawing inspiration from unrelated fields, I integrated principles of human psychology with machine learning. Instead of just recommending based on past behavior, the system also considered users' purpose of purchase. The result was a more dynamic, responsive recommendation engine that resonated deeply with users. This project was a turning point for me, underscoring that while technical skills are vital in AI, it's creativity that often sets groundbreaking solutions apart.

Along with the other skills mentioned throughout this article and the responses, one overlooked capability is the need to be familiar with the data management capabilities in the organization. What are the data governance policies that are applied to data used in the AI? What data quality management requirements and measures have been implemented and measured? What are the business data definitions for the relevant data - and who defined the data? These, and other data management functions are essential for ensuring that the data used in the AI and machine learning routines can be trusted.

Approaching AI successfully isn't solely about mastering coding languages or understanding algorithms; it's equally about the mindset and heartset we bring to the table. Curiosity acts as the engine that drives us to explore new possibilities and ask meaningful questions ??. Adaptability ensures that we can shift perspectives and try things, learning from both success and failure, to iterate effectively ??. Critical thinking is the compass that guides us, especially important in making ethical choices and avoiding unintended consequences ??. As the landscape of AI continues to evolve at a rapid pace, these human-centric soft skills aren't just supplementary; they're foundational to achieving sustainable innovation.

In my view 'Common Sense' is the biggest skill that is needed while working with AI. Common sense helps AI developers & researchers make practical, logical decisions, understand the context of problems, and ensure that AI systems operate effectively in the real world. AI can be powerful but it's driven by humans who need to apply their common sense for data interpretation, problem solving, ethical considerations, user interactions etc.

While programming, mathematics, and critical thinking skills are essential for a career in AI, that alone does not pave the road to success. To be successful in AI or any career, you need to hone three non-negotiable skills: communication, collaboration, and empathy. Success might always feel out of reach without these skills.

One thing I have found helpful is to be aware of the inherent bias that is baked into generative AI. Being aware that it exists is half of the battle towards working out how you are going to approach mitigating that by how you work with the outputs that it creates.