What are the top skills for R&D professionals working with AI and machine learning?

In R&D, focus is on the problem, rather than the programming. Here, problem-solving and innovative thinking are the required skills. There is innovation in "Python, R, Java, and C++", as anyone can program in these languages. Once the problem is defined and the solution is clearly designed, the programming part becomes the normal thing.

R&D (Research and Development) professionals working with AI and machine learning require a diverse skill set to navigate the complex and rapidly evolving landscape of these technologies. Here are some of the top skills for R&D professionals in the AI and machine learning domain: ? Programming Skills: ? Mathematics and Statistics: ? Machine Learning Algorithms: ? Data Science and Analytics: ? Programming Frameworks and Libraries: ? Natural Language Processing (NLP): ? Computer Vision: ? Reinforcement Learning: ? Cloud Computing: ? Version Control and Collaboration: ? Communication and Presentation Skills: ? Problem-Solving and Critical Thinking: ? Continuous Learning: ? Ethical Considerations:

From my experience, the top skills for R&D professionals working in AI and Machine Learning are - One's ability to understand the problem statement and goals of the research - Ability to research on the problem - Mathematics and Statistics Foundation - Previous Domain knowledge and familiarity - Critical Thinking - A little bit of coding skills in Python and R whichever is required - Consistency - Patience

Just forget all the fancy things. Hold it with the intuition. Always rule yourself to follow machine learning lifecycle. I’m providing an overall answer as it will help you to enhance your skills on both data centric and model centric eco system based on your use case. In terms of model centric approach if we focus RnD, overall I feel the most challenging field is Graph Machine Learning. Monumental maths I saw there other than any paradigm. So it’s basically you should be able to write models (mostly with OOP stack) with math formulas which align the model connectivity

Professionals in AI and machine learning R&D should possess strong programming skills in languages like Python and R, coupled with expertise in TensorFlow. A solid foundation in statistics and mathematics is crucial, along with domain-specific knowledge for effective problem-solving in real-world applications. Competence in data handling, preprocessing, and feature engineering is essential for ensuring data quality. A comprehensive understanding of various machine learning algorithms, especially deep learning, is necessary. Additionally, proficiency in ethical considerations, software development practices, and collaborative tools is vital for successful research and development in this field.

You cannot escape mathematics, especially statistics and linear algebra while building ML models. Agreed, Neural Networks / Deep Learning especially with simple frameworks like PyTorch/HuggingFace provide an illusion that "perhaps" one can skip these topics while learning ML. During my time at Dukaan, I was building a Classification model, with Logistic Regression, without actually checking the distribution of the outcome (it resembled a power law distribution), and was not happy to see the results (high precision but very low recall). On doing a mathematical study on Logistic Regression, I applied a log transformation on the outcome to finally fix the model. Such insights are important, and one can know it only from mathematical training

In my view, people working on research and development in machine learning mostly rely on math. They needs to have an in-depth knowledge about how the mathematical concepts used in machine learning tasks. This understanding helps them to improve existing concepts, optimize them, or even derive new concepts. So, I highly recommend them to learn specific mathematical concepts such as ? linear algebra ? calculus ? statistics to those who interested in machine learning R&D.

Statistics (Conditional Probability, Bayes law, distributions functions), Linear Algebra (matrix multiplication, decomposition, vectors etc), Basic Differentiation (partial derivatives, chain rule, sum rule etc).

Indeed, after the first stage of gaining programming, libraries, frameworks and other concepts, you should make yourself well versed with statistics, linear algebra because many a times, R&D will revolve around mathematics.

For an ML/Data science role, it's very important to know linear algebra, probability, and statistics. Most of the foundation ML models are built upon these concepts. Having a thorough understanding of these models is essential in any R&D role. For a data-driven business problem, an AI engineer has to be able to build the use case, train an ML model to solve the supervised and unsupervised task, and evaluate it. So, the following concepts are very essential. 1. Bayes theorem, Conditional probability, central limit theorem, different distributions. 2. Matrices concepts, Similarity measures. 3. p-value, bias and variance, correlation, covariance 4. Linear regression, Logistic regression, Naive Bayes classifier, SVM, and Decision trees.

One time at a conference I heard a talk where a data scientist told of how they were building a model to detect failure in jet engines, and at some point they figured out a way to significantly increase the performance of their model, by realizing that the plane had 2 jet engines, not just one... Domain knowledge informs you modeling! The more you know about the dynamics that are at play represented in your data, the better equipped you are in designing your ML solution.

A person can be a good data scientist without business/domain knowledge. However it takes business acumen and in-depth subject knowledge to be an even better data scientist. During my time at Cactus Communications, I was responsible for working on a data-driven payment solution for balancing company expenses and profits. I started by building an ML model (something along the lines of forecasting), but could not see a clear image until I sat down with the business team to understand how payments worked in such a setting. I ultimately built a model collaborating with the business team, not using ML, but plain old statistics, and achieved a 1.3mil savings that quarter.

Why it's important: Applying AI in a specific domain requires an understanding of the industry's challenges, processes, and nuances to develop solutions that truly address real-world problems.

There are different views on what knowledge in their field an ML specialist should have, but the main question for R&D is how can you research what you don't know? For any researcher, you need to know the field, have enough soft skills to collaborate with stakeholders and produce AI solutions for them, not just a research paper. Hype is exactly what is driving the growth of AI investment, and while AI is the new black, we have several significant developments a year. I've been working in a narrow field for musicians, and if I want to do something really worthwhile, I need to think like a musician and feel the need of musicians

Mastery of AI and ML transcends technical prowess, demanding a fusion of domain expertise and business insight. It's imperative to grasp the product's core context and objectives, discerning precisely how AI and ML can innovate and resolve challenges. This knowledge must then be translated into pragmatic, strategy-aligned recommendations that resonate with the product's vision and user necessities. Collaborative synergy with stakeholders like product managers, designers, engineers, and customers is vital, requiring clear, compelling communication to effectively share and advocate these ideas.

I once worked on a project to fix text generated by a TTS model. At that time, the GPT3 model had just been released and my colleagues and I were trying to apply it to various NLP tasks. But one day we came up with a very unusual idea. What if we predicted a set of most probable words in a sequence of text from the TTS model and checked how likely a word obtained from the transcription could stand in its place. Of course, our approach only worsened the similarity of the text to the original, but it made the embeddings of the sentences closer to each other in terms of meaning. This unusual application of the language model allowed us to increase the target metric.

Critical thinking and creativity are vital skills for AI and machine learning R&D professionals. The ability to approach problems analytically and question assumptions is essential for refining algorithms and models. Creativity is crucial for developing innovative solutions and exploring novel approaches to complex challenges. R&D professionals should be adept at thinking outside traditional boundaries, experimenting with new ideas, and adapting strategies based on outcomes. These skills empower professionals to tackle intricate problems, optimize models, and contribute to advancements in AI and machine learning with inventive solutions.

A critical aspect in research and development with machine learning and AI is cognitive awareness and unconscious bias as it relate to problem solving. One can easily feel tempted to suggest that their solution to a specific problem is easily generalizable and is applicable to other problems until they get disappointed. If a solution is meant to be generalizable then the researcher must consider separate hypotheses for the broader problems. They should not take anything for granted or make assumptions that are not backed by scientific evidence.

In the realm of AI and ML, ethical responsibility is paramount. Developers must meticulously evaluate and ensure their products uphold fairness, transparency, safety, and trust, considering their impact on users, society, and the environment. Adherence to ethical standards like privacy, security, accountability, and explainability is non-negotiable, as is respecting and incorporating diverse perspectives and cultures. This commitment not only aligns with moral imperatives but also drives a culture of inclusive learning and innovation.

Unfortunately in our world we still have the problem of equality. The challenge for any AI professional is to keep the solution from being influenced by such viewpoints. AI should remain neutral to the traits we are born with and rely only on what each of us does as individuals. What's more, some people trust AI with almost everything in their lives. Personally, I'm a big fan of Netflix's Black Mirror series, which very clearly poses the problems we could face if we don't keep an eye on AI ethics and give AI technologies full access to our personal data. AI is a tool to help humans, not a doppelganger

Ethical and social skills are paramount for AI and machine learning R&D professionals. A strong understanding of ethical considerations surrounding AI, including bias mitigation and fairness, ensures responsible development. Social awareness is vital for anticipating and addressing the societal impacts of AI applications. Effective communication skills are necessary for conveying ethical considerations to diverse stakeholders. Collaborative and interdisciplinary approaches facilitate working with experts in ethics, law, and social sciences. R&D professionals with a keen awareness of the ethical implications of their work can contribute to the responsible and positive integration of AI technologies into society.

I would argue that one skill that is rapidly becoming very important for working with AI is learning how to use LLMs. This entails learning how to use natural language for prompt engineering to get LLMs to produce the results we want. Whether we are building a machine learning discriminative model on top of an LLM, or we are brainstorming around a new research direction, being good at "talking" to LLMs is a huge advantage. Some of the best prompt engineers are skilled in writing and psychology in addition to the traditional hard skills of math and coding.

The ability to craft simplest solutions for complex problems. Developing optimal solutions ultimately provide more value and save resources such as time and cost.

From my prospective I should consider Computing, Science, Software development, strong programming skills such C++ etc.. Development tools such Linux, vim/emacs etc… Problem solving

According to my observations, recommendation systems, LLM models, and RL models in advertising are now in demand on the market.

I think it's important acknowledge the fact that sometimes simple solutions are more than enough even for complex problems and we should not complicate the solution unnecessarily. Also the next important factor to understand as a researcher is that sometimes we may fail but keep your spirits up and gather your learnings for your next solution. Also there is never one approach for any solution, there are always multiple approaches to attain a solution hence what matters most is are we prioritizing the right element in our approach to attain the solution. Creativity and thinking through the shoes of the actual domain user is of utmost importance and has to be balanced well for a great solution.