What's your strategy for balancing AI and data science?

From my experience working as a Data Scientist at a public company and working with complex financial data. People often misinterpret "AI" as only when they interact with products like ChatGpt or Bard. The technologies they see; which is interactive AI is called "Natural Language Processing" which is a different branch within the AI domain. Machine Learning is another domain where Data Scientists build models and train on historical data aka 'Supervised Learning'. So isn't that Data Science? I would beg to differ, Data Science is a part of AI just like how NLP is a part of AI. Extracting insights from data is called "Data Analytics" not "Data Science".

It's all about what we we we are looking for. Abundance of Accurate and precise data is helpful in getting insights about what us happening around our area of concern. The AI/ML system's backbone is error free data. The data is not good enough, your model won't be doing justice to your goals and objectives.

AI is more about service and performance oriented. DS is more about the idiosyncratic inferences about data and outputs. AI has more close to Software engineering. DS is more close to Statistics hence domain knowledge. AI has higher ambition on replacing basic human capabilities like language and vision. DS has higher ambition on complex decision making in higher dimension where humans suffer, without too much losing the inference power. Combining both in one service will bring a sweet-point in efficiency and interpretability trade-off.

This article focuses on balancing AI and Data Science in one's career. It's important to recognize that Data Science, along with Machine Learning, is integral to AI. Thus, our conversation centers around "finding the balance between Data Science and Machine Learning" for a career in AI.

Balancing AI and data science is a dynamic journey I navigate with purpose. Collaborating across disciplines amplifies our collective capabilities – melding AI's algorithms with data science's analytical prowess drives impactful insights. Immersing myself in domain knowledge enhances context-driven analysis, while my ethical compass ensures responsible AI integration, safeguarding privacy and fairness. Embracing continuous learning keeps me agile in this evolving landscape, adapting to emerging trends. Sharing findings effectively, I distill complex AI-driven insights for actionable understanding. This synergy of teamwork, domain expertise, ethics, learning, and communication defines my path to harmonizing AI and data science.

By leveraging your strengths and addressing any existing gaps, you can develop a well-rounded skill set that enables you to contribute effectively in this dynamic field. It's important to ensure clear goals to set defining objectives in the field of data science and AI. Accessing the capabilities to deliver complex ideas might require to strategize and plan the growth in field of AI/ML, deep learning, data science and more. Collaborate on projects, share ideas, and learn from each other's experiences. Networking can provide valuable insights and opportunities for growth. Identifying gaps to assess knowledge base and proactively undergo training and continuous learnings can help to keep up with data professionals demand.

In my opinion, knowing your strengths and weaknesses in data science and AI is crucial for effective problem-solving, not just for career planning. When faced with large, messy datasets, you'll know to collaborate with a data engineer if you have a strong foundation in machine learning but lack data engineering skills. These fields are interdisciplinary, so you can work with experts whose abilities complement your own to fill in the gaps.

Understand what are the weakest points in the project. Then understand what you team can do and what segments may potentially need outsourcing... Data Scientists are very versatile and usually understand multiple segments of the AI project.

As a Leader for a team of ML/DL Engineers, Data Engineer, and Data scientists, I see the huge difference in the role, however, I can understand where the confusion is coming from, as most of these team members have overlapping experiences and sometimes they continue doing what the other role should do for many reasons, usually project deadline or excitement about the end results. I think with the rapid growth of these domains, the differences will be crystal clear, as the data is getting more complicated in each industry, and the number of variables is increasing exponentially in every cycle

Taking a step back, the question of how to balance artificial intelligence and data science work is the wrong question to ask. This is partially because it implies that they require balance in all cases. The business must focus on developing whatever solution is best for solving a relevant problem in ways that drive revenue growth and manage costs while doing so. This could be using an AI application, it could be data science, or it could be something else entirely.

You've outlined a solid approach to enhancing your skills in AI and data science. Identifying strengths and gaps is crucial to tailor your learning journey. Accessing diverse resources like online courses, books, podcasts, and joining online communities helps you gain knowledge and interact with peers. Applying what you learn to real-world projects solidifies your skills and demonstrates your expertise effectively. Balancing learning with practical experience is key to growing in both AI and data science and showcasing your value in various contexts.

In my humble opinion, it's important to have a multidisciplinary approach that considers the multiple aspects like technical, ethical, and social dimensions of AI. This may involve assembling diverse teams with expertise in AI, data science, ethics, and other relevant areas, as well as engaging in open communication and collaboration among stakeholders, including policymakers, researchers, industry leaders, and public opinion.

I have found that reading articles or watching lectures isn't enough to fully understand the complexities of AI and data science. You can overcome the particular difficulties of incorporating AI into data-driven solutions by creating your own projects or contributing to those that already exist. This gives you practical experience that cannot be matched by purely academic learning.

There's only so much that an online course or a video can explain. The usual way I've seen the tutorials work is taking a kaggle dataset or something that probably is not representative of the actual dataset you'll be dealing with at work. The challenges that real-world data brings might, and do often involve speaking with people as well, to balance the objective of the problem being solved with the machine learning bits. Examples I can think of off the top of my head are what seemed to be killer features, but they would be generated at a time after the model is actually expecting to generate a prediction

In data science, understanding business knowledge is crucial, and storytelling skills play a vital role. So Effective collaboration allows experts to contribute their unique perspectives, leading to more holistic problem-solving and comprehensive solutions. Similarly, in the dynamic realms of AI and data science, ongoing communication is necessary to swiftly adapt to changes and stay ahead of emerging trends and technologies.

From my point of view, effective communication isn't just about explaining your own work; it's about helping a team of people from different fields understand each other. When you can explain detailed AI algorithms to a data analyst or business stakeholder, you're adding value as an integrator. Your ability to "translate" between various domains of expertise can make a big difference in the success of a project.

This is the most important segment. I was discussing the use of Deep Learning as potentially new use cases when most people were using it for image recognition, today its the most dominant approach in most AI projects. Even if there is resistance to new technology, its important to insist on novel approaches. But its also important to have a good intuition on which novel approaches may actually work.

And if you are at an environment where you need to provide value fast, you need to sometimes look past using 'what's cool out there' or research every possible methods before applying such methods to your use case at hand. Because by the time you may have created something exemplary, the need for that use case may have already expired. Often success can be achieved by using the simpler algorithms over complex ones (adapt to be comforable using simpler solutions), while also making sure you're using the latest state of the art solution (apply the latest model with the best model metric on the leaderboard).

As I see it, successfully navigating the realm of AI and data science demands an adaptive and innovative mindset. The ever-evolving nature of these fields introduces novel challenges, solutions, and avenues. Staying current with the latest trends, tools, and domains is imperative, necessitating an openness to experimentation. Moreover, considering the ethical, social, and environmental dimensions of one's work becomes paramount in order to yield positive outcomes. In today's rapidly transforming landscape, a comprehensive understanding encompassing data engineering, software engineering, and DevOps is essential for a data scientist to remain adept and effective.

Throughout my career, I've learned that having an open mind isn't just a nice-to-have, it's a must. The rate of change in both AI and data science is so fast that techniques that are cutting-edge today could be out of date in a few months. In these fast-changing fields, it's important to be willing to keep learning and changing your skills.

Adapt AI for: - Robotics, - NLP/NLU and - Computer Vision - Gaming related tasks. Adopt DS for: - Predictive/Prescriptive, - Anomaly/Fraud Detection and - Recommendation System related tasks. In any of them, using a deep learning algorithm does not mean that you are working in AI scope!

In addition to the perspectives shared already, I would highlight the following two aspects that I believe to be critical to success: 1) Be very clear on what you are trying to solve for, what does good/great look like, upfront (not mid-development), and define this in terms of customer experience or outcomes vs. just looking at MAE or other error metrics 2) Understand what it takes to scale, whether it is a ML, DS, or AI solution, plan for scale, and scale to the plan, keeping in mind the costs to operate the system and the ROI it can generate for the company and its customers/stakeholders

In my experience, there is great value in the most basic AI algorithms, being Decision Trees for explainability, Linear Regression for simplicity… Most companies’ AI needs can start to be covered with very basic models, even heuristics that are easily understandable by business teams. Once these models are proven to provide value, it may make sense to try to dive deeper into more complex/state of the art AI algorithms, with the extra benefit of having a benchmark to compare against. More complex algorithms tend to take more time to develop and require more resources, people and seniority. To sum up, I believe companies should invest more on the data science aspect at the beginning, and slowly ramp up on AI complexity.

Let's delve into a scenario to understand the relationship between data science and AI within a healthcare institution. A team of AI engineers develops a computer vision system that can scrutinize medical images like X-rays and MRIs. As the AI systems generate more data, the data science team can further refine the models, creating a feedback loop that continuously improves the performance and outcomes. As AI uncovers nuances in medical images that might escape the human eye, data science seizes these revelations to enhance the models.

Balancing AI and data science is like keeping a seesaw steady. AI for Smart Decisions: Think of AI as a friend who helps you make decisions. It uses data to give you insights, like another person sitting on the seesaw's opposite end. Data Science for Insights: Data science is like the balance between you and your friend on the seesaw. It takes the data, analyzes it, and helps you understand what it all means.

It is important to pay attention to the limitations of approaches from both Data Science and AI. For example, when trying to build an ethical and secure AI application, correcting for the biases in the dataset, limitations in the machine learning models and prompt injection attacks an LLM may be subject to can exponentially increase the chance of success of that application.