How can you grow your AI and robotics career?

In my years as a data science leader, I've seen many AI professionals drown in the vast sea of AI because they attempt to master everything. However, the most successful ones have a distinct niche. For instance, while working on a medical imaging project, a colleague dove deep into convolutional neural networks, specific to detecting anomalies in X-rays. This specialisation made her a go-to expert in that niche. My suggestion is to dive deep into a subfield of interest, establish expertise, and then, if desired, branch out or pivot. Specialising not only provides clarity in learning but also makes you invaluable in that specific domain.

Think of diving into AI like exploring a vast ocean. While the waters are interconnected, each region has unique marine life and mysteries. Rather than trying to explore every inch, pick a spot you're passionate about and become the go-to explorer for that area.

AI is a vast technical domain. It has many cross domain areas that are critical. Some suggestions to embark on this would be. 1. Understand the foundations at base level through foundation courses 2 if keen on pursing further get into more hands on courses where you build some models and experience first hand 3. Think about whether this is all more hobby or will you take to your professional requirement. 3.1 If yes work on maybe problems that you want to try to address. 3.2 In specific start exploring if work has been done on this. Follow experts and research done 4 subscribe and follow leading experts and practitioners 5. Have a structured flow to capture your learning’s. 6. Attend workshops, Webinars and joint specialised groups

Identifying Your Niche: 1. Self-Assessment: Begin by understanding your strengths, skills, and passions. Consider what areas of AI and robotics excite you the most. Are you inclined towards machine learning algorithms, computer vision, human-robot interaction, or perhaps ethical considerations? 2. Market Analysis: Research current trends and demands in AI and robotics. Look for gaps or areas that are underserved. This could be a specific application, industry, or technology that aligns with your expertise and interests. 3. Skill Evaluation: Gauge your current skill set and knowledge level in various subfields. Identify areas where you already have proficiency and where you need further development.

Finding your niche is an interesting phrase. How do I know my niche? In reinforcement learning there’s a recognised challenge known as “the explore / exploit trade off “. It’s a balancing act between two opposing strategies: - Is it better to choose the niche/domain best-known from experience? - or is it better to try new things that may deliver better outcomes in the future? The most optimal outcome is usually achieved with a combination of both, but the mix is unique to each person and their context. My 2c: try everything; trust your heart. You’ll know when you’ve found your niche; we change through life. Don’t be afraid to change your niche

1. Leverage a GitHub popular code set and the. create a tool that nontechnical people can use. 1. B Or get certifications from free ai programs from google or Microsoft. 2. Collect testimonials on your projects or app. 3. Leverage the results from testimonials to enhance the product or get a job.

Creating a strong portfolio is crucial in AI and robotics. It showcases your expertise through real-world projects, diverse datasets, and documented processes. By sharing your code on platforms like GitHub and presenting on Medium or personal blogs, you engage with a wider audience and increase visibility. Remember to emphasize practical applications, results achieved, and soft skills. Networking through platforms, events, and collaborations further solidifies your credibility, positioning you as a skilled professional in these dynamic fields.

Focus on the "so what" as you build your portfolio. This also applies to when you may need to do a case study as part of an interview process. This will help you differentiate as someone who focusses on the "job to be done". And, that's what differentiates a great data scientist from a good data scientist.

Here are some strategies to help you create a compelling AI portfolio: 1. Create a diversified project showcase 2. Attend AI workshops/webinars & list relevant certifications 3. Conduct research & publish research papers/white papers 4. Develop your personal branding 5. Engage with the AI community (e.g., write blog posts, start a newsletter, create short Youtube videos showcasing your projects)

The projects you work on, design and showcase should have clear business outcomes as goals. There are many AI experiments out there which aren’t directly connected to business outcomes or business priorities. You want to show that you have the skills to deliver business impact. Demonstrating this outcome centricity will boost the value of your AI skills.

Networking and collaboration are indeed crucial for advancing your career in AI and robotics. Remember that networking is a two-way street. Building meaningful relationships requires active engagement, reciprocity, and a willingness to share your knowledge and experiences as well. Over time, your network can become a valuable resource for learning, collaboration, and career growth.

Networking and collaboration are pivotal to advancing in the dynamic fields of AI and robotics. Building a robust professional network facilitates knowledge sharing, mentorship, and valuable insights from peers. This interaction fosters a broader perspective and enables the discovery of new opportunities, whether they involve job roles, research projects, or partnerships. Concurrently, collaboration amplifies growth by allowing you to leverage diverse skill sets, gain feedback, and foster innovation. Engaging in events, online groups, hackathons, and competitions nurtures connections that enrich your career journey, as you learn, contribute, and collaborate with those who share your passion for these evolving domains.

I would also start publishing value added content on LinkedIn, Twitter or threads ??+ comment on top thought leaders posts to expand your audience

In particular, network outside of your comfort zone rather than only those who fit the same profile as you. This will help you strengthen your skills and capability, diversify your perspectives, and broaden your opportunity.

Industry networking and participation helps you get a practical and peer view of AI systems. In my previous role as the head of AI, we introduced peer review process to improve explainability of model development, code review, and third-eye view on the approach. In addition, we had also set a goal to participate in atleast one industry event to promote peer learning and reflection from industry parallels. I strongly believe that research subjects cannot grow in isolation. Share and learn is the best way once you get out of theoretical learning and start working on complex business problems.

Absolutely, continuous learning and experimentation are crucial for staying relevant and innovative in the ever-evolving domains of AI and robotics. In AI and robotics, ongoing learning and experimentation are imperative. To remain at the forefront, stay updated on trends and breakthroughs within your niche and related areas. Embrace challenges, explore novel concepts, and validate your ideas through experimentation. Utilize resources like online courses, research papers, podcasts, and tools to expand your knowledge and apply it to unexplored problems and domains.

Continual learning and experimentation are at the core of success in AI and robotics. Remember that the field of AI and robotics is characterized by rapid advancements. Staying committed to continuous learning and active experimentation will not only keep you relevant but also position you as a valuable contributor to the ongoing development of these technologies.

As technology rapidly advances, the tools and methodologies of last year might become outdated or be replaced by more efficient alternatives. Professionals aiming to stay at the forefront must adopt a mindset of perpetual learning. This means regularly enrolling in courses, attending workshops, and staying updated with the latest research. But theoretical knowledge isn't enough. Applying what you've learned through hands-on experimentation, building prototypes, or trying out new algorithms is vital. This iterative process of learning and applying ensures a deeper understanding and keeps one adaptable and innovative, whether in generic AI applications or specialized fields like wound care.

Deliberately seek to be disrupted in your learning process. Switch up learning platforms and information sources - it’s surprising how different the world is on different platforms.

Having a strong domain expertise is useful here. Identify the top open challenges in a domain that do not have a satisfactory solution with old technologies, and as new wave of innovations keep coming in, try to see if they can solve a particular problem that was unsolved before. Remember, not every new tehcnology innovation is worth investing time in, since there is always a hype associated with each new innovation. Be mindful of the hype-cycle and keep yourself up-to-date with the latest innovations and the practical usefulness of them.

Algorithmic Enhancement Loop: Treat your career growth in AI and robotics as an algorithmic loop. Begin by collecting data points about your projects, skills, and goals. Create a feedback algorithm by reaching out to mentors, peers, and industry experts for constructive input. Integrate automated tools that analyze your work, providing quantifiable insights into your performance. Based on this feedback, generate a customized improvement strategy. Set measurable goals using data-driven benchmarks, and create an AI-powered virtual assistant to remind you of milestones and guide you through your action plans.

Absolutely, seeking feedback and continuously improving are integral parts of professional growth in AI and robotics. Remember that seeking feedback and embracing improvement is a continuous process. Each piece of feedback received, whether positive or constructive, is an opportunity for growth. By actively seeking ways to enhance your skills and adapt to new challenges, you'll position yourself for long-term success in the dynamic fields of AI and robotics.

Sometimes it’s tough to extract valuable feedback. One of the questions which has helped me unlock feedback and make it more comfortable to discuss is “How could we make/do this better?”

If you are employed, the quality of the solutions that you develop / design gives you the direct feedback on what is working, what is not working for the customer. Understading the customers' requirements and building a solution that caters to those requirements is very important for customer satisfaction. Aim to create a positive experience for the customer that results in positive feedback from the customer, which inturn directly transaltes to your growth in your career. Ofcourse, the converse is also true. So be mindful of how your solution affects your customer, which indirectly affects your career. There are 3 kinds of requirements from customers for any given problem: explicit, expected and implict; Be mindful of all these three

In the realm of AI and robotics, cultivating professional growth while upholding ethical and responsible practices is paramount. Navigating this trajectory necessitates a vigilant integration of principles encompassing fairness, transparency, privacy, safety, and accountability. By prioritizing fairness, we mitigate algorithmic biases, ensuring equitable outcomes and eliminating systemic disparities. The strategic application of transparent methodologies grants stakeholders a lucid understanding of AI model intricacies, fostering trust and confidence in the technology. In parallel, preserving user privacy through robust data protection mechanisms adheres to regulatory standards while respecting individual autonomy.

AI and robotics are necessarily multidisciplinary and cross-hierarchical endeavours. There's a need to consider perspectives of the board, executive, management and frontline as there are risks and opportunities that are only evident when you look at an initiative from each level. And then each initiative needs to be considered from a financial, people, legal/regulatory, strategic and operational perspective. Most importantly, organisational change management must be baked in, and not afterthought. What this means is that the successful AI leaders will be those that can lead and collaborate up, down, across, inside and outside organisations.

AI and robotics are constantly evolving. Continuously update your skills, learn about new technologies, and adapt to changes, and building a career in AI and robotics requires dedication, a growth mindset, and a willingness to adapt to new challenges. By combining education, practical experience, networking, and a passion for innovation, you can make meaningful contributions to these exciting fields.

If I may add on, the choice is always in what makes you passionate on either software or hardware. Are you a handson tangible person who likes creating movement or outcomes or you're that Analyst who programs outputs? Are you a circuit builder? Research on all possible roles, tasks, job type, certification and experiences requirements. Join competitions and speak with participants on their products or outcomes. Learn the process.