What are the best ways to showcase problem-solving skills in AI?

My perspective involves a thorough prelude to any AI endeavor, emphasizing the critical importance of comprehending the problem and stakeholder requirements. Prioritizing questions, collecting pertinent data, and delineating scope and objectives are fundamental steps. This approach allows me to refine the problem statement, pinpoint key assumptions and constraints, and steer clear of futile pursuits, ensuring a focused and effective AI development process that aligns with the real needs of the project.

Understanding and defining the "real" problem clearly is the key, which unfortunately, many miss out on. A leader/project manager can follow many approaches to arrive at the problem statement - brainstorm, affinity mapping exercises with stakeholders, root cause analysis, etc. Explore the problem from different angles. Gather the relevant data to understand the context of the problem and identify patterns & trends. Once you understand the problem, one should also spend time to write the key factors that are to be addressed, the scope of the project and the end objective (what we need to achieve).

I would recommend to start with the endgame and understand how the solution will generate value, and how the value will be measured. This will become your north star, helping you navigate through the design, development, and implementation steps to address any given challenge. This should also inform you whether data & AI is the right approach for problem-solving.

I think an effective way to build trust in your judgment is to explain your methodology not just the end result. Walk people through your process of framing the problem, weighing options, and iterating based on feedback.

Understanding the problem in AI development involves deep analysis to frame the issue clearly. It requires engaging with stakeholders, asking critical questions, and thoroughly researching potential solutions to ensure a targeted and efficient approach.

Once we have understood the problem, defined it clearly, jotted down the scope and objectives of the AI project, we should spend time researching existing solutions available and also existing approaches available that have been used to address similar problems. This can save us time, resources, and provide us with learnings (if we need to still go and build). Choosing the right tools will depend on the problem we are solving ( classification, prediction, regression,...), data size & complexity, data format (certain tools are not compatible with certain formats and environment), budgets available to us, timeline for the project, resources availability and their skill level (for using the tools).

My perspective involves recognizing the expansive nature of AI and its varied tools. Effectively navigating this diversity demands a discerning approach, selecting from machine learning, deep learning, natural language processing, computer vision, or robotics based on the unique demands of the problem at hand. To showcase my proficiency in AI problem-solving, I prioritize not only choosing the right algorithms but also selecting appropriate libraries, platforms, and languages. My decisions are rooted in a thorough understanding of the advantages and limitations of each option, ensuring a tailored and effective solution for the specific challenges posed by the task.

The choice of the right tooling can affect the effectiveness of a solution in quite a major way. A solution can be made up of some very glaring instant gratification features, that can be easily demonstrated by a solution provider. However, like any good solution there are other secondary complexities for implementing an AI solution that may require a better skill set (preferably in-house), more budgetary needs and time to implement. Training an AI, followed by regular retraining, the data upkeep and availability of readily available data lakes or warehouses are all important considerations that get expensive very quickly.

Mastering AI problem-solving is like being a chef in a high-tech kitchen. You need to pick the right tools for the job. Just like a chef selects a particular knife for slicing or a specific pan for sautéing, an AI expert chooses the right algorithms and computing resources for different tasks. The key is knowing which tool from your AI toolkit will make your solution smarter, faster, and more efficient. It’s this smart choice of tools that can take a good AI solution and make it great, solving complex problems in ways we’ve never seen before.

To solve different problems with AI, we match each problem with the right kind of AI tool: Natural Language Processing reads and understands lots of texts like customer reviews, Convolutional Neural Networks act like smart cameras spotting defects in toys, and Machine Learning Algorithms work like detectives using clues to predict customer behaviour. Each tool is like a specialized helper, chosen for its unique ability to handle specific tasks efficiently and accurately. This way, we can use AI to make sense of big data, ensure product quality, and understand customer needs better.

Agree that a PoC is the way to go to show that what you have developed is actually solving a problem the customer has in a way that is timely, proportionate and cost effective.

To develop an AI solution, it is important to understand an organisation’s appetite for building, deploying and maintaining that solution. Most buyers in the current market, more often that not, do not have an in house intelligent client function. This would mean, the cost of acquiring a solution and implementing it to maximise the benefit of that solution is full of hidden costs. Choice of database, choice of libraries, Availability of skill set, ability to maximise adoption /should all be considered.

In my experience, the best way to showcase problem-solving skills in AI is proof of concept. A proof of concept shows without a doubt that you can apply your AI problem-solving skills to clients' business process.

In this stage, starting with a PoC (Proof of Concept) worked best for me. It helped me to showcase what the final solution looks like and gather feedback from the customer.

Developing an AI solution is a complex but rewarding process. By carefully following these steps, you can create robust and effective AI systems that address real-world problems. Remember, adaptability and continuous improvement are key to staying at the forefront of AI development. Whether you're a beginner or an experienced practitioner, embracing these principles will contribute to the success of your AI projects.

We can’t stress the importance of learning from the best, in the field of AI. One of the platforms that can help in this is Kaggle. The new perspectives and the approach of solving a problem is one of the major takeaways and the best thing is that not only the experts we can find innovative ideas even from the beginners, which make this field a lot more exciting.

In my Experience as AI is a new or you can say fastest growing path or field. and I believe new innovation leads to new solution but with new problem so as an AI engineer we have to deal with alot of new problem for that we need knowledge and the best source of knowledge is others.

- By engaging with diverse resources like books, articles, and online communities, one can gain valuable insights and perspectives. - For instance, when faced with a complex image recognition challenge, I sought inspiration from a published research paper detailing a novel neural network architecture. - By adapting and fine-tuning the approach to my specific problem, I successfully enhanced the model's accuracy. - Collaborative learning not only refines individual skills but also fosters a culture of innovation within the AI community. #ProblemSolving #AICommunity #ContinuousLearning #InnovationInAI

AI is constantly evolving, so I stay updated by learning from others. I read the latest AI research, watch tutorials, and join online forums. By doing this, I discover new AI tools and techniques, exchange ideas, and get advice from experts. Staying informed helps me improve my AI solutions and adapt to changes in the field.

The best way to implement AI is learn up to the extent of its limitations. AI is not AGI, even ChatGPT in many ways is still narrow AI. Our ability to solve problems is understanding how much of the problem the AI can solve and how much we need to solve. Novel ways in implementing multiple AI resources in a creative fashion is the best way to prove out your capabilities. For example, simply generating an AI image right now is not engaging enough. If you ask the top Midjourney artists how they are rendering their crazy images, you would be surprised to find that many of them are layering multiple generated images and tools to create the final piece. Art still requires artistic flair at this time. Learning how is our current endeavor.

- Showcasing problem-solving skills in AI necessitates a commitment to continuous learning and skill enhancement. - In the rapidly evolving AI landscape, staying updated is paramount. - Engaging in online courses, attending conferences, and participating in hackathons are effective ways to sharpen problem-solving abilities. - Consider a scenario where an AI professional encounters a complex anomaly detection problem. - By consistently learning and adapting, they can leverage cutting-edge techniques to develop robust solutions, showcasing their problem-solving prowess. #AISkills #ContinuousLearning #ProblemSolvinginAI

Showing passion is great but I want to add the importance of how you choose to show it. Storytelling is very essential in data science as not only it can show what you have achieved but also educate others who might find your findings in an intuitive way. You can also practice your data viz skills along the way too!! :))

- Actively engaging with the AI community, contributing to open-source projects, and sharing your insights through various platforms showcase commitment, dedication, and initiative. - For instance, creating a blog that discusses your problem-solving approaches in AI or participating in collaborative projects highlights your unique perspective and style. - By showcasing both your technical abilities and your passion for the field, you can present a well-rounded image of your problem-solving skills in AI. #AISkills #ProblemSolving #PassionForAI #AICommunityEngagement

To lead with passion means brining your full self to the table - even if it means being vulnerable. In essence, share your trials and tribulations in your AI journey, article it, catalogue it, share lessons learned and equally important share the losses along with the wins - this way we can all learn together.

To showcase my passion for AI, I actively create projects, ranging from automating tasks to building systems from scratch. This hands-on approach is how I learn best. Whether it's developing new applications or exploring innovative solutions, each project sharpens my problem-solving skills. My projects not only demonstrate my technical abilities but also my enthusiasm and dedication to the evolving field of AI.

Spend your time solving difficult, real-world problems rather than posting yet another “ultimate ChatGPT prompt engineering cheat sheet.”

In my opinion the first step involves breaking the problem into steps and understanding the problem dependencies to understand the problem in larger detail. The next step is to understand the big picture in the scheme of things. This should help you understand and articulate the need of a solution towards the problem you intend to solve. The next step is to identify using tool’s parts of solutions to the problem statement and integrating them together to identify the final solution. Test your solution with feedback with peers to improvise the solution if required, learn through failures if any and apply previous experience of solving similar problems.

If you are looking to build with AI, think about which areas are becoming more commoditised (e.g Speech-to-text) and then explore under-digitised or highly specialised verticals to find defensibility.

If we are considering best ways to demonstrate the problem-solving potential of AI, I find that baselining existing manual workflows and then measuring performance again after the introduction of AI allows comparison and the delta to be most easily understood - enabling problem-solving capabilities to be showcased with qualifying evidence.

To showcase your AI problem-solving skills, try tackling real-world projects and sharing your journey, from the challenges you faced to your innovative solutions. Participating in AI Kaggle or Hackaton competitions and contributing to open-source projects can also be great ways to demonstrate your skills. Don't forget the power of a well-crafted portfolio and staying up-to-date with the latest AI trends. And of course, sharing your knowledge through blogs or tutorials can really highlight your expertise.