You’ve encountered a roadblock in Artificial Intelligence. How can you turn it into an opportunity?

When facing a roadblock in AI, turning it into an opportunity starts with a deep dive into the nature of the obstacle. ???♂? Is the issue technical, like a coding bug or algorithm inefficiency? Or perhaps it's conceptual, indicating a misunderstanding or an incomplete model of the problem. Data limitations, such as insufficient or biased datasets, are common culprits, as are performance bottlenecks that hamper your AI's effectiveness.

O primeiro obstáculo no uso da IA é a falta de clareza sobre o poder e os limites, os riscos (medos) e ganhos que ela pode trazer. é assim com qualquer nova tecnologia, mas especialmente mais forte com as IAs. A gente precisa envolver n?o apenas as pessoas de tecnologia, mas todas as pessoas que se relacionam com o resultado da IA, seguran?a, governan?a, produtos, jurídico, DPO. Todos precisam saber o quanto sabemos e o quanto n?o sabemos mas em especial, o que n?o podemos e o que podemos fazer.

Identify Root Cause: Determine if it's a technical limitation, data scarcity, or unforeseen challenge. Learn from Experience: Understand algorithmic or statistical errors and identify areas for improvement. Explore Alternative Approaches: Experiment with algorithms like deep learning or reinforcement learning for a fresh perspective. Collaborate with Others: Seek advice from peers, mentors, or experts in the field through GitHub or Kaggle. Stay Resilient and Persistent: Recognize obstacles as part of AI development process and utilize debugging tools like TensorBoard for resolution. Document Experience: Record roadblocks and resolution steps using tools like Jupyter Notebook to capture insights for future projects.

Being able to define experiments in order to better understand the problem is critical. Some examples of possible problems could be: 1) The data is bad 2) The format of the data is bad 3) The model architecture has a problem 4) The loss function has a problem 5) The evaluation has a problem. And there are different ways to be able to evaluate each one of these.

- Define and document the symptoms - Analyze the impact - Identify the root cause - Research how similar problems had been addressed

Consulting experience showed design thinking isn't a fad, but key to overcoming AI roadblocks. Don't get stuck in technical weeds! Re-focusing on user needs unlocks solutions. Example: a CPG client's demand forecasting system hit data quality issues. Realigning with optimizing production (user need) led to fresh ideas. User interviews revealed using weather data, solving the bottleneck. Human-centric thinking prevents AI roadblocks from derailing progress.

- Internal team discussions: a place to start - Stakeholder engagement and expert consultations: experts in the field, industry professionals, and technology leaders - Community forums and conferences - Partnerships with academic institutions - Professional development and training: the roadblock may be just a new skill to be learned

Navigating AI challenges becomes much more manageable when you tap into collective wisdom. ?? Reaching out to peers, mentors, or online forums can provide fresh perspectives and insights you might not have considered. These communities often harbor a wealth of experience and can offer practical advice, encouragement, and sometimes even direct assistance. ?? Diving into educational resources like books, articles, and online courses can also illuminate your path. They can offer in-depth explanations, showcase best practices, and even provide examples of how others have navigated similar issues. ??

Never be afraid to ask after you've done your due diligence. A healthy workplace always encourages quality questions. It's better to seek assistance rather than spend 3x more time figuring it out on your own, only to realize your work was not quite right. When you ask for help, always communicate what you've tried and where you're stuck at.

Seeking advice from mentors or online community will be a great, as lot of incoming ideas and perspectives may help to resolve and enhance the system

The heart of innovation in AI lies in experimentation and iteration. ?? Don't hesitate to mix things up—alter parameters, test new algorithms, or use different data sets. Each change is a step towards understanding what works best for your specific challenge. Documenting your experiments is crucial. It not only helps in tracking progress but also in understanding the impact of each modification. Embrace failure as a mentor. Mistakes are not setbacks but learning opportunities that pave the way for success. Iteration is your strategy, with each cycle bringing you closer to either a satisfactory solution or a deeper understanding of the problem. ??

Neural networks are probabilistic. Regardless of your fine-tuning, you can't be 100% certain of the output. More significantly, if you use commercial models (even just for the final word-smithing) these models are subject to constant change. As such, AI solutions are never "finished". So, start with a robust set of test data, and have a (mostly) automated mechanism to ensure your solution stays on track. Incorporate feedback when designing your system. The best results come from human tagging (even just a "thumbs up/ down"). The result is training data to fine-tune your model - or further test data if you had stopped fine-tuning. AI projects are never finished. Continuing feedback needs to be in the architecture from the beginning.

Many times the issue is not with the model itself but the data. Experiment with data cleaning, data augmentation using missing data techniques, remove sampling bias etc

Pivoting is pivotal. Yes, as cheesy as that sounds you have to be open to pivoting on your path. This is especially true when blazing trails in technology. Organizations with unlimited resources are throwing everything at the wall to see what sticks, and you shouldn't be afraid to do the same. I started several projects in 2023, only to see large organizations go to market faster than I ever could. This forced me to make adjustments, experiment, engage my community and as a group we were able to add RAG, SLM, and build a strong Corpus (body of knowledge) focused on ethical hacking/programming. Results are better than anything planned in 2022/2023. Bodies in motion stay in motion, so sidestep a roadblock and keep moving -Law of Inertia-

I've found it helpful to first investigate whether the problem I'm facing is unique or if others have encountered it too. Online discussion forums like Stack Overflow are great resources to learn more about the roadblock and see if a solution exists. If not, I try new approaches, experimenting with parameters or model architecture. This may lead to a solution or a new problem with a well-known solution. The key is to persist and keep trying until you succeed!

Writing down the steps you followed when starting a lengthy process will help you go back and choose alternative approaches. This means you will avoid repeating the same iteration twice, saving you time and resources. As an example, when tuning the hyperparameters of a model, it's easy to forget to save all previous results and parameters before reaching the satisfying model, while focusing only on the results. Taking notes prevents this, especially when making iterations and fixes. Once everything is working, it's always great to write the steps: 1) for yourself for future similar projects; 2) for your team so they won't repeat the same errors; and 3) for the community.

After conquering a roadblock, document and share your insights. Consolidate knowledge for personal growth and aid others facing similar challenges. Write a blog post, report, or presentation detailing your solution, learnings, and recommendations. Additionally, contributing to open-source projects or forums are good ways to communicate your findings. Collaboration undoubtedly amplifies the impact of your breakthroughs.

Technical documentation helps everyone out a ton! Speeds up the team's workflow by a lot. I've experienced this first-hand in all my career as I love documenting and sharing requirements, features, processes, and defect resolutions.

Make sure that your results are known not only to your friends and your mom, but to everyone online! Post your findings on Youtube / Medium / own blog / github / anywhere at all.

It is crucial to document the process, including the challenges faced, the experiments conducted, and the results obtained. This documentation serves as a valuable resource for future reference and can be shared with the AI community, contributing to collective knowledge and potentially inspiring others to find innovative solutions.

In this fast paced world, it's common to jump on the next project as soon as we wrap one up. This might lead to burnout in my opinion. It's highly encouraged to take a breather and reflect on your accomplishments before moving on to the next task.

Once the roadblock is successfully overcome, it is important to celebrate the accomplishment. Recognizing and acknowledging the efforts put into solving the problem can boost morale and motivation. Additionally, taking the time to reflect on the experience can provide valuable lessons learned and insights for future AI projects.

After successfully transforming a roadblock into an opportunity, it's crucial to take a moment to celebrate your achievement and engage in thoughtful reflection. This celebration is not only a reward but also a moment to assess your strengths, weaknesses, and the strategies that led to your success. Reflect on how the lessons learned can be applied to future challenges, consider areas for skill enhancement, and identify new goals to push your boundaries further. This process of reflection and celebration fuels personal growth and prepares you for even greater accomplishments ahead.

Celebrate your successes, even the small ones. Take time to reflect on what you have learned and what you can do to improve. Here are some tips for celebrating and reflecting: - Take a break: Take a break to celebrate your successes and recharge your batteries. - Reflect on your learnings: What did you learn from this experience? What could you do better next time? - Set new goals: Set new goals for yourself and continue to learn and grow.

: Celebrate small victories and milestones achieved along the way. Reflect on the lessons learned from overcoming the obstacle and consider how they can inform your future AI endeavors.

Approach to handling roadblocks, whether for AI or something else, would not change drastically. Taking a step back, looking at the issue holistically and articulating the roadblock precisely, is the best place to start. With the availability of Gen AI, finding solutions should only get easier. Start with exploring if the problem is unique to you or something that everyone else has also faced. (I was recently trying to use OpenAI's api to set up a basic LLM powered bot.) The documentation was not very clear, so I was able to search for videos that did this and i was able to resolve my issue quickly. If it is a unique problem, then doing a RCA and brainstorming with others can help get to a solution quickly.

Turn a roadblock in AI into an opportunity by taking out time to deepen your AI concepts and upskill yourself . Secondly, embrace a mindset of endless experimentation and exploration. Try out different approaches and experiments with novel ideas. One should view the roadblock as natural part of iterative process of AI development and never hesitate to restart! Not to miss, identification of root cause is a must which decides the direction of your solution like is it due to data limitations, irrelevant data, algorithms or something else.

It’s important to take into consideration that AI initiatives are multidisciplinary, including data, business, ML …etc Subsequently, prior resolving the issue it’s imperative to pinpoint where the challenge resides! Disclaimer: I am assuming that the Ai has been already assessed as a suitable fit for the problem in the first place!

Facing a roadblock in Artificial Intelligence presents an opportunity to innovate and push boundaries. By analyzing the obstacle, you can identify underlying challenges and gaps in current knowledge. This process encourages creative problem-solving, development of new technologies or algorithms, and ultimately, contributes to the advancement of AI. Embrace challenges as catalysts for breakthroughs.

It's common to face roadblocks in AI projects as these are driven by data. It is important to narrow the scope so that you can create something that is useful for the business and then work on broadening the scope.