¿Qué hacer si quieres evitar repetir los errores de otros en IA?

Stay very close the community and peers. The nature of AI as a scientific field is experimentation. There is a lot to learn from each other to avoid pitfalls on how to leverage the technology efficiently and effectively to realize its true potential.

A student chef wouldn't try every recipe mistake in the book, right? Studying past culinary flops (like burnt soufflés) can help them avoid those disasters and create delicious dishes. Just like in AI development, learning from past mistakes is key to success!

I delve into the history of AI, studying past successes and failures. It's like learning from the collective experiences of the field. This historical perspective helps me identify patterns and pitfalls that others have encountered, allowing me to steer clear of similar mistakes in my own work.

To avoid repeating the mistakes of others in AI, you can take several proactive steps: 1. Learn from Historical Cases: Study past failures and successes in AI development and implementation. Understanding the context and reasons behind these outcomes can help you avoid similar pitfalls. 2. Stay Updated: Keep abreast of the latest research, trends, and best practices in AI. The field is constantly evolving, and new insights can help you make more informed decisions. 3. Follow Ethical Guidelines: Adhere to ethical principles and guidelines, such as those outlined in the ACM Code of Ethics and Professional Conduct or the IEEE Global Initiative for Ethical Considerations in Artificial Intelligence and Autonomous Systems.

My essential books: "The Coming Wave" by Mustafa Suleyman, "AI Superpowers" by Kai-Fu Lee, and "A Thousand Brains" by Hawkins.

Just like a young apprentice chef who watches a master chef refine a dish, learning from their experience (both triumphs and blunders), seeking mentors in AI can guide us through obstacles and prevent us from making the same mistakes.

I actively seek out mentors who have more experience in AI than I do. These mentors provide invaluable insights and guidance, often sharing lessons from their own mistakes. It's like having a personal guide who can help you navigate the complex landscape of AI, ensuring you don't fall into the same traps they or their peers did.

And I would add: in most business fields, knowledge is acquired over years. However, in artificial intelligence, an expert can be as young as 18 years old. This requires a very significant change in perception in the company's management if this expertise is truly sought. At the same time, we need people with poise and experience who can guide this knowledge towards useful goals for the company.

Proper mentorship can guide you in gaining better insights on the pitfalls in past projects thereby helping you to avoid the same mistakes.

Mentors are like experienced guides who help you navigate through unfamiliar territory. In the realm of AI, they can offer valuable insights and advice based on their own experiences, helping you avoid common pitfalls and mistakes that others have made before you. Just like learning from someone who has already walked the path, having a mentor in AI can save you time and effort by showing you the best routes to success while steering you away from potential dangers.

A musical group combining different styles sidesteps a singular sound, producing distinctive and captivating music. In the same way, diverse AI teams provide fresh perspectives to prevent biased algorithms.

I make it a point to work with diverse teams. Different perspectives can highlight potential issues that I might not see on my own. It's like having a multi-faceted lens through which to examine AI projects, ensuring that a wide range of viewpoints and experiences are considered, thereby reducing the likelihood of repeating past mistakes.

Diverse teams in AI are like having a variety of perspectives at the table. Different backgrounds, experiences, and ways of thinking can help catch mistakes and offer creative solutions that a homogenous group might miss. It's like having a team of detectives with different skills solving a case together—they each bring something unique to the table. In AI, diverse teams can help spot potential problems early on and ensure that the technology reflects the needs and values of a wider range of people, reducing the risk of repeating past mistakes.

One of the best ways to get multiple viewpoints (and avoid an echo chamber) is to have a diverse and inclusive culture to AI decision-making. Gen AI is so new - so the legal framework is just taking shape. Guard rails are evolving. Hence, get multiple opinions before you reach a decision. Read industry press regularly - learn from other's experiences. Test and Learn. Experiment in small steps and test whether it works in your specific context. Good luck!

As the president of NIVIDIA announced in his last press conference, the future is not in computer science or coding. The limitations of AI and finding those who are knowledgeable in industry or education is symptomatic of the narrow definition of who AI researchers were, generally confined to CS fields. As the barriers to entry have been lowered so much, and will continue to be lowered, a wide range of expertise is needed to craft a future we all want, including technoethicists, bioethicists, philosophers, historians, social scientists, artists and more.

By thoroughly analyzing past accidents involving similar technology, the self-driving car engineer ensures that their new design avoids any repetition of those mistakes. This commitment reflects the importance of ethical guidelines in guiding AI projects and preventing any potential issues down the road.

Adhering to ethical guidelines is non-negotiable. I familiarize myself with established ethical principles in AI and ensure that my work aligns with them. It's like having a moral compass that guides decision-making and helps avoid mistakes that could lead to unethical outcomes.

In this world of technological advances, it seems, only seems, that technology is everything. However, ethical, philosophical, and moral knowledge has never been so important. Finding a balance between technology, philosophy, and psychology is very important. Applying technological advances in artificial intelligence requires a psychological and philosophical counterweight. This requires a different, but necessary, perspective.

Now, this is an interesting topic as everyone mentions "ethics" in any post/article/mention of AI, but no one seems to know what that means in this new era. There are various technoethical approaches that can be taken, but the regulatory and legal landscape is changing. For instance, everyone notes the importance of transparency and privacy, but those are generational and contextually bound. For instance, it was possible to have a modicum of privacy pre-internet/smart phone. It is nearly impossible not to leave a digital footprint and exist in today's digital world. How easily data was already used for training with search engines, shopping and social media foretold the current state of "data privacy."

My recommendations: "The Coming Wave" by Mustafa Suleyman, "AI Superpowers" by Kai-Fu Lee, and "A Thousand Brains" by Hawkins.

The most important skill any of us can develop now- and we need to teach Gen Alpha (Gen AI) - is metacognition and lifelong learning. Learning how one learns will be the most important skill moving forward as we will need to constantly upskill for at least the next generation (or likely two). As emergent tech rapidly evolves, we need to focus less on "hard skills" and more on durable/transferable/power skills. What we will be doing will change rapidly, but the steps we take in doing it will not- i.e. critically evaluating problems, creatively developing solutions, working across different, clearly communicating in teams, etc.

To sidestep common pitfalls in AI, a solid risk management framework is essential. Anticipate potential setbacks, rigorously test your AI across scenarios to uncover vulnerabilities, and deliberate on its wider impact. Proactive risk preparation fosters resilient AI systems, ready to evolve and mitigate unforeseen challenges.

Join AI-focused online communities, forums, and professional networks like LinkedIn groups or Reddit communities dedicated to AI. These platforms often feature discussions on challenges and learnings.

AI is powerful, but it's not a replacement for human judgment. Ensure appropriate human oversight in decision-making processes involving AI, especially in high-stakes scenarios.