Here's how you can become a valuable asset in the AI field.

The key points that I recommend are the following: -Continuous training -Contributions to AI Projects and Community -Problem Solving and Critical Thinking Skills -Communication and Teamwork Skills

In any career your knowledge is crucial. AI is in the middle between Math, Computer Science and human interaction if you would like to create a vertical NLM with the linguistics of your company/industry. Machine Learning is also about Stats and Neural networks have lots of relations with Probability. Play with the tool and Python is an optimal starting point, inspired by Monty Python's Flying Circus a surreal BBC comedy.

Knowing the foundations of AI and engineering especially understanding the math behind the algorithms and models is essential. I think nowadays, it is even more essential to learn the foundations to deploy AI/ML models for small and large scale applications. Understanding cloud solutions (Azure, AWS, GCP, etc.) and what resources benefits the business team. The knowledge of MLOps, how to work with MLFlow libraries and organize the AI project that can be quickly reproduced for other AI proof of concepts is important. And lastly, the AI expert should have the capacity to lead a potential initiative from step zero to production.

?? Embark on the journey of AI mastery! - Enroll in courses covering machine learning, neural networks, and NLP. - Dive into the mathematics behind AI algorithms, focusing on stats, probability, and linear algebra. - Master programming languages like Python and tools like TensorFlow or PyTorch for practical application. - Stay curious and never stop learning to stay ahead in the dynamic field of artificial intelligence. Let's keep the conversation going and share insights on how we can collectively advance in the realm of AI!

Contribute to open-source AI projects to gain experience, collaborate with others, and enhance your portfolio. Volunteering: Volunteer your AI skills for non-profit organizations or community projects to make a positive impact and gain practical experience.

?? Building Practical Skills in AI: ?? To truly excel in AI, focus on honing practical skills through hands-on projects: - Develop neural networks independently. - Experiment with diverse machine learning models. - Solve real-world challenges to showcase your expertise. ?? Show potential employers your ability to translate theory into actionable solutions. Keep learning and growing in this dynamic field! ??

é muito importante transformar o conhecimento teórico em habilidades práticas. Segue sugest?es de como fazer isso: Comece implementando algoritmos de ML como regress?o linear, KNN, árvore de decis?o e SVM. Depois crie redes neurais simples usando bibliotecas como TensorFlow ou PyTorch para resolver problemas básicos de classifica??o e regress?o. Para projetos intermediários você pode realizar análise de sentimentos (NLTK, spaCy, Hugging Face Transformer) e reconhecimento de imagens (OpenCV, TensorFlow, PyTorch, Keras). Já em projetos mais avan?ados experimente redes neurais convolucionais (CNNs) para vis?o computacional, redes neurais recorrentes (RNNs) para dados sequenciais, e modelos transformadores para tarefas de PLN.

There’s no shortage of courses, books, and YouTube videos available for learning AI skills. My advice is to try a few different resources and find the method that works best for you. Personally, I like combining books with videos from teaching experts like Andrej Karpathy and Andrew Ng. However, to make what you learn truly stick, you need to use it to build something. Without the practical exercise of building a project or solution, the theory quickly evaporates. Engaging in hands-on projects solidifies your understanding and helps you apply theoretical concepts to real-world scenarios.

Building skills in AI requires a multifaceted approach. While foundational knowledge in mathematics, statistics, and programming is essential, the ability to apply these skills in real-world scenarios is equally important. Engage in hands-on projects and collaborative efforts to develop a practical understanding of AI applications. Furthermore, soft skills such as problem-solving, critical thinking, and effective communication are vital. They enable you to convey complex concepts clearly and work efficiently in teams. Combining technical expertise with strong interpersonal skills ensures you are well-rounded and ready to tackle the dynamic challenges in the AI field.

Prompt Example: “AI, suggest practical projects and exercises to build my skills in Generative AI.” Insight: Gain hands-on experience by working on projects that allow you to apply what you’ve learned. Practical skills are crucial in showcasing your abilities. Swagger Tip: Start with small projects like text generation or chatbot creation, and gradually take on more complex tasks like developing AI-driven applications. Document and share your work on GitHub and LinkedIn to build your portfolio.

Here's my personal plan: -FOLLOW key AI researchers, academics, business founders, futurists and contributors on Linkedin. -SUBSCRIBE to newsletters dedicated to AI news -TAKE courses offered by the major AI companies -TALK to fellow AI enthusiasts to hear their perspective

Procure assinar boletins que s?o referências no mercado. Participe de webminars e grupos online sobre assuntos do seu interesse. Utilize plataformas de aprendizagem (Coursera, cursos do Linkedin, edX, Udacity, fast.ai, Udemy, Kaggle). Leia livros técnicos e n?o técnicos sobre IA para realmente entender como tudo funciona. E por último, se acostume a ler artigos do arXiv (repositório de pesquisa onde novos artigos de IA s?o publicados regularmente), é aqui que as novidades aparecem primeiro ;)

Staying current in AI is more than just following trends; it’s about understanding the evolution of technology and its applications. AI is advancing at an unprecedented pace, with new models, algorithms, and use cases emerging regularly. Therefore, professionals need to engage in continuous learning and adapt to new tools and methodologies. Consider enrolling in specialized courses, attending conferences, and actively participating in AI communities online. The ability to adapt to new advancements is crucial. This proactive approach not only enhances your skill set but also positions you at the forefront of AI innovation, making you an invaluable asset in any organization.

It's essential to cultivate a deep understanding of the ethical implications and societal impacts of AI technologies. Engaging with literature, discussions, and workshops on AI ethics and responsible AI practices can equip you with the knowledge and perspective needed to develop products that align with ethical principles and address societal concerns. By incorporating ethical considerations into your AI product management approach, you can contribute to building trust and acceptance of AI technologies among users and stakeholders.

Prompt Example: “AI, identify the latest trends and developments in the Generative AI field.” Insight: Stay updated with the latest advancements and breakthroughs in Generative AI. The field evolves rapidly, and keeping up-to-date is essential for staying relevant. Swagger Tip: Follow AI thought leaders on LinkedIn and Twitter, subscribe to AI newsletters, and participate in online forums and discussions. Attend webinars and conferences to network with peers and experts.

Consider seeking out mentorship programs specifically tailored to AI product management. These programs can provide valuable guidance and insights from experienced professionals in the field, helping you navigate the unique challenges and opportunities in AI product management. Furthermore, actively engaging with online communities and participating in discussions related to AI product management can expand your network and expose you to diverse perspectives and ideas.

Prompt Example: “AI, provide strategies for building a strong professional network in the Generative AI community.” Insight: Networking is key to opening up opportunities and gaining insights from others in the field. Build connections with professionals who can offer guidance and collaboration. Swagger Tip: Use LinkedIn to connect with industry leaders, join AI-focused groups, and participate in discussions. Attend industry events and meetups to build meaningful relationships.

Nunca saberemos tudo sobre IA, é um mundo gigante e com muitas especialidades, tais como: processamento de linguagem natural (PLN), machine learning, redes neurais, deep learning, vis?o computacional, LLM, RAG... leia sobre todas elas, escolha algumas para se especializar, e n?o se esque?a: ninguém sabe tudo.

You need to understand AI, but most importantly, you need to understand the domain, too. That will give you the upper hand over any other data scientist.

Specializing in AI is a double-edged sword. While deep expertise in a niche area can make you highly valuable, it’s essential to choose your specialty wisely. The AI landscape is continuously evolving, and what is cutting-edge today might become obsolete tomorrow. Focus on areas with sustainable growth and broad applications, such as machine learning, natural language processing, or computer vision. Additionally, keep an eye on emerging fields like ethical AI and AI for social good. Diversifying your knowledge base within AI can provide stability and flexibility, allowing you to pivot as the industry shifts.

Combining your business focus or skill with AI is the best way of keeping up with technology advancements and your prior expertise. It also gives you a competitive advantage in your particular field.

Prompt Example: “AI, help me identify niche areas within Generative AI to specialize in.” Insight: Specializing in a niche area within Generative AI can make you an expert in that domain, increasing your value. Identify sectors where Generative AI is making significant impact. Swagger Tip: Focus on high-demand areas like healthcare AI, finance, or creative applications such as content generation and marketing. Tailor your learning and projects to these niches.

Prompt Example: “AI, suggest ways to reflect on my progress and areas for improvement in Generative AI.” Insight: Regular self-reflection helps you assess your progress and identify areas for improvement. Use feedback to enhance your skills and approach. Swagger Tip: Maintain a journal or blog to document your learning journey, challenges, and achievements. Seek constructive feedback from mentors and peers to refine your skills continually.

The AI revolution is here, and you can be a key player! As a top voice in AI, I see a bright future for those who bridge the human-machine gap. Here's your launchpad: First, cultivate a growth mindset. Embrace continuous learning – explore online courses, attend workshops, and stay curious about emerging AI trends. Don't be afraid to experiment! Second, hone your communication skills. AI is powerful, but it needs humans to translate its insights. Learn to explain complex concepts in clear, concise language. Finally, find your niche. The AI field is vast. Identify an area that excites you – computer vision, natural language processing, or perhaps AI ethics. This focus will help you develop expertise and stand out in the crowd.

In addition to enlisted topics, I believe thinking outside the box is critical! So, my suggestions would be to cultivate innovative thinking by challenging conventional approaches and exploring unconventional solutions. This mindset drives creativity, helps identify unique opportunities, and addresses complex problems in novel ways, setting you apart in the rapidly evolving AI landscape.

If you're #opentowork, consider using AI to help a small business in your community solve a real problem. For example, I helped a family-owned optometry business use AI to book patient appointments. This taught me that many of us dealing with AI daily live in a bubble and that the technical portion of AI is just a small part of solving real-world problems. Seeing the amazement on people's faces when they see the final solution in action is extremely rewarding. It highlights the impact AI can have beyond technical circles. Engaging with local businesses not only hones your skills but also provides valuable insights into practical applications and community needs.

To become a valuable asset in AI, apply AI knowledge to relevant business applications, producing actionable results. Stay updated with industry trends, develop strong problem-solving skills, and effectively communicate insights. Focus on creating impactful solutions that drive business value, demonstrating adaptability and continuous learning in the evolving AI landscape.

8. Create something great. AI isn't just about optimising back-office or routine operations; it's about creating the next best customer experience. If your organisation is positioning itself as 'first and innovative', be prepared for your customers' expectations to include services based on AI sooner or later. Start exploring how you can use those opportunities in your organisation. Strive to stay relevant. And please share your case :) it's really hard to find the real implemented case! "Well done is better than well said" (Benjamin Franklin).