What do you do if you want to become an AI expert in IT?

Aquí hay algunos pasos que podrías seguir: Educación formal: considera obtener un título universitario en ciencias de la computación, Ingeniería Informática, Matemáticas o campos relacionados. Busca programas que ofrezcan cursos específicos en IA y aprendizaje automático.

🔍 Embrace Lifelong Learning: The central aspect of the AI-taming is the learning process, which goes beyond the building of skills and includes reskilling. Get familiar the with online courses , workshops and certifications to advance yourself and strive to stay at the frontier of knowledge. 💡 Specialize in Niche Areas: Whereas trying to become a master of many is not the proper route, aim at specific zones in AI such as deep learning, natural language processing, and computer vision among many others. 👩💻 Collaborate with Peers: Be a part of AI communities, coding meetups, attend conferences and join online platforms where you engage with people who share your interests and exchange ideas.

A era digital, que começou há meio século, revolucionou a tecnologia, destacando-se pela decifração da Enigma, um símbolo do triunfo humano sobre a máquina.Esse legado continua à medida que desafiamos a tecnologia para criar valor real. Os atores são os profissionais de negócios, que dirigem programadores, cientistas de dados e estatísticos na jornada para transformar desafios cotidianos em oportunidades. O sucesso não reside na tecnologia em si, mas na sua aplicação para satisfazer necessidades genuínas e resolver problemas reais. É a ciência, aplicada com propósito, que gera impacto, levando à inovação que realmente importa. Não se prenda à tecnologia, mas nas soluções práticas que impulsionem o progresso nos negócios e na sociedade.

It is necessary to know the basics but I would suggest to analyze and work on a demo model first. Practical learning has better impacts over theories. You are blessed if you have access to a practice model for AI learning. Explore opensource platforms first.

To become an AI expert in IT, you can follow these powerful steps: First, master the basics, understanding the core principles of AI, including machine learning, natural language processing, computer vision, and deep learning, along with the essential math and statistics. Next, gain hands-on experience by working on AI projects, starting with simple tasks and progressing to more complex challenges, utilizing platforms like Kaggle and GitHub for collaboration. Stay at the cutting edge of AI by staying informed about the latest developments, research, and technologies through following experts, attending conferences, and engaging in online forums. Network with other AI professionals, joining communities and attending events to learn.

En resumen, por el momento, Python es la elección preferida para trabajar en IA en TI debido a su facilidad de aprendizaje, su amplia adopción en la comunidad de IA, su gran cantidad de herramientas y bibliotecas especializadas, así como su versatilidad y escalabilidad. También depende del lenguaje y herramientas que se utilice en la empresa donde trabajarías.

This really isn't a choice. For all of its drawback, Python is by far the dominant language for AI development today, unless you are working on very low-level performance-sensitive code. But it you are moving from IT into AI, you will almost certainly do so using Python. Fortunately, Python is one of the easier programming languages to learn. But then you discover that it's less about learning the syntax and more about learning how to find and use the cornucopia of AI libraries available to you. Once you master those, you are real AI developer!

Como um general que comanda soldados com habilidades em diversas artes marciais, a vitória não depende de uma técnica específica, mas da estratégia adequada para cada confronto. Na programação, o sucesso não reside em dominar uma linguagem, mas em saber solucionar problemas com as ferramentas disponíveis. O valor de um programador está na sua versatilidade e capacidade de enfrentar desafios, independentemente da linguagem. Assim, é mais importante ser estratégico e adaptável do que especialista em uma única linguagem de programação. Não se prenda à linguagem; o foco deve estar apto a resolver os problemas e não em saber uma única linguagem de programação. Haja como os orientais pense como um general por que você não poder pensar pequeno.

To get started in AI, selecting a programming language is crucial. Python stands out as a top choice due to its simplicity, extensive library support, and vibrant community. However, other languages like R, Java, C++, or MATLAB can also be suitable depending on your project needs. Focus on mastering the basics of coding, including writing, debugging, and testing code, and familiarize yourself with version control and documentation tools to enhance your programming skills.

With respect to those who feel differently, I don't think the main things about AI require coding. Actually, I think AI will more or less render coding irrelevant - an idea expressed very compellingly by the NVIDIA guy the other day. If your goal is to be a programmer and do modeling and so on, yes, obviously, you need those skills and to pick a direction. If your goal is understanding use cases and limitations of AI, coding has nothing to do with it.

Aquí solo algunas de las muchas herramientas y bibliotecas disponibles para trabajar en proyectos de IA en TI. La elección de las herramientas específicas dependerá del tipo de proyecto, los requisitos de rendimiento y las preferencias personales del desarrollador: TensorFlow, PyTorch, scikit-learn, NLTK (Natural Language Toolkit), OpenCV: OpenCV (Open Source Computer Vision Library), spaCy entre otras.

To delve deeper into AI, it's essential to explore the frameworks and tools that streamline model development, training, and deployment. Key frameworks like TensorFlow, PyTorch, Keras, Scikit-learn, and OpenCV offer a range of APIs and functionalities, empowering you to process data, construct neural networks, conduct computations, and visualize outcomes. Familiarize yourself with installing, configuring, and leveraging these tools, and learn to seamlessly integrate them with your programming language and environment for optimal AI implementation.

Participación en proyectos prácticos, aplicar tus conocimientos en proyectos prácticos. Puedes unirte a proyectos de código abierto, participar en competiciones de aprendizaje automático, trabajar en proyectos personales o colaborar con otros en la comunidad de IA.

To enhance your AI skills, actively engage in hands-on projects and challenges tailored to your interests and expertise. Numerous online platforms like Kaggle, Coursera, Udemy, and edX offer a wealth of resources, including datasets, tutorials, and courses, to support your AI endeavors. Additionally, participating in AI competitions and hackathons on platforms like CodaLab, DrivenData, and HackerEarth can provide valuable practical experience. Apply your knowledge, experiment with various strategies, and collaborate with the community to share your insights and receive feedback, fostering continuous learning and improvement.

Here I agree with the premise, if not necessarily the examples. Competitions and hackathons and so on are fine, but AI is more in the quantum realm in terms of real comprehension. That is to say the highest value ideation on AI has nothing to do with these sorts of activities. What can AI do? What can't AI do? What should AI do? What's the use case or business case for...cars, medicine, marketing, whatever?

To become an AI expert in IT, immerse yourself in the ever-evolving landscape of artificial intelligence. Stay updated with the latest advancements through various channels like blogs, podcasts, and reputable journals. Engage with the AI community on social platforms and attend relevant events and conferences. Dedicate time to hands-on learning and practical application of AI concepts through projects and experimentation. Cultivate a curious mindset and embrace continuous learning to adapt to the rapid pace of innovation in this field. By consistently expanding your knowledge and honing your skills, you can navigate the complexities of AI and excel in the IT industry.

It is important to consume the "latest" information about AI but it is also important to find a way to filter BS of which there is an overwhelming amount. Here I would say pick a category. Are you interested in the AI evolution as it relates to written content itself? Or are you interested in the AI evolution as it relates to decision making? Or brewing better beer? Or building better batteries? You can't consume all information across all topics. Pick a topic area or two and read everything you can, with a grain of salt.

Absolutely. This is not unique to AI. Every humanoid reading this should commit or possibly recommit to perpetual learning and continuous improvement. Upskilling and understanding new stuff in IT is a challenge for even fast readers and facile minds, but it a worthy challenge for you and for the future of humanity, not to be too woo-woo about it.

Em um mundo em constante transformação, o aprendizado contínuo é fundamental, independentemente da área. Instituições renomadas como Harvard, MIT, USP, UFPR entre outras, são apenas o início. O verdadeiro estudo transcende as salas de aula tradicionais, estendendo-se a plataformas digitais, como o LinkedIn, YouTube, Chatgpt que oferecem ricos recursos educacionais. O que importa não é de onde vem o conhecimento, mas sua vontade de aprender, aplicar e evoluir. Não há limites para o aprendizado, apenas os desafios pessoais que você escolhe superar. Explore, aplique e ultrapasse suas barreiras por que no final é você contra você mesmo o tempo todo. Só você pode fazer a diferença. Você que vai mudar o jogo não a I.A

Networking within the AI community can provide invaluable opportunities for learning and collaboration. Building relationships with peers, mentors, and professionals in the field can offer insights, guidance, and potential career opportunities. Participating in online forums, joining AI-focused communities, and attending meetups or conferences can help expand your network and foster connections with like-minded individuals. Additionally, seeking out internships, apprenticeships, or mentorship programs can provide hands-on experience and guidance from experienced practitioners. By actively engaging with the AI community and seeking mentorship, you can accelerate your learning and development as an AI expert in IT.