What do you do if you're a late career professional looking to upskill and reskill in AI-related fields?

For someone with no formal CS or coding training, but delivers AI/ML initiatives to organizations, trying to upskill and staying current is often frustrating, considering the fragmented nature of the offering, from a whole slew of companies and educational platforms/universities. My experience is to get grounded first in the fundamentals and look for real time use cases connected to your career or profession, and then drill down to see which learning paths will be most instructive/productive.

I would recommend to take some introductory MooCs on Edx, Coursera or similar to familiarise yourself with the principles and how AI can be applied in various industries. Then depending on your needs, you can move on to more technical courses with hands-on labs to get deeper knowledge. The field has developed so much lately with no-code platforms that one does not often need to code to use AI, but it is always good familiarise oneself with some technical stuff. It is also good to know that a lot of cases organisations can benefit first using data for business intelligence and descriptive analytics before moving into more complex AI models. So starting with data analytics courses is a first step as it also gives a foundation to AI.

Es clave saber que si o si necesitas una base de conocimientos para empezar a aplicar IA en tu trabajo. La base siempre va a ser entender un poco de IA, los alcances actuales y las limitaciones. Y por otro lado es importante saber como comunicarte con la IA, hoy esto se lo conoce como prompting. Una vez cubiertos estos dos espacios iniciales, simplemente empezar a elegir una vía de desarrollo. Mi trabajo requiere más de texto? de creación de piezas gráficas? o de generación de audio y música? Identificar esto es clave para poder marcar un camino de aprendizaje. Si bien uno puede aprender de las tres ramas siempre esta bueno especializarse en una para empezar a aplicar en su trabajo.

In my experience the first step is a deep analysis of the trends and real market needs. It doesn't matter our current skills but where we want to be at the next step

As a late-career professional looking to upskill in AI, start by identifying foundational courses in AI and machine learning from reputable online platforms. Focus on acquiring practical skills through hands-on projects and case studies to apply AI concepts effectively. Network with industry professionals and join AI-focused groups or forums to gain insights and support. Attend workshops and seminars to stay updated on the latest trends and technologies. Consider short-term internships or volunteer projects to gain real-world experience. Tailor your learning to areas of AI that align with your experience and interests, which can provide a unique advantage in the job market.

Considering how Artificial Intelligence continues to develop on a daily basis is important to balance theory with practice. Aside from doing curses by different vendors, you need to dedicate time to run personal projects hands-on to learn how to apply AI in practice

- be curious and try different AI apps - include AI tools in daily work - identify cases where AI can help and become part of the team to execute. Learn from others is very important

Hoy en día existen miles de cursos gratuitos por todas las plataformas. Google ofrece las suyas, Microsoft las propias, hasta LinkedIn largo un curso uniendo varios individuales en este sentido. Todo la formación en IA puede realizarse de forma autodidacta, eso es lo más lindo que tiene. Ahora si este formato es complejo para la persona, sugiero tomar cursos en instituciones formales como universidades nacionales, los cuales suelen tener buenas referencias.

Once you've identified your needs, it's time to choose the right resources to help you acquire the necessary knowledge and skills. Look for online courses, certifications, workshops, and bootcamps specifically designed for professionals like yourself who are looking to upskill in AI. Platforms like Coursera, Udacity, Udemy, and edX offer a wide range of AI-related courses taught by experts in the field. Additionally, consider joining AI communities and forums to connect with like-minded professionals and stay updated on the latest trends and technologies in the AI industry.

Algo que implemento mucho, según lo que esté buscando hacer, es darle “roles” a la IA generativa (chatGPT por ejemplo). Y darle prompts específicos para que me responda que haría o que solución me daría, siendo “x” rol que le asigne. Esto es especialmente útil para obtener respuestas más precisas y objetivas según lo que querramos hacer.

Learning the fundamentals doesn't have to be about reading papers, courses, etc. It can be as simple as watching YT videos or reading posts on LinkedIn. Things like this can give you a great base understanding of AI without having to take the time to "study". The more you get into it the more you will want to learn, eventually leading to more in depth lectures and research paper reading.

En este sentido es clave saber hacia que sector de la IA me quiero dedicar. Si voy más para un lado técnico, se sugiere comenzar con las bases de machine learning. Caso contrario si no soy técnico ni pretendo serlo, existe el camino de la IA generativa. La cual no requiere saber absolutamente nada de código pero si requiere que entiendas como funciona cada modelo a nivel arquitectura, performance, desarrollo, resultado, etc. Y cuales son las mejores para tal o cual tarea. Requiere que sepas promptear, requiere que sepas usar la lógica correcta para obtener el resultado correcto.

With your goals defined, invest time in building that foundational knowledge. I can't emphasize enough how key this is - resist the urge to skip ahead. Take courses, read books/blogs, and solidify your grasp of core concepts like statistics, programming, algorithms. It'll pay dividends later.

With the resources in hand, dive deep into learning the fundamentals of AI. Start with foundational concepts like linear algebra, calculus, statistics, and probability, as these form the basis of many AI algorithms and models. Then move on to studying machine learning algorithms, deep learning frameworks, neural networks, and other AI techniques relevant to your chosen field. It's crucial to have a solid understanding of these concepts to apply them effectively in real-world scenarios.

"Knowledge increases exponentially when shared". I cannot stress enough on getting hands-on when it comes to learning new technologies, especially AI. The beauty of AI is that you can build and start showing off your projects at all skill levels. You can create your own GPT, showcase automation using agents, build a co-pilot extension or an entire RAG app. Your advantage and differentiator is that you will bring your vast industry knowledge and experience to solve a real world problem using AI. And as you invest your time in it, you will share your knowledge, increase your network and build a stronger professional profile for yourself.

⦨ Que tal considerar a construção de projetos que resolvem problemas para a IA? ⦧Você pode começar construindo um ambiente de aprendizado personalizado que fornece dados de alta qualidade, algoritmos eficientes, ou até mesmo feedback do usuário. ⦧Você pode construir sistemas de monitoramento que rastreiam o desempenho da sua IA, ferramentas de depuração que ajudam a identificar e corrigir problemas, ou até mesmo interfaces de usuário que facilitam a interação com a sua IA. ⦧Você pode criar políticas de privacidade e segurança que protegem os dados do usuário, mecanismos de controle que limitam o poder da sua IA, ou até mesmo diretrizes éticas que orientam o comportamento da sua IA. #ai #ia

Try hands-on AI development or implementing AI ready chat-bots. Get some free demo projects (with Step-by-step guide) from google and try that so that your concept can be clear.

Just build. Working on projects can be the best way to learn AI. Taking the knowledge you have learned from study and building something with it is a great way to cement your knowledge. You will also find yourself working for hours without even realizing it, giving you lots of real world type experience working with AI.

Theory is essential, but practical experience is equally important when upskilling in AI. Once you have a good grasp of the fundamentals, start building projects to showcase your skills and knowledge. Work on real-world AI projects that solve practical problems or contribute to existing research in the field. Building projects not only helps you apply your learning but also demonstrates your capabilities to potential employers or clients. Consider collaborating with other professionals or participating in hackathons to gain more hands-on experience and expand your portfolio.

Networking can be great when transitioning into AI from another field. Finding individuals in the industry with experience and lots of valuable information to share with you can be invaluable. Attending networking events and conferences can allow for a valuable yet casual conversation with these professionals.

Esto siempre lo aconsejo, el networking es clave en el área de IA si te queres enterar de las últimas tendencias y también el poder compartir tus conocimientos con otros especialistas. Es muy útil tener esas conexiones y contactos para el beneficio de ambos.

Absolutely true. It’s always good to understand others perspective and lean into your network and industry leaders. Don’t be afraid to contribute on your knowledge or ask questions.

Social media is a great way to stay current in the field of AI. Researches and engineers are always speaking about new/upcoming tools and advancements in the field. These type of ways of staying current will also be much more up to date than courses/papers as they are updated daily.

⦑ Crie uma cenário imaginário onde cada novo avanço na IA é como uma camada de sedimentos em um sítio arqueológico. Cada camada contém artefatos (ou seja, ferramentas, algoritmos, técnicas) que refletem o estado da IA naquele momento. ⦑ Como um arqueólogo da IA, seu trabalho não é apenas descobrir esses artefatos, mas também entender o contexto em que foram criados e usados. ⦑ Isso pode envolver a leitura de artigos antigos, a revisão de códigos obsoletos, ou até mesmo a entrevista de pioneiros da IA. ⦑ Ao fazer isso, você não só ganhará uma compreensão mais profunda da IA, mas também poderá identificar padrões, tendências e ciclos que podem ajudá-lo a prever futuros avanços. #ai #ia

To stay current: Embrace online courses, workshops, conferences, and actively participate in webinars. Collaborate with your peers, join professional networks, follow thought leaders, seek mentorship from industry experts, and experiment with new tools to stay informed.

A great place to find fellow AI professionals and enthusiasts is LinkedIn groups and followers of AI related LinkedIn pages. Start with the folks who edit posts like this since they're already looking to broaden their network and make an impact.

AI moves at lightning speed. What's cutting-edge today may be obsolete in 6 months. Follow the latest research papers, blogs, and thought leaders. Immerse yourself in the community by attending meetups and conferences. Staying endlessly curious is vital. The road ahead won't be easy, but forge ahead with patience and determination. Build your support system of mentors and peers. And remember - you bring immense value from your existing career that AI teams need. Embrace this opportunity to fuse your experience with new skills to unlock AI's potential. The journey will be so worth it.

Be mentally prepared for the transition to take longer than you expect as this is significant change for you as well as the companies adopting AI. So, it will take longer to get into the AI related roles.

I firmly believe that it's never too late to learn anything. Drawing from my own experience, I suggest: Focus on making the base strong in programming by practicing and grasping real-world problem-solving. Start with the basics rather than diving straight into complex concepts. Delve into AI research papers to explore new perspectives and identify areas for innovation. Cultivate your interest by creating simple programs, like predicting financial trends. Get involved in open-source projects and enroll in online courses to expand your knowledge. Connect with industry experts to gain valuable insights and engage in meaningful discussions. Remember that upskilling is a continuous journey, so take small steps every day.

Have a sense of play 🙂 Don’t be afraid to make mistakes and just do some stuff 🛠️ Also don’t worry about the field moving so fast as no one can know everything and are progressing down their own paths, but be prepared to adapt.

Don’t go crazy. Lots of time can be spent on the wrong topics. There is a lot for information out there. Some good and some poor. Lean into your industry segment and learn the fundamentals. Lean into your network and don’t be afraid to create articles for others to follow read and learn. Understand our yesterday and our today before conquering tomorrow.

One who continually strives to learn and unlearn is better adapted to changing market needs. Change is a constant factor in one’s life and career path. Embrace change and continuous learning. And improve your learning by applying following ideas: - if you like to read, then read relevant articles, books, newsletters, join groups or forms online and in-person. - if you learn by doing, then work on building simple small projects using AI - and once you feel comfortable, start sharing your knowledge with others by hosting events, contributing to articles, writing articles.