Here's how you can develop key skills in Artificial Intelligence (AI) through continuing education.

Los cursos en línea sobre inteligencia artificial (IA) ofrecen a los emprendedores una forma flexible y económica de adquirir habilidades en demanda. Aprender IA no solo potencia los resultados del negocio, sino que también abre amplias oportunidades laborales. Son coste-efectivos, eliminan gastos de desplazamiento y fomentan redes profesionales. Además, la IA en los negocios actúa como un consultor invaluable, apoyando la gestión sin reemplazar la participación humana. La educación en línea facilita la actualización continua, proveyendo conocimientos prácticos esenciales para mantener la competitividad en mercados dinámicos.

Resist the urge to skim through the content. It's essential to invest the time to thoroughly grasp each concept. Never underestimate the value of understanding something 100%. It's like learning to ride a bike. Once you learn it, you won't forget it.

Learning new things in AI doesn't follow a one-size-fits-all approach; it varies from person to person. However, leveraging influencers, top company news, and blogs remains a reliable method to stay updated with market trends. Adapting to evolving technology by acquiring new skill sets is crucial, given the rapidly changing landscape.

A measure of impact for university programs would AI’s ability to be taught, understood and researched in important languages of India. Here the state, non-profit actors as well as leading private sector players are already making some laudable efforts. But the project has barely begun. At its heart is the adaptability of India to involve its 1.4 billion people to see AI as a catapult and leveller NOT a potential job slayer that it could well become #Bhashini

Consider enrolling in AI-focused degree programs, certifications, or specialised tracks offered by universities. These formal academic pathways provide comprehensive, structured learning experiences.

University programs are like schools where you can learn about AI in a structured way. They teach everything from the basics to the more complicated stuff like neural networks. Go to classes, do practical activities, and team up with classmates. It's harder work than online courses, but we'll earn important qualifications and really get how AI works.

Working on real projects can help too. Try to apply what you learn in a practical way, like building a small chatbot or using AI to solve problems in your daily life. This experience makes the information stick and shows you the real-world impact of your skills. Another key way to improve is to join AI communities online. Places like GitHub or forums dedicated to AI can be invaluable. You get to learn from others, ask questions, and stay connected to the latest trends and solutions.

There is no better way to be informed of AI programs by universities than reading the respective program prospectus. There are many programs, of which I will mention a few: Harvard University's CS50’s Introduction to Artificial Intelligence with Python - introduces you to the foundations of modern AI; on Python, Machine Learning, TinyML, and others. Stanford University's Artificial Intelligence Professional Program for the Graduate Certificate - provides a thorough grounding in AI principles and technologies. Purdue University in collaboration with Simplilearn has a certificate on AI and ML, which is a good introductory coverage of AI tech. In essence, universities are increasingly providing AI education, from modules to degrees.

Participate in online workshops and webinars focused on AI topics, featuring industry experts, researchers, and practitioners who share case studies, and practical tips. Platforms like Coursera, edX, or specialized AI organizations offer webinars. Many universities offer extension programs or continuing education courses on machine learning, deep learning, natural language processing, and computer vision. Enrol in AI bootcamps. Bootcamps often focus on practical skills, coding, and real-world projects. Participate in AI hackathons and data science competitions. My own most recent involvement is in the Microsoft AI Challenge which I and more than 101,000+ active users managed to complete. Attend AI conferences, meetups, and networks!

Attending workshops and seminars has been a game-changer for me. Not only do they provide invaluable hands-on experience with the latest tools and techniques, but they also offer a unique opportunity to learn directly from industry experts. I've found that the interactive nature of these events really helps solidify concepts and inspires new approaches to problem-solving.

Signing up for workshops & seminars (W&S) is the easy bit Hardly a day passes when you won’t have alerts about “AI” W&S dangling career advancement & networks in the community But be wary The trick is to take your next steps BLENDING two important yardsticks — width & depth. Let me explicate: “Width” is when you can sign up for a W&S hoping to expand your horizons & then bring back the (new) dots to your own arena of expertise. A few sign ups aiming for this goal are welcome, but less than say ~50% On the other hand, pursuit of “depth” seeks to refine your knowledge in an area of AI that you have domain expertise already With ubiquitous internet, signing up for AI W&S offering width *&* depth has never been easier BE CHOOSY. SAY NO

Artificial Intelligence is a continuously evolving field with tons of new research being published regularly. Stay on top of the most recent trends by finding a local paper reading group and listen to other experts explain new developments in their own words. It's a great way to connect with other AI practitioners and find out about new and different lines of research or exciting new libraries and technologies. You can find some meetups and reading groups like this on websites like Meetup.

Attending workshops and seminars creates a platform for learning and collaboration. They not only allow us to build relationships with peers and industry leaders but also expose us to diverse viewpoints, enriching our understanding of the subject matter. By participating in these events, we can stay current with the latest trends, techniques, and technologies in our industry, fostering both personal and professional growth. Moreover, engaging with experts during these sessions often provides deeper insights and unique knowledge that you might not find in traditional learning settings.

Choose a challenge that excites you. Your genuine interest will keep you invested and push you through obstacles. Never let the complexity of the issue deter you. Break the problem into small, manageable segments. Tackle each one at a time. Why it works? By diving into hands-on experimentation with various algorithms and datasets, you gain invaluable practical experience. Far surpassing what textbooks alone can teach.

If you ask me, diving into your own AI projects is an awesome way to level up your skills! It's like a real-world playground where you can let your creativity run wild.

Fuel Your AI Mastery with Passion Projects Dive into personal projects to deeply engage with Artificial Intelligence. Choose problems that stir your passion, transforming your AI journey into a quest for solutions that matter to you and potentially, the world. This approach is not just about honing technical skills through diverse algorithms and data sets; it's about embracing the real-world challenges of AI, fostering creativity, and making an impact. Your projects become a unique blend of your technical prowess and your dedication to positive change, showcasing your ability to innovate and solve problems with empathy and insight. Let your passions guide your AI exploration, making your work a testament to the power of AI to do good.

Personal projects demonstrate your skills and problem-solving abilities to potential employers or clients. 1. Choose a specific AI domain (e.g., machine learning, natural language processing) and identify a problem you're interested in solving. 2. Break it down into smaller tasks, research relevant techniques, and implement solutions. 3. Document your process, challenges, and lessons learned. 4. Participating in online coding challenges or hackathons can also provide valuable practical experience.

Peer Collaboration Join AI-focused online communities, forums, and discussion groups to engage with like-minded individuals and experts. Participate in study groups or peer learning circles to collectively explore AI topics, share knowledge, and provide feedback. Attend local AI meetups or join regional AI societies to network, collaborate, and learn from professionals in your area. Collaborate on open-source AI projects or research initiatives with peers from diverse backgrounds and expertise. Engage in code reviews, pair programming sessions, or hackathons to enhance your coding skills and learn from others.

Connecting with peers in the field of AI is a great way to boost our learning. Imagine joining groups or online forums where people who are also interested in AI hang out. Here, we can chat about ideas, ask questions, and share what we know. It's like having a bunch of study buddies who are on the same journey as us. Plus, working on projects together is like having a team of experts helping us out. Everyone brings their own skills and ideas to the table, making the learning experience richer and more enjoyable. So, don't be shy to reach out and collaborate with others—it's a win-win for everyone involved!

Some of the most powerful applications of Artificial Intelligence are through solutions which blend the use of traditional Machine Learning and AI methodologies with software engineering approaches like object oriented programming and stateful vs. stateless programming. Getting involved in open source projects and in collaborative spaces will give you a better understanding of how others are leveraging the power of AI and what role AI plays in a variety of functional solutions.

- Busque compartilhar seu conhecimento com a comunidade/grupos - Procure profissionais que você possa se espelhar - Escreva sua experiência ou gere conteúdo sobre o tema

Keeping an eye on blogs published by big AI labs will keep you on the cutting edge of what top experts are working on. Labs like Google's DeepMind, OpenAI Research, Stanford's SAIL lab, UC Berkeley's BAIR lab, and many more regularly post updates on recently published papers, interesting projects, and new technologies as soon as they are released.

I make it a habit to stay up-to-date with the latest research, case studies, and industry news by regularly reading relevant journals, blogs, and publications. This continuous learning approach has not only broadened my understanding of AI but has also sparked countless innovative ideas that I've been able to apply in my work.

PLUG INs are our enhancer to GPT4 (etc). Do try them, if you haven’t, to play with real-time stuff; customisation; functionalities, scalability Plug ins are step towards making AI models more dynamic & adaptable BUT remember: OpenAI, for eg, DOES NOT validate or control the content. Responsibility for accuracy, security, & reliability typically lies with the developer As users we therefore need to be discerning about which plug ins OpenAI (etc) enables, understand the sources of data & potential risks involved in using 3rd-party integrations OpenAI only provides guidelines or frameworks for developers to build & integrate plug ins safely. Repeat, OpenAI DOESN’T monitor or verify individual transactions or data spewed by these tools

For those here hooked to ChatGPT-4. Well, here’s to remind you what it (still) CAN’T do (IN ITS OWN WORDS): Access Real-Time Information: Can’t browse the web in real time or provide up-to-the-minute information. Responses are pre-trained knowledge base & don't reflect recent developments or real-time events. 2. Make Decisions: It can assist in decision-making by offering advice or information, but the final choice remains in the hands of the user. 3. Perceive or Interpret Non-Textual Data: It can’t interpret non-textual inputs directly, such as images, sounds, or physical gestures, unless specific tools are integrated for those purposes. Its responses are also limited to the information explicitly provided in text form. So, be wary ??

“Technologies such as AI, data analytics, robotics, digital platforms, SM, blockchain, & 3-D printing affect many parts of the organization simultaneously, enabling new interdependencies within & between units & with actors that many organizations have typically considered to be outside their boundaries. Consequently, today’s emerging technologies have the potential to fundamen- tally shape all aspects of organizing” — We Are All Theorists of Technology Now: A Relational Perspective on Emerging Technology & Organizing By Diane E. Bailey,Samer Faraj, Pamela J. Hinds, Paul M. Leonardi, Georg von Kroghe

Attending AI meetups, participating in online forums, and engaging with fellow professionals has been an invaluable source of learning for me. Sharing ideas, discussing challenges, and exploring different perspectives has helped me gain insights that I may have missed otherwise.

Recently, we were introduced to this SMART goal-setting framework to break down our learning goals in my current organization. Personally, It has helped me become more structured over time(a reason for me to brag about it in this article). In this ever-evolving industrial paradigm, you must step back and categorize your learning goals into short-term(G1), mid-term, and long-term goals(G2). G1. Short-term goals: Tangible enough to help in your current work & career trajectory, small enough to be achieved in a small period of time. Think of it as baby steps to achieve long-term goals. G2. Mid-term and long-term goals: Something that will get you close to your ideal self or something that will bring you closer to your dream role.

Continuous learning by staying updated with AI evolution, so makes it a habit to stay updated with the latest advancements, tools, and techniques. In addition to online courses enroll in advanced courses, workshops, or webinars to continue expanding your knowledge and skills in AI.

Algumas habilidades humanas s?o fundamentais para que você tenha um bom desempenho em AI. Por exemplo, a extens?o vocabular. Se você n?o sabe a diferen?a entre comprimento e cumprimento, possivelmente você terá um output indesejado da IA. Estimular a cria??o vocabular, com leitura de livros, participando de conversas em grupos de especialidades distintos do seu, ou mesmo ouvir um Podcast, pode te ajudar nesta frente. Uma outra habilidade, é a capacidade de fazer perguntas, pois essa habilidade será bastante utilizada na cria??o de Prompts. Como fomos educados a responder perguntas, acabamos n?o desenvolvendo muito bem essa habilidade.

To develop key skills in Artificial Intelligence (AI) through continuing education, start with foundational online courses and books on AI fundamentals and machine learning. Progress to specialized training through advanced courses and workshops. Gain practical experience by working on AI projects, participating in competitions, and securing internships or research opportunities. Expand your network by attending AI conferences and joining online forums. Stay updated with the latest AI advancements and develop soft skills like communication and critical thinking to effectively apply your knowledge in AI projects. Embrace continuous learning to master AI skills effectively.