What do you do if you want to stay ahead of the emerging trends and advancements in AI as an intern?

1. Develop strong fundamentals - it is important to have a strong grasp in statistics, model building, feature engineering, model performance evaluations, and most importantly applying the right AI use case given the business context 2. Be curious - ask meaningful questions, understand the "why" and not just "how" around AI deployments 3. Be coachable - learn from experts, seek mentorship not just advise, demonstrate progress by following up on initial conversations 4. Focus on learning and development - understand industry trends, come up with learning plans, seek industry certification, attend conferences, and ask for help when needed.

Combining internal mentorship and practical experience with external learning and networking provides a well-rounded approach. 1. Internal Mentorship: Connect with in-house AI experts for guidance and insights. Don't hesitate to ask for informational meetings to learn from their experiences. 2. Online Learning: Enroll in online courses and webinars on platforms like Coursera or edX to stay updated. 3. Professional Networks: Follow AI thought leaders on social media to gain insights into trends 4. Hands-On Experience: Volunteer for AI-related projects within your organization to gain practical experience. 5. AI Communities: Join forums and attend meetups or virtual events to network with AI professionals

To stay ahead of emerging trends in AI as an intern, prioritize continuous learning and skill development. Engage in online courses, webinars, and workshops to deepen your understanding of AI technologies and applications. Follow industry thought leaders, subscribe to relevant publications, and join AI-focused communities to stay updated on the latest advancements. Seek mentorship from experienced professionals in the field and actively participate in AI-related projects or research initiatives. Embrace curiosity, adaptability, and a growth mindset to thrive in the dynamic landscape of AI innovation.

Experiential learning is the best kind of learning - pick a small AI project in an area you're deeply interested in, and make something you can share with others. Along the way, you'll get frustrated and stuck. Then you'll start looking for newsletters, tutorials, bloggers, courses, and other resources that will help you move forward. You'll start to find the people and platforms that work best for your learning. And you'll aggregate a list of resources that will help you stay engaged with the sector and layer of AI that you're most excited about. Plus, at the end of it all, you'll have a portfolio piece to show for your work. Anyone can watch YouTube videos - set yourself apart by learning with doing.

Además de estudiar lo último en técnicas e investigación de IA, presta atención a cómo la IA está impactando y siendo aplicada en diversas industrias y sectores. Comprender las tendencias comerciales, regulatorias y sociales que dan forma a las demandas de IA te permitirá anticipar mejor las próximas olas de innovación.

Tu red debería incluir no solo expertos técnicos, sino también líderes de pensamiento en ética, políticas públicas, derechos civiles y otros dominios que abordan las implicaciones más amplias de la IA. Su perspectiva te ayudará a desarrollar un enfoque más holístico e informado.

1. AI Time Capsule: Create a digital time capsule of AI predictions and projects. Contribute and invite AI enthusiasts to add their predictions, then open it annually to see which technologies emerged or evolved. 2. Cross-Disciplinary Expeditions: Pair with experts from unrelated fields (artists, biologists, historians) to explore how AI can solve problems outside tech. Document these explorations in a blog or podcast series. AI-powered Storytelling Marathon: Organize an online event where participants use AI to create stories or art within 48 hours. Global AI Idea Exchange: Start a virtual exchange program with AI students or interns across different countries to share insights on AI trends, projects, and cultural impacts.

Networking is often seen as a dirty word. It can also be a beautiful art form. And it's never been easier online. Network strategically: 1) Identify influencers and network nodes (individuals with lots of loose ties) and follow them 2) Start sharing their content and commenting on threads + groups where they're engaged 3) Add insightful commentary and other value in every post 4) Post on your own profile and at mention target individuals and their companies or projects 5) Start at the middle of the pack and work your way up If you have time and resources, nothing beats in-person events. If you're not living in a hub or can't afford travel, don't let that stop you from connecting virtually with the best and brightest.

Think of it as designing a blueprint for professional connections. Instead of merely casting a wide net, focus on forging meaningful relationships with individuals who possess insights and expertise in niche areas of AI. Just as different architectural styles require unique approaches, diverse perspectives within the AI community can illuminate unexplored pathways and foster collaborative innovation.

No amount of theory can replace actual project learning. There are a lot of open source projects you can contribute you and it will make a significant impression when you are looking for a job

Hands-on projects allow you to apply theoretical knowledge to real-world problems, enhancing your understanding and skills in AI. The gaps or issues that arise during the course of projects also force you to find new ways of doing things, helping you to stay updated with the latest technologies and methodologies, while fostering a habit of continuous learning.

Just as architects learn the nuances of their craft through designing and constructing buildings, engaging in AI projects enables interns to apply theoretical knowledge to real world scenarios. Whether it's collaborating on research initiatives or contributing to prototype development, active involvement in projects not only deepens understanding but also fosters creativity and problem solving skills essential for staying ahead of emerging trends in AI architecture.

Projects lead to portfolio pieces. Portfolio pieces inspire questions and conversation. Questions and conversations can be used to highlight the skills you learned and challenges you overcame along the way. Projects also speak to a bias to action and a can do, go get 'em attitude. Voila! Projects are incredibly valuable for anyone to gain a foothold in the AI industry.

Al trabajar en proyectos, adóptate un enfoque centrado en el problema, no solo una mentalidad de "resolver el rompecabezas". Desafía los supuestos iniciales y ponte en los zapatos de los usuarios/clientes para comprender sus necesidades reales.

Understanding AI ethics and responsible AI practices is essential for interns who want to thrive in their future careers. AI's influence will only expand, shaping diverse aspects of our lives. By proactively learning responsible AI principles, you'll gain the ability to build systems that prioritize fairness, transparency, and user well-being. This knowledge doesn't just make you a more ethical professional; it gives you a critical edge in a field where responsible technology development is increasingly valued by both employers and the public.

AI ethics is a nuanced and complicated field. Identify the top 10 researchers and industry commentators, then read some of their work. Or use AI to summarize the key points and point you in the right direction. No one will expect you to be an overnight expert. Grasping the main considerations and being able to cite specific sources will set you far apart when the conversation (or interview) turns to ethics.

La ética de la IA no debe verse como un conjunto estático de reglas, sino como un campo en continua evolución que requiere un pensamiento crítico constante. Mantente abierto a cuestionar y repensar los marcos éticos establecidos.

In my experience, staying adaptable is more fun than ever. I started with a product called SharePoint 2010; if I had stuck with it, I would not be writing about AI. Since then, I have moved from SharePoint 2010 to SharePoint 2013, Jira, then to Egnyte, Office 365, Microsoft Teams, Yammer, Microsoft Defender, ServiceNow, SharePoint Online with SkyDrive and OneDrive in between, and finally Microsoft 365 and more. The constant relearning and adapting of skills to new challenges is more fun than sticking with the "one tool" you know by heart. There is no "one tool to rule them all" ???? and you only find the right solution by exploring others. You can try the competitors to the tool you know, something I love to learn what your tool is missing.

Más que aprender herramientas y lenguajes específicos, enfócate en desarrollar habilidades de aprendizaje y resolución de problemas transferibles que te permitan dominar cualquier nueva tecnología de manera eficiente.

Documenta tus procesos de pensamiento, supuestos y razonamientos al trabajar en proyectos de IA. Revisar esta "caja negra" te ayudará a identificar sesgos inconscientes, brechas de conocimiento y áreas de mejora. La capacidad de explicar y justificar tus decisiones es crucial para sistemas de IA responsables.

Use your preferred AI toolkit to help you reflect on your AI journey. Here are some prompts and considerations you can explore: - What areas and applications of AI are you interested to explore? How is that changing over time? - How can you establish a series of projects or a curriculum to learn about those areas? Document your progress along the way. - Can you establish a scorecard for the skills or ideas you want to internalize? Grade your progress weekly or monthly. - Ask a foundational AI model what questions you should be asking yourself. They can be surprisingly insightful. Reflecting is key to avoid mission drift. Know where you want to go, draft a map to get there, and check your course often.