🎯 Learning Goals
- Explore new opportunities in the field of machine learning and AI
- Understand the diverse array of career pathways in AI/ML
- Identify next steps in the learning journey
Introduction
Welcome to the next step in your AI journey! Over the course of this camp, you’ve explored the fascinating world of machine learning and artificial intelligence, so what’s next? In this lesson, we’ll introduce you to career pathways, additional resources, and communities where you can continue learning, collaborate with others, and build on your newfound knowledge. Let’s dive in and discover how to make your passion for technology a lifelong adventure!
What’s Next in AI?
The new frontiers of AI are incredibly dynamic and full of promise. Today, AI is expanding to creating new content, powering autonomous systems, and even building immersive virtual worlds. Researchers are pushing AI into physical spaces through robotics and spatial intelligence, which enables machines to understand and interact with the three-dimensional world—leading to innovations in fields like autonomous vehicles and healthcare robotics. Many of us are familiar with smart homes with thermostats and lights that adjust to new inputs, but what if we could build a smart city? Many cities are already planning ways to use data and technology to improve sustainability and enhance quality of life for people living and working there!
At the same time, there’s a growing focus on ethical, transparent, and explainable AI to ensure these powerful systems benefit everyone. Overall, the field is moving toward AI that is not only more creative and autonomous, but also more human-centric, promising to transform industries and solve complex global challenges.
Career Pathways
No matter which career path you choose, having a strong understanding of AI is like having a secret superpower. In today’s world, AI is everywhere, from the smartphones in your pocket to the systems that help make decisions in business and medicine. Whether you're drawn to technology, business, or even the arts, learning about AI will empower you to be part of the future, no matter what field you decide to pursue. But if you’re especially excited about specializing in AI and machine learning, there’s a world of opportunity even within this one domain!
You could be an AI Research Scientist or Machine Learning Engineer, creating new algorithms and designing smart systems that revolutionize how we interact with technology.
If you're fascinated by data, consider a role as a Data Scientist or Big Data Engineer. Here, you’ll dive into mountains of data to find patterns that help companies make smarter decisions. For those who love language and visuals, positions like Natural Language Processing Engineer or Computer Vision Engineer let you develop everything from chatbots to systems that recognize images.
Not to mention, there are also exciting opportunities in robotics, product management, and even ethics. As a Robotics Engineer, you could design and build the next generation of robots. And as an AI Product Manager or AI Ethicist, you'd blend technical know-how with business strategy and ethical decision-making.
In short, a career in AI and machine learning isn’t just about coding. It’s about pushing the boundaries of innovation and creating technologies that can change the world. Whether you’re a problem solver, a creative thinker, or someone who loves exploring data, or all of the above, there’s a place for you in this exciting field!
Industry Icons
There are so many women advancing the field of AI, but we’ll highlight a few to see the many different ways you could have an impact in this field.
Dr. Fei-Fei Li
Dr. Li is a pioneering Chinese–American computer scientist who revolutionized AI with ImageNet, a vast, meticulously labeled database that revolutionized computer vision and deep learning by enabling machines to recognize objects with unprecedented accuracy. Remember when we used Teachable Machine to train an image classifier? Her work made that possible! Beyond academia, she champions diversity through initiatives like AI4ALL and recently raised $230 million for her startup, World Labs, which is developing spatial intelligence to help AI understand our 3D world. 🤯
Dr. Ayanna Howard
Dr. Ayanna Howard, PhD, is a pioneering robotics engineer, entrepreneur, and educator who has made incredible contributions to autonomous systems. She began her career at NASA’s Jet Propulsion Laboratory, where she contributed to Mars rover projects, designing systems that enabled rovers to autonomously navigate the rugged Martian terrain, analyze rock composition, and collect scientific data, all without constant human control. She also worked on climate-monitoring robots known as SnoMotes, small autonomous devices deployed in extreme environments like the Antarctic ice shelves to gather critical data on temperature, ice dynamics, and environmental changes caused by global warming.
Allie K. Miller
Allie K. Miller is a leading artificial intelligence innovator, advisor, and investor known for shaping AI strategy in major technology companies. She has held roles in major corporations, supporting the development of IBM’s Watson as a lead product manager and later scaling Amazon’s machine learning business development as the Global Head of Machine Learning Business Development. Beyond her corporate achievements, Miller has become a prominent public speaker and thought leader in AI. She has addressed global audiences, including policymakers at the European Commission, and authored multiple guidebooks that educate businesses on building successful AI projects.
AI & Machine Learning Tools
Many of the tools you used in this course are relevant and widely used by industry professionals, but there are definitely other tools you may want to explore!
- Continue learning Python or branch out into R! R is another programming language commonly used for statistical analysis, data visualization, and manipulation. There are many free tutorials available on YouTube to get you started.
- Master the basic Python libraries commonly used in machine learning. Building skill and confidence with the many tools available in the pandas and NumPy libraries will set you up for more challenging tasks later on.
- Explore more ways to apply scikit-learn. This robust Python library for common machine learning algorithms is great for beginners due to its user-friendly interface. We used some of the tools available in this library, but there’s so much more to learn!
- Ready for a challenge? Dive into the world of machine learning frameworks with PyTorch or TensorFlow! These powerful frameworks provide building blocks for developing and training deep learning models.
This is only a small sampling of the many machine learning tools you may encounter, but exploring any of them is a great next step in your AI learning journey!
Resource Roundup
If you’re looking for structured courses or detailed guides, we put together a list of suggestions for continuing to learn in AI and machine learning.
- Check out this machine learning cheat sheet from DataCamp, listing popular algorithms for machine learning and their use-cases.
- There are a few YouTube channels that have excellent content related to AI and machine learning. CrashCourse Computer Science offers short, engaging videos on computing concepts and 3Blue1Brown breaks down complex math and ML concepts visually. The video we saw explaining neural networks was one from 3Blue1Brown and its part of a series he created!
- Keep learning on Hugging Face! The 🤗 Team created a Natural Language Processing course that goes into more detail about how the transformer architecture and how to use their platform to its full extent.
- Coursera offers a range of machine learning courses taught by industry experts. Although most of these courses are paid, they remain an affordable way to access high-quality instruction in machine learning and AI. We recommend exploring courses by the DeepLearning.AI team—many of their shorter courses are even available for free!
Find a Community
Learning is even more rewarding when you do it with others! If your school offers computer science classes, take full advantage of them – you’re more than ready to tackle new challenges. Similarly, sign up for advanced math classes! At their core, many machine learning models rely on mathematical relationships—like those you encounter in AP Statistics and AP Calculus—to analyze data and make predictions. Concepts such as functions, derivatives, probability, and statistics all play a big role in how these models learn from data and improve over time. While some machine learning techniques involve more advanced math, the foundation is very much built on the principles you're already exploring in high school. Beyond your classes, consider exploring other opportunities:
- Join a robotics or computer science club
- Attend hackathons
- Team up with a study buddy
- Find local workshops or coding communities
The journey of learning never stops, and surrounding yourself with a supportive community will help you continue to grow and innovate.
Think About It:
Take a few minutes to reflect on which career pathways in AI excited you most today.
- How do you plan to continue exploring AI?
- What new ideas or perspectives from the lesson do you want to explore further because they resonated with you?
- What part of today’s lesson inspired you the most and why?
Back up your files before you go!
If you are using a Kode With Klossy loaner laptop and/or attending an in-person camp, please take a moment to back up your work so you can continue your coding journey beyond camp.
Any files you’ve saved locally (meaning they are stored only on this computer and not in the cloud) could be lost when you return your laptop or leave camp. To make sure you don’t lose anything, send any local documents to yourself on Slack.
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