A comprehensive guide on the magic behind generative AI

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So you’ve been playing ChatGPT, Dall-E, and Whisper…Here’s how to actualize your interest in generative AI and take your first step in creating the AI of your dreams. 

First, let’s demystify the daunting concept of artificial intelligence. Artificial intelligence is a blanket term for a machine that uses technology to automate human intelligence. AI works by processing large amounts of data with intelligent algorithms, learning from patterns in the data.

Artificial intelligence is a subfield of computer science that includes the following major subjects:

  • Machine learning: The processes computers use to interpret large datasets. Utilizing statistical mathematics, computers gain insight from data without explicitly being programmed for where to look or what to conclude. Furthermore, computers can make predictions or classifications based on their learnings.
  • Deep learning: A subset of machine learning that utilizes neural networks, layers of interconnected units that process information like a real brain. Common applications include image detection and speech recognition.
  • Computer vision: The way computer’s use pattern recognition and deep learning to recognize what’s in a picture or video. When machines can process, analyze and understand images, they can capture images or videos in real time and interpret their surroundings.
  • Natural language processing: A practice that derives machine learning algorithms to understand and generate human language. This technology allows humans to communicate with computers using normal, everyday language to perform tasks.
  • Robotics: As an interdisciplinary practice, Robotics greatly benefits by employing Natural Language processing, Computer vision, and/or Machine learning algorithms to create machines that can operate autonomously.

As technology develops, more and more subfields are established. Each Subfield is split into their own respective algorithms, frameworks, and practices. This all may be extremely overwhelming, but these subject areas progressively build upon or derive from each other. Deep learning, for example, is a subset of machine learning. It operates in the same algorithmic context, but defines itself by employing new technology called artificial neural networks to better solve problems. 

To gauge your interest in a prospective career within these fields, before committing to a computer science or data science degree, you may wonder how to get involved. Here are a few methods to get your feet wet!

  1. Coursera/EDX:

Introduction courses give great overviews on subjects. By building a foundational understanding, you can gauge your passion for a particular subject. If you are starting out in the computer science space you could take Harvard’s CS50x: Introduction to Computer Science course. If you are already confident in the basics of programming, you can jump right into Harvard’s subsequent CS50: Introduction to AI with Python.

To delve deeper into a particular subfield of artificial intelligence, you can take a free specialization course on coursera or EDX. Courses such as DeepLearningAI’s Machine Learning Course will give you a ground up understanding of the mathematical basics that compose AI systems. With no previous knowledge, you can build your first statistical prediction model in less than a day.

These courses won’t replace a college education, but they can give you the skills to build fascinating projects. The problem sets and projects ingrained in these courses can help build your portfolio and expose you to the diverse applications of such technology. 

  1. Hackathons: 

Hackathons can be found all over the internet. Websites such as devpost.org and kaggle.com host contests where you employ technology to solve problems. They are great places to meet new people, showcase your skills, and win prestigious prizes. Some hackathons are geared specifically towards a specific field of computer science, like machine learning for example. 

  1. Online community based learning resources: 

Resources such as Kaggle offer machine learning practice from beginner to expert level. Although they aren’t as accredited as coursera or EDX courses, these resources are a great way to get to building. Engage with industry professionals and other artificial intelligence geeks as you tackle coding problems using real world data.

  1. On Campus Clubs: 

Washington High School’s very own AI Mobile development, Robotics, and Computer Science clubs are great in-person resources to learn how to code and expand your passion on the topic of artificial intelligence. Who knows, the projects you can build with other club members can change the world!

Congrats, you’ve taken your first step in building epic technologies like generative AI. Don’t be afraid to fail, throw yourself into topics to challenge yourself and build exciting projects. Remember, you can do anything you put your mind to! Goodluck!

Zahi Imaduddin is a senior at Washington High School. He was born and raised in Fremont, California, and this is his first year at the paper. He hopes to discuss the intersections of technology and psychology and how technology shapes culture. His hobbies include coding, cooking, creating 3D art, and working out. He hopes to study computer science in college and eventually start a company. With a strong interest in the ocean, he hopes to one day sell all his belongings and live on a boat.

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