Levels of AI Expertise

--- tags: kevin --- Here is a start that I made with lots of Googling (and help from GPT). What maybe could be done (in a hypothetical universe) is to integrate the list of tools that MW made into this document...(Her list: https://hackmd.io/nOTyDO12SpOZExTjm6rj8Q) # Levels of AI Expertise ## Three Basic Levels: 1. Beginner • Knowledge: Basic understanding of what AI is and some popular applications of AI. • Skills: Can interact with AI-driven software or tools, can articulate general ideas about AI, maybe has some basic programming knowledge, perhaps in Python. 2. Intermediate User • Knowledge: Grasps the differences between AI, Machine Learning (ML), and Deep Learning (DL). Familiar with AI ethics and potential biases in AI. (Of some use: the document of resources that Sabrina made: https://docs.google.com/document/d/1DzKD2uCjJ4wbARQDb4xcnevwbVlYCcofTX2ISJuYomA/edit.) Other YouTube tutorials and articles. • Skills: Can use pre-existing ML models, libraries, or tools such as Scikit-learn, TensorFlow, or Keras for simple tasks. Comfortable with some data pre-processing. 3. Advanced User • Knowledge: In-depth understanding of various ML algorithms (e.g., regression, clustering, neural networks) and architectures. Awareness of the limits and challenges of current AI methods. • Skills: Can train, fine-tune, and evaluate ML models. Has experience with different datasets and practical AI projects. Familiar with cloud AI services and platforms. __ ## A bit more specific (three levels broken down further): 1. Beginner I • Knowledge: Familiar with the concept of AI and its potential. Recognizes popular AI-driven applications and services in daily life. • Skills: Basic computer literacy. Curiosity about AI. 2. Beginner II • Knowledge: Understanding of AI, ML, and DL as distinct yet related concepts. Can name some prominent AI tools or platforms. • Skills: Basic programming knowledge in languages like Python. Can interact with AI-driven software or tools, and perhaps run basic pre-made AI scripts or models. 3. Intermediate User I • Knowledge: Aware of key ML concepts like training, testing, and validation. Familiar with common AI terminology such as epochs, layers, and neurons. • Skills: Can use popular ML libraries or tools such as Scikit-learn for structured data tasks. Comfortable with basic data pre-processing and visualization. [Gets more technical:] 4. Intermediate User II • Knowledge: Has a grasp of various ML algorithms (e.g., regression, clustering) and understands some foundational neural network architectures. • Skills: Capable of using deep learning libraries like TensorFlow or Keras for image or text-based tasks. Can fine-tune pre-trained models and understands model evaluation metrics. 5. Advanced User I • Knowledge: Recognizes the challenges and pitfalls of ML models, such as overfitting and underfitting. Familiar with AI ethics and potential biases in AI. Aware of current trends and research directions in AI. • Skills: Proficient in training ML models from scratch. Experienced in tackling real-world AI projects with moderate complexity. Familiar with cloud AI services and platforms. 6. Advanced User II • Knowledge: In-depth understanding of specialized ML algorithms and architectures. Familiar with advanced topics like transfer learning, ensemble methods, and adversarial attacks. • Skills: Capable of designing, implementing, and optimizing custom AI solutions for specific challenges. Comfortable working with large datasets, and possesses debugging and optimization skills. __ ## List of these six levels — with *specific tools, software, and programs* (and could add more from MW's list) that might be used and explored along the way: 1. Beginner I • Tools & Software: • Analog: Wikipedia articles. Books like "AI For Everyone" by Andrew Ng or "Artificial Intelligence: A Guide to Intelligent Systems" by Michael Negnevitsky. Board games like "Robot Turtles" to introduce programming concepts. • Digital: Websites like [AI4ALL](https://ai-4-all.org/) for exposure to AI concepts. Playing with ChatGPT and other LLMs. Use of AI-driven apps. 2. Beginner II • Tools & Software: • Programming: Introduction to Python via classes like CS50 and platforms like [Codecademy](https://www.codecademy.com/) or [Coursera](https://www.coursera.org/). • AI-specific: Experimentation on platforms like [Google's Teachable Machine](https://teachablemachine.withgoogle.com/) which offers interactive/hands-on experience without requiring coding. 3. Intermediate User I • Tools & Software: • Data Analysis: Use of tools like [Pandas](https://pandas.pydata.org/) for data manipulation and [Matplotlib](https://matplotlib.org/) or [Seaborn](https://seaborn.pydata.org/) for data visualization. • Machine Learning: Introduction to Scikit-learn for building basic ML models. Tutorials on platforms like [Kaggle](https://www.kaggle.com/) can be really beneficial. 4. Intermediate User II • Tools & Software: • Deep Learning: Initiation into deep learning frameworks like [TensorFlow](https://www.tensorflow.org/) or [Keras](https://keras.io/). • Online Platforms: Explore Semantle! (which uses TensorFlow). Advanced courses on platforms like [Udacity](https://www.udacity.com/) or fast.ai. • Environments: [Google Colab](https://colab.research.google.com/) for experimenting with deep learning models without local setup. 5. Advanced User I • Tools & Software: • Cloud Services: Introduction to cloud ML platforms like [Google AI Platform](https://colab.research.google.com/), [Amazon SageMaker](https://aws.amazon.com/sagemaker/), or [Azure Machine Learning](https://azure.microsoft.com/en-us/products/machine-learning/). • Advanced ML: Tools like [XGBoost](https://xgboost.readthedocs.io/en/stable/) or [LightGBM](https://lightgbm.readthedocs.io/) for gradient boosting. Exploration of H2O.ai for automated machine learning. • Data Management: Databases like [SQLite](https://www.sqlite.org/index.html) or platforms like Airtable (!) for structured data management. 6. Advanced User II • Tools & Software: • Deep Learning Advanced: Exploration of more specialized libraries such as [PyTorch](https://pytorch.org/) and tools like [TensorBoard](https://www.tensorflow.org/tensorboard) for visualization. • Natural Language Processing: Libraries like [spaCy](https://spacy.io/) or [NLTK](https://www.nltk.org/). • Optimization & Debugging: Tools like [Weights & Biases](https://wandb.ai/site) for experiment tracking and [Optuna](https://optuna.org/) for hyperparameter optimization.