What about AI?

Data Analysis and Prediction

On the surface, these may not seem like student tasks, but analyzing statistics and being able to make informed choices based on your findings is exactly what students should be able to do.

• Tasks: Extracting insights from data, identifying patterns, and forecasting future trends.
  • Predictive Analytics:  Forecasting future events (e.g., customer churn, sales forecasting, fraud detection).
  • Data Cleaning and Preparation:  Automating the process of making data ready for analysis.
  • Anomaly Detection:  Identifying unusual patterns or outliers in data (e.g., fraudulent transactions).
  • Business Intelligence (BI) Augmentation:  Enhancing traditional BI with AI-driven insights.
  • Risk Modeling:  Assessing and quantifying financial or operational risks.

• Programs/Platforms:
  • Google Cloud AI Platform (Vertex AI, BigQuery ML): Comprehensive suite for building, deploying, and managing ML models, including predictive analytics.  These are really big picture programs for creating entirely new AI programs.       
  • AWS SageMaker:  Fully managed service for building, training, and deploying machine learning models.
  • Azure Machine Learning: Microsoft's cloud-based platform for ML.
  • TensorFlow & PyTorch:  Underlying frameworks for building custom predictive models.
  • R:  Statistical programming language widely used for data analysis and machine learning.
  • Databricks:  Data platform that integrates AI/ML capabilities for large-scale data analysis.

Natural Language Processing (NLP)

These are your big, broad tools that do just about anything.  This is where good prompting skills come into play.

• Tasks: Understanding, interpreting, and generating human language.  This includes:
  • Text Summarization:  Condensing long texts into shorter, coherent summaries.
  • Sentiment Analysis:  Determining the emotional tone of text (positive, negative, neutral).
  • Chatbots/Conversational AI:  Enabling human-like conversations, answering questions, and providing support.
  • Translation: Converting text from one language to another.
  • Named Entity Recognition (NER):  Identifying and classifying entities like names, organizations, and locations.
  • Topic Modeling:  Discovering abstract topics in a collection of documents.
  • Text Generation:  Creating new text, like articles, emails, or creative content.

• Programs/Platforms:
  • Google Cloud Natural Language API (GEMINI):  Pre-trained models for sentiment analysis, entity extraction, content classification, and more.  This is Gemini.  This is free all the time and is a pretty good tool.  I use it every day. 
  • OpenAI's GPT series (ChatGPT):  Excellent for general text generation, summarization, and conversational AI.       There is a free level of this.
  • NLTK (Natural Language Toolkit):  A popular Python library for research and development in NLP, offering tools for tokenization, stemming, tagging, parsing, and more.  Python can do anything, but it is a language you will need to learn first.

After going through the big AI answer, I realized that there is no way I will be discussing seven types.  Right off the top, I am going to drop all the programs that write AI programs.  If you have a specialty need for your business, these are great, but this blog is meant for students, staff, and faculty of a community college.  When you are in graduate school, you might need those tools. When you are ready, do an AI search, prompting it to name the tool that will best suit your task.

Another thing I dropped are multiple programs from the same company, specifically Amazon, Microsoft, and Google.  They show up in the list multiple times with different names.  As an example, Amazon has AI programs called Rekognition, Comprehend, and SageMaker and none of them are free.  Essentially, they are all the same – programs that write programs.  All Microsoft programs come with associated costs.  Google has at least nine programs.  They boil down to Gemini and a variety of prompting techniques. 

Python is an open-source programming language that is closer to human language than any other programming language.  It is really flexible and, for our purposes here, can be used to write just about anything.  It is a first-level skill you would have to acquire, like the ability to write HTML.  From there, the possibilities are endless. 

The robotics, healthcare, and financial services modeling tools are simply unrelated to our needs.  I suspected that when I got started, but decided to leave them in, thinking there might be something useful.  There probably is, but for this blog, they are just clutter.  My target audience may never need tools like that.  Once a profession is chosen, the employer will have a solution that fits their needs. 

Computer vision tools are pretty cool, but are pretty deep for our needs.  This is image manipulation, OCR reading, and facial recognition.  You may use these tools a lot in life but probably not much in school.

So, after a false start, over the next four weeks we will take a deep dive into:

1. Natural Language Processing (NLP)
2. Generative AI (Art, Music, Code, etc.)

3. Data Analysis and Prediction

   This is another reason why you should always check your AI response.  It may not fit your needs.