## AI-Introduction

- Presenter: Nils Seipel

### Premises and Terms

- The goal of artifical intelligence is to simulate human intelligence
- This achieved by simulation functions of signal neurons.
- Problem: Human intelligence is not well defined
- Artificial intelligence > Machine Learning > Deep Learning
- Artificial intelligence(knowledge-based systems)
- StrongAI: General AI, solves Problems of its own (Holy Grail/Singularity)
- WeakAI(narrowAI): helps with specific problems,
not capable of knowledge transfer

Algorithm:
- An algorithm is like a recipe, defined input and expected output are clear
- The problem is: algorithms are difficult to adept to reality
 we need to cover **every** possible case

### Artificial neural networks (ANN)

- Solve the problem with algorithms
- Can adapt to different inputs
- Have to be trained by Humans or another Artificial neural network
- Training data must be complex and we need **a lot**. And this data needs to be labled.
- Training is really crucial, bad training creates bad ai.
- Biased data will create a biased ai, diverse data is important to remove bias.
This can become dangerous for societies that relay on AI. It has the potential 
to perpetuate prejudice.
- AI learns similar to learning child by pointing on things
- try yourself: GOOGLETOOL
- A shallow neural networks is build out of input layer, processing layer and
output layer.
- Deep neural networks means: lots of processing layers of "neurons".
- After training, the artificial neural network is **fixed**, it doesnt learn any more.
- All ANN work based on probability.

### Use-cases:

- Cancer diagnosis, histological images are rated by an AI that supports the doctor.
- Self-driving-cars
- Learn sign-language assisted by ai
- DALL-E
- Deep-Fake Videos
- openAI-Playground
- Universal Translation

### Bias in neural networks:

- Really important issue, if we give important tasks/decisions to ai
- Reality is sometimes not diverse enough to provide sufficiant data
- Currently to focused on western white males, due to limits in training data

### Big data is required for training

- 45 petabyte pure text as training data for ChatGPT
- Example: In order to classify a handwritten 0 as a zero, with 95% likelyhood
of correct classification you need 50.000 handwritten samples of this singel digit.

### Future of AI

- WeakAI will be better than humans in every discipline.
- AI will not drive us into unemployment, but more that we'll be interacting with AI.
  Simlar to a conductor and the orchestra working together.
- <https://makerspace-giessen.de/ki/> further reading and nice resources
- [AI for Start-Ups](https://www.tig-gmbh.de/projekte/ki-fuer-startups)



## Assist me, AI: The pun-ishingly good future of productivity
- presenter: Johannes Hammp
- Why this talk: Plagiarism detector software didn't recognize plagiarims.
- The whole talk is available as [pdf](https://github.com/HackyHour/Giessen/blob/gh-pages/notes/2023-01-25-jh-assist-me-ai.pdf)

### Quillbot 
- Paraphraser-Tool is often used by students for homework
- Quality is often lacking
- Problem: PlagAware (anti-plagiarism) Software does not recognize anything

### Semantic Scholar  
- Alternative to Google Scholar
- Creates references for you
- Has integrated "unpaywall"
- AI-feature TLDR (Beta). Shorter then abstracts, oftern better content summary

### ChatGPT

- does everything what all the other tools do
- higher usability through the chat like interface
- keywords to circumvent blocks: hypothetical,educational
- Uses "However" to often in its answers
- Context can also be a primer into a fault direction
- Awesome prompts, great prompts for chat gpt (<https://prompts.chat/>)

Use-cases:
- Essay-writing
- Code-writing/Debugging
- Summariser
- Assistant to create fundamentals of a research paper.
  - Advantage: new way of writing, less writing blockade.
   not necessarily less time consuming.

Problem: Teachers are confronted with content which is often difficult to veryfi
The border between human and AI generated content blurs.

- Enhance your own creativity with suggestions from ChatGPT

### Transparency Issues

- not everything labeled AI is real AI
- we dont know how it works
- it has some quality issues, e.g. creating eloquent bullshit
- reproducibility