Possible AI Applications

1. Co-Pilot for Developers
2. Designer
3. Vacation Planner
4. Cooking Assistant
5. Fitness Trainer

Microsoft Power Automation

1. Power Automate (https://powerautomate.microsoft.com/)

   1. AI Builder

   2. Models

   3. Training Document

   4. Information to Extract

   5. My Flows

   6. Cloud Flows

      1. Use form processing to extract data from documents triggered manually.
      2. Flow Checker Feedback Flows and Run

   7. How to : https://learn.microsoft.com/en-us/microsoft-365/community/machine-learning-and-managed-metadata

Python and Sci-Kit

1. Subject Terms (Keyword)

   • Rapid Automatic Keyword Extraction (RAKE)
   • Python using RAKE and the Natural Language Toolkit (NLTK) module, with a character minimum of 5, a phrase maximum of 3 words, and a minimum word frequency of 4 words. The SMART stop list was used instead, which contains 571 words.
   • DBPedia Spotlight22 / Aho-Corasick algorithm

2. Clustering

   • Weka to test Simple k-Means, one of the most popular clustering algorithms. k-Means partitions a set of documents into k clusters where each document belongs to the cluster with the nearest mean.
   • To start, the algorithm will randomly choose “centroids” for each cluster, then iteratively recalculate the position of those centroids until either there is no change in position or some set number of iterations has been reached.
   • Identifying meaningful relationships is a matter of trial and error, adjusting the value of k and then reviewing the composition of each cluster.

MALLET for classifying documents.

-  Machine Learning for LanguagE Toolkit, or MALLET, is a Java application
for classification, information extraction, and topic modeling, and like
Weka, is free and easy to use.24 
-  MALLET uses an implementation of Latent
-  Dirichlet Allocation (LDA)25

Chat GPT (3.5, 3.5-turbo, 4.0, 4.0-=turbo) as a spell checker, voice response, or Image Creator :

1. Chat GPT (OpenAI) : https://openai.com/blog/chatgpt

• Github : https://github.com/openai
• Explore : https://github.com/openai/openai-cookbook
• Uses the openai-client: https://github.com/itikhonenko/openai-client
  • API KEY
  • ORGANIZATION_ID
• Dall-E (OpenAI) : https://openai.com/product/dall-e-2

1. Spell Checker

   request_body = { model: 'text-davinci-edit-001', input: 'What day of the wek is it?', instruction: 'Fix the spelling mistakes' } Openai::Client.edits.create(request_body)

2. Image Creator

   request_body = { prompt: 'A cute baby sea otter', n: 1, # between 1 and 10 size: '1024x1024', # 256x256, 512x512, or 1024x1024 response_format: 'url' # url or b64_json }

   response = Openai::Client.images.create(request_body)

3. Connect in Ruby

   require 'openai-client'

   Openai::Client.configure do |c| c.access_token = 'access_token' c.organization_id = 'organization_id' # optional end

4. Find Engine

   Openai::Client.models.find(‘babbage’) Openai::Client.models.find(‘davinci’)

5. Build Request Body

   request_body = { prompt: 'high swim banquet', n: 1, # between 1 and 10 size: '1024x1024', # 256x256, 512x512, or 1024x1024 response_format: 'url' # url or b64_json }

1. Playground interface : https://platform.openai.com/playground?mode=chat

Other LLM Tools

1.  Repository : https://huggingface.co/
1.  The Bloke : https://huggingface.co/TheBloke
1.  Lone Striker : https://huggingface.co/LoneStriker
1.  WebGUI : https://github.com/oobabooga/text-generation-webui
1.  Stable Diffusion : https://github.com/AUTOMATIC1111/stable-diffusion-webui
1.  Voice Changer : github.com/w-okada/voice-changer
1.  Real Time Voice : https://github.com/RVC-Project/Retrieval-based-VOice-Conversion-WebUI
1.  RVC : voice-models.com  and weighs.gg
1.  Dify : https://dify.ai/#llms-app-stack
1.  Cognition : https://www.cognition.ai/blog/introducing-devin

Amazon Sage Maker

1.  Canvas
    1. Low Code.  Drag and Drop.
1.  Studio
1. Code and Models

Used for creating LLM in AWS.
AI to determine bank loans.

Other AI Engines to Explore

1. Stable Diffusion (Stability) : https://stablediffusionweb.com/ or civitai.com
2. Watson (IBM) : https://www.ibm.com/products/watson-explorer
3. Chess
4. Content Hub. IBM Watson can propose relevant tags based on content.
5. Bard/Palm 2 (Google)
6. Google blog post about BERT,18 an ML technique for NLP, the benefit shown was simply the ability to link a preposition with a noun.
7. Aladin (BlackRock)
8. Mindjourney (MindJourney) : https://www.midjourney.com/home/?callbackUrl=%2Fapp%2F
9. Kaaros
10. Tensor Flow (Google)
11. IRIS : https://iris.ai/
12. Claude https://www.anthropic.com/index/claude-2
13. https://marketplace.atlassian.com/apps/1224655/scrum-maister?hosting=cloud&tab=overview
14. Bing (free)
15. Claude 2 (free) by Anthropic
16. Grok by X (Twitter)
17. Open-source models (FREE) available on Huggingface https://huggingface.co/
18. Llama 2 by Meta
19. Flan, Falcon, Orca, Beluga, Mistral, Mixtral, Phi2
20. LMStudio (Windows, Mac, Linux) - install and run models
21. Pinokio.computer browser - install and run models
22. Atlassian Rovo - https://www.atlassian.com/blog/announcements/introducing-atlassian-rovo-ai

References

1. PubMED : https://pubmed.ncbi.nlm.nih.gov/30153250/
2. Other :https://www.aiforlibrarians.com/ai-cases/
3. Science Direct : Weed Collections : https://www.sciencedirect.com/science/article/pii/S0099133317304160?via%3Dihub
4. Apache Mahout : https://mahout.apache.org//
5. Spark MLlib Apache : https://spark.apache.org/mllib/
6. Stanford : https://library.stanford.edu/blogs/stanford-libraries-blog/2022/07/working-students-library-collections-data
7. M-Files. Smart subjects provide tag suggestions based on document content
8. Magellan's AI capabilities include speech and text analytics from contextual hypothesis and meaning deduction.
9. AWS Innovate: Data and AI/ML Edition
10. Data Science : https://www.dataplusscience.com/GenerativeAI.html
11. AI Got Talent : https://dataplusscience.com/files/UCCBAGenAI20240206.pdf

Regulation

MEPs substantially amended the list to include bans on intrusive and discriminatory uses of AI systems such as:

1. “Real-time” remote biometric identification systems in publicly accessible spaces;
2. “Post” remote biometric identification systems, with the only exception of law enforcement for the prosecution of serious crimes and only after judicial authorization;
3. Biometric categorisation systems using sensitive characteristics (e.g. gender, race, ethnicity, citizenship status, religion, political orientation);
4. Predictive policing systems (based on profiling, location or past criminal behaviour);
5. Emotion recognition systems in law enforcement, border management, workplace, and educational institutions; and
6. Indiscriminate scraping of biometric data from social media or CCTV footage to create facial recognition databases (violating human rights and right to privacy).

AI Feedback Loop

• Programming : Algorithm + Input => Answers
• Supervised Learning : Answer + Input = > Algorithm
• Feedback Loop : Re-perturbed feeds classification and classification feed perturbed
• Decision Tree : mathematically produced else
• Neural Network : binary tree diagrams
• Natural Language Processing (NLP_ : the identification of patterns in spoken or written text.
  • Read Understand Derive meaning from Human Languages
  • Lanaguage Structures
  • INteract Transfer Data
  • Feed Document -> encode -> segmentation into sentences by punctuations. words in the sentences into constiutainet words into tokens. tokenize. remove no$
  • ALgorithm
  • Explain (skip, skipping skipped _ same stemming.
  • limitization lemmatizaition
  • verbs particle - speech tagging
  • Pop culture references movies places news locations- named entitity tagging

Segmentation into sentences and store them. Tokenizing into words and store them. Remove Stop Words (Non Essential Words.) like are, and, the from sentence segments. Stemming treats skip, skipping, skipped as the same words. Lemmetization Am Are Is for all genders ich bin, du bist, er/sie/es ist base word (lemma)= be Named Entity Tagging of Proper Nouns Sentiment and Speech with naive bayes

• Reinforcment Learning : data - > model
• Cumulative Selection : Building off the last step. Not starting over everyt ime
• Semi-Supervisied Machine Learning : Supervised means there is some human involvement in setting up the tool,
• Inductive Reasoning
  • The corpus-based approach using a training set, as described above, uses the process of inductive reasoning. This is the kind of thinking that states ‘the sun rose yesterday, the sun rose today, so the chances are the sun will rise tomorrow’. Now, philosophers will argue that inductive reasoning is not scientific. Just because the sun rose yesterday does not mean the sun will rise tomorrow.

AI Terms

• A Corpus : All text documents in Scholar

• A Training Set : is a subset of the corpus, which has been tagged in some way to identify the characteristic you are looking for Common Crawl RefinedWeb The Pile C4 Starcoder BookCorpus o ROOTS Wikipedia o Red Pajama

• A Test Set : collection of documents to be used for trialling the algorithm, to see how successfully it carries out the operation.

  • Example : Modified National Institute of Standards and Technology (MNIST) database of handwritten numbers,10

• An Algorithm : The ‘algorithm’ is simply the tool that looks at each item in the corpus and enables a decision to be made. An algorithm may be as simple (and frequently is as simple) as matching a pattern. selecting and applying an algorithm or method

AI Process Cycle

1. Identify
2. Explore / Analyze / Encode (Change Everything into a number)
3. Model
4. Predict
   1. Clarity (Makes Sense)
   2. Original (Novelty)
   3. Useful
5. Feedback

Resources https://www.youtube.com/watch?v=awGJkRe9m50