Tag Archives: AIML

AIML, Liferay

Liferay & AIML / Generative AI – Part 1

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What all is possible with Liferay & AIML / Generative AI? – Part 1. Liferay being open source we can integrate GAI / AIML with Liferay for:

  • Generating content & images in web content section
  • Generating automated blogs, tags, categories and UIUX flows
  • Assistant which could redirect us to relevant help pages
  • Assistant which could suggest which portlets or features to use from documentation
  • Assistant for suggesting configurations of Liferay
  • Intelligent chatbot which is domain specific
  • Assistant for fault finding & log analysis recommendations
  • Assistant for detecting status of liferay, integrations, file store & database
  • Assistant for analysis of networked components like web server, CDN, hosting / cloud and so on
  • List is endless and this seems to be the just the start of this new area of integrating AIML / GAI into enterprise applications for monitoring, suggestions and assistance

Refer my earlier blog for what kind of disclaimers might be required when working with GAI: What points can be part of fair use disclaimer for Generative AI? – Part 1 | LinkedIn

Email me: Neil@HarwaniSystems.in

AIML, Analytics, Data Science, Machine Learning, Mathematics

Short list of good courses / links / books on Mathematics, Operating Systems and AIML / ChatGPT – Part 1

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Here is a short list of good courses / links / books on Mathematics, Operating Systems and AIML / ChatGPT – Part 1:

Email me: Neil@HarwaniSystems.in

Analytics, Data Science

Four waves of Artificial Intelligence & Machine Learning

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While teaching students in two different courses (AIML & “Data Science and Analysis”), there was a requirement to categorize historical AI & ML along with it’s interface with Data Science.

To start: AI is the superset, ML is a subset of AI, Neural Networks (Deep Learning) are specialized subsets of ML.

Below is a categorization of AIML across four waves and it’s interface with Data Science:

Wave 1:

Concepts: Traditional topics like state space search, heuristics, knowledge representation, expert systems, fuzzy logic, problem solving languages and such.

UseCases: Think a small basic robot moving through your home and taking decisions on avoiding obstacles.

Wave 2:

Concepts: Standard algorithms built on top of Regression, Statistics, Algebra, Probability, Calculus and such – Classification, Decision Trees, Association Mining, Clustering, Ensemble methods, Random Forest, SVM and so on. NLP, Computer vision, scanning solutions, advanced search and such areas also evolved here in parallel or with the help of these algorithms.

UseCases: Spam detection, Decision making, Co-related variables related predictions, Prescriptive Analytics and so on.

Wave 3:

Concepts: Replicating human / animal brain. Neural Networks. Storing and managing very large amount of data (structured & un-structured)

UseCases: BigData, Self driving cars, Image recognition, Complex reasoning, Medical diagnosis, Chat bots, Personal assistants, potentially unlimited usecases interfacing with all usecases across AIML & Data Science.

Wave 4:

Concepts & UseCases: Explanability, Interpretability – Understanding the complexity of artificial intelligence & machine learning models. UI & Low code driven AIML (Neural Networks), one shot learning, hardware optimized AIML. Deep Learning. BERT and newer context driven algorithms also are in this area, Natural Language Generation is another area here.

Where does Data Science interface with AIML:

  • Non structured data analysis
  • Natural language generation
  • Sentiment analysis
  • Use of standard algorithms to analyse structured data
  • Building insights & making predictions / prescriptions and so on

Email me: Neil@TechAndTrain.com

Analytics, Data Science, Machine Learning

What are we doing in AI / ML / Data Science / Decision Science / Analytics World? – Glossary

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Over the last few years I have explored, programmed, worked in, researched and taught Data Science / AI / ML / Analytics / Decision Science to multiple students and with many software professionals. I have collected many keywords that you can google and explore. This will help you to keep pace and learn about things happening is these areas. It’s like a glossary of words to search over internet. It’s a mix and match of technologies, algorithms, concepts, AI / ML / Information Technology terms, BigData words and so on in no particular order. I will keep expanding this till it’s a relatively exhaustive list.

  • Automatic Machine Learning
  • Transfer Learning
  • Explainable Machine Learning
  • Keras
  • PyTorch
  • MLR
  • R
  • Python
  • Ggplot2
  • MathplotLib
  • MLib
  • Spark
  • Hadoop
  • Tableau
  • Chatbots
  • Talend
  • MongoDB
  • Neo4j
  • Kafka
  • ELK
  • NoSQL
  • Cassandra
  • AWS SageMaker
  • SVM
  • Decision Trees
  • Regression: Logistic, Multiple, Simple Linear, Polynomial
  • Scikit Learn
  • KNIME
  • BERT
  • NLG
  • NLP
  • Random Forest
  • Hyper parameters
  • Boosting
  • Association rules / mining – Apriori, FP-Growth
  • Data mining
  • OpenCV
  • Self driving cars
  • AI / Memory embedded SOCs, GPUs, TPUs
  • Neural engine chipsets
  • Neural Networks
  • Deep Learning
  • EDA
  • Statistical & Algorithmic modelling
  • Sampling
  • Probability distributions
  • Hypothesis testing
  • Intervals, extrapolation, interpolation
  • Scaling
  • Normalization
  • Agents, search, constraint satisfaction
  • Rules based systems
  • Semantic net
  • Propositional logic
  • Fuzzy reasoning
  • Probabilistic learning
  • First order logic
  • Game theory
  • Pipeline building
  • Ludwig
  • Bayesian belief networks
  • Anaconda Navigator
  • Jupyter
  • Synthetic data
  • Google dataset search
  • Kaggle
  • CNN / RNN / Feed forward / Back propagation / Multi-layer
  • Tensorflow
  • Deepfakes
  • KNN
  • K means clustering
  • Naive Bayes
  • Dimensionality reduction
  • Feature engineering
  • Supervised, unsupervised & reinforcement learning
  • Markov model
  • Time series
  • Categorical & Continuous data
  • Imputation
  • Data analysis
  • Classification / Clustering / Trees / Hyperplane
  • Differential calculus
  • Testing & training data
  • Visualization
  • Missing data treatment
  • Scipy
  • Pandas
  • LightGBM
  • Numpy
  • Dplyr
  • Google Collaboratory
  • PyCharm
  • Plotly
  • Shiny
  • Caret
  • NLTK, Stanford NLP, OpenNLP
  • Artificial intelligence
  • SQL / PLSQL
  • Data warehousing
  • Cognitive computing
  • Coral
  • Arduino
  • Raspberry Pi
  • RTOS
  • DARPA Spectrum Challenge
  • 100 page ML book
  • Equations, Functions, and Graphs
  • Differentiation and Optimization
  • Vectors and Matrices
  • Statistics and Probability
  • Operations management & research
  • Unstructured, semi-structured & structured data
  • Five Vs
  • Descriptive, Predictive & Prescriptive analytics
  • Model accuracy
  • IoT / IIoT
  • Recommendation Systems
  • Real Time Analytics
  • Google Analytics

If you are learning something by googling these topics, feel free to provide suggestions for adding more words here. You are welcome to discuss / suggest on top of this article as well. Thank you for reading.

Email me: Neil@TechAndTrain.com

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