Generative AI 101 Slides

Learn how software evolved from coding to data driven ML. Now genertive AI is even generating data and code. This will change product business model and also product management.

20 Latest Update of Generative AI

Latest announcement from Meta - Confirmed $20B investment for GenAI, Llama3. Llama2 is available 2 at Hugging Face. Meta announced the plan and their serious ness to take this forward

Google Gemini and Bard

Google Gemini latest model is used in Bard. Gemini is different as it is trained for multimodal. Google Bard us Gemini and available as AI Assistant.

Open AI GPT Store

Open AI introduced GPT store for users.
Open AI also introduced ChatGPTTeam for enterprises. Mistral also join LLM race and announced a large model that handle context of 32K length.

Large Language Model

  • LLMs are changing the way how documents are analyzed, summarized
  • LLMs are influential in creating new text and getting used in content generation
  • Code generation is area that will significantly getreefined by LLMs
  • With Generative AI progress - one should reconsider how software should be developed.

    LLM Impact

  • LLMs building blocks are Data, Architecture, Training, Inference
  • While making decisions about Closed vs Open model you should consider above factors, control needed, IP risks etc
  • Lllama and Hugging Face are 2 famous open source model. ChatGPT, Bard, Palm , Claude are closed models.
  • LLM Comparision Criteria

  • Model parameter indicate complexity. Bigger paramter size indicate ability to handle complexity
  • Top most criteria should be - accuracy for given task
  • In many cases ability to fine tune a model, reasoning capabilities are impotant too.
  • Foundation Model

  • Foundation model work out of box for universal scenarios.
  • Specific models can be trained on these.
  • Reduce the labeling requirements
  • As FMs are exposed to internet scale data and various forms and myriad of patterns, FMs learn to apply their knowledge within a wide range of contexts.
  • Consideration to Extend Foundation Model

  • Extend and Fine tune for specific task
  • Train custom model for specific domain
  • Build datasets on which you can train model
  • Prompt Engineering

  • Give persona to model
  • Provide sub steps to complete task
  • Specify output format
  • Prompt Engineering

  • Give instruction , Give persona, add delimeter
  • Add examples, provide steps to complete task
  • Specify output format such as HTML, JSON, Table to produce
  • OpenAI Fine tuning

  • Finetuning and building custom model give you edge
  • With Fine tuning you have your own model, your IP based on data
  • With Fine tune model, you can handle complex scenarios
  • Before Fine tuning - try prompt engineering, few shot learnings.

    Fine tuning Steps

  • One Fine tune - evaluate the results
  • Make a inference calls/li>
  • Do Error Analysis.
  • Tech Stack and modeling architectures

    Generative AI is based on "comprehend existing" data and determine trajectories data can take. It uses it for geenration

  • Diffusion architecture is suitable for generation
  • Transformer are suitable for language gentration in sequence
  • How to Evaluate Gen AI

    Traditional machine learning model has evaluaition metrics like accuracy. Generative AI creates new data. It evaluation is based on subjecive measures like diversity of data, realism, nobalty, creativity. It is hard to evaluate or benchmark geenrative models.

    Generative AI adoption framework

    Use above dimensions to identify quardant of your use case(s). Low risk and applicability of generic data e.g content writing for sales,travel guide are easy to adopt.

  • Areas where universal data is available but risk of geenrating wrong results are high - it is opportunity for companies that want to train and sell custom models
  • Areas where task specifci fine tuning is needed, is opprtunity for services companies
  • Companies that want to build defendable IP will focus on create new dataset and model training on these dataset for areas where risk is high and universal dataset are not available.
  • Trade off and Conflicts

    Generative AI has potential, but there are many challenges and open questions. One should consider these before using geenrative AI in enterprise(s)

    Uncontrolled data production

    Generative AI is based on "comprehend existing" data and determine trajectories data can take. It uses it for creating new data. However the method of producing new variation of data makes it controllable.

  • Generative Model output is unpredicatble and uncontrollable. Main issue is - how to get confidence if you want to use it in mission critical envioronment.
  • Large language Model (LLM) inherit bias from data they are trained on.
  • There are open questions on who have copy right on generated content. In future there may be new laws that will impact consumers of generative AI
  • LLM or Image model that are trained on universal data and produce new data are compute intensive. There are impact of enviornment one need to consider.
  • Enviornment concern and legal question

    There is significant amount of compute, energy usage. There is significant amount of carbon emission. One need to ensure it is for good cause and not for producing variation(s)

  • Dataknobs has created set of controls to handle this
  • GPT 4 vs GPT 3.5

    GPT (Generative Pre-Trained Transformer) is family of large language model. developed by OPEN AI. GPT4 is latest generaral purpose LLM released by OpenAI. ChatGPT4 is chatbot focused LLM.

  • ChatGPT4 has large token length compared to GPT3.5 ChatGPT4 can process 25000 words of context. It is 8 times higher than chatGPT3.5
  • ChatGPT4 can understand and process visual input.
  • ChatGPT 4 has better programming capabilities compared to ChatGPT 3.5
  • ChatGPT 4 has fewer hallunication compared to ChatGPT 3.5
  • Digital Human

    Here is framework for Digital Agent, Virtual Assistant, Digital Inluencer and Digital Human.

    Virtual Agent to Digital Human

  • Virtual Agent/Digital Agent are for one off task.
  • Virtual assistant carry context and are ongoing engagement
  • Digital influencer add experience and emotion into interaction
  • Digtial Human provide experience/emotion for ongoing engagement.
  • Generative AI Vendors

  • Vendors: OpenAI, Microsoft, GCP, AWS, Anthropic, Dataknobs, Snorkel and more
  • Evaluation criteria : Features, accuracy, flexibility, ability to fine tune model, cost of inference, how reliable reults are
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  • Open AI and Micorosoft model is most used
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  • GCP bard provide up to date information. GCP also has TPU
  • AWS has large cloud share
  • Anthropic released caluse with 100K tokens
  • Hugging Face provide many smaller models
  • ChatGPT3.5 ChatGPT4 and Bard

  • Open AI has chatGPT3.5 and chatGPT4.0
  • Microsoft Azure provide OPEN AI services integrated with Azure
  • Google has Bard, Vertex AI with generative AI studio. In addition google has TPUs
  • AWS provide cloud to use existing capabilities

  • AWS bedrock enable using Hugging face, anthropic or other model on AWS cloud
  • Hugging Face is providing various small model like BERT, GPT-3, ROBERTA, XLNET
  • Anthropic has build Claude. Available to use at "poe . com". There are 3 flavors even with 100K tokens.
  • Generative AI Applications in Security

  • Generative AI is extremly useful for Cyber Security
  • Simulate phishing attack to check robustmess of security solution
  • Automate the analysis of security logs
  • Generative AI Applications in Payments Industry

  • Payments is highly regulated area
  • Customer Service can be made effective in Payment industry
  • Audit can be made more robust in payment industry
  • Check validation, Fraud detection can be improved
  • Ad