Generative AI 101 Slides

Agenda - Overview, history, current state, whats coming, limitations and mitigations

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.

Applications of Generative AI

Generative AI applications today Evolving areas for enterprises

Writing content, summarizing text, Q&A, Assisting in code writing are areas where generative AI has shown promising results.

Generating experience e.g Product KREATE, reinventing workflows, creating ideas for new products - are ares where more pregress is coming

In addition experienced team are including geenrative AI for creating and augmenting datasets.

With Generative AI progress - one should also consider how AI threats are changing too - new threats, change to existing threats and expansion of new threats

Tech Stack and modeling architectures

There are many architectures - Transformer Auto encoder, GAN, Diffusion model and RL

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

Identify what is important - Creativity, Realisim , Diversity and then evaluate

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

Consider High Risk vs Low Risk application. Also consider generic data vs custom data dimension

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

Challenges, Ethical Issues, Copyright issues, Data ownership question, Environmental issues

Slide 17 Generative AI challenges - generative AI 101

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

LLM, Image generation can produce variety but There is very less control how new data will be generated

Uncontrolled data generation challenge - generative AI

Data ownership challenges - generative AI

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

One need to responsibly use Generative AI

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

GPT4 has one trillon parameters. It has longer context, multimodal capabilities. It has lower rate of hallunication.

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

Digital Human is type of generative AI to handle complete interaction with customers

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

Virtual Agent to Digital Human

Use this framework to determine what kind of capabilities generative AI should add

Framework for AI Human conversation generative AI 101

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.

Foundation Model

Act as platform. They can be use as out of box and advanced model can be trained on these.

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.