Dataknobs

Generative AI 101 Slides | GenAI Guide

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.

20 Latest Update of Generative AI

Llama 3 released. Google code Assist announced

Meta Releasd Llama 3.
Microsoft announced investment in G42
AWS invested in Anthropic Claude
Tesla announce RoboTaxi for Aug 2024

Gen AI offering/update from Vendors

Closed Model From Hyper scalers

Google has Gemini, Vertext AI and also building semi conductor

Microsoft has partned with OpenAI. Now Microsoft is also working on next generation model.

AWS has released AWS Q and bedrock for Gen AI.

Gen AI offering fron Non Hyper Scalers

Other options OpenAI Meta and Hugging Face

Open AI is leading GPT research and development. They are focusing on building state of art API and assistant.

Hugging Face is providing Open source small LLM in user friendly manner.

Meta is providing tool for ad creation as well as very advance and big open source LLM.

GenAI vs Predictive AI

Gen AI creates, Predicitve AI classify ability to create make GenAI very powerful

Large Language Model

LLMs are build by training on large text data . These are useful to generate text, summarize document, answer questions, reason, translate, do SEO of web pages

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.

How to use LLMs and build applications

Consideration while selecting - Open Source vs Closed LLM Closed Model can be access via API and have a cost. Open Model have less cost, more control but may require significant work

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

Consideration while comparing - LLM for accuracy and size More complexity and size can be good or bad

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.

Guardrails for GenAI LLM and Chatbots

Safety for LLM and GenAI

Guardrails are essentially guidelines and controls that steer the LLM's outputs in the desired direction.

Here are some ways to keep your LLM on track:

Input Validation: Set criteria for what kind of information the LLM can process, preventing nonsensical or malicious inputs.
Output Filtering: Review and potentially edit the LLM's outputs before they are used, catching any biases or factual errors.
Real-time Monitoring: Continuously track how the LLM is being used and intervene if it generates harmful content.
Human oversight: Ensure humans are always involved in the LLM interaction

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.

Consideration to Extend Foundation Model

Consider extending Foundation model for compettive advantage

Extend and Fine tune for specific task

Train custom model for specific domain

Build datasets on which you can train model

Prompt Engineering

Design input in natural language to get results you want from LLM

Give instruction , Give persona, add delimeter

Add examples, provide steps to complete task

Specify output format such as HTML, JSON, Table to produce

Retrieval-Augmented Generation (RAG)

Add memory to LLM and GenAI

Retrieval-Augmented Generation. It's a technique that helps large language models. Before answering your question, the LLM uses RAG to search a vast external knowledge base for relevant information.

With this extra knowledge, the LLM can provide more accurate and informative answers. RAG combines the vast knowledge of LLMs with your data, enhancing AI's ability to provide contextually rich responses.

RAG is extremly ueful when you need to answer question on specific domain, latest and up to date information or use internal knowledge. Use RAG is
Domain-specific knowledge: If your assistant needs to be an expert in a specific domain, RAG can be used to integrate relevant databases or knowledge repositories to enhance its understanding.
Accuracy is crucial: If your LLM assistant needs to provide highly accurate information, especially on factual topics like science, history, or specific procedures, RAG can ensure responses are grounded in real-world knowledge.
Combating hallucinations: LLMs can sometimes make up information, called hallucination. RAG combats this by providing verifiable evidence to support the response.
Building trust: By allowing users to see where the information comes from (think footnotes!), RAG fosters trust and transparency in the assistant's responses.

Unleash the power of Similarity Search

Vector DB and Vector Embeddings

Vector DB store date in vector embeddings which are high dimension vectors that represent features or attributes of data. Traditional DB store dat in rows and columns. In traditional db each columen has signle field and each row is a record.

Traditional DBs are good for extact match vs vector dbs are great for similarity search.

Vector Dbs have great use in many use cases like:

Text Search, Question Answer Bot
Vision search
Find similar design
Efficient analysis of audio
Find visually similar images e.g. blue water

OpenAI Fine tuning

Fine tune Open AI by your training data to handle edge cases, complex scenarios

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

Fine tuning Steps- Prepare Data in same format as Completion API Create 10-100 examples of each type

One Fine tune - evaluate the results

Make a inference calls/li>

Do Error Analysis.

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

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

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

Governace for GenAI LLM and Chatbots | governance Framework for GenAI

Trustyworthy and reliable LLM and GenAI

We recognize the immense potential of LLMs to revolutionize various aspects of our lives. However, we also acknowledge the critical need to ensure their development and deployment are guided by ethical principles and safeguard human values. Above are guiding principles and framework for AI. It is further extended for GenAI. Click to see detail slides for personalziation, automation and creative scenario to specific governance items

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

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.

GenAI for Technology Domain

Improve Search , Cyber Security and Audience Intelligence

Understand user intent better and improve search with embeddings. With Generative AI - do persona development and create personalized responsed. With GenAI do Proactive threat detection. Generative AI can also simulate cyber attack and help prepare model and advance technqies to handle such attacks.

Generative AI Applications in Security

As GenAI generate new data, it is extremly useful to create synthenic attacks and test security solutions

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

Payment industry need reliability. GenAI can be used for imporving compliance, audit and fraud detection.

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

Gen AI for Mortgage Industry

Loadn Origination Customer Service

GenAI is useful for Mortgage Industry

It can do document analysis in loan origination process and streamline it. It can help in customer service. Most important it can automate compliance in mortgage industry

Gen AI for Mobile Development

Creativity App Development

GenAI can revolutionze mobile app development.

New apps willemerge for creativity. Better personalization will be provided in future apps.

Generative AI Vendors

Open AI, Microsoft, GCP, AWS, Anthropic ..

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

ChatGPT3.5 ChatGPT4 and Bard

GPT4 has good reasoning. ..

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

Companies like Hugging Face, Anthropic are providing their own model

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.

build Data Products using GenAI

Data Products build Right From Start

Dataknobs capablities - KREATE, KONTROLS and KNOBS.
KREATE focus on creatibvity and generation
KONTROLS provide guardrails, lineage, compliance, privacy and security.
KNOBS enable experimentation and diagnosis