Generative AI/AI Capability deck
Budget: $250 – $750 USD
Looking for a presentation on generative AI/AI that can be used in sales calls. The presentation needs to cover in detail the following sections:
Key Capabilities in GAI
o Machine Learning & Deep Learning
§ Supervised, Unsupervised, and Reinforcement Learning:
§ Deep Neural Networks (DNNs)
o Natural Language Processing (NLP)
§ Tokenization and Word Embeddings
§ Named Entity Recognition (NER) and Sentiment Analysis
o Language Models: Utilizing pre-trained models like BERT, GPT (Generative Pre-trained Transformer), or their variants to perform various NLP tasks.
o Computer Vision
§ Convolutional Neural Networks (CNNs):
§ Transfer Learning:
o Reinforcement Learning
o Robotics and Automation
Approach to GAI
o LLM Utilization
§ Utilizing Pre-trained Models: BERT/GPT etc
§ Fine-tuning Pre-trained Models on domain-specific data to improve performance on particular tasks.
§ Applications of LLMs in Text Generation & Summarization & Contextual Understanding
§ Ethical Considerations: Bias Mitigation & Explainability
o Data analysis and model development
o Iterative model refinement for accuracy and efficiency
o Agile Development
o Cross-Disciplinary Teams: Collaborating among data scientists, domain experts, ethicists, and developers for holistic GAI development.
o Client Collaboration: Involving clients throughout the process to align GAI solutions with their specific needs and goals.
GAI Case Studies
o Supply chain
o Retail
Common Focus Areas with AI/GAI
o Fairness, Accountability, and Transparency: Implementing fairness metrics, bias detection, and interpretability techniques to ensure ethical AI deployment.
o Regulatory Compliance: Adhering to legal frameworks and industry standards (like GDPR, HIPAA) to protect user data and privacy.
Key Capabilities in GAI
o Machine Learning & Deep Learning
§ Supervised, Unsupervised, and Reinforcement Learning:
§ Deep Neural Networks (DNNs)
o Natural Language Processing (NLP)
§ Tokenization and Word Embeddings
§ Named Entity Recognition (NER) and Sentiment Analysis
o Language Models: Utilizing pre-trained models like BERT, GPT (Generative Pre-trained Transformer), or their variants to perform various NLP tasks.
o Computer Vision
§ Convolutional Neural Networks (CNNs):
§ Transfer Learning:
o Reinforcement Learning
o Robotics and Automation
Approach to GAI
o LLM Utilization
§ Utilizing Pre-trained Models: BERT/GPT etc
§ Fine-tuning Pre-trained Models on domain-specific data to improve performance on particular tasks.
§ Applications of LLMs in Text Generation & Summarization & Contextual Understanding
§ Ethical Considerations: Bias Mitigation & Explainability
o Data analysis and model development
o Iterative model refinement for accuracy and efficiency
o Agile Development
o Cross-Disciplinary Teams: Collaborating among data scientists, domain experts, ethicists, and developers for holistic GAI development.
o Client Collaboration: Involving clients throughout the process to align GAI solutions with their specific needs and goals.
GAI Case Studies
o Supply chain
o Retail
Common Focus Areas with AI/GAI
o Fairness, Accountability, and Transparency: Implementing fairness metrics, bias detection, and interpretability techniques to ensure ethical AI deployment.
o Regulatory Compliance: Adhering to legal frameworks and industry standards (like GDPR, HIPAA) to protect user data and privacy.