5 OCI AI Portfolio

AI Infrastructure:

a. Oracle AI Stack: Components not specified. b. AI Infrastructure Components: GPU, Networking, Super Clusters, Storage. c. GPU Architecture: i. Capabilities: Parallel computing, rapid computations, processing large datasets. d. OCI GPU Instances: Ideal for model training and interference computation.

ML Services Overview:

a. Oracle AI Stack: Components not specified. b. Oracle Cloud Infrastructure Data Science: i. Definition: Cloud service for data scientists throughout ML lifecycle. ii. Core Principles: Accelerated, Collaborative, Enterprise-Grade. iii. What, Whom, Where, How: Builds, serves data scientists, Jupyter Lab, Model Catalog. c. Data Science Features and Terminology: i. Components: Projects, Notebook Sessions, Conda Environments, ADS SDK, Models, Model Catalog, Model Deployment, Jobs.

First Principles:

a. RDMA: i. Definition Remote Direct Memory Access. b. Clos Fabric: i. Definition: Multistage circuit-switching network. Visioned by Charles Clos in 1950s. c. Cable Distance: Considerations for higher maintenance; account for worst-case scenarios.

Overview of AI Services:

i. AI Service Types: Language, Vision, Speech, Document Understanding, Anomaly Detection, Digital Assistant.

AI for the Enterprise:

i. Components: SaaS Apps, AI Services, Infrastructure, Data. ii. Oracle Focus: Bringing AI to every layer of the enterprise stack. iii. Approach: Extensive investment from infrastructure to SaaS apps. iv. Recent Steps: Emphasis on Generative AI and massive scale models. v. Oracle AI Stack: Components not specified.

Human Ethics and Fundamental Rights:

i. Human Ethics: Respect for human dignity, freedom, democracy, justice, equality. ii. AI Ethics: Respect for autonomy, prevention of harm, fairness, explicability. iii. Responsible AI Requirements: Human-centric, technical robustness, privacy, transparency, diversity, accountability.

Ways to Access Oracle Cloud AI Services:

i. Methods: OCI Console, Rest API, Language SDKs, CLI.

Language Overview:

i. Pretrained Models: Language Detection, Sentiment Analysis, Key Phrase Extraction. ii. Custom Models: Named Entity Recognition, Text Classification. iii. Text Translation: Neural machine translation for various languages. iv. Vision: Pretrained and custom models for image analysis. v. Speech: Not detailed. vi. Document Understanding: OCR, Text Extraction, Key-Value Extraction, Table Extraction, Document Classification. vii. Anomaly Detection: Multitenant service analyzing time series data. viii. Digital Assistant: Interacts, lists capabilities, routes request, handles disambiguation.

Healthcare AI Challenges:

Balancing AI benefits with concerns about fairness, transparency, and regular evaluation.

Trustworthy AI:

Driven by ethical principles.

Guiding Principles for Trustworthy AI:

Follow applicable laws, be ethical, be robust.

Responsible AI Cycle and Roles:

Implementing and managing AI according to responsible AI requirements. Roles: Developers, Deployers, End Users.

AI Needs to be Lawful:

Operates within legal frameworks.

Responsible AI requirements

Set up governance to be put into place. Develop policies and procedures. Ensure Compliance