Deepdive in AI, AI agents, AGI & LLMs providers

1. AI Types, AI Levels, AI Subsets
2. AI Layers: AI Models, AI Workflow, Frameworks | Platforms | Hardware
3. AI Ethics: Responsible, Trustworthy, Compliant

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1. AI types, AI Levels, AI Subsets

AI Types

AI types are classified by capability:

AI Levels

Below a re the five levels of AI, as defined by OpenAI to track progress toward AGI. These levels represent a progression from AI that can only chat (Level 1) to increasingly sophisticated systems that can reason, act, invent, and eventually perform the work of an entire organization (Level 5).

1	Conversational AI	AI systems capable of interacting with people through conversational language.	- Modern chatbots used for customer service
2	Reasoners	AI that can perform complex problem-solving tasks at a human-level, such as those a doctorate-level professional might handle.	- Analyzing medical data to suggest diagnoses - Get key insights from resumes
3	Agents	Systems that can take actions	- AI frameworks that allow multiple agents with specific roles to collaborate on complex tasks.
4	Innovators	AI that can generate new ideas and new inventions	- AI that can self-select goals and create a path to achieve them.
5	Organizations	AI that can perform the work of an entire organization	- An organization that operates with AI managing and executing the majority of its functions.

AI Subsets

AI → ML → DL → Gen AI & LLM   AI families

AI	Learn → Analyse → Predict • AI is a family made up of smaller, specialized parts called subsets of artificial intelligence: The learner, the talker, the creator, the watcher • Together they weave the magic you see in apps, games, hospitals, and even cars. • Problem-solving, Decision-making, Natural Language Processing, Perception
ML	Data → Stats and Math Algorithms → Prediction • Machine Learning is a subset of AI that focuses on training machine to solve a specific AI problem • Supervised Learning Models, Unsupervised Learning Models, Selfsuper Learning Models, Reinforcement Learning Models
DL	Data → Neural Networks → Predictions • Deep Learning is a subset of ML that uses artificial neural networks with multiple layers (hence "deep") to model and understand complex patterns in large datasets: • Image recognition, Speech recognition, NLP Natural Language Processing, Game playing • CNN Convolution Neural Networks
Gen AI	Data → Neural Networks (Transformers) → New Content • Generative AI is a branch of AI that focuses on creating models capable of analyzing and generating new content that resemble human-created content, such as: Text / Code, Images, Videos, Music • Gen AI uses LLM to invent. Large Language Model are very large deep learning models that are pre-trained on vast amounts of data. The underlying transformer is a set of neural networks that consist of an encoder and a decoder with self-attention capabilities • Transformer models • BERT Bidirectional Encoder Representations from Transformers (Biderctional encoder-only) • GPT Generative Pre-trained Transformers (Unidirectional decoder-only) • GAN Generative Adversarial Networks used for image generation
Other Subsets	• Robotics: AI driven machines performing physical tasks • Genetic Algorithms: AI takes lessons from nature. Genetic Algorithms are modeled on evolution itself. NASA designed antennas with evolution that had a new type of shape and outperformed any existing design.

2. AI Layers: AI Models, AI Workflow, Frameworks | Platforms | Hardware

AI Models

What is an AI model?
An artificial intelligence (AI) model is a computer program or algorithm that has been trained on a large dataset of information. This training process allows the AI model to learn patterns and relationships in the data so that it can make predictions or decisions about new data that it has never seen before.

AI workflow

AI workflow is the sequence of tasks and processes followed to develop, train, deploy, and maintain artificial intelligence models.

AI frameworks

	PyTorch	A popular deep learning framework developed by Facebook's AI Research lab, favored for its flexibility and Pythonic feel. Get Started | Tutorials | Learn the Basics | Recipes | Youtube
	TensorFlow	An open-source framework developed by Google for large-scale machine learning and deep learning, known for its flexibility and ability to scale from small to large projects.
	Scikit-learn	The standard Python library for traditional machine learning algorithms, including classification, regression, and clustering.

AI Platforms

	GCP VerteX AI
	Azure	- Azure Machine Learning studio is a managed web-based environment for building, training, deploying, and operating machine learning models end-to-end documentation. - Azure AI Foundry portal is a unified platform for developing and deploying generative AI apps and Azure AI APIs documentation.
	AWS	- SageMaker - Bedrock
	Nvidia	Free platform for developers to explore, test, and prototype with over 80+ AI models, including popular LLMs like Llama and Mistral. Users sign up for a free Nvidia developer account to receive API keys.
	Hugging Face	The platform where the machine learning community collaborates on models, datasets, and applications.
	Arxiv	arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science / AI, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics.

AI Hardware

Company	Platform	Models
Nvidia	CUDA	• 2026 Rubin, 2024 Blackwell, 2022 Hopper, 2020 Apere
Google TPU	Tensorflow	• 2023 TPU v5e, 2022 TPU v4, 2021 TPU v3
Huawei	CANN	• 2023 Kunlun, 2022 Ascend, 2021 Kirin
AMD Instinct		• 2023 MI300, 2022 MI200, 2021 MI100

3. AI Ethics: Responsible, Trustworthy, Compliant

AI ethics focus on ensuring that artificial intelligence systems are developed and deployed in ways that are fair, transparent, and beneficial to society. This includes addressing issues such as bias in algorithms, privacy concerns, accountability for decisions made by AI systems, and the potential societal impact of AI technologies. Ai solutions are: Responsible, Trustworthy, Compliant

Responsible	- Human-Centered Design - Awareness - Data Understanding, Privacy & Security - Fairness & Non-Discrimination - Social & Environmental Responsibility - Repeatability & Testing - Accountability
Trustworthy	(Guidelines for Trustworthy AI - European Commission) - Lawful: respecting all applicable laws and regulations - Ethical: respecting ethical principles and values - Robust: both from a technical perspective while taking into account its social environment Key requirements that AI systems should meet in order to be deemed trustworthy. - Human agency and oversight - Technical Robustness and safety - Privacy and data governance - Transparency - Diversity, non-discrimination and fairness - Societal and environmental well-being - Accountability
Compliant	The AI Risk Categories - Unacceptable risk is the highest risk category - High-risk AI is likely to constitute the majority of AI systems currently in practice. - Low-risk AI systems do not directly pose harm onto individuals or society but still require transparency and accountability mechanisms to ensure they remain at this risk level. - Minimal risk is the lowest risk category