Mobile to AI era

We are arguably at the very beginning of the AI era, which is poised to subsume and redefine the mobile era, much like the mobile era redefined the PC era.

The AI era signals the decline of this model in three keyways:

1. From GUI to NUI (Natural User Interface)

The primary way of interacting with technology is shifting from tapping on glass to something more intuitive.

• Old: Tapping icons, swiping through menus.
  
  
• New: Speaking or typing a request in natural language. The AI understands intent, context, and ambiguity. The interface becomes a conversation.

2. From Apps to Agents

The siloed nature of apps is becoming a bottleneck. An AI-first world replaces them with intelligent agents.

• Old: To plan a trip, you open Kayak for flights, Airbnb for lodging, and Google Maps for directions. You are the system integrator.
  
  
• New: You tell your AI agent, "Book me a weekend trip to Napa Valley for next month, find a quiet place to stay near a good vineyard, and arrange a car." The agent interacts with the APIs of all the necessary services in the background and presents you with a complete plan.

3. From Device-Centric to Ambient Computing

The smartphone's dominance is challenged as AI becomes embedded in the environment around us.

• Old: Your digital life is centered on the device you pull from your pocket.
  
  
• New: AI is accessible through various endpoints—smart glasses, headphones, cars, home speakers, and yes, still your phone. The "computer" is no longer a specific object but a layer of intelligence that is everywhere. The hardware becomes a simple portal to your personal AI.

Industry leaders are emphasizing this shift in recent times / news.

AI Journey VBlog

 

 

Blog content is created by AI from my first V(ideo)Blog at https://www.youtube.com/playlist?list=PLClRWhkU0HEcZo2jDgscHa0UGcld6v7ZP.

• Evolution of AI in Industry Ganesan Senthilvel outlined the four phases of AI's industrial evolution, beginning in 1943 with neuron research and progressing to machine learning and deep learning. They explained that the rise of social media around 2010 shifted data from structured to unstructured formats, necessitating high-powered GPU computing and neural algorithms to process large volumes of unstructured data.
• AI Concepts and Models Ganesan Senthilvel detailed key AI concepts such as training data, model building, and automatic inference, noting that ML data mining identifies patterns for prediction. They explained regression models are foundational for big data predictions and that recurrent neural networks process sequences of words by feeding results back into the processing layer.
• Generative AI and Recent Innovations Ganesan Senthilvel discussed the recent explosion in AI's popularity, attributing it to generative AI, particularly ChatGPT, which reached one million users in just five days. They highlighted the difference between traditional AI, which analyzes existing information, and generative AI, which produces entirely new content like text, images, or code. They also introduced Retrieval-Augmented Generation (RAG) for trusted information retrieval and Agentic AI for building complex business workflows, in addition to the Model Context Protocol (MCP) framework for universal AI model communication.
• AI System Layers and Learning Approach Ganesan Senthilvel described the five distinct layers of an AI system, starting with the interaction layer as the foundation and moving up through intelligent, engineering, observability, and agent layers, where human and AI interact. They emphasized that hands-on coding and consistent daily learning are crucial for staying current in computer engineering and becoming an engineering leader.

AI system layers

A comprehensive approach to building and managing AI systems can be structured into the following five distinct layers

1. Infrastructure Layer: Core Computational Power

At the base of the stack, the Infrastructure Layer handles the most demanding computational tasks. It is responsible for running complex models, managing large-scale data storage, and ensuring seamless scalability of workloads. A key function of this layer is to provide the power required to host a large language model serving thousands of queries per hour.

Leading Platforms: OpenAI, Hugging Face, Mistral, Anthropic

2. Intelligence Layer: Advanced Cognitive Functions

This layer augments the raw capabilities of AI models by integrating memory, reasoning frameworks, and retrieval mechanisms. It enables the creation of sophisticated systems, such as a RAG-powered financial assistant capable of accessing and referencing specific corporate reports upon request.

Enabling Technologies: LangChain, LlamaIndex, Pinecone

3. Engineering Layer: From Prototype to Production

The Engineering Layer provides the tools and processes necessary to convert experimental AI models into fast, reliable, and scalable products. It bridges the gap between a simple proof-of-concept, like a notebook-based chatbot, and a production-ready application capable of supporting a large user base.

Deployment and Scaling Tools: Lamini, Relevance AI, Modal

4. Observability Layer: Ensuring AI Integrity and Safety

This critical layer is dedicated to monitoring, evaluating, and governing AI behavior. Its primary function is to ensure that AI systems operate in an ethical, accurate, and safe manner. For example, it can be used to identify and flag hallucinations in a medical diagnostic tool before it is used in a real-world clinical setting.

Monitoring and Governance Solutions: WhyLabs, Guardrails AI, Lakera

5. Agent Layer: The Human-AI Interface

The Agent Layer is where users interact with the AI system. This is accomplished through various interfaces, including copilots, virtual assistants, and autonomous agents that perform tasks on behalf of the user. A well-known example is GitHub Copilot, which integrates directly into a developer's workflow to autocomplete code and suggest solutions.

User-Facing Applications: Cursor, GitHub Copilot, Cognition