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AI revolutionizes technology, advancing towards Human Intelligence
accelerate journey toward AGI
AI agents unveil new reasoning skills—are we on the path to ASI ?
Conversational AI, Code Assistant, Virtual Assistant, AI Agent, Specialized Agents open up new possibilities.

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We could only be a few years, maybe a decade away.

Demis Hassabis

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Semantic Segmentation Tasks, "Classifying each pixel in an image into meaningful categories."

Interactive Agents, "AI systems capable of dynamic, real-time interactions with users."

Cloud Transformation Challenges: do they favor the emergence of Low-Code and No-Code platforms?

This research investigates the challenges associated with cloud transformation and explores whether these challenges create a conducive environment for the emergence of low-code and no-code (LCNC) platforms as viable solutions for digital innovation. The study focuses on cloud-native development strategies, cloud migration models, and the growing role of LCNC platforms in enabling faster application development and deployment

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Study trends in code smell in microservices-based architecture, Compare with monoliths

The code quality of software applications is usually affected during any new or existing features development, or in various redesign/refactoring efforts to adapt to a new design or counter technical debts. At the same time, the rapid adoption of Microservices-based architecture in the influence of cognitive bias towards its predecessor Services-oriented architecture in any brownfield project could affect the code quality.

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Discover various ways to implement Retrieval Augmented Generation (RAG)

RAG

Transfer Learning, "Reusing a pre-trained model's knowledge for a different but related task."

Fine-Grained Control in Generative AI, "Allowing users to control specific aspects of generative outputs."

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"Machine learning is a core, transformative way by which we are rethinking everything we are doing."
Sundar Pichai
"We are now confident that we know how to build AGI."
Sam Altman

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Model Robustness, "Ability of models to perform well under various conditions."

Neural Network, "Computational models inspired by the human brain that process information using interconnected nodes (neurons) to recognize patterns and solve problems."

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AGI Elements

Artificial General Intelligence (AGI) represents the next frontier in artificial intelligence, aiming to develop machines with human-like cognitive abilities. Unlike narrow AI, which excels at specific tasks, AGI encompasses a broad range of capabilities, including generalized learning, reasoning, creativity, and adaptability. It can process diverse data sources, apply logical problem-solving strategies, and generate innovative solutions across multiple domains. Additionally, AGI integrates common sense, social intelligence, and ethical reasoning, enabling it to interact meaningfully with humans and make responsible decisions. With self-awareness, autonomy, and continuous learning, AGI aspires to function independently, adapting to new challenges and refining its knowledge over time.

Generalized Learning

AGI should be capable of efficiently acquiring new skills and solving novel problems without explicit prior training, emphasizing adaptability over memorization. [2412.04604v1]

Reasoning and Problem Solving

The ARC-AGI benchmark tests AGI's ability to deduce solutions from abstract reasoning, rather than relying on pre-learned patterns. [2412.04604v1]

Creativity and Innovation

AGI must demonstrate the ability to synthesize knowledge and generate new solutions, as observed in LLM-guided program synthesis for solving ARC-AGI tasks. [2412.04604v1]

Common Sense and Contextual Understanding

ARC-AGI tasks are designed to be solvable without domain-specific knowledge, relying instead on core cognitive concepts such as objectness and spatial reasoning. [2412.04604v1]

Self-Awareness and Self-Improvement

Test-time training (TTT) allows AI models to adapt dynamically by refining themselves at inference time based on new tasks. [2412.04604v1]

Social and Emotional Intelligence

While not explicitly covered, AGI's ability to generalize and adapt suggests potential for understanding social contexts and responding appropriately to human interactions. Ethical considerations in AI evaluation further imply an awareness of human values. [1911.01547v2]

Adaptability

The concept of skill-acquisition efficiency defines intelligence as the ability to generalize knowledge across domains with minimal prior exposure. [1911.01547v2]

Ethical and Responsible Decision Making

AI evaluation should consider not just skill acquisition but also fair comparisons and responsible benchmarking practices to avoid overfitting and bias. [1911.01547v2]

Autonomy and Independence

Measuring AI intelligence should focus on broad abilities, allowing systems to operate without constant human intervention. [1911.01547v2]

Continuous Learning and Adaptation

AGI should exhibit extreme generalization, meaning the ability to learn and adapt to novel tasks without predefined training. [1911.01547v2]

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Singularity, "A theoretical point where AI surpasses human intelligence, potentially leading to rapid technological advancements that could reshape society."

Text-to-Image Models, "AI systems generating images from textual descriptions."

Fueling the AI Revolution,

In recent years, the AI landscape has undergone a seismic shift, powered by the advent of Large Language Models (LLMs) like GPT-4, Claude, and Llama. These groundbreaking technologies are not just transforming the way we interact with artificial intelligence; they are turning the AI world upside down. Social media is flooded with discussions, research papers, and news showcasing how Agentic AI is shaping the future of technology, work, and enterprise.

The rise of AI Co-pilots has become a defining feature of this revolution. From enhancing workplace productivity to reimagining collaborative workflows, Co-pilot-like AI systems are emerging as the face of modern AI. These intelligent agents are bridging the gap between humans and machines, creating intuitive and transformative ways to work. They are not only tools but active participants in reshaping industries.

The surge in AI research has further amplified this momentum. Academic and industrial spheres alike are producing an unprecedented volume of papers, pushing the boundaries of what AI can achieve. From algorithmic innovations to enterprise-ready solutions, AI is becoming more powerful, adaptable, and ubiquitous.

In the enterprise world, AI is rapidly embedding itself into core operations. Algorithms are the backbone of this transformation, driving efficiency and enabling businesses to harness data in new and impactful ways. Social media and news platforms are brimming with stories of AI’s enterprise adoption, making it clear that Agentic AI is not just a trend—it is a revolution defining the next era of technological advancement.

Deep Dive into Transformers & LLMs.,

This insight explores the architecture of Transformer models and Large Language Models (LLMs), focusing on components like tokenization, input embeddings, positional encodings, attention mechanisms (self-attention and multi-head attention), and encoder-decoder structures. It then examines Large Language Models (LLMs), specifically BERT and GPT, highlighting their pre-training tasks (masked language modeling and next token prediction), and their impact on natural language processing, shifting the paradigm from feature engineering to pre-training and fine-tuning on massive datasets. Finally, it discusses limitations of current transformer-based LLMs, such as factual inaccuracies.

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Insights

"AI should not be designed to replace humans, but to augment human capabilities."

Fei-Fei Li

"AI could execute tasks more complexly through 'agents' acting on behalf of users."

Dario Amodei