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Geoffrey Hinton

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Sam Altman

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Insights

AI Industry Pulse: April 2025 Monthly Roundup

Big Tech Makes Strategic AI Moves

Meta

  • Launched Llama 4 family of multimodal AI models
  • Llama 4 Scout: 17B parameters, 10M token context window, outperforms competitors in its class
  • Llama 4 Maverick: 17B parameters with 128 experts, beats GPT-4o and Gemini 2.0 Flash
  • Llama 4 Behemoth: 288B parameters, outperforms GPT-4.5 and Claude Sonnet 3.7 on STEM benchmarks
  • Models available on llama.com, Hugging Face, and integrated across Meta platforms

Google

  • Unveiled Gemini 2.5 Pro and Veo 2 with enhanced multimodal capabilities
  • Introduced Ironwood TPU v6 with 2x performance gains and 30% improved efficiency
  • Launched Agent2Agent (A2A) Protocol for cross-vendor AI collaboration
  • Expanded Vertex AI with Lyria, Chirp, and specialized fine-tuning frameworks
  • Added 50+ industry-specific models to Model Garden for Enterprise

Anthropic

  • Rolled out Claude for Education
  • Enhanced citation capabilities for academic environments
  • Published research acknowledging limitations in AI self-explanation

NVIDIA

  • Released Nemotron Ultra, a 253B-parameter model
  • Launched AgentIQ toolkit
  • Strengthened position in enterprise AI development

Creative Industries Accelerate AI Integration

Canva

  • Introduced Dream Lab powered by Leonardo.AI's Phoenix model
  • Added AI-driven coding environments
  • Expanded into spreadsheet functionality

YouTube

  • Partnered with Universal Music Group
  • Launched AI Music Incubator
  • Implemented digital watermarking for AI-generated content

Adobe & Runway

  • Integrated first true AI Agent into Photoshop
  • Gen-4 Turbo model reduced video generation to 30 seconds
  • James Cameron collaborating with Stability AI

Enterprise AI Deployment Accelerates

HubSpot

  • Unveiled Breeze Agents at Spring Spotlight event
  • 50% ticket resolution without human intervention
  • 40% efficiency gains in pipeline building

Shopify

  • Implemented AI-first hiring freeze
  • Requires justification for non-automatable roles

IBM

  • Enhanced z17 mainframe with multi-model AI processing
  • Handles 2.1 billion daily transactions
  • Improved security and reduced latency

Supply Chain Intelligence Network

  • Formed by consortium of logistics companies
  • Using federated learning for supply chain prediction
  • Successfully forecasting transportation bottlenecks

Autonomous Systems Expand Commercial Footprint

Amazon's Zoox

  • Began mapping Los Angeles streets
  • Planning autonomous taxi trials for summer 2025
  • Commercial service planned for Las Vegas and San Francisco

Samsung

  • Announced Ballie home robot launch
  • Enhanced with Google Gemini AI
  • Priced at $2,499

John Deere

  • Deployed Autonomous Farm Platform
  • Demonstrated 22% yield improvements
  • Reduced herbicide usage through precision application

Legal and Regulatory Developments

OpenAI vs Musk

  • Trial date set for March 2026
  • Allegations of monopolization attempts
  • Judge confirms likely Musk testimony

Content Rights

  • Publishers call for government action
  • Wikimedia reports 50% bandwidth spike from AI crawlers

EU Regulation

  • Published AI Act implementation framework
  • Established compliance guidelines for high-risk AI
  • Providers publishing compliance roadmaps

Research Highlights and Emerging Trends

ARC Prize 2025

  • $1 million purse for human-easy reasoning tasks
  • Leading models scoring below 30%

Looking Ahead

This April of 2025, the AI industry continues its rapid evolution on multiple fronts. The emerging focus on agent-to-agent communication protocols, multimodal integration, and autonomous systems suggests continued acceleration in AI capabilities. Meanwhile, legal battles, regulatory frameworks, and ethical concerns underscore the growing importance of governance mechanisms that can keep pace with technical innovation. The month's developments reveal an industry in transition – moving from experimental technologies toward mainstream deployment while still grappling with fundamental questions about transparency, accountability, and economic impact. How these tensions resolve will likely shape AI's trajectory throughout the remainder of 2025 and beyond.

Check out updates from AI influencers

"We are at 'peak data'; the way AI is built is about to change."
Ilya Sutskever
"I'm a tech optimist, not a tech utopian."
Reid Hoffman

Human vs Machines

Human senses are the body's way of perceiving and interacting with the world. The six primary senses or sensory faculties—eye/vision faculty (cakkh-indriya), ear/hearing faculty (sot-indriya), touch/body/sensibility faculty (kāy-indriya), tongue/taste faculty (jivh-indriya), nose/smell faculty (ghān-indriya), and thought/mind faculty (man-indriya)—help us navigate our environment, while additional senses like balance and temperature awareness enhance our perception. These sensory inputs are processed by the brain, shaping our experiences, emotions, and understanding of reality.

Unlike the physical senses, the thought/mind faculty (man-indriya) processes abstract concepts, memories, and emotions, enabling higher cognitive functions such as reasoning, creativity, and self-awareness. It is the core of human intelligence, allowing for introspection, imagination, and ethical decision-making. This cognitive aspect makes human perception unique, as it integrates sensory data with experiences, knowledge, and emotions to create a deep understanding of the world.

While these senses are fundamental to human experience, technological advancements have enabled machines to replicate many of them in various ways. Cameras function as artificial vision, microphones capture sound, tactile sensors detect touch, chemical sensors mimic taste and smell, and gyroscopes provide a sense of balance. These innovations allow machines to perceive and interact with the world in ways increasingly similar to humans.

Motor skills, including fine and gross movements, are closely linked to touch, proprioception (body awareness), and balance. Speech, as a refined motor function, involves intricate coordination of the vocal cords, tongue, and breath, guided by sensory feedback. Machines can mimic these capabilities using robotics for physical movement and speech synthesis for verbal communication, combining sensors, actuators, and AI-driven models to enable dexterous manipulation, fluent speech generation, and expressive voice modulation.

Beyond individual senses, AI is evolving toward multimodal capabilities, where it can integrate multiple sensory inputs—such as combining vision and language understanding—to analyze images, interpret speech, and generate context-aware responses. This enhances human perception and decision-making in fields like healthcare, accessibility, and robotics.

Advancements in AI are also paving the way for higher-order capabilities like reasoning, emotional recognition, and real-time adaptive learning. AI systems can process vast amounts of data, detect patterns, and generate insights that mimic certain aspects of human cognition.

However, AI lacks true consciousness, self-awareness, the deep intuition, and the rich subjective experience derived from the thought/mind faculty. Unlike humans, AI does not possess genuine emotions, ethical judgment, or the ability to reflect on its own existence.

These fundamental gaps highlight the distinction between artificial intelligence and human intelligence. While AI can augment human decision-making and automate complex tasks, it remains limited in replicating the depth of perception, consciousness, and meaningful experiences that arise from the human thought/mind faculty.

The question of whether artificial intelligence (AI) poses a threat to human existence is complex and multifaceted. While AI offers significant benefits, such as augmenting human capabilities and improving efficiency, it also presents potential risks that warrant careful consideration.

One concern is the potential for AI to surpass human intelligence, leading to scenarios where AI systems operate beyond human control. Experts like Dario Amodei, co-founder and CEO of AI start-up Anthropic, predict that superintelligent AI could emerge as soon as next year, capable of surpassing human intelligence across various fields.

Elon Musk has also expressed concerns about AI, estimating a 20% chance that AI could pose existential risks to humanity. These perspectives underscore the importance of proactive measures to ensure AI development aligns with human values and safety.

To mitigate these risks, it is crucial to establish robust ethical frameworks and regulatory measures that guide AI development and deployment. This includes addressing issues such as data privacy, algorithmic bias, transparency, and accountability. As AI continues to evolve, fostering collaboration among governments, industry leaders, and the public is essential to navigate the challenges and opportunities presented by this transformative technology.

In conclusion, while AI holds immense potential to drive progress and innovation, it is imperative to approach its development with caution and ethical consideration. By implementing responsible practices and policies, we can harness the benefits of AI while safeguarding against potential threats to human existence.

Bill Gates recently stated that while artificial intelligence is transforming many aspects of our work, it won't replace humans in all professions. In his view, AI will significantly enhance efficiency in tasks like disease diagnosis and DNA analysis, yet it lacks the creativity essential for groundbreaking scientific discoveries. According to his comments, three specific professions are likely to remain indispensable in the AI era:

Coders

Although AI can generate code, human programmers are still vital for identifying and correcting errors, refining algorithms, and advancing AI itself. Essentially, AI requires skilled coders to build and continually improve its systems.

Energy Experts

The energy sector is characterized by its intricate systems and strategic decision-making requirements. Gates argues that the field is too complex to be fully automated, necessitating the expertise of human professionals to manage and innovate within this domain.

Biologists

While AI can analyze vast amounts of biological data and assist with tasks like disease diagnosis, it falls short in replicating the intuitive, creative insight required for pioneering scientific research and discovery.

Bill Gates envisions AI as a tool that will augment human capabilities, particularly in professions requiring complex judgment and innovation, such as coding, energy expertise, and biology. Conversely, Elon Musk predicts a future where AI and robotics could render traditional employment obsolete, suggesting that "probably none of us will have a job" as AI provides all goods and services. He introduces the concept of a "universal high income" to support individuals in such a scenario. These differing perspectives highlight the ongoing debate about AI's role in the workforce. While AI's influence is undeniable, many experts believe that human creativity, emotional intelligence, and complex problem-solving abilities will continue to hold significant value, suggesting that AI will serve more as a complement to human labor rather than a wholesale replacement.

The future is not a place to visit, it is a place to create. In the age of AI, while machines may shoulder routine tasks, the true breakthroughs will always be born from human ingenuity. Our future isn't solely about coders, energy experts, or biologists—it's about every professional harnessing technology to amplify their unique strengths. Whether you're a creative, an educator, a healthcare worker, or in any other field, your vision and passion remain irreplaceable. Embrace AI as a powerful tool to elevate your work, and never lose hope in your chosen path. Your journey, like our collective future, is full of promise and possibility.

Future of Software Development

by Dr. Amit Puri

Review research work to get a useful perspective of the tech landscape - Citizen Development

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

  • Part 1 - Low-Code and No-Code Platforms and Cloud Transformation
  • Part 2 - Low-Code and No-Code Platforms and Cloud Transformation

Published in the Global Journal of Business and Integral Security.

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.

  • Paper - Study trends in code smell

Unlock expert advice on AI, cloud, and citizen development today!

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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]

Check out updates from AI influencers

"The implausibility of intelligence explosion."
François Chollet
"AI could execute tasks more complexly through 'agents' acting on behalf of users."
Dario Amodei

Levels of AGI

In Defining AGI: Six Principles, the paper argues that AGI should be defined in terms of capabilities rather than processes, while also emphasizing both generality and performance. It stresses that an AGI definition should focus on cognitive and metacognitive tasks, not necessarily physical embodiment, and should assess potential rather than requiring full real-world deployment. Finally, it highlights the importance of ecological validity (tasks people truly value) and proposes viewing AGI as a path or set of levels rather than a single end-state.

AGI Level Narrow AI Examples General AI (AGI) Examples
Level 0: No AI Calculator software; compiler Human-in-the-loop computing (e.g. Mechanical Turk)
Level 1: Emerging Early rule-based systems (e.g. SHRDLU, GOFAI) Frontier LLMs (ChatGPT, Bard, Llama 2, Gemini)
Level 2: Competent Toxicity detectors; smart speakers (Siri, Alexa); VQA systems Competent AGI (not yet achieved)
Level 3: Expert Spelling/grammar checkers (e.g., Grammarly); image models (Imagen, DALL·E 2) Expert AGI (not yet achieved)
Level 4: Virtuoso Deep Blue; AlphaGo Virtuoso AGI (not yet achieved)
Level 5: Superhuman AlphaFold; AlphaZero; StockFish Artificial Superintelligence (ASI; not yet achieved)

Autonomy Considerations Across AGI Levels

AGI Level Autonomy Characteristics
Level 0: No AI Fully non-autonomous; entirely operated by humans.
Level 1: Emerging Limited autonomy; capable of basic task execution but relies heavily on human oversight.
Level 2: Competent (Not yet achieved) Expected to operate semi-autonomously – can perform tasks independently but still requires oversight.
Level 3: Expert (Not yet achieved) Anticipated to have increased autonomous capabilities while still needing human intervention in edge cases.
Level 4: Virtuoso (Not yet achieved) Likely to be near fully autonomous in task execution; robust safeguards would be essential.
Level 5: Superhuman (Not yet achieved) Would operate fully autonomously, introducing significant risk and safety considerations.

Autonomy Levels, Example Systems, Unlocking AGIL Levels, and Example Risks

Autonomy Level Example Systems Unlocking AGIL Level(s) Example Risks Introduced
Level 0: No AI
(Human does everything)
  • Analogue approaches (e.g., sketching with pencil, no code)
  • Non-AI digital workflows (e.g., a spreadsheet with no macros)
No AI
  • status quo
  • No automation benefits
  • De-skilling or inefficiency in repeated tasks
Level 1: AI as a Tool
(Human fully controls tasks but uses AI to automate sub-tasks)
  • Rewriting with the aid of a grammar tool
  • Reading a sign with a translator (no AI planning)
  • Simple web search using an AI plugin
Likely: Competent Narrow AI
Emerging AGI (for some tasks)
  • Over-reliance on AI output
  • Potential user complacency
Level 2: AI as a Consultant
(AI not in ultimate role, but only consults)
  • Complex computer programming assistant or code completion
  • Recommending strategy in a multi-step domain
  • Summarizing text or providing advanced suggestions
Likely: Competent Narrow AI
Emerging AGI
  • Outright overconfidence in AI suggestions
  • Risk of biased or manipulative advice
Level 3: AI as a Collaborator
(AI shares decisions with human in near‐equal partnership)
  • Co-creating text entertainment via advanced chat-based AI
  • Training an expert system integrated with AI chess-playing engine
  • AI co-ideation with generalist personalities
Possible: Expert AGI
Virtuoso Narrow AI
  • Societal-scale emulation of human experts
  • Mass displacement of certain roles
Level 4: AI as an Expert
(AI fully owns or surpasses sub-tasks; human is present for oversight)
  • Autonomously diagnosing & prescribing in medical contexts
  • Designing complex systems without direct human input
Likely: Virtuoso AGI
  • Decline of human expertise in specialized domains
  • Escalating risk from emergent AI behaviors
Level 5: AI as an Agent
(Fully autonomous AI; not yet unlocked)
  • Hypothetical AGI-powered personal assistants controlling entire workflows
  • Recursive self-improvement & robust open-world autonomy
Possible: Virtuoso AGI → ASI
  • Concentration of power
  • Complete loss of human oversight
  • Unpredictable emergent properties

Reference: Paper: Levels of AGI for Operationalizing Progress on the Path to AGI, on Alphaxiv

Mind Map

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.

more insights in our Insights section

We introduce you our Open AGI Codes | Your Codes Reflect! Team! Get more information about us here!

About Us

Key Elements of Explainable AI (XAI)

Explainable AI (XAI) aims to make artificial intelligence systems more transparent, interpretable, and accountable, ensuring users understand and trust AI-driven decisions.

Transparency

AI models should clearly disclose how they function, including their architecture, training data, and decision-making processes.

Citation: DARPA XAI Program, 2016

Interpretability

Model outputs should be understandable to humans, enabling users to grasp why a decision was made.

Citation: Lipton, 2018

Accountability

AI systems should have mechanisms to trace responsibility for decisions, ensuring ethical and legal compliance.

Citation: EU AI Act, 2021

Fairness

AI models should avoid bias and ensure equitable treatment across different user groups.

Citation: Bellamy et al., 2018

Causality

Explanations should reveal cause-and-effect relationships rather than just correlations in data.

Citation: Pearl, 2000

Trustworthiness

Users should have confidence in AI decisions through consistent, reliable, and fair outputs.

Citation: NIST AI Risk Management Framework, 2023

Robustness

AI systems should perform reliably across different scenarios, minimizing susceptibility to adversarial attacks or errors.

Citation: Goodfellow et al., 2015

Generalizability

AI models should apply learned knowledge to new, unseen situations effectively.

Citation: Bengio et al., 2019

Human-Centered Design

XAI should prioritize user needs, ensuring explanations are useful and accessible to diverse audiences.

Citation: Google People + AI Research, 2019

Counterfactual Reasoning

AI explanations should explore 'what-if' scenarios, helping users understand alternative outcomes.

Citation: Wachter et al., 2017

Check out updates from AI influencers

"AI will make us more human, not less."
Satya Nadella
"To solve really hard problems, we'll have to use several different representations."
Marvin Minsky

more coverage in our Featured section

Open AGI Codes by Amit Puri is marked with CC0 1.0 Universal