doctornuke_AI

What is Agency AI?

by with No Comment AI-artificial-intelligence

What is Agency or Agentic AI?

Agency AI of Agentic AI refers to artificial intelligence systems that exhibit a degree of autonomy, goal-setting, and decision-making in executing tasks or achieving objectives. These systems are designed to act as “agents” that can interact with their environment, make choices, and pursue specific goals based on their programming and learned experiences.

Here’s a detailed breakdown of Agency AI and its role in the AI hierarchy:

What is Agency AI?

  1. Autonomy:
    • Agency AI operates independently within its environment.
    • It does not require constant human intervention to perform tasks.
    • Example: Autonomous drones or robots navigating complex terrains.
  2. Goal-Oriented Behavior:
    • Agency AI systems are designed to achieve specific outcomes.
    • They can prioritize tasks, adapt strategies, and refine their actions to meet objectives.
    • Example: A stock-trading AI that adjusts its trading strategy in real-time based on market trends.
  3. Perception and Action Loop:
    • These systems perceive the environment (using sensors, cameras, or other inputs), process the data, and take actions to influence the environment.
    • Example: AI-powered virtual assistants like Siri or Google Assistant that respond to user queries and execute commands.

Types of Agency AI

  1. Reactive Agents (Basic Agency):
    • Respond to stimuli in real-time without internal memory or learning capabilities.
    • Example: Pac-Man AI that reacts to ghosts and pellets in the game.
  2. Deliberative Agents (Goal-Oriented):
    • Use internal models to plan and execute actions.
    • Can assess the consequences of their actions to make decisions.
    • Example: A chess-playing AI like AlphaZero that plans moves based on the game’s state.
  3. Learning Agents:
    • Capable of improving their performance over time by learning from their experiences.
    • Combine perception, action, and adaptation for better decision-making.
    • Example: Self-driving cars that learn from millions of miles driven.
  4. Multi-Agent Systems:
    • Multiple agents interact, collaborate, or compete to achieve goals.
    • Often used in simulations, distributed AI, or swarm robotics.
    • Example: AI systems coordinating logistics in a warehouse.

Characteristics of Agency AI

  • Adaptability: Can adjust to changing environments or situations.
  • Decision-Making: Evaluates options and chooses the most suitable course of action.
  • Learning: May incorporate machine learning to refine actions based on outcomes.
  • Interaction: Communicates with humans, other agents, or systems in its environment.

Examples of Agency AI in the Real World

  1. Autonomous Vehicles:
    • Perceive the environment using sensors.
    • Make decisions about speed, direction, and obstacle avoidance.
    • Operate without direct human input.
  2. Robotic Process Automation (RPA):
    • Automates repetitive tasks in industries like finance and healthcare.
    • Acts as an “agent” performing tasks like data entry, invoice processing, or customer support.
  3. AI-Powered Chatbots:
    • Interact with users autonomously to resolve queries, book appointments, or assist in e-commerce.
  4. Smart Assistants:
    • Devices like Amazon Alexa or Google Nest that act as agents to control smart home devices and provide information.
  5. Gaming AI:
    • Characters or agents in video games that autonomously respond to player actions and dynamically adapt strategies.

Agency AI in the AI Hierarchy

Agency AI spans across all levels of the AI hierarchy (ANI, AGI, and ASI):

  1. ANI Level:
    • Basic reactive and deliberative agents operating within narrow domains.
    • Example: A delivery drone that follows a specific route.
  2. AGI Level:
    • More advanced agents capable of general-purpose tasks.
    • Example: A personal AI that understands and adapts to the full spectrum of a user’s needs.
  3. ASI Level (Theoretical):
    • Superintelligent agents with complete autonomy and the ability to set goals, solve problems, and innovate beyond human capabilities.
    • Example: A global AI system optimizing resources to prevent climate change.

Potential Concerns with Agency AI

  • Ethical Risks: Misaligned goals or unintended consequences.
  • Control Issues: Autonomous agents acting beyond intended boundaries.
  • Security Threats: Vulnerabilities to hacking or misuse.

artificial car

The hierarchy of Artificial Intelligence (AI)

by with No Comment AI-artificial-intelligence

The hierarchy of Artificial Intelligence (AI) is typically structured in layers or levels that reflect the scope and complexity of intelligence and its development. Here’s an overview of the hierarchy of AI:

1. Artificial Narrow Intelligence (ANI)

  • Definition: Also known as Weak AI, this is AI specialized in performing a single task or a set of related tasks.
  • Examples:
    • Voice Assistants (e.g., Siri, Alexa)
    • Recommendation systems (e.g., Netflix, Spotify)
    • Chatbots and virtual assistants
    • Image and speech recognition systems
  • Characteristics:
    • Task-specific
    • Cannot generalize beyond its programming
    • Most AI systems today fall into this category.

2. Artificial General Intelligence (AGI)

  • Definition: Also known as Strong AI, this level of AI possesses the ability to understand, learn, and perform any intellectual task that a human can do.
  • Examples:
    • Hypothetical systems (currently no real-world examples)
    • Human-like cognitive systems
  • Characteristics:
    • Can generalize knowledge across multiple domains
    • Can think, reason, and adapt like a human
    • Still under research and development.

3. Artificial Superintelligence (ASI)

  • Definition: This is the hypothetical stage where AI surpasses human intelligence in all respects, including creativity, problem-solving, and emotional intelligence.
  • Examples:
    • None (currently theoretical)
  • Characteristics:
    • Exceeds human capabilities across all domains
    • Possesses independent reasoning, decision-making, and innovation
    • Often associated with existential risks (e.g., potential misuse or loss of control).

Supporting Hierarchical Layers in AI Systems:

1. Core AI Technologies

  • Machine Learning (ML): The ability to learn from data (e.g., supervised, unsupervised, reinforcement learning).
  • Deep Learning: A subset of ML using neural networks with multiple layers to process data (e.g., image and speech processing).
  • Natural Language Processing (NLP): Understanding and generating human language (e.g., ChatGPT).

2. Data Layers

  • Data Collection: Raw data is gathered from various sources.
  • Data Preprocessing: Cleaning, organizing, and preparing data for training models.

3. Infrastructure Layers

  • Hardware: CPUs, GPUs, and specialized hardware (e.g., TPUs).
  • Frameworks: TensorFlow, PyTorch, Keras, etc.
  • Cloud Platforms: AWS, Google Cloud, Microsoft Azure, etc.

4. Application Layer

  • AI-powered solutions applied in industries like healthcare, finance, entertainment, and transportation.

Example of AI Hierarchy in Practice (Use Case: Autonomous Vehicles):

  1. ANI Level: Specific tasks like object detection, lane following, and traffic sign recognition.
  2. AGI Level (Future): A car capable of driving in any environment, adapting to unpredictable conditions like a human driver.
  3. ASI Level (Theoretical): A system that designs smarter traffic systems, optimizes global transportation, and anticipates future needs autonomously.
fake doctor

Reels and Short Content: A Challenge for AI and Information Quality in Thailand

by with No Comment AI-artificial-intelligence

Reels and Short Content: A Challenge for AI and Information Quality in Thailand

Over the next five years, the prevalence of short content such as reels and clips is likely to impact the quality and type of data that AI can utilize in Thailand.

Content Quality

The landscape of content creation has shifted dramatically. Previously, high-quality content was typically produced by experts deeply specialized in their fields—engineers, doctors, or professionals with formal education in communication or other disciplines. However, with the rise of social media platforms like Facebook, TikTok, and YouTube over the past decade, content creation has become accessible to anyone. This shift has led to a flood of content that often lacks depth, originality, or copyright infringement.

A concerning trend is the prevalence of individuals pretending to be professionals, offering in-depth reviews or expert opinions while copying or misrepresenting copyrighted material.

Information Shallowness

In Thailand, social media’s focus on entertainment has led to a shallow consumption of information. Content that emphasizes humor, sensationalism, or emotional appeal tends to dominate over thoughtful, educational, or in-depth discussions. This often includes:

  • Superficial Sharing: Users frequently share headlines or partial information without fully engaging with the content.
  • Misinformation: The spread of fake news, misinterpretations, or unverified information without proper references is rampant.

This shallowness diminishes critical thinking and analytical skills, particularly in areas requiring careful consideration, such as medical or life-related knowledge. Short, incomplete data often prioritizes engagement over accuracy, contributing to a decline in deep learning.

Content Copying

Copyright infringement has become a widespread issue in the transition from traditional web platforms to social media. This includes:

  • Direct Copying: Republishing articles, images, or videos without proper credit to the creators.
  • Slight Modifications: Altering text or visuals slightly while retaining the original ideas.
  • Monetized Copying: Using stolen content to generate revenue, such as reposting clips on platforms like TikTok or YouTube to maximize views or promote products.

This practice discourages original creators, reduces creativity, and leads to the repetition of low-value content. Many creators abandon originality to mimic trends, further saturating social media with repetitive and shallow material.

Platform Responsibility

Social media platforms play a significant role in shaping content trends. Algorithms often prioritize engagement metrics over quality, favoring viral or entertaining content over informative or insightful material. For example, Facebook, TikTok, and Instagram frequently promote short reels or live sales, while informative posts struggle to gain visibility.

This focus undermines creators aiming to provide high-quality, in-depth content, as they are often overshadowed by clickbait and low-effort material.

Solutions and Management Strategies

  1. For Individuals:
    • Encourage the creation of sustainable, “evergreen” content that remains relevant and valuable over time, akin to planting long-lasting trees.
    • Foster critical thinking skills among users to discern credible content from shallow or fake information.
  2. For Platforms:
    • Develop AI tools to detect and reduce redundant, copied, or low-quality content.
    • Adjust algorithms to prioritize high-quality, informative posts over shallow or purely entertaining material.
    • Implement stricter filters to prevent AI from learning and perpetuating low-quality or plagiarized content.
  3. For Society and Government:
    • Launch digital literacy programs to educate the public on distinguishing real from fake information and encourage creative content production.
    • Enforce copyright laws more rigorously to deter online infringement.

Final Thoughts

Although it is challenging to align financial incentives with high-quality content creation, small steps can make a difference. By filtering algorithms to discourage shallow content and encouraging platforms to support originality, we can foster a more informed and creative digital space in Thailand. Governments, platforms, and users all have a role to play in promoting a culture of meaningful and responsible content creation.

IMG_E6106

Short history of phpnuke

by phpnuke-postnuke

Francisco Burzi, describes the history of PHP-Nuke as follows:
PHP-Nuke is a free software, released under the GNU GPL License, version 2.0. PHP-Nuke is the result of many years administrating a news site called Linux Preview. First, around August 1998, I wrote my own code in Perl called NUKE and used it for about 1 year, then my site grew big, so I needed a more powerfull system and decided to use Slash, the same used in the Slashdot site. It’s good, but you realy need to know Perl to modify it, need too many modules, need to load a damn daemon that sucks all your CPU power. My Pentium III just appears to be a 386 each minute the daemon make its work.
Well, then I discovered Thatware, a good project to have a news site under PHP. I learned PHP in less than a week and began modifying it. There are too many mods to mention, it was practicaly a rewrite. I added some cool stuff, deleted some others and after more than 380 hours of hard work in 3 weeks! PHP-Nuke was born.
On August 17, 2000 I sold LinuxPreview.org to LinuxAlianza.com and now I have all the time to dedicate to the development of PHP-Nuke.
From January 2001 to January 2002, PHP-Nuke has been financially supported by MandrakeSoft, the folks that made Mandrake Linux. This gave me and PHP-Nuke a lot of oxygen and made possible a lot of stuff.
Now, I’m alone with this killer project. There is a lot of help from the people that use and develop modules and themes. Now, phpnuke.org is a big site with a lot of users and helpful information for any user around the world. There are also strong users community sites in almost any language you can imagine. Just go to phpnuke.org and enjoy this great community!

PHP-Nuke

Founder: Francisco Burzi
Initial Release: June 17, 2000
Written In: PHP
Purpose: PHP-Nuke was created as a web portal system (web 2.0) that allowed users to build dynamic websites focused on ease of use , content based management. It’s a pioneering open-source Content Management System (CMS) created by Francisco Burzi in 1998.

Key Features:

  • Modular architecture (e.g., news, forums, downloads, links).
  • Simple administration panel for non-technical users.
  • Multi-language support.
  • MySQL as the primary database backend.

Evolution and Criticism:

  • PHP-Nuke gained significant popularity early on due to its ease of setup and wide functionality. It was a fork of the Thatware news portal system.
  • PHP-Nuke became a significant force in the early days of web development, inspiring many other CMS platforms. Its modular architecture, where developers could create and add new functionalities (modules),
  • However, it faced criticism for security vulnerabilities, frequent bugs, and the introduction of paid licensing in later versions, which alienated parts of the open-source community.

Decline:

Over time, security vulnerabilities and a lack of consistent development led to a decline in PHP-Nuke’s popularity. However, its influence is undeniable. Many popular CMS platforms today, such as WordPress, Joomla, and Drupal, owe a debt to PHP-Nuke’s innovative approach and modular architecture.

By the mid-2000s, newer CMS platforms like Joomla, WordPress, and Drupal offered more robust and secure systems, leading to PHP-Nuke’s decline.